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Levels and trends in sex ratio at birth in provinces of Pakistan from 1980 to 2020 with scenario-based missing female birth projections to 2050: a Bayesian modeling approach

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  • Chao, Fengqing

    (King Abdullah University of Science and Technology)

  • Wazir, Muhammad Asif
  • Ombao, Hernando

Abstract

BACKGROUND: Pakistan has a strong preference for boys over girls; previous evidence on sex preference is primarily reported at the postnatal stage in which the child mortality rate is higher for females than males. Prenatal sex discrimination in Pakistan, reflected in the inflated sex ratio at birth (SRB; ratio of male to female births) has been barely mentioned before this study. OBJECTIVE: We estimate the SRB and missing female births in Pakistan provinces from 1980 to 2020 and identify provinces with imbalanced SRB. We provide scenario-based projections of missing female births in provinces without the existing SRB inflation. METHODS: An extensive SRB database of 832,091 birth records was compiled from all available surveys and censuses. To synthesize different data sources and provide annual estimates and their associated uncertainties of SRBs across provinces, we adopted a Bayesian hierarchical time series model. RESULTS: As per our model, Balochistan has had SRB imbalance since 1980. The maximum SRB was estimated as 1.121 (95% credible interval [1.066; 1.142]) in 1997. Assuming different start year of SRB inflation process in provinces without existing imbalance, the largest female birth deficit is projected to be 76.2 thousand in Punjab in 2033 when the SRB inflation starts in 2021. CONTRIBUTION: This is the first study on estimating the SRB from 1980 to 2020 and providing scenario-based projections of missing female births up to 2050 by Pakistan province. We identified the Balochistan province with imbalanced SRB and demonstrated important disparities in the occurrence and quantity of female birth deficits before 2050.

Suggested Citation

  • Chao, Fengqing & Wazir, Muhammad Asif & Ombao, Hernando, 2021. "Levels and trends in sex ratio at birth in provinces of Pakistan from 1980 to 2020 with scenario-based missing female birth projections to 2050: a Bayesian modeling approach," SocArXiv 5n3y8, Center for Open Science.
  • Handle: RePEc:osf:socarx:5n3y8
    DOI: 10.31219/osf.io/5n3y8
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    References listed on IDEAS

    as
    1. Fengqing Chao & Christophe Z Guilmoto & Samir K. C. & Hernando Ombao, 2020. "Probabilistic projection of the sex ratio at birth and missing female births by State and Union Territory in India," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-17, August.
    2. Géraldine Duthé & France Meslé & Jacques Vallin & Irina Badurashvili & Karine Kuyumjyan, 2012. "High Sex Ratios at Birth in the Caucasus: Modern Technology to Satisfy Old Desires," Population and Development Review, The Population Council, Inc., vol. 38(3), pages 487-501, September.
    3. M. Khan & Ismail Sirageldin, 1977. "Son preference and the demand for additional children in pakistan," Demography, Springer;Population Association of America (PAA), vol. 14(4), pages 481-495, November.
    4. Christophe Z. Guilmoto & Fengqing Chao & Purushottam M. Kulkarni, 2020. "On the estimation of female births missing due to prenatal sex selection," Population Studies, Taylor & Francis Journals, vol. 74(2), pages 283-289, June.
    5. Syed Mubashir Ali & Jafar Hussain, 2001. "Fertility Transition in Pakistan: Evidence from Census," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 40(4), pages 537-550.
    6. Fengqing Chao & Patrick Gerland & Alex R. Cook & Leontine Alkema, 2019. "Systematic assessment of the sex ratio at birth for all countries and estimation of national imbalances and regional reference levels," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 116(19), pages 9303-9311, May.
    7. Chao, Fengqing & Gerland, Patrick & Cook, Alex R. & Guilmoto, Christophe Z. & Alkema, Leontine, 2021. "Projecting sex imbalances at birth at global, regional, and national levels from 2018 to 2100: scenario-based Bayesian probabilistic projections of the sex ratio at birth and missing female births," SocArXiv zps2m, Center for Open Science.
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