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    On the estimation of female births missing due to prenatal sex selection.

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    RPST-2020-2023.R1_Proof_hi.pdf
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    Description:
    Accepted Manuscript
    Embargo End Date:
    2021-12-04
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    Type
    Article
    Authors
    Guilmoto, Christophe Z
    Chao, Fengqing cc
    Kulkarni, Purushottam M
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-06-03
    Online Publication Date
    2020-06-03
    Print Publication Date
    2020-05-03
    Embargo End Date
    2021-12-04
    Submitted Date
    2020-01-17
    Permanent link to this record
    http://hdl.handle.net/10754/663440
    
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    Abstract
    This research note is prompted by a paper by Kashyap (Is prenatal sex selection associated with lower female child mortality? Population Studies 73(1): 57-78). Kashyap's paper, which provides 40 original estimates of missing female births, relies on an alternative definition of missing female births, leading to estimates of about half the magnitude of other estimates. There appears, therefore, a real need to take stock of the concept of missing female births widely used by statisticians around the world for assessing the demographic consequences of prenatal sex selection. This research note starts with a brief review of the history of the concept and the difference between Amartya Sen's original method and the alternative method found elsewhere to compute missing female births. We then put forward three different arguments (deterministic and probabilistic approaches, and consistency analysis) in support of the original computation procedure based on the number of observed male births and the expected sex ratio at birth.
    Citation
    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. doi:10.1080/00324728.2020.1762912
    Publisher
    Informa UK Limited
    Journal
    Population studies
    DOI
    10.1080/00324728.2020.1762912
    PubMed ID
    32489140
    Additional Links
    https://www.tandfonline.com/doi/full/10.1080/00324728.2020.1762912
    ae974a485f413a2113503eed53cd6c53
    10.1080/00324728.2020.1762912
    Scopus Count
    Collections
    Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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