Page 143 - IJPS-11-5

International Journal of
Population Studies Fertility desire of married women

(2022 DHS), and Zambia (2018 DHS). The DHS program, 2.2.2. Independent variables
which provides nationally representative household data, Explanatory factors were chosen based on the body of
is carried out by national statistics agencies in many existing literature (Adilo & Wordofa, 2017; Ahinkorah et al.,
developing countries, with support from international 2020; Casterline & Agyei-Mensah, 2017; Phiri et al., 2023a;
partners such as Inner City Fund International and the 2023b; Shiferaw et al., 2019). The following variables were
United States Agency for International Development. The included: a woman’s age, place of residence, educational
DHSs employed a stratified two-stage sampling approach. attainment, partner’s educational attainment, parity, living
In the first stage, enumeration areas were selected with
a probability based on the size of each stratum. In the children, household’s wealth status, employment status,
second stage, households are systematically selected with decision-making about health, informed about family
planning at a health facility, and exposure to media family
equal probability in each enumeration area. All women planning information. These variables were divided into
aged 15 – 49 years and men aged 15 – 59 years who had two groups: Variables at the individual and contextual
stayed overnight in the selected household preceding the
interview date were eligible for inclusion in the survey. levels. The variables were categorized as age (15 – 24 years,
The DHS utilizes standardized questionnaires to gather 25 – 34 years, and 35 – 49 years), education attainment
data, including the Household Questionnaire, Woman’s (no education, primary level, secondary level, and tertiary
Questionnaire, Man’s Questionnaire, and Biomarker level), partners’ education (no education, primary level,
Questionnaire (Croft et al., 2018). Each country’s survey secondary level, and tertiary level), employment status
report provides a detailed explanation of the methodologies (working or not working), parity (1 – 2, 3 – 4, and 5+), and
used in these surveys (Croft et al., 2018). Our study other individual variables (yes or no), such as was informed
used the module on Contraceptive Use and Fertility about family planning at health facilities, visited a health
Preferences, which is included in the individual women’s facility in last the 12 months, and exposure to mass-media
recode file. The DHS datasets are publicly available from family planning information. Contextual level variables
the DHS website (https://dhsprogram.com/; Croft et al., included residence (urban or rural) and household wealth
2018). The samples of women interviewed in each country status (poor, middle, or rich).
were as follows: Gabon (n = 11,043), Mali (n = 10,519), The initial analysis included the following variables:
Tanzania (n = 13,266), and Zambia (n = 13,683). These Contraceptive use, ideal number of children, age at first
samples translated into overall response rates of 96%, 95%, sexual activity, and age at first marriage. However, after
97%, and 97%, respectively. However, in this analysis, we a thorough review of the literature, these variables were
restricted the samples to married women who had the removed from the analysis due to ambiguity in their
desire to limit childbearing. This resulted in the following relationship significance to explain the desire to limit
study sample (n = 3,664) for Gabon, (n = 6,782) for Mali, childbearing among women. Contraceptive use may
(n = 6,946) for Tanzania, and (n = 6,674) for Zambia. not directly indicate a desire to limit childbearing, as it
could also reflect spacing intentions. The ideal number
2.2. Measurement of study variables
of children is often influenced by cultural and social
2.2.1. Outcome variable desirability biases, making it unreliable as a predictor of
The outcome variable for this study is the desire to limiting behavior. Furthermore, age at first sexual activity
limit childbearing. For this analysis, the desire to limit and age at first marriage may have no direct influence on
childbearing refers to an individual woman’s preference the desire to limit childbearing, as reproductive intentions
to control the number of children she wants to have. In are shaped more by factors such as socioeconomic status,
the DHS, all women who had given birth were asked a health concerns, and access to family planning rather than
question: “Would you like to have another child, or would the timing of these life events. Their exclusion ensured a
you prefer not to have any more children?” To facilitate more consistent analysis of the true drivers of the desire to
our analysis, we restricted the analysis to married women limit childbearing among women.
who indicated that they desired to have another child or 2.3. Statistical analysis
wanted no more children. Women whose responses were
coded as undecided were removed from the analysis “Statistical analysis was performed using Stata version 17
sample. A binary variable was created to facilitate binary software with a 95% confidence interval (CI). The
logistic regression. The outcome variable was coded “1,” analysis considered survey design, cluster effect, and
representing women who indicated that they wanted no post-stratification weights in the DHS datasets. Statistical
more children, and “0,” representing women who wanted analysis was conducted in three stages. In the first
to have another child in the future. stage, a univariate analysis was conducted to describe

Volume 11 Issue 5 (2025) 137 https://doi.org/10.36922/ijps.5584