Evaluation of Fertility Data Collected from Population Censuses

1. Evaluation of Fertility Data Collected from Population Censuses United Nations Statistics Division

2. Outline of the presentation • For two items that can be collected to obtain fertility statistics in census: • Children ever born • Recent births • Discuss • What information to collect • What fertility indicators can be derived • Possible quality issues related to each question • Methods of data evaluation

3. Children ever born – what information to collect? • How many children has [this woman] had in her lifetime? • (a) “Total number of sons living in the household”; • (b) “Total number of sons living elsewhere”; • (c) “Total number of sons born alive who have died before the census date”; • (d) “Total number of daughters living in the household”; • (e) “Total number of daughters living elsewhere”; • (f) “Total number of daughters born alive who have died before the census date”. • incl. all live births • Could be elaborated into a number of questions • Asked to all women

4. Children ever born – the use of When is it used? • Widely used for over 50 years • Important for countries without complete birth registration • Also important for countries with complete birth registration • Study fertility by detailed socio-economic characteristics What can we get? Parity distributions Average number of children ever born Age-specific fertility rate Total fertility rate (TFR)

5. Children ever born - Tabulation • - Do not group the numbers of children, except for the last open category • - Distinguish children ever born not stated from no children

6. Children ever born – possible errors (1) Group A: Errors because of misunderstanding the question: • Mortality error: reported only children living rather than ever born • Non-resident error: overlooking ever born children living elsewhere • Marriage error: women not including her children born from previous marriage/not reporting children born out of wedlock Group B: Errors because of respondents’ lapse of memory or neglect: • Memory error: forgot some children, especially from older women Group C: Enumerators’ failure to reach individuals: • The not-at-home error: information provided by neighbors • Coverage error: omit an area or forgot to record the answer Group D: Recording error: • Childless women mis-classified into parity not stated

7. Children ever born – possible errors (2) • Not all errors can be detected • However, many steps can be taken to find inconsistencies, to understand better the data quality and to provide information for improvement in the next census

8. Children ever born – quality assessment Methods (1) • Initial assessment: • Any missing values in children ever born data? • Missing value for any relevant variables? (age of mother, sex of child, survival status of the child) • Comparing children ever born and children surviving data

9. Children ever born – quality assessment Methods (2) • For tabulated data: • Sex ratio of children ever born consistent with national average of sex ratio at birth? • Check whether women with parity “not stated” are childless instead (El-Badry method) • Plausibility of data – graphics help and best with other sources • Average children ever born should increase with age (Group B error) – with constant or declining fertility assumption • Proportion of women by parity, for older age groups • Time plot of mean number of children ever born, based on multiple data sources (cohort analysis) • Age-specific fertility rates • TFR

10. Children ever born – quality assessment Methods (3) P/F ratio method • Usually used to adjust current fertility data derived from recent births question • May also be used to assess the quality of both current (derived from recent births) and life time fertility data (derived from children ever born) • Certain assumptions (discuss in more details later)

11. Children ever born – quality assessment examples (1) Any missing/implausible value for children ever born data? Source: Estimation of fertility from the 2001 South Africa census data, Tom A Moultrie & Rob Dorrington, Centre for Actuarial Research, University of Cape Town

12. Children ever born – quality assessment examples (2)Comparing sex ratio at birth Data source: graph produced based on data from United Nations Demographic Yearbook

13. Children ever born – quality assessment examples (3): Plot mean number of children ever born by age of women Data source: graph produced based on data from United Nations Demographic Yearbook

14. Children ever born – quality assessment examples (4): mean children ever born from multiple sources Data source: graph produced based on data from United Nations Demographic Yearbook

15. Children ever born – quality assessment examples (5): parity distribution for age group 45-49 High level of 0 parity for older age group: 1950 and 1970 censuses: possible combined group of not stated parity with 0 parity group Flat curve: probably some form of mis-reporting, seems to be improving over time Mexican fertility survey: shape of the curve more plausible (although with small sample sizes) Source: Child survivorship estimation: methods and data analysis, Griffith Feeney, Asian and Pacific Population Forum, Vol. 5, Nos. 2-3, 1991

16. Children ever born – quality assessment examples (6): Cohort analysis of mean number of children ever born • Year = census yr – (age – 25) • 1960 and 1970 censuses: an increase of fertility • Erroneous data from 1980 census (conclusion was reached after comparing with data from other surveys) Source: Child survivorship estimation: methods and data analysis, Griffith Feeney, Asian and Pacific Population Forum, Vol. 5, Nos. 2-3, 1991

17. Children ever born – quality assessment examples (7): Comparing age specific fertility rates Data source: graph produced based on data from United Nations Demographic Yearbook and Measure DHS country report

18. Children ever born – quality assessment examples (8): Comparing total fertility rates Data source: graph produced based on data from United Nations Demographic Yearbook and Measure DHS country report

19. Children ever born – quality assessment examples (9): El-Badry method Data source: United Nations Demographic Yearbook, Burundi 1990

20. Children ever born – quality assessment examples (9): El-Badry method (cont.) Data source: United Nations Demographic Yearbook, Burundi 1990

21. Children ever born – quality assessment examples (9): El-Badry method (cont.) % women with not stated # of children = -0.49 + 0.48 * % childless women (linear regression)  32% of the real childless cases were reported as “number of children not given”  real childless women = observed childless women / (1-32%) El-badry method: Failure of enumerators to make entries of zero: Errors in recording childless cases in population censuses, JASA, Vol. 56, No. 296, 1961

22. Children ever born – quality assessment examples (10)Using the P/F ratio method Rational: • Compare cumulative fertility level derived from current fertility data F(trusting the distribution but not level) and life time fertility data P (trusting the overall level but assumes under-reporting varies by age) • The method is typically used to adjust current fertility level (may be generated from recent births question in census or birth data from civil registration) • However the method is also used to assess the quality of children ever born data and sometimes, the age reporting of mother Current fertility data: • Births in the last 12 months (from censuses or civil registration) Life time fertility data: • Children ever born in the life time of women

23. Children ever born – quality assessment examples (10)Using the P/F ratio method (cont.) Assumptions: • Constant proportion of under-reporting of current fertility for all age groups • Increasing under-reporting of parity (children ever born) by age of women • Constant fertility (relaxed by a modification of the original P/F ratio method)

24. Children ever born – quality assessment examples (10):P/F ratio method (cont.) Typical P/F ratio, relative good data Data source: Manual X, Bangladesh 1974 census

25. Children ever born – quality assessment examples (10): P/F ratio method (cont.) • Typical “look” of P/F ratios: • Similar level of P/F ratios for age groups 20-24, 25-29 and 30-34 • P/F ratios becomes smaller for older ages Deviation from the above typical pattern: indicates either violations of the assumptions or different patterns of under-reporting • Example 1: a rising trend in the P/F ratios by age of women: fertility could have been decreasing in the past • Example 2: a declining trend in the P/F ratios by age of women: fertility could have been increasing or that reported data on children ever born suffer from progressively increasing omissions of children as age of women increases • Example 3: large fluctuation in P/F ratios may reflect either differential coverage by age or selective age misreporting of women

26. Recent births - basics How? Date of birth of last child born alive (preferred question) OR Births in the last twelve months (to a woman) or in the household What can we get? Age specific fertility rate and TFR However, The questions are subject to under-reporting of births Age of women during the census to be adjusted to age at giving birth (usually -0.5 year)

27. Recent births – Possible errors • Reporting errors: • Enumerator’s error • “reference period error”: uncertain of the date of birth vs the reference period • Proxy respondent • Births reported not including: • Women had a birth recently but died before the census • Household had a birth recently but the household dissolved before the census • Not significant in most cases, however could become an issue when many deaths occurred in a short period (HIV/AIDS)

28. Recent births – assessment (1)Methods • Initial assessment: • any missing values in data? (month/date/year of births) • Missing data for any relevant variables? (age of mother, sex of child, survival status of the child) • For tabulated data: • Sex ratio at birth consistent with national average? • Plausibility of data – graphics help and best with other sources • Age-specific fertility rate • TFR

29. Recent births – assessment (1)Methods (cont.) • Compare with civil registration data on live births • P/F ratio method to compare with children ever born data for coverage

30. Recent births – assessment (2): any missing values? Source: Estimation of fertility from the 2001 South Africa census data, Tom A Moultrie & Rob Dorrington, Centre for Actuarial Research, University of Cape Town

31. Recent births – assessment (3): Comparing age-specific fertility rate patterns Source: Graph created using data from United Nations Demographic Yearbook; 2000 Census of Population, Social and Economic Characteristics of Population, State Institute of Statistics, Turkey; Measure DHS Final Report Turkey 1998 and 2003

32. Recent births – assessment (4): Comparing TFR Source: Graph created using data from United Nations Demographic Yearbook; 2000 Census of Population, Social and Economic Characteristics of Population, State Institute of Statistics, Turkey; Measure DHS Final Report Turkey 1998 and 2003

33. Recent births – assessment (6): Comparing TFR • Yes under-estimated: but may reflect recent fertility pattern; adjusting for under-count and will be useful for sub-national and small area fertility estimates. • How to adjust: use household survey data • Only source for small area • Date of last birth data better quality than births in the last 12 months

34. Illustration of using MortPak FERTPF for indirect estimates of fertility – input data

35. Illustration of using MortPak FERTPF for indirect estimates of fertility – results

36. Thank you!