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Document Type
Methodological Reports
Publication Topic(s)
Household and Respondent Characteristics
Language
English
Recommended Citation
Pullum, Thomas W., and Sarah Staveteig. 2017. An Assessment of the Quality and Consistency of Age and Date Reporting in DHS Surveys, 2000-2015. DHS Methodological Reports No. 19. Rockville, Maryland, USA: ICF.
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Publication ID
MR19

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Abstract:

This report assesses the quality and consistency of age and date reports in DHS surveys conducted since 2000 in 67 countries. It is the most recent of several reports on various aspects of DHS data quality. The first chapter describes the steps of editing and imputing during fieldwork and data processing. Great care is taken to train and supervise the interviewers to obtain the best possible estimates of ages and dates. The eligibility of adults for the surveys of women and men depends on obtaining accurate values of age near the lower and upper age boundaries within the household survey. The eligibility of young children for the detailed health questions depends on obtaining accurate estimates of when they were born within the surveys of women. Age-specific fertility rates, under-five mortality rates, immunization rates, anthropometry scores, and many other DHS indicators depend on accurate estimates of age. An appendix provides an inventory of all the locations in DHS surveys where the respondents are asked for ages and dates. This assessment focuses on just a few of those locations: the ages of all household members, provided by the household respondent during the household survey; the self-reported ages and birthdates of women and men in the surveys of women and men; women’s self-reports of age and date of first union in the survey of women; the birthdates (and ages, if living) of children in the birth histories, provided by the mother; and the women’s and men’s estimates of their respective spouses’ ages in the surveys of women and men. The second chapter assesses the ages listed above, other than spousal estimates, in terms of three types of measures: incompleteness, heaping, and transfers. A total of 11 indicators are used. For each indicator, the distribution across all surveys is described and the surveys with the most extreme levels are identified. All of these measures vary substantially. There are many surveys with values close to zero on all measures, and others with very high values. There are some surveys in which month of birth is hardly ever given. Age/date transfers are sometimes large but in a direction opposite to what we would expect, particularly around age 15 or around the date for the health questions, clearly as a result of over-correction during training and supervision. Surveys with extreme values are listed. Summary indices of incompleteness, heaping, and transfers are constructed and tracked over time. The indicators fluctuated substantially from 2000 to 2015 and did not show a systematic trend. A single composite index is constructed for each of the 67 countries. The countries in the highest quintile (with the most problems) and the lowest quintile (with the fewest problems) are identified. The third chapter investigates the quality and consistency of spousal age estimates compared to self-reports in the surveys that included interviews of men and where women and/or men were asked to estimate the age of their spouse(s). In the absence of an age gap between spouses, women tend to estimate that their husbands are older than their self-reported age and men tend to estimate that their wives are younger than their self-reported age. Evidence indicates that where there is an age difference between spouses, women tend to estimate in a way that reduces the gap: they underestimate the age of older husbands and overestimate the age of younger husbands. Men underestimate the age of wives who are older than they are, which reduces the gap, but they also tend to underestimate the age of wives who are younger, which increases the gap. In the vast majority of countries, there was more heaping for estimates of spouse’s age than for self-reported age. There is evidence that displacement and heaping, in particular, can be reduced, through training and supervision, but there is also evidence that too much focus on displacement of children or on heaping at final digit 0 can lead to over-correction. The biggest determinant of good age reporting is probably the value, to the respondents, in everyday life, of knowing their ages or the ages of their children. This component of data quality varies from one setting to another and is outside the control of a survey operation.