Sat. Nov 23rd, 2024

. 2023 Apr 4;18(4):e0284032.


doi: 10.1371/journal.pone.0284032.


eCollection 2023.

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Mashudu Lucas Bidzha et al.


PLoS One.


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Abstract

This paper investigates the effect of under-five mortality, child support grant (CSG) coverage and the rollout of antiretroviral therapy (ART) on fertility in South Africa. The study employs the quality-quantity trade-off framework to analyse the direct and indirect factors affecting fertility using the two stage least squares fixed effects instrumental variable approach. The analysis uses balanced panel data covering nine provinces from 2001-2016. This period was characterised by significant increases in the child support grant coverage and ART coverage. Furthermore, this period was characterised by a significant decline in the under-five mortality rate. We find no evidence to support the hypothesis that increases in the CSG coverage are associated with an increase in fertility. This finding aligns with previous literature suggesting that there are no perverse incentives for childbearing associated with the child support grant. On the other hand, results indicate that an increase in ART coverage is associated with an increase in fertility. Results also show that a decrease in under-five mortality is associated with a decline in fertility over the sample period. HIV prevalence, education, real GDP per capita, marriage prevalence and contraceptive prevalence are also important determinants of fertility in South Africa. Although the scale up of ART has improved health outcomes, it also appears to have increased fertility in HIV-positive women. The ART programme should therefore be linked with further family planning initiatives to minimise unintended pregnancies.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures


Fig 1



Fig 1. Provincial under-five mortality rate over time, 2001–2016.

Source: Authors’ calculation using data from the Thembisa model by Johnson and Dorrington [26].


Fig 2



Fig 2. Provincial TFR over time, 2001–2016.

Source: Authors’ calculation using data from the Thembisa model by Johnson and Dorrington [26].


Fig 3



Fig 3. Provincial ART coverage over time, 2001–2016.

Source: Authors’ calculation using data from the Thembisa model by Johnson and Dorrington [26].


Fig 4



Fig 4. Provincial CSG coverage in South Africa 2001–2016.

Source: Authors’ calculation using beneficiary data from SASSA [27] and population data from Stats SA [33].


Fig 5



Fig 5. Comparison of TFR and under-five mortality rate estimates for South Africa.

Source: Authors’ calculation using data from Johnson and Dorrington [26], Stats SA [61] and World Bank [62].


Fig 6



Fig 6. Scatter plots of total fertility rate and independent variables.

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Grant support

MLB’s work was funded by the United Nations University World Institute for Development Economics Research (UNU-WIDER) through its Southern Africa – Towards Inclusive Economic Development (SA-TIED) PhD Research Programme. Grant No (605UU-000003011). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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