COVENANT UNIVERSITY DEPT. OF ECONS & DEV. STUDIES DEMOGRAPHIC DATA EVALUATION

1. COVENANT UNIVERSITYDEPT. OF ECONS & DEV. STUDIESDEMOGRAPHIC DATA EVALUATION ASSESSMENT OF DEMOGRAPHIC DATA Lecturer: Miss Adetoro Gbemisola W.

2. Introduction Data evaluation is the assessment of the quality of the data. In evaluating the data, sometimes it is adjusted in order to ensure that it is of an acceptable standard. The adjustment is done on the basis of the responses to the following questions, which were asked during the census: • Sex of members of household • Age (in completed years) of members of household • Residential status of household • Children still living (with household or elsewhere), and • Children dead

3. Concepts and Definitions • Census of Population: Complete enumeration of persons during a specified period in a demarcated geographical area. • Content Error: Mistake made in the recorded information in the census questionnaire either by the respondent or by the interviewer. • Coverage Error: Under or over-enumeration in a population census due to either omission or duplication. • De facto Population: This refers to the usual household members present and visitors who spent the census night at any given household. This however excludes: (a) Foreign diplomatic personnel accredited to Nigeria; and

4. (b) Nigerian nationals accredited to foreign embassies and their family members who live with them abroad and , Nigerian migrant workers and students in foreign countries who were not in the country at the time of the census. • De jure Population: This refers to usual household members present and usual household members temporarily absent at the time of the census. These include institutional populations in places such as hospitals/health centers, prisons and academic institutions (universities, colleges, boarding schools). Thus, the de facto and the de jure population can be diagrammatically represented as follows:

6. Child-Woman Ratio: Number of children age 0-4 years in a population to every 1000 women age 15-49 years in the same population. • Dependency Ratio: Ratio of children age 0-14 and persons age 65 years and older, per 100 persons in the age-group 15-64 years old. • Digit Preference: Reporting of age by respondents often ending in certain preferred digits. This results in heaping of population in ages ending with certain digits. • Evaluation of Census Data: Measurement of the quality of Census data. • Sex Ratio: Number of males per 100 females in a population.

7. Type of Population used in Evaluating the Coverage and Content Errors • In the analysis of the coverage and content errors, the de facto population has been used. This is so because we would like to analyse the information obtained from the people who gave us their details and not those we did not talk to or collect the information from. Methods of Evaluation • During enumeration, checks and controls are instituted to minimize errors in the census. Despite instituting data control measures, there are usually several errors in the census data. • For instance, some people may be completely omitted, others may be enumerated more than once, or some characteristics of an individual such as age, sex, fertility and economic activity of the canvassed individual may be incorrectly reported or tabulated. In general, two approaches are used to evaluate the quality of data, direct and indirect methods.

8. The direct method basically involves the carrying out of what is referred to as a Post Enumeration Survey (PES). In a PES, a sample of households is revisited after the census and data are again collected but on a smaller scale and later compared with that collected during the actual census. The matching process of the two sets of data can then be used to evaluate the quality of the census data. • Indirect methods usually employ the comparison of data using both internal and external consistency checks. Internal consistency checks compare relationships of data within the same census data, whereas external consistency checks compare census data with data generated from other sources. For instance, one can compare data on education obtained during a census with administrative data maintained by the Ministry of Education.

9. Detection of Errors in Age Data • Demographic data are usually classified by age and sex. Despite this importance; a variety of irregularities and misstatements have been noted with respect to age-related data. These irregularities must be detected, adjusted or corrected before demographic data could be used for any meaningful analysis. Common Errors in Age Data • Digital preference or aversion • Deliberate misstatement • Ignorance of correct age

10. Some Indices of Detection of Errors in Age Data • Whipple’s Index • Myer’s Blended Index • Un Age-sex Accuracy Index • Carriag-farrag Index

11. WHIPPLE’S INDEX • Essentially designed to detect concentration or heaping in terminal digits 0 and 5 • Applicable when ages are reported in single years • Designed over the range 23-62 inclusive • Assumptions of rectangularity (ages are evenly distributed )

12. Assumption of Rectangularity • For example, employing again the assumption of rectangularity in a 10-year range, we may measure heaping on terminal digit “0” in the range 23 to 62 very roughly by comparing the sum of the populations at the ages ending in “0” in this range with one-tenth of the total population in the range:

13. Similarly, employing either the assumption of rectangularity or of linearity in a 5-year range, we may measure heaping on multiples of five (terminal digits “0” and “5” combined) in the range 23 to 62 by comparing the sum of the populations at the ages in this range ending in “0” or “5” and one-fifth of the total population in the range:

14. Steps in Computing Whipple’s Index Terminal digits ‘0’ and ‘5’ • Step1: Add all ages ending in terminal digits 0 and 5 over the range 23-62 (Numerator) (P25+P30+P35+P40+…+P60) • Step2 : Add population in the entire age range 23-62 inclusive (denominator) • Step 3: Put the sum of the numerator on the one-fifth of the sum of the denominator and express in percentage.

15. Whipple’s Index (Terminals 0 & 5) This gives the relative preference for terminal digits ‘0’ and ‘5’

16. Steps for Terminal Digit ‘0’ • Step1: Add all population with ages ending in terminal digits 0 over the range 23-62 (Numerator) (P30+P40+ 50+P60) • Step2 : Add population in the entire age range 23-62 inclusive (denominator) • Step 3: Put the sum of the numerator on the one-tenth of the sum of the denominator and express in percentage.

17. Whipple’s Index (Terminal digit 0) • This gives the relative preference for terminal digit ‘0’ only

18. Single Year Age Data

19. Check the Whipple’s Index for: • Terminal Digits 0 and 5 =100.5 • Terminal Digit 0 only = 72.0 • Terminal Digit 5 only = 129.0

20. INTERPRETATION OF WHIPPLE’S INDEX • Whipple’s index varies from 0 to 500. • Index=0 digits ‘0’ and ‘5’ are not reported, • Index =100 no preference for ‘0’ or ‘5’, • Index=500 only the digits ‘0’ and ‘5’ are reported in the age data. • Index<105 highly accurate age data • Index 105-109.9 = fairly accurate • Index 110-124.9= approximate • Index=125-174.5 Rough • Index>=175 very Rough Age Data

21. MYERS BLENDED INDEX • Designed for evaluating single - year age – sex data. • It can give the extent of digit preference for all the digits 0, 1, 2, 3... 9. It can be used to report errors for all ages 10 – 99 years. • Based on the assumption of rectangularity.

22. Steps in Computing Myers Index • Sum all the populations ending in each digit over the whole range i.e. 10 - 99 • Sum figures between ages 20 - 99. • Multiply the sums in (1) by coefficients; 1, 2, 3,4,5,6,7,8,9 and 10. • Multiply the sums in (2) by coefficients from 9 descending to 0 i.e. 9 ,8 ,7, 6 , 5 ,4 ,3 ,2 ,1 ,0. • Add the product of (3) and (4) , to obtain the blended sum

23. Steps Cont’d • Add up the blended sum in (5). • Find the percent (%) of the total blended sum at different digit ends. • Take the deviations of each % in (7) from 10.0. This result indicates the extent of concentration or avoidance of a particular digit. • A summary index of preference for all terminal digits is derived as one half of the sum of the deviations from 10.0%, each without regard to signs

24. Example: Myers Index

25. INTERPRETATION • If the sum at any given digit exceeds 10% of the total blended population, it indicates over selection of ages ending in that digit (i.e. digit preference). • On the other hand, a negative deviation or sum that is less than 10% of the total blended population indicates an under selection of the ages ending in that digit (i.e. digit avoidances). • If age heaping is non-existent, the index would be approximately zero.

26. Theoretically Myers index can vary from 0 to 180. If ages are reported accurately, all “blended” sums are very nearly equal and deviations from 10 percent are negligible, resulting in an index of almost zero. If all ages were reported with the same terminal digit (for example digit 0), then 100 per cent of the “blended” total would be at this digit; the absolute sum of deviations from 10 percent would then amount to 180. • In the present example, they are markedly unequal and, by adding together irrespective of sign, the deviations from 10 percent of the grand total an index of 62.96 was obtained.

27. UN AGE –SEX ACCURACY INDEX • This method consists essentially in the computation of sex-ratios and age –ratios for five-years groups of ages, up to age 70. in the case of sex- ratios, successive differences between one age group and the next are noted, and their average is taken, irrespective of sign. In the case of age-ratios, for either sex, deviations from 100 are noted and averaged irrespective of sign. Three times the average of sex-ratio differences is then added to the two averages of deviations of age ratios from 100, to compute the index.

28. AGE ACCURACY INDEX In the absence of extreme fluctuations in the past vital events, the age ratios for all age groups should be about equal to 100. The sum of the deviations from 100 of the age ratios for males divided by number of age groups gives the mean deviation for males and the same procedure also gives the mean deviation for females. The average of the mean deviations of males and females is a measure of the overall accuracy of the age data, i.e., age accuracy index.

29. UN AGE-SEX ACCURACY INDEX • This index which was proposed by the United Nation is used for the evaluation of five-years age-sex data. The index is also referred to as Joint Score. It has three components; COMPONENT (1) • This score is obtained by first calculating the sex ratio at each age group. Successive differences irrespective of sign are added and averaged. • Age – specific sex ratio = 5Pxm X 100 5PXf • 5Pxm =males aged x to x + 5 • 5Pxf = females aged x to x + 5

30. COMPONENT 2 Average male age ratio • For each age group for males, calculate the age ratios computed as • Age ratio = 5Px X 100 ½ (5Px – 5 + 5Px + 5) • The deviations from unity irrespective of sign are added and averaged (M). • NOTE THE AGE RATIO FORMULA HERE!

31. COMPONENT 3 Average female age ratio score (F) • For each age group for females, the age ratios are calculated using the same formulae as for males. The deviations from unity irrespective of sign are added and averaged (F). • The index is then computed as: UN INDEX = 3(S) + average male age ratio + Average Female Age Ratio.

32. Age-Sex Data 1991 Census Nigeria

33. Age-Sex Data 1991 Census Nigeria

34. UN AGE-SEX ACCURACY INDEX NIGERIA 1991

35. UN AGE-SEX ACCURACY INDEX NIGERIA 1991

36. INTERPRETATION • The reported age-sex data for a given population is presumed to be accurate if the age-sex accuracy index is between 0 and 19.9, inaccurate if the index is between 20 and 39.9, and highly inaccurate if the index is above 40.

37. References • Demographic Methods and Concepts by Donald T. Rowland. • Methods and Materials of Demography by Shryock and Siegel • UN Manuals IV by the United Nations. • Population Handbook (PRB) published by Arthur Haupt and Thomas T. Kane (2004). Population Reference Bureau’s, 5th Edition, Washington D.C. • Essentials of Demographic Analysis for West Africa by G.M. Kpedekpo. • Techniques of Population Analysis by D. W. Baclays

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