DHS METHODOLOGICAL 

REPORTS 19

An Assessment of the Quality 

and consistency of Age and 

Da te Reporting in DHS Surveys, 

2000-2015

AUGUST 2017

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

Thomas Pullum and Sarah Staveteig.

DHS Methodological Reports No. 19 

An Assessment of the Quality and Consistency of Age and 

Date Reporting in DHS Surveys, 2000-2015 

Thomas Pullum1,2,§

Sarah Staveteig2,3,§

ICF 

Rockville, Maryland, USA 

August 2017 

1

 ICF 

2

 The DHS Program 

3

 Avenir Health 

§ Both authors contributed equally to this work 

Corresponding author: Thomas Pullum, International Health and Development, ICF, 530 Gaither Road, 

Suite 500, Rockville, MD 20850, USA; phone: 301–572–0950; fax: 301–572–0999; email: 

tom.pullum@icf.com 
Acknowledgments: The authors wish to thank Stan Becker for valuable comments, and Fred Arnold, 

Trevor Croft, and Anjushree Pradhan for helpful discussions of DHS fieldwork procedures. 

Editor: Bryant Robey 

Document Production: Christopher Gramer 

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

Development (USAID) through The DHS Program (#AIDOAA-C-13-00095). The views expressed are 

those of the authors 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, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA. Phone: +1 301-407-6500; 

fax: +1 301-407-6501; email: reports@dhsprogram.com; Internet: www.dhsprogram.com. 

Recommended citation: 

Pullum, Thomas, and Sarah Staveteig. 2017. An Assessment of the Quality and Consistency of Age and Date 

Reporting in DHS Surveys, 2000-2015. DHS Methodological Reports No. 19. Rockville, Maryland, USA: 

ICF. 
iii 

Contents 

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

Figures ........................................................................................................................................................ vii 

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

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

1 Background .................................................................................................................................... 1 

1.1 DHS Age and Date Questions Analyzed in this Report ...................................................... 1 

1.1.1 Household Questionnaire ....................................................................................... 1 

1.1.2 Woman’s Questionnaire ........................................................................................ 1 

1.1.3 Man’s Questionnaire .............................................................................................. 2 

1.2 The DHS Program’s Procedures for Obtaining Accurate Age and Date Information ........ 2 

1.3 The DHS Program’s Procedure for Editing Inconsistent and Imputing Incomplete Dates 3 

1.4 Prior DHS Studies on Data Quality .................................................................................... 4 

1.5 Report Structure .................................................................................................................. 4 

2 Incompleteness, Heaping, and Displacement in DHS Surveys .................................................. 7 

2.1 Data and Methods ............................................................................................................... 7 

2.2 Incompleteness .................................................................................................................. 10 

2.3 Heaping ............................................................................................................................. 14 

2.4 Displacement .................................................................................................................... 18 

2.5 Composite Measures of Data Quality ............................................................................... 23 

3 The Quality and Consistency of Spousal Age Estimates in DHS Surveys .............................. 27 

3.1 Data and Methods ............................................................................................................. 27 

3.2 Composition of Couples by Direction of Age Difference ................................................ 28 

3.3 Difference between Spousal Age Estimates and Self-Reports ......................................... 28 

3.4 Heaping in Spousal Age Estimates ................................................................................... 31 

3.5 Consistency of Spousal Estimates by Age ........................................................................ 35 

3.6 Consistency of Spousal Age Estimates by Education ....................................................... 36 

4 Conclusion .................................................................................................................................... 41 

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

Appendix A: All Ages, Dates, and Durations Asked of DHS Respondents and their Evolution 

between DHS Phases 4 to 7 ......................................................................................................... 49 

Household and Biomarker Questionnaires .................................................................................... 49

Woman’s Questionnaire ................................................................................................................. 49 

Man’s Questionnaire ...................................................................................................................... 52 

Appendix B: Example National Historical Events Calendar Used for Date Estimation during 

the 2012-13 Pakistan DHS ........................................................................................................... 55

Appendix C: List of Surveys Analyzed in Chapter 2 ............................................................................. 57 

Appendix D: List of Surveys Analyzed in Chapter 3 ............................................................................. 61 

v 

Tables 

Table 2.1 Meaning of DHS completeness codes ................................................................................................ 7 

Table 2.2 Surveys with at least 50% incompleteness in women’s reported birthdate/age ......................... 11 

Table 2.3 Surveys with at least 50% incompleteness in women’s reported age at first union .................. 12 

Table 2.4 Surveys with at least 50% incompleteness in men’s reported birthdate/age .............................. 12 

Table 2.5 Surveys with at least 10% heaping in reported age from the household survey ........................ 15 

Table 2.6 Surveys with at least 10% heaping in reported age from the woman’s interview ..................... 16 

Table 2.7 Surveys with at least 10% heaping in reported age from the man’s interview ........................... 16 

Table 2.8 Surveys with age transfers from age 15 to 14 outside the range of -15% to +15%, 

and the statistical significance of those transfers ............................................................................ 20 

Table 2.9 Surveys with age transfers from age 49 to 50 outside the range from -15% to 50%, 

and the statistical significance of those transfers ............................................................................ 21 

Table 2.10 Surveys with average transfers for health that are 15% or greater and statistical 

significance of those transfers ........................................................................................................... 22 

Table 2.11 Countries in the poorest quintile of data quality for ages and dates, based on a 

combination of standardized scores for incompleteness, heaping, and displacement 

in DHS surveys during 2000-2015 ................................................................................................... 25 

Table 2.12 Countries in the highest quintile of data quality for ages and dates, based on a 

combination of standardized scores for incompleteness, heaping, and displacement 

in DHS surveys during 2000-2015 ................................................................................................... 26 

vii 

Figures 

Figure 1.1 Procedures for the collection of an individual’s age and date of birth from The DHS 

Program’s interviewer manual .................................................................................................. 3 

Figure 2.1 Histograms showing the level of any type of incompleteness of reported women’s age, 

women’s age at first union, men’s age, or children’s ages ...................................................... 11 

Figure 2.2 Scatterplots showing the incompleteness of women’s age at first union, men’s age, or 

children’s age, versus the incompleteness of birthdates/ages of women ................................. 13 

Figure 2.3 Histograms showing the level of heaping in reported age in the household survey, in the 

survey of women, in the survey of men, and of children in the birth histories ....................... 15 

Figure 2.4 Scatterplots showing the level of heaping in reported age in the survey of women, the 

survey of men, and the children in the birth histories, versus the heaping in reported 

age in the household survey .................................................................................................... 17 

Figure 2.5 Histograms showing the level of displacement from age 15 to 14, or from age 49 to 50, 

for women in the household survey, and displacement across the boundary for the health 

questions in the birth histories ................................................................................................. 19 

Figure 2.6 Scatterplot showing the level of displacement from age 15 to 14 versus the level of 

displacement from age 49 to 50, for women in the household survey .................................... 22 

Figure 2.7 Changes in the levels of incompleteness, heaping, displacement, and a combination of all 

three in DHS surveys conducted from 2000 to 2015............................................................... 24 

Figure 2.8 Distribution of the summary measure of incompleteness, heaping, and displacement 

across the 148 surveys in this analysis .................................................................................... 25 

Figure 3.1 Distribution of the share of marriages by direction of age difference ..................................... 28 

Figure 3.2 Distribution of average difference between wife’s estimate of husband’s age and 

husband’s self-reported age, by marriage type ........................................................................ 30 

Figure 3.3 Distribution of average difference between husband’s estimate of wife’s age and 

wife’s self-reported age, by marriage type .............................................................................. 31 

Figure 3.4a Myers’ Index for men’s ages, self-reports versus spousal estimates ....................................... 32 

Figure 3.4b Myers’ Index for men’s ages, self-reports versus spousal estimates (continued) ................... 33 

Figure 3.5 Myers’ Index for women’s ages, self-reports versus spousal estimates .................................. 34 

Figure 3.6 Coefficient for wife’s self-reported age from regression on absolute difference between 

husband’s self-reported age and wife’s estimate of husband’s age ......................................... 35 

Figure 3.7 Coefficient for husband’s self-reported age from regression on absolute difference 

between wife’s self-reported age and husband’s estimate of wife’s age ................................. 36 

Figure 3.8 Coefficient for wife having primary education (versus none) from regression on absolute 

difference between husband’s self-reported age and wife’s estimate of husband’s age ......... 37 

Figure 3.9 Coefficient for wife having secondary or higher education (versus none) from 

regression on absolute difference between husband’s self-reported age and wife’s 

estimate of husband’s age ........................................................................................................ 38 

Figure 3.10 Coefficient for husband having primary education (versus none) from regression on 

absolute difference between wife’s self-reported age and husband’s estimate of wife’s age . 39 

Figure 3.11 Coefficient for husband having secondary or higher education (versus none) from 

regression on absolute difference between wife’s self-reported age and husband’s 

estimate of wife’s age .............................................................................................................. 40 

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 assesses the quality and consistency of age and date reports in DHS surveys conducted since 

2000 in 67 countries. It is the most recent of several reports on various aspects of DHS data quality. 

The first chapter describes the steps of editing and imputing during fieldwork and data processing. Great 

care is taken to train and supervise the interviewers to obtain the best possible estimates of ages and dates. 

The eligibility of adults for the surveys of women and men depends on obtaining accurate values of age 

near the lower and upper age boundaries within the household survey. The eligibility of young children for 

the detailed health questions depends on obtaining accurate estimates of when they were born within the 

surveys of women. Age-specific fertility rates, under-five mortality rates, immunization rates, 

anthropometry scores, and many other DHS indicators depend on accurate estimates of age. 

An appendix provides an inventory of all the locations in DHS surveys where the respondents are asked for 

ages and dates. This assessment focuses on just a few of those locations: the ages of all household members, 

provided by the household respondent during the household survey; the self-reported ages and birthdates 

of women and men in the surveys of women and men; women’s self-reports of age and date of first union 

in the survey of women; the birthdates (and ages, if living) of children in the birth histories, provided by 

the mother; and the women’s and men’s estimates of their respective spouses’ ages in the surveys of women 

and men. 

The second chapter assesses the ages listed above, other than spousal estimates, in terms of three types of 

measures: incompleteness, heaping, and transfers. A total of 11 indicators are used. For each indicator, the 

distribution across all surveys is described and the surveys with the most extreme levels are identified. All 

of these measures vary substantially. There are many surveys with values close to zero on all measures, and 

others with very high values. There are some surveys in which month of birth is hardly ever given. Age/date 

transfers are sometimes large but in a direction opposite to what we would expect, particularly around age 

15 or around the date for the health questions, clearly as a result of over-correction during training and 

supervision. Surveys with extreme values are listed. Summary indices of incompleteness, heaping, and 

transfers are constructed and tracked over time. The indicators fluctuated substantially from 2000 to 2015 

and did not show a systematic trend. A single composite index is constructed for each of the 67 countries. 

The countries in the highest quintile (with the most problems) and the lowest quintile (with the fewest 

problems) are identified. 

The third chapter investigates the quality and consistency of spousal age estimates compared to self-reports 

in the surveys that included interviews of men and where women and/or men were asked to estimate the 

age of their spouse(s). In the absence of an age gap between spouses, women tend to estimate that their 

husbands are older than their self-reported age and men tend to estimate that their wives are younger than 

their self-reported age. Evidence indicates that where there is an age difference between spouses, women 

tend to estimate in a way that reduces the gap: they underestimate the age of older husbands and 

overestimate the age of younger husbands. Men underestimate the age of wives who are older than they 

are, which reduces the gap, but they also tend to underestimate the age of wives who are younger, which 

increases the gap. In the vast majority of countries, there was more heaping for estimates of spouse’s age 

than for self-reported age. 

There is evidence that displacement and heaping, in particular, can be reduced, through training and 

supervision, but there is also evidence that too much focus on displacement of children or on heaping at 

final digit 0 can lead to over-correction. The biggest determinant of good age reporting is probably the 

value, to the respondents, in everyday life, of knowing their ages or the ages of their children. This 

component of data quality varies from one setting to another and is outside the control of a survey operation. 

1 

1 Background 

Surveys conducted by The Demographic and Health Surveys Program (DHS) are a principal source of data 

for vital rates and health in developing countries. Ages and dates are foundational to a number of key 

demographic indicators computed by The DHS Program. Periodic assessments of the quality of age and 

date data are an important element of program monitoring and can help improve data quality. To that end, 

this report examines the quality and consistency of age and date reporting in DHS surveys conducted since 

2000. It includes household, individual, and couples reports of ages and dates as well as birth histories. We 

begin with an overview of ages and dates collected by DHS surveys, a description of the process of editing 

and imputation, in the field and during data processing, and prior study findings about age and date quality 

of DHS data. 

1.1 DHS Age and Date Questions Analyzed in this Report 

The DHS Program has fielded multistage household surveys in low- and middle-income countries since 

1985. In these surveys, after sampling and household selection are complete, interviewers approach each 

eligible household for an interview with an adult respondent1

 for the household using the household 

questionnaire, which includes an inventory of all household members. Household members are defined as 

individuals who are usual residents as well as individuals who stayed with the household as guests the 

previous night. Based on the household inventory, interviewers determine which women in the household 

are eligible for individual interviews and—if the survey includes men and the household is selected for 

male interviews—which men are eligible for individual interviews. If eligible women and men consent to 

individual interviews, they are conducted separately, typically within a few days of the household interview. 

In the period covered by this report—from 2000 until present—The DHS Program has spanned four phases: 

DHS-4 (1997-2003), DHS-5 (2003-2008), DHS-6 (2008-2013), and DHS-7 (2013-present). The DHS 

Program publishes core questionnaires for each phase. Household respondents and individual women and 

men are asked a number of questions related to ages, dates, and durations of time. A full list of these 

questions and a description of their evolution is included in Appendix A. Below we describe the survey 

questions used for analysis in this report. 

1.1.1 Household Questionnaire 

All core household questionnaires from DHS-4 through DHS-7 (ICF International 2011, 2015b; Macro 

International 2008a; ORC Macro 2001a, 2001b) ask an adult household respondent to provide the name 

(Q2)2

 and age (Q7) of all household members, to identify a head of household, and to describe each 

member’s relationship to the household head (Q3). Specifically, the household respondent is asked “How 

old is (NAME)?”

1.1.2 Woman’s Questionnaire 

All woman’s core questionnaires from DHS-4 through DHS-7 (ICF International 2011, 2015d; Macro 

International 2008c; ORC Macro 2001a, 2001b) ask women the following items analyzed in this study: 

 

1

 The respondent for the household questionnaire may be any resident adult who consents to and is capable of 

answering the questions. 

2

 Question numbers given here and in subsequent sections are from DHS-4 Model “A” questionnaire (ORC Macro 

2001a). Questions may be numbered differently in subsequent rounds. 
2 

• Birthdate: “In what month and year were you born?” (Q105) 

• Age: “How old were you at your last birthday?” (Q106) 

• Age of each child who is still alive: “How old was (NAME) at his/her last birthday?” (Q217) 

• Age at first union—if date of first union is known, this is computed from date of birth and date of 

first union; if unknown, respondents are asked age at first union: 

• If married or lived with man as if married only once: “In what month and year did you start 

living with your husband/partner?” (Q511) 

• If married or lived with a man as if married more than once: “Now we will talk about your 

first husband/partner. In what month and year did you start living with him?” (Q511) 

• If the year first started living with first husband/partner is unknown: “How old were you 

when you first started living with him?” (Q512) 

• If currently married or living with a man as if married, “How old was your husband/partner on 

his last birthday?” (Q702) 

1.1.3 Man’s Questionnaire 

Although DHS surveys included men as early as 1987, the DHS core questionnaires did not include a model 

man’s questionnaire until DHS-5. In the DHS-5 model man’s questionnaire and in all subsequent man’s 

questionnaires through DHS-7 (ICF International 2011, 2015c; Macro International 2008b), men are asked 

the following questions related to age and dates that are used in this report: 

• Birthdate: “In what month and year were you born?” (Q106) 

• Age: “How old were you at your last birthday?” (Q107) 

• Age of all current wives/partners: “How old was (NAME) on her last birthday?” (Q408) 

1.2 The DHS Program’s Procedures for Obtaining Accurate Age and Date Information 

The DHS Program has established a number of field procedures to obtain accurate information on age and 

date of birth from respondents. Such procedures are of particular importance in countries and areas where 

innumeracy is prevalent and vital recordkeeping is deficient. Prior to each survey, interviewer training 

typically lasts several weeks, with modules about age and date reporting that include practical exercises. 

The DHS Program’s interviewer manuals (ICF 2017; ICF International 2012) contain a number of 

instructions for interviewers on obtaining complete and accurate age and date information from individual 

respondents. Figure 1.1 illustrates these procedures. Interviewers are also instructed to use these methods 

for the household age roster if the age of any household member is unknown. In countries lacking vital 

registration systems, identity cards may themselves be inaccurate. For this reason, as noted in Figure 1.1, 

if interviewers ask for a card in the process of establishing date of birth they are advised to check with the 

respondent about the veracity of the date printed on the card. When both age and date of birth are unknown, 

interviewers are instructed to reference respondent’s age at particular life events (for example, birth of first 

child) and number of years ago the event took place, to relate their age to someone in household whose age 

is more reliably known, or to use the local and/or national historical events calendar they received during 

training. An example of a national historical events calendar distributed to interviewers for use in date of 

birth estimation is given in Appendix B. 

In practice, during individual interviews the process of obtaining ages and dates may be slightly different 

from what is shown here; in particular it may be more iterative. While interviewers are explicitly instructed 
3 

to ask age and date of birth independently from the household interview, if there is uncertainty about the 

respondent’s age then the age assignment from the household interview may be taken as a starting point for 

a discussion about the individual respondent’s age and date of birth. 

Figure 1.1 Procedures for the collection of an individual’s age and date of birth from The DHS Program’s 

interviewer manual 

Oversight and quality-checking are important aspects of ensuring the accuracy of age and date reporting. 

In the absence of adequate supervision there may be a tendency toward age displacement that reduces the 

time burden on interviewers and respondents. For example, if a household member is outside the range for 

individual interviews (typically age 15-49), an individual interview is not administered; if a child is older 

than age 5, the interviewer can skip a battery of questions about the child’s circumstances of birth, health, 

and nutrition. DHS field supervisors and survey managers are cognizant of this motivation for a possible 

bias. Field editors and supervisors may ask interviewers to return to a household where ages and dates of 

birth appear overly rounded or are slightly outside of the thresholds. Supervisors routinely examine field 

check tables for age heaping and may return to a household or to an entire survey cluster in an attempt to 

obtain more precise information. 

1.3 The DHS Program’s Procedure for Editing Inconsistent and Imputing Incomplete Dates 

After data collection is complete, The DHS Program follows a standard procedure for checking the 

consistency of dates and for imputing missing dates (and, by extension, ages). These procedures are 

described in detail in the DHS data editing and imputation manual (Croft 1991). Inconsistent dates are 

flagged and incomplete dates are imputed for the following demographic events: 
4 

• Date of birth of the respondent 

• Date of first union 

• Date of birth of each child 

• Date of conception and current pregnancy 

• Date of sterilization of respondent or partner 

Data editing and imputation require the creation of an event table involving the aforementioned events plus 

employing ancillary data such as the duration of breastfeeding, amenorrhea, and abstinence after the birth 

of a child. These pieces of information create logical constraints on demographic events; for example, a 

minimum seven-month interval between non-multiple births, the impossibility of becoming pregnant during 

a period of postpartum abstinence, and so forth. Inconsistent data are flagged and resolved prior to 

imputation. Imputation is a four-stage process during which logical ranges for the date of each event are 

established and iteratively narrowed based on the sequence of events until a final random value is assigned. 

Imputed dates are flagged in final DHS datasets. 

1.4 Prior DHS Studies on Data Quality 

Given the essential need for correct and consistent age and date data for the computation of demographic 

indicators, The DHS Program and its predecessor, the World Fertility Survey, have undertaken a number of 

studies on age and date data quality. In an early study, Arnold (1991) found that eligibility errors—including 

birth displacements past the five-year line—may affect fertility rates and child and infant mortality rates by 

a few percentage points. However, the overall quality of DHS-I data was quite good. Rutstein et al. (1990) 

found similar issues with misplaced eligibility and birth displacement and similar probable effects on 

demographic rates. Curtis (1995) examined the quality of data used to compute infant and child mortality 

in DHS surveys and found that the date of birth was consistently less complete for dead children than for 

surviving children but did not substantially affect mortality rates. Gage (1995) examined the quality of 

DHS-II data on the age at first union, at first birth, and at first sexual intercourse and found evidence of 

heaping in age at first union and years of first birth, but not a directional bias. 

More recently, a DHS report on age and date quality (Pullum 2006) examined nine symptoms of poor data 

quality in DHS surveys from 1985 to 2003. There was evidence of age heaping and age displacement, but 

an overall improvement in data quality over the time period studied. A second study that examined 

fieldwork found that data quality was worse in rural areas, at the beginning and end of fieldwork, and in 

instances where a translator was used (Johnson et al. 2009). Schoumaker (2014) examined the quality and 

consistency of DHS fertility estimates and found that most estimates were of good or acceptable quality but 

that not all should be taken at face value due to the omission of recent births, a tendency to report the first 

birth as more recent than it was (the Potter effect), and differences in sample composition. Pullum and 

Becker (2014) examined birth histories to study the effects of the omission of births and/or deaths, potential 

displacement, and misreporting. They found that the quality of recent surveys was high; omission and 

displacement of births is typically around 2%, while omission and displacement of deaths may be up to 5%. 

1.5 Report Structure 

This report examines the quality of age and date reporting in DHS surveys conducted during the interval 

from 2000 to 20153

. Chapter 2 reviews 148 surveys conducted in 67 countries, using four indicators of 

 

3

 The fieldwork for two surveys, Angola 2015-16 and Myanmar 2015-16, extended into 2016, but it would be 

misleading to include 2016 in the reference interval of time. For all surveys included in this report, the range of years 

for the start of fieldwork was 2000 to 2015. 
5 

incompleteness, four indicators of heaping, and three indicators of displacement. All of the indicators are 

expressed as percentages that ideally would be at a level of 0%, except for possible random variation. 

Incompleteness refers to ages that should be accompanied by the month and year of birth (except for 

women’s age at first union, which should be accompanied by the month and year of first union). If any 

component of age, month, and year is missing or the combination provided is inconsistent, then the response 

is considered incomplete. Heaping refers to digit preference, most commonly for final digits 0 or 5 in 

reported age. Displacement refers to an irregularity in the age distribution that suggests the interviewer has 

tended to shift women out of the eligible age range 15-49 for the survey of women, or children out of the 

eligible range of birthdates for the questions on child health.

Chapter 2 also describes the distribution of the level of each indicator across countries and identifies the 

specific surveys with the highest levels. It also combines the indicators into groups to summarize the levels 

of incompleteness, heaping, and displacement for each survey, and it describes year-to-year levels from 

2000 to 2015. Finally, a single composite indicator is used to identify the countries in the worst quintile or 

in the best quintile of age and date reporting, so that problems in future surveys can be better anticipated. 

For each DHS survey that includes a survey of men as well as a survey of women, a “couples’ file” is 

constructed, consisting of a single record for each co-resident woman and man who self-identify as partners. 

For women—and in some cases, men—the data include the person’s own age and the person’s report 

(estimate) of their partner’s age. Chapter 3 considers another aspect of the quality of age reports—the 

correspondence between a respondent’s reported age and their spouse’s estimate of the respondent’s age in 

113 surveys for men and 67 surveys for women. The correspondences and differences between the two 

statements of age for each partner are examined systematically, with reference to a set of hypotheses. 

Heaping of the spousal age estimate is compared with heaping of the partner’s self-reported age. 

Characteristic patterns of discrepancies are identified. In this chapter, two covariates—age and education—

are included. 

The report also includes, as Appendix A, a comprehensive review of all ages, dates, and time durations 

asked in the core questionnaires from DHS phases 4 through 7, and their evolution over time. 

7 

2 Incompleteness, Heaping, and Displacement in DHS Surveys 

2.1 Data and Methods 

This chapter presents a systematic review of misreporting of ages and dates using virtually all of the DHS 

surveys conducted between 2000 and the closing date for this report—148 surveys in 67 countries. These 

numbers do not include Malaria Indicator Surveys (MIS) or AIDS Indicator Surveys (AIS) or so-called 

Interim Surveys but do include groupings of rounds of the Continuous Surveys in Peru and Senegal. The 

total number of cases is approximately 7.98 million persons of all ages in the household surveys, 1.67 

million women in the surveys of women, 0.72 million men in the surveys of men, and 3.79 million children 

in the birth histories. 

Misreporting will be described with indicators of incompleteness, heaping, and displacement. For each of 

these, three or four indicators will be employed, a total of eleven, variously using the household survey, the 

survey of women, the survey of men, and the birth histories. We will propose a synthesis of these indicators 

that can serve as a general indicator of misreporting, and will describe overall trends since 2000. 

Incompleteness applies to an instance in which the respondent provides less information about an age or 

date than is expected, requiring some imputation during data processing, or when there is an inconsistency 

between the types of information provided that requires some reconciliation. There are eight possible codes, 

representing the possible combinations of age, year of birth, and month of birth, being provided (and 

consistent) or not provided (or not consistent).4

 Definitions of incompleteness codes below are from the 

DHS-6 recode manual (MEASURE DHS and ICF International 2013): 

Table 2.1 Meaning of DHS completeness codes 

DHS completeness code Meaning 

1: month and year - 

information complete 

Both month and year of the event were specified so no imputation was necessary. 

2: month and age - year 

imputed 

The year of the event was not given, but the month of the event and the age of the respondent 

or child or, in the case of the date of first union, the respondent’s age at first union were 

specified. In most cases this information uniquely identifies the exact date of the event. In a few 

cases the year of the event was imputed from a choice of two possible years. 

3: year and age - month 

imputed 

The year of the event, but not the month, and the age of the respondent or child or, in the case 

of the date of first union, the respondent’s age at first union were specified and only the month 

of the event was imputed. 

4: year and age - year ignored The year of birth, but not the month, and the age of the respondent or child were specified. In

surveys where it is believed that the year of birth was calculated by just subtracting age from 

the current calendar year, year and may be imputed within a fully twelve-month range that is 

consistent with stated years of age and date of interview. 

5: year - age/month imputed The year of the event was given but the month of the event was not specified, and neither was 

the age. The month of the event was imputed. 

6: age - year/month imputed Neither the month nor the year of the event was specified, but age was given and the year and 

month of the event were imputed from the age. 

7: month - age/year imputed Only the month of the event was given, without the year or age. The year of the event was 

imputed from other information. 

8: none - all imputed No information was given concerning the date of the event. But month and year of the event 

were imputed from other information. 

 

4

 In the birth histories for the most recent surveys, day of birth is also asked. If the respondent can provide day, month, 

and year of the child’s birth, then the relevant completeness variable (b10) is assigned code 0. In this report, the only 

affected surveys are Malawi 2015-16 and Tanzania 2015-16. 
8 

Type 1 is most desirable. Several of the other logically possible combinations hardly ever occur. Types 3, 

6, and 4, in that order, account for virtually all of the incomplete cases. The general rule during imputation 

is to give higher priority to stated age than to stated year or month of birth. If age in years is given, then 

there are typically 12 possible months of birth, spread across two adjacent calendar years of birth, that 

would be consistent with the month and year of interview. Imputation is typically at random within this 

range. In this analysis, we simply aggregate types 2-8 as a percentage of the total for types 1-8 and call this 

the “percentage incomplete.” It will be calculated for: age in the women’s survey (v014 is the 

incompleteness code for age, v012); age at first union in the women’s survey (v510 is the incompleteness 

code for age at first union, v509); age in the men’s survey (mv014 is the incompleteness code for age, 

mv012); and age of a child in the birth history (b10 is the incompleteness code for month and year of birth 

and current age if alive). Each completeness code refers to a combination of age, month, and year. 

Heaping refers to a tendency to provide an age that disproportionately has specific final digits, typically 0 

or 5, although other final digits such as 2 or 8 can also occur more often than would be expected. The easiest 

way to describe heaping or digit preference is to calculate the percentage of cases with each possible final 

digit (0, 1, ..., 9), add up the absolute deviations of those percentages from 10%, and divide the total by 

two. This indicator is an application of the Index of Dissimilarity, which summarizes the deviation of any 

observed categorical distribution from a distribution that is “expected” under some model. The index is 

interpreted as the percentage of cases that would have to be shifted from an over-represented category to 

an under-represented category in order to match the “expected” distribution. The division by two is required 

because when one case is moved it will simultaneously reduce the excess by one and reduce the deficit by 

one. 

A longstanding indicator of age heaping is Myers’ Blended Index (herein Myers’ Index). This index is a 

minor modification of the Index of Dissimilarity that takes into account the overall pattern of most observed 

age distributions, such that the percentage of the cases at age x+1 tends to be less than the percentage at age 

x. Such a pattern results from the cumulative impact of mortality, as well as, often, an increasing number 

of births every year compared with the previous year. For that reason, even in the absence of digit 

preference, there would tend to be fewer cases with final digit 1 than with final digit 0, fewer cases with 

final digit 2 than with final digit 1, etc., and fewer cases with final digit 9 than with final digit 8. Myers’ 

Index adjusts the Index of Dissimilarity for that gradient. For either index to be applied correctly, the age 

range must be a multiple of 10 years. We will calculate this index for age in the household survey (hv105, 

using ages 0-79), age in the survey of women (v012, using ages 20-49), age in the survey of men (mv012, 

using ages 20-49), and age of living children in the birth histories (b8, for ages 0-29). 

It would be possible to examine digit preference in the stated calendar year of birth, rather than age at the 

time of the survey. For many surveys, for example, there appears to be a preference for calendar years 

ending in 0, particularly for the year 2000. This will not be investigated systematically here, partly because 

heaping on calendar years would be confounded with heaping on age for interviews conducted in calendar 

years ending with 0 or 5, that is, in 2000, 2005, 2010, and 2015. 

Incompleteness and heaping probably tend to result from the respondent genuinely not knowing the age in 

question. The procedure for field imputation of ages and dates was described in Chapter 1. In many surveys, 

some assistance is provided in the form of identity cards, reference events, and relative ages. But if the 

respondent really does not know the birth year or age, there is a limit to what can be done during the 

interview to obtain a correct response. 

Displacement—the third potential type of age misreporting considered here—refers to a shifting of age or 

birthdate across some relevant boundary. Three examples of potential displacement will be considered. The 

first is a possible transfer from age 15 to age 14 for females in the household survey. One function of the 

household survey is to ascertain eligibility for the survey of women. The lowest age of eligibility is 15 (with 
9 

very few exceptions). If the age of a woman is reported to be 14, rather than 15, then she will not be eligible 

for the household survey. The second example is a possible transfer from age 49 to age 50 for women in 

the household survey, which will also remove women from eligibility because the upper age limit is 49. 

Note that transfers from age 49 to 50 related to the eligibility criterion are confounded with heaping at age 

50. Heaping at age 50, as such, would tend to be drawn from true ages just above or just below 50. Transfers 

out of eligibility for the survey of women would only displace from true ages below 50. Nevertheless, 

transfers from age 49 to 50 can arise from either source. 

Another example of displacement is a possible transfer of children in the birth histories across the boundary 

for eligibility for the child health questions. Eligibility is normally determined by whether the child was 

born within the five years before the month of interview, or, more precisely, in January of the fifth calendar 

year before the beginning of fieldwork, or later.5

 The first calendar month for the health questions is the 

same for all interviews and is specified in the data files (with a century month code) as v017. Some health 

data are dropped during data processing if they refer to the interval between that specific month and the 

60th month before the month of interview. For a few surveys, the interval for the health questions is less 

than five years; for a few surveys, v017 is a month other than January. These potential transfers are 

estimated with a data file that lists all births in the birth histories (the BR file), including those that are 

reported with a birthdate before month v017, as well as those during and after month v017, which were 

eligible for the health questions. 

The three age/date transfers would be attributable to the interviewer, motivated by a possible desire to 

reduce the required number of in-depth interviews. At the point when the interviewer asks these questions 

about age, the respondent does not know that the responses will be used to determine eligibility for further 

questioning. The interviewer, of course, does know how they will be used. Nevertheless, on the basis of 

training and field observations, it is generally believed that the tendency of interviewers to displace an 

age/date in a direction that would avoid extended interviews occurs mainly when there is ambiguity or 

uncertainty from the respondent. 

The indicator of potential age/date transfers was first used in Methodological Report #5 (Pullum 2006, pp. 

81-82). It is based on an assumption that the true number of cases in a short sequence of four successive 

years—two of which are before the boundary and two of which are after the boundary—is linear on a log 

scale. Another way to describe the expected regularity is that the ratios of the frequencies at successive 

years should all be equal. Referring to the four successive years of age generically, as years 1, 2, 3, and 4, 

the assumption is that the true number of cases at year 2, divided by the number at year 1, is the same as 

the number at year 3 divided by the number at year 2, or the number at year 4 divided by the number at year 

3. We also assume that the displacement only involves years 2 and 3 and that the total for years 2 and 3 is 

correct.6

 These assumptions provide leverage to estimate the percentage of true cases in year 3 that were 

shifted downward to year 2, or conversely, the percentage of true cases in year 2 that were shifted upward 

to year 3.7

When this method is applied to ages 13, 14, 15, and 16, the expected direction of transfers is downward, 

and a positive sign for the shift will indicate evidence of a transfer in the expected direction. When applied 

to ages 48, 49, 50, and 51, the expected direction of transfers is upward, and a positive sign for the shift 

will indicate evidence of a transfer in the expected direction. When applied to child transfers, year 1 is the 

second year before v017, year 2 and year 3 straddle the boundary, and year 4 is the second year after v017; 

 

5

 This potential transfer is sometimes described as being across the fifth birthday, that is, from age 4 to age 5. The 

transfer is actually across a specific calendar month and calendar year of birth, as described. 

6

 Displacement, like rounding, is probably over a wider range than adjacent years. This is a simplifying assumption. 7

 A detailed description will not be given here; a link is provided in the References. 
10 

the expected direction of transfers is downward (from year 3 to year 2), and a positive sign for the shift will 

indicate evidence of a transfer in the expected direction. 

Heaping and displacement are closely related. For both of them, the methods are based on comparing the 

observed frequencies with the frequencies that would be expected under a model that smooths the data. 

The data files include other distributions of ages and dates that could be investigated for heaping or 

displacement. For example, another outcome that has been included in previous assessments of data quality 

is heaping of age at death on exactly 12 months. Age at death is asked in months for children who died 

before two years of age, and asked in years for age 2 and above. There is usually a tendency for some 

responses to occur disproportionately at exactly 12 months, probably through a thought process that 

converts a less precise “one year” to more precise “12 months”. In terms of completed years and months, 

however, the range of 0-11 months is understood by DHS to refer to age 0 years (infancy), and 12 months 

is the first (completed) month at age 1. As an illustration, in the Afghanistan 2015 survey, among children 

born in the past five years the numbers of deaths reported at 10, 11, 12, 13, and 14 completed months of 

age is 11, 31, 76, 14, and 6. It is clear that many of the 76 deaths reported at exactly 12 months should have 

been placed at earlier months or later months. The displacement of deaths from age 0 to age 1 resulted in a 

slight underestimate of recent infant mortality (and over-estimate of mortality for age 1 to age 4). There are 

a number of possible ways to measure this type of reporting error, interpreting it as a result of heaping or 

displacement, but the weakness of any measure based on this particular type of error is that it involves small 

frequencies and more sampling error than the other indicators. For that reason, it is not included in this 

report. 

For each measure of incompleteness, heaping, and displacement, we will describe the distribution across 

all surveys since 2000 and will identify the surveys with the most extreme values of the measures. All 

measures treat all observations equally, ignoring sampling weights. 

2.2 Incompleteness 

Incompleteness of age and date reporting will be assessed for two ages in the survey of women—the 

woman’s stated age and age at first union; for one age in the survey of men—the man’s stated age; and for 

one age in the birth histories—the woman’s report of the child’s birth date and age. The household survey 

is not included for this type of indicator.8

 As stated earlier, the indicator is the percentage of cases for which 

the age, year of birth, and month of birth are not all provided or are not all consistent with the month and 

year of interview. Figure 2.1 gives the distribution of this percentage for the four indicators, across all 

surveys. The four graphs in the figure have the same horizontal scales, the percentage of responses that are 

incomplete, but different vertical scales. 

When age is incomplete, it is usually because age and year of birth were provided, but not a month, or the 

month was inconsistent with the year and age, so the month had to be imputed or modified. It is rare for 

age itself to require imputation. Over all the surveys, the average level of incompleteness is 21.8% for 

women’s birthdate/age. Incompleteness of woman’s age in excess of 50% was found in the 22 surveys 

listed in Table 2.2. Overwhelmingly, the incompleteness was only in terms of month, but a threshold of 

50% is clearly a high level of any kind of incompleteness. 

 

8

 The age variable in the household survey, hv105, is not accompanied by a month and year of birth, or by a separate 

indicator of completeness, and is not subject to imputation or editing during data processing. The variable has a 

potential code for “missing,” 98, but this code is rarely required and has little diagnostic value. The greatest use of 

this code, by far, was in the Angola 2015-16 survey, in which it was assigned to 1.2% of the respondents. It was 

assigned to 0.3% of the cases in the Mozambique 2000 survey and 0.2% of the cases in the Namibia 2000 and Senegal 

2010-11 surveys. In all other household surveys the level is 0.1% or less. 
11 

Figure 2.1 Histograms showing the level of any type of incompleteness of reported women’s age, women’s 

age at first union, men’s age, or children’s ages 

Table 2.2 Surveys with at least 50% incompleteness in women’s reported birthdate/age 

Survey % 

Rwanda 2005 55.0 

Burundi 2010 55.1 

Nepal 2001 56.2 

Ethiopia 2005 57.2 

Niger 2006 62.1 

Ethiopia 2011 63.3 

Chad 2014-15 63.5 

Benin 2006 63.8 

Mali 2006 68.5 

Yemen 2013 70.5 

Pakistan 2006-07 73.6 

Survey % 

Benin 2001 74.1 

Pakistan 2012-13 75.6 

Ethiopia 2000 79.4 

Mali 2001 81.1 

Burkina Faso 2003 81.2 

Niger 2012 86.3 

Bangladesh 2014 86.9 

Guinea 2005 89.4 

Bangladesh 2011 91.4 

Bangladesh 2007 91.8 

Bangladesh 2004 93.7 

Four of the five surveys with greatest incompleteness of women’s age were conducted in Bangladesh (2004, 

2007, 2011, and 2014). Each of these four surveys had slightly less incompleteness than the preceding one, 

suggesting some improvement, but not a great deal. The underlying problem is that many women in 

Bangladesh simply do not know their birthdates. Several other countries appear more than once, e.g., Niger 

(2006 and 2012), Mali (2001 and 2006), Ethiopia (2000, 2005, and 2011), and Benin (2001 and 2006). 

The average level of incompleteness for age at first union is considerably higher, at 30.8%. Table 2.3 lists 

the 32 surveys with a level above 50%. In addition to many of the surveys that were on the previous list, 
12 

this list includes five surveys from Senegal (2005, 2010-11, 2012-13, 2014, and 2015)—all rounds of the 

Senegal Continuous Survey since 2010, plus an earlier standard DHS from 2005. 

Table 2.3 Surveys with at least 50% incompleteness in women’s reported age at first union 

Survey % 

Cameroon 2004 51.4 

Gabon 2012 51.8 

Senegal 2012-13 52.8 

Senegal 2010-11 53.6 

Ghana 2008 54.3 

Madagascar 2003-04 55.9 

Senegal 2015 56.0 

Cote d’Ivoire 2011-12 56.3 

Ethiopia 2011 57.0 

Senegal 2014 57.6 

Comoros 2012 58.6 

Niger 2006 61.3 

Bangladesh 2007 61.7 

Yemen 2013 62.3 

Sierra Leone 2013 62.7 

Ghana 2003 63.3 

Survey % 

Senegal 2005 64.6 

Sierra Leone 2008 65.1 

Bangladesh 2014 65.2 

Benin 2011-12 65.6 

Bangladesh 2011 66.9 

Sao Tome and Principe 2008-09 67.9 

Ethiopia 2000 68.7 

Mali 2006 68.7 

Nepal 2001 69.9 

Mali 2001 71.9 

Chad 2014-15 75.4 

Benin 2006 76.3 

Benin 2001 84.7 

Burkina Faso 2003 88.2 

Niger 2012 90.8 

Guinea 2005 93.1 

For men’s birthdate/age, the mean level of incompleteness is 20.8%. Table 2.4 lists the 17 surveys that have 

a level above 50%. The surveys on this list match closely with the surveys on the list for incompleteness of 

women’s birthdate/age. Some of the differences are simply due to the fact that not every DHS survey 

includes a survey of men. For example, the Bangladesh 2014 survey did not include a survey of men. The 

Bangladesh 2004, 2007, and 2011 surveys are at the extreme end of the list, with levels of incompleteness 

that show a slight increase, rather than decline, from one to the next. The Niger 2006 survey had a level of 

50.7%, and the 2012 survey had a much higher level, 80.3%. Three surveys from Ethiopia are on the list. 

The 2000 survey had a level of 69.4%; the 2005 and 2011 surveys were considerably lower, at 53.2% and 

56.8%, respectively. When following successive surveys in the same country, it is difficult to generalize 

about whether there has been a trend toward less incompleteness or toward more. 

Table 2.4 Surveys with at least 50% incompleteness in men’s reported birthdate/age 

Survey % 

Niger 2006 50.7 

Chad 2014-15 51.5 

Burundi 2010 52.5 

Ethiopia 2005 53.2 

Ethiopia 2011 56.8 

Benin 2006 58.6 

Rwanda 2005 59.3 

Benin 2001 63.0 

Mali 2006 66.3 

Survey % 

Guinea 2005 68.9 

Ethiopia 2000 69.4 

Burkina Faso 2003 74.7 

Mali 2001 75.2 

Niger 2012 80.3 

Bangladesh 2004 84.3 

Bangladesh 2007 84.9 

Bangladesh 2011 86.5 

The level of incompleteness of birthdates and ages in the birth histories is far less, only 4.6% on average. 

The only survey above 50% is Guinea 2005. 

Across surveys, there is close correspondence between the measures of incompleteness. The correlation 

between the incompleteness levels for women’s age and men’s age is 0.97. The correlation between 
13 

incompleteness of age at first union and incompleteness of women’s age is 0.72. The correlation between 

incompleteness of age at first union and incompleteness of men’s age is 0.67. Whether incompleteness 

arises from the respondents’ own knowledge of ages and dates, or from the quality of the data collection, 

we would expect high levels of correspondence, because the men and women are in the same households 

and the interviewers all have the same training and supervision. Incompleteness of children’s birthdates and 

ages has much lower correlation with the other three measures—0.18 with incompleteness of women’s age 

or men’s age and 0.21 with incompleteness of age at first union. 

Figure 2.2 Scatterplots showing the incompleteness of women’s age at first union, men’s age, or children’s 

age, versus the incompleteness of birthdates/ages of women 

Figure 2.2 provides scatterplots of pairings of these four indicators. The reference indicator is the level of 

incompleteness of women’s age, which is the horizontal axis for all three graphs. In each graph, a red 

diagonal line represents hypothetical equality of the indicator on the vertical axis with incompleteness of 

women’s age, and a green line is the fitted line when the indicator on the vertical axis is regressed on 

incompleteness of women’s age. 

In the upper left graph, the nearness of the two lines is due to the high correlation between incompleteness 

of men’s age and incompleteness of women’s age. Far more points are below the line of equality than are 

above it. That is, given the woman’s level of incompleteness, the man’s level tends to be lower. A handful 

of surveys are 20 to 30 points below the line, implying substantially less incompleteness for men than for 

women. Our interpretation would be that knowledge of birthdate and age may be more salient for men than 

for women, in the countries represented by the points farthest below the red diagonal. 

In the upper right graph, there is much more scatter, and far more points are above the line of equality, 

rather than below it. That is, incompleteness of women’s age at first union is not just more common than 

incompleteness of women’s age—the former is more likely for any given level of the latter. Even so, another 

handful of surveys are 20 to 30 points below the line, a deviation from the typical pattern. Incompleteness 
14 

of women’s age at first union is probably due partly to potential ambiguity about the criterion for the first 

union and partly due to the fact that it is not as useful or salient as the woman’s own birthdate and age. 

The lower left graph shows that the correspondence between incompleteness of the woman’s birthdate/age 

and her children’s birthdates/ages is much less than the other correspondences, and also shows that the level 

of incompleteness is much lower for children than for adults. This graph includes two points that appear to 

have extreme levels of incompleteness for children. These points are for the Malawi 2015-16 and Tanzania 

2015-16 surveys. These two surveys included day of birth for children (the completeness code is b10=0 if 

day, as well as month and year, is provided). When that is taken into account, only 4.5% and 1.6%, 

respectively, of children’s birthdates/ages did not include day of birth; only 2.8% and 0.4%, respectively, 

had incompleteness codes 2-8. That is, taking into account the revised coding, those two surveys had very 

low incompleteness. In the future, all DHS surveys will obtain day of birth, as well as month and year, for 

all children in the birth histories, although not for adults. 

2.3 Heaping 

As described earlier, age heaping is measured with Myers’ Blended Index, which can be interpreted as the 

percentage of cases that would have to be shifted from over-represented final digits to under-represented 

final digits, with an adjustment to take account of the general gradient in the age distribution. A heaping 

index of 0 implies perfect uniformity across final digits 0 through 9, but simply because of randomness we 

would never expect to achieve that lower limit. 

The four graphs in Figure 2.3 show the distributions of the heaping index for the four ages used in this 

analysis: age in the household survey; age in the women’s survey; age in the men’s survey; and age in the 

birth histories. The woman’s estimate of her husband’s age and the man’s estimate of his wife’s age will be 

examined separately in Chapter 3. All four graphs have the same horizontal scale, so the differences in 

distributions appear clearly, but different vertical scales. 

Across surveys, the mean level of heaping is 5.3% in the household survey. The mean, median, and 

maximum of the level of age heaping are substantially lower for age in the household survey than for age 

in the surveys of men and women. A lower level of heaping in the household survey, for ages 0-79, than in 

the surveys of men or women, for ages 20-49, would not be expected, but it results from the difference in 

age ranges. The wider age range in the household survey includes ages below 20, for which there is 

generally not much heaping. It includes ages 50-79, for which considerable heaping at 50, 60, and 70 is 

often found, but the number of people age 0-19 is greater than the number age 50+, so the net effect is less 

heaping within ages 0-79 than within ages 20-49. Age in the surveys of men and women is sometimes a 

revision of age in the household survey, with additional probing about month and year of birth, but those 

revisions are usually small. 

Twenty-five surveys, listed in Table 2.5, have a level of heaping above an arbitrary threshold of 10%. The 

highest level, 15.2%, is in the India 2005-06 survey, and other surveys from the Indian Subcontinent are 

also on this list—two from Pakistan (2006-07 and 2012-13) and three from Bangladesh (2004, 2011, and 

2014). There are also three surveys from Nigeria (2003, 2008, and 2013), three from Benin (2001, 2006, 

and 2011-12), two from Niger (2006 and 2012), two from Sierra Leone (2008 and 2013), and a mix of other 

countries that appear on other lists. 
15 

Figure 2.3 Histograms showing the level of heaping in reported age in the household survey, in the survey 

of women, in the survey of men, and of children in the birth histories 

Table 2.5 Surveys with at least 10% heaping in reported age from the household survey 

Survey % 

Mali 2012-13 10.3 

Yemen 2013 10.5 

Bangladesh 2011 10.5 

Bangladesh 2014 10.6 

Niger 2012 10.9 

Bangladesh 2004 10.9 

Benin 2001 11.0 

Guinea 2012 11.0 

Niger 2006 11.2 

Guinea 2005 11.5 

Chad 2004 12.0 

Benin 2011-12 12.1 

Sierra Leone 2008 12.4 

Survey % 

Benin 2006 12.5 

Afghanistan 2015 12.6 

Pakistan 2012-13 12.8 

Pakistan 2006-07 13.1 

Sierra Leone 2013 13.2 

Ethiopia 2005 13.2 

Nigeria 2013 13.5 

Ethiopia 2011 13.7 

Chad 2014-15 13.8 

Nigeria 2008 14.2 

Nigeria 2003 14.3 

India 2005-06 15.2 

The mean level of heaping is 7.1% in the women’s survey. The level is 10% or more in 26 countries, listed 

in Table 2.6. This list does not include India 2005-06. That survey had a high level of heaping for age in the 

household survey (15.2%), but only 6.3% for women’s age, 7.9% for men’s age, and 2.8% for children’s 

birthdates and age. This is an exceptional combination of high heaping on age in the household survey but 

low heaping in the women’s and men’s surveys, suggesting that additional attention was given to obtaining 

better reports of age in the interviews with women and men. The three surveys in Bangladesh that were 

included on the list for high age heaping in the household survey have also moved off the corresponding 
16 

list for the women’s survey. The two surveys of Pakistan remain on the list, as do most of the other surveys 

seen above. 

Table 2.6 Surveys with at least 10% heaping in reported age from the woman’s interview 

Survey % 

Ghana 2008 10.1 

Comoros 2012 10.5 

Pakistan 2012-13 11.3 

Senegal 2010-11 11.4 

Togo 2013-14 11.4 

Pakistan 2006-07 11.9 

Mali 2001 12.8 

Ethiopia 2000 13.0 

Mali 2006 15.4 

Benin 2001 15.5 

Yemen 2013 15.6 

Benin 2011-12 16.6 

Nigeria 2003 16.6 

Nigeria 2013 17.0 

Survey % 

Mali 2012-13 17.6 

Chad 2004 17.7 

Guinea 2012 18.2 

Benin 2006 19.2 

Niger 2012 19.3 

Sierra Leone 2013 19.4 

Niger 2006 19.4 

Ethiopia 2011 20.0 

Guinea 2005 20.2 

Afghanistan 2015 20.3 

Nigeria 2008 20.6 

Ethiopia 2005 21.9 

Sierra Leone 2008 23.2 

Chad 2014-15 25.1 

The mean level of heaping in the men’s survey is 7.0%. Twenty-three surveys are at or above the arbitrary 

threshold of 10% and are listed in Table 2.7. Sierra Leone 2008 has the highest level, at 21.1%, and Sierra 

Leone 2013 is fourth highest, at 18.1%. As with the other lists, Nigeria, Afghanistan, Chad, Guinea, Benin, 

Ethiopia, and Nigeria are among the countries with the highest levels of heaping. Two surveys from 

Bangladesh that were below 10% for heaping in the women’s survey are above 10% in the men’s survey. 

Table 2.7 Surveys with at least 10% heaping in reported age from the man’s interview 

Survey % 

Mali 2006 10.1 

Ethiopia 2000 10.3 

Bangladesh 2007 10.7 

Benin 2001 11.1 

Pakistan 2012-13 11.5 

Mali 2012-13 12.0 

Ethiopia 2011 12.6 

Bangladesh 2011 12.8 

Niger 2006 13.0 

Chad 2004 13.2 

Ethiopia 2005 13.3 

Niger 2012 15.0 

Survey % 

Benin 2011-12 15.4 

Nigeria 2003 15.6 

Guinea 2005 15.7 

Benin 2006 16.6 

Nigeria 2013 17.2 

Guinea 2012 17.7 

Chad 2014-15 18.1 

Sierra Leone 2013 18.1 

Afghanistan 2015 19.9 

Nigeria 2008 20.0 

Sierra Leone 2008 21.1 

Finally, the mean level of heaping for children’s ages in the birth histories is only 2.5%. No survey reaches 

a level of 10%. Only five surveys have Myers’ Index values at or above 5%: Mali 2012-13 (5.3%), Sierra 

Leone 2008 (5.8%), Guinea 2005 (5.8%), Afghanistan 2015 (6.8%), and Chad 2014-15 (7.1%). These are 

all surveys that appear with high levels of the other heaping indicators. 

A Myers’ Index of 5% is very low. For example, compared with the expectation of 10% of cases at each 

final digit 0 through 9, the index would be 5% if we observed 13% at final digit 0, 12% at final digit 5, and 

less than 10% at all other final digits. With this hypothetical distribution, if the 3% excess at final digit 0 
17 

and the 2% excess at final digit 5 were redistributed to the final digits with deficits, then we could achieve 

a uniform distribution. The generally low value of this index for children ever born to the women in the 

survey of women, within the 30 years before the survey, may be due to the manner in which the birth history 

is constructed, starting from the first birth and working toward the most recent birth. The procedure places 

more emphasis on a birthdate, if it can be obtained, than on age. The children are positioned in a sequence. 

There may be other forms of age-related misreporting, such as tending to list children two years apart, but 

the age distribution of children is less heaped than the other age distributions. 

Figure 2.4 shows the correspondences among the measures of age heaping. Age heaping in the household 

survey is the standard and is the horizontal axis in all three graphs. The red line represents hypothetical 

equality with the household survey’s level of heaping. The green line is produced by regressing on the 

household survey’s level of heaping. 

Figure 2.4 Scatterplots showing the level of heaping in reported age in the survey of women, the survey of 

men, and the children in the birth histories, versus the heaping in reported age in the household 

survey 

The upper left graph compares the age heaping in the survey of women with that in the household survey. 

The great majority of points are above the line of equality, showing that within each survey there tends to 

be worse heaping in the survey of women than in the household survey. As stated above, the difference is 

due to the different age ranges. The fitted line is above the line of equality, but roughly parallel to it, 

implying that the difference is mostly independent of the level. However, the correspondence is much closer 

when both levels are below a level of about 10% than when either of them is higher. There is considerable 

scatter when the levels are high. Four surveys have relatively high heaping in the household survey but 

relatively low heaping in the survey of women, and when the heaping in the household survey is in the 

range of 10%-15% there are about 20 surveys with heaping of women’s ages in the range of 15%-25%. A 

similar pattern is seen in the upper right graph, for heaping of men’s ages compared with the household 

survey, and in the lower left graph, for heaping of children’s ages compared with the household survey. In 
18 

each graph, the point farthest to the right and farthest below the line of equality is for the India 2005-06 

survey. 

The pairwise correlations among the four heaping indicators are in a range from 0.79 to 0.91. The highest 

correlation (0.91) is between the women’s index and the men’s index. In all surveys, the interviewers are 

the same sex as the respondent. The high similarity in the extent of heaping—and of incompleteness, which 

was observed above—in the surveys of men and of women is probably due to the initial assessment of age 

during the household interview, in which the household interviewer and the household respondent 

determine (or negotiate) the ages of all the household members. Once those numbers are set, there is 

relatively little adjustment during subsequent interviews with the men and women who have been identified 

as eligible respondents for the surveys of women and men. 

2.4 Displacement 

Age displacement is an issue when a specific age range or time interval is used as a criterion for some 

purpose. In some situations, respondents may be motivated to adjust their age relative to an age of legal 

majority (e.g., 21) or military service (e.g., 18) or eligibility for retirement (e.g., 65). In the context of DHS 

surveys, it is the interviewer who may have an incentive to shift respondents out of an age range or time 

interval that is a criterion for eligibility for a longer interview. DHS has been sensitive to this phenomenon 

at age 14-15 and age 49-50 for women, and at the boundary for the health questions about children under 

age 5. Figure 2.5 shows the distributions of the displacement index for these three potential transfers. All 

of them are interpretable as percentages. For example, the index for displacement from age 15 to 14 is an 

estimate of the percentage of women with true age 15 who are misstated to be age 14. The procedure for 

calculating the “true” numbers at ages 15 and 14 was described earlier. Note that both the horizontal and 

vertical scales are different in the three graphs in Figure 2.5. 

All three indicators are oriented such that a positive value is interpretable as a shift in the expected direction 

and a negative value as a shift in the opposite direction. A reference value of 0 means that there is no 

evidence of a shift—at least not with this particular indicator—but because of sampling variation we would 

expect some variation around 0 even if there were not a systematic tendency for cases to be shifted. In all 

three graphs, the great majority of transfers are in a positive direction, but some are in a negative direction 

(to repeat, the measure is aligned so that shifts in the expected direction are positive). 

The average transfer from age 15 to 14 is only 2.6%, but with a very wide range, from -31.6% to 50.8%. 

Table 2.8 lists the surveys with the most extreme levels of transfers from age 15 to 14—including reverse 

transfers, indicated with negative values. These are all the transfers outside an arbitrary range from -15% 

to 15%, and they are accompanied by an approximate z statistic for a test of the null hypothesis that the 

population value was 0%, and by symbols to describe the level of significance. 
19 

Figure 2.5 Histograms showing the level of displacement from age 15 to 14, or from age 49 to 50, for 

women in the household survey, and displacement across the boundary for the health questions 

in the birth histories 

In Tables 2.8 through 2.10, a “+” sign indicates significance in the expected direction and a “-” indicates 

significance in the opposite direction. One sign indicates significance with a two-tailed .05 test, two signs 

with a .01 test, and three signs with a .001 test. Adjustments for weights, clustering, and stratification are 

not included. Test statistics and p-values are included explicitly in the discussion of transfers because 

transfers can occur in either direction, and we emphasize that the listed values are not random variations 

around zero, but can be highly significant in either a positive (expected) or a negative (opposite) direction. 

In the lists of surveys with high levels of incompleteness or heaping, the measures were highly significant 

but test statistics and p-values were omitted. 
20 

Table 2.8 Surveys with age transfers from age 15 to 14 outside the range of -15% to +15%, and the 

statistical significance of those transfers 

Survey % z Sig. 

Sierra Leone 2013 -31.6 -11.77 --- 

Nigeria 2013 -29.6 -15.58 --- 

Nigeria 2003 -25.0 -6.19 --- 

Ethiopia 2011 -19.7 -7.46 --- 

Nepal 2006 -18.2 -5.23 --- 

Cameroon 2011 -17.1 -5.91 --- 

Ethiopia 2000 -16.1 -5.97 --- 

Liberia 2013 -15.9 -4.26 --- 

Ukraine 2007 15.2 2.89 ++ 

Myanmar 2015-16 16.9 4.76 +++ 

Namibia 2013 17.1 4.84 +++ 

Gabon 2012 17.2 4.55 +++ 

Azerbaijan 2006 17.4 4.33 +++ 

Sao Tome and Principe 2008-09 18.9 2.83 ++ 

Swaziland 2006-07 19.6 4.62 +++ 

Congo Democratic Republic 2007 23.4 7.15 +++ 

Niger 2012 23.9 7.03 +++ 

Madagascar 2003-04 27.0 7.17 +++ 

Mali 2012-13 27.8 8.27 +++ 

Sierra Leone 2008 50.8 14.57 +++ 

The highest level of transfers in the expected direction, 50.8%, is found for the Sierra Leone 2008 survey. 

However, the highest level of transfers in the contrary direction, -31.6%, is found for the next survey in 

Sierra Leone, conducted in 2013. It is apparent that the high level in 2008 was noted, during or after 

fieldwork, and some procedures were adopted for the 2013 survey, through special training and supervision, 

in an attempt to prevent such transfers. Unfortunately, the net effect was an over-correction that moved 

older children into the time frame for the child health questions. 

The Nigeria 2003 and 2013 surveys have the next-worst level of transfers from age 15 to 14 in a negative 

direction (that is, from age 14 to 15), almost certainly because the level of positive transfers was very high 

in the Nigeria 1999 survey (as noted in Pullum 2006) and an intentional effort was made to prevent such 

transfers, but the result was an over-correction. It is likely that other surveys with negative transfers also 

experienced over-correction for anticipated shifts from age 15 to 14. 

In terms of relative frequency, the most serious type of transfer is from age 49 to 50, some of which is 

certainly due to heaping at age 50. Across all household surveys, the mean level is 21.3%, with a very wide 

range from -57.1% to 83.1%. Table 2.9 includes six surveys with negative shifts in excess of 15%. Four of 

the six surveys with extreme shifts in the unexpected direction, in effect from 50 to 49, were in 

Bangladesh—the surveys of 2004, 2007, 2011, and 2014. Clearly, these are the result of over-correction for 

an expected shift from age 49 to 50. 

Many surveys had large shifts from age 49 to 50, so the threshold for identifying specific surveys will be 

set at a much higher level than 15%. Table 2.9 includes 14 surveys with shifts from 49 to 50 in excess of 

50%. The list includes two surveys from Niger (2006 and 2012) and two surveys from Benin (2006 and 

2011-12). It does not appear that there was any attempt to reverse the level of transfers following the first 

survey in each pair, because the second survey had the same or a higher level of transfers. 
21 

Table 2.9 Surveys with age transfers from age 49 to 50 outside the range from -15% to 50%, and the 

statistical significance of those transfers 

Survey % z Sig. 

Bangladesh 2007 -57.1 -9.91 --- 

Bangladesh 2014 -41.8 -8.96 --- 

Bangladesh 2011 -36.9 -8.24 --- 

Rwanda 2014-15 -24.1 -4.10 --- 

Bangladesh 2004 -23.6 -4.03 --- 

Cambodia 2014 -17.7 -4.26 --- 

Niger 2012 54.2 9.95 +++ 

Niger 2006 54.2 8.44 +++ 

Benin 2006 54.2 13.24 +++ 

India 2005-06 55.3 31.14 +++ 

Gambia 2013 57.2 10.60 +++ 

Pakistan 2006-07 57.2 43.66 +++ 

Senegal 2014 57.3 8.22 +++ 

Angola 2015-16 60.0 14.17 +++ 

Senegal 2010-11 60.5 13.32 +++ 

Yemen 2013 66.5 18.77 +++ 

Comoros 2012 70.6 9.87 +++ 

Mali 2012-13 75.0 12.30 +++ 

Benin 2011-12 79.8 20.05 +++ 

Sierra Leone 2008 83.1 13.87 +++ 

Transfers across the boundary for the health questions have a mean value of 7.5%, with a range from -6.7% 

to 28.3%. The surveys listed in Table 2.10 had more than 15% displacement, all in the expected direction. 

The only countries appearing more than once are Malawi (2000 and 2004), Madagascar (2003-04 and 2008-

09), and Egypt (2008 and 2014). Most of the countries on this list appear for early surveys within the time 

interval (2000-15) but are not repeated for later surveys. Both Malawi and Madagascar have had more 

recent surveys in which these kinds of transfers were less serious. It appears that this kind of transfer can 

be remediated without serious over-correction in the opposite direction. 

 
22 

Table 2.10 Surveys with average transfers for health that are 15% or greater and statistical significance of 

those transfers 

Survey % z Sig. 

Sao Tome and Principe 2008-09 15.0 3.81 +++ 

Yemen 2013 15.2 11.43 +++ 

Egypt 2008 15.3 9.60 +++ 

Uganda 2006 15.8 8.67 +++ 

Swaziland 2006-07 16.1 5.01 +++ 

Egypt 2014 16.2 12.02 +++ 

Malawi 2004 16.6 9.87 +++ 

Ethiopia 2005 16.6 9.95 +++ 

Kenya 2008-09 17.1 8.04 +++ 

Madagascar 2003-04 17.2 7.60 +++ 

Madagascar 2008-09 17.7 12.17 +++ 

Afghanistan 2015 17.8 19.17 +++ 

Ghana 2008 18.3 6.00 +++ 

Benin 2006 18.4 13.72 +++ 

Indonesia 2002-03 18.5 13.87 +++ 

Malawi 2000 22.3 13.93 +++ 

Sierra Leone 2008 23.9 10.96 +++ 

Mali 2006 24.9 17.99 +++ 

Niger 2006 25.1 14.77 +++ 

Niger 2012 28.3 19.33 +++ 

Figure 2.6 Scatterplot showing the level of displacement from age 15 to 14 versus the level of displacement 

from age 49 to 50, for women in the household survey 
23 

Figure 2.6 gives a scatterplot of the two types of shifts in the in the household age distribution. There is no 

evidence of statistical association between them (there is also no evidence of association with transfers of 

children). However, the Sierra Leone 2008 survey had the highest level of transfers from age 49 to 50 

(83.1%) and the highest level of transfers from age 14 to 15 (50.8%). It also had the second highest level 

of transfers of children across the boundary for the health questions (23.9%), second only to Niger 2012 

(28.3%). In the Sierra Leone 2013 survey, the level of transfers from age 14 to 15 was reversed because of 

over-correction of the direction of transfers in the earlier survey; the other two transfers remained in the 

expected direction but were more moderate. 

2.5 Composite Measures of Data Quality 

Finally, we attempt to summarize changes from 2000 to 2015 by constructing standard scores for the three 

types of misreporting, with the following three steps: 

First, the displacement indicators are replaced with their absolute values. The incompleteness and heaping 

indicators all have natural zeros—they can never be negative, and under ideal conditions they would all be 

zero except for sampling error—but the displacement indicators can be positive or negative. They were 

constructed to be positive for displacement in the expected direction, but they can be negative if, say, there 

was over-correction. The absolute value measures the magnitude of displacement, regardless of the 

direction. 

Second, the mean and standard deviation of each indicator were calculated, and then a standard score (or zscore) was calculated by subtracting the mean and dividing by the standard deviation. That is, each indicator 

is represented by a new score that has a mean of 0 and a standard deviation of 1. By doing this, we put all 

11 indicators, which vary widely in their means and standard deviations, onto the same scale. 

Third, we calculate the average of the z-scores within each group. “Incompleteness” is the average of the 

four z-scores in that group of indicators, “Age heaping” is the average of the four z-scores in that group, 

and “Displacement” is the average of the three z-scores in that group. In each case, the average is based on 

the number of indicators of each type that are available. “Combination” is an average of the z-scores for all 

11 indicators, giving the same weight to all of them. 

The purpose of Figure 2.7 is to see how each of these averages has changed over time, within the interval 

2000 to 2015. If, say, there were clear and steady improvement in the level of incompleteness, then the 

upper left graph would start well above 0 in 2000 and steadily move downward, ending well below 0 in 

2015. For all of the figures, a downward trend would be interpreted as improvement in the quality of the 

age and date reporting. Each indicator measures an undesirable characteristic, so low values are preferable 

to high values. 
24 

Figure 2.7 Changes in the levels of incompleteness, heaping, displacement, and a combination of all three 

in DHS surveys conducted from 2000 to 2015 

All four lines show some decline during the final three years, 2013-2015, but for the full interval 2000-

2015 there is no convincing evidence of improvement. All the figures show an upward spike in 2006, to the 

maximum values for incompleteness, heaping, and displacement, with the exception of the spike for 

incompleteness in 2001.9

 Each type had a minimum in 2002, moved upward to 2006-2008, had another 

minimum in 2009-2010, moved upward to 2013, and then moved back down to the most recent year, 2015. 

Some short-term variation is clear, but, to repeat, there is no clear evidence of long-term improvement 

between 2000 and 2015. 

The combination indicator, which averages the 11 z-scores for the full set of indicators, varies considerably 

across surveys.10 In Figure 2.8, we take the indicator one step further by calculating an average of the 

combination indicator for all the surveys that have been conducted in a given country. This step allows us 

to rank countries in the quality of their data collected during 2000-2015. It should be helpful, when planning 

another survey in a country where DHS has worked before, if DHS has objective evidence of past problems 

and can target training and resources to avoid a recurrence of those problems. 

 

9

 There might appear to be a downward trend with incompleteness, which would continue an earlier pattern noted in 

Pullum (2006), but this impression is simply due to the spike in incompleteness in 2001. 

10 The Combination indicator is equivalent to the first principal component of a principal component analysis of the 

11 indicators. 
25 

Figure 2.8 Distribution of the summary measure of incompleteness, heaping, and displacement across the 

148 surveys in this analysis 

In Figure 2.8, lower values on the horizontal axis indicate better quality data. The reference value of 0 is 

the mean for all 148 surveys, and is approximately the mean for all 67 countries represented in the figure. 

The distribution is skewed to the right, with 13 countries having a separation from the main body of the 

figure and a combination level of 0.6 or above. It is somewhat arbitrary where to draw a boundary, but these 

countries represent the bottom quintile in data quality. We list them in Table 2.11, ordered by the 

combination level but including the standard scores for incompleteness, heaping, and displacement, to give 

a clearer idea of what dimension(s) of age and date reporting are most problematic. Sierra Leone and Niger 

have virtually identical combination scores, the highest on the list. Most countries in the table are in subSaharan Africa, with the exceptions of Afghanistan, Bangladesh, Pakistan, and Yemen. 

Table 2.11 Countries in the poorest quintile of data quality for ages and dates, based on a combination of 

standardized scores for incompleteness, heaping, and displacement in DHS surveys during 

2000-2015 

Country Surveys Incomp. Heaping Displacement Combination 

Comoros 1 0.53 0.65 0.72 0.63 

Pakistan 2 1.31 1.67 0.02 0.95 

Guinea 2 0.83 2.01 -0.01 0.98 

Bangladesh 4 2.23 1.01 -0.13 1.00 

Nigeria 3 -0.05 2.11 1.03 1.01 

Mali 3 1.08 1.02 1.26 1.12 

Yemen 1 1.64 1.11 0.83 1.14 

Ethiopia 3 1.55 1.36 0.46 1.16 

Benin 3 1.60 1.43 0.94 1.32 

Chad 2 1.30 2.28 0.18 1.39 

Afghanistan 1 0.58 2.82 0.96 1.45 

Niger 2 2.01 1.59 1.86 1.82 

Sierra Leone 2 0.64 2.39 2.47 1.83 
26 

The 13 countries comprising the lowest quintile of the combination score, that is, the best quintile of data 

quality, are listed in Table 2.12. Guatemala, which had only one DHS survey (in 2014-15), has the lowest 

combination score of all surveys. This list includes other countries in Latin America and the Caribbean, 

namely Colombia, Guyana, Honduras, and Peru. It also includes Albania, Armenia, Kyrgyzstan, Moldova, 

and Tajikistan, in Eastern Europe and Central Asia, and one country in Southeast Asia—the Philippines—

and one in sub-Saharan Africa—Zimbabwe. 

Of the countries in Table 2.11, there are three where DHS conducted only one survey within the time 

interval. In Table 2.12, there are seven where DHS conducted only one survey within the time interval. 

However, it cannot be inferred that the quality of age and date reporting has nothing to do with accumulated 

experience, because many of the countries in the best quintile had DHS surveys prior to 2000. 

The identification of specific surveys and countries having relatively poor data, or relatively good data, in 

terms of age and date reporting, is based on diagnostic tools that are limited in their reliability and validity 

and are themselves subject to sampling and measurement error. Good quality and poor quality have many 

potential sources, of which one of the most important is simply the relevance of knowing one’s age or 

birthdate with any accuracy in the context of daily life in each country. If respondents do not know their 

age, because they do not need to know their age for practical purposes, for example, then no amount of 

training of interviewers can substitute. Nevertheless, knowledge of the surveys in which the reporting of 

ages and dates has had a relatively high frequency of problems may be useful for redirecting resources in 

anticipation of potential problems in future surveys. 

Table 2.12 Countries in the highest quintile of data quality for ages and dates, based on a combination of 

standardized scores for incompleteness, heaping, and displacement in DHS surveys during 

2000-2015 

Country Surveys Incomp. Heaping Displacement Combination 

Guatemala 1 -1.04 -0.87 -0.90 -0.93 

Colombia 4 -0.83 -0.89 -0.80 -0.83 

Jordan 3 -0.94 -0.94 -0.68 -0.83 

Philippines 3 -0.95 -0.92 -0.57 -0.80 

Honduras 2 -0.74 -0.80 -0.75 -0.76 

Albania 1 -1.02 -0.67 -0.57 -0.75 

Peru 6 -0.79 -0.67 -0.73 -0.73 

Republic of Moldova 1 -0.97 -0.54 -0.64 -0.72 

Kyrgyzstan 1 -1.03 -0.51 -0.60 -0.71 

Guyana 1 -0.59 -0.81 -0.68 -0.69 

Armenia 3 -1.04 -0.70 -0.30 -0.68 

Tajikistan 1 -1.08 -0.74 -0.28 -0.64 

Zimbabwe 3 -0.92 -0.73 -0.23 -0.63 

Viet Nam 1 -0.99 -0.52 -0.45 -0.62 
27 

3 The Quality and Consistency of Spousal Age Estimates in 

DHS Surveys 

Core DHS questionnaires for women first began to include questions about age of husband or live-in partner 

in DHS Phase 3 (Macro International 1995a, 1995b). Since then, in most DHS surveys women who are 

married or living with a man as if married are asked the age of their partner. Core questionnaires for men 

were first included in DHS core questionnaires in Phase 5 (Macro International 2008b); men who are 

married or living with a woman or with multiple women as if married are asked the age of their partner(s). 

These questions facilitate an analysis of their partner’s age and their relative age difference in relation to 

demographic and health outcomes even in cases where the partner is not interviewed or is not a member of 

the household. 

In surveys and households where men are interviewed, it is possible to compare women’s estimates of their 

husband’s or partner’s (herein: spouse’s) age and men’s self-reported age. Additionally, in many recent 

surveys where men are asked the age of their spouse(s) a similar comparison between women’s selfreported age and their spouse’s estimate of their age is possible. 

While it may be presumed that self-reported ages are more accurate than spousal estimates of age, when 

the two reports conflict it is unknown which—if either—reflects the ‘true’ value of age. Instead, we can 

examine consistency and heaping of self-reports and spousal estimates of age. In this section we explore 

the composition of partnerships by self-reported age difference and the consistency of spousal estimates 

and self-reports of age. Additionally, we test four hypotheses about spousal versus self-reported age: 

• Hypothesis 1: The age difference between partners tends to be reduced by spousal estimates. 

• Hypothesis 2: Spousal age estimates have more heaping than self-reported age. 

• Hypothesis 3: The older the individual, the less consistent estimates of their spouse’s age will 

be with self-reported age. 

• Hypothesis 4: Individuals with primary, secondary, and higher education will provide more 

consistent estimates of their spouse’s age than individuals with no education. 

3.1 Data and Methods 

Data for this analysis come from DHS couples’ files, which include all couples where both the man and 

woman were living in the same household, were individually interviewed by DHS, and each identified the 

other as someone they were married to or living together with as if married. Women, who are never asked 

by DHS surveys about multiple husbands, may appear in the couples file only once. Men married to more 

than one woman who was interviewed by DHS will appear in each applicable couple in the dataset. 

Surveys used in this analysis are Standard and Continuous DHS surveys fielded in 2000 or after where men 

were interviewed and where women and/or men were asked about their spouse’s age. In total, 113 surveys 

qualified for inclusion. Appendix D shows a list of these surveys. As the table indicates, women were asked 

about their spouse’s age in all of these surveys, but men were asked about their spouse’s age in only 67 

surveys. All analysis is unweighted, both at the survey and couple level. 

We analyze the composition of couple’s age differences, the direction of differences of spousal estimates, 

heaping in spousal estimates, and consistency of estimates by age and education. For tests of heaping 

(Myers’ Index), all ages had to be restricted such that they would be allowed to span an equal number of 

ages ending in digits 0 to 9. The minimum and maximum age bounds for heaping analysis and for all other 
28 

analysis are shown in Appendix D. Note that in 16 surveys men’s age was allowed to be as low as 20, but 

in the couple’s file men’s minimum age was actually 21 or 22. For consistency of estimates by age and 

education, regression analysis was run to predict the absolute and percentage difference of spousal age 

estimates based on the age or education of the husband or wife who was assessing their partner’s age. 

3.2 Composition of Couples by Direction of Age Difference 

In each of the 113 surveys included in this study, for marriages and live-in partnerships (herein marriages) 

where both members of a co-resident couple were individually interviewed and where each member 

reported the other as a spouse or someone with whom the respondent was living with as if married, we 

examined the responses for differences between the two members in self-reported ages. Figure 3.1 shows 

the distribution of the share (proportion) of marriages by direction of age difference in all 113 surveys. The 

share of marriages where the man is older than the woman ranges from 65% in Myanmar (2015-16) to 99% 

in Guinea (2012), Mali (2006), and Niger (2006, 2012), with an average of 89% and a median of 91% 

across surveys. The distribution of the percentage of marriages where the woman’s self-reported age is 

greater than the man’s self-reported age ranges from <0.01% in Niger (2006, 2012) to 25% in Myanmar 

(2015-16), with a survey average of 7% and a survey median of 5%. The share of marriages where the 

man’s and woman’s self-reported ages are equal averages and has a median of 4% across surveys, and 

ranges from 0.04% in Niger (2012) to 11% in Bolivia (2003), Moldova (2005), Myanmar (2015-16), and 

Ukraine (2007). 

Figure 3.1 Distribution of the share of marriages by direction of age difference 

3.3 Difference between Spousal Age Estimates and Self-Reports 

On average across surveys, the difference between women’s estimates of their husband’s age and husband’s 

self-reports is -0.03 years, and the average absolute difference is 0.77 years. The cross-survey average 

difference between men’s estimates of their wife’s age and wife’s self-reports is -0.15 years and the average 

absolute difference is 0.53 years. These near-zero averages disguise important variation; the average survey 

standard deviation of the difference between women’s estimates of their husband’s age and husband’s self-
29 

reports of their age is 2.21 years, and the average survey standard deviation of the difference between men’s 

estimates of their wife’s age and wife’s self-reports is 1.46 years. These standard errors suggest some 

important differences between spousal estimates and self-reports of age. 

In this section we test the first of our four hypotheses about spousal age estimates—that the age difference 

between partners tends to be reduced by spousal estimates. To test this hypothesis we examine, for each of 

the differential assessments—wife’s estimate of husband’s age versus husband’s self-report, and husband’s 

estimate of wife’s age versus wife’s self-report—the distribution of the difference across three types of 

marital age differences: man is older, woman is older, and man and woman are the same age. Note that 

these classifications of marital age difference are themselves based on self-reported ages. 

Figure 3.2 shows the distribution of the survey average difference between wife’s estimate of husband’s 

age and husband’s self-reported age in the three types of marriages. In marriages where the man is older, 

the average of this difference across surveys ranges from -1.03 to 0.59 years, with a mean of -0.11; in other 

words, women whose husbands are older tend to estimate that their husbands are slightly younger than their 

self-reported age. This lends support to the hypothesis that spousal age estimates tend to reduce the age 

difference between spouses. 

Figure 3.2 also shows the distribution of the same survey metric—average difference between wife’s 

estimate of husband’s age and husband’s self-reported age—is shown for marriages where the woman is 

older. Here we see nearly universal support for the hypothesis that women’s estimates of their husband’s 

age tends to reduce the age difference between them: women who are older than their husband tend to 

estimate their husband’s age as older than their husband’s self-reported age. The survey average of this age 

difference ranges from -0.03 to 6.86 years, with a cross-survey average of 1.20 years older. 

The distribution of the average difference between wife’s estimate of husband’s age and husband’s selfreported age is also shown in Figure 3.2 for the baseline group: marriages where the woman and man selfreport the same age. Here, women tend to estimate their husband as older than the husband’s self-report, 

with an average of 0.70 years across surveys, and a range from -0.06 to 3.37 years. This makes the earlier 

result for marriages where the woman is older appear more normative, although still greater than baseline. 

It also makes the result for marriages where the man is older appears more impressive: women may 

normally tend to estimate their husbands as older than husband’s self-reports, but in marriages where the 

men are older, women estimate them to be younger than men’s self-reports. Thus in marriages where a 

spousal age difference exists, the evidence is consistent with the hypothesis that women’s estimates of their 

spouses ages tend to reduce the age difference between them; in the absence of a spousal age difference, 

women tend to estimate their husbands to be older than husbands self-report. 
30 

Figure 3.2 Distribution of average difference between wife’s estimate of husband’s age and husband’s selfreported age, by marriage type 

Figure 3.3 shows the results of our test of the same hypothesis on men’s estimates of their wife’s age. The 

average difference across surveys between husband’s estimate of wife’s age and wife’s self-reported age 

for marriages where the man is older ranges from -0.40 to 0.10 years, and averages -0.08 years. This is 

generally inconsistent with our hypothesis that spousal estimates tend to reduce the age difference: men 

who self-report as older than their wife tend to slightly underestimate their wife’s age. In the distribution 

of the same survey metric for marriages where the woman self-reports as older, we see a near-universal 

average of less than zero, meaning men tend to underestimate their wife’s age when their wife self-reports 

as older. The average of this metric across surveys ranges from -6.83 years to 0.05 years, with an average 

of -1.31 years. Finally, the distribution of the average difference across surveys between husband’s estimate 

of wife’s age and wife’s self-reported age in the baseline group—marriages where both partners self-report 

the same age—ranges from -4.04 to 0.04 years, with an average of -0.74. These results suggest that men 

generally tend to underestimate their wife’s ages, more so if the wife self-reports to be older and less so if 

the wife self-reports to be younger than the husband. 
31 

Figure 3.3 Distribution of average difference between husband’s estimate of wife’s age and wife’s selfreported age, by marriage type 

3.4 Heaping in Spousal Age Estimates 

In this section we test our second hypothesis—that spousal age estimates have more heaping than selfreported age. As in the earlier chapter, we compare the Myers’ Index estimate of heaping, which can be 

interpreted as the minimum percentage of cases that would have to be shifted from one digit to another in 

order to achieve a uniform distribution across ages. 

Figures 3.4a and 3.4b show a comparison of the Myers’ Index for self-reported ages and spousal estimates 

of age. In six surveys, all from West Africa, Myers’ Index for self-reported ages among men is above 20%—

Nigeria 2008, Sierra Leone 2008, Nigeria 2013, Benin 2006, Nigeria 2003, Sierra Leone 2013—and in all 

of these cases Myers’ Index is higher for spousal estimates than for self-reports. In five surveys—

Dominican Republic 2002, Indonesia 2012, Zambia 2013-14, Haiti 2012, and Dominican Republic 2007—

Myers’ Index is below 3%, and in all except Gabon Myers’ Index is higher for spousal estimates. In all, the 

correlation coefficient for Myers’ Index of self-reported age and Myers’ Index of spousal estimates of age 

is 87%. In 91 of 113 surveys (81%) Myers’ Index is higher for spousal estimates than for self-reports of 

age. These results for men support the hypothesis that there is more heaping in spousal estimates of age 

than in self-reports. 

To compare heaping of spousal age estimates versus self-reported age among women, we computed the 

Myers’ Index for all 67 surveys in which men were asked to estimate their wife’s age. Figure 3.7 shows a 

comparison of Myers’ Index for self-reported ages among women and Myers’ Index for spousal estimates 

of age. The estimates for self-reported age range from 1% in Myanmar 2015-16 to 24% in Sierra Leone 

2008. In 53 of 67 surveys (79%), Myers’ Index is higher for spousal estimates of age than for self-reports. 

Hence, the results for women also support the hypothesis that there is more heaping in spousal estimates of 

age than in self-reports. 
32 

Figure 3.4a Myers’ Index for men’s ages, self-reports versus spousal estimates 
33 

Figure 3.4b Myers’ Index for men’s ages, self-reports versus spousal estimates (continued) 
34 

Figure 3.5 Myers’ Index for women’s ages, self-reports versus spousal estimates 
35 

3.5 Consistency of Spousal Estimates by Age 

Here we test our third hypothesis—that the older the individual, the less consistent estimates of their 

spouse’s age will be with self-reported age in absolute terms. To test this hypothesis we ran unweighted 

regressions of the absolute difference between spousal estimates and self-reported age by age of spouse for 

each survey. Figure 3.6 shows the coefficients of woman’s self-reported age on the absolute difference 

between women’s estimates of their husband’s age and husband’s self-reported age, in ascending order of 

coefficient. In 45 of 113 surveys the coefficient is negative, but in only one survey (Kenya 2014) is it 

significantly less than zero. This indicates that in Kenya the lower the wife’s age the less consistent her 

estimation of her husband’s age is with his own reported age. In 21 of the 68 surveys where the coefficient 

on wife’s age is positive it is also statistically significant—that is, the higher the wife’s age the less 

consistent her estimate of her husband’s age is with the husband’s own report. Notably, in the Maldives 

2009 and in Benin 2001 the coefficient is significant and quite high in relative terms (0.07 and 0.16, 

respectively), indicating for example in Benin 2001 that for each additional year of wife’s age the absolute 

difference between her estimate of her husband’s age and his self-reported age is predicted to increase by 

0.16 years. These results provide support for our hypothesis in almost one-fifth of the surveys studied, and 

provide contrary evidence in just 1 of the 113 surveys studied.

Figure 3.6 Coefficient for wife’s self-reported age from regression on absolute difference between 

husband’s self-reported age and wife’s estimate of husband’s age 

We also tested the age hypothesis on husband’s estimates of wife’s age. The results are shown in Figure 

3.7, which—as with subsequent charts—uses a different x-axis scale. As Figure 3.7 shows, in 11 of 67 
36 

surveys the coefficient on women’s age is negative, but not significant. In 31 of the remaining 56 surveys 

where the coefficient is in the expected direction (positive), it is also statistically significant. Notably, the 

coefficients never reach even half of the level they did in Maldives 2009 or Benin 2001 for men, which 

suggests that while age may be statistically significant it is less salient of a factor in accuracy. Taken 

together, these findings provide modest support for the hypothesis that the absolute value of differences 

between spousal estimates and self-reports of age is higher at older ages. 

Figure 3.7 Coefficient for husband’s self-reported age from regression on absolute difference between 

wife’s self-reported age and husband’s estimate of wife’s age 

3.6 Consistency of Spousal Age Estimates by Education 

To test our fourth hypothesis—that individuals with primary or secondary-plus education will provide more 

consistent estimates of their spouse’s age than individuals with no education—we ran unweighted 

regressions of the absolute difference between spousal estimates and self-reported age by education of 

spouse (primary versus none, secondary and higher versus none) for each survey. Armenia 2005 and 

Ukraine 2007 were excluded from this analysis because in these two surveys all women in the couples file 

had some education. 

Figure 3.8 shows the coefficients on wife’s primary education (versus none) on the absolute difference of 

her estimate of husband’s age and his self-reported age, in ascending order of coefficient size. In nearly half 

of the surveys the coefficient is negative, and in 22 surveys it is negative and statistically significant, 

consistent with our hypothesis. The significant effect of primary education on accuracy was largest in 

Senegal 2010-11, Zimbabwe 2010-11, Benin 2001, Senegal 2015, Cameroon 2011, and Dominican 
37 

Republic 2002. However, in six surveys—Mali 2006, Zambia 2007, Burkina Faso 2010, Timor-Leste 2009-

10, Afghanistan 2015, and Kenya 2014—the coefficient is positive and statistically significant, counter to 

our hypothesis. 

Figure 3.8 Coefficient for wife having primary education (versus none) from regression on absolute 

difference between husband’s self-reported age and wife’s estimate of husband’s age 

Figure 3.9 shows coefficients on wife’s secondary education (versus no education) from the same 

regression, in ascending order. Here, we would expect the effect of education on consistency of ages to be 

stronger, and in general it is, both in magnitude and in frequency. In 70 of 111 surveys, the coefficient on 

secondary education is negative, and in 29 cases the coefficient is both negative and statistically significant, 

which supports our hypothesis. The significant effect of secondary education on accuracy is strongest in 

Maldives 2009, Benin 2001, Zimbabwe 2010-11, and Senegal 2010-11. Meanwhile, in six cases the 

coefficient is positive and statistically significant, counter to our hypothesis. 
38 

Figure 3.9 Coefficient for wife having secondary or higher education (versus none) from regression on 

absolute difference between husband’s self-reported age and wife’s estimate of husband’s age 

The same regression model on the absolute difference between spousal estimates and self-reported age was 

run for husband’s education (primary versus none and secondary or higher versus none). Figure 3.10 shows 

results for the coefficient for primary education versus none, in ascending order. Here, in over half of the 

surveys (36 of 67), the coefficient is in the expected direction (negative). In six surveys—Cameroon 2011, 

Guyana 2009, Ghana 2008, Benin 2006, Nigeria 2008, and Tanzania 2015-16—the coefficient is both 

negative and statistically significant. The results in six other surveys show positive and statistically 

significant coefficients. Thus there is insufficient support for our fourth hypothesis among husbands with 

primary education. 
39 

Figure 3.10 Coefficient for husband having primary education (versus none) from regression on absolute 

difference between wife’s self-reported age and husband’s estimate of wife’s age 

Figure 3.11 displays the effect of husband’s secondary-plus education (versus none). As expected, the effect 

of secondary education on consistency of estimates is stronger than the effect of primary education: it is 

negative and statistically significant in 13 of 67 surveys, and positive and statistically significant in 6 other 

surveys, thus providing only limited support for the education-consistency hypothesis among husbands. 
40 

Figure 3.11 Coefficient for husband having secondary or higher education (versus none) from regression on 

absolute difference between wife’s self-reported age and husband’s estimate of wife’s age 
41 

4 Conclusion 

The goal of this report has been to assess the quality and consistency of age and date reports in DHS surveys 

conducted since 2000 in 67 countries. It is the most recent of several reports on various aspects of DHS 

data quality. Ages and dates are well suited to statistical analysis because they are represented numerically 

and have true and fixed values, even if we do not know exactly what the true values are. 

The first chapter described the steps of editing and imputing during fieldwork and data processing. Great 

care is taken to train and supervise the interviewers to obtain the best possible estimates of ages and dates. 

The eligibility of adults for the surveys of women and men depends on obtaining accurate values of age 

near the lower and upper age boundaries within the household survey. The eligibility of young children for 

the detailed health questions depends on obtaining accurate estimates of when they were born within the 

surveys of women. Age-specific fertility rates, under-five mortality rates, immunization rates, 

anthropometry scores, and many other DHS indicators depend on accurate estimates of age. 

Appendix A supplements the first chapter with an inventory of all the locations in DHS surveys where the 

respondents are asked for ages, dates, and durations since an event. This detailed assessment focused on 

just a few of those locations: the ages of all household members, provided by the household respondent 

during the household survey; the self-reported ages and birthdates of women and men in the surveys of 

women and men; women’s self-reports of age and date of first union in the survey of women; the birthdates 

(and ages, if living) of children in the birth histories, provided by the mother; and the women’s and men’s 

estimates of their respective spouses’ ages in the surveys of women and men. These are a mix of self-reports 

of one’s own age and reports or estimates of someone else’s age. It would be possible to analyze the other 

ages or dates that are asked about, such as age at sterilization, but we focus on ages that we expect to be 

most salient to the respondents. 

The second chapter assessed the ages listed above, other than spousal estimates, in terms of three types of 

measures: incompleteness, heaping, and transfers. A total of 11 indicators were used. For each indicator, 

the distribution across all surveys was described and the surveys with the most extreme levels were 

identified. DHS surveys include explicit incompleteness codes to identify which ages and dates were 

adjusted during machine editing, and the type of adjustment. There is no incompleteness code for age in the 

household survey, because only age is asked—not month and year of birth. In the surveys of men and 

women, age and birthdate are flagged for about 21% of cases, usually because month of birth is not provided 

or is inconsistent with age, year of birth, and date of interview. Across surveys, the levels of incompleteness 

for men and for women are very highly correlated (r=0.97). In several surveys the level is considerably 

lower for men than for women. The reverse pattern is never found, suggesting that knowledge of age and 

birthdate may be more important for men than for women in some contexts. Women’s reports of their age 

and date at first union have higher levels of incompleteness than their age and birthdate, on average about 

31%, mainly because month is missing. Birthdates and ages of all children in the birth histories (not just 

those born in the past five years) have the lowest levels of incompleteness, on average only 5%. The birth 

histories involve a good deal of probing and adjusting during fieldwork, often including comparisons with 

a list of significant national or regional events, and proper sequencing of successive births. Across surveys, 

incompleteness in the birth histories has little correspondence with incompleteness of women’s age and 

birthdate. 

Heaping, measured with Myers’ Blended Index, is mostly due to preference at final digits 0 and 5. Heaping 

is low, only about 5%, on average, in the household surveys (for ages 0-79). Heaping is very conspicuous 

at ages such as 50, 60, 70, but not at ages below 30 in most surveys. In general, the numbers of cases in the 

ages with greatest heaping are relatively small, and that is the reason why the heaping index is usually low 

in the household survey. It tends to be a little larger in the surveys of women and men (for ages 20-49), on 
42 

average 6% for women and 7% for men, and only 3% for living children (for ages 0-29) in the birth histories. 

Ages reported in these other locations are generally the same as ages reported in the household survey; 

differences in the heaping index are largely due to differences in the age range. Pairwise correlations 

between heaping indices, across surveys, are high, in a range from 0.79 to 0.91. 

Age/date displacement can be measured in different ways, and here we use a measure developed in an 

earlier assessment (Pullum 2006). The most important potential transfers affect eligibility for further 

interviewing. It is generally understood that such transfers tend to be made by interviewers, as part of 

discussion or negotiation with respondents who are not confident in the age of the person they are reporting 

on. It is estimated that on average only about 3% of girls or women who are actually age 15 are misreported 

as being age 14, and therefore not eligible for the survey of women. These reports are not made by the 15-

year-old herself, because DHS does not interview respondents below age 15, but by a different person who 

is the household respondent, often the girl’s mother. Much more serious, it is estimated that on average 21% 

of women who are actually age 49 are misreported as being age 50, and therefore not eligible for the survey 

of women. Some of these transfers are due to heaping at age 50. Displacement of children in the birth 

histories, to a birthdate that is outside the range for additional questions on child health, is estimated to 

occur on average for about 8% of the children whose true birthdate is in the 12 months nearest the threshold 

for those questions. The correspondence among these potential transfers, across surveys, is low. 

All of these measures vary substantially. There are many surveys with values close to zero on all measures, 

and others with very high values. There are some surveys in which month of birth is hardly ever given. 

Age/date transfers are sometimes large but in a direction opposite to what we would expect, particularly 

around age 15 or around the date for the health questions, clearly as a result of over-correction during 

training and supervision. Surveys with extreme values were listed. Summary indices of incompleteness, 

heaping, and transfers were constructed and tracked over time, indexed by the year of the survey (or the 

first year of data collection, for surveys spanning two years). The indicators fluctuated substantially from 

2000 to 2015 and did not show a systematic trend. A single composite index was constructed for each of 

the 67 countries. The countries in the highest quintile (with the most problems) and the lowest quintile (with 

the fewest problems) were identified. 

The third chapter investigated the quality and consistency of spousal age estimates compared to self-reports 

of age, in the surveys that included interviews of men as well as women. Our first set of findings indicated 

that in the absence of an age gap between spouses, women tend to estimate that their husbands are older 

than their self-reported age and men tend to estimate that their wives are younger than their self-reported 

age. Evidence indicates that where there is an age difference between spouses, women tend to estimate in 

a way that reduces the gap: they underestimate the age of older husbands and overestimate the age of 

younger husbands. Men underestimate the age of wives who are older than they are, which reduces the gap, 

but they also tend to underestimate the age of wives who are younger, which increases the gap. The 

magnitude of underestimation is smaller when wives are younger. The results lend general support to the 

hypothesis that spousal age estimates tend to reduce the actual age gap between spouses. 

The third chapter also examined metrics of age heaping—excess age reports at digits ending in 0 and 5, and 

Myers’ Blended Index (the measure of heaping also used in chapter 2)—for both spousal and self-estimates 

of age among husbands and wives. We hypothesized that heaping would be greater for spousal reports than 

for self-reports. Our results gave fairly strong support for this hypothesis: in the vast majority of countries, 

there was more heaping for estimates of wife’s and husband’s age than for self-reported age. Our third 

hypothesis was that the older the individual, the less consistent their spousal age estimate would be with 

their spouse’s reported age. Here the evidence was more mixed. Regression results for women’s age on the 

absolute difference of their husband’s age and their report of their husband’s age were consistent with this 

hypothesis in one-fifth of countries and consistent in the reverse scenario in over half of the surveys in 

which men were asked the age of their wives. The fourth and final hypothesis was that more educated 
43 

spouses would provide more consistent estimates of their partner’s age than uneducated spouses. The 

hypothesis was supported modestly among women for their estimates of their husband’s age and only very 

weakly among men. 

Each interview is an interaction between an interviewer and a respondent, both of whom contribute to the 

quality of the age and date responses—the respondent to the degree that she or he actually knows the answer, 

and the interviewer to the degree that she or he is able to elicit the answer. If the respondent really does not 

know her or his age, then a skilled interviewer can nevertheless produce a good estimate by referring to 

historical events on a list or other household members whose age may be better known, but estimates by 

their nature will tend to be affected by digit preference and/or a desire to comply with guidelines related to 

heaping and to eligibility boundaries. 

Misreporting of ages and dates can potentially affect many of the indicators in DHS surveys. There has not 

been a thorough inquiry into these effects, but other features of the surveys and indicators mitigate against 

major distortion. For example, the age range for eligibility for the survey of women is 15 to 49, but at the 

ends of that range fertility is low and unmet need for contraception is low. The beginning of the time interval 

for the additional health questions is always at least 60 months before the month of interview, easily long 

enough to include abundant information about feeding practices, immunizations, child illness and treatment, 

and so on. Heaping at women’s ages ending at 0 and 5 could have some ramifications for age-specific 

fertility rates, but the standard age intervals (15-19, 20-24, etc.) apply to the mother’s age at the child’s 

birth, not to her current age, so the fact that the age intervals begin with a multiple of five is unrelated to 

any heaping of current age at multiples of five. Errors in reported ages and dates would be more serious if 

they include bias—for example, if there is a consistent tendency for respondents to be reported as older 

than they actually are—rather than being heaped by symmetric shifts from both directions. Systematic 

omission of cases, for example of births that resulted in a neonatal death, will have more serious 

ramifications than age and date errors. 

Problems with the reporting of ages and dates are potentially symptoms of broader data quality issues that 

are harder to detect. This report does not systematically investigate this assumption, but many of the surveys 

and countries that have been identified with the highest levels of the misreporting indicators would appear 

on lists of surveys and countries that have posed the greatest challenges during fieldwork. 

It is hoped that future surveys in the countries that have had the most problems in the past can be targeted 

for special training and supervision of interviewers. There is evidence that displacement and heaping, in 

particular, can be reduced—but there is also evidence that too much focus on displacement of children or 

on heaping at final digit 0 can lead to over-correction. The biggest determinant of good age reporting is 

probably the value, to the respondents, in everyday life, of knowing their ages or the ages of their children. 

This component of data quality varies from one setting to another and is outside the control of a survey 

operation. 

47 

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49 

Appendix A All Ages, Dates, and Durations Asked of DHS 

Respondents and their Evolution between DHS 

Phases 4 to 7 

During each five-year phase of the project, The DHS Program has published model household, women’s, 

and men’s questionnaires that form the basis of standard data collection during that phase. Known as ‘core 

questionnaires’, these are adapted for each country during each survey phase. Hence, questions for 

individual surveys—located in the back of each final survey report—may vary slightly from what is 

described here. However, collection of most basic indicators including ages and dates is almost fully 

standardized throughout each five-year phase. 

In the period covered by this report—from 2000 until present—The DHS Program has spanned four phases: 

DHS-4 (1997-2003), DHS-5 (2003-2008), DHS-6 (2008-2013), and DHS-7 (2013-present). The collection 

of information related to ages and dates in each questionnaire type during all four phases is described in the 

next three sections of this appendix. Note that in DHS-4 there were two types of questionnaires: Model “A” 

for countries with high contraceptive prevalence (ORC Macro 2001a) and Model “B” for countries with 

low contraceptive prevalence (ORC Macro 2001b); in subsequent program phases, questionnaires for high 

and low contraceptive prevalence countries were unified into single questionnaires for households, women, 

and men. 

Household and Biomarker Questionnaires 

In DHS-4, the core household questionnaire for both Model “A” and Model “B” countries asks an adult 

household representative to list the name (Q2) and age (Q7) of all household members, to identify a head 

of household, and to describe each member’s relationship to the household head (Q3). Additionally, the 

weight and height measurement section, which is located at the end of the household questionnaire, asks 

for the date of birth of each household child under age 6 (Q39). However this section is not necessarily 

intended to be filled in during the household interview, as the measurements require separate consent from 

each woman and are typically done by a biomarker specialist. 

In DHS-5 to DHS-7, the model household questionnaires continue to ask the household representative for 

the names and ages of all household members and their relationship to the head of household (ICF 

International 2011, 2015b; Macro International 2008a). Starting in DHS-5, the household questionnaire 

section on height, weight, and hemoglobin instructs interviewers that, if the mother of children age 0 to 5 

was interviewed, they should copy the dates of birth from the mother’s questionnaire (Q503), otherwise 

they should ask an adult respondent for the child’s birth date. This practice continued in DHS-6 and DHS7. In DHS-7 weight, height, and hemoglobin measurement were moved to a separate biomarker 

questionnaire (ICF International 2015a). 

Woman’s Questionnaire 

In the DHS-4 Model “A” and Model “B” women’s questionnaires (ORC Macro 2001a, 2001b), interviewed 

women are asked the following items related to ages and dates: 

• Duration in years of continuous residence at current city, town, or village (Q103) 

• Month and year of birth (Q105) 

• Age at last birthday (Q106) 

• Month and year of birth for each child ever born (Q215) 
50 

• For all living children, the age of each child at the child’s last birthday (Q217) 

• For all children who have died, the age of each child at death in days, months, or years (Q220) 

• If currently pregnant, the number of completed months pregnant (Q227) 

• If have ever had a pregnancy that did not end in a live birth, the month and year the last such 

pregnancy ended (Q230); if ended in past five years1

, the number of months that pregnancy lasted 

(Q232) 

• For any and all other pregnancies in the past five years that did not result in a live birth, the month 

and year the pregnancy ended and the number of months each pregnancy lasted (Q234) 

• If more than five years before the survey there were any other pregnancies that did not result in a 

live birth, the month and year that the last such pregnancy ended (Q236) 

• The number of days, weeks, months, or years since the respondent’s last menstrual period started, 

or the date it started (Q237) 

• If sterilized, month and year when sterilized (Q316) 

• If currently using a method, month and year when started using method without stopping (Q316A) 

• Contraceptive calendar: starting month and year and ending month and year of each period of 

contraceptive use or non-use for the past five years (Model “A” only: Q317 and calendar page) 

• If have given birth in the past five years and received any antenatal care, number of months pregnant 

when first received antenatal care for the most recent pregnancy (Q408); if received antenatal care 

more than once during that pregnancy, number of months pregnant when last received antenatal 

care (Q411) 

• If have given birth in the past five years and a health professional or traditional birth attendant 

checked on respondent’s health after the most recent child was born, the number of days or weeks 

after delivery that the first check took place (Q430) 

• If have given birth in the past five years and period has since resumed, the number of months after 

most recent birth that the respondent did not have a period (Q436, column 1); if have given birth 

more than once in the past five years and period resumed between second-to-last birth and most 

recent pregnancy, the number of months after second-to-last birth that the respondent did not have 

a period (Q436, column 2) 

• If have resumed sexual relations since birth of child in the past five years, number of months 

abstinent after birth (Q439, column 1); if have given birth more than once in past five years, number 

of months after second-to-last birth the respondent abstained from sex (Q439, column 2) 

• If have breastfed either of last two children born in past five years, number of hours or days after 

birth first put child to breast (Q441, columns 1 and 2) 

• If not still breastfeeding last two children born in past five years, number of months each child was 

breastfed (Q446, columns 1 and 2) 

 

1

 Here and elsewhere, ‘past five years’ means that the questionnaire refers to the period starting January of the calendar 

year five years prior to the year of fieldwork; depending on the dates of fieldwork this may span up to six years before 

the date of interview. Consult individual survey reports for details on dates of fieldwork and the dates of reference 

periods used in the country-specific questionnaires. 
51 

• If ever married or lived with man as if married, month and year started living with first 

husband/partner (Q511); if the year first started living with first/husband partner is unknown, age 

when started living with first husband/partner (Q512) 

• Marital calendar: month and year of beginning and end of each union for the past five years (Model 

“A” only: Q513 and calendar page) 

• If ever had sex, age at first sex (Q514) 

• If ever had sex, time since last sex in days, weeks, months, or years (Q515) 

• If last sex was with a man who is not a husband or cohabiting partner, duration of sexual 

relationship in days, weeks, months, or years (Q518) 

• If have had sex in past 12 months with any other man who is not a husband or cohabiting partner, 

for the next-most-recent sexual partner, the duration of that sexual relationship in days, months, 

weeks, or years (Q522) 

• If currently married or living with a man as if married, husband/partner’s age at last birthday (Q702) 

Dates of immunization for each child age 5 and under are recorded from the vaccination card. If the card is 

not available, the dates of immunization(s) are not asked. DHS interviewers are instructed to reconcile a 

woman’s reported age and date of birth (see Figure 1.1 “Field imputation procedures from The DHS 

Program’s interviewer training manual” for more details). However the questionnaire does not include any 

formal instructions to reconcile disparities between child’s age and date of birth. 

The DHS-5 woman’s core questionnaire (Macro International 2008c) includes all DHS-4 age and date 

questions at some point in the questionnaire, with the exception of the marital calendar, the number of 

months pregnant when last received antenatal care, and the time after birth first breastfed second-to-last 

baby born in past five years. Additionally, women are asked a number of additional timing questions related 

to antenatal care and post-delivery care for the most recent birth in the past five years. They are asked the 

month and year of last tetanus injection (if any) prior to most recent pregnancy that resulted in a birth in the 

past five years (Q419); if year is unknown, they are asked how many years ago they received the injection 

(Q420). If, for the most recent birth in the past five years the woman delivered at a health facility, she is 

asked the number of hours, days, or weeks she stayed there after birth (Q437); if someone at the facility 

checked on her health prior to discharge she is asked the number of hours, days, or weeks after birth that 

the first check took place (Q440). The original question about how long after birth a health care provider 

checked on the woman’s health (Q430 in DHS-4 woman’s questionnaire) is asked slightly differently for 

two groups of women: women who gave birth in a facility are asked about the time after delivery that a 

health care provider or traditional birth attendant first checked on their health post-discharge, while women 

who did not deliver in a facility are asked about the time after delivery that a health care provider or 

traditional birth attendant first checked on their health, in hours, days, or weeks (Q445 for both groups). If, 

for the most recent birth in the past five years, a health care provider or traditional birth attendant checked 

on the child’s health within the first two months after birth, women are asked how many hours, days, or 

weeks after birth the first check took place (Q450). Also, in DHS-5, questions about resumption of 

menstrual period after birth and duration of abstinence after birth are asked for up to three births in the past 

five years (Q456, Q459, columns 1 to 3). 

Additional questions about dates and timing asked in the DHS-5 woman’s questionnaire relate to sexual 

history and HIV testing. In DHS-5, women are asked not only the time since last sexual intercourse but also 

the time since last sexual intercourse, for up to two most recent additional sexual partners in the past 12 

months (Q627 columns 2 and 3), the duration of that sexual relationship (Q631, columns 1 to 3), and ages 

of the three most recent sexual partners in the past 12 months (Q633, columns 1 to 3). If age is unknown, 

the respondent is asked whether the partner is older, younger, or the same age (Q624) and, if older, whether 
52 

there is at least a 10-year age gap (Q625). If the respondent has given birth in the past five years and was 

tested for HIV2

 during antenatal care for that pregnancy but has been tested since then, she is asked whether 

the last time she was tested is less than 12 months ago, 12 to 23 months, or 2 years or more (Q921). If the 

respondent was not tested during her pregnancy or has not had a birth in the past five years but has ever 

been tested for HIV, she is asked whether the last time she was tested for HIV is less than 12 months ago, 

12 to 23 months, or 2 years or more (Q923). 

In the DHS-6 woman’s core questionnaire (ICF International 2011), all age and date questions from DHS5 are asked, with the exception of the duration at current residence and the month and year of the last tetanus 

injection prior to pregnancy that resulted in a birth in the past five years. Instead, women who have given 

birth in the past five years are asked the number of years before their pregnancy they last received a tetanus 

injection. Additionally, the separate questions about health checkups for the respondent after most recent 

delivery at the facility and after discharge are combined—as in DHS-4—into a single question for all 

women who have given birth in the past five years about how long after delivery a health professional or 

traditional birth attendant checked on her health (Q440). For the last three sexual partners in the past 12 

months, instead of the duration of the sexual relationship, women are asked how many days, weeks, months, 

or years ago they first had sexual intercourse with this person (Q622). If age of the last three sexual partners 

in the past 12 months is unknown, women are no longer asked to estimate whether the partner is older, 

younger, or the same age. For HIV testing within the past two years, women are asked to specify the number 

of months since the most recent test (Q925 and Q927). 

In the DHS-7 woman’s core questionnaire (ICF International 2015d), women are asked all age and date 

questions from the DHS-6 core questionnaire except duration of breastfeeding. Additionally, they are asked 

the duration in years of continuous residence in current city, town, or village (Q102), and the day of birth 

for all children ever born (Q215). For respondents whose most recent birth in the past five years was at a 

health facility, the question about health checkups after the most recent delivery for the respondent is 

divided into two questions—as in DHS-5—one about the amount of time after delivery someone checked 

on her health inside the facility if at all (Q436), and another about the amount of time after delivery that 

someone checked on her health post-discharge if at all (Q442). As in all other survey rounds described here, 

women who gave birth in the past five years but delivered their most recent birth outside of a health facility 

are asked a single question about the amount of time after delivery their first health check took place if at 

all (Q450). New in DHS-7, respondents who have most recently given birth in the past five years at a health 

facility are asked two separate questions about health checks on the baby in the first two months after birth: 

one about the amount of time after birth someone checked on the baby’s health while at the facility if at all 

(Q439), and another about the amount of time after birth that someone checked on the baby’s health postdischarge if at all (Q446). Women who whose most recent birth in the past five years was outside of a health 

facility are asked a separate question about the timing of the first health checkup for the baby if it occurred 

within the first two months after birth (Q454). In DHS-7—as in DHS-4—questions about number of months 

after birth the respondent did not have her period (Q460), and did not have sexual intercourse (Q463), are 

only asked for the two most recent live births in the past five years. 

Man’s Questionnaire 

DHS surveys have included men as early as 1987, however the DHS core questionnaires did not include a 

model man’s questionnaire until DHS-5, in 2003. In the DHS-5 model man’s questionnaire (Macro 

International 2008b), men are asked for the following information related to age and dates: 

 

2

 In DHS-5, referred to as ‘tested to see if you have the AIDS virus.’ In DHS-6, the preface question is ‘tested to see 

if you have the AIDS virus’ and the subsequent question is ‘How many months ago was your most recent HIV test.’ 

In DHS-7, referred to as HIV throughout. 
53 

• Duration of continuous residence in current home (Q102) 

• Month and year of birth (Q106) 

• Age at last birthday (Q107) 

• If have had children, age when first child was born (Q212) 

• If have any living children, age of youngest child (Q214) 

• Age of all current wives/partners at last birthday (Q408) 

• If ever married or lived with woman as if married, month and year started living with first 

wife/partner (Q411); if year began living with first wife/partner is unknown, age began living with 

first wife/partner (Q412) 

• If ever had sex, age at first sexual intercourse (Q414) 

• If ever had sex, number of days, weeks, months, or years since last sexual intercourse (Q419); if 

have had sex with more than one partner in the past 12 months, the number of days, weeks, or 

months since last sexual intercourse with the next two most recent partners (Q421, columns 2 and 

3) 

• If any of the last three sexual partners in the past 12 months is not a wife or live-in partner, the 

duration of the sexual relationship with that partner in days, months, or years (Q425, columns 1 to 

3) 

• If ever tested for HIV3

, whether the time since last test was less than 12 months ago, 12 to 23 

months ago, or 2 or more years ago (Q714) 

For the model man’s questionnaire, DHS interviewers are instructed to reconcile any conflicts between 

men’s reported age and date of birth, as with the model woman’s questionnaire. 

In the DHS-6 man’s core questionnaire, men are not asked their duration of current residence, but they are 

asked the age of their last three sexual partners in the past 12 months (Q425, columns 1 to 3). Instead of the 

duration of the sexual relationship with the last three non-marital partners in the past 12 months, they are 

asked how long ago they first had sexual intercourse with each partner, regardless of whether married to 

them or not (Q423, columns 1 to 3). And, instead of categorical values for time since last HIV test, if the 

last test was less than two years ago men are asked the number of months since their last HIV test (Q713). 

Additionally, if men are circumcised, they are asked their age at circumcision (Q802). 

In the DHS-7 man’s questionnaire (ICF International 2015c), men are asked all of the age and date questions 

asked in DHS-6. Additionally, they are asked the duration in years of continuous residence in current city, 

town, or village (Q102). 

 

3

 See previous note about use of the term ‘AIDS virus’ in earlier survey rounds. 

55 

Appendix B Example National Historical Events Calendar 

Used for Date Estimation during the 2012-13 

Pakistan DHS 

Main Events in Pakistan 

Date Event Date Event

Oct. 27, 1958 Field Marshal Ayub Khan imposed Martial Law in 

Pakistan 

Nov. 13, 1993 Farooq Leghari elected President

Sep. 6, 1965 War between Pakistan & India Nov. 5, 1996 Benazir Bhutto’s government sacked 

Mar. 26, 1969 General Yahya Khan imposed Martial Law Feb. 17, 1997 Nawaz Sharif became Prime Minister (Second time) 

Dec. 16, 1971 War between Pakistan & India (Fall of Dhaka) May 28, 1998 Pakistan made first nuclear test 

Dec. 20, 1971 Mr. Zulfikar Ali Bhutto became President of Pakistan May/Jun. 1999 Kargil event 

Aug./Sep. 1973 Floods in Punjab Oct. 12, 1999 Gen. Pervez Musharraf impose Martial Law

Jun./Jul. 74 & 76 Floods in Sindh Aug. 2000 Local Bodies Elections (Nazims system) 

Jul. 5, 1977 Gen. Zia-ul-Haq declared Martial Law Sep. 11, 2001 Attack on World Trade Centre (USA) 

Apr. 4, 1979 Z. A. Bhutto was hanged Oct. 2002 Provincial & National Assemblies Elections 

1985 Mr. Muhammad Khan Jonaijo became Prime Minister Oct. 2002 Attacked on Afghanistan by USA 

Dec. 1985 Sohrab Goth clean-up operation in Karachi Mar. 18, 2003 Attacked on Iraq by USA 

Apr. 10, 1988 Disaster of Ojri Camp in Rawalpindi Sep. 2005 2nd Local Bodies Elections (Nazims system) 

Aug. 17, 1988 Gen. Zia’s plane crash Oct, 2005 Earthquake in AJK and parts of Pakistan 

Dec. 2, 1988 Benazir Bhutto became Prime Minister.(First Time) Dec. 27, 2007 Banazir Bhutto was Murdered in Rawalpindi 

Dec. 13, 1988 Ghulam Ishaq Khan became President Feb. 2008 Election of National and Provincial Assemblies 

Aug. 6, 1990 Benazir Bhutto’s government sacked Mar. 2008 Syed Yousaf Raza Gillani elected as Prime Minister 

Nov. 6, 1990 Nawaz Sharif elected Prime Minister (First time) 2008 Asif Ali Zardari elected as President of Pakistan 

Mar. 22-23, 1992 Pakistan won the final of Cricket World Cup Jul./Aug. 2010 Floods in Pakistan 

Aug./Sep. 1992 Floods in Punjab and Sindh 2012 Syed Yousaf Raza Gillani’s Government dismissed by 

the Supreme Court

Apr. 18, 1993 Nawaz Sharif’s government dismissed 2012 Raja Parvez Ashraf elected as Prime Minister 

Oct. 19, 1993 Benazir Bhutto became Prime Minister (Second time) 

57 

Appendix C List of Surveys Analyzed in Chapter 2 

Survey Household Women Men Children 

Afghanistan 2015 203,708 29,461 10,760 114,905 

Albania 2008-09 31,099 7,584 3,013 12,208 

Angola 2015-16 74,902 14,379 5,684 37,223 

Armenia 2000 26,371 6,430 1,719 10,517 

Armenia 2005 25,028 6,566 1,447 9,734 

Armenia 2010 23,629 5,922 1,584 8,067 

Azerbaijan 2006 30,637 8,444 2,558 12,465 

Bangladesh 2004 55,883 11,440 4,297 28,285 

Bangladesh 2007 53,413 10,996 3,771 26,104 

Bangladesh 2011 83,731 17,842 3,997 40,253 

Bangladesh 2014 81,624 17,863 - 38,894 

Benin 2001 30,417 6,219 2,709 15,689 

Benin 2006 90,650 17,794 5,321 47,808 

Benin 2011-12 88,174 16,599 5,180 42,617 

Bolivia 2003 81,669 17,654 6,230 39,272 

Bolivia 2008 77,757 16,939 6,054 35,834 

Burkina Faso 2003 60,766 12,477 3,605 33,006 

Burkina Faso 2010 82,095 17,087 7,307 46,357 

Burundi 2010 42,420 9,389 4,280 20,682 

Cambodia 2000 66,285 15,351 - 34,696 

Cambodia 2005 73,010 16,823 6,731 34,854 

Cambodia 2010 76,920 18,754 8,239 33,437 

Cambodia 2014 74,122 17,578 5,190 30,443 

Cameroon 2004 51,976 10,656 5,280 24,747 

Cameroon 2011 72,622 15,426 7,191 36,057 

Chad 2004 29,614 6,085 1,887 16,914 

Chad 2014-15 99,620 17,719 5,248 57,995 

Colombia 2000 47,520 11,585 - 20,117 

Colombia 2005 157,840 41,344 - 67,390 

Colombia 2010 204,459 53,521 - 86,947 

Colombia 2015 162,459 38,718 35,783 59,761 

Comoros 2012 24,499 5,329 2,167 10,700 

Congo 2005 31,481 7,051 3,146 14,519 

Congo Democratic Republic 2007 48,291 9,995 4,757 24,255 

Congo 2011-12 51,449 10,819 5,145 28,337 

Congo Democratic Republic 2013-14 95,949 18,827 8,656 50,637 

Cote d’Ivoire 2011-12 51,187 10,060 5,135 23,870 

Dominican Republic 2002 110,758 23,384 2,833 49,566 

Dominican Republic 2007 123,738 27,195 27,975 54,136 

Dominican Republic 2013 41,267 9,372 10,306 16,942 

Egypt 2000 91,173 15,573 - 47,982 

Egypt 2005 112,710 19,474 - 55,411 

Egypt 2008 92,120 16,527 - 45,196 

Egypt 2014 120,276 21,762 - 56,155 

Ethiopia 2000 68,642 15,367 2,607 33,741 

Ethiopia 2005 67,540 14,070 6,033 32,591 

Ethiopia 2011 77,744 16,515 14,110 37,796 

Gabon 2000 32,391 6,183 2,004 14,934 

Gabon 2012 41,675 8,422 5,654 20,903 

Gambia 2013 52,691 10,233 3,821 24,163 

Ghana 2003 26,601 5,691 5,015 12,976 

Ghana 2008 46,536 4,916 4,568 10,378 

Ghana 2014 43,945 9,396 4,388 20,678 
58 

Survey Household Women Men Children 

Guatemala 2014-15 102,510 25,914 11,145 51,876 

Guinea 2005 38,182 7,954 3,174 20,876 

Guinea 2012 45,049 9,142 3,782 22,957 

Guyana 2009 22,845 4,996 3,522 10,197 

Haiti 2000 47,361 10,159 3,171 21,963 

Haiti 2005-06 47,319 10,757 4,958 21,481 

Haiti 2012 59,746 14,287 9,493 25,468 

Honduras 2005-06 93,867 19,948 - 46,725 

Honduras 2011-12 100,555 22,757 7,120 46,456 

India 2005-06 534,161 124,385 74,369 229,017 

Indonesia 2002-03 149,222 29,483 8,310 71,607 

Indonesia 2007 178,843 32,895 8,758 76,392 

Indonesia 2012 185,345 45,607 9,306 76,043 

Jordan 2002 46,755 6,006 - 24,051 

Jordan 2007 82,470 10,876 - 41,828 

Jordan 2012 80,822 11,352 - 40,705 

Kenya 2003 37,612 8,195 3,578 19,319 

Kenya 2008-09 38,515 8,444 3,465 20,137 

Kenya 2014 153,840 31,079 12,819 76,876 

Kyrgyz Republic 2012 35,805 8,208 2,413 15,445 

Lesotho 2004 40,490 7,095 2,797 13,022 

Lesotho 2009 44,546 7,624 3,317 12,903 

Lesotho 2014 40,197 6,621 2,931 10,540 

Liberia 2007 34,670 7,092 6,009 17,826 

Liberia 2013 48,219 9,239 4,118 25,016 

Madagascar 2003-04 38,325 7,949 2,432 18,249 

Madagascar 2008-09 85,858 17,375 8,586 42,922 

Malawi 2000 63,823 13,220 3,092 31,402 

Malawi 2004 60,747 11,698 3,261 29,156 

Malawi 2010 118,850 23,020 7,175 60,461 

Malawi 2015-16 120,492 24,562 7,478 60,399 

Maldives 2009 42,050 7,131 1,727 18,598 

Mali 2001 66,505 12,849 3,405 35,778 

Mali 2006 73,685 14,583 4,207 39,972 

Mali 2012-13 58,330 10,424 4,399 29,585 

Morocco 2003-04 64,044 16,798 - 29,098 

Mozambique 2003 63,496 12,418 2,900 29,419 

Mozambique 2011 62,750 13,745 4,035 32,312 

Myanmar 2015-16 55,584 12,885 4,737 20,428 

Namibia 2000 31,675 6,755 2,954 13,748 

Namibia 2006-07 42,633 9,804 3,915 17,836 

Namibia 2013 41,646 10,018 4,481 16,847 

Nepal 2001 47,523 8,726 2,261 24,279 

Nepal 2006 44,057 10,793 4,397 22,891 

Nepal 2011 49,791 12,674 4,121 23,786 

Nicaragua 2001 61,351 13,060 - 31,206 

Niger 2006 47,964 9,223 3,549 26,853 

Niger 2012 64,011 11,160 3,928 35,983 

Nigeria 2003 35,820 7,620 2,346 17,736 

Nigeria 2008 156,809 33,385 15,486 83,640 

Nigeria 2013 178,894 38,948 17,359 97,736 

Pakistan 2006-07 727,493 10,023 - 34,759 

Pakistan 2012-13 94,169 13,558 3,134 44,832 

Peru 2000 131,062 27,843 - 58,396 

Peru 2004-08 187,286 41,648 - 81,741 

Peru 2009 105,225 24,212 - 46,451 
59 

Survey Household Women Men Children 

Peru 2010 101,409 22,947 - 43,404 

Peru 2011 98,662 22,517 - 42,890 

Peru 2012 103,211 23,888 - 44,139 

Philippines 2003 61,864 13,633 4,766 28,430 

Philippines 2008 60,901 13,594 - 26,851 

Philippines 2013 71,893 16,155 - 30,121 

Moldova 2005 32,110 7,440 2,508 9,483 

Rwanda 2000 45,247 10,421 2,717 21,612 

Rwanda 2005 47,851 11,321 4,820 24,042 

Rwanda 2010 56,505 13,671 6,329 27,682 

Rwanda 2014-15 54,905 13,497 6,217 26,391 

Sao Tome and Principe 2008-09 13,430 2,615 2,296 6,852 

Senegal 2005 69,059 14,602 3,761 33,494 

Senegal 2010-11 77,269 15,688 4,929 36,828 

Senegal 2012-13 41,593 8,636 - 19,977 

Senegal 2014 40,723 8,488 3,371 19,964 

Senegal 2015 41,902 8,851 3,734 20,727 

Sierra Leone 2008 41,985 7,374 3,280 17,003 

Sierra Leone 2013 75,299 16,658 7,262 37,294 

Swaziland 2006-07 22,143 4,987 4,156 10,113 

Tajikistan 2012 38,805 9,656 - 18,598 

Timor-Leste 2009-10 67,834 13,137 4,076 31,922 

Togo 2013-14 46,577 9,480 4,476 22,914 

Turkey 2003 47,894 8,075 - 20,413 

Uganda 2000-01 37,951 7,246 1,962 19,444 

Uganda 2006 45,439 8,531 2,503 24,822 

Uganda 2011 44,977 8,674 2,295 24,618 

Ukraine 2007 34,123 6,841 3,178 7,762 

Tanzania 2004-05 49,921 10,329 2,635 25,750 

Tanzania 2010 50,414 10,139 2,527 26,011 

Tanzania 2015-16 64,880 13,266 3,514 33,234 

Vietnam 2002 31,529 5,665 - 13,538 

Yemen 2013 120,923 25,434 - 58,905 

Zambia 2001-02 38,089 7,658 2,145 19,427 

Zambia 2007 35,562 7,146 6,500 18,032 

Zambia 2013-14 83,058 16,411 14,773 43,234 

Zimbabwe 2005-06 42,698 8,907 7,175 17,834 

Zimbabwe 2010-11 41,946 9,171 7,480 17,674 

Zimbabwe 2015 43,706 9,955 8,396 19,054 

61 

Appendix D List of Surveys Analyzed in Chapter 3 

Survey 

Women 

asked to 

state their 

husband’s 

age 

Men asked

to state 

their wife’s 

age 

Men’s age range Women’s age range 

For heaping 

analysis 

For all other 

analysis 

For heaping 

analysis 

For all other 

analysis 

Afghanistan 2015 • • 20-49 15-49 20-49 15-49 

Albania 2008-09 • • 21-49 21-49 20-49 16-49 

Armenia 2000 • 25-54 19-54 - 15-49 

Armenia 2005 • 20-49 20-49 - 16-49 

Armenia 2010 • 20-49 19-49 - 18-49 

Azerbaijan 2006 • • 20-59 18-59 20-49 16-49 

Bangladesh 2004 • 25-54 16-54 - 13-49 

Bangladesh 2007 • 25-54 18-54 - 15-49 

Bangladesh 2011 • • 25-54 16-54 20-49 13-49 

Benin 2001 • 25-64 16-64 - 15-49 

Benin 2006 • • 25-64 17-64 20-49 15-49 

Benin 2011-12 • • 25-64 16-64 20-49 15-49 

Bolivia 2003 • 25-63 17-63 - 15-49 

Bolivia 2008 • 25-64 15-64 - 15-49 

Burkina Faso 2003 • 21-59 19-59 - 15-49 

Burkina Faso 2010 • • 20-59 18-59 20-49 15-49 

Burundi 2010 • • 20-59 15-59 20-49 16-49 

Cambodia 2005 • • 20-49 15-49 20-49 15-49 

Cambodia 2014 • 20-49 17-49 - 15-49 

Cameroon 2004 • 21-59 18-59 - 15-49 

Cameroon 2011 • • 21-59 17-59 20-49 15-49 

Chad 2004 • 20-59 18-59 - 15-49 

Chad 2014-15 • • 20-59 17-59 20-49 15-49 

Comoros 2012 • • 20-59 19-59 20-49 15-49 

Congo 2005 • • 20-59 16-59 20-49 15-49 

Congo 2011-12 • • 20-59 17-59 20-49 15-49 

Congo Democratic Republic 2007 • • 20-59 16-59 20-49 15-49 

Congo Democratic Republic 2013-14 • • 20-59 16-59 20-49 15-49 

Cote d’Ivoire 2011-12 • • 20-59 18-59 20-49 15-49 

Dominican Republic 2002 • 20-59 16-59 - 15-49 

Dominican Republic 2007 • 20-59 15-59 - 15-49 

Dominican Republic 2013 • 20-59 16-59 - 15-49 

Ethiopia 2000 • 20-59 18-59 - 15-49 

Ethiopia 2005 • 20-59 16-59 - 15-49 

Ethiopia 2011 • • 20-59 15-59 20-49 15-49 

Gabon 2000 • 20-59 15-59 - 15-49 

Gabon 2012 • • 20-59 17-59 20-49 15-49 

Gambia 2013 • • 20-59 20-59 20-49 15-49 

Ghana 2003 • 20-59 18-59 - 15-49 

Ghana 2008 • • 20-59 18-59 20-49 15-49 

Ghana 2014 • • 20-59 19-59 20-49 15-49 

Guinea 2005 • 21-59 18-59 - 15-49 

Guinea 2012 • • 22-59 17-59 20-49 15-49 

Guyana 2009 • • 20-49 17-49 20-49 15-49 

Haiti 2000 • 21-59 18-59 - 15-49 

Haiti 2005-06 • • 20-59 17-59 20-49 16-49 

Haiti 2012 • • 20-59 17-59 20-49 15-49 

India 2005-06 • 25-54 15-54 - 15-49 

Indonesia 2002-03 • 25-54 16-54 - 15-49 

Indonesia 2007 • 25-54 17-54 - 15-49 
62 

Survey 

Women 

asked to 

state their 

husband’s 

age 

Men asked

to state 

their wife’s 

age 

Men’s age range Women’s age range 

For heaping 

analysis 

For all other 

analysis 

For heaping 

analysis 

For all other 

analysis 

Indonesia 2012 • • 25-54 16-54 20-49 15-49 

Kenya 2003 • 25-54 18-54 - 15-49 

Kenya 2008-09 • • 25-54 17-54 20-49 15-49 

Kenya 2014 • • 25-54 17-54 20-49 15-49 

Kyrgyz Republic 2012 • 20-49 19-49 - 16-49 

Lesotho 2004 • 21-59 18-59 - 15-49 

Lesotho 2009 • • 21-59 16-59 20-49 15-49 

Lesotho 2014 • • 20-59 17-59 20-49 15-49 

Liberia 2007 • • 20-49 18-49 20-49 15-49 

Liberia 2013 • • 20-49 18-49 20-49 15-49 

Madagascar 2003-04 • 20-59 16-59 - 15-49 

Madagascar 2008-09 • • 20-59 15-59 20-49 15-49 

Malawi 2000 • 25-54 17-54 - 15-49 

Malawi 2004 • 25-54 17-54 - 15-49 

Malawi 2010 • • 25-54 16-54 20-49 15-49 

Malawi 2015-16 • • 25-54 17-54 20-49 15-49 

Maldives 2009 • 25-64 19-64 - 18-49 

Mali 2001 • 21-59 19-59 - 15-49 

Mali 2006 • • 21-59 20-59 20-49 15-49 

Mali 2012-13 • • 21-59 20-59 20-49 15-49 

Moldova 2005 • 20-59 18-59 - 15-49 

Mozambique 2003 • 25-64 18-64 - 15-49 

Mozambique 2011 • • 25-64 15-64 20-49 15-49 

Myanmar 2015-16 • • 20-49 16-49 20-49 15-49 

Namibia 2000 • 20-59 17-59 - 15-49 

Namibia 2006-07 • • 20-49 17-49 20-49 15-49 

Namibia 2013 • • 25-64 18-64 20-49 15-64 

Nepal 2001 • 20-59 15-59 - 15-49 

Nepal 2006 • 20-59 15-59 - 15-49 

Nepal 2011 • • 20-49 17-49 20-49 15-49 

Niger 2006 • • 22-59 18-59 20-49 15-49 

Niger 2012 • • 20-59 16-59 20-49 15-49 

Nigeria 2003 • 22-59 18-59 - 15-49 

Nigeria 2008 • • 20-59 17-59 20-49 15-49 

Nigeria 2013 • • 20-49 16-49 20-49 15-49 

Philippines 2003 • 25-54 17-54 - 15-49 

Rwanda 2000 • 20-59 18-59 - 16-49 

Rwanda 2005 • 20-59 18-59 - 17-49 

Rwanda 2010 • • 20-59 18-59 20-49 18-49 

Rwanda 2014-15 • • 20-59 18-59 20-49 16-49 

Sao Tome and Principe 2008-09 • • 20-59 18-59 20-49 15-49 

Senegal 2005 • 20-59 19-59 - 15-49 

Senegal 2010-11 • • 20-59 17-59 20-49 15-49 

Senegal 2014 • • 20-59 18-59 20-49 15-49 

Senegal 2015 • • 21-59 19-59 20-49 15-49 

Sierra Leone 2008 • • 20-59 18-59 20-49 15-49 

Sierra Leone 2013 • • 20-59 16-59 20-49 15-49 

Swaziland 2006-07 • • 20-49 19-49 20-49 15-49 

Tanzania 2004-05 • 20-49 18-49 - 15-49 

Tanzania 2010 • • 20-49 18-49 20-49 15-49 

Tanzania 2015-16 • • 20-49 17-49 20-49 15-49 

Timor-Leste 2009-10 • • 20-49 18-49 20-49 15-49 

Togo 2013-14 • • 20-59 16-59 20-49 15-49 
63 

Survey 

Women 

asked to 

state their 

husband’s 

age 

Men asked

to state 

their wife’s 

age 

Men’s age range Women’s age range 

For heaping 

analysis 

For all other 

analysis 

For heaping 

analysis 

For all other 

analysis 

Uganda 2000-01 • 25-54 16-54 - 16-49 

Uganda 2006 • • 25-54 18-54 20-49 15-49 

Uganda 2011 • • 25-54 18-54 20-49 15-49 

Ukraine 2007 • 20-49 18-49 - 16-49 

Zambia 2001-02 • 21-59 17-59 - 15-49 

Zambia 2007 • • 20-59 16-59 20-49 15-49 

Zambia 2013-14 • • 20-59 17-59 20-49 15-49 

Zimbabwe 2005-06 • • 25-54 18-54 20-49 15-49 

Zimbabwe 2010-11 • • 25-54 18-54 20-49 15-49 

Zimbabwe 2015 • • 25-54 16-54 20-49 15-49