Page 68 - IJPS-7-2

International Journal of
Population Studies Age-adjusted measures for fertility transition

Table 1. Myer’s Indices of age misreporting in the DHS data for women from Ghana, Kenya, Rwanda, and Zimbabwe
DHS1988 DHS1993 GHS1998 GDHS2003 GDHS2008 DHS2014
Ghana
n 3,156 3,204 3,229 3,694 2,950 5,456
MI 15.6 12.3 12.6 10.7 12.8 9.0
DHS1989 DHS1993 DHS1998 KDHS2003 KDHS2008 DHS2014
Kenya
n 4,778 4,583 4,847 4,876 5,041 19,036
MI 10.6 8.3 9.6 7.0 9.9 9.6
DHS1992 DHS2000 RDHS2005 RDHS2010 DHS2014
Rwanda
n 3,698 4,891 5,458 6,834 6,890
MI 6.8 6.8 6.8 6.8 6.8
DHS1988 DHS1994 DHS1999 DHS2005/06 DHS2010/11 DHS2015
Zimbabwe
n 2,973 3,469 4,203 6,154 6,543 6,015
MI 8.5 8.6 10.8 6.2 8.8 6.1

& Pazvakawambwa, 2012; Locoh, 2002; Potts & Marks, and has a predictable constant change over time.
2001; Upadhyay & Karasek, 2012). The DHS data are Socioeconomic variables such as household wealth status,
publicly available on Measure DHS portal. rural-urban residence, and contraceptive which are widely
used in fertility analysis do not have a constant rate of
2.1.1. Ethics requirements change over time and are, therefore, not reliable for basing
This study did not require ethics clearance, because it fertility rates on. However, they are important factors for
was based on secondary data. The DHS data are collected understating fertility transitions. We used education as one
with ethics clearance from each host country’s relevant of the independent variables, because it has been widely
institutional review boards (IRBs). The data are publicly shown to play a significant role in the onset and progress of
available on Measure DHS website https://dhsprogram. fertility transition in sub-Saharan African countries.
com/data/available-datasets.cfm. To access the data, 2.3. Data quality analysis
researchers must register as a DHS data user. The access
to the datasets is granted to legitimate research purposes. The first consideration when conducting fertility analysis
using DHS data is the quality of the data. The early surveys
2.2. Variables especially from the DHS Phases I and II from some SSA
The dependent variables for this study were ASMFRs and countries have been noted to have problems of data quality
CEB. These two variables were used, because they represent due to misreporting of dates and ages which adversely
age-adjusted and cumulative and non-adjusted measures affect the accuracy of fertility rates for age. The adverse
of fertility, respectively. The ASMFRs constitute the impact of poor quality in DHS data is that if subsequent
constituents of the TMFR. Because the TMFR is derived surveys have improved quality, demographic trends may
from ASMFRs, it is defined as the total number of live be erroneously shown to have occurred when in fact it was
births that a woman is expected to have by the end of her only improvements in the data. The previous assessments
reproductive career if she remains married and experiences of quality issues in DHS data have indeed highlighted the
the given ASMFRs. The CEB measures that the cumulative problem of age heaping whereby respondents showed bias
toward stating ages ending in digits zero and five (Pullum,
total number of children a woman has given birth to in her 2006). In analyzing fertility rates, age misreporting can
lifetime, thus reflects actual achieved fertility.
have an adverse effect on the resulting age distribution of
The independent variables were age group and fertility rates and can distort the results on the differences/
education. Age is the main demographic characteristic similarities between two time points of the same country.
used as the basis for calculating fertility indicators, because Given that this study was designed to determine the
it does not change its form from population to population accuracy of two types of measures of fertility levels which

Volume 7 Issue 2 (2021) 62 https://doi.org/10.36922/ijps.v7i2.354