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Heavy-tailedness and Threshold Sex Determination

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  • Ibragimov, Rustam
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    Abstract

    This paper studies the properties of the sex ratio in two-period models of threshold (e.g., polygenic or temperature-dependent) sex determination under heavy-tailedness in the framework of possibly skewed stable distributions and their convolutions. We show that if the initial distribution of the sex determining trait in such settings is moderately heavy-tailed and has a finite first moment, then an excess of males (females) in the first period leads to the same pattern in the second period. Thus, the excess of one sex over the other one accumulates over two generations and the sex ratio in the total alive population in the second period cannot stabilize at the balanced sex ratio value of 1/2. These properties are reversed for extremely heavy-tailed initial distributions of sex determining traits with infinite first moments. In such settings, the sex ratio of the offspring oscillates around the balanced sex ratio value and an excess of males (females) in the first period leads to an excess of females (males) in the second period. In addition, the sex ratio in the total living population in the second period can stabilize at 1/2 for some extremely heavy-tailed initial distributions of the sex determining trait. The results in the paper are shown to also hold for bounded sex determining phenotypes.

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    File URL: http://dash.harvard.edu/bitstream/handle/1/2623659/ibragimov_sex.pdf
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    Bibliographic Info

    Paper provided by Harvard University Department of Economics in its series Scholarly Articles with number 2623659.

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    Date of creation: 2008
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    Publication status: Published in Statistics and Probability Letters
    Handle: RePEc:hrv:faseco:2623659

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    1. An, Mark Yuying, 1998. "Logconcavity versus Logconvexity: A Complete Characterization," Journal of Economic Theory, Elsevier, Elsevier, vol. 80(2), pages 350-369, June.
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    Cited by:
    1. Jin, Hao & Tian, Zheng & Qin, Ruibing, 2009. "Bootstrap tests for structural change with infinite variance observations," Statistics & Probability Letters, Elsevier, Elsevier, vol. 79(19), pages 1985-1995, October.

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