Levels and trends in the sex ratio at birth in seven provinces of Nepal between 1980 and 2016 with probabilistic projections to 2050: a Bayesian modeling approach

Image credit: © 2020 Chao et al.


We aim to estimate sex ratio at birth (SRB) for the seven provinces of Nepal from 1980 to 2016, and to compute probabilistic projections for provincial SRB through 2050. We use a Bayesian hierarchical time series model based on the 2001, 2006, 2011, and 2016 Nepal Demographic and Health Surveys (NDHSs) and 2011 Census. Based on data from four NDHSs and one census with 151,663 births, we estimate the SRB trajectories to be more diverse post-2000 than before. In 2016, the highest SRB is estimated in Province 5 at 1.102 with a 95% Bayesian credible interval (1.044, 1.127) and the lowest SRB is in Province 2 at 1.053 (1.035, 1.109). During 1980–2016, the provincial SRB was around the same level as the national SRB baseline of 1.049. The SRB imbalance probabilities in all provinces are generally low and vary from 16% in Province 2 to 81% in Province 5. SRB imbalances are estimated to have begun at the earliest in 2001 in Province 5 with a 95% credible interval (1992, 2022) and the latest in 2017 (1998, 2040) in Province 2. We project SRB in all provinces to begin converging back to the SRB national baseline in the mid-2030s. By 2050, the SRBs in all provinces are projected to be around the SRB baseline level. Our findings imply that the majority of provinces in Nepal have a low risk of SRB imbalance for the period 1980–2016. However, we identify a few provinces with higher probabilities of having SRB inflation. Although the projected SRB is based on the assumption of potential future SRB inflation, it is an important illustration of potential future prenatal sex discrimination and shows the need to monitor SRB in provinces with higher possibilities of SRB imbalance.

arXiv preprint arXiv:2007.00437

This preprint is under review. This is the 2nd version. The 1st version can be found on arXiv.

Fengqing Chao
Postdoctoral Fellow

My research interests include statistical demography, global health, Bayesian modeling, and time series analysis.