AccScience Publishing / IJPS / Volume 8 / Issue 2 / DOI: 10.36922/ijps.v8i2.332
Cite this article
15
Download
44
Citations
241
Views
Journal Browser
Volume | Year
Issue
Search
News and Announcements
View All
RESEARCH ARTICLE

Levels and trends estimate of sex ratio at birth for seven provinces of Pakistan from 1980 to 2020 with scenario-based probabilistic projections of missing female birth to 2050: A Bayesian modeling approach

Fengqing Chao1* Muhammad Asif Wazir2 Hernando Ombao1
Show Less
1 Statistics Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
2 Population and Development Advisor (Freelance), Islamabad (ICT), Pakistan
IJPS 2022 , 8(2), 51–70; https://doi.org/10.36922/ijps.v8i2.332
Submitted: 27 August 2022 | Accepted: 14 November 2022 | Published: 14 December 2022
© 2022 by the Author(s). Licensee AccScience Publishing, Singapore. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract

Most evidence on son preference in Pakistan is reflected in the higher child mortality among females than males. The sex discrimination before birth is rarely reported in Pakistan. This is the first study to quantify prenatal sex discrimination in Pakistan on a subnational level. We provide annual estimates of the sex ratio at birth (SRB) from 1980 to 2020 and scenario-based projections of the number of missing female births up to 2050 by Pakistan province. The results are based on a comprehensive database consisting of 832,091 birth records from all available surveys and censuses. We adopted a Bayesian hierarchical time series model to synthesize different data sources. We identified Balochistan with an existing imbalanced SRB since 1980. For the rest provinces without past or ongoing SRB inflation, we projected the largest female birth deficit to occur in Punjab in 2033 under the scenario that the SRB transition process starts in 2021. We demonstrated important disparities in the occurrence and quantification of missing female births up to 2050.

Keywords
Bayesian hierarchical model
Pakistan
Scenario-based projection
Sex ratio at birth
Son preference
Sex-selective abortion
Subnational modeling
Time series models
Funding
None.
References
[1]

Ali, S.M., Hussain, J., & Chaudhry MA. (2001). Fertility transition in Pakistan: Evidence from census [with Comments]. The Pakistan Development Review, 40(4):537-550.

[2]

Alkema, L., Chao, F., You, D., Pedersen, J., & Sawyer, C.C. (2014). National, regional, and global sex ratios of infant, child, and under-5 mortality and identification of countries with outlying ratios: A systematic assessment. The Lancet Global Health, 2(9):e521-e530. https://doi.org/10.1016/s2214-109x(14)70280-3

[3]

Alkema, L., Chou, D., Hogan, D., Zhang, S., Moller, A.B., Gemmill, A., et al. (2016). Global, regional, and national levels and trends in maternal mortality between 1990 and 2015, with scenario-based projections to 2030: A systematic analysis by the UN Maternal Mortality Estimation Inter- Agency Group. The Lancet, 387(10017):462-474. https://doi.org/10.1016/S0140-6736(15)00838-7

[4]

Alkema, L., Wong, M.B., & Seah, P.R. (2012). Monitoring progress towards Millennium Development Goal 4: A call for improved validation of under-five mortality rate estimates. Statistics, Politics and Policy, 3(2):1-19. https://doi.org/10.1515/2151-7509.1043

[5]

Atif, K., Ullah, M.Z., Afsheen, A., Naqvi, S.A., Raja, Z.A., & Niazi, S.A. (2016). Son preference in Pakistan; A myth or reality. Pakistan Journal of Medical Sciences, 32(4):994-998. https://doi.org/10.12669/pjms.324.9987

[6]

Attané, I., & Guilmoto, C.Z. (2007). Watering the Neighbour’s Garden: The Growing Demographic Female Deficit in Asia. Paris: Committee for International Cooperation in National Research in Demography.

[7]

Bearak, J., Popinchalk, A., Alkema, L., & Sedgh, G. (2018). Global, regional, and subregional trends in unintended pregnancy and its outcomes from 1990 to 2014: Estimates from a Bayesian hierarchical model. The Lancet Global Health, 6(4):e380-e389. https://doi.org/10.1016/S2214-109X(18)30029-9

[8]

Brown, C.E. (1998). Coefficient of variation. In: Applied Multivariate Statistics in Geohydrology and Related Sciences. Berlin, Heidelberg: Springer, p. 155-157.

[9]

Chao F, Guilmoto CZ & Ombao H. (2021c). Sex ratio at birth in Vietnam among six subnational regions during 1980-2050, estimation and probabilistic projection using a Bayesian hierarchical time series model with 2.9 million birth records. PLoS One, 16(7):e0253721. https://doi.org/10.1371/journal.pone.0253721

[10]

Chao, F., & Yadav, AK. (2019). Levels and trends in the sex ratio at birth and missing female births for 29 states and union territories in India 1990-2016: A Bayesian modeling study. Foundations of Data Science, 1(2):177-196. https://doi.org/10.3934/fods.2019008

[11]

Chao, F., Gerland, P., Cook, A.R., & Alkema, L. (2019a). 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, 116(19):9303-9311. https://doi.org/10.1073/pnas.1812593116

[12]

Chao, F., Gerland, P., Cook, A.R., & Alkema, L. (2019b). “Web appendix 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, 116:9303-9311. https://doi.org/10.6084/m9.figshare.12442373

[13]

Chao, F., Gerland, P., Cook, A.R., & Alkema, L. (2021a). Global estimation and scenario-based projections of sex ratio at birth and missing female births using a Bayesian hierarchical time series mixture model. Annals of Applied Statistics, 15(3):1499-1528. https://doi.org/10.1214/20-AOAS1436

[14]

Chao, F., Gerland, P., Cook, A.R., Guilmoto, C.Z., Alkema, L. (2021b). Projecting sex imbalances at birth at global, regional and national levels from 2021 to 2100: Scenario-based Bayesian probabilistic projections of the sex ratio at birth and missing female births based on 3.26 billion birth records. BMJ Global Health, 6(8):e005516. https://doi.org/10.1136/bmjgh-2021-005516

[15]

Chao, F., Guilmoto, C.Z., Samir, K.C., & Hernando, O. (2020). Probabilistic projection of the sex ratio at birth and missing female births by State and Union Territory in India. PLoS One, 15(8):e0236673. https://doi.org/10.1371/journal.pone.0236673

[16]

Chao, F., Samir, K.C., & Ombao, H. (2022). Estimation and probabilistic projection of levels and trends in the sex ratio at birth in seven provinces of Nepal from 1980 to 2050: A Bayesian modeling approach. BMC Public Health, 22(1): 1-5. https://doi.org/10.1186/s12889-022-12693-0

[17]

Chao, F., You, D., Pedersen, J., Hug, L., & Alkema, L. (2018a). National and regional under-5 mortality rate by economic status for low-income and middle-income countries: A systematic assessment. The Lancet Global Health, 6(5):e535-e547. https://doi.org/10.1016/S2214-109X(18)30059-7

[18]

Chao, F., You, D., Pedersen, J., Hug, L., & Alkema, L. (2018b). National and regional under-5 mortality rate by economic status for low-income and middle-income countries: A systematic assessment. The Lancet Global Health, 6:e535-e547. https://doi.org/10.6084/m9.figshare.12442244

[19]

De Tray, D. (1984). Son preference in Pakistan: An analysis of intentions vs. behavior. Research in Population Economics, 5:185-200.

[20]

Dréze, J., & Sen, A. (1990). Hunger and Public Action. Oxford: Oxford University Press.

[21]

Duthé, G., Meslé, F., Vallin, J., Badurashvili, I., & Kuyumjyan, K. (2012). High sex ratios at birth in the Caucasus: Modern technology to satisfy old desires. Population and Development Review, 38(3):487-501. https://doi.org/10.1111/j.1728-4457.2012.00513.x

[22]

Efron, B., & Gong, G. (1983). A leisurely look at the bootstrap, the jackknife, and cross-validation. The American Statistician, 37(1):36-48. https://doi.org/10.2307/2685844

[23]

Efron, B., & Tibshirani, RJ. (1944). An Introduction to the Bootstrap. Abingdon: Chapman and Hall/CRC.

[24]

Feeney, G., & Alam, I. (1998). Fertility, population growth, and accuracy of census enumeration in Pakistan: 1961-1998. In: A Kemal, M Irfan and N. Mahmood (eds.). Population of Pakistan: An Analysis of 1998 Population and Housing Census. Islamabad: Pakistan Institute of Development Economics (PIDE) and UNFPA.

[25]

Ge, T., Mei, L., Tai, X., & Jiang, Q. (2020). Change in China’s SRB: A dynamic spatial panel approach. International Journal of Environmental Research and Public Health, 17(21):8018. https://doi.org/10.3390/ijerph17218018

[26]

Gelman, A., & Rubin, D.B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7(4):457-472. https://doi.org/10.1214/ss/1177011136

[27]

Gerland, P., Raftery, A.E., Ševčíková, H., Li, N., Gu, D., Spoorenberg, T., et al. (2014). World population stabilization unlikely this century. Science, 346(6206):234-237. https://doi.org/10.1126/science.1257469

[28]

Goodkind, D. (2011). Child underreporting, fertility, and sex ratio imbalance in China. Demography, 48(1):291-316. https://doi.org/10.1007/s13524-010-0007-y

[29]

Guilmoto, C. (2012). Sex Imbalances at Birth: Current Trends, Consequences and Policy Implications. Bangkok, Thailand: UNFPA Asia and Pacific Regional Office. Available from:https://www.unfpa.org/publications/sex-imbalances-birth [Last accessed on 2022 Nov 07].

[30]

Guilmoto, C.Z. (2009). The sex ratio transition in Asia. Population and Development Review, 35(3):519-549. https://doi.org/10.1111/j.1728-4457.2009.00295.x

[31]

Guilmoto, C.Z., & Ren, Q. (2011). Socio-economic differentials in birth masculinity in China. Development and Change, 42(5):1269-1296. https://doi.org/10.1111/j.1467-7660.2011.01733.x

[32]

Guilmoto, C.Z., Chao, F., & Kulkarni, P.M. (2020). On the estimation of female births missing due to prenatal sex selection. Population Studies, 74(2):283-289. https://doi.org/10.1080/00324728.2020.1762912

[33]

Guilmoto, C.Z., Hoang, X., & Van, T.N. (2009). Recent increase in sex ratio at birth in Viet Nam. PLoS One, 4(2):e4624. https://doi.org/10.1371/journal.pone.0004624

[34]

Gupta, M.D., Zhenghua, J., Bohua, L., Zhenming, X., Chung, W., & Hwa-Ok, B. (2003). Why is son preference so persistent in East and South Asia? A cross-country study of China, India and the Republic of Korea. The Journal of Development Studies, 40(2):153-187. https://doi.org/10.1080/00220380412331293807

[35]

Hussain, R., Fikree, F.F., & Berendes, H. (2000). The role of son preference in reproductive behaviour in Pakistan. Bulletin of the World Health Organization, 78:379-388.

[36]

ICF International. (2012). Demographic and Health Survey Sampling and Household Listing Manual. Calverton, Maryland, U.S.A.: Measure DHS, p. 78-79. Available from: https://dhsprogram.com/pubs/pdf/DHSM4/DHS6_Sampling_Manual_Sept2012_DHSM4.pdf [Last accessed on 2022 Nov 07].

[37]

ICF International. (2022). The DHS Program. Available from: https://dhsprogram.com [Last accessed on 2022 Nov 07].

[38]

Jiang, Q., & Zhan, C. (2021). Recent sex ratio at birth in China. BMJ Global Health, 6(5):e005438. https://doi.org/10.1136/bmjgh-2021-005438

[39]

Khan, M.A., & Sirageldin, I. (1977). Son preference and the demand for additional children in Pakistan. Demography, 14(4):481-495. https://doi.org/10.2307/2060591

[40]

Lin, T. (2009). The decline of son preference and rise of gender indifference in Taiwan since 1990. Demographic Research, 20:377. https://doi.org/10.4054/DemRes.2009.20.16

[41]

Liu, P., & Raftery, A.E. (2020). Accounting for uncertainty about past values in probabilistic projections of the total fertility rate for most countries. The Annals of Applied Statistics, 14(2):685. https://doi.org/10.1214/19-aoas1294

[42]

Masquelier, B., Hug, L., Sharrow, D., You, D., Mathers, C., Gerland, P., et al. (2018). Global, regional, and national mortality trends in older children and young adolescents (5-14 years) from 1990 to 2016: An analysis of empirical data. The Lancet Global Health, 6(10):e1087-e1099. https://doi.org/10.1016/S2214-109X(18)30353-X

[43]

Minnesota Population Center. (2019). Integrated Public Use Microdata Series, International: Version 7.2 [dataset]. Minneapolis, MN: IPUMS. https://doi.org/10.18128/D020.V7.2

[44]

National Institute of Population Studies (NIPS) [Pakistan], & ICF. (2019). Pakistan Demographic and Health Survey 2017-18. Islamabad, Pakistan, and Rockville, Maryland, USA: NIPS, ICF. Available from: https://dhsprogram.com/pubs/pdf/FR354/FR354.pdf [Last accessed on 2022 Nov 07].

[45]

Pakistan Bureau of Statistics (PBS). (2019). Pakistan Social and Living Status Measurement Survey: 2013-14 and 2018- 19. Statistics Division, Planning Commission, Islamabad. Government of Pakistan: Pakistan Bureau of Statistics (PBS). Available from: https://www.pbs.gov.pk/content/pakistan-social-and-living-standards-measurement [Last accessed on 2022 Nov 07].

[46]

Park, C.B., & Cho, N.H. (1995). Consequences of son preference in a low-fertility society: Imbalance of the sex ratio at birth in Korea. Population and Development Review, 21(1):59-84. https://doi.org/10.2307/2137413

[47]

Pedersen, J., & Liu, J. (2012). Child mortality estimation: Appropriate time periods for child mortality estimates from full birth histories. PLoS Medicine, 9(8):e1001289. https://doi.org/10.1371/journal.pmed.1001289

[48]

Plummer, M. (2003). JAGS: A Program for Analysis of Bayesian Graphical Models Using Gibbs Sampling. Vol. 124. Vienna, Austria: Proceedings of the 3rd International Workshop on Distributed Statistical Computing. pp. 1-10.

[49]

Plummer, M. (2018). RJAGS: Bayesian Graphical Models using MCMC. R Package Version 4-8. Available from: https://CRAN.R-project.org/package=rjags [Last accessed on 2022 Nov 07].

[50]

Plummer, M., Best, N., Cowles, K., & Karen, V. (2006). CODA: Convergence diagnosis and output analysis for MCMC. R News, 6(1):7-11.

[51]

Qayyum, K., & Rehan, N. (2017). Sex-selective abortion in rural Pakistan. Journal of Advances in Medicine and Medical Research, 22(12):1-7.

[52]

R Core Team. (2022). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Core Team. Available from: https://www.R-project.org [Last accessed on 2022 Nov 07].

[53]

Sathar, Z., Rashida, G., Hussain, S., & Hassan, A. (2015). Evidence of son preference and resulting demographic and health outcomes in Pakistan. Islambad: Population Council. Available from: https://knowledgecommons.popcouncil.org/departments_sbsr-pgy/665 [Last accessed on 2022 Nov 07].

[54]

Sathar, Z., Singh, S., Rashida, G., Shah, Z., Niazi, R. (2014). Induced abortions and unintended pregnancies in Pakistan. Studies in Family Planning, 45(4): 471-491. https://doi.org/10.1111/j.1728-4465.2014.00004.x

[55]

Sen, A. (1990). More than 100 Million Women are Missing. Vol. 37. New York City: New York Review of Books. pp. 61-66. Available from: https://www.nybooks.com/articles/1990/12/20/more-than-100-million-women-are-missing [Last accessed on 2022 Nov 07].

[56]

Su, Y.S., & Yajima, M. (2015). R2jags: Using R to Run ’JAGS’. R Package Version 0.5-7. Available from: https://CRAN.R-project.org/package=R2jags [Last accessed on 2022 Nov 07].

[57]

United Nations Development Programme. (2022). Gender Inequality Index (GII). Available from: https://hdr.undp.org/data-center/thematic-composite-indices/gender-inequality-index/indicies/GII [Last accessed on 2022 Nov 07].

[58]

Verma, V., & Le, T. (1996). An analysis of sampling errors for the demographic and health surveys. International Statistical Review, 64(3):265-294. https://doi.org/10.2307/1403786

[59]

Wang, H., Abbas, K.M., Abbasifard, M., Abbasi-Kangevari, M., Abbastabar, H., Abd-Allah, F., et al. (2020). Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019: A comprehensive demographic analysis for the Global Burden of Disease Study 2019. The Lancet, 396(10258):1160-1203. https://doi.org/10.1016/S0140-6736(20)30977-6

[60]

Wazir, M.A. (2018). Fertility preferences in Pakistan. In: S Gietel-Basten, J Casterline, and M Choe, (eds.). Family Demography in Asia: A Comparative Analysis of Fertility Preferences. Cheltenham: Edward Elgar, pp. 247-259.

[61]

Wazir, M.A., & Goujon, A. (2019). Assessing the 2017 Census of Pakistan Using Demographic Analysis: A Sub-National Perspective. Austria: Vienna Institute of Demography Working Papers. Available from: https://www.oeaw.ac.at/fileadmin/subsites/Institute/VID/PDF/Publications/Working_Papers/WP2019_06.pdf [Last accessed on 2022 Nov 07].

[62]

Wazir, M.A., & Shaheen, K. (2016). Understanding and Measuring Pre-and Post-Abortion Stigma about Women Who Have Abortions: Results from Explorative Study. In: 2016 Annual Meeting. Washington, DC: Population Association of America (PAA). Available from: https://paa.confex.com/paa/2016/mediafile/ExtendedAbstract/Paper3246/Extended%20abstract%20PAA%202016.pdf [Last accessed on 2022 Nov 07].

[63]

You, D., Hug, L., Ejdemyr, S., Idele, P., Hogan, D., Mathers, C., et al. (2015). Global, regional, and national levels and trends in under-5 mortality between 1990 and 2015, with scenario-based projections to 2030: A systematic analysis by the UN Inter-agency Group for Child Mortality Estimation. The Lancet, 386(10010):2275-2286. https://doi.org/10.1016/S0140-6736(15)00120-8

[64]

Zaidi, B., & Morgan, S.P. (2016). In the pursuit of sons: Additional births or sex-selective abortion in Pakistan? Population and Development Review, 42(4):693-710. https://doi.org/10.1111/padr.12002

Conflict of interest
No conflicts of interest were reported by all authors.
Share
Back to top
International Journal of Population Studies, Electronic ISSN: 2424-8606 Print ISSN: 2424-8150, Published by AccScience Publishing