Error detection in census data age reporting

1
International Journal of
Science, Engineering and Innovative Research Vol. 7, January 2016
ERROR DETECTION IN CENSUS DATA AGE
REPORTING
Obisesan K.O.1
, Mojoyinola M.O.2
Department of Statistics
University of Ibadan, Nigeria
(1
obidairo@gmail.com, 2
olumidemubarak@gmail.com)
Abstract- Age is an important demographic variable that must
be carefully considered in all demographic survey. The
objective of this study is to conduct a comprehensive
assessment of the age reporting in Census data. The study
gave an estimate of age misrepresentation in the Nigeria 2006
Population and Housing Census Data.
The data used in this study was obtained from the 2006
Population and Housing Census Priority Table,
Volume(III)published by the National Population
Commission, Abuja, Nigeria, in April
2010.
Age heaping and digit preference were measured using
modified Whipple's index and Myers index. Age Sex accuracy
was also measured using the United Nation's age-sex accuracy
index.
The reported Whipple's index for both sexes was 251
indicating presence of age heaping and it also showed age
heaping at terminal digit 0 and 5 as 268 and 233 respectively.
The Myers index had an overall index of 50.9, 49 for male and
52.82 for female population.
The evaluation of Nigeria 2006 Population and Housing
Census Data based on the technique applied in this study
indicates that the data is of poor quality as a result of the
presence of age heaping and digit preference in recorded ages.
Therefore modern methods such as a systematic data
management system, compulsion to register birth, and
standard smoothing techniques are thereby recommended for
future data collection.
Keywords- Census, Age Heaping , Digit Prefernce.
I. INTRODUCTION
In the demographic study of a population, age is an important
variable used in the description of the population structure and
growth rate forecasting. Age data are important in determining
basic factors of population dynamics and studying fertility,
mortality, and migration. Data on age are essential in
population estimates and projections. From age distribution of
any population, estimates of school-age population can be
made as well as number of voters, entrants in labour force, and
in planning of social services for instance, for mother,
children, aged e.t.c has to be based on age distribution of the
population. The presence of error in this important variable is
an obstacle to proper planning and decision making in both
governmental and non-governmental agencies, a decision
based on inaccurate data will definitely not produce the
desired outcome. Age misrepresentation is a common problem
in developing countries (Gonzalez et al, 2014).Thereby
making its study an essential step to reducing its occurrence.
The most common irregularity in age data is the age heaping.
Age data frequently display excess frequencies at round or
preferred ages, such as even numbers and multiples of 5,
leading to age heaping. Age heaping is considered to be a
measure of data quality and consistency (Pardeshi, 2010).
Bello (2012), while assessing the quality of outpatients’ age
data found age heaping to be one of the irregularities in survey
reporting of age in Nigeria.
II. BACKGROUND OF STUDY
Borkotoky and Unisa (2014) examined the quality data on
large scale survey data. In their study, age misreporting was
observed and it differs by region to region and individual
characteristics.
They also identified illiteracy, rural residence and poor
economic conditions as some of the factors associated with
age misreporting. It was concluded in the study that “age
misreporting, inconsistency and incomplete response are three
sources of error that needed to be considered before drawing
conclusion from any survey".
In the paper “Error Detection in Outpatients Age Data Using
Demographic Techniques" by Bello (2012), where he
evaluated the accuracy of age reporting by the outpatients in
General Hospital Dutsin-ma, Katsina state, Nigeria. Using
demographic techniques, which includes Whipple's Index,
Myer's Blended Index and UN Age-sex Accuracy Index. His
research showed very rough age data reporting for both male
and female outpatients. He found `5' and `0' to be the most
preferred digits, and `1' as the most avoided digit for both
sexes.

International Journal of Science, Engineering and Innovative Research, Volume 7, January 2016 2
WWW.IJSEIR.ORG Paper ID: 45734716ISSN: 2412-513X
Susuman et.al (2012) assessed the quality of age reported in
the Tanzania 2012 population census data by measuring age
heaping and digit preference using the Whipple's Index,
Myer's index, and Age-Sex Accuracy Index. They recorded a
Whipple's Index of 154.43 for both sexes, the male population
was found to have a lower Index of about 152.65 while the
females had the higher Index of about 156.07. Digit
preferences were at `0' and `5' while avoidances were at digit
`1' and `3' for both sexes. In a similar study by Pardeshi
(2010), the quality of age data collected during a community
survey in the district of Yavatmal, Maharashtra, India was
assessed. Whipple's Index, Myre's Blended Index, Age Ratio
Score and Age Accuracy Index were used in measuring the
age heaping and digit preference. The age data collected was
found to be of very poor quality. 42% of the population
reported incorrect digit and preferences were found at digits
`0' and `5'.
Also in the study “Age Reporting Behavior, A case study of
1991 and 2000 Population and Housing Census, Malaysia" by
Talib et al.(2007). They applied Pyramid chart, Whipple's
Index and test differences of the terminal digits to analyse the
quality of single years of age population. The study found that
misstatements in age reporting were due to digit preference
and avoidance in both censuses.
Denic et al.(2004) also studied the quality of age data in
patients from developing countries. The study used a data of
3874 cancer patients from 72 developing countries, mainly
from the Indian subcontinent and the Middle East. The
Whipple's Index and Myer's Index were used.
Age data quality was found to be low in cancer patients from
the Indian subcontinent and Middle Eastern countries, the
male citizens of the UAE did not show preference for terminal
digits `0' while preference for terminal digits `0' and `5' were
found in other populations. Finally, the study concluded that
age data quality should be analysed as it may bias results and
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III. METHODOLOGY
This section gives a description of the demographic methods
used for measuring the extent of age heaping and digit
preference. The methods are Whipple's index and Myers'
blended index respectively. Age-sex accuracy index was also
calculated.
1. WHIPPLE'S INDEX
Age misreporting constitutes one of demography's most
frustrating problems" (Ewbank1981:88). To detect it,
demographers have developed different methods to assess the
quality of age data. Among these methods, age heaping
indices have been proposed to detect the degree of preference
or avoidance for specific digits in age reporting. The index
was invented by the American demographer George C.
Whipple (1866 - 1924). Whipple's index is highly sensitive to
age heaping on ages ending in 0 and 5. This index applies to
single years of age returns between ages 23 and 62 inclusive.
It is obtained by summing the number of persons in this age
range, and calculating the ratio of reported ages ending in 0 or
5 to one-fifth of the total sample. It varies between 1
(indicating no preference for ages ending by 0 and 5) and 5
(indicative of a complete report on ages ending by 0 and 5):
ACKNOWLEDGMENT(HEADING 5)
The preferred spelling of the word “acknowledgment” in
America is without an “e” after the “g”. Avoid the stilted
expression, “One of us (R. B. G.) thanks . . .” Instead, try “R.
B. G. thanks”.
Later modifications of the W index gave a more precise and
synthetic mean to assess the accuracy of single-year age data.
W measures only preferences for (or avoidance of) ages
ending by 0 and 5 indistinctively. However, opposite effects
of digit preferences and avoidances can potentially cancel
each other out and affect the original Whipple's index. To
remove this constraint, two modifications have been made to
the original formulation of W (Roger et al. 1981;Noumbissi
1992).The first change made it possible to distinguish between
preferences for ages ending on 0 and those ending in 5 as
follows (Roger et al., 1981, p. 148):
By summing W0 and W5 and dividing the sum by 2, we
return to the original Whipple's index (W)
Though this first modification provides a means to distinguish
between preferences for ages ending in 0 and those ending in
5, it is nonetheless based on the unrealistic assumption of
linearity over a ten-year age range. The second modification,
proposed by Noumbissi (1992), returns to a more reasonable
assumption of linearity over an age range of five years rather
than ten. Based on the same basic principles of the original
Whipple's index (linearity and rectangularity over a 5-year age
range), the latter modification allows to measure age heaping
for all digits. Through the digit-specific modified Whipple's

index (Noumbissi 1992), age heaping could hence being
computed distinctively for all ending digits (and not the sole 0
and 5 as in the original Whipple's index). Age heaping can be
calculated thus:
Age heaping can thus be calculated for all ten digits (0-9). For
each digit, the degree of preference or avoidance can be
determined as follows:
Where Px is the population of completed age x and 5Px the
population of the age range (x, x+4). If there is no digit
preference or avoidance this “digit-specific modified
Whipple's index" is equal to 1. An index above or below 1
signifies, respectively, preference for or avoidance of the digit
in question .
The index can be summarized through the following
categories:
Table 1: Value of Whipple's index
Highly accurate data <= 105
Fairly accurate data 105 – 109.9
Approximate data 110 – 124.9
Rough data 125 – 174.9
Very rough data >= 175
1. MYRES INDEX
Myers Index is usually used to measure degree of preference
for each digit and it provides summary index for all terminal
digits. The summary index is an estimate of the minimum
proportion of persons in the population for whom an age with
an incorrect final digit is reported. The theoretical range of the
index is from zero to ninety (Hobbs, 2004).
It can be used to report errors for all ages 10 – 89 years
(Kpedekpo, 1982). The underlined assumption of this method
is that in the absence of systematic irregularities in the
reporting of age, the blended sum at each terminal digit should
be approximately equal to 10% of the total blended
population. If the sum at any given digit exceeds 10% of the
total blended population, it indicates over selection of ages
ending in that digit (digit preference). On the other hand, a
negative 30 deviation (or sum that is less than 10% of the total
blended population) indicates under-selection of the ages
ending in that digits (digit avoidance). If age heaping is non-
existent, the index would be approximately 0 (Kpedekpo,
1982). The procedure for computation is as follows:
1. Sum all the population ending in each terminal digit
over the whole range for the ages 10 – 89.
2. Sum all the population ending in each terminal digit
over the whole range for the ages 20 – 89.
3. Multiply the sums of ages at each terminal digit in (1)
above by co-efficient, 1,2,3,4,5,6,7,8,9,10.
4. Multiply the sums of ages at each terminal digit in (2)
above by co-efficient 9,8,7,6,5,4,3,2,1,0.
5. Add the product of (3) and (4) above to obtain the
blended sum at each terminal digit.
6. Add up the blended sum in (5) above.
7. Find the percentage of the blended sum at each
terminal digit to the total of the blended sum.
8. Find the deviation of the percentage distribution from
10.
2. UNITED NATION AGE SEX ACCURACY
INDEX.
This index which was proposed by the United Nation is used
for evaluation of five-year age-sex data. The index is also
referred to as Joint Score. It has three components:
1. Average Sex Ratio Score (ASRS)
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 =
5𝑃𝑥 𝑚
5𝑃𝑥 𝑓
𝑋 100 (15)
5Pxm
= males aged x to x +5
5Pxr
= females aged x to x +5
2. Average Male Age Ratio Score (AMRS)
For each age group for males, calculate the age ratios
computed as
𝐴𝐺𝐸 𝑅𝐴𝑇𝐼𝑂 =
5𝑃𝑥
0.5(5𝑃𝑥+1 + 5𝑃𝑥−1)
𝑋 100 (16)
The deviations from unity irrespective of sign are added and
averaged (AMRS)
3. Average Female Age Ratio Scores (AFRS)
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 (AFRS)
The index is then computed as UNAI = 3 (S) + (M+ F):
Where;
S = Average Sex Ratio Difference.

M = Average Male Deviation from 100.
F = Average Female Deviation from 100.
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.
IV. RESULTS
1. WHIPPLE’S INDEX
TABLE 2: WHIPPLE’S INDEX RESULT
TERMINAL
DIGITS
TOTAL MALES FEMALES
W0
267.87
265.46 270.36
W1 36.30 37.96 34.55
W2 72.05 73.27 70.76
W3 64.99 66.21 63.79
W4 56.05 57.80 54.34
W5 233.32 227.18 239.32
W6 64.54 67.16 61.99
W7 67.28 67.76 66.81
W8 81.37 79.46 83.31
W9 51.09 52.98 49.17
WI 251 247 255
Table (2) shows the Whipple's Index for Nigeria 2006
population data, it shows age preference for each of the ten
terminal digit (0-9) developed by Noumbissi(1992). From the
table one can conclude that ages with terminal digits 0, and 5
are very inaccurate. And the overall Whipple's index value of
251 shows the presence of Age heaping for both sexes, the
male population has an index of 257 and the female 255 which
are both high and qualify the data as very rough.
FIGURE 1: NIGERIA WHIPPLES INDEX FOR EACH TERMINAL DIGIT
FIGURE 2: MALE WHIPPLE’S INDEX FOR EACH TERMINAL DIGIT.
FIGURE 3: FEMALE WHIPPLE’S INDEX FOR EACH TERMINAL DIGIT.
2. MYRES INDEX
Note: Terminal digit(1), Sum (10 - 79) (2), Sum (20 - 79) (3),
Coefficients(4),Coefficients(5),Blendedsum(6)=(2*4)+(3*5),P
ercentage distribution (%)(7),Deviation from 10%(8),Re-
marks(9).

Coefficients(4),Coefficients(5),Blendedsum(6)=(2*4)+(3*5),
Percentage distribution (%)(7),Deviation from 10%(8),Re-
marks(9).
Coefficients(4),Coefficients(5),Blendedsum(6)=(2*4)+(3*5),
Percentage distribution (%)(7),Deviation from 10%(8),Re-
marks(9).
Table (3, 4 & 5) show Myers Blended Index for the whole
population, male population and female population
respectively, they reveal their respective indices of 50.9, 49
and 52.82. However, ages ending with `1' have the highest
avoidance for both male and female population while ages
ending with `0' have the highest preference, followed by ages
ending with `5' and `8'.
Figure 4: MALES MYERS INDEX FOR EACH TERMINAL DIGIT
Figure 5: FEMALES MYERS INDEX FOR EACH TERMINAL DIGIT
Figure 6: MALES MYERS INDEX FOR EACH TERMINAL DIGIT

3. UN AGE-SEX ACCURACY INDEX
The analysis of the UN Age-sex Accuracy Index shows that
the male and female populations have an approximately equal
value for the Age ratio, except for age group 70 – 74 and 85+
where that of the male was higher. The population has an
average sex ratio value of approximately 9.02 and male and
female age ratio of about 19.81 and 21.39 respectively. The
UNAI of 68.29 was recorded which according to the proposed
scale by the UN qualifies the data as of poor quality.
V. RECOMMENDATIONS
1. Increase awareness about birth registration with the
National Population Commission.
2. Make birth registration at birth compulsory for parents.
3. Using analytical approaches such as smoothing technique to
improve age data.
4. Adoption of accurate Age and Sex data collection model
used in developed countries.
5. Age data should be collected with date of birth as this
reduces error of digit preference.
VI. CONCLUSION
In conclusion, all analyses in this study indicate that age-sex
reporting in Nigeria 2006 Population and Housing Census
Data is erroneous and needs to be properly checked. The next
census in Nigeria is reported to take place in the year 2017
thus the need for the implementation of all recommendations
in this study.
REFERENCES
[1] Abd. Latib Talib, Mohd SofiAli, Martini Sahul Hamid and Khamsiah M,
Ohd Zin, (2007). “Age reporting behaviour: a case study of 1991 and
2000 population and housing census, Malaysia”. Department of
Statistics Malaysia.
[2] A. Sathiya Susuman, et al. (2012) “An assessment of the age reporting
in Tanzania population census 2012”. Journal of Social Sciences
Research Vol. 8, No. 2.
[3] Bello Y. (2012). “Error detection in outpatients age data using
demographic techniques”. International journal of Pure and Applied
Science and Technology. 10(1): 27 – 36.
[4] Borkotoky K, Unisa S (2014).” Indicators to examine the quality of
large scale survey data:an example through district level household and
facility survey”. journal.pone.0090113.
[5] Denic, S., Khatib, F. & Saadi, H. (2004). “Quality of age data in patients
from developing countries”. Journal of Public Health, 26(2):168- 171.
[6] Ewbank, D.C. (1981). `Àge misreporting and age-selective
underenumeration: sources, patterns, and consequences for demographic
analysis”. Washington: NationalAcademy Press, Committee on
Population and Demography.
[7] Gonzalez J. F., Attanasio L. & Trang Ha J. (2014). `Àn assessment of
the age reporting in the IPUMS-I Microdata”. Paper submitted for
presentation at the 2014 Annual Meeting of the Population Association
of America.
[8] Hobbs, F. B. (2004). Age and sex composition. In Siegel, J.S., &
Swanson, D.A., (Eds). The methods and materials of demography (2nd
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[9] Kpedekpo G.M.K. (1982). Essentials of Demographic Analysis for
Africa. Hernerman Educational Books Inc., New Hemisphere.
[10] Noumbissi, A. 1992. “Modofied whipples index : an application to the
Cameroon, Sweden amd Belgium data” “ L'indice de Whipple modified
: une application aux donnees du Cameroun, de la Su_ede et de la
Belgique”. Population 47 (4) : 1038-1041.
[11] Pardeshi, G.S. (2010). `Àge heaping and accuracy of age data collected
during a community survey in the Yavatmal district, Maharashtra”.
Indian Journal of Community Medicine, 35(3): 391-395.
[12] Roger, G., Waltisperger D., Corbille-Guitton C. 1981. ``Structure by sex
and age in africa”. African demography group, Paris. “Les structures par
sexe et age en Afrique. Paris : Groupe de Demographie Africaine, IDP-
INED-INSEE MINCOOPORSTOM.
[13] United Nation Age-sex Accuracy test for census age distributions
tabulated in five yearand ten year groups. Population Bulletin of
United Nations. 2:59 - 79.

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