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

evaluate population, health, nutrition, and HIV/AIDS programs. Funded by the United States Agency for 

International Development (USAID) under Contract No. GPO-C-00-03-00002-00, MEASURE DHS is 

implemented by Macro International Inc. in Calverton, Maryland. 

The main objectives of the MEASURE DHS project are: 

x To provide decisionmakers in survey countries with information useful for informed policy choices; 

x To expand the international population and health database; 

x To advance survey methodology; and 

x To develop in participating countries the skills and resources necessary to conduct high-quality 

demographic and health surveys. 

Additional information about the MEASURE DHS project is available on the Internet at 

http://www.measuredhs.com or by contacting Macro International Inc., MEASURE DHS, 11785 Beltsville 

Drive, Suite 300, Calverton, MD 20705 USA; Telephone: 301-572-0200, Fax: 301-572-0999, E-mail: 

reports@orcmacro.com. 
DHS Methodological Reports No. 5 

An Assessment of Age and Date Reporting 

in the DHS Surveys, 1985-2003 

Thomas W. Pullum 

The University of Texas at Austin 

Macro International Inc. 

Calverton, Maryland, USA 

December 2006 

The author’s views expressed in this publication do not necessarily reflect the views of the 

United States Agency for International Development or the United States Government. 
Editor: Sidney Moore 

Document Production: Kaye Mitchell 

This publication was made possible through support provided by the U.S. Agency for International Development 

under the terms of Contract No. GPO-C-00-03-00002-00. 

Recommended citation: 

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

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

Contents

Preface ............................................................................................................................. v 

Acknowledgments............................................................................................................ vi 

Executive Summary.........................................................................................................vii

1 Introduction 

1.1 Objectives ........................................................................................................1 

 1.2 Methods ...........................................................................................................2 

2 Household Survey 

2.1 Digit Preference or Age Heaping.....................................................................8 

 2.2 Digit Preference or Age Heaping: Household Respondent 

 Compared With Other Household Members .................................................10 

 2.3 Age Displacement of Women ........................................................................12 

 2.4 Age Displacement of Men..............................................................................16 

3 Survey of Women 15-49 

3.1 Incompleteness..............................................................................................19 

 3.2 Age and Birth Year .......................................................................................24 

 3.3 Year of First Marriage and Years Since First Marriage .................................27 

 3.4 Fertility Rates from Successive Surveys in the Same Country .....................31 

4 Children in the Birth Histories 

4.1 Incompleteness..............................................................................................36 

 4.2 Birth Dates and Ages of Children ..................................................................38 

 4.3 Birth Intervals.................................................................................................43 

 4.4 Age at Death..................................................................................................45 

 4.5 Infant Mortality Rates from Successive Surveys in the Same Country .........48 

5 Effect of Education on Misreporting 

5.1 Aggregate-level Effects..................................................................................52 

 5.2 Individual-level Effects...................................................................................53 

6 Conclusions 

6.1 Variation by Region, Time Period, and Type of Problem ..............................61 

 6.2 Recommendations.........................................................................................67 

References ......................................................................................................................69
iv

Appendix A Surveys Conducted by DHS 1985-2003 and Surveys 

 Included in this Report ..........................................................................75 

Appendix B Previous Assessments of Age and Date Reporting in DHS Surveys....75 

Appendix C Data Quality Tabulations Included in DHS Reports ..............................79 

Appendix D Detailed Description of Method to Detect Age Displacement................81 
v

Preface

The Demographic and Health Surveys (DHS) program has become one of the principal sources of international 

data on fertility, family planning, maternal and child health, nutrition, mortality, and HIV/AIDS. The quality of 

this data is of utmost importance to researchers worldwide. 

Because survey methodology has a major impact on data quality, one of the objectives of the MEASURE DHS 

project is to advance the methodology and procedures used to carry out national-level surveys. This will 

improve the accuracy and depth of information relied on by policymakers and program managers in developing 

countries.

The topics in the DHS Methodological Reports series are selected by MEASURE DHS staff in consultation with 

the U.S. Agency for International Development. While data quality is a main topic of the reports, they also 

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

Some reports are updates of previously published reports. 

This report deals with the quality of age and date reporting in DHS surveys. Most of the indicators produced by 

DHS surveys depend on accurate reporting of ages of women and children, as well as dates when events 

occurred. From 1985 to 2003, 141 DHS surveys of households and women were carried out in 66 countries. 

These data are examined here. 

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

particularly those engaged in work in developing countries. 

Martin Vaessen 

Project Director 
vi

Acknowledgments

Several DHS staff members provided valuable assistance during the preparation of this report. Special thanks go 

to Fred Arnold, Bridgette James, Sidney Moore, Shea Rutstein, and Jerry Sullivan. Khatuna Doliashvili helped 

during the early stages of data preparation. 
vii

Executive Summary 

Complete and accurate reports of dates and ages are critically important for DHS surveys. Eligibility for 

inclusion in the survey of women age 15-49, as well as most surveys of men and special surveys, depends on the 

age given in the household survey. Eligibility of children for questions on health depends on the birth dates 

given in the birth histories. Both the numerators and the denominators of age-specific fertility rates, infant 

mortality rates, and other rates depend on reported age. In addition, the quality of the reports of ages and dates 

reflects on the quality of other information in the surveys. 

This assessment is based on virtually all of the surveys of households and surveys of women conducted between 

1985 and 2003 that were available in standard recode format: 102 household surveys and 128 surveys of 

women, including complete child files from the surveys of women. Incompleteness of reporting (such as giving 

age but not a birth year or month), heaping of ages or birth years (stating final digit 0 or 5, or age at death 12 

months, for example), and displacement (transfers below age 15, above age 49, or outside the interval for the 

child health questions) are examined. Results are presented mainly in the form of histograms showing the 

distribution of indexes of these kinds of problems and lists of surveys that exceed specific thresholds. The effect 

of displacement on estimates of fertility and infant mortality is explored, as well as evidence that education of 

the household respondent, household head, and the mother tend to affect misreporting. Trends over time and 

differences between major regions are presented. 

In general, surveys in Latin America have the highest quality of age and date reporting. The South Asian 

surveys show the strongest evidence of incompleteness and heaping, although these problems are also prevalent 

in Africa. Surveys in Sub-Saharan Africa have the highest levels of age displacement. Over time, DHS surveys 

show a marked reduction in the incompleteness of children’s birthdates and in the upward displacement of older 

women, two of the most important types of misreporting. No indexes have deteriorated, but several have 

fluctuated or remained much the same.
1

1 Introduction 

The Demographic and Health Surveys (DHS) project has become one of the principal sources of international 

data on fertility, family planning, child health, child mortality, and reproductive health. From 1985 through 

2003, DHS conducted 141 surveys of households and women in 66 different countries. In addition, it has 

conducted many surveys on the availability of health and family planning services, surveys of men, and other 

special surveys. 

Most of the measures produced from DHS surveys depend to a critical degree on reports of ages and dates. At 

the most basic level, inclusion of women age 15-49 in the surveys depends on accurate reports of the ages of 

women near the boundaries of that age interval in the household survey. The inclusion of children under five (or 

another specified age) for the questions about child health, immunizations, and nutrition also depends on 

accurate reports of their birth dates. 

Many measures are age-specific, such as estimates of age-specific fertility rates and infant and child mortality 

rates. Estimates of levels and trends in such rates may be affected by misreporting of ages and dates of birth for 

a woman and her children, or dates of death for her children. Misreporting of adult ages often takes the form of a 

preference for numbers ending in the digits 0 or 5. Since standard age intervals begin with those digits, 

misreporting can shift women into the next higher age interval. Age displacement of children can seriously 

distort estimates of current levels and recent trends in fertility and mortality. 

1.1 Objectives 

This report uses household and individual data from nearly all of the DHS surveys to identify evidence of 

misreporting of ages and dates. It focuses on the levels of incompleteness, digit preference, and transfers across 

specific boundaries such as ages 5, 15, and 50. Omission and misreporting of variables other than ages and 

dates, while important, are not included in this report. 

The data examined are drawn from surveys conducted in four phases of DHS data collection: DHS-I (1985-89), 

DHS-II (1990-92), DHS-III (1993-98), and MEASURE DHS+ (1999-2003)—a total of 141 surveys. The 

countries and survey dates are listed in Appendix A. This analysis includes 128 of those surveys; 13 are omitted. 

Eleven surveys are excluded because of restrictions imposed by the host countries (Botswana 1988; Cambodia 

2000; Egypt 1988 and 1998; Eritrea 1995 and 2002; Jordan 1990, 1997, and 2002; Turkmenistan 2000; and 

Yemen 1997). One is excluded (Peru 2003) because it had a different design, and one (Senegal 1999) is 

excluded because no standard recode file was ever prepared for it. 

Each reference in Appendix A is to a pair of surveys, one for households and one for women age 15-49. The 

principal purposes of the household survey are to identify eligible respondents for the survey of women and to 

collect information on housing characteristics and household assets. Household surveys conducted between 

1985 and 1989 are not included in the data analysis because standard recode files were not prepared for them. 

This analysis is thus based on 102 household surveys conducted between 1990 and 2003 and 128 surveys of 

women age 15-49 conducted between 1985 and 2003. Sixty-one countries are represented, with one to five 

surveys from each country. Table 1.1 describes the data files used in the report. Tables in Section 6.1, where the 

results are summarized, describes the distribution across regions and time periods. 
2

Table 1.1 Number of data files used in the report and number of cases in those files 

Types of files 

Number

of files Smallest Median Mean Largest 

Household 102 5,764 37,235 54,267 488,839 

Woman 128 1,286 7,198 9,973 90,303 

Child 128 3,256 24,562 31,079 285,599 

Note: The household files consist of one record for each person in each household. The 

smallest files are all from the Dominican Republic 1999 survey. The largest household and 

woman files are from the India 1998-99 survey. The largest child file is from the India 

1992-93 survey. 

DHS has carried out several earlier studies of age and date reporting, specifically examining data collected 

during DHS-I and DHS-II. A brief description of those earlier studies can be found in Appendix B. The quality 

of DHS data is also monitored during the data collection process, and each country report includes appendix 

tables with some discussion of this topic. Those tables are described in Appendix C. 

It is important to conduct overall assessments of this type from time to time. However, the kinds of problems 

that appear in DHS surveys are largely endemic in settings where ages and dates are not important in the daily 

lives of most people and do not necessarily indicate faulty data collection procedures. For example, heaping is 

repeatedly observed in all censuses and surveys in some countries, and cannot be eliminated even with special 

training of interviewers. One function of this analysis is to identify the countries, surveys, and topics that show 

the strongest evidence of reporting problems, so that efforts can be made to reduce—if not eliminate—such 

problems in the future. Another function is to determine whether, as would be expected because of cumulative 

experience and increasing average educational attainment of respondents, there has been a general improvement 

in data quality over time. 

There is a large demographic literature on age and date reporting in surveys, censuses, and vital statistics data. 

This report adapts and advances those methods by fitting them into the more general framework of statistical 

models. The procedures used are described in Section 1.2, with some details deferred to Appendix D. 

1.2 Methods

This report focuses on measuring the levels and patterns of four kinds of potential errors: incompleteness, 

heaping or digit preference, transfers across boundaries, and inconsistencies between successive surveys. 

1.2.1 Missing or Incomplete Data 

For the most important kinds of dates, DHS includes a code to indicate whether imputation was necessary. Each 

such item is recoded to the value 0 if the original response was valid and complete or 1 if the response was 

incomplete in any way. For each dataset, the output measure will be the proportion of cases with code 1. 

1.2.2 Heaping or Digit Preference 

One of the most frequently employed indicators of data quality is the degree of digit preference in stated age. If 

age is not known, and is estimated by either the respondent or the interviewer, there is often a tendency to heap 

on a number ending in 0 or, to a lesser extent, ending in 5. Heaping tends to increase as age increases. 

The standard summary measure of digit preference is produced by Myers’ blended method. The blending 

procedure adjusts for the natural tendency of a final 0 to occur more often than a final 1, and a final 1 more often 
3

than a final 2, and so on, simply because population growth and mortality tend to result in more people age x

than age x+1. The blending procedure for the index requires that the age range be a multiple of ten years, so that 

each final digit (0 through 9) occurs the same number of times. 

Myers’ Index is equivalent to the index of dissimilarity for a comparison between the blended percentage 

observed at each digit and the expected percentages (uniformly 10 percent). It is half the sum of the absolute 

deviations of the observed percentages from the expected percentages, and can be interpreted as the minimum 

percentage of the cases that would have to be shifted from one digit to another to achieve a uniform distribution 

across ages. 

A large value of the index can result from large deviations at any digit, not necessarily 0 or 5. For example, age 

distributions of children will often suggest a preference for even numbers. In any case, the preferred digits will 

be those with the largest positive deviations. In addition to Myers’ Index, this report gives the excess 

percentages at final digits 0 and 5, calculated as the blended percentages at those two digits minus 20. This 

measure does not use absolute deviations and can be negative. If Myers’ Index is large, but the excess at 0 and 5 

is not, then the heaping is on other digits. 

If age is not known, it is possible that an estimate is made of year of birth, and age is calculated by subtracting 

year of birth from year of interview. Digit preference could then be traced to the year of birth. For example, if 

the survey was done in 2002, a preference for birth years ending in 0 will produce an excess of ages ending in 2. 

Similarly, if birth year is calculated by subtracting age from survey year, heaping on ages ending in 0 will 

produce an excess of birth years ending in 2. To check on this possibility, a modification of Myers’ blended 

method will be applied to the reported or implied year of birth, as well as to reported age. 

Myers’ blended method is illustrated in Siegel and Swanson (2004: 139) using the single-year age distribution 

from the 1990 census of the Philippines. This report uses an equivalent formulation as a statistical model, which 

has been verified to agree with the usual approach. A routine written in Stata is applied to a file of individual 

data, using multinomial logit regression on the final digit (as a category of a 10-category categorical dependent 

variable) with no predictor variables and with the Myers’ weights, extracting the coefficients from that 

regression, converting the coefficients to proportions at each integer, and calculating the summary index. 

Another context in which heaping can occur is in the reported age of a child at death. In DHS surveys, if a child 

died before the second birthday, age at death is reported in months. Not surprisingly, the data often show some 

heaping at 12 months (and, to a lesser degree, at 6 and 18 months). If the excess at 12 months is partly due to 

misreporting of deaths that actually occurred at 10 or 11 months, this can lead to an underestimate of the 

probability of dying in the first year of life (months 0-11), 1q0. It can also cause an overestimate of the 

probability of dying at ages 1-4 (months 12-59), 4q1. Earlier assessments have measured this phenomenon by 

dividing the reported number of deaths in month 12 by the average of the numbers in months 10, 11, 13, and 14. 

That is, if the numbers reported in months 10, 11, 12, 13, and 14 are labeled a, b, c, d, and e, respectively, this 

heaping measure is calculated as 

4 /( ). ( )/ 4c a b d e

a b d e

c   

  

It is the ratio of the observed number of deaths in month 12 to the expected number, under a model of linear 

change in the true monthly frequencies from month 10 to month 14. Heaping will produce a ratio greater than 

one.

In this report, this measure of heaping of age at death at 12 months is calculated with a logit regression (limited 

to births in the ten years before the survey). In the file of births, a binary variable is constructed, coded 1 if a 

death was reported at 12 months, 0 if a death was reported at 10, 11, 13, or 14 months, and missing otherwise 
4

(that is, if the death occurred outside these months or the child did not die at all). In a logit regression with this 

constructed variable as the outcome and no predictor variables, the coefficient for the intercept is exponentiated 

and multiplied by four to find the measure of heaping. This approach allows for easy incorporation of weights 

and clusters and also yields a statistical test of the null hypothesis that there is no heaping in the population. 

All measures in this report must be accompanied by a warning that evidence of misreporting may be a spurious 

consequence of genuine variation. Such a misdiagnosis is probably unlikely for this measure because it uses 

only a five-month range of age within a ten-year interval of time. 

1.2.3 Transfers Across Boundaries 

The household survey contains a considerable amount of information that can be analyzed in its own right, but 

its main purpose is to identify women age 15-49 who are eligible for the individual survey (and sometimes to 

identify eligible men or other respondents). The interviewers have some incentive, in order to reduce their 

workload, to misstate the ages of some women by entering them as too young or too old to participate in the 

main survey. Such an effect could cause a woman who is actually age 15-19 to be reported as 10-14, or a 

woman who is actually age 45-49 to be reported as 50-54. It is virtually impossible to know which specific 

women have been transferred across a boundary, but evidence of the effect will appear as an excess of women 

reported at ages 10-14 and 50-54. Two main approaches to identifying such displacement have been used in the 

literature: sex ratios and age ratios. This report does not use the method of sex ratios, because it requires an 

assumption that displacement is restricted to women and that reporting of age is correct for men. Many DHS 

surveys have included male samples as well as female samples, so that assumption would not be valid. 

 Age ratios: original use 

The age ratio for an age interval is defined as the ratio of the reported number of cases in that interval, divided 

by the reported number of cases in the preceding age interval (sometimes multiplied by 100). Because of a 

history of population growth in almost all countries, and the increase in mortality with age, we expect age ratios 

to be a little less than one (or 100) at most ages and to decline fairly regularly as age increases. Looking for 

possible transfers outside the age 15-49 interval, Rutstein and Bicego (1990:8) reported the following relevant 

age ratios from the 1988 household survey of Botswana: 

Age Age 

 interval ratio 

 10-14 126 

 15-19 76 

 ....... 

 45-49 73 

 50-54 152 

The irregularity of these age ratios clearly indicates displacement from age 15-19 to 10-14 and from 45-49 to 

50-54. To summarize this displacement, Rutstein and Bicego calculate ARi-ARo, which is the age ratio inside (i) 

the boundary minus the age ratio outside (o) the boundary. For Botswana 1988, this indicator is 76-126 = -50 for 

the lower age boundary and 73-152 = -79 for the upper age boundary, or a total of (-50) + (-79) = -129. This 

report builds on the logic of this approach, first with aggregated data, and then adapting it to a statistical model 

that can be used with individual-level data. 
5

 Age ratios: reformulation 

Focusing on the lower boundary for eligibility, age 15, we identify two age intervals below the boundary and 

two above it: 

 Age Observed Fitted 

 interval frequency frequency 

 5- 9 a a

 10-14 b b

ˆ

 15-19 c cˆ

 20-24 d d

Thus, for Botswana 1988, b/a=1.26 and c/b=.76; frequency d was not used by Rutstein and Bicego. We propose 

a model to estimate the second and third frequencies (b and c) using the first and fourth frequencies (a and d),

which are assumed to be reported correctly. This will be done with two requirements. First, we assume that the 

total of the second and third frequencies is correct: b

ˆ + cˆ = b + c. Second, we assume that the three successive 

fitted age ratios, d/ cˆ , cˆ /b

ˆ , and bˆ /a, are linear on a log scale. The second assumption seems plausible because 

changes in both cohort size and the force of mortality tend to be linear on a log scale. 

As shown in Appendix D, the second requirement, after some algebra, leads to ) [ln( / )]/ 3 ˆ ln(cˆ/b d a .

Defining 1 / 3 u (d / a) , we obtain

( )[1/(1 )] ˆb b  c  u

and cˆ (b  c)[u /(1 u)] . 

The proportion age 15-19 who were misreported downwards as 10-14 is then estimated as 

(cˆ  c)/ cˆ 1 c / cˆ .

To identify transfers upwards across age 50, the four intervals would be 40-44 through 55-59, and the proportion 

of 45-49 year olds who were misreported as 50-54 would be estimated as 

b b b b b

ˆ 1 / ˆ )/ ˆ (   .

This approach can be illustrated by applying it to the females in the Zambia 2001-02 household survey 

(unweighted). The frequencies in age intervals 5-9 through 20-24 are a=3047, b=2684, c=2039, and d=1789, 

respectively. The age ratio for age 10-14 is 2684/3047=.88 and for age 15-19 is 2039/2684=.76, leading to 

ARi-ARo,=-12, suggesting some, but not a large amount of, displacement. Our method gives 

) [ln( / )]/3 .1775 ˆ ln(cˆ/b d a  ,

leading to

u exp(.1775) .8374
6

and then 

(2684 2039)/1.8374 2570.5 ˆb  

and

cˆ (2684  2039)(.8374/1.8374) 2152.5.

This suggests that a proportion 1  c / cˆ 1  2039 / 2152.5 .053 of women age 15-19 were misreported as 

10-14. We suggest that this modest extension of the traditional age ratio approach gives a more interpretable 

parameter estimate. The application of this model to individual-level data is described in Appendix D. 

In this report, the four-category method for identifying age transfers is applied in two contexts other than 

possible transfers outside the age range of eligibility for the survey of women. First, in a male survey, we 

identify transfers outside the age range of eligibility. Second, possible transfers of children outside the interval 

of eligibility for the extra questions about child health are investigated. In these contexts, the four intervals are 

single calendar years, two prior to the boundary and two after the boundary. For example, in the 2001-02 survey 

of Zambia, the extra questions applied to all children born in January 1996 or later. We look at the births 

reported for calendar years 1994, 1995, 1996, and 1997. Distortion would appear as an excess of births in 1995 

and a deficit of births in 1996 because some children actually born in 1996 were misreported as being born in 

1995. We assume that such transfers affect only the single calendar year on each side of the boundary (e.g., 

1995 and 1996), and that the other two years (e.g., 1994 and 1997) are reported correctly. It is possible, of 

course, that more years are involved in the misreporting. It is also possible that in some contexts there are 

patterns of transfers of children’s ages that have nothing to do with the questionnaire. If either of these 

conditions applies, our method will be deficient. 

1.2.4 Inconsistencies Between Successive Surveys 

The report checks for agreement between two successive surveys in the estimates of fertility prior to the first 

survey. Such disagreements can be due to misreporting of the ages of women—and possibly to mortality of 

women between the surveys—but are more likely due to misreporting of dates of childbirth. We focus on agespecific fertility rates and the total fertility rate (TFR) for the three calendar years prior to the first month of 

interview in the first survey. The rates for this reference period are estimated from the birth histories in the first 

survey and then from the birth histories in the second survey. 

The rates are estimated with a Poisson regression procedure that is equivalent to the usual DHS method and 

allows easy adjustment for sampling weights and clusters. The difference between the logarithms of two 

estimated age-specific rates will have an approximately normal distribution with a standard error that is the 

square root of the sum of the squares of the two log rates’ standard errors. It is thus relatively easy to perform 

tests of statistical significance. 

Most successive surveys are five years apart. The second survey will include few or no women who had 

exposure to the risk of childbearing during age 45-49 during the reference period. Therefore the comparisons 

will be restricted to ages 15-44. 

As an example, Zambia has had three DHS surveys, in 1992, 1996, and 2001-02. For the comparison between 

the 1992 and 1996 surveys, the reference period was 1989-91. The TFR (age 15-44, rather than 15-49) estimate 

for this period from the first survey was 6.25, and from the second survey was 6.40. These estimates are quite 

close; there are no statistically significant differences within any pairs of age-specific rates or between the two 

Tars.
7

In the comparison between the 1996 and 2001-02 surveys, the reference period was the calendar years 1993-95. 

The estimated TFR (age 15-44) for this period from the 1996 survey was 6.05; from the 2001-02 survey it was 

7.08. The difference is statistically significant for four of the six age-specific fertility rates as well as for the 

TFR.

When this type of comparison between surveys is carried out, discrepancies will be measured in terms of the 

difference in TFR, the relative difference in TFR, and a chi-square statistic that synthesizes the z-scores for the 

six age-specific rates. 

A similar comparison between successive surveys is made for the infant mortality rate (IMR). This comparison 

uses the files of children rather than the files of women. The reference time period is the five, rather than three, 

calendar years prior to the first month of interview in the first survey. For example, in the comparison between 

the 1996 and 2001-02 Zambia surveys, the reference period for infant mortality is the calendar years 1991-95. 

DHS normally uses three-year intervals for recent fertility and five-year intervals for recent mortality, but their 

intervals extend back from an individual woman’s date of interview, which would not give a fixed reference 

period.

The IMR for the reference period is calculated for both surveys using a log probability procedure (the IMR is 

actually an estimated probability) that is equivalent to the standard DHS procedure and allows for sampling 

weights and clustering. The difference between the logarithms of two estimated probabilities will have an 

approximately normal distribution with a standard error that is the square root of the sum of the squares of the 

two log probabilities’ standard errors. Therefore the difference between the two estimates of the IMR can be 

evaluated for both magnitude and statistical significance. 

It is important to recognize the limitations on our ability to identify misreporting of ages and dates, which can 

lead to two complementary types of misinterpretations. First, there may be instances in which the reporting is 

actually better, or more accurate, than we infer. For example, the true age distribution is the consequence of an 

irregular history of births and deaths, and any assumption about its shape, across either twenty years or four 

years in our applications, is probably false to some degree. In some of the datasets, the frequencies in successive 

age groups appear very erratic, but any kind of smoothing procedure runs the risk of removing true variation. 

Second, there may be other instances in which the reporting is actually accurate than we infer. 

Test statistics can be calculated for all of the indices in this report, but they are only presented for the 

comparison of fertility rates and infant mortality rates from successive surveys, the heaping of age at death at 

twelve months, and estimates of an effect of education on misreporting. In other contexts there is too great a risk 

of over-interpreting a test statistic by confounding sampling errors with systematic nonsampling errors and 

giving too much credence to a particular method for smoothing or adjusting data. Instead, we list the surveys 

that have the highest levels of each index, above an admittedly arbitrary cutoff such as 10 percent or 20 percent. 

Before reaching the conclusion that a survey is indeed flawed, we would advise a thorough country-specific 

analysis that would go into more detail than can be applied to every country in this kind of report. 
8

2 Household Survey 

This chapter analyzes age reporting in 102 household surveys. Each survey was processed as a file of the 

individual household members, defined by de facto residence and omitting persons above age 79. Topics are age 

heaping; differences between the quality of age data for the household respondent and other household 

members; evidence of transfers outside the age range of eligibility for women age 15-49; and evidence of such 

transfers for men when there was a male survey. 

2.1 Digit Preference or Age Heaping 

Figures 2.1 and 2.2 give the distribution of the Myers’ Index and the percentage excess at 0 and 5 for reported 

ages 0-79. These figures are followed by Table 2.1, which lists the countries for which either index is above 10 

percent. This cutoff is at least twice the median values (5.0 percent for Myers’ Index and 3.7 percent for the 

heaping at 0 and 5). In Table 2.1 and other tables, if the survey’s fieldwork extended across two successive 

calendar years, the calendar year listed is the year in which a majority of the interviews occurred, referred to as 

the median year of interview.

The two indexes have similar distributions and are almost perfectly correlated with each other (r=.98), implying 

that most digit preference for age is indeed at final digits 0 and 5. With the exception of the 1991-92 survey of 

Yemen, the countries listed are either in South Asia (India, Bangladesh, and Pakistan) or Western/Middle Africa 

(Benin, Niger, Nigeria, and Chad, which are adjacent to one another). In successive surveys in Niger and 

Nigeria, the level of heaping appears to have declined. In Bangladesh there was a decline between the 1993-94 

survey and the 1996-97 survey, but the level was higher in 1999-2000 than in either of the two earlier surveys. 

In India, Myers’ Index was the same for both the 1993 and 1999 surveys. 

The household surveys do not include year of birth, but it can be estimated approximately as year of interview 

minus reported years of age. We calculated Myers’ Index and the excess at final digits 0 and 5 for this implied 

year of birth. Myers’ Index was about the same as in Figure 2.1, with no evidence of heaping at calendar years 

of birth ending in 0 or 5, so the results need not be presented here. 
9

9

14 14

17

10

8

11

4

1

2

1

2

1 1

2

1

3

1

0.0 5.0 10.0 15.0 20.0

Myers' Index

Figure 2.1 Myers' Index for household members (0-79 years)

Number of surveys

3

12

15 15

14

12

8

3

2

4

2

1 1

2

1 1

3

2

1

Number of surveys

0.0 5.0 10.0 15.0 20.0

Percent excess at digits 0 or 5

Figure 2.2 Heaping at digits 0 or 5 for household members (0-79 years)
10

Table 2.1 Household surveys with strongest evidence of 

heaping by age 

Country 

Median

year of 

survey 

Myers’ 

Index

Percent 

excess at 

final digit 

0 or 5 

Bangladesh 1994 11.6 10.4 

Bangladesh 1996 10.4 9.7 

Bangladesh 2000 12.4 12.1 

Benin 2001 10.6 8.4 

Chad 1997 12.4 11.0 

India 1993 17.1 15.0 

India 1999 17.1 15.3 

Niger 1992 15.3 14.3 

Niger 1998 13.2 12.8 

Nigeria 1990 19.7 19.6 

Nigeria 1999 15.6 15.2 

Nigeria 2003 14.7 13.4 

Pakistan 1991 17.9 16.9 

Yemen 1991 16.6 16.6 

Note: The table lists surveys for which either the Myers’ Index 

or the percent excess at final digit 0 or 5 is greater than 10. 

2.2 Digit Preference or Age Heaping: Household Respondent Compared with Other 

Household Members 

The information about household members is obtained from a specific person, usually the household head or 

spouse. It is reasonable to expect that this person will report information more accurately about himself or 

herself than about other household members. 

Figures 2.3 and 2.4 give the reduction, or improvement, in Myers’ Index and the percentage excess at final digits 

0 and 5 for the household respondent compared with other members of the household. The comparisons are 

limited to ages 20-59, because the household respondent is almost always in this age range. Table 2.2 lists the 

surveys with the largest difference. 

The differences are almost always positive, but they also are generally small, only occasionally above 5 percent. 

The only large differences, listed in Table 2.2, are for the three surveys in Bangladesh. They imply a very low 

level of heaping, less than 5 percent, when the respondent is giving his or her own age, but a very high level, 20 

percent to 30 percent, when the same person reports the age of other household members. The net effect, as 

given in Table 2.1, is a high overall level of heaping in all the Bangladesh surveys, but the overall level 

disguises the relatively accurate self-reports of the household respondent. No other country listed in Table 2.1 

shows this kind of bifurcation. 
11

1

5

6

16

19

18

5

7

3

2

11 1

Number of surveys

0 10 20 30

Difference in Myers' Index

Figure 2.3 Reduction in Myers' Index for respondent (20-59 years)

5

12

25

13

16

9

3

11 1 1 1

Number of surveys

0 10 20 30

Difference in percent excess at digits 0 or 5

Figure 2.4 Reduction in heaping at digits 0 or 5 for respondent (20-59 years)
12

Table 2.2 Household surveys with largest difference in heaping by age between other household members and the 

household respondent 

Myers’ Index Excess at 0 or 5 

Country 

Median

year of 

survey 

Nonhousehold

respondents

age 20-59 

Household

respondents

age 20-59 Difference

Nonhousehold

respondents

age 20-59 

Household

respondents

age 20-59 Difference

Bangladesh 1994 25.0 4.7 20.29 23.8 3.0 20.75 

Bangladesh 1996 25.5 3.5 21.99 25.3 1.0 24.26 

Bangladesh 2000 29.7 3.7 25.99 29.7 1.3 28.36 

Note: The table lists surveys for which the reduction in either the Myers’ Index or the percent excess at digits 0 or 5 

is greater than 10. 

2.3 Age Displacement of Women 

The principal function of the household survey is to identify eligible respondents for the survey of women age 

15-49. Errors of inclusion have potential ramifications for several measures produced by the surveys of women. 

The errors are most likely due to intentional efforts by interviewers to reduce their workload, because the 

respondents themselves are not aware of the screening function and have no incentive to shift ages across the 

boundaries of 15 and 50. This section examines internal evidence of downward and upward transfers. 

Evidence of net transfers from ages 15-19 into 10-14 is given in Figure 2.5 and Table 2.3. The figure gives the 

distribution of the index, expressed as the estimated percentage of women with true age 15-19 who were 

misreported at 10-14. The table lists the countries for which the percentage exceeds 10 percent. 

There is one data set, the 1999 survey of Nigeria, for which the method indicates a 17.4 percent increase in the 

number of women 15-19, compared to the estimated true number. A transfer in this direction is not expected. As 

Table 2.3 shows, the previous Nigeria survey (for 1990) appears to have had a high level of transfers in the 

expected direction—indeed, the highest level of any survey. 

An effort was apparently made in the 1999 survey of Nigeria to reduce the displacement out of age 15-19 that 

had been observed in 1990. According to the report on the 1999 survey, household screening for the survey of 

women age 15-49 included an eligibility code for ages 10-49, with ages 10-14 screened out later. However, as 

the report notes, this change induced substantial transfers from ages 10-14 into 5-9. The weighted age 

distribution of females in the household survey included 2762 at ages 0-4, 3445 age 5-9, 1817 age 10-14, 1918 

age 15-19, and 1622 age 20-24. The obvious transfers from ages 10-14 into 5-9 would cause our method to give 

a false diagnosis of transfers from 10-14 into 15-19, so we do not conclude that there was a such a shift. Indeed, 

it appears likely that the two-step screening procedure for the 1999 survey did give a more plausible count for 

age 15-19.

In any case, 12 different countries are represented in the list of 18 surveys given in Table 2.3, with estimated 

transfer rates above 10 percent. Six countries appear twice. With the exception of Kyrgyzstan, all of the 

countries are in sub-Saharan Africa: Burkina Faso, Benin, Ghana, Kenya, Madagascar, Mali, Malawi, Nigeria, 

Uganda, and South Africa. 
13

Table 2.3 Household surveys with strongest evidence of downward age transfers for younger women 

 

Observed number 

Estimated “true” 

number

Country 

Median

year of 

survey 

Estimated 

percentage 

of women 

15-19

misreported

at 10-14 

Age

5-9

Age

10-14

Age

15-19

Age

20-24

Age

10-14

Age

15-19

Benin 1996 11.2 2240.8 1787.3 1139.8 1067.0 1643.6 1283.5

Burkina Faso 1993 12.1 2879.8 2394.2 1465.9 1265.0 2193.0 1667.1

Burkina Faso 1999 10.1 2610.2 2350.3 1522.6 1228.9 2178.3 1694.6

Ghana 1993 17.8 1742.0 1479.0 842.0 861.0 1296.2 1024.8

Ghana 1998 14.1 1602.5 1482.5 949.1 924.9 1326.8 1104.7

Kenya 1993 16.1 3409.5 3110.4 1838.7 1709.5 2758.0 2191.1

Kenya 1998 13.3 2754.0 2996.9 1965.0 1638.0 2695.3 2266.7

Kyrgyzstan 1997 11.1 971.0 1050.4 740.5 640.1 957.6 833.3

Madagascar 1992 10.7 2105.3 2060.1 1431.9 1286.7 1889.0 1603.0

Malawi 1992 14.8 1909.9 1899.4 1160.8 987.9 1697.5 1362.6

Malawi 2000 11.7 4859.1 4355.4 2991.6 3043.9 3959.3 3387.7

Mali 1996 11.6 4234.7 3353.3 1996.4 1649.7 3091.7 2258.0

Mali 2001 15.4 5476.2 4754.2 2738.2 2403.4 4257.2 3235.2

Mozambique 1997 12.5 3297.6 3068.1 2018.8 1883.4 2780.2 2306.7

Nigeria 1990 19.7 3973.9 3258.6 1733.0 1760.4 2832.4 2159.2

Uganda 1995 10.3 3142.4 2519.9 1672.8 1613.9 2328.2 1864.5

Uganda 2001 12.0 3121.3 2723.9 1734.2 1555.6 2486.6 1971.5

Zambia 1998 16.1 3442.8 3825.7 2462.7 2303.7 3354.5 2934.0

Note: The table lists surveys for which the estimated percentage of women 15-19 misreported at 10-14 is greater than 10. 

1 1

2

3

10

19

13

16

10

9

7

4

3 3

1

Number of surveys

-20.0 -10.0 0.0 10.0 20.0

Percentage 15-19 shifted downwards

Figure 2.5 Estimated downward transfers of women age 15-19
14

To interpret the other numbers provided in Table 2.3, consider the age distribution given for the 1993 survey of 

Ghana. The observed ratio of women age 15-19 to 10-14 is 842.0 / 1479.0 = .57, an implausible decline from 

one age group to the next. After adjustment, retaining the total for these two age groups, the ratio becomes 

1024.8 / 1296.2 = .79. The adjusted decline is more plausible, in a context of high fertility and nonnegligible 

mortality across these years of age, and gains credibility by comparison with the pattern in Ghana’s 1998 age 

distribution, in which each cohort was five years older. However, there are also some age distributions in Table 

2.3 that would be a challenge to any method. The age distribution for the 1998 survey of Kenya reported 2754.0 

women age 5-9, then an increase to 2996.9 age 10-14, a precipitous drop to 1965.0 women age 15-19, and then 

a more modest decline to 1638.0 women age 20-24. In order to preserve the observed numbers age 5-9 and 20-

24, and the total for the interval 10-19, our method produces estimates for 10-14 and 15-19 that are probably 

more plausible than the observed numbers, but they are certainly themselves in error. It is likely that there is a 

broader pattern of misstatement of age than the method assumes. Nevertheless, although the percentage 

transferred from age 15-19 into age 10-14 may not be estimated very well, there is clearly something wrong 

with the Kenya distribution, and it belongs on this list. 

We now look for evidence of upward transfers, across age 50. Using a similar methodology, Figure 2.6 shows 

the distribution of estimated transfers of women from age 45-49 into 50-54, and Table 2.4 lists the data sets 

believed to have shifts greater than 20 percent. This is a higher threshold than used for Table 2.4, but so many 

surveys are between 10 percent and 20 percent, or between -10 percent and -20 percent, that it would be hard to 

see a pattern with a 10 percent threshold (no surveys are below -20 percent). Evidence of widespread transfers 

across age 50 is very strong, much stronger than across age 15. 

11111

3

2222

5 5

7

12

8

11

7

8

6

4

6

1

222

Number of surveys

-20.0 -10.0 0.0 10.0 20.0 30.0

Percent 45-49 shifted upwards

Figure 2.6 Estimated upward transfers of women age 45-49
15

Table 2.4 Household surveys with strongest evidence of upward age transfers for older women 

 

Observed number 

Estimated “true” 

number

Country 

Median

year of 

survey 

Estimated 

percentage 

of women 

45-49

misreported

at 50-54 

Age

40-44

Age

45-49

Age

50-54

Age

55-59

Age

45-49

Age

50-54

Burkina Faso 1993 32.0 521.1 394.1 730.9 435.9 579.2 545.8 

Cameroon 1991 27.3 323.0 221.7 365.6 255.7 305.1 282.2 

Comoros 1996 20.4 212.0 219.0 313.0 173.0 275.0 257.0 

Ghana 1993 20.6 433.0 337.0 444.0 257.0 424.4 356.6 

Kenya 1993 28.5 684.7 452.9 722.7 428.7 633.6 542.0 

Madagascar 1992 21.8 550.9 355.7 474.6 309.8 454.8 375.4 

Namibia 2000 22.5 697.4 465.8 621.6 369.2 601.1 486.3 

Niger 1992 27.6 544.9 354.2 551.3 334.7 489.4 416.0 

Nigeria 1990 28.8 887.8 642.0 1051.7 603.5 901.2 792.4 

Senegal 1993 21.5 619.0 398.0 542.0 387.0 506.7 433.3 

Togo 1998 21.4 693.7 583.9 854.4 569.0 742.9 695.4 

Uganda 1995 31.3 430.4 290.6 510.7 307.2 423.2 378.2 

Zimbabwe 1999 21.3 512.2 399.5 537.3 309.0 507.8 429.1 

Note: The table lists surveys for which the estimated percentage of women 45-49 misreported at 50-54 is greater than 20. 

There are several reasons to expect a higher probability of transfers across age 50 than across age 15. First, 

because of improvements in education, birth registration, and greater use of age in everyday life, age is better 

documented and more accurately known for younger cohorts. Second, it is generally harder to estimate the age 

of someone who is older than someone who is younger. Third, any motivation for interviewers to reduce their 

workload by displacing potential respondents will be greater for older women, because much more of the 

questionnaire, in particular a longer birth history, will apply to them. 

Each of the 13 countries in Table 2.4 appears in only one survey. All are in sub-Saharan Africa: Burkina Faso, 

Cameroon, Ghana, Kenya, Comoros, Madagascar, Nigeria, Niger, Namibia, Senegal, Togo, Uganda, and 

Zimbabwe. Half of the countries in Tables 2.3 and 2.4 appear on both lists. Indeed, the duplicates on the list 

consist of six surveys conducted about the same time: Burkina Faso (1992-93), Ghana (1993), Kenya (1993), 

Madagascar (1992), Nigeria (1992), and Uganda (1995). The most stunning example is the 1992-93 survey in 

Burkina Faso, which reported 394.1 women age 45-49 and 730.9 women 50-54. Transfers of this magnitude 

should have been (and probably were) detected during the fieldwork for the household survey. However, this 

entire part of the age distribution is problematic for that survey. The total number of women reported in the 

middle two age groups, 45-49 and 50-54, was 18 percent more than the total number reported at 40-44 and 55-

59. The true subtotals would probably be much closer to each other. 

The rates of boundary transfer tend to be higher around age 50 than around age 15, but the actual number of 

women affected is smaller at 50 than at 15. For example, we estimate that in the 1992-93 Burkina Faso survey, 

the downward transfer rate was 12.1 percent and involved the loss of 1667 - 1466 = 201 women age 15-19. The 

upward transfer rate was much larger, at 32.0 percent, but involved the loss of slightly fewer women age 45-49, 

namely 579 - 394 = 185. 

The total loss of cases due to age displacement in this survey is estimated to be 201 + 185 = 386 women. The 

number of women age 15-49 in the main survey was 6354. If the 386 women had been included, the sample size 

would have been 6354 + 386 = 6740. Compared with this potential sample size, the 386 displaced women would 

represent a 5.7 percent loss to the sample. We have not done this calculation for all surveys, but few if any 

surveys had a combined loss in excess of this level. 
16

2.4 Age Displacement of Men 

Many DHS surveys include a survey of males, who are screened for eligibility with the household data. This 

section checks for internal evidence of age transfers of men who were eligible for such a survey.

The beginning age was 15 or 20 for 55 male surveys, and the ending age was 54, 59, or 64 for 51 male surveys. 

In this section we check for evidence of displacement around the beginning or ending ages of those surveys. 

Surveys are not included here if the starting or ending age was not specific or if the last digit of the ending age 

was not 4 or 9. As with the women, the threshold for the younger age transfers is 10 percent and for the older 

ages is 20 percent. 

Four of the 55 surveys with a clear indication of the starting age of eligibility indicate displacement of at least 10 

percent of males out of the five-year age group just above the lowest boundary (15-19 or 20-24): Armenia 2000, 

Benin 2001, Côte d’Ivoire 1998-99, and Ghana 2003. Two countries, Benin and Ghana, also showed serious 

transfers of women across the lower boundary of eligibility in Table 2.3, but for different surveys. 

The age distribution of young men in the 2000 male survey of Armenia is strikingly irregular. This was a survey 

in which about a third of men in the household survey who were age 15-54 were selected. The weighted age 

distribution, unadjusted, gives 1087 males age 5-9, 1325 at age 10-14, 920 at age 15-19, and 814 at age 20-24. 

Our method adjusts the two middle age groups to estimated “true” frequencies of 1176 at age 10-14 and 1068 at 

age 15-19. The “bump” for age 10-14 is substantially reduced, but cannot be eliminated. The distribution for 

females at these ages in Armenia does not show the same pattern as the males, and gives very irregular sex 

ratios. Even after adjustment, the sex ratios in these ages are implausible, but they bolster the evidence that the 

Armenia survey had substantial transfers across age 15 for males but not (to nearly as great a degree) for 

females. 

Percent in youngest interval shifted downwards

1 1

2

3

6

16

11

5

9

1

3

Number of surveys

-20.0 -10.0 0.0 10.0 20.0

Figure 2.7 Estimated downward transfers of younger men
17

Table 2.5 Household surveys with strongest evidence of downward age transfers for younger men 

 

Observed number 

Estimated “true” 

number

Country 

Median

year of 

survey 

Estimated 

percentage 

of men

age x 

misreported

at x-1 

Age

x-2

Age

x-1

Age

x

Age

x+1

Age

x-1

Age

x

Armenia 2000 13.9 1087.4 1324.8 919.6 814.2 1176.3 1068.1 

Benin 2001 14.4 2472.9 2167.6 1214.3 934.2 1962.9 1419.0 

Côte d’Ivoire 1999 13.3 887.3 874.4 568.1 510.2 787.6 654.9 

Ghana 2003 10.6 1952.3 2014.0 1212.7 747.0 1869.5 1357.2 

Note: Age x is the first five-year interval in a male survey; x-1 is the immediately preceding interval, etc. The table lists 

surveys for which the estimated percentage of men age x misreported at x-1 is greater than 10. 

As with the surveys of women, there is considerably stronger evidence of transfers across the upper age 

boundary than across the lower boundary, so the threshold will again be 20 percent rather than the 10 percent 

used for downward transfers. Figure 2.8 shows the distribution of the estimated transfer rate in the 51 male 

surveys with a clear upper age boundary, and Table 2.6 lists the four surveys that were above the 20 percent 

cutoff. The four surveys are Bolivia 1998, Dominican Republic 2002, Ghana 1993, and Rwanda 2000. All of 

them show far more men in the age group just above the upper age boundary than in the age group just below it. 

The Ghana 1993 survey appeared earlier in Table 2.3 and Table 2.4 for its high levels of both downwards and 

upwards transfers out of the survey of women age 15-44. The other three surveys on this list do not give other 

evidence of high transfer rates.

111 1

2 2

6

5

2 2

3

4 4

5

4

3

2 2

3

1

Number of surveys

-20.0 -10.0 0.0 10.0 20.0

Percent in oldest interval shifted upwards

Figure 2.8 Estimated upward transfers of older men
18

Table 2.6 Household surveys with strongest evidence of upward age transfers for older men 

 

Observed number 

Estimated “true” 

number

Country 

Median

year of 

survey 

Estimated

percentage 

of men

age x 

misreported

at x+1 

Age

x-1

Age

x

Age

x+1

Age

x+2

Age

x

Age

x+1

Bolivia 1998 24.1 704.3 480.4 638.0 318.3 632.8 485.6

Dominican Republic 2002 21.3 2170.7 1280.9 1632.0 1071.4 1627.0 1285.8 

Ghana 1993 20.5 290.0 196.0 444.0 136.0 246.5 191.5 

Rwanda 2000 20.4 408.0 239.4 600.5 224.7 300.8 246.5 

Note: Age x is the last five-year interval in a male survey; x+1 is the next higher interval, etc. The table lists surveys for which the 

estimated percentage of men age x misreported at x+1 is greater than 20. 
19

3 Survey of Women Age 15-49 

This chapter examines the main surveys of women age 15-49. The data file consists of the information in 128 

such surveys. This file contains the DHS-I surveys, conducted before 1991, that could not be included in 

Chapter 2. Topics concern the completeness of reporting, age and birth date, age at marriage, date of marriage, 

years since marriage, and a comparison between two successive surveys’ estimates of fertility in a three-year 

reference period prior to the first survey. 

3.1 Incompleteness

Three variables to describe the quality of age and date reporting are included in all the surveys of women. The 

first one refers to the woman’s age. Ideally, each woman will provide three items: her age in completed years, a 

year of birth, and a month of birth. At a minimum, there should be an age or a birth year. However, some 

women do not provide all three items, and even if all information is provided, there may be inconsistencies that 

require the imputation of one or even two of the items. 

The first index is the proportion of women in a survey who did not provide all three items or for whom there 

was at least one inconsistency requiring imputation. The distribution of this proportion is given in Figure 3.1. 

The second index refers to the completeness (or incompleteness) of data about age and date at first marriage. 

Ideally a woman provides both her age and the year and month at first marriage. The index is the proportion of 

women who did not provide all three items or for whom there was at least one inconsistency requiring 

imputation. The distribution of this proportion is given in Figure 3.2. 

The third index summarizes the completeness of date information in the birth history. Each woman is asked 

about the month and year of each of her births. In some settings it is fairly common to omit the month or to be 

able to provide only the current age or years since the birth. The data files include a variable for each birth, 

describing whether the month and year were provided and were acceptable or had to be imputed (for example, if 

one birth date was too close to another birth date, then one or both would be adjusted to achieve compatibility 

with an imputation procedure). These variables were summed to obtain the third index. This index can be 

greater than 1, because there is a potential contribution from each birth, and it will tend to be larger for women 

who have had more children. (Section 4.1 will look again at the incompleteness of birth dates, using births 

rather than women as the units of analysis.) 

Table 3.1 identifies the surveys with the highest levels of incompleteness, defined by the age index being greater 

than 0.6, or the marriage index being greater than 0.6, or the birth index being greater than 1.0. There were 29 

such surveys, representing 19 different countries. About half of these surveys are above the threshold on two or 

three of the indices, not just one, because there is a moderate level of positive association among the indices. 

The affected surveys include all of the South Asia surveys in this study: Bangladesh (both 1996-97 and 1999-

2000), India (both 1993 and 1999), Nepal (both 1996 and 2001), and the only survey conducted in Pakistan 

(1990-91). They include three North Africa/West Asia surveys: Morocco 1992, Egypt (1995 only), and Yemen 

1991. All the remaining countries are in Western, Middle, and Eastern Africa, with the exception of the small 

country of Comoros, in Southern Africa. 
20

The imputation procedures used by DHS have been carefully developed and tested, and it is likely that 

incomplete data on ages and dates has a relatively minor impact on rates, means, or other calculations. Imputed 

values are almost always within a year of any plausible alternatives, given the information that is available. 

Nevertheless, this problem serves as a warning about other possible deficiencies. Many of the countries and 

surveys in Table 3.1 appear in other tables of this report. 

47

7 8

18

13

9

5

8

13

0.00 0.20 0.40 0.60 0.80

Index of incompleteness

Figure 3.1 Incompleteness of women's age data

29 29

18

16

7 7

13

6

2

1

Number of surveys

0.00 0.20 0.40 0.60 0.80 1.00

Index of incompleteness

Figure 3.2 Incompleteness of women's marriage data

Number of surveys
21

56

16

12

6

8

3

5 5

3 3

1

4

2 1 1 1 1

0.00 0.50 1.00 1.50 2.00 2.50

Index of incompleteness

Figure 3.3 Incompleteness for women's birth history data

Number of surveys
22

Table 3.1 Surveys of women age 15-49 with highest levels of 

incompleteness of age, marriage, or birth data 

Proportion of women 

missing any information 

Country 

Median

year of

survey 

Age or 

birth date 

Marriage 

age or 

date

Sum, across 

births, of 

proportions

missing any 

birth history 

Bangladesh 1997 0.78 0.10 0.03 

Bangladesh 2000 0.92 0.66 0.11 

Benin 1996 0.81 0.69 1.74 

Benin 2001 0.74 0.67 1.42 

Burkina Faso 1993 0.72 0.57 0.94 

Burkina Faso 1999 0.82 0.70 1.58 

Burkina Faso 2003 0.81 0.71 0.28 

Burundi 1987 0.62 0.30 0.60 

Comoros 1996 0.66 0.45 1.00 

Egypt 1995 0.51 0.38 1.01 

Ethiopia1

 1992 0.79 0.51 0.30 

Guinea 1999 0.87 0.73 2.12 

India 1993 0.79 na 0.10 

India 1999 0.72 na 0.13 

Mali 1987 0.91 0.89 2.46 

Mali 1996 0.84 0.62 0.17 

Mali 2001 0.81 0.62 0.26 

Morocco 1987 0.87 0.77 1.77 

Nepal1

 2952 0.57 0.70 0.01 

Nepal1

 2057 0.56 0.70 0.00 

Niger 1992 0.87 0.69 1.54 

Niger 1998 0.86 0.71 0.22 

Pakistan 1991 0.70 0.44 0.42 

Rwanda 1992 0.60 0.17 0.26 

Senegal 1986 0.66 0.68 0.75 

Senegal 1993 0.50 0.65 1.53 

Senegal 1997 0.48 0.60 0.75 

Sudan 1990 0.84 0.64 1.99 

Togo 1988 0.73 0.63 1.58 

Togo 1998 0.69 0.64 1.02 

Yemen 1991 0.88 0.85 2.71 

Note: The table lists surveys for which the index of incompleteness of 

women’s age is greater than 0.6, or the index of incompleteness of women’s 

marriage is greater than 0.6, or the index of incompleteness of the birth 

history is greater than 1.0. 

1 The Ethiopian survey was conducted in 1992 in the Ethiopian calendar, 

2000 in the Western calendar. The Nepalese surveys were conducted in 

2052 and 2057 in the Nepalese calendar, 1996 and 2001 in the Western 

calendar. 

na = Not applicable 

To clarify the pattern of missing information, Table 3.2 gives the distributions of types of responses for age and 

for first union in a pooling all 128 surveys in this study. Across all those surveys, 452,052 women required some 

imputation of their age data. Because of the large size of the two surveys of India and endemic problems with 

reports of ages and dates in that country, 30 percent (135,061) of these imputations can be attributed to those 

two surveys. A total of 292,054 women required some imputation of their marriage or first union data. The 

1992-93 survey of India did not ask about age or date at first union. The 1998-99 survey of India asked only 

about age at first cohabitation, not the date, and a satisfactory response was given by all but 155 women. 
23

Table 3.2 includes a brief description of each type of incompleteness and its resolution, but some further 

explanation may be helpful. The data are stated in terms of age and birth date, but with rephrasing would also 

apply to marriage. Ideally, the woman provides an age, year of birth, and month of birth, and that information 

will be internally consistent and consistent with the date of interview. If this happens, DHS assigns code 1. If the 

responses are inconsistent, consistency is imposed, with priority given first to stated age, then to year of birth, 

and then to month of birth. Similarly, if an item is missing, a value is imputed with the same priorities. That is, 

imputation will be done if an item is given but is inconsistent with a higher priority item, as well as if it was 

omitted entirely. This is probably the best strategy for handling missing data, and our analysis does not lead to 

any suggestions for modifying the imputation procedures. 

There are several possible combinations of acceptable or unacceptable responses to these three items: 

DHS code 1: Current age and month and year of birth are given and are compatible. 

DHS code 2: Month of birth and age are given. Year of birth is not given, or is incompatible with age 

and must be imputed. 

DHS code 3: Year of birth and age are given. Month is not given, or is incompatible with age and must 

be imputed. 

DHS code 4: Year of birth and age are given, but year is inconsistent with age. Year of birth, and if 

necessary, month of birth, are imputed. 

DHS code 5: Year of birth is given. Both age and month of birth must be imputed. 

DHS code 6: Age is given. Both year of birth and month of birth must be imputed. 

DHS code 7: Month of birth is given. Both age and year of birth must be imputed. 

DHS code 8: None given, all imputed. 

DHS code 9: Not defined (found only in the 1992 survey of Namibia). 

For both types of data—age and birth date—the most common type of incompleteness is that age (current age or 

age at first union) is given but year and month (of birth or first union) must be imputed. These cases receive 

DHS code 6. 14 percent and 17 percent, respectively, of all responses are of this type (this includes the cases for 

the 1998-99 survey of India for which code 6 applied to all responses on first unions other than the 155 cases 

with code 8). Another common type of incompleteness is when year and age are given, and only month must be 

imputed (DHS code 3), accounting for 12 percent and 8 percent, respectively, of all responses. For age, another 

6 percent of cases include year and age but year is incompatible with age (DHS code 4). For marriage, another 

11 percent of cases include only the year, so both age and month must be imputed (DHS code 5). 

The data files do not contain information about the magnitudes of discrepancies. If, say, year of birth and current 

age are incompatible with the date of the survey, in which case year of birth will be revised, we expect that it 

usually needs only to be shifted by one or two years, but this could only be checked by comparing the files 

before and after imputation. 
24

Table 3.2 Percent distribution of types of imputation of age or marriage data, 

across all 128 surveys of women age 15-49 

DHS

code Information Age Marriage 

1 Age, month and year given and okay 63.6 58.8 

2 Month and age given, year imputed 3.5 2.6 

3 Year and age given, month imputed 12.4 7.8 

4 Year and age given, year ignored 6.4 1.8 

5 Year given, age and month imputed 0.0 10.6 

6 Age given, year and month imputed 13.9 17.4 

7 Month given, age and year imputed 0.2 0.1 

8 None given, all imputed 0.1 0.8 

9 Not defined 0.0 0.1 

 

Total 100.0 100.0 

Number of women 1,276,546 960,586 

3.2 Age and Birth Year 

All women in a main survey also appeared in the corresponding household survey, and their age distributions 

were analyzed in that context. We now look just at the women who were included in the main surveys, looking 

for evidence of heaping. These women would have provided their age information themselves, whereas in the 

household survey it could have come from someone else (although the eligible respondent for the main survey 

is often the household informant for the household survey). In order to have equal representation of all final 

digits, we look only at the thirty-year age interval 15-44. This age distribution is examined for overall evenness 

using Myers’ Index (Figure 3.4) and for heaping on final digits 0 and 5 in (Table 3.4). The strong similarity 

between Figures 3.4 and 3.5 is due to the fact that digit preference is almost entirely in favor of 0 and 5. 

2

9

26

23

25

10

8

3

4

222

1

2

1 111 1

2 2

Number of surveys

0.0 5.0 10.0 15.0 20.0 25.0

Myers' Index

Figure 3.4 Myers' Index for ages of women (15-44 years)
25

Surveys with measures in excess of 10 percent are given in Table 3.3. The same countries listed earlier for their 

conspicuous age heaping in the household schedule and incompleteness in age and date reports tend to reappear 

here. The list includes Pakistan 1990-91 and Yemen 1991-92, relatively early surveys and the only surveys in 

those two countries. Apart from Egypt 1995, all other surveys are in Western, Middle, and Eastern Africa. 

Table 3.3 Surveys of women age 15-49 with highest 

levels of incompleteness of age, marriage, or birth data 

Country 

Median

year of

survey 

Myers’ 

Index

Percent 

excess at 

final digit 

0 or 5 

Benin 2001 14.2 11.4 

Chad 1997 18.0 16.5 

Egypt 1995 11.6 10.3 

Ethiopia1

 1992 10.1 7.8 

Ghana 1988 13.1 10.4 

Ghana 1993 10.2 7.7 

Guinea 1999 12.0 11.6 

Mali 1996 11.9 10.6 

Mali 2001 12.3 10.3 

Niger 1992 18.3 17.8 

Niger 1998 14.0 13.9 

Nigeria 1990 26.1 26.1 

Nigeria 1999 19.3 18.8 

Nigeria 2003 15.7 14.3 

Ondo State2

 1986 24.3 22.2 

Pakistan 1991 21.8 20.6 

Sudan 1990 24.6 24.6 

Yemen 1991 26.0 26.0 

Note: The table lists surveys for which either the Myers’ 

Index or the percent excess at final digit or 5 is greater 

than 10. 

1 The Ethiopian survey was conducted in 1992 in the 

Ethiopian calendar, 2000 in the Western calendar. 

2

 Ondo State is in Nigeria. 

4

9

25

16

15

11

14

5

4

5

4 4

2

11 111 1 1 1

2

Number of surveys

0.0 10.0 20.0 30.0

Percent excess at digits 0 or 5

Figure 3.5 Heaping at digits 0 or 5 for ages of women (15-44 years)
26

As mentioned earlier, it is possible that some age heaping can be traced indirectly to a preference for calendar 

years ending in 0 and 5, rather than ages ending in 0 and 5. Figure 3.6 gives the distribution of Myers’ Index 

calculated for birth year. As would be expected, it is similar in shape to Figure 3.5. Figure 3.7 gives the 

distribution of the excess percentage at calendar years ending in 0 and 5. This figure is fairly symmetric, by 

comparison with Figure 3.5, which showed the preference for ages ending in 0 and 5, and has only five values 

above 10 percent. 

Table 3.4 lists the surveys that are above 10 percent on either index. Surveys with a survey year ending in 0 or 5 

are omitted because age and birth year will be confounded for them (this includes surveys conducted across two 

years, either of which ended in 0 or 5). If a survey shows heaping on birth years ending in 0 and 5, the country 

will show a high percentage in the last column of Table 3.4. The only surveys with this pattern are Guinea 

(1999), Ondo State (Nigeria 1986), and Yemen (1991). However, these surveys occurred in calendar years 

adjacent to years ending in 0 and 5, and there is enough ambiguity about the timing of the respondents’ 

birthdays that heaping on age could be confounded with heaping on birth year. 

Thus there is little evidence to support the hypothesis of a preference for birth years ending in 0 and 5. The 

source of heaping appears to be a preference for ages ending in 0 or 5. 

1

10

30

35

16

14

9

1 1

3 2 1 2 11 1

Number of surveys

0.0 5.0 10.0 15.0 20.0 25.0

Myers' Index

Figure 3.6 Myers' Index for birth years of women (15-44 years ago)
27

Table 3.4 Surveys of women age 15-49 with strongest 

evidence of heaping by birth year 

Country 

Median

year of

survey 

Myers’ 

Index

Percent 

excess at 

final digit 

0 or 5 

Guinea 1999 11.7 11.7 

Mali 2001 11.6 -5.2 

Niger 1992 10.0 -0.0 

Niger 1998 10.0 0.2 

Nigeria 1999 12.9 6.6 

Ondo State1

 1986 20.1 19.1 

Pakistan 1991 16.5 -3.5 

Yemen 1991 24.3 24.3 

Note: The table lists surveys for which either the Myers’ 

Index or the percent excess at final digit 0 or 5 is greater 

than 10. 

1 Ondo State is in Nigeria. 

3.3 Year of First Marriage and Years Since First Marriage 

The most important date provided by a woman, other than date of birth and dates of childbirth, is the date when 

she was first married or lived together with a man as married (following standard practice, the term “marriage” 

is used regardless of whether there was a marriage ceremony). Typically, the woman is asked for the month and 

year when she married or started living with her first partner. If she gives at least the year, then her age at that 

time is imputed, and she is not asked for it. If she does not give the year, she is asked her age at that time. Date 

and age at first marriage were not included in the two India surveys because of the prevalence of early arranged 

1 1

2

7

25

29

20

16

10

5

2

1

4

1 1 11 1

Number of surveys

-10.0 0.0 10.0 20.0 30.0

Percent excess at digits 0 or 5

Figure 3.7 Heaping at digits 0 or 5 for birth years of women (15-44 years ago)
28

marriages there and the frequent gap between marriage and cohabitation in these cases. (Age at first cohabitation 

was asked in the second India survey.) 

Regardless of whether the year or age response is given, it is possible that the response is based on some 

calculations (by the woman or by the interviewer) involving some combination of year of marriage, years ago, 

and/or age at marriage. We now look for evidence of heaping and digit preference involving these data. 

First, Figures 3.8 and 3.9 give the distribution of Myers’ Index and the excess at final digits 0 and 5 for year of 

marriage, which is the preferred response. Only one survey exceeds a level of 10 percent on either measure. This 

survey, Nigeria 1990, is listed in Table 3.5. Most of the heaping is indeed on 0 and 5. 

Second, Figures 3.10 and 3.11 give the distribution of Myers’ Index and the excess percentage on final digits 0 

and 5 for implied years since marriage (calculated as the year of interview minus the stated year of marriage, 

ignoring months). No survey exceeds the 10 percent threshold; the maximum is about 7 percent. 

Third, there may be heaping or digit preference of age at marriage, but our methods are unable to evaluate it. In 

every survey, the reported age at first marriage is compressed into a very narrow age range, usually less than ten 

years, such as 15-24. 

This section thus finds no noteworthy evidence of heaping or digit preference that directly or indirectly involves 

age at marriage. 

Table 3.5 Survey of women age 15-49 with strongest 

evidence of heaping by year of marriage 

Country 

Median

year of

survey 

Myers’ 

Index

Percent 

excess at 

final digit 

0 or 5 

Nigeria 1990 10.8 9.3 

Note: The table lists the only survey for which either the 

Myers’ Index or the percent excess at final digit 0 or 5 is 

greater than 10. 
29

2

4

16

35

38

18

11

11 1

Number of surveys

-5.0 0.0 5.0 10.0

Percent excess at digits 0 or 5

Figure 3.9 Heaping at digits 0 or 5 for year of marriage (15-44 years ago)

5

19

54

27

15

3

1 2 1

Number of surveys

0.0 2.0 4.0 6.0 8.0 10.0

Myers' Index

Figure 3.8 Myers' Index for year of marriage (15-44 years ago)
30

3

25

44

30

16

5

2 2 1

Number of surveys

-4.0 -2.0 0.0 2.0 4.0 6.0

Percent excess at digits 0 or 5

Figure 3.11 Heaping at digits 0 or 5 for year of marriage (15-44 years ago)

5

19

54

27

15

3

1 2 1

Number of surveys

0.0 2.0 4.0 6.0 8.0 10.0

Myers' Index

Figure 3.10 Myers' Index for years since marriage (15-44 years ago)
31

3.4 Fertility Rates from Successive Surveys in the Same Country 

An important function of DHS surveys is to monitor levels and trends in fertility. All of the main reports give 

the age-specific rates and Total Fertility Rate (TFR, five times the sum of the age-specific rates) for a period 

before the survey, generally the three years prior to the date of interview. These estimates are subject to error if 

women’s ages are misreported, if some children are omitted, or if the dates of recent childbirths tend to be 

shifted systematically. One of the best ways to assess such misreporting is to reestimate those rates using a 

subsequent survey in the same country. 

This section uses 67 pairs of successive DHS surveys in the same country, usually about five years apart, to 

identify such discrepancies. The definition of “recent fertility” is only slightly different from that used by DHS: 

the three calendar years prior to the median year of interview in the first survey, rather than the three years (36 

months) prior to each respondent’s date of interview. Age-specific rates and the TFR for ages 15-44 are 

calculated for this reference period from the birth histories in the first survey, and are then reestimated from the 

birth histories in the second survey, using statistical methods described in Section 1.2. 

Figure 3.12 describes the difference as the TFR estimate from the second survey minus that from the first 

survey. Thus, if the second survey gave a higher estimate than the first, the difference in this figure will be a 

positive number. Figure 3.13 is similar but expresses the difference as a relative difference: the difference in 

Figure 3.13 is divided by the TFR estimate from the first survey and multiplied by 100. Figure 3.14 gives a chisquare test statistic for the differences between the two sets of age-specific rates. This chi-square has six degrees 

of freedom, and the .05 critical value for the null hypothesis that the estimates agree is 16.8. The intervals in 

Figure 3.13 are multiples of 16.8, so this null hypothesis would be rejected for all surveys outside the first 

interval. (The figure omits three surveys with chi-square values above 400, but they are included in Table 3.6.) 

Note that this test statistic does not test whether the TFR estimates differ. Frequently the differences in the agespecific rates have different signs in different age groups that tend to cancel one another out in the summation. 

Some pairs have very large chi-square statistics but only modest differences in TFR estimates. 

A little more than half of the pairs have a difference in TFRs that is less than half a child in absolute value, 

relative differences that are less than 10 percent in absolute value, and acceptable chi-square test statistics (less 

than 16.8). Most analysts would probably be satisfied with this level of agreement. 

Table 3.6 lists those pairs of surveys for which the difference in Figure 3.12 was one of the “worst” six (about 

10 percent of all pairs) by any of the three measures that appear in the figures. This table omits Brazil because 

its first survey, conducted in 1991, was not national and the differences do not necessarily imply measurement 

errors. Some changes in coverage may account for other apparent differences. 

Some discrepancies are very large and reflect poorly on the quality of the data. Table 3.6 shows that the 

arithmetic difference between the two TFR estimates is as large as 2.08 children (for Burkina Faso 1992-93 and 

1998-99) and 1.80 children (for Mali 1995-96 and 2001). Other countries in this table (ignoring Brazil, for 

which the comparison is questionable) are Benin (1996 and 2001), Egypt (1995 and 2000), Indonesia (1991 and 

1994; 1994 and 1997; 1997 and 2002), Morocco (1987 and 1992), Mozambique (1997 and 2003), Nigeria (1999 

and 2003), Niger (1992 and 1998), Peru (1992 and 1996), Senegal (1986 and 1992-93), and Turkey (1993 and 

1998). African countries predominate, but the presence of Indonesia and Turkey make this a truly international 

list.

There is a tendency for the differences to be positive, so that the second survey produces a larger TFR for the 

reference period than the first survey does. In some instances (Egypt, Indonesia, Morocco, and Turkey) the 

difference is negative. When a negative difference occurs, it is small but highly significant, implying that the 

age-specific differences are large but inconsistent in sign. Indonesia appears in three pairings of surveys. Each 
32

time, the second survey gave a lower estimate than the first survey, by an amount .39, .42, and .20 of a child, 

respectively. Pairs of surveys tend to agree in the amount of change in the TFR over time.

Discrepancies between surveys probably stem from systematic errors in the statements of children’s birth dates 

in one or both surveys. For example, upward age transfers can arise from the use of a calendar or window for 

detailed questions about child health, or other indicators. The interviewer may be motivated to reduce her 

workload by placing some children above the upper age boundary for those questions. This type of transfer has 

already been mentioned and will be discussed further in Section 4.2. 

Upward age transfers are also endemic in some cultures, regardless of whether any extra information is to be 

gathered for young children. This phenomenon has long been observed for Indonesian surveys and censuses. It 

has also been observed in Pakistan, although Pakistan had only one survey and cannot be included in this 

particular comparison. 

Tables 3.7 and 3.8 give more detail on the two pairs of surveys that show the largest discrepancies in TFR 

estimates: Burkina Faso (1992-93 and 1998-99) and Mali (1995-96 and 2001). For both of these pairs, all agespecific estimates are higher in the second survey than in the first one. The distortion is almost certainly due to a 

shifting of birth dates that is fairly uniform across different ages of women. 

A more thorough analysis of all pairs of surveys in Table 3.6 is recommended, looking at age groups, cohorts of 

women, and all overlapping calendar years in more detail. Without such checks it is impossible to tell whether 

discrepancies are due to age displacement in the first survey, the second survey, or both surveys. 

1 11

4

3

1

4

5

8

7

3

2 2

4

5

3

1

3

1 1

2

1

2

1 1

0

2

4

6

8

Frequency

-1.00 0.00 1.00 2.00

Delta TFR for 15-44

Figure 3.12 TFR for second survey minus TFR for first survey, for ages 15-44
33

1 1

3

6

3

13 13

8

10

5

1

2

1

0

5

10

15

Frequency

-20.00 0.00 20.00 40.00

Percentage difference in TFR for 15-44

Figure 3.13 Percentage difference between TFR estimates, for ages 15-44

34

8

6

1 2 1 1 2 2 1 1 11 2

0

10

20

30

40

Frequency

0.00 100.00 200.00 300.00

Chi-square for 15-44

Figure 3.14 Chi-square test of differences between ASFRs for ages 15-44

Note: Three surveys with chi-square 

values over 400 are omitted. 
34

Table 3.6 Pairs of surveys with greatest discrepancies between fertility rates in the reference period 

 TFR (15-44) 

Century month code 

Median year 

Country 

Beginning 

month of 

3-year 

window

Final 

month of 

3-year 

window

First

survey 

Second

survey 

Window

estimated 

from first

survey 

Window

estimated 

from

second

survey Difference

Relative

difference 

as a 

percentage 

Chi-square

for

difference 

between 

age-specific 

fertility 

rates1

Benin 1117 1152 1996 2001 5.76 7.15 1.39 24.10 65.22 

Burkina Faso 1081 1116 1993 1999 6.23 8.32 2.08 33.40 117.99 

Egypt 1105 1140 1995 2000 4.71 4.31 -0.40 -8.50 1058.29 

Indonesia 1057 1092 1991 1994 3.93 3.54 -0.39 -9.90 310.14 

Indonesia 1093 1128 1994 1997 3.76 3.35 -0.42 -11.20 314.04 

Indonesia 1129 1164 1997 2002 3.55 3.35 -0.20 -5.60 425.49 

Mali 1117 1152 1996 2001 6.54 8.34 1.80 27.50 123.73 

Morocco 1009 1044 1987 1992 6.21 4.90 -1.31 -21.10 2547.75 

Mozambique 1129 1164 1997 2003 4.97 6.51 1.54 31.00 13.28 

Niger 1069 1104 1992 1998 6.85 8.24 1.40 20.40 10.11 

Nigeria 1153 1188 1999 2003 4.75 5.92 1.16 24.40 74.88 

Senegal 997 1032 1986 1993 6.40 7.86 1.46 22.80 30.31 

Turkey 1081 1116 1993 1998 3.51 3.20 -0.30 -8.50 428.61 

Note: The table lists pairs of surveys in the highest decile of values of the absolute difference, the absolute relative difference, or chi-square. 1

 Six degrees of freedom; .01 critical value is 16.8 

Table 3.7 Fertility rates for the period 1990-1992 in Burkina Faso estimated 

from surveys conducted in 1992-93 and 1998-99 

Estimated age-specific 

fertility rates 

Five-year 

age interval 

1993

survey 

1999

survey 

Difference

1999-1993

z statistic for 

(4), adjusted for 

sample weights 

and clustering 

15-19 146.6 181.8 35.2 3.21 

20-24 281.7 354.0 72.3 5.80 

25-29 282.4 353.5 71.1 6.13 

30-34 233.9 332.5 98.6 4.80 

35-39 196.9 266.0 69.1 3.22 

40-44 105.4 175.9 70.5 1.75 

 

15-44 6.23a

 8.32a 2.08 117.99b

a

 Total fertility rate (15-44) 

b

 Sum of squares of z scores, which has a chi-square distribution 
35

Table 3.8 Fertility rates for the period 1993-1995 in Mali estimated from 

surveys conducted in 1995-96 and 2001 

Estimated age-specific 

fertility rates 

Five-year 

age interval 

1996

survey 

2001

survey 

Difference

2001-1996

z statistic for 

(4), adjusted 

for sample 

weights and 

clustering 

15-19 185.5 225.3 39.8 3.94 

20-24 294.9 337.1 42.2 6.10 

25-29 293.2 350.5 57.3 5.88 

30-34 241.7 308.9 67.2 4.50 

35-39 200.2 260.0 59.8 3.50 

40-44 92.1 185.7 93.6 1.98 

 

15-44 6.54a

 8.34a 1.80 123.73b

a

 Total fertility rate (15-44) 

b

 Sum of squares of z scores, which has a chi-square distribution 
36

4 Children in the Birth Histories 

This chapter examines the ages, dates, and intervals that are given within the birth histories collected from the 

128 surveys of women age 15-49. The data file contains all births in these birth histories. Topics include the 

completeness of the responses; heaping on birth dates and ages, and transfers outside the calendar interval for 

the additional information; heaping of the length of preceding birth intervals; heaping of the age at child death; 

and a comparison between two successive surveys’ estimates of infant mortality in a five-year reference period 

prior to the first survey. 

4.1 Incompleteness 

Each birth was given a code by DHS to indicate the completeness of information about the birth date of the child 

and the amount of imputation that was required. For this report, the information is considered incomplete if 

anything less than the month and year of birth was provided. The distribution of the proportion of births that 

were incomplete is described in Figure 4.1. 

Much the same information was given in the summary measure on birth dates in Figure 3.1 and Table 3.1. 

However, in that context a country with high fertility—i.e., more births per woman—would tend to get a higher 

score. Here, with births as the unit, there is no confounding with the level of fertility. A lower threshold is used 

here. Surveys with a woman’s birth-date incompleteness index of 1.0 or more were included in Table 3.1, but if 

the child’s incompleteness index exceeded .2—that is, if 20 percent or more of the births were incomplete—the 

survey is listed in Table 4.1. 

Twenty-four surveys, representing 17 countries, exceeded this threshold. There is considerable overlap with the 

surveys listed in Table 3.1, except that no South Asia surveys are above the .2 threshold. Except for Egypt, 

Morocco, Yemen, and the earliest of the Indonesian surveys, all countries are in Western and Middle Africa. 

88

16

10

3

8

3

Number of surveys

0 .2 .4 .6

Index of incompleteness

Figure 4.1 Incompleteness of birth data
37

Table 4.1 Surveys with highest levels of missing 

data in birth histories 

Country 

Median

year of 

survey 

Proportion of 

births with 

missing data 

Benin 1996 .470 

Benin 2001 .426 

Burkina Faso 1993 .272 

Burkina Faso 1999 .435 

Cameroon 1991 .283 

Comoros 1996 .343 

Egypt 1995 .264 

Ghana 1988 .230 

Ghana 1993 .203 

Guinea 1999 .592 

Indonesia 1987 .234 

Madagascar 1997 .206 

Mali 1987 .625 

Morocco 1987 .418 

Mozambique 1997 .313 

Niger 1992 .403 

Sudan 1990 .452 

Senegal 1986 .218 

Senegal 1993 .435 

Senegal 1997 .218 

Tanzania 1991 .218 

Togo 1988 .465 

Togo 1998 .307 

Yemen 1991 .558 

Note: The table lists surveys for which the 

proportion missing exceeds .2. 

On average, about 11 percent of the births in a survey had incomplete information about date or age. Across the 

full set of 128 surveys, 392,450 births (out of 3,688,462) received some kind of imputation. The distribution of 

imputation types is given in Table 4.2. (See Chapter 3, Section 3.1 for the meaning of the DHS codes.) 

The most common type of imputation, affecting about 4 percent of births (or 38 percent of the births that 

required any imputation at all) involved just the imputation of the calendar month of birth (DHS code 3). This is 

a relatively innocuous type of imputation that should have only a trivial effect on most kinds of calculations 

from the birth histories because the calendar year of birth is accepted. Year of birth is also unaffected for DHS 

code 5, which applies to 3 percent of all births (27 percent of imputed births). The next largest category is DHS 

code 4, in which both year and month are modified to become consistent with the stated age of the child. There 

is more potential impact on various rates from this kind of adjustment, but it only affects 2 percent of all births 

(19 percent of imputations). These three categories together account for 83 percent of all imputations. About 0.3 

percent of all births have code 8, which is most serious, because both age and year of birth must be imputed. All 

types of incompleteness become more common as the elapsed time between the birth and the interview 

increases. 
38

Table 4.2 Percent distribution of types of imputation of children’s birth dates 

across all 128 surveys of women age 15-49, by years since birth (unweighted) 

Years since birth DHS

code Information 0-9 10-19 20+ Total 

1 Month and year given and okay 93.2 86.9 82.9 89.4 

2 Month and age given, year imputed 0.4 0.6 0.8 0.5 

3 Year and age given, month imputed 2.4 5.1 6.7 4.1 

4 Year and age given, year ignored 1.4 2.5 2.6 2.0 

5 Year given, age and month imputed 1.6 3.4 5.2 2.8 

6 Age given, year and month imputed 0.3 0.4 0.5 0.4 

7 Month given, age and year imputed 0.5 0.7 0.8 0.6 

8 None given, all imputed 0.2 0.3 0.5 0.3 

 

Total 100.0 100.0 100.0 100.0 

Number of births = 3,688,462 

4.2 Birth Dates and Ages of Children 

The birth histories include the ages of children who were still alive at the date of interview. The distribution of 

Myers’ Index and the percentage excess at final digits of age 0 and 5 are shown in Figure 4.2 and Figure 4.3, 

respectively. Surveys exceeding a threshold of 10 percent on either measure are listed in Table 4.3. 

There is virtually no evidence of heaping on age of child. Only two surveys exceed the threshold of 10 percent 

on either measure. The relatively symmetric distribution of the second measure, shown in Figure 4.3, is due to 

the fact that the small deviations from a uniform distribution tend not to be at final digits 0 and 5. The two 

surveys in Table 4.3 are El Salvador 1985 and Ondo State, Nigeria 1986—two of the very earliest DHS surveys. 

4

38

57

21

4

11 1 1

Number of surveys

0.0 5.0 10.0 15.0 20.0

Myers' Index

Figure 4.2 Myers' Index for age of child (0-29 years)
39

Table 4.3 Surveys with highest levels of heaping on 

children’s ages 

Country 

Median

year of

survey 

Myers’ 

Index

Percent 

excess at 

final digit 

0 or 5 

El Salvador 1985 13.1 1.5 

Ondo State1 1986 16.5 -1.0 

Note: The table lists surveys for which either the Myers’ 

Index or the percent excess at final digit 0 or 5 is greater 

than 10. 

1

 Ondo State is in Nigeria. 

Although there is virtually no evidence of heaping on children’s age, year of birth does not fare so well. Figure 

4.4 and Figure 4.5 give the distribution of Myers’ Index and the percentage excess at final digits 0 and 5 for year 

of birth. No surveys have a conspicuous excess at final digits 0 and 5, but 27 surveys have Myers’ Index above 

10 percent and are listed in Table 4.4. This list includes surveys from 21 countries. Apart from early surveys in 

Brazil (1991) and El Salvador (1985) and a more recent one in Guatemala (1998-99), all these surveys are from 

sub-Saharan Africa. All three of the Uganda surveys, all three of the Zambia surveys, and both of the 

Madagascar surveys show strong heaping of children’s year of birth. 

4

47

54

20

3

Number of surveys

-2.0 -1.0 0.0 1.0 2.0 3.0

Percent excess at digits 0 or 5

Figure 4.3 Heaping of age at digits 0 or 5 (0-29 years)
40

2

4

22

32

40

18

3 4

2

Number of surveys

0.0 5.0 10.0 15.0 20.0

Myers' Index

Figure 4.4 Myers' Index for year of birth (0-29 years ago)

2

4

13

19

9

20

21

19

11

3

4

1 1

Number of surveys

-5.0 0.0 5.0 10.0

Percent excess at digits 0 or 5

Figure 4.5 Heaping of birth years at digits 0 or 5 (0-29 years ago)
41

Table 4.4 Surveys with highest levels of heaping on birth 

year in the birth histories 

Country 

Median

year of

survey 

Myers’ 

Index

Percent 

excess at 

final digit 

0 or 5 

Brazil1

 1986 10.1 3.3 

Burkina Faso 1993 10.3 1.9 

Burundi 1987 10.9 2.1 

Chad 1997 10.5 3.5 

El Salvador 1985 18.5 3.7 

Ghana 1988 10.5 0.2 

Guatemala 1999 10.0 0.6 

Kenya 1998 10.0 0.2 

Kenya 2003 11.5 0.2 

Liberia 1986 12.0 7.8 

Madagascar 1992 10.3 2.2 

Madagascar 1997 10.3 1.4 

Malawi 2000 12.1 4.9 

Namibia 1992 10.5 2.3 

Ondo State2

 1986 19.4 8.7 

Rwanda 2000 10.5 3.2 

Senegal 1986 10.1 4.4 

Tanzania 1991 10.5 3.0 

Tanzania 1996 10.1 4.7 

Togo 1988 10.1 0.1 

Uganda 1988 11.1 0.4 

Uganda 1995 12.1 1.6 

Uganda 2001 11.5 4.4 

Zambia 1992 10.1 1.7 

Zambia 1996 12.2 5.1 

Zambia 2002 10.0 2.8 

Zimbabwe 1988 10.3 0.3 

Note: The table lists surveys for which either the Myers’ 

Index or the percent excess at final digit 0 or 5 is greater 

than 10. 

1

 The 1986 Brazil survey was restricted to the northeastern 

part of the country. 

2

 Ondo State is in Nigeria. 

The next type of potential problem with children’s ages to be considered is transfers outside a window or 

calendar of eligibility for the special questions on child health. This has long been known to be a risk because 

interviewers may be motivated to reduce their workload by shifting children to a higher age. The issue and 

methods to estimate its prevalence were introduced in Section 1.2 of this report. 

Except for the earliest surveys, the standard recode file includes a variable in the survey of women 15-49 (v017) 

that gives the century month code for the start of the calendar for contraception and the proximate determinants. 

It is generally January of the fifth full calendar year preceding the year when interviewing began. 

The standard recode file does not include a specific code for the start of the child health questions—that is, the 

earliest month of birth for which a child would become eligible for the child health questions. It is recommended 

that such a code be added to the standard recode file. It is also generally January of the fifth full calendar year 

preceding the year when interviewing began, but sometimes (for six surveys) was in the fourth preceding 
42

calendar year and sometimes (in twenty surveys) was in the third preceding calendar year. There were four other 

exceptions (Institute for Resource Development, 1990:90): for the 1987 survey in Mali, the calendar started in 

March 1982; for the 1986 survey in Senegal, the calendar started in April 1981; for the 1988 survey in Tunisia, 

the calendar started in May 1983; and for the 1988 survey in Togo, the calendar started in June 1983. 

Data on child health are collected for all children born in or after this beginning month, but such data are 

discarded if the birth date is more than 59 months before the month of interview. For example, if an interview 

occurred in April 1998, the data on child health (for a typical survey) would have been collected for births 

occurring in January 1993 and later. After the fieldwork, data on births in January through April of 1993 would 

have been discarded. Months 0 to 59 before the interview would be May 1993 through April 1998. 

In terms of the interviewer’s workload, the first month of the child health questions is pivotal. Displacement of 

births refers to any tendency to transfer births which actually occurred in that month, or later, backwards into an 

earlier month. This tendency was assessed for 125 surveys—all surveys except Ecuador 1987 and Indonesia 

1987, the only surveys that did not include child health questions, and India 1998-99, because it used different 

start dates in different states. 

In 34 surveys it appears that at least 10 percent of the children actually born in the earliest year of the calendar 

were transferred to the preceding year. For no survey is there a suggestion of more than a 10 percent shift in the 

other direction (that is, into the window). The distribution of the transfer rate is given in Figure 4.6, and the 

surveys over the 10 percent threshold are listed in Table 4.5. 

The majority of the listed surveys are in sub-Saharan Africa. The exceptions are Egypt 2000, Guatemala 1987, 

Haiti 1994-95, Indonesia 1997 and 2002, Pakistan 1990-91, Turkey 1993, and Vietnam 1997. The Pakistan 

survey has the highest transfer rate of all, 28 percent, but it is also one of the earliest surveys on the list. For all 

the surveys in Table 4.5, substantially more births were reported for the year just before the window (column 5 

of the table) than for the preceding year (column 4) or the following year (column 6), which is the earliest year 

in the window.

Several of the countries on this list also appeared in Table 3.6, because of discrepancies between successive 

surveys in their estimates of fertility rates in a window before the first survey. Several also appeared on lists of 

countries with other kinds of age transfers.
43

1

2

10

18

24

21

25

12

3

4 4

1

Number of surveys

-10.0 0.0 10.0 20.0 30.0

Percent shifted upwards

Figure 4.6 Percent of births transferred outside window (one year)
44

Table 4.5 Surveys with strongest evidence of transfer outside the calendar 

 

 

Observed number of births 

Estimated “true” 

number of births 

Country 

Median

year of 

survey 

Estimated 

percentage 

of births in 

year x 

misreported

at x-1 Year x-2 Year x-1 Year x Year x+1 Year x-1 Year x 

Burkina Faso 1993 21.3 1126.5 1417.9 922.2 1134.3 1168.7 1171.4 

Burkina Faso 1999 12.7 1193.5 1356.2 1059.1 1225.2 1202.4 1212.9 

Burkina Faso 2003 13.2 2222.2 2425.6 1864.3 2241.9 2141.8 2148.1 

Burundi 1987 13.5 720.8 861.3 655.4 718.5 758.7 757.9 

Chad 1997 18.2 1303.0 1669.3 1189.4 1444.3 1404.8 1453.9 

Comoros 1996 11.6 395.0 464.0 368.0 396.0 415.8 416.2 

Côte d’Ivoire 1994 13.2 1487.4 1614.3 1218.2 1411.2 1428.6 1403.8 

Côte d’Ivoire 1999 13.7 381.7 456.1 334.8 340.4 403.0 387.9 

Egypt 2000 12.0 2197.4 2553.8 2028.2 2275.6 2277.6 2304.4 

Ghana 1993 23.4 685.0 965.0 605.0 711.0 780.1 789.9 

Ghana 2003 14.1 753.9 824.7 627.2 781.2 721.7 730.2 

Guatemala 1987 11.8 889.0 1041.0 835.0 937.0 929.8 946.2 

Haiti 1994 12.8 658.8 740.4 584.8 707.1 654.8 670.4 

Indonesia 1997 11.0 3906.8 4083.9 3159.2 3457.1 3695.4 3547.7 

Indonesia 2002 14.2 3417.7 3784.7 2734.7 2981.9 3333.8 3185.6 

Kenya 1989 18.9 1068.5 1617.5 1189.2 1393.8 1341.2 1465.5 

Liberia 1986 22.9 943.3 1251.6 800.8 1013.3 1014.0 1038.4 

Malawi 2000 23.0 2018.0 2684.5 1702.3 2111.3 2176.9 2209.9 

Mozambique 1997 19.6 1384.7 1827.7 1198.7 1263.9 1536.2 1490.2 

Mozambique 2003 13.5 1954.7 2233.7 1700.7 1947.9 1940.5 1993.9 

Namibia 2000 11.0 742.6 839.3 694.4 821.6 754.0 779.8 

Niger 1992 11.1 1399.3 1526.8 1219.4 1391.5 1374.4 1371.8 

Nigeria 1990 19.3 1480.7 1997.3 1381.6 1607.4 1666.3 1712.6 

Ondo State1

 1986 17.9 653.0 825.0 576.0 661.0 699.1 701.9 

Pakistan 1991 27.8 1790.0 2032.9 1052.4 1284.7 1627.8 1457.4 

Rwanda 2000 15.7 1372.9 1737.8 1342.3 1682.0 1487.9 1592.1 

Senegal 1986 11.9 838.0 966.0 771.0 879.0 861.6 875.4 

Tanzania 1996 11.8 1218.9 1482.9 1192.8 1304.5 1322.7 1353.0 

Togo 1988 10.9 656.0 665.0 534.0 655.0 599.7 599.3 

Turkey 1993 12.0 883.1 949.0 710.8 754.1 851.7 808.1 

Uganda 1995 17.3 1251.9 1587.1 1150.5 1379.5 1330.4 1407.2 

Uganda 2001 14.1 1218.3 1458.5 1139.1 1377.2 1272.2 1325.3 

Vietnam 1997 11.1 763.3 741.6 570.1 666.0 673.2 638.5 

Zambia 1996 13.1 1181.6 1434.9 1162.0 1412.5 1259.8 1337.1 

Note: Year x is the first year within the calendar; year x-1 is the first year before the calendar, etc. Example: Survey begins

in 2001; calendar begins January 1996; x=1996, x-1=1995. The table lists surveys for which the estimated percentage of 

births in year x misreported in x-1 is greater than 10. 

1

 Ondo State is in Nigeria. 

4.3 Birth Intervals 

Another perspective on the accuracy of birth dates comes from examining the intervals between births, 

expressed in months. For each birth, DHS data files include the months since the immediately preceding birth 

(for second and higher order births) and the months until the immediately following birth (when applicable). 

The subsequent interval for one birth will equal the preceding interval for the next birth, except that it will be 

censored for the most recent birth. The focus, therefore, is just on the preceding birth interval. As described in 

Section 1.2, a modification of Myers’ Index will identify heaping on multiples of six months. Most birth 

intervals are five years or less in length, and few are less than a year because of the nine months of pregnancy 
45

and postpartum amenorrhea. This method requires the use of a maximum interval that is a multiple of 12 

months. The index, therefore, is restricted to intervals 12 to 59 months in length. 

Figure 4.7 gives the distribution of surveys by the value of Myers’ Index, and Figure 4.8 gives the percent 

excess at multiples of six months. Those surveys that exceed a 10 percent threshold on either measure are listed 

in Table 4.6. There is no evidence of heaping on multiples of six months, but five surveys exceed the 10 percent 

threshold of Myers’ Index for any irregularities: Guinea 1999, Comoros 1996, Nigeria 1999, Pakistan 1990-91, 

and Yemen 1991-92. All of these surveys have appeared on earlier lists of possible misreporting. 

Although these five surveys exceed the threshold, the results are generally quite favorable, probably because the 

imputation procedures are effective in avoiding heaping at intervals of 24, 36, and 48 months. When birth year 

is given but month is not, month is imputed with a random component to avoid heaping at such intervals. The 

results of Figure 4.8 show clearly that this kind of heaping has been completely averted. There is still some 

heaping at other values, but probably not enough to distort the analysis of birth intervals. 

Table 4.6 Surveys with highest levels of heaping on length 

of previous birth interval (12-59 months) 

Country 

Median

year of

survey 

Myers’ 

Index

Percent 

excess at 

a multiple 

of 6 months 

Comoros 1996 15.5 2.6 

Guinea 1999 12.0 -1.5 

Nigeria 1999 10.5 -1.6 

Pakistan 1991 11.6 -1.6 

Yemen 1991 13.7 -0.5 

Note: The table lists surveys for which either the Myers’ 

Index or the percent excess at a multiple of 6 months is 

greater than 10. 

2

12

24

35

22

7

10

7

4

111 1 1

Number of surveys

0.0 5.0 10.0 15.0

Myers' Index

Figure 4.7 Myers' Index for months in previous birth interval (12-59 months)
46

4.4 Age at Death 

For each child who died, DHS provides a code for age at death, which may involve some imputation. If the child 

died before the second birthday, age at death is given in months. There is a tendency for these reports to be 

heaped at multiples of six months, and particularly at twelve months. As was mentioned in Section 1.2, this type 

of heaping, immediately above the boundary for infant deaths, can be a source of underestimation of the infant 

(age 0) mortality rate and overestimation of the child (ages 1-4) mortality rate. 

Our measure of heaping at twelve months is the ratio of the observed cases to the expected cases, where the 

expected number is the average for months 10, 11, 13, and 14. This is calculated for all deaths under age 2 that 

were reported for the ten years before the survey. The distribution of this ratio of observed to expected numbers 

of cases is given in Figure 4.9. Almost all surveys show some heaping of this sort. For 22 surveys, the ratio of 

observed to expected is 10 or more, which most analysts would consider to be very high. The distribution of the 

z statistic for testing the significance of this heaping is given in Figure 4.9. The z statistic is above 2, 

approximately the critical value for a two-tailed .05 test, for 42 surveys. 

For many surveys the ratio is well below 1.00, and z is below -2 for 17 surveys. The reason for this is that some 

surveys—El Salvador 1985 is a good example—have very substantial heaping at “months ago” that are 

multiples of 10 rather than 12. It is likely that this pattern resulted from an imputation procedure that is no 

longer in use. 

2

6

10

29

54

18

6

3

Number of surveys

-4.0 -2.0 0.0 2.0 4.0

Percent excess at multiples of six months

Figure 4.8 Heaping of birth interval at 6X months (12-59 months)
47

Table 4.7 lists the surveys with heaping ratios at 12 months that exceed 10.0 or z statistics that exceed 5.0. This 

is a very high and rather arbitrary threshold for z, but because of considerable variation from one survey to 

another in the frequencies of deaths reported at months 10-14, there are a few surveys with high heaping ratios 

but small z statistics, as well as the other way around. The 24 surveys on this list come from 21 different 

countries. Again, a majority are from sub-Saharan Africa. The others are Bangladesh 1993-94, Bolivia 1989, 

Dominican Republic 1999, Ecuador 1987, Egypt 2000, India 1998-99, Liberia 1986, Thailand 1987, Tunisia 

1988, and Yemen 1991, representing all regions. The highest ratio by far, 36.3, is from the 1988 Ghana survey. 

10

41

30

11

14

6

444 3

1

Number of surveys

0.0 10.0 20.0 30.0 40.0

Ratio of observed to expected for age at death 12 months

Figure 4.9 Heaping of age at death at 12 months
48

Table 4.7 Surveys with highest levels of heaping on months of 

age at child death (0-59 months) 

Country 

Median

year of

survey 

Ratio of 

observed to 

expected at

12 months

z test 

statistic for 

significance

of (3) 

Bangladesh 1994 12.2 5.6 

Bolivia 1989 11.8 5.9 

Burundi 1987 21.2 6.3 

Dominican Republic 1999 17.1 1.3 

Ecuador 1987 13.3 3.6 

Egypt 2000 10.1 4.3 

Ghana 1988 36.3 7.8 

Ghana 2003 16.3 4.5 

Guinea 1999 20.3 9.3 

India 1999 6.8 6.6 

Liberia 1986 14.6 6.6 

Mali 1987 21.5 8.6 

Mali 2001 8.4 7.2 

Morocco 1987 10.1 4.9 

Nigeria 1999 11.9 6.3 

Senegal 1986 16.8 7.8 

Senegal 1997 10.4 6.1 

Sudan 1990 14.2 7.5 

Togo 1988 17.9 5.6 

Thailand 1987 15.9 2.1 

Tunisia 1988 13.1 4.1 

Uganda 1988 13.4 6.8 

Yemen 1991 14.8 6.8 

Zambia 1998 10.1 3.4 

Note: The table lists surveys for which either the ratio is greater 

than 10 or z is greater than 5. 

2

3

2

3

7

14

20

15

18

8

12

8

3

7

4

2

Number of surveys

-10.0 -5.0 0.0 5.0 10.0

z test statistic

Figure 4.10 Statistical test of heaping of age at death at 12 months
49

4.5 Infant Mortality Rates from Successive Surveys in the Same Country 

Infant mortality rates from successive surveys in the same country can be compared for a fixed reference period 

before the first survey. This comparison will be described for the same pairs of countries that were used in 

Section 3.4 to check the consistency of the fertility data. Section 3.4 used the files of women, and the window or 

reference period was the three calendar years prior to the year in which the fieldwork began. This section uses 

the files of births with the reference period extended backward two more years (for a total of five years) to yield 

a maximum number of events. As with the fertility comparison, we take the first survey as the reference because 

the estimates are commonly believed to be accurate. 

In the case of fertility, the estimate for the reference period tended to be higher for the second survey than the 

first. When this discrepancy occurred, it was interpreted as the likely result of distortion in one or both surveys 

that pushed some children with true ages 0-4 into reported ages 5-9—these age intervals are approximate; the 

actual ages depend on the calendar-related questions for children—combined with the typical five-year interval 

between surveys. 

Figures 4.11 and 4.12. show that the second survey also tends to produce a higher estimate of infant mortality 

than the first survey. Figure 4.11 expresses the difference arithmetically: second estimate minus first estimate, 

with a factor of 1,000 in the infant mortality rate. Figure 4.12 expresses it in relative terms as the percentage by 

which the second estimate exceeds the first estimate. For about three-quarters of the comparisons, the second 

estimate is higher than the first estimate. The greatest arithmetic differences are 49 points for Nigeria (1999 and 

2003 surveys) and 47 points for Mali (1987 and 1995 surveys). The greatest relative difference, by far, is for 

Colombia, where the 1995 survey estimate for 1985-89 was 102 percent greater than the 1990 survey estimate 

for the same period. 

Figure 4.13 gives the distribution of z test statistics to test the null hypothesis that the two estimates are 

compatible or consistent. Of the 63 pairs of surveys for which this statistic was computed, 49 are in a range from 

-2 to +2 and would be judged to be consistent. In several cases, the difference is fairly large, arithmetically or 

relatively, but is not statistically significant. For example, the Dominican Republic 2002 survey gives an 

estimate for 1994-98 that is 40 percent higher than the 1999 survey. However, both of the estimates were fairly 

low (an IMR of 25 using the 1999 survey and 35 using the 2002 survey), so there were few deaths, and the z

statistic was only 0.98. 

Thirteen pairs of surveys with significantly different estimates (at the .05 two-tailed level) are listed in Table 

4.8. (The pair of surveys from Brazil are omitted because the first survey was not national.) One pair of surveys 

had a significant difference in a negative direction: the Namibia 2000 survey gave a lower estimate for 1987-91 

than the 1992 survey. For the other twelve pairs of surveys in Table 4.8, the second estimate was higher than the 

first. These include surveys from Bolivia (1989 and 1993), Comoros (1991 and 1998), Colombia (1990 and 

1995), Egypt (1992 and 1995), Haiti (1994 and 2000), Indonesia (1997 and 2000), Mali (1987 and 1995), 

Nigeria (1999 and 2003), Niger (1992 and 1998), Nepal (1996 and 2001), and Tanzania (1996 and 1999). All 

geographical regions are represented on this list. Although many countries had three or more surveys, and 

therefore appear in two or more pairs of successive surveys, no country had more than one pair of surveys with a 

high level of this kind of inconsistency. 

A comparison between the discrepancies in the two fertility estimates and the discrepancies in the two infant 

mortality estimates shows considerable similarity. Combining the summary data file for this section with the one 

in Section 3.4, and calculating the correlation (r) between the difference in TFRs with the difference in IMRs 

gives r=.44 for all pairs, increasing to r=.83 when limited to the twelve pairs of surveys that have statistically 

significant discrepancies for both fertility and mortality. 
50

There is no obvious reason why an inconsistency in fertility estimates should be associated with an 

inconsistency in mortality estimates, apart from the fact that both kinds of inconsistencies are more likely if 

knowledge of dates is poor. If some children who died early (for example, in month 0) were omitted from a birth 

history, then there would be an underestimate of both fertility and infant mortality. However, such omissions 

would be less likely if the event was recent, and we tend to observe lower estimates for the first survey, a pattern 

that is inconsistent with this mechanism. 

The association between fertility and mortality estimates may depend on whether there is a consistent pattern of 

misreporting in both surveys. Distortion in the first survey or in the second survey, or in both surveys, may 

account for inconsistencies. Further research into the underlying sources of error, with a view toward 

anticipating such error during data collection, would be worthwhile. 

Regardless of the mechanism, it is not unusual in DHS surveys to show an inconsistency between earlier and 

later estimates of infant mortality in the same reference period. It is general practice in DHS reports to describe 

trends in infant mortality, not just levels. This practice should be continued by piecing together successive 

surveys and, additionally, by going backward within each survey to attempt to identify and reconcile 

discrepancies for the periods of overlap. 

1

222

11

17

11

9

4

2 2

1

2

0

5

10

15

20

Pairs of surveys

-50 0 50

Difference

Figure 4.11 1q0 from second survey minus 1q0 from first survey
51

3

2

13

20

13

8

4

1 1 1

0

5

10

15

20

Pairs of surveys

-100.00 -50.00 0.00 50.00 100.00

Percentage difference

Figure 4.12 Percentage difference between 1q0 estimates.

2

4

12

19

16

5

4

3

1

0

5

10

15

20

Pairs of surveys

-10.00 -5.00 0.00 5.00 10.00

z test statistic

Figure 4.13 z test of difference between 1q0 estimates
52

Table 4.8 Pairs of surveys with statistically significant (at the .05 level) discrepancies between estimates of infant mortality during 

a reference period preceding the first survey 

Century month code 

IMR estimate for 

reference period 

Country 

Median

year of 

first

survey 

Beginning 

month of 

5-year 

window

Final 

month of 

5-year 

window

Median

year of 

second

survey 

First

survey 

Second

survey Difference

Relative

difference 

z

statistic 

Bolivia 1989 1009 1068 1993 83 97 15 17.84 2.02 

Brazil 1991 1033 1092 1996 77 57 -20 -26.03 -2.41 

Cameroon 1991 1033 1092 1998 62 79 18 28.41 2.03 

Colombia 1990 1021 1080 1995 17 34 17 102.77 4.31 

Egypt 1992 1045 1104 1995 63 78 15 23.48 3.07 

Haiti 1994 1069 1128 2000 74 109 35 46.96 4.37 

Indonesia 1997 1105 1164 2002 44 53 9 20.32 2.10 

Mali 1987 985 1044 1995 106 153 47 44.85 4.60 

Namibia 1992 1045 1104 2000 58 42 -16 -27.81 -2.07 

Nepal1 2052 1765 1824 2057 82 94 12 15.05 2.12 

Niger 1992 1045 1104 1998 123 147 25 20.06 3.03 

Nigeria 1999 1129 1188 2003 74 124 49 66.73 6.96 

Tanzania 1996 1093 1152 1999 83 117 33 40.14 3.14 

1

 The Nepalese surveys were conducted in 2052 and 2057 in the Nepalese calendar, 1996 and 2001 in the Western calendar. 
53

5 Effect of Education on Misreporting 

It is likely that many characteristics are associated with the probability that a response will be inaccurate, and 

would provide partial explanations, at least in the statistical sense, of the observed variations in data quality. 

Some of these characteristics are aggregate level and would account for variation between countries, and others 

are individual level and would account for variation within specific surveys. In this report, some interpretations 

have referred to the interviewers’ natural desire to reduce their workload, but it could be argued that even this 

source of misreporting can be traced back to the social context and the respondents’ backgrounds. The role of 

the interviewer in age transfers, for example, is probably contingent on the respondents being uncertain or vague 

about their own ages or the ages of children or other household members. 

It would be possible to conduct a multivariate analysis of the association between several respondent 

characteristics and various indicators of misreporting, but this report focuses on the role of education, i.e., years 

of schooling, measured as an interval-level variable (hv108 in the household survey and v133 in the survey of 

women). We will examine how education is related to age heaping, incompleteness of reporting of ages and 

dates, heaping of age at death at 12 months, and age transfers. This is one of the few parts of this report in which 

test statistics will be reported because there is probably less risk here of confusing reporting errors with genuine 

irregularities in age distributions. We will employ a conservative .01 level of significance rather than the .05 

level used earlier. 

5.1 Aggregate-level Effects 

In the household survey, it is reasonable to look for variation according to education of the household head and 

education of the household respondent—the person who provides the information on the household. The 

education of individual household members would be unlikely to matter, except to the extent that it is correlated 

with the education of the household head or household respondent. 

The data file that consists of one record for each of the 102 household surveys used in Chapter 2 includes the 

mean education of the household head and usually includes the mean education of the household respondent. 

We would expect data quality to be more strongly associated with the education of the household respondent 

than the household head. Both measures will be used because they are highly correlated, and in some surveys it 

is not possible to link to the education of the respondent. 

Myers’ Index for the age heaping of household members is regressed on these two measures of education 

(separately) using ordinary least squares (OLS) regression, with results given in the first two lines of Table 5.1. 

The table shows that when Myers’ Index of age heaping for a survey is regressed on the mean years of schooling 

for the household heads in that survey, the slope is -0.6208. That is, an increase of one year in that mean is 

associated with a decline of .6208 in Myers’ index, and this variable accounts for 11.16 percent of the variation 

in Myers’ Index. If the predictor variable comes from the household respondent rather than the household head, 

the effect increases in magnitude to -0.6680 and the percentage of variation explained increases to 18.15 

percent. As expected, the effect of education on heaping is greater when it refers to the household respondent 

than when it refers to the household head. 

As with a multilevel analysis, the appropriate way to obtain unbiased estimates and evaluate statistical 

significance is to inflate each household survey by the number of cases in the sample. When education of the 

household head is the predictor, the combined sample size is 5,408,173; when education of the household 

respondent is the predictor, the total number of cases is 4,719,118 (2 of the 102 surveys are missing the mean 

education of household head and 15 are missing the mean education of the household respondent). Because of 

the huge combined sample size, all coefficients given in Table 5.1 are highly significant. This analysis has not 
54

made a correction for within-survey clustering, which would not alter the estimates but would reduce the 

effective sample sizes somewhat. 

Turning to the 128 surveys of woman age 15-49, Table 5.1 also gives the results of regressing the three 

measures of incompleteness for each survey on the mean years of schooling for the women in the survey. 

Overall, an increase of one year in the mean years of schooling tends to reduce the level of incompleteness by 

0.0923 for age and birth date, 0.0666 for marriage, and 0.1145 for birth dates. Mean education accounts for a 

substantial amount of the variation between surveys, with R2

 values of .6196, .4952, and .2860, respectively. 

The association with the woman’s own age and birth date, in particular, is very strong. 

Finally, using the 128 files of birth histories as cases, we regress the measure of heaping of child deaths at 12 

months (the ratio of the observed number to the expected number at 12 months) on the mean years of schooling 

of the mother. Again, there is a negative relationship, with a coefficient of -0.3756, but R2

 is small, only .0299. 

Table 5.1 Between-survey effect of mean years of schooling on selected 

measures of misreporting (results of OLS regressions)

Measures of education and misreporting Slope R2

Education of household head 

 Myer’s Index of age heaping -0.6208 .1116 

Education of household respondent 

 Myers’ Index of age heaping -0.6680 .1815 

Education of woman 

 Incompleteness of age and birth date -0.0923 .6196 

 Incompleteness of marriage age and date -0.0666 .4952 

 Incompleteness of birth dates of children -0.1145 .2860 

Education of mother 

 Heaping of child deaths at 12 months -0.3756 .0299 

To summarize, we find consistently negative and statistically significant effects of mean education on 

misreporting. The strongest effect, by far, is on the measures of incompleteness. For example, it is far more 

common to give an age but not a birth date, or to give ages and birth dates that are inconsistent in contexts 

where the mean level of education is low. The next strongest relationship is the negative effect of respondent’s 

schooling on age heaping in the household surveys. This kind of heaping, like incompleteness, also indicates 

poor knowledge of ages and birth dates. Age heaping and heaping of age at death at 12 months are also 

negatively related to mean education but to a much lower degree. 

5.2 Individual-level Effects 

Another way to measure the effect of education on misreporting is with individuals as units of analysis. All the 

coefficients in this report have been calculated with individual-level statistical models, and covariates can be 

added to those models. Logit regression will now be used to estimate the within-survey effects of woman’s 

education on the incompleteness of her age/birth date and marriage information, and the effects of the mother’s 

education on incompleteness of birth dates and on the heaping of child deaths at 12 months. In this section, 

incompleteness of birth date information comes from the child file rather than from the woman file, because it is 

only in the child file that it is coded as a binary variable. 

A code of 1 is assigned if a type of date or age information is missing, incomplete, or inconsistent, and must be 

given at least some degree of imputation, and a code of 0 otherwise. Within each survey, the code is regressed 
55

on years of schooling (v133) using logit regression. The results will be presented on a logit scale. As an aid to 

interpretation, a coefficient of -0.2 indicates that, on average, a one-year increase in education corresponds with 

an 18 percent reduction in the odds of giving complete information. A coefficient of -0.50 indicates a 39 percent 

reduction, and a coefficient of -1.00 indicates a 63 percent reduction. (If b is the logit regression coefficient, then 

the percentage reduction in the odds is 100[1 exp(b)].) Figures 5.1, 5.2, and 5.3 show the distributions of 

these logit regression coefficients for the three kinds of incompleteness across all the surveys of women. 

The effect of education on age incompleteness is always negative and almost always large and statistically 

significant. The chi-square statistics for the logit regressions described in Figure 5.1 are often very large (the one 

for India 1998-99 is 2525, with one degree of freedom, partly because of the size of that survey.) The largest 

(most negative) coefficient is -1.16, for Guatemala 1987, and it is below -0.50 for 27 other surveys. The 

distribution of coefficients for the effect of education on marriage incompleteness is more concentrated, and 

never below -0.50, although for two surveys, Kazakhstan 1995 and Uzbekistan 1996, it is actually in the 

opposite direction from what would be expected. The chi square test statistics are large, reaching a maximum of 

1520. The effect of education on children’s birth dates is always negative, and is below -0.50 for three countries. 

The chi-square values tend again to be very large, reaching a maximum of 1048. 

Table 5.2 lists the 27 surveys, from 17 countries, for which at least one of the three coefficients in Figures 5.1-

5.3 is below the arbitrary threshold of -0.50. There is a relative preponderance of Latin American countries in 

this category, with at least one survey each from Bolivia, Brazil, Colombia, Dominican Republic, Ecuador, 

Guatemala, Haiti, Mexico, Peru, Paraguay, and Trinidad and Tobago. The countries outside Latin America 

include Burkina Faso, Benin, Comoros, and Togo in sub-Saharan Africa, and Armenia and the Philippines. 

These countries differ greatly in their levels and internal variation of education, and many of them have low 

levels of incompleteness. 

The three coefficients to describe the education effect in these logit regressions are strongly and positively 

correlated with one another. The correlation between the first and second coefficient is 0.35; between the first 

and third is 0.60; and between the second and third is 0.50. That is, education has a generalized effect on 

knowledge of ages and dates, which is expressed with all three indices but most clearly with the reporting of the 

woman’s own date and age. 
56

1 1 2 1

8

15

23

20

39

13

2

Number of surveys

-1.50 -1.00 -0.50 0.00

Logit regression slope

Figure 5.1 Effect of women's education on age incompleteness

3

29

62

26

4

1 1

Number of surveys

-1.50 -1.00 -0.50 0.00 0.50

Logit regression slope

Figure 5.2 Effect of women's education on marriage incompleteness
57

Table 5.2 Surveys with the strongest negative effect of women’s 

education on incompleteness of dates 

Logit regression coefficient 

for incompleteness 

 

Country 

Median

year of

survey Age/birth Marriage 

Children’s 

births

Armenia 2000 -0.52 -0.19 -0.19 

Benin 1996 -0.62 -0.27 -0.35 

Benin 2001 -0.57 -0.25 -0.30 

Bolivia 1989 -0.51 -0.27 -0.33 

Bolivia 1998 -0.51 -0.28 -0.35 

Brazil 1991 -0.86 -0.24 -0.29 

Burkina Faso 1999 -0.54 -0.33 -0.31 

Burkina Faso 2003 -0.53 -0.35 -0.16 

Cameroon 1991 -0.67 -0.38 -0.54 

Cameroon 1998 -0.55 -0.31 -0.14 

Colombia 1986 -0.66 -0.33 -0.37 

Colombia 1995 -0.88 -0.28 -0.36 

Dominican Republic 1999 -0.54 -0.24 -0.38 

Ecuador 1987 -0.53 -0.31 -0.41 

Guatemala 1987 -1.16 -0.39 -0.57 

Haiti 1994 -0.53 -0.22 -0.40 

Mexico 1987 -0.57 -0.41 -0.54 

Paraguay 1990 -0.64 -0.27 -0.35 

Peru 1986 -0.93 -0.36 -0.61 

Peru 1991 -0.60 -0.33 -0.43 

Peru 1996 -0.75 -0.26 -0.29 

Peru 2000 -0.60 -0.26 -0.20 

Philippines 1993 -0.51 -0.10 -0.12 

Philippines 1998 -0.62 -0.34 -0.21 

Togo 1988 -0.54 -0.31 -0.33 

Togo 1998 -0.65 -0.29 -0.33 

Trinidad and Tobago 1987 -0.57 -0.14 -0.31 

Note: The table lists surveys in which any of the three coefficients are 

less than -0.50. 

1

4 5

27

42

33

15

Number of surveys

-1.50 -1.00 -0.50 0.00

Logit regression slope

Figure 5.3 Effect of mother's education on child's birth incompleteness
58

The effect of mother’s education on heaping of child’s age at death at 12 months can also be assessed by adding 

years of schooling to the logit regression that produced the ratio of observed to expected cases at month 12. 

Figure 5.4 shows the distribution of the logit regression coefficients while Table 5.3 lists the countries in which 

the coefficient is less than -0.50 or the coefficient is significant at the .01 level, for which the critical value of 

chi-square with one degree of freedom is 6.63. There are only a few countries for which more education tends to 

reduce this kind of heaping by a statistically significant amount, and the chi-square values are much smaller than 

for incompleteness, reaching a maximum of only 8.83. Some coefficients are statistically significant even 

though they are close to zero, simply because of the large sample sizes. For 32 surveys, the effect is actually 

positive, such that women with more education tend to have more heaping at 12 months, but those positive 

coefficients are never significant. Two of the most negative coefficients are for the two surveys of Vietnam. 

Table 5.3 Surveys with the strongest negative effect of women’s 

education on heaping of child deaths at 12 months 

Country 

Median

year of

survey 

Logit

regression

coefficient 

Chi-square test 

statistic (has 

one degree 

of freedom) 

Egypt 1992 -0.16 8.35 

India 1999 -0.08 7.28 

Peru 1996 -0.17 8.83 

Rwanda 1992 -0.17 8.72 

Trinidad & Tobago 1987 -1.02 2.51 

Turkey 1998 -0.39 8.31 

Uzbekistan 1996 -0.60 1.60 

Vietnam 1997 -0.54 2.34 

Vietnam 2002 -0.69 4.16 

Zambia 1992 -0.13 7.59 

Note: The table lists surveys in which the coefficient is less than 

-0.50 or chi-square is greater than 6.63. 

1 1 2 2

6 6

21

56

23

4

11 1 1 1

Number of surveys

-1.00 -0.50 0.00 0.50 1.00 1.50

logit regression slope

Figure 5.4 Effect of mother's education on month 12 heaping
59

We use a simpler strategy to analyze the individual-level effect of education on upward and downward age 

transfers of women in the household survey and the backward age transfers of children in the birth histories. 

Using the household surveys, the estimates of upward and downward transfers of women are recalculated 

separately for households in which the household head is below the median in years of schooling, or above the 

median; and separately for households in which the household respondent is below the median in years of 

schooling, or above the median. As mentioned above, we expect data quality to be more strongly associated with 

the characteristics of the household respondent than the household head. 

In the birth histories, the measure of backward transfers will be recalculated separately for children whose 

mother is below the median in years of schooling, or above the median. (As before, the measure of schooling for 

the household survey is hv108 and for the mothers is v133. Note that hv108 is sometimes missing for an entire 

survey or cannot be linked to the household head or respondent, and both hv108 and v133 are missing for some 

individuals.) 

The results of these comparisons between the lower and upper halves of the education distribution are shown in 

Table 5.4. The table lists only the surveys in which the difference between the two halves exceeds 10 percent in 

the expected direction and is statistically significant with a one-tailed .01 test. The expected direction of 

transfers is downward for younger women, upward for older women, and backward for children, and the 

hypothesized effect of more education is to reduce the level of such transfers. The z statistic given in the table is 

based on a test of the difference between the two “transfer” statistics, t, described in Appendix D. 

There are a few surveys in which the displacement is reversed or the education effect is reversed, but rarely with 

statistical significance. A high standard for statistical significance is also required for inclusion in the table. For 

these reasons, the table includes only 16 surveys. One of them, Bolivia 1998, appears in two panels. Two of 

them, Peru 2000 and Zimbabwe 1999, appear in three panels each. 

Only one of the surveys listed in Table 5.4 (Malawi 2000) was listed earlier as having high displacement. There 

is a tendency for surveys with a large differential by education to have low overall displacement levels. If 

education (or presumably another covariate) has a large effect on age transfers, then the serious transfers will be 

concentrated in a smaller subgroup. Hence there is actually a negative correlation between the overall net level 

of transfers and the inhibiting effect of education upon displacement. 

Although there are good reasons to believe that the education of the household respondent is more relevant than 

that of the household head, there is very little difference in terms of whose education is more strongly associated 

with age displacement. In many countries, the household respondent tends to be female and to have very little 

education, and the household head tends to be male and to have greater variation in years of schooling as well as 

a higher mean. Greater dispersion in the head’s years of schooling will in itself induce a stronger relationship. 

This section serves mainly to suggest a strategy for identifying important covariates of misreporting. It could be 

applied to type of place of residence and other background variables, many of which are country-specific. As 

noted above, a covariate may actually be most important in a context in which there is a low level of 

misreporting, so a search for useful covariates should not be limited to countries in which the net level of 

misreporting is high.
60

Interviewers may affect the quality of the data, but we are not aware of earlier studies of DHS reporting that 

have examined this issue and we are unable to examine it here. There may be some settings in which either fixed 

or random effects for interviewers could be added to the various statistical models that produce our summary 

measures. In most contexts, however, interviewers are confounded with clustering and other characteristics of 

the survey design. This is because a specific interviewer will generally work in only a few clusters, nested 

within one region or part of a country, and not assigned at random. Some inferences might be possible if DHS 

could identify at least one survey with relatively poor quality data and a relatively small number of interviewers 

who tended to cover relatively large areas. We suspect that some kinds of misreporting, especially age 

displacement, are caused primarily by the interviewer. 

Table 5.4 Differences between the upper and lower halves of the education distribution in the net level of 

displacement

 

Estimated percentage transfers 

Country 

Median

year of 

survey 

Up, down, 

backward

For low 

education

For high 

education

Difference

(high

education

minus low 

education)

z test 

statistic 

(absolute

value

>1.96)

Surveys with a significant effect of education of household head on downward displacement 

Bolivia 1998 2.83 10.06 -3.08 -13.14 -3.80 

Peru 2000 5.52 13.33 2.19 -11.14 -4.09 

Zimbabwe 1999 9.88 14.89 3.11 -11.78 -2.50 

Surveys with a significant effect of education of household head on upward displacement 

Brazil 1991 4.78 14.27 -8.82 -23.09 -2.96 

Gabon 2000 17.86 30.31 3.03 -27.27 -3.23 

Tanzania 1996 6.24 13.26 -2.90 -16.17 -2.35 

Surveys with a significant effect of education of household respondent on downward displacement 

Dominican Republic 1999 4.58 20.43 -6.36 -26.79 -2.62 

Guatemala 1995 1.05 7.74 -3.51 -11.25 -3.28 

Peru 1991 1.10 9.84 -5.00 -14.84 -5.25 

Peru 1996 4.73 14.90 0.32 -14.57 -5.31 

Peru 2000 5.52 13.97 1.66 -12.31 -4.33 

Philippines 1993 6.24 14.51 3.65 -10.87 -3.12 

Philippines 1998 -1.40 8.36 -8.03 -16.39 -4.81 

Philippines 2003 8.54 14.14 3.98 -10.16 -3.00 

Zimbabwe 1994 9.53 17.91 2.13 -15.78 -3.41 

Zimbabwe 1999 9.88 16.58 2.86 -13.71 -2.83 

Surveys with a significant effect of education of household respondent on upward displacement 

NO SURVEYS 

Surveys with a significant effect of education of mother on backward displacement 

Bolivia 1998 6.68 12.83 1.61 -11.22 -2.61 

Malawi 2000 22.97 29.31 16.30 -13.01 -3.38 

Peru 2000 4.40 11.74 0.15 -11.59 -3.51 

Turkey 1998 9.28 21.70 3.47 -18.23 -2.93 

Zambia 1998 8.00 15.94 2.30 -13.64 -2.35 

Zambia 2002 9.53 17.52 4.48 -13.04 -2.98 

Note: Restricted to surveys in which the displacement is at least 10 percent less for the more educated than 

for the less educated and the difference is statistically significant at the .01 level. 
61

6 CONCLUSIONS 

In an attempt to summarize and integrate the findings in Chapters 2, 3, and 4, we now focus on nine specific 

symptoms of reporting errors. First we describe how these indicators have varied according to region and time 

period, or phase of DHS. Then we identify the specific surveys that have the highest overall levels on these 

indicators.

6.1 Variation by Region, Time Period, and Type of Problem 

This analysis covers 128 surveys of women and 102 household surveys. Table 6.1 describes their distribution 

across four major geographic regions and time periods. The regions are sub-Saharan Africa, Latin America and 

the Caribbean, South and Southeast Asia, and “other.” These are the same regions DHS uses in comparative 

analyses, except that “other” consists of a consolidation of regions that are sometimes given separately as North 

Africa, West Asia, and Central Asia. Even with consolidation, “other” includes fewer surveys than the other 

three regions.

Time period refers to the four phases of DHS data collection: DHS-I (1985-89), DHS-II (1990-92), DHS-III 

(1993-98), and MEASURE DHS+ (1999-2003). The omitted household surveys are the 26 conducted during 

DHS-I.

Table 6.1 Distribution of the surveys in this analysis according to major region and time period 

Time period 

Region

DHS-I

(1985-89)

DHS-II

(1990-92)

DHS-III

(1993-98)

MEASURE

DHS+

(1999-2003) Total

Sub-Saharan Africa 11 11 22 19 63 

Latin America/Caribbean 10 5 9 7 31 

South and Southeast Asia 3 3 8 6 20 

Other 2 3 5 4 14 

 

Total 26 22 44 36 128 

Of the nine selected indicators of misreporting, three refer to household data, three to the women’s data, and 

three to the birth histories. The three indicators based on the household data are Myers’ Index for age heaping, 

the measure of downward transfers of women across age 15, and the measure of upward transfers of women 

across age 50. The three based on the survey of women refer to incompleteness of reports of age, marriage, and 

births. The three based on the birth histories refer to upward transfers of children’s ages, Myers’ Index for 

heaping of the preceding birth interval, and heaping of age at death on 12 months. 
62

Selected indicators of misreporting 

Source of data 

Indicator

Household

survey 

Women’s

survey 

Birth

histories

1. Age heaping X 

2. Downward age transfers of women across age 15 X 

3. Upward age transfers of women across age 50 X 

4. Age incompleteness X 

5. Marriage incompleteness X 

6. Birth history incompleteness X 

7. Upward age transfers of children’s age X 

8. Myers’ Index for heaping of preceding birth interval X 

9. Heaping of age of death on 12 months X 

The nine indicators were selected because they refer to manifestations of age and date reporting that would 

generally be considered important, but are relatively independent of one another. For example, evidence of age 

heaping in the household survey is included, but age heaping in the survey of women is not included. 

Incompleteness of birth dates is included for the women files, but not for the birth files. No indicator is included 

for discrepancies between fertility rates or infant mortality rates in successive surveys. Such discrepancies are 

important, but it generally is not possible to say whether the problem is with the first survey or the second one, 

or both, and it would be misleading to assume that both are at fault. 

Each indicator is coded “1” if the underlying continuous measure of misreporting was in its worst quartile; 

otherwise it is coded “0.” For example, the first indicator refers to age heaping in the household survey, 

measured with Myers’ Index. Twenty-five surveys (which rounds to 25 percent of the 102 household surveys) 

exceeded 7.660. A household survey is coded “1” for this indicator if Myers’ Index exceeded that value; 

otherwise it is coded “0.” As another example, the fourth indicator refers to age incompleteness. It is coded “1” 

if the incompleteness index exceeded .542, because 31 surveys (which rounds to 25 percent of the surveys of 

women) exceeded that value; otherwise it is coded “0.” 

For each indicator, and within each cell of Table 6.1, we then calculate the percentage of surveys that are coded 

“1.” For example, of the 11 surveys in sub-Saharan Africa during DHS-II, 3 (or 27 percent) were in the overall 

worst quartile on the first indicator. Four (or 36 percent) were in the worst quartile on the fourth indicator. Table 

6.2, with nine panels, gives the results of these calculations. 

Some combinations of region and time period contain very few surveys. Indeed, half of the combinations 

contain five or fewer surveys. The interpretation of the panels of Table 6.2 will focus on the margins and on the 

trends for sub-Saharan Africa and Latin America, since these two regions account for the great majority of the 

surveys. 
63

Table 6.2 Percentage of surveys in the worst quartile of nine measures of data quality, by region 

and time period 

Time period 

Region

DHS-I

(1985-89)

DHS-II

(1990-92)

DHS-III

(1993-98)

MEASURE

DHS+

(1999-2003) Total

Indicator 1: Percentage of household surveys with age heaping in worst quartile 

Sub-Saharan Africa 27 27 37 31 

Latin America/Caribbean 0 0 0 0 

South and Southeast Asia 100 25 33 41 

Other 33 20 25 25 

 

Total 32 20 28 25 

Indicator 2: Percentage of household surveys with downward age transfers of women 

in worst quartile 

Sub-Saharan Africa 36 50 47 46 

Latin America/Caribbean 0 0 0 0 

South and Southeast Asia 0 0 17 6 

Other 0 20 0 8 

 

Total 18 30 24 25 

Indicator 3: Percentage of household surveys with upward age transfers of women 

in worst quartile 

Sub-Saharan Africa 73 41 37 46 

Latin America/Caribbean 0 11 0 5 

South and Southeast Asia 33 0 0 6 

Other 0 0 0 0 

 

Total 41 23 19 25 

Indicator 4: Percentage of women surveys with age incompleteness in worst quartile 

Sub-Saharan Africa 55 36 32 26 35 

Latin America/Caribbean 0 0 0 0 0 

South and Southeast Asia 0 67 38 60 40 

Other 50 33 0 0 14 

 

Total 27 32 23 22 25 

Indicator 5: Percentage of women surveys with marriage incompleteness in worst quartile 

Sub-Saharan Africa 55 36 41 35 41 

Latin America/Caribbean 0 0 0 0 0 

South and Southeast Asia 0 0 13 50 20 

Other 50 33 0 0 14 

 

Total 27 23 23 28 25 

Indicator 6: Percentage of women surveys with birth history incompleteness in worst quartile 

Sub-Saharan Africa 55 64 41 11 38 

Latin America/Caribbean 0 0 0 0 0 

South and Southeast Asia 33 33 13 0 15 

Other 50 67 20 25 36 

 

Total 31 45 25 8 25 

 Continued... 
64

Table 6.2—Continued

Time period 

Region

DHS-I

(1985-89)

DHS-II

(1990-92)

DHS-III

(1993-98)

MEASURE

DHS+

(1999-2003) Total

Indicator 7: Percentage of surveys with upward age transfers of children in worst quartile 

Sub-Saharan Africa 45 27 45 32 38 

Latin America/Caribbean 0 0 20 25 14 

South and Southeast Asia 0 33 13 20 17 

Other 11 0 11 0 7 

 

Total 25 18 30 23 25 

Indicator 8: Percentage of surveys with heaping of previous birth interval in worst quartile 

Sub-Saharan Africa 36 9 18 21 21 

Latin America/Caribbean 0 40 0 0 6 

South and Southeast Asia 0 100 25 33 35 

Other 100 100 40 50 64 

 

Total 23 45 18 22 25 

Indicator 9: Percentage of surveys with heaping ratio of child deaths 

at 12 months in worst quartile 

Sub-Saharan Africa 82 0 9 26 25 

Latin America/Caribbean 30 0 0 14 13 

South and Southeast Asia 33 33 38 0 25 

Other 100 67 20 50 50 

 

Total 58 14 14 22 25 

Five of the nine indices either had their lowest level (sometimes by only a small amount) in the most recent time 

period, or declined monotonically across the last three time periods. Perhaps the greatest improvement has been 

in upward age transfers of women, which was serious for 41 percent of DHS-II surveys but only 18 percent of 

MEASURE DHS+ surveys. No real improvement is observed for age heaping, heaping of previous birth 

interval, downward age transfers of women, and incompleteness of date of marriage. 

As a region, South and Southeast Asia has the most serious incidence of age heaping and incompleteness of age. 

Sub-Saharan Africa is above average on most indicators, but especially age transfers—downward for women, 

upward for women, and upward for children—and incompleteness of all kinds of dates. Over time, countries in 

this region show progressively less of a problem with upward age transfers of women and incompleteness. 

Across the board, the Latin American surveys show the fewest problems, frequently having no surveys at all in 

the worst quartile of an indicator.

Table 6.3 summarizes the distribution of the nine types of problems listed in Table 6.2. An overall index is 

constructed which is the total number of indicators that are in the worst quartile. Thus a score of 0 means that a 

survey was not in the worst quartile for any of the nine indicators. Table 6.3 shows that 33 of the 128 surveys 

had a score of 0 on this overall index. A score of 9 (never observed) would mean that a survey was in the worst 

quartile on all nine indicators. Because the DHS-I surveys do not include the three household indicators, the 

maximum possible score for those surveys would be 6, and two surveys receive that maximum. Across DHS-II, 

DHS-III, and MEASURE DHS+ there is a steady increase in the proportion of surveys with favorable scores on 

all indicators. 
65

Table 6.3 Frequency distribution and percent distribution of the number of indicators that are in 

the worst quartile, by time period 

DHS-I DHS-II DHS-III

MEASURE

Overall DHS+ Total

index n % n % n % n % n % 

0 7 27 3 14 11 25 12 36 33 26 

1 9 35 6 27 10 23 5 14 30 23 

2 2 8 2 9 8 18 5 14 17 13 

3 1 4 5 23 4 9 6 17 16 13 

4 3 12 0 0 6 14 5 14 14 11 

5 3 12 2 9 3 7 1 3 9 7 

6 1 4 4 18 2 5 0 0 7 5 

7 0 0 0 0 0 0 2 6 2 2 

 

Total 26 100 22 100 44 100 36 100 128 100 

Note: Percentages may not add to 100 percent because of rounding. 

Next we will identify the specific surveys that have had the highest incidence of reporting problems. These 

surveys are, of course, familiar from the various lists in Chapters 2, 3, and 4. Table 6.4 includes 8 DHS-I 

surveys with 3 or more indicators in the worst quartile, and 14 later surveys with 5 or more indicators in the 

worst quartile. Somewhat different lists would be obtained with different indices and different thresholds, but 

most of the surveys listed in table 6.1.3 would probably show up on most plausible alternatives. 

The only surveys outside of Sub-Saharan Africa that appear in Table 6.4 are Morocco 1987, Egypt 2000, and 

Yemen 1991-92. Surveys from Burkina Faso, Ghana, Mali, and Togo appear twice on the list. There is some 

overlap, but not a great deal, with the lists of successive surveys that had discrepancies in estimates of fertility 

rates or infant mortality rates. For example, the two surveys from Burkina Faso that are listed in Table 6.4 are 

the same two surveys that had the greatest of all inconsistencies in successive TFR estimates. The two surveys 

from Mali that are listed in Table 6.4 were involved in inconsistencies of fertility estimates or infant mortality 

estimates. Surveys from Comoros, Egypt, Ghana, Morocco, Niger, Nigeria, and Senegal appear in Table 6.4 and 

also in the tables on fertility and mortality estimates, but are not always the same surveys. 
66

Table 6.4 Surveys with the highest levels of age and date misreporting 

Indicator1

Country Region 1 2 3 4 5 6 7 8 9 Total

SURVEYS CONDUCTED AS PART OF DHS-I 

Ghana 1987 Sub-Saharan Africa . . . 0 1 1 0 0 1 3 

Liberia 1986 Sub-Saharan Africa . . . 1 1 0 1 0 1 4 

Mali 1987 Sub-Saharan Africa . . . 1 1 1 0 0 1 4 

Togo 1988 Sub-Saharan Africa . . . 1 1 1 0 0 1 4 

Burundi 1987 Sub-Saharan Africa . . . 1 0 1 1 1 1 5 

Morocco 1987 Other . . . 1 1 1 0 1 1 5 

Senegal 1986 Sub-Saharan Africa . . . 1 1 1 0 1 1 5 

Sudan 1990 Sub-Saharan Africa . . . 1 1 1 1 1 1 6 

SURVEYS CONDUCTED AS PART OF DHS-II, DHS-III, and MEASURE DHS+ 

Cameroon 1991 Sub-Saharan Africa 1 0 1 0 1 1 0 1 0 5 

Comoros 1996 Sub-Saharan Africa 0 0 1 1 0 1 1 1 0 5 

Egypt 2000 Other 1 0 0 0 0 1 1 1 1 5 

Ghana 1993 Sub-Saharan Africa 0 1 1 0 1 1 1 0 0 5 

Mozambique 1997 Sub-Saharan Africa 0 1 0 0 1 1 1 1 0 5 

Nigeria 1990 Sub-Saharan Africa 1 1 1 0 0 1 1 0 0 5 

Burkina Faso 1992-93 Sub-Saharan Africa 0 1 1 1 1 1 1 0 0 6 

Burkina Faso 1998-99 Sub-Saharan Africa 0 1 1 1 1 1 1 0 0 6 

Niger 1992 Sub-Saharan Africa 1 0 1 1 1 1 1 0 0 6 

Pakistan 1990-91 South/Southeast Asia 1 0 1 1 0 0 1 1 1 6 

Togo 1998 Sub-Saharan Africa 1 0 1 1 1 1 0 1 0 6 

Yemen 1991-92 Other 1 0 0 1 1 1 0 1 1 6 

Guinea 1999 Sub-Saharan Africa 1 1 0 1 1 1 0 1 1 7 

Mali 2001 Sub-Saharan Africa 1 1 1 1 1 0 0 1 1 7 

1 Key to indicators: 

1: Age heaping in worst quartile 

2: Downward age transfers of women in worst quartile 

3: Upward age transfers of women in worst quartile 

4: Age incompleteness in worst quartile 

5: Marriage incompleteness in worst quartile 

6: Birth history incompleteness in worst quartile 

7: Upward age transfers of children in worst quartile 

8: Heaping of previous birth interval in worst quartile 

9: Heaping ratio of child deaths at 12 months in worst quartile 

This report has focused on omission and inconsistencies, using somewhat arbitrary thresholds for evidence of 

problems. It is important to recognize that most surveys had no apparent problems or only a few problems. 

Surveys done in Latin America or Southeast Asia, in particular, are relatively free of problems. The problems 

found in sub-Saharan Africa and South Asia can almost certainly be traced to the low salience of ages and dates 

and the relatively low levels of education, especially for women respondents. There are limits to what can be 

achieved in such contexts, even with careful construction of survey instruments, careful training of interviewers, 

and maintenance of the highest standards in all aspects of survey implementation. 

Finally, the most important caveat in the first section of this report must be repeated: almost all of the measures 

confound misreporting with real, or genuine, variation. The appearance of age heaping and age transfers may be 

spurious and be the result of genuine irregularities in the history of fertility and mortality. On the other hand, 

some heaping and age transfers may go unrecognized because they are effectively cancelled out by the history 

of fertility and mortality. The only exceptions to this confounding of real variation with errors in reporting occur 
67

when there are two reports on the same objective reality, as with estimates of fertility rates or mortality rates in 

the same reference period using successive surveys. 

6.2 Recommendations 

This analysis leads to three sets of recommendations regarding fieldwork, imputation, and research into the 

sensitivity of important output measures to misreporting of ages and dates. 

First, regarding the implementation of surveys, it is recommended that additional checks be developed and 

applied during fieldwork. DHS is well known for its high standards of training and fieldwork, but improvement 

is always possible, especially in countries that have previously had surveys with high levels of apparent 

misreporting.

Second, it is possible that the date imputation procedures can be extended to mitigate some of the negative 

consequences of misreporting. For example, the age of a child at death could perhaps be adjusted with a 

probabilistic model, even when a valid age is reported, in order to avoid heaping at 12 months. Again, DHS 

imputation procedures are already unusually sophisticated, especially as applied to dates in the birth histories, 

but some further adjustments may be feasible. 

Third, and perhaps most important, is the issue of whether the level of problems observed in even the worst 

settings has much of an impact on the proportions, means, and rates that constitute the main output of these 

surveys. DHS could undertake a sensitivity analysis, for example, in which the output measures would be 

calculated for a survey with good data, and then recalculated after varying degrees of random displacement were 

artificially imposed on the data. It is quite possible that considerable shifting of eligible respondents outside of 

the boundaries of eligibility will have only a negligible effect on these measures. Age heaping, in itself, may not 

actually produce much distortion of rates unless it has a systematic component of bias. Age transfers of young 

children outside of the calendar may have little effect on estimates of the treatment of childhood illnesses, for 

example. There may also be analytic strategies to cope with such transfers, such as shifting the reference period 

or window for the calculation of rates, which would reduce their impact. 

There has already been some research into the sensitivity of fertility and mortality rates to misreporting of ages 

and dates, but new strategies could relate more directly to the specific characteristics of DHS surveys and to 

reporting requirements. It would be cost effective to resolve these analytical issues before implementing 

significant changes in the fieldwork and imputation procedures.
69

References

Chidambaram, V.C., and T.W. Pullum. 1981. Estimating fertility trends from retrospective birth histories: 

Sensitivity to imputation of missing dates. Population Studies 35: 307-320. 

Chidambaram, V.C., and Z.A. Sathar. 1984. Age and date reporting. WFS Comparative Studies No. 5. 

Voorburg, Netherlands: International Statistical Institute. 

Chidambaram, V.C., J.G. Cleland, and V. Verma. 1980. Some aspects of WFS data quality: A preliminary 

assessment. WFS Comparative Studies No. 16. Voorburg, Netherlands: International Statistical Institute. 

Curtis, S.L. 1995. Assessment of the quality of data used for direct estimation of infant and child mortality in 

DHS-II surveys. DHS Occasional Papers No. 3. Calverton, Maryland: Macro International Inc. 

Curtis, S.L., and F. Arnold. 1994. An evaluation of the Pakistan DHS survey based on the reinterview survey. 

DHS Occasional Papers No. 1. Calverton, Maryland: Macro International Inc. 

Ewbank, D.C. 1981. Age misreporting and age-selective underenumeration: Sources, patterns and 

consequences for demographic analysis. Committee on Population and Demography, Report No. 4. 

Washington, D.C.: National Academy Press. 

Gage, A.J. 1995. An assessment of the quality of data on age at first union, first birth, and first sexual 

intercourse for Phase II of the Demographic and Health Surveys Program. DHS Occasional Papers No. 4. 

Calverton, Maryland: Macro International Inc. 

Goldman, N., A.J. Coale, and M. Weinstein. 1979. The quality of data in the Nepal fertility survey. WFS 

Scientific Reports, No. 6. Voorburg, Netherlands: International Statistical Institute. 

Goldman, N., S.O. Rutstein, and S. Singh. 1985. Assessment of the quality of data in 41 WFS surveys: A 

comparative approach. WFS Comparative Studies, No. 44. Voorburg, Netherlands: International Statistical 

Institute.

Hobbs, F. 2004. Age and sex composition. In The methods and materials of demography, second edition, ed. 

J.S. Siegel and D.A. Swanson. San Diego, California: Elsevier Academic Press. 125-173. 

Institute for Resource Development. 1990. An assessment of DHS-I data quality. DHS Methodological Reports 

No. 1. Columbia, Maryland: Institute for Resource Development/Macro Systems, Inc. 

Marckwardt, A.M., and S.O. Rutstein. 1996. Accuracy of DHS-II demographic data: Gains and losses in 

comparison with earlier surveys. DHS Working Papers No. 19. Calverton, Maryland: Macro International Inc. 

Mason, K.O., and L.G. Cope. 1987. Sources of age and date-of-birth misreporting in the 1900 U.S. census. 

Demography 24: 563-73. 

Nag, M.H., E.G. Stockwell, and L.M. Snavely. 1973. Digit preference and avoidance in the age statistics of 

some recent African censuses: Some patterns and correlates. International Statistical Review 41:165-74. 

Potter, J.E. 1977. Problems in using birth history analysis to estimate trends in fertility. Population Studies 

31:335-64. 
70

Pullum, T.W. 1987. Analytic methodology. In The World Fertility Survey: An assessment of its contribution, ed. 

J. Cleland and C. Scott. New York: Oxford University Press. 644-676. 

Pullum, T.W. 1988 An approach to the reconciliation of demographic survey data from the Philippines. 

Occasional Paper No. 2. Population Technical Assistance Project. Arlington, Virginia: International Science and 

Technology Institute.

Pullum, T.W. 1990. Statistical methods to adjust for date and age misreporting to improve estimates of vital 

rates in Pakistan. Statistics in Medicine 10:191-200. 

Pullum, T.W. 2005. A statistical reformulation of demographic methods to assess the quality of age and date 

reporting, with application to the Demographic and Health Surveys. Paper presented at 2005 Annual Meeting of 

the Population Association of America, Philadelphia, March 31-April 2. 

Pullum, T.W., N. Ozsever, and T. Harpham. 1984. An assessment of the machine editing policies of the World 

Fertility Survey. WFS Scientific Reports, No. 54. World Fertility Survey. Voorburg, Netherlands: International 

Statistical Institute. 

Pullum, T.W., T. Harpham, and N. Ozsever. 1986. The machine editing of large sample surveys: The experience 

of the World Fertility Survey. International Statistical Review 54: 311-326. 

Pullum, T.W., and S.L. Stokes. 1997. Identifying and adjusting for recall error, with application to fertility 

surveys. In Survey measurement and process quality, ed. L. Lyberg and P. Biemer et al. New York: John Wiley 

and Sons. 711-732. 

Rutstein, S.O., and G.T. Bicego. 1990. Assessment of the quality of data used to ascertain eligibility and age in 

the Demographic and Health Surveys. In An assessment of DHS-I data quality. DHS Methodological Reports, 

No. 1. Columbia, Maryland: Institute for Resource Development/Macro Systems, Inc. 

Siegel, J., and D. Swanson, eds. 2004. The methods and materials of demography, second edition. San Diego, 

California: Elsevier Academic Press. 

Stanton, C., N. Abderrahim, and K. Hill. 1997. DHS maternal mortality indicators: An assessment of data 

quality and implications for data use. DHS Analytical Report No. 4. Calverton, Maryland: Macro International 

Inc.

United Nations. 1967. Manual II: Methods of appraisal of quality of basic data for population estimates. 

Population Studies, No. 23. New York: United Nations Department of Economic and Social Affairs. 

United Nations. 1987. A comparative evaluation of data quality in thirty-eight World Fertility Surveys. New 

York: United Nations Department of International Economic and Social Affairs. 

United Nations. 2003. World population prospects: The 2002 revision. Volume II: Sex and age distributions of 

populations. New York: United Nations Department of Economic and Social Affairs. 
71

Appendix A Surveys Conducted by DHS 1985-2003 and Surveys 

 Included in This Report 

Table A.1 Surveys conducted by DHS 1985-2003. This report includes all surveys of women except for 

Senegal 1999, Peru 2003, and restricted surveys. It also includes all corresponding household surveys except 

those conducted as part of DHS-I. Age displacement in 55 of the surveys of men is examined in section 2.4. 

Country/year 

Code or 

status

Household

sample 

Female 

sample 

Male 

respondents

Age

of men 

Male 

sample 

DHS-I 1985-89 

Sub-Saharan Africa 

Botswana 1988 Restricted 4,473 4,368 na na na 

Burundi 1987 BU01 3,868 3,970 Husbands na 542 

Ghana 1988 GH02 4,406 4,488 Husbands na 943 

Kenya 1989 KE03 8,173 7,150 Husbands na 1,133 

Liberia 1986 LB01 5,023 5,239 na na na 

Mali 1987 ML01 3,048 3,200 All men 20-55 970 

Ondo State, Nigeria 1986 OS01 3,437 4,213 na na na 

Senegal 1986 SN02 3,736 4,415 na na na 

Sudan 1990 SD02 6,891 5,860 na na na 

Togo 1988 TG01 3,432 3,360 na na na 

Uganda 1988 UG01 5,101 4,730 na na na 

Zimbabwe 1988 ZW01 4,107 4,201 na na na 

North Africa/West Asia/Europe 

Egypt 1988 Restricted 9,805 8,911 na na na 

Morocco 1987 MA01 6,960 5,982 na na na 

Tunisia 1988 TN02 5,645 4,184 na na na 

South and Southeast Asia 

Indonesia 1987 ID01 14,142 11,884 na na na 

Sri Lanka 1987 LK02 7,669 5,865 na na na 

Thailand 1987 TH01 9,045 6,775 na na na 

Latin America/Caribbean 

Bolivia 1989 BO01 8,439 7,923 na na na 

Brazil 1986 BR01 13,283 5,892 na na na 

Colombia 1986 CO01 4,273 5,329 na na na 

Dominican Republic 1986 DR01 7,152 7,649 na na na 

Ecuador 1987 EC01 4,578 4,713 na na na 

El Salvador 1985 ES00 4,922 5,207 na na na 

Guatemala 1987 GU01 5,459 5,160 na na na 

Mexico 1987 MX00 7,786 9,310 na na na 

Peru 1986 PE01 4,497 4,999 na na na 

Trinidad & Tobago 1987 TT01 4,122 3,806 na na na 

DHS-II 1990-92 

Sub-Saharan Africa 

Burkina Faso 1992-93 BF21 5,143 6,354 All men 18+ 1,845 

Cameroon 1991 CM22 3,538 3,871 Husbands na 814 

Madagascar 1992 MD21 5,944 6,260 na na na 

Malawi 1992 MW22 5,323 4,850 All men 20-54 1,151 

Namibia 1992 NM21 4,101 5,421 na na na 

Niger 1992 NI22 5,242 6,503 Husbands na 1,570 

Nigeria 1990 NG21 8,999 8,781 na na na 

Rwanda 1992 RW21 6,252 6,551 Husbands na 598 

Senegal 1992-93 SN21 3,528 6,310 All men 20+ 1,436 

Tanzania 1992 TZ21 8,327 9,238 All men 15-60 2,114 

Zambia 1992 ZM21 6,209 7,060 na na na 

 Continued... 
72

Table A.1—Continued 

Country/year 

Code or 

status

Household

sample 

Female 

sample 

Male 

respondents

Age

of men 

Male 

sample 

DHS-II 1990-92 (continued) 

North Africa/West Asia/Europe 

Egypt 1992 EG21 10,760 9,864 Husbands na 2,466 

Jordan 1990 Restricted 8,333 6,461 na na na 

Morocco 1992 MA21 6,577 9,256 All men 20-70 1,336 

Yemen 1991-92 YE21 12,836 5,687 na na na 

South and Southeast Asia 

India 1992-93 IA22 88,562 89,777 na na na 

Indonesia 1991 ID21 26,858 22,909 na na na 

Pakistan 1990-91 PK21 7,193 6,611 Husbands na 1,354 

Latin America/Caribbean 

Brazil 1991 (Northeast) BR21 6,064 6,222 Husbands na 1,266 

Colombia 1990 CO21 7,412 8,644 na na na 

Dominican Republic 1991 DR21 7,144 7,320 na na na 

Paraguay 1990 PY21 5,683 5,827 na na na 

Peru 1992 PE21 13,479 15,882 na na na 

DHS-III 1993-98 

Sub-Saharan Africa 

Benin 1996 BJ31 4,499 5,491 All men 20-64 1,535 

Burkina Faso 1998-99 BF31 4,812 6,445 All men 15-59 2,641 

Cameroon 1998 CM31 4,697 5,501 All men 15-59 2,562 

CAR 1994-95 CF31 5,551 5,884 All men 15-59 1,729 

Chad 1996-97 TD31 6,840 7,454 All men 15-59 2,320 

Comoros 1996 KM32 2,252 3,050 All men 15-64 795 

Côte d’Ivoire 1998-99 CI3A 2,122 3,040 All men 15-59 886 

Côte d’Ivoire 1994 CI34 5,935 8,099 All men 15-59 2,552 

Eritrea 1995 Restricted 5,469 5,054 All men 15-59 1,114 

Ghana 1993 GH31 5,822 4,562 All men 15-59 1,302 

Kenya 1998 KE3A 8,380 7,881 All men 15-54 3,407 

Kenya 1993 KE33 7,950 7,540 All men 20-54 2,336 

Madagascar 1997 MD31 7,171 7,060 na na na 

Mali 1995-96 ML32 8,716 9,704 All men 15-59 2,474 

Mozambique 1997 MZ31 9,282 8,779 All men 15-59 2,335 

Niger 1998 NI31 5,928 7,577 All men 15-59 3,542 

Senegal 1997 SN32 4,772 8,593 All men 20+ 4,306 

South Africa 1998 ZA31 12,247 11,735 na na na 

Tanzania 1996 TZ3A 7,969 8,120 All men 15-59 2,256 

Togo 1998 TG31 7,517 8,569 All men Dec-59 3,819 

Uganda 1995 UG33 7,550 7,070 All men 15-54 1,996 

Zambia 1996 ZM31 7,286 8,021 All men 15-59 1,849 

Zimbabwe 1994 ZW31 5,984 6,128 All men 15-54 2,141 

North Africa/West Asia/Europe 

Egypt 1995 EG33 15,567 14,779 na na na 

Jordan 1997 Restricted 7,335 5,548 na na na 

Turkey 1993 TR41 8,619 6,519 na na na 

Yemen 1997 Restricted 10,701 10,414 na na na 

Central Asia 

Kazakhstan 1995 KK31 4,178 3,771 na na na 

Kyrgyz Republic 1997 KY31 3,672 3,848 na na na 

Uzbekistan 1996 UZ31 3,703 4,415 na na na 

 Continued... 
73

Table A.1—Continued 

Country/year 

Code or 

status

Household

sample 

Female 

sample 

Male 

respondents

Age

of men 

Male 

sample 

DHS-II 1993-98 (continued) 

South and Southeast Asia 

Bangladesh 1996-97 BD3A 8,682 9,127 Currently married men 15-59 3,346 

Bangladesh 1993-94 BD31 9,174 9,640 Husbands na 3,284 

Indonesia 1997 ID3A 34,255 28,810 na na na 

Indonesia 1994 ID31 33,738 28,168 na na na 

Nepal 1996 NP31 8,082 8,429 na na na 

Philippines 1998 PH3A 12,407 13,983 na na na 

Philippines 1993 PH31 12,995 15,029 na na na 

Vietnam 1997 VN31 7,001 5,664 na na na 

Latin America/Caribbean 

Bolivia 1998 BO3B 12,109 11,187 All men 15-64 3,780 

Bolivia 1994 BO31 9,114 8,603 na na na 

Brazil 1996 BR31 13,283 12,612 All men 15-59 2,949 

Colombia 1995 CO31 10,112 11,140 na na na 

Dominican Republic 1996 DR32 8,831 8,422 All men 15-64 2,279 

Guatemala 1995 GU33 11,754 12,403 na na na 

Haiti 1994-95 HT31 4,818 5,356 All men 15-59 1,610 

Nicaragua 1997-98 NC31 11,528 13,634 All men 15-59 2,912 

Peru 1996 PE31 28,122 28,951 All men 15-59 2,487 

MEASURE DHS+ 1999-2003

Sub-Saharan Africa 

Benin 2001 BJ41 5,796 6,219 All men 15-64 2,709 

Burkina Faso 2003 BF41 10,000 12,000 All men 15-59 4,000 

Eritrea 2002 Restricted 9,389 8,754 na na na 

Ethiopia 2000 ET41 14,072 15,367 All men 15-59 2,607 

Gabon 2000 GA41 6,203 6,183 All men 15-59 2,004 

Ghana 2003 GH4Z 6,500 4,500 All men 15-59 4,500 

Ghana 1998 GH41 6,003 4,843 All men 15-59 1,546 

Guinea 1999 GN41 5,090 6,753 All men 15-59 1,980 

Kenya 2003 KE40 8,561 8,195 All men 15-54 3,578 

Malawi 2000 MW41 14,213 13,220 All men 15-54 3,092 

Mali 2001 ML41 12,285 12,817 All men 15-59 3,390 

Mozambique 2003 MZ41 12,087 12,193 All men 15-59 2,849 

Namibia 2000 NM41 6,392 6,755 All men 15-59 2,954 

Nigeria 2003 NG4A 7,225 7,620 All men 15-59 2,346 

Nigeria 1999 NG41 7,647 9,810 All men 15-64 2,680 

Rwanda 2000 RW41 9,696 10,421 All men 15-59 2,717 

Senegal 1999 No SR 9,085 17,189 All men 15-59 7,850 

Tanzania 1999 TZ41 3,615 4,029 All men 15-59 3,542 

Uganda 2000-01 UG41 7,885 7,246 All men 15-54 1,962 

Zambia 2001-02 ZM41 7,126 7,658 All men 15-59 2,145 

Zimbabwe 1999 ZW41 6,369 5,907 All men 15-54 2,609 

North Africa/West Asia/Europe 

Armenia 2000 AM41 5,980 6,430 All men 15-54 1,719 

Egypt 2000 EG41 16,957 15,573 na na na 

Egypt 1998 Restricted 6,759 6,406 na na na 

Jordan 2002 Restricted 7,825 6,006 na na na 

Turkey 1998 TR41 8,059 8,576 Husbands na 1,971 

 Continued... 
74

Table A.1—Continued 

Country/year 

Code or 

status

Household

sample 

Female 

sample 

Male 

respondents

Age

of men 

Male 

sample 

MEASURE DHS+ 1999-2003 (continued) 

Central Asia 

Kazakhstan 1999 KK41 5,844 4,800 All men 15-59 1,440 

Turkmenistan 2000 Restricted 6,303 7,919 na na na 

South and Southeast Asia 

Bangladesh 1999-2000 BD41 9,854 10,544 Currently married men 15-59 2,556 

Cambodia 2000 Restricted 12,236 15,351 na na na 

India 1998-99 IA42 92,486 90,303 na na na 

Indonesia 2002 ID41 33,088 29,483 Currently married men 15-54 8,310 

Nepal 2001 NP41 8,602 8,726 Ever-married men 15-59 2,261 

Philippines 2003 PH40 12,586 13,633 All men 15-54 4,766 

Vietnam 2002 VN41 7,048 5,665 na na na 

Latin America/Caribbean 

Colombia 2000 CO41 10,907 11,585 na na na 

Dominican Republic 2002 DR4A 27,135 23,384 All men 15-59 2,833 

Dominican Republic 1999 DR41 1,381 1,286 All men 15-64 1,112 

Guatemala 1998-99 GU41 5,587 6,021 na na na 

Haiti 2000 HT41 9,595 10,159 All men 15-59 3,171 

Nicaragua 2001 NC41 11,328 13,060 na na na 

Peru 2003 New design 6,000 6,000 na na na 

Peru 2000 PE41 28,900 27,843 na na na 

Note: All surveys of women include ages 15-49 except for India 1992-93 (ages 13-49). 

na = Not applicable 

Restricted = Country will not allow release of data without special permission 
75

Appendix B Previous Assessments of Age and Date Reporting 

 in DHS Surveys 

As background to this report it is helpful to summarize the relevant methods of earlier assessments of DHS 

surveys. For the sake of completeness, the report’s bibliography includes references to assessments of World 

Fertility Survey data and to some assessments of specific topics not included in this report, such as maternal 

mortality, but this appendix is confined to five DHS reports, listed below in chronological order and with an 

acronym for easy reference. The most recent assessment was published in 1996, and it appears that no surveys 

conducted after 1993 have been included in any general assessment. 

Methodological Report No. 1, 1990 (MR1) 

Institute for Resource Development. 1990. An assessment of DHS-I data quality. DHS Methodological Reports 

No. 1. Columbia, Maryland: Institute for Resource Development/Macro Systems Inc. 

The authors of this report, in alphabetical order, are Fred Arnold, George Bicego, Ann Blanc, Naomi Rutenberg, 

Shea Rutstein, and Jeremiah Sullivan. A revision of the report by Fred Arnold was presented at the DHS World 

Conference in 1991 and appears in Volume II of the Conference Proceedings, pp. 785-806. 

The report is a collection of four papers covering 22 DHS-I surveys between 1986 and 1989. The approach and 

measures for the present assessment of DHS data are fundamentally similar to those in MR1, which are outlined 

below.

Household survey 

Completeness: Non-response rates 

Misclassification: Household residency (misclassification of de jure and de facto residence) 

Age heaping (using Myers’ Index and a UN index of age-sex composition) 

Age displacement across ages 15 and 49 (using age ratios and sex ratios) 

The report includes some simulations of the effect of net age transfers outside the 15-49 age interval on the TFR, 

under-five mortality, and contraceptive use. 

Individual survey, age and date of birth 

Completeness 

Age heaping (using Myers’ Index) 

Distortion of age distribution (simply identifies deviations from monotonicity) 

Individual survey, age at first marriage 

Completeness 

Heaping of calendar year and years ago on 0 or 5 

Consistency of trends in median age at first marriage between DHS and previous WFS surveys 

Age bias due to marriage (trends in percentage of women ever-married by age 15-19 and 20-24 and comparison 

with previous survey or census) 
76

Individual survey, age at first birth 

Completeness 

Heaping of calendar year and years ago on 0 or 5 

Consistency of trends in median age at first birth between DHS and previous WFS survey 

Age bias due to childbearing (trends in percentage of women with a first birth by age 15-19 and 20-24 and 

comparison with previous survey or census) 

Individual survey, age at first sexual intercourse 

Completeness, indicated by omission of age (date not asked) 

Consistency of dates, indicated by age at first sex greater than age at first union or first birth 

Birth histories 

Completeness 

Birth displacement (excess/deficit of births five or six years before survey, with three-year ratio, separately for 

dead children and living children) 

Age heaping (using three-year ratio) 

Miscalculation of year of birth (for children with age and birth year reported, month imputed) 

Omission of births (using age at first birth by current age, and sex ratios at birth by years since birth) 

Comparison of cumulative fertility rates for ages 15-34 for same reference period, measured by DHS and 

previous WFS survey 

Mortality data in the birth histories 

Completeness of date of birth, as above, separately for dead and living children 

Birth displacement, as above, for dead and living children 

Completeness of age at death 

Heaping of age at death at 12 months for children who died at less than two years of age 

Internal consistency among mortality rates 

Comparison of under-five mortality rates for same reference period, measured by DHS and previous WFS 

survey 

Occasional Paper No. 1, 1994 (OP1) 

Curtis, Siân L. and Fred Arnold. 1994. An evaluation of the Pakistan DHS Survey based on the reinterview 

survey. DHS Occasional Papers No. 1. Calverton, Maryland: Macro International Inc. 

This report is limited to a comparison between matched cases in the Pakistan DHS survey and a reinterview 

survey of 528 cases approximately six months later. Topics involving dates include agreement between year, 

month, and age of the woman’s birth, first marriage, first live birth, and most recent live birth. 

Occasional Paper No. 3, 1995 (OP3) 

Curtis, Siân L. 1995. Assessment of the quality of data used for direct estimation of infant and child mortality in 

DHS-II Surveys. DHS Occasional Papers No. 3. Calverton, Maryland: Macro International Inc. 

The relevant topics in this assessment are the dates of birth and dates of death in the birth histories that could 

affect the calculation of infant and child death rates. It included all 50 surveys conducted between 1986 and 

1993 (28 DHS-I surveys and all 22 DHS-II surveys). 
77

Birth histories 

Completeness of reporting of births, by survival status 

Displacement of births in the fifth calendar year before the start of the survey 

Completeness of reporting of deaths at 0-24 months 

Heaping on 12 months for deaths at 0-24 months (five point ratio) 

Comparison between rates for ages 0 and 1-4, unadjusted and adjusted for heaping at 12 months 

(adjustment is to shift 25 percent of excess at 12 months to the period 6-11 months) 

Internal consistency among mortality rates 

Comparison of under-five mortality rates for same reference period, measured by successive DHS surveys 

Occasional Paper No. 4, 1995 (OP4) 

Gage, Anastasia J. 1995. An assessment of the quality of data on age at first union, first birth, and first sexual 

intercourse for Phase II of the Demographic and Health Surveys Program. DHS Occasional Papers 

No. 4. Calverton, Maryland: Macro International Inc. 

This occasional paper is largely a repetition of the chapter in MR1 assessing data on first union, first birth, and 

first sexual intercourse, but utilizing data primarily from DHS-II. It included a total of 26 surveys conducted 

between 1988 and 1993 (4 from DHS-I; 21 from DHS-II; and one, a repeat survey of Kenya, from DHS-III). 

Individual survey, age at first union 

Completeness 

Heaping of calendar year and years ago on 0 or 5 

Internal consistency of trends in median age at first marriage 

Consistency between successive surveys in estimates of percent marrying by age 20 

Age bias due to marriage (trends in percentage of women ever-married by age 15-19 and 20-24) 

Individual survey, age at first birth 

Completeness 

Heaping of calendar year and years ago on 0 or 5 

Internal consistency of trends in median age at first birth 

Age bias due to childbearing (trends in percentage of women with a first birth by age 15-19 and 20-24) 

Individual survey, age at first sexual intercourse 

Response rate 

Internal consistency of trends in median age at first sex 

Consistency of dates, indicated by age at first sex greater than age at first union or first birth 

Working Paper No. 19, 1996 (WP19) 

Marckwardt, Albert M. and Shea Oscar Rutstein. 1996. Accuracy of DHS-II demographic data: Gains and 

losses in comparison with earlier surveys. DHS Working Papers No. 19. Calverton, Maryland: Macro 

International Inc. 

This assessment included 25 DHS surveys done between 1989 and 1993. The main interest was in evidence of 

improvements in reporting when WFS, DHS-I, and DHS-II surveys are compared. 

Household survey 

Misclassification: Household residency (misclassification of de jure and de facto residence) 

Age displacement across ages 15 and 50 (using age ratios and sex ratios) 
78

Birth histories 

Completeness of reporting of births, by survival status 

Birth displacement (excess/deficit of births five or six years before survey, with three-year ratio, separately for 

dead children and living children) 

Heaping on 12 months for deaths at 0-24 months (five point ratio) 

Indexes of heaping in these earlier assessments 

Earlier DHS assessments made considerable use of age ratios, sex ratios, and Myers’ Index. Two other measures 

of heaping that have been used are based on a type of three-point ratio and five-point ratio. These are clarified 

below.

Three-point ratio: Let a, b, and c be the number of events in three consecutive intervals (e.g., ages 4, 5, 6). The 

“correct” value of b is estimated to be (a  c)/ 2 ; the ratio of observed to “correct” is

a c

b

a c

b

r

  2( )/ 2 . (This may be multiplied by 100.) 

The relative excess in b is described with r 1; if this is 0 then the progression across the three intervals is 

exactly linear. 

Five-point ratio: This is the standard basis for identifying heaping at 12 months for age at death 0-24 months. 

Let a, b, c, d, e be the number of events in five consecutive intervals (e.g., months 10, 11, 12, 13, 14). 

In MR1 and WP19, the “correct” value of c is estimated to be (a  b  d  e)/ 4; the ratio of observed to 

“correct” is 

a b d e

c

a b d e

c

r

      4( )/ 4 .

In OP3, the “correct” value of c is estimated to be (a  b  c  d  e)/5 and the ratio of observed to “correct” 

is

a b c d e

c

a b c d e

c

r

        5( )/5 .

Singh Index of heaping: This measure first appeared in a chapter by Susheela Singh in a WFS publication 

(Goldman, Rutstein, and Singh, 1985). It is based on the second five-point ratio, calculated over a total time 

span of 25 years, and typically used to describe the “excess” reporting at final digits 0 and 5. For example, in its 

original use the time span was 1963-1987 and the interest was in possible heaping on 1965, 1970, 1975, 1980, 

and 1985. The underlying ratio of observed to “correct” was 

a b c d e

c

a b c d e

c

r

        5( )/5 ,

centered successively on 1965, 1970, 1975, 1980, and 1985.

The index is the average of these five ratios. 
79

Appendix C Data Quality Tabulations Included in DHS Reports

The final report for each DHS survey includes an appendix with a standard set of data quality tables, usually 

C.1-C-6. If the survey includes men, Table C.2 has a panel for women and a panel for men. The formats and 

titles of these tables have been fixed since about 1990, when MR1 was published. Some reports have slightly 

different subtitles for the tables, but the content is always the same. 

Usually, the only reference to data quality in the body of a DHS report is the discussion of response rates in the 

introductory chapter and a brief discussion in the chapter on infant and child mortality. 

The tables in the appendix provide the most useful information on the quality of age and date data, but the 

appendix usually does not include any analysis or interpretation of the data. The following is a description of the 

DHS data quality tables and some comments on their use. 

Table C.1. Household age distribution. Single-year age distribution of the de facto household population by sex 

(weighted)

This table can be used for calculation of Myers’ Index, heaping at multiples of 0 and 5, and all measures of 

transfers across the age boundaries of eligibility. Measures of transfers based on age-specific sex ratios, not used 

in this report, are also possible with this table. 

Table C.2. Age distribution of eligible and interviewed women. Five-year age distribution of the de facto 

household population of women age 10-54, and of interviewed women age 15-49, and percentage of eligible 

women who were interviewed (weighted) 

The main value of this table is to identify whether the percentage of eligible women who were interviewed is 

constant across age, that is, whether the age distribution of respondents is consistent with the age distribution of 

eligible respondents. If there are substantial variations across age, it would be possible to revise the sample 

weights (v005) to improve some estimates, but it is not normal practice to do this. 

Table C.3. Completeness of reporting. Percentage of observations missing information for selected 

demographic and health questions (weighted) [The selected items are birth date for births in the past 15 years, 

age at death for dead children in past 15 years, age/date at first union for ever-married women 15-49, and some 

additional selected variables.] 

Completeness is a very important indicator of data quality, and it is essential to include this table. 

Table C.4. Births by calendar years. Number of births, percentage with complete birth date, sex ratio at birth, 

and calendar year ratio by calendar year, according to living, dead, and total children (weighted) [The 

“calendar year ratio” is based on a three-year ratio. The calendar years are in single years for the most recent 

ten years, and then in five-year intervals going back to the earliest births in the birth histories.] 

This table can be used to identify possible displacement or transfers of births. It can suggest possible omission 

of births, especially if the child died, but not reliably. Note that the sex ratio at birth, particularly for dead 

children, has a large standard error. A confidence interval can be obtained from a logit regression in which the 

dependent variable is sex of child, coded 1 for males and 0 for females, with no covariates (repeated within each 

combination of year of birth and survival status). The confidence interval for the intercept, after exponentiation 

and multiplication by 100, would be a confidence interval for the sex ratio. 
80

Table C.5. Reporting of age at death in days. Distribution of reported deaths under one month of age by age at 

death in days and the percentage of neonatal deaths reported to occur at ages 0-6 days, for five-year periods of 

birth preceding the survey (weighted)

This table typically shows substantial heaping at 7, 14, and 21 days that could have some effect on estimates of 

very early mortality, but DHS reports do not generally give rates for shorter intervals than the first month 

(neonatal mortality). It is not clear that much can be done with this table with respect to the particular mortality 

rates currently included in DHS reports. 

Table C.6. Reporting of age at death in months. Distribution of reported deaths under two years of age by age at 

death in months and the percentage of infant deaths reported to occur at age under one month, for five-year 

periods of birth preceding the survey (weighted) 

In most surveys, this table shows a very high degree of heaping at 12 months, and substantial heaping at other 

months such as 6 and 18. DHS staff sometimes attempt to adjust the infant mortality rate (IMR) for heaping at 

12 months by shifting one-fourth of the excess at 12 months down to 0-11. (The excess is the observed 

frequency at 12 months minus the expected frequency, which is calculated as the average at months 10, 11, 13, 

and 14.) Although this approach does give an impression of the sensitivity of 1q0 and 4q1 to the heaping at 12 

months, it does not seem to have a good rationale. As noted, the ratio of observed to expected at 12 months is 

often 10 or more, and the heaping probably extends over a broader range than the two months below and above. 

Procedures for applying a model to Table C.6 to get a more robust adjustment are given in Pullum (2005). 

Although we do not recommend changes to these tables, we suggest that the most important ones, namely 

Tables C.1 and C.4, be used routinely to calculate measures of heaping and transfers using the procedures in this 

report.
81

Appendix D Detailed Description of Method to Detect Age Displacement 

Section 1.2 of this report described a procedure for detecting age displacement across a boundary such as age 

15, or age 50, or the beginning month of the reproductive health calendar. This appendix provides more details 

on the procedure, for both aggregated and individual-level data, and will describe an empirical validation. 

Application to aggregated data 

As described in Section 1.3, the method uses four successive age intervals of equal width (single years or fiveyear age groups), two of which come before the boundary and two of which come afterwards. The intervals can 

be numbered 1, 2, 3, and 4, in increasing order of age. For example, for boundary 15, the intervals would be (1) 

age 5-9, (2) 10-14, (3) 15-19, (4) 20-24. The observed frequencies in the four intervals are a,b,c,d and the 

fitted frequencies are a b c d

ˆ , ˆ, ˆ ˆ, , respectively. 

There are two crucial assumptions. The first is that the only error of reporting is in the allocation across the 

second and third intervals. That is, the first and fourth frequencies are “correct,” aˆ a and d d ˆ , and the 

sum of the middle two frequencies is “correct,” b  cˆ b  c ˆ . Thus it is important that the intervals be wide 

enough for it to be plausible that displacement does not extend beyond the middle two intervals. 

The second assumption is that the “correct” frequencies have a linear pattern of progression on a log scale (most 

models for frequencies or odds assume some form of linearity on a log scale). That is, it is possible to find 

constants D and E such that

ln(b

ˆ/ aˆ) D , ) D  E ˆ ln(cˆ/b , and / ˆ) D 2E ˆ ln(d c  .

It is not actually necessary to solve for the two constants, because the sum of the three equations gives 

/ ˆ) 3 3 3( ) ˆ ) ln( ˆ / ˆ) ln(ˆ/ ˆ ln(d c  c b  b a D  E D  E , or )ˆ / ˆ) 3ln(ˆ/ ˆ ln(d a c b . Therefore 

1 / 3 1 / 3 / ˆ) ( / ) ˆ ( ˆ cˆ/b d a d a , which leads to 

1 / 3

1 / 3

1 / 3

1 / 3

1 ( / )

( / ) ( ) 1 ( / )( / ) ˆ) ˆ ˆ ( d ad a b c

d a

d a

c b c

   and b b c cˆ ˆ   .

A coefficient for the amount of transfer could be calculated as the log of the observed ratio in the middle two 

categories minus the log of the expected ratio: t ln(c /b) [ln(d / a)]/3, which (with the usual Poisson-based 

approximations) has an estimated variance of (1/9 ) (1/ ) (1/ ) 1/9 ) 2

s a  b  c  d . A simple test of the null 

hypothesis that there are no net transfers between categories 1 and 2 would be given by z t /s, which would 

have an approximately normal sampling distribution if the null hypothesis is true. 

The proportion of cases in category 2 that are estimated to be shifted out of that category and into category 3 

will be )ˆ 1 ( / ˆ )/ ˆ (b  b b  b b . Similarly, the proportion of cases in category 3 that are estimated to be shifted 

out of that category and into category 2 will be (cˆ  c)/ cˆ 1 (c / cˆ).
82

This aggregated-data form of the model can be used to conduct a simple check for early evidence of age 

displacement in contexts where it seems likely, using unweighted numbers of cases in four successive age 

intervals.

Application to individual-level data 

We now describe how this method may be easily applied to individual-level data using logit regression. We 

construct two binary variables, yad and ybc. Both refer to females only; they are “missing” for males. Variable 

yad is defined to be 1 if the woman is 20-24 and 0 if she is 5-9; otherwise it is missing. Variable ybc is defined 

to be 1 if the woman is 15-19 and 0 if she is 10-14; otherwise it is missing. 

In a logit regression of yad, with no covariates, the intercept is equal to ln(d/a). If this coefficient is divided by 

3, we have )ˆ ln(cˆ/b . In a logit regression of ybc, with no covariates, the intercept is equal to ln(c /b). Using the 

relationship between odds and proportions, we can then readily obtain the measure of upward shift, )ˆ 1 (b /b ,

or the measure of downward shift, 1 (c / cˆ). One of the advantages of the individual-level format with logit 

regression is that it allows us to incorporate sampling weights and clustering. It also can produce a test statistic 

of whether the measures of shift are significantly different from zero. That test statistic was not included in this 

report, but all evidence of shifting that was reported is highly significant. 

The logit regression approach will be illustrated with Stata code to estimate net transfers from age 15-19 to age 

10-14 in the household survey. Here, agevar is age coded in five year intervals, with ages 0-4 coded 0, ages 5-9 

coded 1, etc. The weight variable is hv005 and the cluster variable is hv001. 

gen yad=. 

gen ybc=. 

replace yad=0 if agevar==1 

replace yad=1 if agevar==4 

replace ybc=0 if agevar==2 

replace ybc=1 if agevar==3 

logit yad [pweight=hv005],cluster(hv001) 

matrix b=e(b) 

matrix V=e(V) 

scalar slopead=b[1,1] 

scalar varad=V[1,1] 

scalar slopebchat=slopead/3 

scalar varbchat=varad/9 

logit ybc [pweight=hv005], cluster(hv001) 

matrix b=e(b) 

matrix V=e(V) 

scalar slopebc=b[1,1] 

scalar varbc=V[1,1] 

* t is the measure of transfer, log of ratio of observed odds to expected odds 

* where odds is category above boundary to category below boundary 

* negative for downward shift, positive for upwards shift 

* set is the estimated standard error of t 

scalar t=slopebc-slopebchat 

scalar set=sqrt(varbc+varbchat) 

scalar z=t/set 
83

scalar list t set z 

scalar bobs=1/(1+exp(slopebc)) 

scalar cobs=1-bobs 

scalar bhat=1/(1+exp(slopebchat)) 

scalar chat=1-bhat 

scalar pshiftb=1-(bobs/bhat) 

scalar pshiftc=1-(cobs/chat) 

scalar list pshiftb pshiftc 

Validation of the method 

To assess the validity of this procedure, the version for aggregated data was applied to several five-year age 

distributions estimated for 1995 by the Population Division of the United Nations (United Nations, 2003). The 

year 1995 was selected partly because it was within the range of the DHS survey dates. It also preceded the 

2000-2001 censuses that most countries conducted and was sufficiently in advance of the UN publication date 

(2003) that the tabulations are probably final estimates for 1995 and not contaminated with any projections. It is 

possible that in some cases the UN data were smoothed or interpolated with statistical and demographic 

methods, which would tend to make them more consistent with our model, but we have tried to avoid smoothed 

data.

Nine tabulated age distributions were selected, representing countries that showed the highest levels of transfers 

in the report. Six distributions refer to women in African countries. The other three refer to men in Armenia, 

Kazakhstan, and Kyrgyzstan. The model was applied to five-year age groups in the range from 0-4 to 55-59, 

leading to an estimate of outward shifts for ages 5-9 through 50-54. The results are given in Table D.1. The 

percentages in column 3 are estimates of the percentage of “correct” cases that were shifted upward to the next 

interval, assuming that the age interval occupied position 2 in the four-interval model. The percentages in 

column 4 are estimates of the percentage of “correct” cases that were shifted downward to the next interval, 

assuming that the age interval occupied position 3 in the model. If the UN tabulations were correct, and if the 

assumptions of the model were correct, all of the percentages in columns 3 and 4 would be zero. Our interest is 

mainly in whether the model gives spurious evidence of shifts in the vicinity of ages 15 and 50, specifically in 

the estimates for age 15-19 in column 4 and for age 45-49 in column 3. 

The results given in Table D.1 indicate that the model worked very well in the African countries. The spurious 

estimates of downward transfers from age 15-19 into 10-14 range from -0.3 percent to 2.5 percent. These are 

well below the threshold of 10 percent used in Chapter 2. The spurious evidence of upward transfers from 45-49 

into 50-54 range from -4.0 percent to 2.8 percent, consistently well below the thresholds of 20 percent used in 

Chapter 2. 

Considering that these results come from the African countries with greatest evidence of age transfers in Chapter 

2, we infer that the method was successful in that context. A somewhat different conclusion is reached for men 

in Armenia, Kazakhstan, and Kyrgyzstan, given in the last two panels of Table D.1. The percentages in columns 

3 and 4 for these panels are generally larger in magnitude and more erratic than for the African women. These 

spurious estimates of transfers do not exceed the thresholds of 10 percent and 20 percent used in Chapter 2, but 

they would exceed thresholds of, say, 5 percent and 10 percent. There appear to be some genuine inconsistencies 

between the assumptions of our model and the actual age distributions for these countries, which may have 

caused an exaggerated appearance of transfers. 
84

Table D.1 Estimated percentages shifted out (+) or in (-) when the four-category 

method is applied to UN age distributions (United Nations, 2003) 

Beginning

of age interval 

Population

(in thousands) 

Estimate when the 

age interval is the 

second in the 

sequence of four 

Estimate when 

the age interval is 

the third in the 

sequence of four 

Burkina Faso Females 1995 

0 1000 na na 

5 813 0.7 na 

10 687 -0.3 -0.8 

15 577 1.0 0.3 

20 490 -0.1 -1.2 

25 394 0.3 0.2 

30 303 -2.2 -0.3 

35 219 0.5 3.0 

40 174 0.6 -0.6 

45 147 0.0 -0.7 

50 127 na -0.0 

55 112 na na 

Ghana Females 1995 

0 1385 na na 

5 1261 2.1 na 

10 1168 -2.1 -2.4 

15 960 0.9 2.5 

20 805 -1.4 -1.1 

25 647 1.0 1.7 

30 546 -0.1 -1.2 

35 453 -0.4 0.1 

40 371 0.4 0.0 

45 307 -0.1 -0.5 

50 250 na 0.2 

55 202 na na 

Kenya Females 1995 

0 2214 na na 

5 2200 -1.1 na 

10 1931 -0.4 1.2 

15 1605 -0.1 0.4 

20 1295 -0.9 0.1 

25 1019 0.6 1.1 

30 830 0.1 -0.8 

35 672 1.2 -0.1 

40 538 -0.7 -1.6 

45 392 -4.0 1.0 

50 274 na 5.2 

55 243 na na 

 Continued... 
85

Table D.1—Continued

Beginning

of age interval 

Population

(in thousands) 

Estimate when the 

age interval is the 

second in the 

sequence of four 

Estimate when 

the age interval is 

the third in the 

sequence of four 

Madagascar Females 1995 

0 1238 na na 

5 999 0.7 na 

10 846 0.2 -0.8 

15 719 -0.2 -0.3 

20 604 -0.2 0.2 

25 507 0.2 0.0 

30 430 0.4 -0.2 

35 364 1.1 -0.5 

40 299 -3.8 -1.3 

45 222 2.8 4.7 

50 192 na -3.5 

55 160 na na 

Nigeria Females 1995 

0 8843 na na 

5 7386 0.7 na 

10 6288 -0.2 -0.8 

15 5207 -0.4 0.3 

20 4255 -0.0 0.4 

25 3513 0.0 0.0 

30 2938 0.2 -0.0 

35 2487 1.5 -0.3 

40 2099 -3.2 -1.8 

45 1602 2.3 3.9 

50 1371 na -2.8 

55 1128 na na 

Uganda Females 1995 

0 2031 na na 

5 1610 0.6 na 

10 1329 -0.1 -0.8 

15 1095 0.8 0.1 

20 904 -0.2 -1.0 

25 708 -1.9 0.3 

30 535 1.1 2.4 

35 443 0.4 -1.3 

40 374 0.4 -0.5 

45 314 1.1 -0.4 

50 256 na -1.4 

55 188 na na 

 Continued... 
86

Table D.1—Continued

Beginning

of age interval 

Population

(in thousands) 

Estimate when the 

age interval is the 

second in the 

sequence of four 

Estimate when 

the age interval is 

the third in the 

sequence of four 

Armenia Males 1995 

0 140 na na 

5 176 -3.0 na 

10 167 -2.2 3.0 

15 143 -0.9 2.4 

20 127 -1.3 1.0 

25 124 5.8 1.3 

30 144 1.7 -5.6 

35 141 -2.4 -1.8 

40 105 -0.7 3.0 

45 70 -17.3 1.0 

50 44 na 19.0 

55 79 na na 

Kazakhstan Males 1995 

0 753 na na 

5 940 -7.4 na 

10 810 1.1 7.4 

15 750 1.2 -1.2 

20 696 -6.5 -1.4 

25 599 9.6 6.6 

30 708 -3.3 -9.8 

35 642 3.6 3.4 

40 545 -7.8 -4.5 

45 340 -5.5 10.4 

50 271 na 6.2 

55 393 na na 

Kyrgyzstan Males 1995 

0 287 na na 

5 297 -2.5 na 

10 259 -1.2 2.7 

15 222 1.9 1.4 

20 202 -3.3 -2.1 

25 173 4.0 3.6 

30 172 0.9 -4.4 

35 154 -0.5 -1.1 

40 117 -1.0 0.6 

45 78 -14.1 1.4 

50 49 na 16.5 

55 74 na na 

na = Not applicable 
DHS Methodological Reports Series 

1. Institute for Resource Development. 1998. An Assessment of DHS-I Data Quality.

2. Macro International Inc. 1993. An Assessment of the Quality of Health Data in DHS-I Surveys.

3. Aliaga, Alfredo and Pradip K. Muhuri. 1994. Methods of Estimating Contraceptive Prevalence Rates for 

Small Areas: Applications in the Dominican Republic and Kenya.

4. Landers, Alynne and Melissa McNiff. 1994. Comparability of Questionnaires. 

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