Large Shocks and Small Changes in the Marriage Market for Famine Born Cohorts in China

Source: RePEc


Between 1958 and 1961, China experienced one of its worst famines in history. Birth rates plummeted during these years, but recovered immediately afterwards. The famine-born cohorts were relatively scarce in the marriage and labor markets. The famine also adversely affected the health of these cohorts. This paper decomposes these two effects on the marital outcomes of the famine-born and adjacent cohorts in the rural areas of two hard hit provinces, Sichuan and Anhui. Individuals born pre and post-famine, who were in surplus relative to their customary spouses, were able to marry. Using the Choo Siow model of marriage matching, the paper shows that the famine substantially reduced the marital attractiveness of the famine born cohort. The modest decline in educational attainment of the famine born cohort does not explain the change in spousal quality of that cohort. Thus, the famine-born cohort, who were relatively scarce compared with their customary spouses, did not have significant above average marriage rates.


Available from: Loren Brandt
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    • "Choo and Siow [4] proposed such a marriage matching function using a transferable utility model of the marriage market. This model has been used to study the effects of the legalization of abortion on marital behavior in the United States (Choo-Siow [4]), the decomposition of marital behavior of famine born cohorts in China into quantity versus quality effects (Brandt, Siow and Vogel [2]), changes in marital matching in the United States in recent decades (Chiappori, Selanie and Weiss, [3]), and to test Becker's model of positive assortative matching (Siow [13]). Siow [15] surveys other applications. "
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    ABSTRACT: In a transferable utility context, Choo and Siow (2006) introduced a competitive model of the marriage market, and derived its equilibrium output, a marriage matching function. The marriage matching function denes the gains generated by a marriage between agents of prescribed types in terms of the observed frequency of such marriages within the population, relative to the number of unmarried individuals of the same types. Left open in their work is the question of whether, for a given population whose frequency of types is known, this gains data captures all of the statistical information used to dene it. Equivalently, it is not known whether the Choo-Siow model of the marriage market admits a unique equilibrium. We resolve this question in the armative, assuming the norm of the gains matrix (viewed as an operator) to be less than two. The analytical diculty of showing uniqueness of positive roots of polyno- mial systems has generated a growing literature that provides numerical techniques for tackling such problems. Our method adapts a strategy called the continuity method, more commonly used to solve elliptic par- tial dierential
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    X Meng · N. Qian ·

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    ABSTRACT: The paper surveys the Choo and Siow (2006a; CS) marriage matching model and its extensions. CS derives a behavioural marriage-matching function. The collective model of intra-household allocations can be integrated into this framework. Spousal labour supplies respond to changing marriage market conditions. Marriage market tightness, the ratio of unmarried type i men to unmarried type j women is a sufficient statistic for marriage market conditions for those types of individuals. The hypothesis that spousal labour supplies vary to equilibrate the marriage market has overidentifying restrictions. The framework extends to a dynamic marriage-matching environment. Empirically, this paper shows how the famine caused by the great leap forward in Sichuan affected the marital behaviour of famine-born cohorts. Marriage market tightness is shown to be a useful statistic for summarizing marriage market conditions in the United States. Marriage market conditions in the contemporary United States primarily affect spousal labour force participation rather than hours of work.
    Canadian Journal of Economics/Revue Canadienne d`Economique 02/2008; 41(4):1121-1155. DOI:10.1111/j.1540-5982.2008.00498.x · 0.61 Impact Factor
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