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Assortative mating in the online market for sperm donation

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Journal of Bioeconomics

Everybody would prefer a higher-fitness mate rather than a same-fitness mate. But the opposite sex feels the same way too. For a male to mate above his fitness, a female would have to mate below her fitness. Her response to his offer will be “Dream on, loser.” Likewise for females trying to mate above their fitness. Individuals have no realistic hope for mating far above their own fitness level, or any willingness to mate below their fitness. (Miller 2011, p. 197).

Abstract

Previous studies on assortative mating have struggled to isolate preferences from actual constraints faced throughout the matching process, including the geographic and social propinquity that limit the availability of possible mates. Because such passive factors restrict the possibility set of potential partners, they may either restrict the chance of fulfilling mating preferences or lead to a high level of positive assortative mating. The possibility set may be further reduced by competition in the mating market. It is also unclear from couple’s data how much assortative mating is driven by partner selection to reduce anticipated child rearing problem and how much by a desire for parental assistance and altruistic preferences for offspring. Adopting the online market for sperm donation as the research setting reduces such problems: the more controlled setting ensures isolation of a male’s genetic impact on his offspring from other factors. By identifying the factors that influence the symmetry of characteristics between recipients and partners and recipients and donors chosen, we provide empirical evidence that even with limited constraints on available choice, women still exhibit homogamous donor preferences. Likewise, by exploring how potential donors’ characteristics match partner characteristics, we offer insights into what drives recipients’ desires to find donors who surpass both their own and their partners’ characteristics.

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Notes

  1. Economists and Nobel laureates Lloyd Shapley and David Gale began the early microeconomic work on individual matching. Their seminal contribution is the Gale–Shapley Algorithm (Gale and Shapley 1962), which seeks to model matching scenarios and outcomes. Using this algorithm, Gale and Shapley show that for any equal number of men and women, it is possible to solve the stable marriage problem. The equilibrium (for the stable marriage problem) outcome indicates that players have no reason to deviate from the highest ranked partner selection available to them. The stability of the match results from the fact that players are paired with a partner in kind. Although mathematically such pairing is expressed as an assigned numerical value or Roman numeral, in a real world scenario, it can imply symmetry in demographics, aesthetics, cognitive ability and status or attitudes between the paired mates. Sloman and Sloman (1988) put forward a similar evolutionary theory in which individuals are most likely to settle on similarly mates similarly endowed in terms of characteristics or attractiveness. Studies of online dating also show that in practice, the Gale–Shapley algorithm does in fact explain stable matches (Hitsch et al. 2010).

  2. Nevertheless, future studies could explore whether or not female choices are still affected by such factors. Formal (clinical) donation settings can provide a rich source of information on donor’s personality, talents, and hobbies that can be used for testing corresponding conjectures.

  3. Co-Parent.net, Co-ParentMatch.com, PrideAngel.com, and Modamily.com.

  4. My annual wage would be in the range of 1 = below $20,000, 2 = $20,000 – $50,000, 3 = $50,000 – $80,000, 4 = $80,000 – $110,000, 5 = $110,000 – $150,000, 6 = $150,000 – $180,000, 7 = $180,000 – $210,000, 8 = $210,000 – $240,000, 9 = $240,000 – $270,000, 10 = $270,000 – $300,000, and 11= above $300,000.

  5. Question: My highest level of education achieved at this point in time (1 = below Grade 10, 2 = Grade 10, 3 = Grade 11, 4 = Grade 12, 5 = Technical college (prevocational, trade college, apprenticeship), 6 = undergraduate university study (diploma, bachelor’s), 7 = university (graduate diploma, graduate certificate, master’s), 8 = doctorate/PhD.

  6. 9 = over 220cm (taller than 7ft 1in), 8 = 210cm – 220cm (6ft11in–7ft 1in), 7 = 200–210 cm (6ft 7in–6ft 11in), 6 = 190–200 cm (6ft 3in–6ft 7in), 5 = 180–190 cm (5ft 11in–6ft 3in), 4 = 170–180 cm (5ft 7in–5ft 11in), 3 = 160–170 cm (5ft 3in–5ft 7in), 2 = 150–160 cm (4ft 11in–5ft 3in), 1 = under 150cm (4ft 11in).

  7. 1 = under 50kg (110lb), 2 = 50–60 kg (110lb–132lb), 3 = 60–70 kg (132lb–154lb), 4 = 70–80 kg (154lb–176lb), 5 = 80–90 kg (176lb–198lb), 6 = 90–100 kg (198lb–220lb), 7 = 100–110 kg (220lb–242lb), 8 = 110–120 kg (242lb–264lb), 9 = 120–130 kg (264lb–286lb), 10 = 130–140 kg (286lb–308lb), 11 = over 140 kg (308lb).

  8. 1 = Green, 2 = Hazel, 3 = Brown, 4 = Dark brown, 5 = Black, 6 = Blue, 7 = Grey Blue.

  9. 1 = Light Blonde, 2 = Blonde, 3 = Strawberry Blonde, 4 = Blonde Brown, 5 = Brown, 6 = Dark Brown, 7 = Ginger, 8 = Red, 9 = Black.

  10. 1 = Fair/Light, 2 = Olive, 3 = Freckles, 4 = Tan, 5 = Dark.

  11. 1 = Caucasian, 2 = Asian, 3 = African, 4 = African-American, 5 = Middle Eastern (North African, Arab), 6 = Sub-Continental (Indian, Pakistani, Sri Lankan), 7 = Aboriginal and Torres Strait Islander, Polynesian, Maori, Pacific Islander, 8 = Other.

  12. All things considered, how would you describe your health (1 = very unhealthy, 7 = very healthy).

  13. All things considered, how satisfied are you with your life (1 = very unsatisfied, 7 = very satisfied).

  14. For an overview of studies in regards to the different correlations see Table 11 in Appendix.

  15. These results remain robust even after individual income is controlled for.

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Acknowledgments

We acknowledge financial support from the Australian Research Council (FT110100463).

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Authors and Affiliations

  1. School of Economics and Finance, Queensland University of Technology, Gardens Point, 2 George St., Brisbane, QLD, 4001, Australia

    Stephen Whyte & Benno Torgler

  2. CREMA—Center for Research in Economics, Management and the Arts, Basel, Switzerland

    Benno Torgler

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  1. Stephen Whyte
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Correspondence to Stephen Whyte.

Appendix

Appendix

See Table 11.

Table 11 Empirical studies into human assortative mating
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Whyte, S., Torgler, B. Assortative mating in the online market for sperm donation. J Bioecon 18, 169–194 (2016). https://doi.org/10.1007/s10818-016-9232-9

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