Stephen Whyte Thesis

DECISION MAKING IN MATE CHOICE MARKETS

Stephen Whyte

BBus(Econ) with Distinction, MRes(Econ)

Principal Supervisor: Prof. Benno Torgler

Associate Supervisor: Prof. Uwe Dulleck

Associate Supervisor: Dr David A. Savage

Submitted in fulfilment of the requirements for

Doctor of Philosophy (Economics)

School of Economics & Finance

QUT Business School

Queensland University of Technology

2018

Abstract

This thesis contributes to the behavioural economic and behavioural science literature by providing empirical evidence of factors that impact large scale decision making in mate choice settings. The thesis consists of five studies. The first three explore human decision making in the online dating market. They utilise both stated preference and actual decision outcomes to explore differences between preference and choice, positive assortment, and specificity of preference for different sexes. Studies four and five concern male and female behaviour in the clinical and informal (online) sperm donation markets. These studies explore factors that impact female’s decision to choose specific donors, and the characteristics, personality and behaviour of males who choose to donate.

Keywords

Decision making; Mate choice; Sex differences; Online dating; Online sperm donor; Nonbinary gender; Parental investment theory; Positive assortment; Homogamy; Preference; Choice.

Acknowledgments

Thank you to Benno Torgler and Uwe Dulleck for your incredible support and encouragement across my research journey. Your generosity, knowledge, wisdom, empathy, leadership, humour and understanding has inspired me every day. No words can express how much I appreciate all that you have done for myself and my family, and how dearly I value our friendship.

To all the QUT Econ team, past and present, thank you for listening, helping, crying with, laughing at and with, drinking with, bitching and whinging, and most of all supporting me in any and every way that you did. I hope you enjoyed it, because I loved every second and I had an absolute blast working with you all. Thank you to Ben, Naomi, David, Marco, Sam, Juliana, Suzanne, AKK, Dr Mueller, Jieyang, Harry, Azhar, Jonas, Louisa, Tommy, Tony, Lionel, Megan, Jason, Gaurav, Chris, Poli, Lisa, Alice, Zili, Vlad, and anyone else I’ve missed.

To Pascalis, Michelle, Kristin, Katalina, my best buds in Finance Mary and Alice, Gareth, Lee, Diane, Brian, Takae, Maria, and any and all other QUT admin I’ve missed, thank you for all your support and help.

To all of the amazingly talented scientists who have been gracious enough to give their time in supporting me and my research along the way. Thank you to Douglas T. Kenrick, Steven Neuberg, Dan Conroy-Beam, Todd K. Shackelford, Jaimie Krems, Jordan Moon, Bill von Hippel, Karin Hammarberg, Khandis Blake, Dax Kellie, Mike Kasumovic, Amany Gouda-Vossos, Ahmed Skali, Gigi Foster, Barnaby J. Dixon, David Stadelman, and Robert C. Brooks.

Thank you to Marilyn Healy and all the Kelvin Grove QUT Human Research Ethics Team. Thank you for being the Batman to my Joker, or the Joker to my Batman, I often got confused who was playing which role in the soap opera that was my research journey. Your feedback on my work taught me so much about myself, thank you.

I would like to recognize and thank Lifeline for its valued role in Australian human behavioural research support, as well as acknowledge funding support from an Australian Government Research Training Program Scholarship. Thank you to all the external stakeholders who took a chance on me and helped me do something great. Erika Tranfield at PrideAngel, James Templeman at GIGA, Dave Heyson at RSVP and Keith Harrison at QFG. Without your faith in me and your passion for science I wouldn’t have had any data. Thank you for the trust and belief in my ability that you showed. Thank you to the more than 50,000 people globally that willingly provided data for the studies in this thesis. Thank you to Ash, Ethan, Will and Poppy for all your support and encouragement. I’m sorry that my PhD took so much time away from you all. I hope all my hard work has inspired you to follow your dream, no matter what that may be. I will always be your biggest supporter. I hope you believe in yourself as much as I believe in you, the future will always be bright for you.

Thank you to my mum Carmel for sticking by me through thick and thin, you never stopped believing in me.

Thank you to Diz, Avi, Leela, for all your love and support. Thank you to Ciara and Emily for being a special part of our family at a time when we needed it most.

And finally thank you to Priscilla, who is the reason I started this journey a decade ago. Thank you for helping me to realise my dream, I will always be thankful for all of your support across the last nine years, I couldn’t have done it without you.

Table of Contents

List of Figures 10 List of Tables 12 List of Publications 15 Statement of Authorship 16 Chapter 1: General Introduction 17 Outline of Thesis 20 Chapter 2: Things change with age: Educational assortment in online dating 22 Introduction 23 Background 24 Methods 27 Participant Pool 27 Data Collection 29 Experimental Model 29 Results 31 Discussion 38 Conclusion 40 Chapter 3: Preference vs. Choice in online dating 42 Introduction 43 Method 45 Participants 45 Procedures 46 Results 47 Discussion 52 Conclusion 54 Chapter 4: Do women know what they want? Sex differences in online daters 57 educational preferences Introduction 58 Method 59 Participants 59 Data collection and ethical practice 60 Measures 61 Preference specificity 61 Preference for homogamy or hypergamy 62 Minimum acceptable level of education 63 Empirical analysis 63 Results 64 Discussion 71

Chapter 5: What women want in their sperm donor: A study of more than 73 1000 women’s sperm donor selections Introduction 74 Method 77 Data 77 Results 82 Discussion 88 Limitations 90 Conclusion 93 Chapter 6: Online sperm donors: The impact of family, friends, personality 95 and risk perception on behaviour Introduction 96 Method 100 Data collection 100 Multivariate analysis 108 Results 109 Discussion 113 Chapter 7: Summary and Conclusion 119 Summary of findings 119 Limitations 121 Chapter 2, 3 & 4: Online dating 121 Chapter 5 & 6: Sperm donation in clinical and informal markets 121 Directions for future research 122 Bibliography 125 Appendices: Statement of author contribution 134

List of Figures

Figure 1. Age distribution 28

Figure 2. Educational hypergamy to hypogamy 30

Figure 3. Predicted margins for assortative mating, with a focus on age slopes as a 35 function of education (linear prediction). Figure 4 Predicted margins for educational hypergamy, with a focus on age slopes by 37 gender (linear prediction). Figure 5. Predicted margins for educational hypogamy, with a focus on age slopes by 37 gender (linear prediction). Figure 6. Percentage of participants contacted matching up to 7 characterist ics 47

Figure 7. Average marginal effect: Age and Gender 51

Figure 8. Average Marginal effect: Age and Education 52

Figure 9. Age distribution differentiated by sex 60

Figure 10. Percentage of sample stating an explicit educational preference 65

Figure 11. Marginal effects of sex and member’s age on the probability of stating an 68 explicit educational preference with effects of control variables reported in Table 1 taken out Figure 12. Specificity of educational preference: Pickiness 68

Figure 13. Preference for educational homogamy or hypergamy in ideal partner by 69

Figure 14. Lowest minimum acceptable educational preference in ideal mate 70

Figure 15. Frequency of sperm sample reservation (90.82% of sample) 80

Figure 16. Frequency of sperm sample reservation (49.48% of sample) 80

Figure 17. Average days elapsed between reservations (N = 1546) 81

Figure 18. Average days elapsed between reservations (49.48% of sample) 82

Figure 19. Average marginal effects: age and reservation order 87

Figure 20. Distribution of donor age 102

Figure 21. Distribution of donor risk perception towards informal donation 102

Figure 22a. Predicted margins for total number of women donated to 112

Figure 22b. Predicted margins for number of years informally donating 112

List of Tables

Table 1. Descriptive statistics on participant and contact’s educational background 29

Table 2. Positive educational assortment (based on OLS & probit regressions) 33

Table 3. Educational hypogamy and hypergamy 34

Table 4. OLS analyzing matches of stated preference and actual choice 49

Table 5. Negative binomial model analyzing matches of stated preference and actual choice 50

Table 6. Regression Coefficients from Multivariate Models of Participant Educational 66 Preference Table 7. Factors influencing number of days until reservation 84

Table 8. Interaction effects: Donor Age and reservation order 86

Table 9. Average Marginal Effects: age and reservation order 88

Table 10. Donor descriptive statistics 101

Table 11. Factors impacting number of average monthly informal donations (Nbreg) 103

Table 12. Factors impacting number of years donating informally (Nbreg) 104

Table 13. Factors impacting total number of women informally donated to in lifetime (Nbreg) 105

Table 14. Factors impacting number of offspring from donation (Nbreg) 106

Table 15. Factors impacting risk attitudes to informal donation (OLS) 107

List of Publications

This thesis is presented in the form of peer reviewed published papers. The thesis is comprised of the following five manuscripts.

1. Whyte, S., & Torgler, B. (2017). Things change with age: Educational assortment in online dating. Personality and Individual Differences, 109, 5-11. 2. Whyte, S., & Torgler, B. (2017). Preference versus choice in online dating. Cyberpsychology, Behavior, and Social Networking, 20(3), 150-156. 3. Whyte, S., Chan, H.F., & Torgler, B. (2018) Do women know what they want? Sex differences in online daters educational preferences. Psychological Science. forthcoming 4. Whyte, S., Torgler, B., & Harrison, K. L. (2016). What women want in their sperm donor: A study of more than 1000 women’s sperm donor selections. Economics & Human Biology, 23, 1-9. 5. Whyte, S., Savage, D. A., & Torgler, B. (2017). Online sperm donors: the impact of family, friends, personality and risk perception on behaviour. Reproductive BioMedicine Online, 35(6), 723-732.

Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet requirements for an award at this or any other higher education institution. To the best of my knowledge and belief, the thesis contains no material previously published (except by myself) or written by another person or where due reference is made.

Signature: QUT Verified Signature

Date: 25th January 2018

Chapter 1: GENERAL INTRODUCTION

Choosing a mate is arguably the largest economic decision a human can make. Mate choice decisions can have significant short and long run impacts on the individual decision maker, as well as macroeconomic impacts for society. By observing and studying individuals mating preferences and behaviours, and the factors at play when individuals make mate choice decisions, behavioural science can build a more developed understanding of the unseen mechanisms that drive large scale decision making processes. Exploring the interplay of such factors as an individual’s biology, personality, education, income, sex, gender, sexuality, religion, political beliefs and micro level behaviour, can inform and develop sciences understanding of how humans make decisions. The study of mate choice decision making then provides considerable advantages for the applied microeconomic analysis of human behaviour. The application of a more nuanced and holistic understanding to the factors at play in mate choice decision processes is the necessary foundation for future policy initiatives that assist frontline psychological healthcare and economic support services, as well as legislative frameworks that seek to combat sex, gender and sexuality based discrimination and inequity.

In many mate choice settings participants (to reduce their search costs) advertise a preference for the characteristics of the individual they are searching for. To further increase the likelihood of finding a partner, individuals also willingly provide significant amounts of their socio-demographic characteristics and discerning features. Their preferences and behaviours, and most importantly their “decisions” are often readily observable. Because one of the lowest cost and most readily accessible modern conduits in searching for a mate is the internet, a myriad of data is now readily accessible for behavioural research. These data allow

15 for the opportunity to test a range of economic, psychological, and wider social science theories using large populations of real world participant’s actual behaviour. For this reason, this thesis primarily focuses on the cyber mate choice domains of online dating and online reproductive tissue (sperm) donation.

Human males and females face different reproductive costs and constraints. For males, the cost of reproduction can be as little as the time spent in copulation. For females however internal gestation, lactation, paternity certainty and ongoing maternal investment all create a significantly disproportionate opportunity cost of reproductive market participation. Just as economics is the study of scarcity and finite resources, evolutionary psychology too recognises the costs and constraints involved in reproduction, and the evolutionary pressure it places on the decision makers involved. Triver’s (1972) seminal work titled Paternal

Investment and Sexual Selection proposed that the sex who bears the heavier reproductive burden (human females) should naturally be more selective in their mate choice behaviour.

Further behavioural research work has built on this core theory of female choice (Buss 1989;

1991) by proposing that females should not only be more selective than males, but that females should exhibit an evolved adaptive preference for male with resources, to compensate for this disproportionate opportunity cost in reproduction. Resources including not just accrued wealth, but other proxies for possible increased or ongoing paternal investment, such as education (Buss 1989; Buss & Barnes, 1986; Shackelford et al., 2005) and age (Buunk et al., 2002; Kenrick & Keefe, 1992). This thesis applies many of these core economics and evolutionary psychological normative constructs to explore key sex differences in human mate choice behaviour, and the contributing individual factors that impact the decision mechanism. By looking through an applied microeconomics lens this thesis is able to quantitatively explore and test these overlapping theories of large scale mate choice decision making, using uniquely large samples of actual human behaviour.

This thesis consists of five different studies presented in Chapters 2 through Chapter 7.

In Chapter 2 I provide empirical evidence of positive educational assortment in online daters contact behaviour. While previous research has shown that humans exhibit a preference for the same level of education in their partner as their own (Buss, 1985; 1989), this study goes a step further and explores educationally hypergamous and homogamous mate choice behaviour. From a neoclassical economics perspective rational maximisation (ceterus paribus) should motivate individuals to prefer/favour those with equivalent or higher levels of education relative to their own. Such online dating market data provides a uniquely observable setting in which to explore factors that impact homogenous preference (positive assortment), as well as strategic and utility maximising behaviours associated with heterogenous educational partner selection.

In Chapter 3 I provide empirical evidence that individuals ex ante preference for the characteristics of their ideal mate show little to no explanatory power in their actual contact behaviour. The online dating market provides the perfect observational setting for exploring the difference between what humans say they want, and how they behave in practise. This study utilises the ideal partner preferences individuals’ state before they begin their online search for a mate, and compares those selections with the characteristics of the individuals that they actually contact on the dating website in question. As many of our core economic, societal and legal processes heavily rely on eliciting individual’s true preference (census data, voting preferences, welfare transfers, education, healthcare, infrastructure investment, but to name a few) establishing factors that impact individual’s decision to deviate from their prior is critically important. Such research can also provide insight into the cognitive biases and constraints humans have and employ when making search or consumption decisions.

Chapter 4 presents (to the best of the authors knowledge) the first ever large scale empirical evidence of sex differentiated selectiveness in mate choice preference for different

17 age groups. While a significant amount of literature (Buss, 1989; 1991) has demonstrated females evolved adaptive preference for proxies for resources (namely, education) no study has used such a large sample to conclusively demonstrate female preferences (when compared to males) across all ages are more stringent and become increasingly more selective through the years of peak fertility. Exploring how males and females differ in the way they search and select mates can provide insight into how different sexes form and select their preference, and why individual’s preferences change across time.

Even after more than four decades of commercial sperm banking, no study has ever quantitatively analysed the characteristics of the sperm donors that females have actually selected to use in clinical invitro fertilisation (IVF) and donor insemination (DI) settings.

Chapter 5 is thus the first ever behavioural study of its kind. For the first time in human history males are (from a legal standpoint) consciously instructed not to invest in their resulting offspring in any way. Studying the impact (of zero obligation for paternal investment from the offsprings biological father) on female preference and behaviour provides a unique and unparalleled large-scale decision setting for behavioural research.

Males who donate their sperm in clinical donation settings are not seeking (or permitted) to invest in their offspring in the traditional sense (eg. contact, financially, emotionally). For millennia females have evolved to seek cues for males that will paternally invest in their offspring before they choose to reproduce with them (Trivers, 1972). As the opportunity cost of reproduction is far greater for women compared to men (eg. 9 months internal gestation,

12-18 months of lactation), identifying and securing males who could historically compensate and support this disproportionate female reproductive cost seems an obvious stipulation or criteria for their choice in a reproductive mate. Clinical sperm donation provides arguably the greatest possible natural field experiment setting to explore and delineate female mate choice psychology from male’s signals for accrued resources or those that may be proxies for

18 paternal investment. The Chapter 5 study uses a unique sample of more than a decade of women’s sperm donation reservations (selections) at an Australian fertility organisation. It uses the speed at which donor’s sperm samples were reserved (purchased) for use by women to explore female’s preference for specific characteristics in the donor males they chose for procreation.

Markets often fail to allocate efficiency. A lack of supply in the global clinical sperm donation market is one such real world example. Since IVF and commercial sperm banking began more than four decades ago, male supply has consistently fallen short of female demand. Regulatory factors that have excluded donors based on genetic testing, as well as socio-cultural and sexuality based forms of discrimination have further compounded this shortage. The commercialisation & regulated global trade in clinically donated semen

(although with good intention) has by its very nature forced men and women unable to access clinical donation opportunities to seek and create alternative options. The development of a cyber marketplace for sperm and egg donors has provided men and women with a low

(opportunity) cost alternative to the historically oligopolistic global supply of clinical reproductive tissue donation. The Chapter 6 study explores determinants of men that have made the large scale decision to participate as a sperm donor using the online connection website PrideAngel. The study provides unique empirical evidence on how males perceive the risks involved in online participation, and factors that drive success (ie. number of donations, number of women donated to, and number of offspring generated). It also explores the role and links between kinship, social networks and personality in donor psychology and behaviour. As informal (online) donation is an unregulated market facilitated from the advent of the internet, exploring individual’s decision processes and behaviour in an unregulated setting can provide insight into mechanisms and forces that trigger market development. And

19 more importantly, factors that drive individuals to participate, collaborate and act altruistically in large scale decision settings.

Outline of thesis

I present the five essays comprising this PhD thesis in individual chapters. Each chapter is self-contained and thus provides the relevant literature independently for each study. Due to the requirements set down by the Queensland University of Technology (QUT) in their “thesis by publication” requirements some minor adjustments and changes have been made from the published version of the manuscripts included. These changes primarily relate to the reformatting of tables, figures, and references and their aggregation into single lists for this thesis document.

Firstly, I present three explorative studies of online dating behaviour utilizing data from the Australian online dating website RSVP. Chapter 2 titled Things change with age:

Educational assortment in online dating is a joint work with Benno Torgler and published in

Personality and Individual Differences. Chapter 3 is another collaboration with Benno

Torgler published in Cyberpsychology, Behavior and Social Networking, its title Preference vs. Choice in Online Dating. Chapter 4 is the final study using data from RSVP and is a collaborative work with Ho Fai Chan and Benno Torgler titled Do women know what they want? Sex differences in online daters’ educational preferences.

The second section of the thesis contributes to the field of reproductive medicine and wider behavioural science by exploring sperm donor and recipient psychology and behaviour in both clinical and informal settings. Chapter 5 was a collaborative work with Benno Torgler and Keith Harrison using data from the records of Queensland Fertility Group (QFG). This

20 work is published in Economics and Human Biology and is titled What women want in their sperm donor: a study of more than 1000 women’s sperm donor selections. Chapter 6 explored donor characteristics and behaviour in the informal (unregulated) sperm donation market using data collected from the online connection website PrideAngel. This study was a joint work with David A. Savage and Benno Torgler. The study is published in Reproductive

Biomedicine Online, and is titled Online sperm donors: the impact of family, friends, personality and risk perception on behaviour.

Finally, in the Conclusion chapter I provide a summary of key findings and limitations for each study. I then conclude the thesis by providing future directions for work in these unique and innovative fields for behavioural science research.

Chapter 2: THINGS CHANGE WITH AGE: EDUCATIONAL ASSORTMENT IN ONLINE DATING

Stephen Whyte & Benno Torgler

Personality and Individual Differences (2017) 109, 5-11

Abstract

To identify the factors that influence educational assortment in an online dating setting, we analyse 219,013 participant contacts by 41,936 members of the Australian online dating website RSVP. Consistent with prior research, we find more educated online daters are consistently likely to assort positively (homogamy), meaning that they are more likely to contact potential mates with the same level of education. However, as the more educated cohort gets older they care less about homogamy while less educated daters become more interested in homogamy which leads to an increase in similarity towards caring for the same educational level. Older and more educated online daters are less likely to contact those with lower educational levels compared to their own, while women are more likely to contact a potential mate with higher education relative to their own (hypergamy). Our interaction analysis reveals fewer differences in educational hypergamy among older participants but a greater likelihood of online daters contacting mates with lower levels of education among younger males and older females. Further research is warranted on technology’s impact on human mating behaviour; particularly the psychology employed by humans using the Internet to maximize their chances of matching their educational preferences in a mate.

Mate choice is not a random roll of the dice, nor is it the search for universal signs of beauty upon which everyone agrees. There is, as the maxim goes, “a lid for every pot”. Aside from searching for those signs which everyone finds attractive, each individual is also searching for his or her own version of that perfection. That person is the one who is most like themselves.

Keller et al. 1996. p. 221

Introduction

Assortative mating (or homogamy) refers to the non-random matching of individuals based on a preference for a similar or shared feature (Buss, 1985). For over half a century, assortative mating research has identified and explored a range of characteristics and traits that individuals not only prefer but actually choose in a partner, including symmetry in factors such as age, aesthetics, attractiveness, personality, culture, education, religion and race

(Berscheid et al., 1971; Buss, 1985; Little et al., 2003; Mare, 1991; Vandenburg, 1972). More recent research has even used genome-wide genotype data to measure the magnitude to which married couples assort genetically (Guo et al., 2014, p. 2). This plethora of research underscores the importance of understanding the psychology of actual mate choice behaviour rather than mere stated preference in human mating scenarios.

The Internet has created a completely new conduit through which humans can search for a mate. It constitutes a non-sequential decision-making setting for mate choice, one that permits multiple partner choices in real time facilitating significantly greater available choice of potential mates, particularly on factors such as education that may have historically constrained the number of potential mates. Thus, not only does this technology reduce and possibly even eliminate previous propinquity and sequential constraints in the human mating market, it increases the opportunity set (for available choice) for potential mates. This

23 increased pool of potential mates also means greater opportunity for selection of partners with lower, similar or even higher levels of certain characteristics, traits or endowments. Education is also an interesting factor as it is commonly used in human mating behaviour as a proxy for resources and future provision helping to gain reproductive or (economic) advantages (Buss,

1985). The online dating environment allows to observe whether individuals select lower, similar, or higher levels of education than themselves providing insights how the selection changes for individual differences (e.g., related to gender or education) across age. Thus, to understand how technology is impacting or facilitating mate choice decisions based on education, we analyse the mating behaviour (rather than mere preferences) of over 41,000 members of the Australian online dating web site RSVP1 and their 219,013 contact decisions across the four months of January to April 2016.

Background

Because choosing a mate can be one of the largest psychological and economic decision humans can make, social science’s extensive explorations of mate choice behaviour represent a broad range of disciplines, including sociology, economics, (evolutionary) psychology and reproductive medicine (Buss, 1985). All these disciplines however, no matter their differences, uniformly acknowledge one phenomenon: positive assortative mating behaviour (homogamy) among humans. Such homogamy is essentially generated by two preference sets in the mating market. In the first, both males and females prefer partners with characteristics, traits or endowments that are symmetrical to their own and so choose each other. In the second, both males and females prefer a particular characteristic, trait or endowment (Schwartz, 2013) – for example, wealth, education or career success – and so choose a similarly wealthy, educated or successful mate. In either case, whether through

1 See https://www.rsvp.com.au/

24 matching or competition, individuals select partners that are more alike than would be the case based on random choice alone.

One advantage of positive assortment is its evolutionary payoff: it ensures the gene transmission optimization that comes from mating with those who share common genes

(Thiessen & Gregg, 1980) while also “increas[ing] the degree to which parents share genes with offspring” (Thiessen et al., 1997, p. 162), which elevates fitness. Symmetrical preferences may thus stem not only from risk minimization but also from an innate recognition mechanism, such as sexual imprinting (Bereczekei et al., 2004). The benefits of positive assortment, however, may go beyond the biological: at a micro level, it can mean increased socio-economic and productivity gains in both the short and long term (Rushton

1988). For example, education-based assortment can confer a range of benefits from improved lifetime health and well-being to increased wages and access to healthcare. It can also bring about greater economic understanding and increased gender equity within a marriage (Shafer, 2013). Assortative mating may be a way to optimise mate choice by selecting a partner with a certain degree of symmetry (Bateson, 1983). This may foster altruistic behaviour inside the family unit, increase marital stability and may even help realise greater fecundity (Rushton, 1988; 1989; Penton-Voak et al., 1999). As such, humans (and other animals) may in fact be able “to detect genetic similarity between themselves and others” (Russell et al., 1985, p.183) leading to a preference for stronger similarity on (highly heritable traits and or) factors like intelligence or education.

As female participation in the labour force and tertiary education increasingly comes to resemble male participation, gender equity between men and women could lead to increasingly symmetrical preferences based on education (Mare, 1991). Because education can be a proxy for resources and their ongoing provision, from a sexual selection perspective,

25 there should be more competition for men and women at the top of the education spectrum

(Rose, 2005). This should normatively further encourage positive educational assortment.

The task of finding a mate has always had opportunity costs because individuals have certain preferences, face constrained supplies and also compete with others to maximize their mate choice (Schwartz, 2013). For example, the traditional geographic constraints of local neighbourhoods, cities and towns meant that the available supply of potential mates was finite, generating a choice constraint in the mating market of the type generally labelled propinquity (Vandenburg, 1972). Yet even in local populations, assortative mating patterns at the phenotypic level always had benefits because individuals could “avoid the costs of leaving the immediate environment to mate” (Penton-Voak et al., 1999, p. 105). The arrival of the

Internet however, has expanded available choices to the point of a quasi-unbounded human mating market. On line, the click of a button results in a myriad of potential mates, thereby reducing potential search costs to virtually zero.

Even though available choices are notionally infinite, socio-economic (and particularly educational) barriers remain. The resulting distance, whether economic or social, means higher inequality, which may in fact increase education or income-based homogamy

(Schwartz, 2013) across the socio-economic distribution. Normatively, these increases in variance may facilitate or encourage males and females to favour the convenience of individuals at each socio-economic equity extreme (Buss, 1985). Such a preference may then propel the remaining individuals with less extreme characteristics (e.g., average educational levels) to pair up based on similarity (Sloman & Sloman, 1988). Generational increases in positive educational homogamy could thus increase inequality and constrain socio-economic opportunity and achievement for offspring (Mare, 1991). Theoretically then greater educational homogamy should increase economic inequality (Schwartz, 2013) because of the education-earnings link. Research shows that residents of less educationally favourable

26 marriage markets are more likely than those in more highly educated markets to marry hypogamously based on education, with the chance of women doing so increasing with age

(Lewis & Oppenheimer, 2000). Research into whether the Internet is facilitating or accentuating changes in educational assortment behaviour and its impact on social equity is still in its infancy.

In addition, as the Internet increases in popularity as a mating tool it may be crowding out more historical social intermediaries like work, school and local neighbourhoods

(Rosenfeld & Thomas, 2012). This is not to say that the geographic proximity of potential partners has become redundant but rather that online searches for mates generally cover a much greater geographic area than the small radius of local neighbourhoods (Rosenfeld &

Thomas, 2012). As this greater coverage translates into a significant increase in available choices, it may also change the structure of mate searches. In particular, new dating technology like the Internet may actually increase the possibility of homogamous selections

(Schwartz, 2013), and in some online dating segments – particularly those facing thin dating markets – it may encourage the convergence of particular characteristics.

This research seeks to identify the factors that influence internet educational assortment behaviour, thereby expanding behavioural science’s understanding of mate choice psychology and decision-making in large scale settings. In particular, by exploring deviations from positive educational assortment, it aims to distinguish the factors influencing education based mate choice decisions in the 2016 online dating market.

Method

Participant pool

Our data set, collected from the Australian online dating web site RSVP, encompasses

41,936 unique online dating individuals (2016), who, as part of their web site membership, have provided a wealth of personal details, including height, hair colour, eye colour, body type, sexuality, marital status, ethnicity, religious views, political affiliations, personality type and offspring2. As on most online dating web sites, RSVP users also have the option to provide their ideal preference for each of the characteristics for which they are searching or would prefer in an ideal partner. For the purposes of this study, the participant set is limited to online daters who self-identified as heterosexual (99.60% of the original sample), yielding a final sample with an age range of 18 to 80 years (M = 48.33; SD = 11.25) in which 77.80% of all participants are male.

The data set also includes information on the potential mates contacted by each individual during the four-month investigative period (January 1 to April 26, 2016), which totalled 219,013 contacts with 57,670 separate individuals. Table 1 shows the distribution of educational attainment for the total sample (both participants and their contacts).

2 Percent

0 20 40 60 80 Age of participant

2 Body type, education level and personality type are all measured on a five-point scale (lightest to heaviest, high school to post graduate study, and very private to very social, respectively).

Fig. 1. Age distribution (N = 27,695)

Table 1.

Descriptive statistics on participant and contact’s educational background

Participant Participant Freq. Percent /contact Freq. Percent

High school 5,991 14.29% High school 6,533 11.33% Some college 5,387 12.85% Some college 7,039 12.21% Diploma 7,475 17.82% Diploma 10,587 18.36% Degree 10,800 25.75% Degree 16,305 28.27% Post graduate 7,770 18.53% Post graduate 10,429 18.08% Not specified 4,513 10.76% Not specified 6,777 11.75% Total 41,936 100.00% Total 57,670 100.00%

Data Collection

All data were collated by RSVP and provided to the researchers as a single data set.

As collection of member profile demographics and contact behaviour for research purposes are covered under RSVP (FairFax media) Privacy Policy3, all participants have provided informed consent. All demographics collected and used as part of the study (eg. age, sex, body type, etc) was information that was visually available to all RSVP members in each individual participant’s online dating profile.

Empirical model

Our multivariate analysis employs three types of regression modelling: ordinary least squares (OLS), probit and negative binomial. Table 2 reports the regression results using the

OLS and probit models, which treat positive assortment as a binary dependent variable

3 Fairfax Media Privacy Policy – All personal information collected may be used for: “administrative, marketing (including direct marketing), promotional, planning, product/service development, quality control and research purposes, or those of our contractors or external service providers”

29 clustered by each individual online participant. The independent factors chosen for our multivariate analysis are all commonly identified determinants of mate choice across a wide range of behavioural science research (Buss, 1985; Buss & Barnes, 1986; Botwin et al., 1997;

Hitsch et al., 2010a). All of the relevant factors used are also immediately observable by online daters when viewing and deciding on potential mates dating profiles. As such participants’ decision to contact a specific member of the dating website community is done in the complete knowledge of similarity or difference within each particular variable stated.

20 Percent

0 -4 -3 -2 -1 0 1 2 3 4 < Hypergamy - Hypogamy >

Fig. 2. Educational hypergamy to hypogamy (N = 43,147).

The independent variables are online dater demographics, including age, sex (dummy for male), height, body type, personality, education and marital status (dummy for single), as well as dummies for no children, wanting (more) children, ethnicity, political views and religious ideology4. Because each online dating contact initiation is independent of all others, we also employ a negative binomial regression (using a count-data model) to more accurately explore discrete probability in online dating behaviour.

4 For a description, see Table 2.

Deviation from positive educational assortment can be referred to as educational hypergamy (choosing a mate with a higher level of education relative to your own) or educational hypogamy (choosing a mate with a lower level of education relative to your own).

To measure this deviation from positive assortment (see Table 2) and derive a new hypergamy/hypogamy variable, we subtract the value of the contact’s continuous education variable from the value of the participant’s to provide a positive or negative measure of difference from positive assortment (the zero value). A positive (negative) value (i.e., one greater (less) than zero) means that the individual is contacting another online dater with a lower (higher) level of education than his or her own (hypogamy or hypergamy, respectively).

We graphically depict this variable in Figure 2, with the zero value representing positive educational assortment by the online daters. Then, in Table 3, we identify the factors at play when online daters contact others with lower education levels (educational hypogamy) by using only the positive values (specifications 5 & 7) of the (dependent) hypergamy/hypogamy variable in OLS and negative binomial regressions.

We similarly identify the relevant factors for online daters contacting those with higher educational levels (educational hypergamy) using the hypergamy/hypogamy variable’s negative values (specifications 6 & 8, Table 3) in OLS and negative binomial regressions

(Nbreg)5 that are clustered by unique individual online user. For all OLS results, we show the relative strength of the variables by reporting robust beta-standardized coefficients, and for all the probit models, we identify the quantitative effects by reporting marginal effects.

Results

Specification (1) (first column of Table 2) uses OLS and the binary dependent variable of participants who contacted another online dater with the same educational level

5 Because negative binomial regression requires positive values, all values below zero are converted to positives.

(educational homogamy). Here, positive educational assortment takes the value of one, while all other combinations of participant and contact educational level are equal to zero. In all models, dummy variables control for the categorical factors of ethnicity, political views and religious ideology. Specification (2) then extends this specification by controlling for the participant’s political views and religious beliefs.

Overall, the analyses indicate that higher educational levels are positively correlated with contacting online daters with symmetrical educational levels. Specification (4), for example, reveals that a one unit increase in education raises positive assortment by 5.3%. The beta coefficients also show that education has the largest standardized coefficient, with a one standard deviation increase raising the standard deviations in positive assortment by 0.152

(specification (2)). The results also indicate that those with no children are more likely to contact online daters with a symmetrical educational level, while wanting (more) children is positively correlated with positive assortment but only at a 10% statistical significance in the fuller specifications. The analyses also show that those with heavier body types are less likely to assort based on educational level, although age, sex, height, personality and being single exhibit no statistically significant relation with positive educational assortment in this setting.

Table 2. Positive educational assortment (based on OLS & probit regressions) Dependent variable: Educational homogamy (1) (2) (3) (4) OLS OLS Probit Probit Age -5.7e-05 -7.7e-05 -7.1e-05 -1.4e04 (1.4e-04) (1.4e-04) (4.9e-04) (4.9e-04) -0.002 -0.002 -2.1e-05 -4.1e-05 Male -0.004 -0.003 -0.017 -0.014 (0.004) (0.004) (0.013) (0.013) -0.004 -0.003 -0.005 -0.042 Height -2.2e-04 -2.5e-0.4 -0.001 -0.001 (2.2e-04) (1.9e-04) (0.001) (0.001) -0.004 -0.005 -2.4e-04 -2.8e-04 Body type -0.004** -0.003** -0.010** -0.010** (0.001) (0.001) (0.005) (0.005) -0.007 -0.007 -0.003 -0.003 Personality 0.002 0.002 0.007 0.009 (0.002) (0.002) (0.006) (0.006) 0.003 0.004 0.002 0.003 Education 0.051*** 0.049*** 0.175*** 0.171*** (0.001) (0.001) (0.003) (0.004) 0.162 0.157 0.053 0.052 Marital status: single 3.3e-04 3.7e-04 0.002 0.002 (0.004) (0.004) (0.013) (0.013) -2.3e-04 0.000 0.001 -5.5e-04 No children 0.008*** 0.007** 0.024** 0.021** (0.003) (0.003) (0.010) (0.010) 0.009 0.008 0.007 0.006 Wanting (more) children 0.006 0.007* 0.019 0.023* (0.004) (0.003) (0.014) (0.014) 0.006 0.016 0.006 0.007 Ethnicity Yes Yes Yes Yes

Political views No Yes No Yes

Religion No Yes No Yes

N 187,713 187,713 187,713 187,713 N Clusters 35,135 35,135 35,135 35,135 Prob > F / Prob > χ2 0.0000 0.0000 0.0000 0.0000 Pseudo R2 0.0280 0.0284 0.0268 0.0271 Notes: Standard errors are in parentheses; beta coefficients (OLS regressions) and marginal effects (probit regressions) are in italics. The beta coefficients were derived by running separate regressions without clustering. Political views (dummy) = left wing, right wing, no strong beliefs or swing voter; religion (dummy) = agnostic, atheist, Anglican, Catholic, born again

Christian, other Christian, New Age, Islamic, Jewish, Buddhist, Hindu, Sikh, or other; ethnicity (dummy) = Northern European, Southern European, Eastern European, Hispanic/Latino, other Caucasian, Black African, Middle Eastern, Asian, Indian, Aboriginal, Other Islander, mixed race, other ethnicity. *, ** and *** denote statistical significance at the 10%, 5% and 1% levels, respectively.

Table 3. Educational hypogamy and hypergamy (based on OLS and negative binomial regressions)

Dependent variable: (5) (6) (7) (8) Educational Hypogamy Hypergamy Hypogamy Hypergamy hypogamy & hypergamy OLS OLS Nbreg Nbreg Age -0.002*** 0.003*** -0.001*** 0.002*** (4.1e-0.4) (0.001) (0.000) (3.2e-0.4) -0.029 0.037 -0.002 0.003 Male -0.007 -0.102*** -0.006 -0.061*** (0.011) (0.014) (0.006) (0.008) -0.003 -0.053 -0.010 -0.101 Height -1.2e-04 -0.003*** 6.4e-05 -0.002*** (0.001) (0.001) (0.000) (4.2e-0.4) -0.001 -0.034 0.000 -0.003 Body type -0.021*** 0.053*** -0.010*** 0.032*** (0.004) (0.005) (0.002) (0.003) -0.019 0.052 -0.018 0.052 Personality 0.008 0.001 0.004 0.001 (0.005) (0.006) (0.003) (0.004) 0.006 0.001 0.007 0.002 Education -0.525*** 0.221*** -0.307*** 0.140*** (0.004) (0.004) (0.002) (0.003) -0.630 0.232 -0.526 0.229 Marital status: single 0.005 0.012 4.8e-04 0.007 (0.008) (0.013) (0.006) (0.008) 0.002 0.004 0.001 0.011 No children 0.057*** -0.033*** 0.031*** -0.020*** (0.008) (0.011) (0.007) (0.007) 0.031 -0.019 0.053 -0.033 Want (more) children 0.039*** -0.067*** 0.020*** -0.042*** (0.012) (0.015) (0.006) (0.009) 0.016 -0.030 0.034 -0.067 Ethnicity Yes Yes Yes Yes

Political views Yes Yes Yes Yes

Religion Yes Yes Yes Yes

N 68,682 54,113 68,682 54,113 N Clusters 20,116 17,949 20,116 17,949 Prob > F 0.0000 0.0000 0.0000 0.0000 R-squared 0.3857 0.0586 0.0641 0.0096 Notes: Standard errors are in parentheses; beta coefficients (OLS regressions) and marginal effects (probit regressions) are in italics. The beta coefficients were derived by running separate regressions without clustering. Political views (dummy) = left wing, right wing, no strong beliefs or swing voter; religion (dummy) = agnostic, atheist, Anglican, Catholic, born again

To further explore the effect of the age-education interaction on positive educational assortment, we graph the average marginal effects in Figure 3 (based on specification (2)), which shows that the higher the educational level online daters (in particular younger ones), the more likely they are to contact others with homogenous educational levels. However, the slope for individuals with higher educational levels is negative and steeper (see the three highest educational groups) indicating that the older cohorts care less about educational homogamy while cohorts with lower educational levels care more (see positive slope) as they get older. Thus, older online daters become more similar in regards to how much they care about homogamy.

High School Some College Diploma Degree Post Graduate

.2 Linear Prediction Linear .1

0 18 80 Age of participant

Fig. 3. Predicted margins for assortative mating, with a focus on age slopes as a function of education (linear prediction).

Table 3 reports the results for the four specifications (two OLS and two Nbreg) that explore the factors which influence online daters’ educational hypogamy ((5) and (7)) and educational hypergamy decisions ((6) and (7)). In these analyses, those who are older, have heavier body types, or higher educational levels are all less (more) likely to contact those with lower

(higher) educational levels than themselves. For example, a one unit increase in education decreases hypogamy by 52.5% (see (5)) while increasing hypergamy by 22.1% (see (7)).

Conversely, online daters who have no children and those who want more children are more

(less) likely to hypogamously (hypergamously) sort based on education. Yet again, sex, height, personality and being single have no statistically significant coefficients. Overall, therefore, education has the largest relative importance, as indicated by the largest standardized coefficient.

To further explore decisions to deviate from positive educational assortment, we identify the interaction effects between age and educational hypogamy or hypergamy by gender (see Figures 4 and 5). We find that younger male online daters are less likely than younger females to contact daters with higher educational levels (Figure 4). On the other hand, females across all age groups show very little change in their willingness to contact males with higher educational levels (a linear prediction of 1.7 to 1.75) and are in fact more likely to mate hypergamously based on education. Nevertheless, older online daters do exhibit similar levels of sex-based differences in hypergamous contact with other potential mates online.

In addition, as Figure 5 shows, younger males are more likely than younger females to use educational hypogamy as a strategy when contacting online daters of the other sex. On the other hand, online daters over 50 show the opposite trend: older females are more likely than older males to contact individuals with educational levels lower than their own.

Female Male

1.75

1.7

1.65

1.6 Linear Prediction

1.55

1.5 18 80 Age of participant

Fig. 4. Predicted margins for educational hypergamy, with a focus on age slopes by gender (linear prediction).

Female Male

1.9

1.85

1.8 Linear Prediction Linear 1.75

1.7 18 80 Age of participant

Fig. 5. Predicted margins for educational hypogamy, with a focus on age slopes by gender (linear prediction).

Discussion

Our analysis of the factors influencing homogamous, hypogamous and hypergamous decisions to contact potential mates online indicates that, consistent with previous research

(Buss, 1985), more highly educated online daters are more likely to contact others with the same educational level. The interaction effects further demonstrate that positive assortment becomes similar among older online daters across all levels of educational achievement, while younger more educated online daters are more likely to mate homogamously. The results for hypogamy similarly show that older more educated online daters are less likely than other participants to contact those with lower educational levels.

On the other hand, sex differentiating these results in our analysis of interaction effects reveals that younger males are more likely than younger females to contact someone with lower educational levels, which confirms previous evidence of males not being averse to contacting less educated females (Skopek et al., 2008). Interestingly, older women are also more likely to contact less educated men, suggesting a sex difference intersection of male and female educational hypogamy preference at approximately 50 years of age. One possible explanation offered by qualitative research on older online dater preferences is that males favour committed relationships, whereas females may “desire companionship without demanding caring roles” (McWilliams & Barrett, 2014, p. 411). If females do in fact become less selective with age relative to males, then their participation in the online dating market is

38 likely to focus more on ongoing interaction rather than securing an exclusive relationship with a potential male companion.

Both the educational hypergamy and the interaction effects analyses suggest that older online daters and women are more likely to contact a potential mate with a higher educational level, although the sex difference diminishes considerably among older RSVP members.

From an evolutionary perspective, the concept of “female choice” (Buss & Barnes, 1986, p.

559) may provide some insight into women’s (vs. men’s) relative selectivity for those with higher educational levels. That is, the opportunity cost of participating in the reproductive market is inherently greater for women (Trivers, 1972), so whether seeking more offspring or not, females in the online dating market may still exhibit evolved adaptive preference for resources (higher education) and proxies for the ability or willingness to provide them.

Naturally then, as in traditional settings, female online daters initiate contact and reply more selectively than males (Fiore et al., 2010). Online dating research on middle age to older online daters has also shown that female selection criteria centre more on a male’s abilities

(McWilliams & Barrett, 2014), which could explain males’ comparatively lower preference for educational hypergamy and increased preference for educational hypogamy in potential female mates. Given that male education historically exceeds that of females (particularly among older participants), hypogamy implies a larger potential market of females seeking more educated males from which male daters can choose.

Positive assortment is also more likely among online dating participants with no children and those who want more children, although these are also more (less) likely to contact less (more) educated others. These results align with research findings among online daters of significant homophily for life course preferences such as wanting children (Fiore &

Donath, 2005). Previous research has also shown significant homophily in self-reported physical build for online daters (Fiore & Donath, 2005). Our analysis of the body-type

39 education relation, shows those with heavier body types are less likely to positively assort, less likely to mate hypogamously and more likely to contact more highly educated

(hypergamy) potential mates.

The authors also acknowledge several limitations to the current study. This study is limited by its use of a sample derived from only one dating web site, meaning that the research findings, although based on an extremely large and diverse sample, may in part reflect the platform provided by the particular web site and its participants. The time of year may also have impacted behaviour given that different seasons or months can translate into a selection effect. In addition, even though humans have an amazing ability to collate and analyse information through interaction and observation (Wilson & Dugatkin, 1997) because the Internet can theoretically provide infinite choice in a non-sequential setting, some online daters may have experienced cognitive overload or similarly negative cognitive outcomes during their cyber search for a potential mate. Research has also demonstrated that, for both sexes, increased choice can mean reduced utility and increased difficulty in decision making

(Lenton et al., 2008). Whether or not these difficulties facilitate or accentuate homogamous, hypogamous or hypergamous contact behaviour in an online dating setting is less clear and thus warrants further investigation.

A further limitation of the study is information asymmetry and deception by online dating participants. Whether consciously or not the conduit of the Internet certainly allows for exaggeration in self-representation. However, this does not mean that deception is the most common or successful strategy employed in the cyber dating world. As the payoff for online dating is how well the face to face meeting goes, participants are constrained in their ability to over or understate particular features. In fact research has shown that while online daters may provide false information in their profiles, the inaccuracies are limited and often so minute

(eg. height, weight, age) that they would be difficult to identify in any face to face contact

(Ellison et al., 2006; Toma et al., 2008; Guadagno et al., 2012). It is for this very reason that wider social science uses such readily available (online) data sources to study human decision making and mate choice behaviour.

Conclusion

Because the burgeoning online dating conduit has significant potential to create a more efficient and successful mechanism for finding and securing both short-term and long- term romantic relationships (Finkel et al., 2012), ever greater numbers of people are choosing to use it to find a partner. Practical implications for understanding human cyber-dating behaviour extend not just too individual relationship formation, but also couples counselling psychology and psychotherapy. It is for these reasons that further research is warranted into the impact of technology in the mating market, and particularly on the psychology employed by humans when using the Internet to maximize their educational preference in a mate.

Chapter 3: PREFERENCE VS. CHOICE IN ONLINE DATING

Stephen Whyte & Benno Torgler

Cyberpsychology, Behavior and Social Networking (2017) 20(3), 150-156

Abstract

This study explores factors that influence matches of online dating participants’ stated preference for particular characteristics in a potential partner and compares these with the characteristics of the online daters actually contacted. The nature of online dating facilitates exploration of the differences between stated preference and actual choice by participants, as online daters willingly provide a range of demographics on their ideal partner. Using data from the Australian dating website RSVP, we analyze 219,013 contact decisions. We conduct a multivariate analysis using the number of matched variables between the participants’ stated preference and the characteristics of the individuals contacted. We find that factors such as a person’s age, their education level and a more social personality all increase the number of factors they choose in a potential partner that match their original stated preference. Males (relative to females) appear to match fewer characteristics when contacting potential love interests. Conversely, age interaction effects demonstrate that males in their late 60’s are increasingly more selective (than females) regarding who they contact. An understanding of how technology (the Internet) is impacting human mating patterns and

the psychology behind the participants, informs the wider social science of human behavior in large scale decision settings.

Introduction

The Internet has become such a common and widely used form of technology that the online population is increasingly homogenous with the offline population (Valkenburg & Peter,

2007). Since its inception (less than two decades ago) the Internet has facilitated a fundamental shift in how humans are able search and interact with potential partners (Finkel et al., 2012). The cyber world is becoming one of the most widely accepted and popular tools for finding both short and long term love interests (Heino et al., 2010; Dröge & Voirol, 2011)

. It has even partially displaced more traditional avenues such as family and friends, work and the local community as a conduit for first contact with that special someone (Rosenfeld &

Thomas, 2012). Research from Britain shows the third most popular method of finding a date is the Internet (Couch & Liamputtong, 2008). More than one in ten American adults – and

38% of those who identify as “single and looking” for a partner – have used some form of online dating platform (Smith & Duggan, 2013) In the United States alone there is in excess of 40 million unique American dating website users as of 2010 (Sritharan et al., 2010)

At a micro level, people use online dating to search for a partner for a range of reasons. Increased information, convenience and accessibility, age, greater choice, or an inability to meet potential mates via their own social networks (Couch & Liamputtong, 2008;

Frost et al., 2008). It is little wonder then, that online dating has become a setting for behavioral science to explore the psychology of human interaction and decision making.

There have been numerous studies using online dating for the observation of behaviour

(Bapna et al., 2013; Hitsch et al., 2010a; Lee et al., 2008; Pizzato et al., 2010; Wu & Chiou,

2009). Online dating is a unique domain, often requiring individuals to provide extensive information on themselves, on their preferences for their ideal partner, and even their resulting mate choice. The contact and interaction history offers a rich source of implicit information

(Lee et al., 2008). From a research perspective, the greatest benefit of online dating data is that it is in individuals’ best interests to provide as much (honest) information as possible.

Minimizing asymmetric information between potential matches reduces transaction and search costs for all market participants.

While online dating provides many individuals an otherwise inaccessible market of new potential mates, questions may arise around such platforms ability to allow participants to exaggerate or lie in user profile formation. On the surface this may seem the most efficient strategy, but the payoff for any online dating interaction is to meet in person or a date. While it may be possible to exaggerate one’s potential to get selected, online self-representation cannot be too exaggerated if the end goal is to have a successful and enjoyable face-to-face date (Toma & Hancock, 2010). It is costly to send deceptive or noisy signals, assuming that the final payoff for all participants is to have a successful first meeting. In fact, research shows that honesty is the best policy, as there is often little or no evidence of strategic behavior in online dating settings (Hitsch et al., 2010a). While online daters may emphasize their positive characteristics (Guadagno et al., 2012) in their advertised dating profiles, inaccuracies are often small (e.g., related to factors such as age, height and weight) perhaps because they are difficult to detect in person (Toma et al., 2008). Ellison, Heino and Gibbs

(2006) go as far as to say that the idea that online daters “frequently, explicitly, and

44 intentionally “lie” online is simplistic and inaccurate”. Moreover, when comparing the reported height of the people in our data set with information from the Australian Bureau of

Statistics we find that the stated height of both men and women is only slightly above the

Australian average (by less than 4 centimeters) which indicates little evidence of misrepresentation.

Such findings are also pertinent irrespective of the sexual (short term) or relationship

(long term) objectives or motivations of the individuals involved. Whether searching for romantic or sex-only relationships, participant’s end goal of face-to-face contact ensures men and women’s online dating information (profiles) are an apt reflection of their actual demographic characteristics. A significant (social and) market mechanism ensuring participants are accurate and honest in their representations. It is for this very reason that social science now extensively uses internet interactions, and particularly online dating, as a rich and credible source of quantitative behavioral data (Hitsch et al., 2010a; Lee & Niederle,

2015).

The domain of online dating, then naturally facilitates exploration of the differences between stated (advertised) preference and actual choices of participants. In line with adaptive decision making theories such as satisficing (Simon, 1972), online dating preference research shows that explicit preferences are weak indicators of the success of user interactions (Frost,

2009). In fact, research into actual choices made by individuals is far more informative as dating participants’ actions may be somewhat contrary to their original stated preference

(Heino et al., 2010). Exploring the progression employed between what humans say they want and what they actually choose is of critical importance for behavioral science. And as wider social science is often the academic narrative for public policy (e.g. health and education expenditure), the benefits of understanding how humans match and choose cannot be

45 understated. Large scale settings such as online human mating scenarios provide a fitting domain for such psychological investigation.

Method

Participants

Our data set consists of 41,936 unique individual online dating members’ “dating profile” information from the Australian dating website, RSVP (Whyte & Torgler 2017). A member’s profile contains a range of demographic self-reported data, such as age, height, eye colour, hair colour, body type, sexuality, marital status, ethnicity, religious views, political affiliations, and number of offspring. Data are also captured on participants’ stated preference regarding the ideal mate or love interest that they are searching for on RSVP. Males make up

77.80% of the sample, with close to one hundred percent (99.60%) of all participants identifying themselves as heterosexual. For the purpose of this study, only heterosexual participants are used in our analysis. Participant age ranges from 18 to 80 years of age. RSVP members mean age being 48.33 years with a SD of 11.25 years. RSVP members mean age is

48.33 years with a SD of 11.25 years with 50% of all participants being between the ages of

39 and 56 years old. Comparing our sample with statistics from the wider Australian population taken from the Australian Bureau of Statistics (ABS, 2016) indicates that our cohort is on average 11 years older. In relation to the sex ratio of participants our sample mirrors previous research with males being over represented (Hitsch et al., 2010a). This may reflect the fact that men tend to visit online dating websites more often compared to women

(Heino et al., 2010) and are more likely to contact women whose profiles they viewed

(Valkenburg & Peter, 2007).

Procedures

The data set also contains actual decision (choice) information on whom the 41,936 individuals contacted via the dating website between the 1st of January 2016 and the 26th April

2016. There were a total of 219,013 contacts made, to 57,670 unique individuals. The number of matching variables between the participants’ stated preference (what they want) and the characteristics of the individuals they actually contacted are used as our dependent variable in the multivariate analysis. The seven matching factors are: hair colour, eye colour, body type, education level, personality type, political view, and religious affiliation6.

As Figure 6 shows, more than one in every three contact decisions by RSVP members was to make contact with a potential partner that matched zero of the seven preferences they had originally stated. And more than 65% of the total 219,013 contacts initiated had one or less variables matching their stated preference.

20 Percent

0 02468 Number of matching characteristics

Fig. 6. Percentage of participants contacted matching up to 7 characteristics

6 Body type, education level and personality type all being five point scales (lightest to heaviest, high school to post graduate study, and very private to very social, respectively).

Results

To gain an overview of determinants impacting the psychology of mate preference matching actual mate choice, we perform an ordinary least squares (OLS) regression analysis, clustering by individual. In specification (1) we begin with the physical and aesthetic features of participant age, a dummy variable for sex difference, height, body type, and ethnicity. We find that older participants contact potential partners that match more of their preferences, while male and taller individuals match less. Body type shows no influence. In specification

(2), we now introduce personality based characteristics (a self-identified personality measure of how “social” the participant regards themselves). Due to missing values in our data set (not all online daters state all of their preferences/personal characteristics) our sample size then falls from 213,843 observations to 187,713 observations. People with more social personalities are more inclined to contact those who match more of their original preferences.

In specification (3) we introduce and control for circumstantial factors like current level of education, current relationship status, offspring from previous relationships, and future intention for more children. All of these new factors appeared not to matter for actual matching choices that align stated preferences. Finally, in specification (4) we include a further set of controls (dummies for political views and religious ideology) as a further robustness check. The key results for age, males, and personality remain robust. We present also beta/standardized coefficients to explore the relative importance of the variables, and put all the factors into a common metric. In all equations age has the largest standardized coefficient. For example, in specification (4) we can see that an increase of one standard deviation in age is correlated with an increase of 0.096 standard deviation in matching.

Taking into account that our dependent variable is a count variable with a high proportion of zeros (see Fig. 6), we conduct a further robustness check by generating results using the zero-inflated model (see Table 4). To discriminate between the negative binomial and the zero-inflated model we apply a Vuong’s test. The test statistics (z between 20.09 and

29.52) indicate that the zero-inflated model should be used. We find consistent results. The coefficients of the variables age, male dummy, and personality remain statistically significant.

However, height is now also statistically significant in all four specifications.

Table 4. OLS analyzing matches of stated preference and actual choice

Dependent Variable: (1) (2) (3) (4) Number of matches Age 0.018*** 0.018*** 0.018*** 0.013*** (0.001) (0.001) (0.002) (0.002) 0.138 0.133 0.130 0.096 Male dummy -0.137*** -0.136*** -0.133*** -0.158*** (0.041) (0.042) (0.046) (0.043) -0.038 -0.038 -0.036 -0.043 Height -0.004* -0.004* -0.004* -0.003 (-0.002) (0.002) (0.002) (0.002) -0.021 -0.022 -0.025 -0.016 Body type -0.021 -0.019 -0.018 -0.028 (0.017) (0.017) (0.018) (0.018) -0.011 -0.010 -0.010 -0.015 Personality 0.082*** 0.086*** 0.088*** (0.019) (0.021) (0.020) 0.039 0.040 0.041 Education 0.012 0.021 (0.011) (0.011) 0.011 0.019 Marital Status: Single 0.123 0.111 (0.076) (0.074) 0.025 0.022 No children dummy 0.004 0.002 (0.037) (0.036) 0.001 0.001 Want more children 0.014 -0.038

(0.042) (0.042) -0.003 -0.010 Ethnicity dummy Yes Yes Yes Yes

Political dummy No No No Yes

Religion dummy No No No Yes

N 213,843 204,986 187,713 187,713 N Clusters 40,601 38,765 35,135 35,135 Prob > F 0.0000 0.0000 0.0000 0.0000 R-squared 0.0616 0.0608 0.0575 0.0792 Notes: Ordinary Least Squares Regression standard errors are presented in parentheses. Beta coefficients are presented in italics. Separate regressions were run without clustering to produce beta coefficients. Seven characteristics are: Hair colour, eye colour, body type, education, personality, political view, religious affiliation. Categories for dummy controls are, Political: Left wing, right wing, no strong beliefs, swinging voter. Religion: Agnostic, Anglican, atheist, born again Christian, Buddhist, Catholic, Hindu, Islamic, Jewish, New Age, other, other Christian, Sikh. Ethnicity: North European, South European, East European, Hispanic Latino, Other Caucasian, Black African, Middle Eastern, Asian, Indian, Aboriginal, Other Islander, Mixed Race, Other Ethnicity. *, ** and *** represent statistical significance at 10, 5 and 1% levels, respectively.

Table 5. Negative binomial model analyzing matches of stated preference and actual choice

Dependent Variable: (1) (2) (3) (4) Number of matches Age 0.011*** 0.011*** 0.009*** 0.006*** (0.001) (0.001) (0.001) (0.001) Male dummy -0.044 -0.057 -0.064* -0.088*** (0.034) (0.035) (0.034) (0.031) Height -0.005*** -0.005** -0.004** -0.003* (0.002) (0.002) (0.004) (0.002) Body type -0.015 -0.014 -0.008 -0.007 (0.015) (0.015) (0.008) (0.013) Personality 0.087*** 0.078*** 0.074*** (0.023) (0.021) (0.020) Education -0.007 -0.002 (-0.007) (0.009) Marital Status: Single 0.071 0.069 (0.050) (0.047) No children dummy -0.027 -0.027 (0.028) (0.027) Want more children -0.064 -0.065 (0.039) (0.041) Ethnicity dummy Yes Yes Yes Yes

Political dummy No No No Yes

Religion dummy No No No Yes

Ethnicity dummy No No No Yes

N 213,843 204,986 187,713 187,713 Non zero N 137,905 134,556 126,703 126.073 N Clusters 40,601 38,765 61,640 61,640 Prob > chi2 0.0000 0.0000 0.0000 0.0000 Vuong Test z=20.27 z=20.09 z=23.62 z=29.52 Notes: Zero-Inflated Negative Binomial Regression robust standard errors are presented in parentheses. Seven characteristics are: Hair colour, eye colour, body type, education, personality, political view, religious affiliation. Categories for dummy controls are, Political: Left wing, right wing, no strong beliefs, swinging voter. Religion: Agnostic, Anglican, atheist, born again Christian, Buddhist, Catholic, Hindu, Islamic, Jewish, New Age, other, other Christian, Sikh. Ethnicity: North European, South European, East European, Hispanic Latino, Other Caucasian, Black African, Middle Eastern, Asian, Indian, Aboriginal, Other Islander, Mixed Race, Other Ethnicity. *, ** and *** represent statistical significance at 10, 5 and 1% levels, respectively.

Due to the strongest effect of age on matching we take a closer look at whether age interacts with other factors by applying specification (4) in Table 4. Thus, we fit a separate slope for the relationship between age and matching for male and female. The age slope for male and female is significantly different (t=3.01). The hypothetical predicted values from such a model are shown in Figure 7. The slopes visually depict younger men matching fewer characteristics than women from the age of 18 up until their late 60’s. After this males become more selective with whom they contact, compared to their female counterparts, matching more of their stated preference when contacting potential mates.

The same modelling process is repeated to explore interaction effects between age and education (Figure 8). The slopes represent visually those online daters who have greater levels of education matching less characteristics of their preference in who they contact for younger ones, relative to those with less education. Inversely those with higher levels of education and over the age of 40 are more selective (matching more characteristics to their stated preference) relative to those with less education.

1.8 1.6 1.4 1.2 1

18 80 Age of Participant

Female Male

Fig. 7. Average marginal effect: Age and Gender

2 1.8 1.6 1.4 1.2 1

18 80 Age of Participant

High School Some College Diploma Degree Post Graduate

Fig. 8. Average Marginal effect: Age and Education

Discussion

This study explores factors that influence matches between online dater’s stated preference for particular characteristics in a potential partner and the characteristics of the online daters they actually contact. Factors such as a person’s age, their height, and their personality are all positively correlated with the number of factors in a chosen potential partner that match their originally advertised preference. Males (relative to females) appear to match fewer characteristics when contacting potential love interests online, at least until they reach their late 60’s. Also, relative to those with less education, more educated younger online daters match fewer characteristics when contacting a potential mate. Conversely older online daters with higher levels of education match more of their preferences in the people they contact.

The finding that older participants match more preferred characteristics in those they contact is interesting. While online dating has been extensively studied across behavioural science for more than a decade, (arguably) less attention has been given to the psychology and behavior of older online daters. Millennials and university students are an exhaustively used subject pool in both online and laboratory research, as more mature samples are often much harder and costly to observe. But as the internet facilitates increased participation across generations, more and more non-millennials engage in the conduit for mating that it provides.

Of the current research on mature daters, evidence shows that men are more likely to date

(Watson & Stelle, 2011) and those participating in the dating market have higher levels of education, having accrued more assets, and self-identify as having more social connectedness

(Brown & Shinohara, 2013). Older daters also appear to set higher standards for potential mates’ educational levels (Buunk et al., 2002), a finding that is not surprising considering educational attainment naturally accrues with age. Moreover, research in speed dating settings has also shown that men and women do not necessarily adjust (with age) how selective they

53 are (Kurzban & Weeden, 2005). Older online daters may not necessarily be more selective than their younger counterparts; rather they just contact less of those who do not meet their preferred characteristics in a mate.

Evolutionary psychology has put forward motivations for strategic sex difference in human mating (Trivers, 1972). Our results indicate that males contact more online daters with less characteristics that match their stated preference, building on the idea of “female choice”, and that men are less selective in who they choose to mate with (Buss & Barnes, 1986).

Whether men realize the evolutionary payoff of more offspring from this strategy in an online dating market is unclear. While the technology of online dating has changed the medium of the dating market, women still appear to be more selective than males with whom they contact online (Alterovitz & Mendelsohn, 2009). It is also interesting that males in their sixties surpass their female equivalents and match more of their advertised preference in who they contact. While the composition of the online dating market may reflect disproportionate sex difference across different age groups, such a significant change warrants further research.

As Australian males have shorter life expectancy than females (Mathers et al., 2003), one hypothesis may be that older males may in fact be looking for a cooperative care arrangement as well as a love interest. Thus, men in their late 60s may prioritise their preferences accordingly, giving greater weighting to women who have the potential to also fill the role of caregiver (McIntosh et al., 2011). Or alternatively, for the older cohort significant mating pool sex difference may just mean females are less selective because they have less potential mates to choose from.

That more outgoing or social personality types realise more matches in preference and choice is also a notable finding. On the surface we would expect more extroverted people to cast a wider net of both attention and interaction in the mating market. In any market, the ability to sell or advertise a good or service can facilitate increased consumption. Being a

“good seller” may translate into less necessity to contact as many potential mates. Ofcourse searching for and matching characteristics in suitable partners (to your preference) comes at a higher opportunity cost. From an evolutionary sense, it would also significantly reduce any pool of potential mating options, as fewer participants could match all stated preferences.

More social and extroverted individuals may in fact believe their personality type is more appealing (Hall et al., 2010). Having a more social personality type may also grant participants the confidence to be more selective in who they contact in the cyber dating world.

Conclusion

Disclosure of partner preferences is a widely offered and commonly used option for participants of online dating websites. The core premise is that it allows participants to advertise (signal) to potential love interests who may be endowed with or in possession of said characteristics and traits. The efficiency of this mechanism in eliciting an understanding of actual choice and decision behavior is not as clear. Speed dating research shows that advertised preference can be an indication of willingness to participate or attend, but may provide very little predictive capacity with respect to actual choice. Economics alludes to this fact with the idea that individuals generally tend to overstate their valuations (Murphy et al.,

2005), and advertising one’s ideal partner preferences online may just be another microeconomic example. Of course as online dating’s natural progression is towards face to face dating, preferences may just be a “wish list” of individual demographics, rather than a rigid mate choice criterion. Meeting that special someone (with enough of the characteristics we prefer) may be the optimal and most flexible strategy (Wiederhold, 2015).

Certainly, both men and women know that they are in a mating market, and respond or adjust to market forces accordingly (Pawlowski & Dunbar, 1999). Humans are conscious of the changing conditions of online markets, and how it differs from other sequential settings

55 like local night spots, school, work, or social gatherings. As the psychology employed by humans when choosing a partner can certainly be environmentally sensitive, online dating may trigger changes in underlying preferences and decision behavior (Kurzban & Weeden,

2007). A disproportionate amount of choice relative to other everyday consumption decisions may impact or even change our original stated preference. Rather than seeking the specifically highest utility payoff available in a mate, human beings may make decisions that are optimal in relation to the mental, resource, age, and environmental constraints they are faced with when searching and choosing (Simon, 1972).

In this fast paced world, and with the myriad of options the internet now provides, time spent searching and exploring all available potential partners is extremely costly. More than ever before, our cognitive bounds may in fact be aiding us in our adaptive decision making (Todd, 2007). This means searching and evaluating through suitable options until one meets an acceptable threshold, rather than matching a single or set of specific idealized preferences. The online dating decision making process and our results demonstrating a lack of matching preference with actual choice reflects a wider willingness to contact any and all potential mating options available. As the Internet and online dating exponentially grow, further research is warranted in the psychology and decision processes employed by online daters and the relationship between stated preference and choice.

Chapter 4: DO WOMEN KNOW WHAT THEY WANT? SEX DIFFERENCES IN ONLINE DATERS EDUCATIONAL PREFERENCES

Stephen Whyte, Ho Fai Chan & Benno Torgler

Psychological Science (2018) forthcoming

Abstract

Using a unique cross-sectional data set of dating site members’ educational preferences for potential mates (N = 41,936), we show that females are more likely than males to stipulate educational preferences at all ages. When members indifferent to educational level are excluded, however, the specificity of male and female preferences does differ across age groups. That is, whereas females express more refined educational preferences during their years of maximum fertility, their demand specificity decreases with age. Male specificity, in contrast, remains stable until the 40s, when it surpasses that of post-reproductive females and then increases as males reach their peak career earnings potential. The greater female investment in offspring also translates into more selective preferences for potential mates when the offspring is still living at home.

Introduction

Sexual selection decisions are at the core of evolutionary psychology human mating strategies, including parental investment, whose levels tend to differ between males and females (Trivers, 1972). Because “[t]hose who hold valuable resources do not give them away cheaply or unselectively” (Buss, 1994, p. 33), this parental investment asymmetry from different minimum physiological obligations (fertilization, gestation and lactation), sex cell size and mobility implies that men, having lower sex investment, will be less choosy in mate preferences (Schmitt, 2005). Evolution thus favours women who are highly selective about their males, focusing on quality over quantity. These males, however, vary significantly across multiple factors, which forces a trade-off that over millennia has trained females to

58 identify adaptively valuable characteristics while recognizing their own wants and preferences for particular traits. This act of choosing, therefore, is a difficult task in which the decision maker must not only weigh the immediate attractiveness of options but also anticipate the consequences of choice (Schwartz, 2004). This latter is particularly relevant in mate selection because the impossibility of knowing the consequences of relationship commitment beforehand (Buss, 1994) makes the confrontation with trade-offs incredibly unsettling and may engender choice resistance (Schwartz, 2004). Hence, women who can solve the adaptive problem in choosing a mate should have a comparative advantage.

We are able to examine this issue empirically because our unique cross sectional data set from the commercial online dating service, RSVP, permits a large-scale analysis of whether female participants are more likely than males to express their explicit wants and/or show actual preferences for mate characteristics. The fact that this large online population encompasses many age groups also enables exploration of how static such preferences are throughout the human life cycle. We can investigate, for example, what happens when parental investment becomes less relevant. The use of this data set also exemplifies the increasing tendency over the last two decades for behavioural science researchers to leverage the plethora of information recorded by commercial Internet dating web sites and organizations (Lee et al., 2008; Fiore et al., 2010; Whyte & Torgler, 2017a, 2017b).

The major benefit for mate choice researchers is that males and females willingly disclose large amounts of information about their personal demographics and mate preferences. Moreover, because the participation objective is the payoff of meeting a mate face-to-face, participants employ only minimal amounts of deception in their responses

(Toma et al., 2008). Yet, whereas previous research demonstrates a female preference for (and mate selection based on) education level (Hitsch et al. 2010a, 2010b, Skopek et al. 2010), psychology research into online mate choice behaviour as yet provides no large scale

59 evidence of (a distal cause for) changes in female selectiveness at different life or fertility stages. In our analysis, therefore, we leverage the openness of participant disclosure to focus on educational level, a choice characteristic that in many cultures is valued more by women than men (Buss & Barnes, 1986; Shackelford et al., 2005), possibly because of its frequent association with social status and intelligence (Correia, 2003), both common proxies for resources.

Method

Participants

Our study covers 41,936 heterosexual individuals aged 18 to 80 years, with a total of

15,194 females (M = 48.68; SD = 11.22) and 26,742 males (M = 46.93; SD = 11.79) (see

Figure 9). The dataset was generated for active members (those accessing the account at least once during the sample period) of the Australian online dating web site RSVP between

January and April 2016. The sample includes a variety of membership durations, from those who joined as far back as the RSVP launch in 1997 to those who joined as recently as April

2016 (median join date = March 2015). Large data sets such as ours are well suited for life cycle analysis in that 90% of the individuals surveyed are over 30, with only 1.29% under 25;

14.82%, 26.14%, 30.2%, and 19.95% aged between 25 and 34, 35 and 44, 45 and 54, and 55 and 64, respectively; and a mere 0.8% over 65. In addition to age, whose distribution is differentiated by sex in Figure 1, the participant online profile data also records members’ self-reported education level and number of dependent offspring. According to these data, women in the sample have higher education levels than men ( < 0.001), broken down on the web site into high school, some college, diploma, degree and post graduate. As is standard practice for online dating web sites, participants also stipulate the (educational) characteristics they prefer or are seeking in a potential partner.

Fig 9: Age distribution differentiated by sex

Data Collection and Ethical Practice

Data for this study were freely supplied to the researchers by RSVP as a single cross sectional data set. All RSVP members provided informed consent for the use of their demographic information and online dating behaviour for research purposes as part of their initial membership registration7. Once collected, however, all such information was de- identified to protect participant anonymity.

Measures

To provide a detailed analysis of sex differentiated educational preference in the online dating market, we utilise four different dependent variables:

1) Individual’s preference specificity differentiating between

a) Whether or not an individual preference was communicated (Fig. 10 and

11/Table 6).

7 FairfaxMedia Privacy Policy: all personal information collected may be used for “administrative, marketing (including direct marketing), promotional, planning, product/service development, quality control and research purposes, or those of our contractors or external service providers.”

b) The level of definitiveness (“pickiness”) based on a scale from 0 to 1 (Fig. 12).

2) Preference for homogamy or hypergamy (Fig. 13).

3) Minimum acceptable level of education in a mate (Fig. 14).

The following outlines each measure’s construction, validity and use in this study.

Preference specificity. When participants first register their RSVP account, they are asked to select as many or as few of the five educational levels as would be preferable in their ideal partner8. Individuals are classified as “picky” (or choosy) if they explicitly state a preference for education, with the selection of fewer (more) educational categories equated with higher

(lower) levels of pickiness. Because stating a preference for all options (i.e. all five levels of education) is viewed methodologically as complete indifference (Fiore et al., 2010) and effectively mirrors those who make no discrimination (choose no options) between alternatives, we code these participants as “indifferent”. In total, 70.8% (N=10,754) of females and 82% (N=21,934) of males of the whole sample did not select an option while

5.3% (N=811) and 5.8% (N=1,559) of females and males marked all five education options.

In other words, of all female participants, 23.9% state a specific preference for educational level in their preferred partner, whilst 12.2% of male registered members expressed a specific preference.

Moreover, for participants who are selective, that is, those who stated between one and four preferences (females: N = 3,629; males; N = 3,249), we also measure the extent to which individual preferences are definitive (i.e. level of pickiness) by creating a scale based on the number of categories not selected. For example, if an individual selects only one educational

level, the score equals =1 (i.e. most selective). Those who select more categories are thus

8 1. High school 2. Some college 3. Diploma 4. Degree 5. Postgraduate.

62 assigned values closer to zero. The mean pickiness score for female participants is 0.49

(SD=0.35), compared with 0.52 (SD=0.37) for male participants.

Preference for Homogamy or Hypergamy. Defining the empirical concept of pickiness based on number of boxes selected, however, is not free of problems; for example, an individual ticking only the one lowest educational category (equal or less educated mate) would be designated picky, yet if the goal is to choose a mate more able to provide—implying selection of higher educational categories—then this individual is not choosy. To overcome this problem, we instead create a variable measuring how specific educational preference relates to the participant’s own education. This measure allows us to explore whether those with more specific preferences are seeking partners with lower (hypogamous), the same

(homogamous) or higher (hypergamous) educational levels than their own. As our dependent variable, we use a simple binary variable that has the value 1 if a person has a preference for the same or higher educational level in an ideal partner, and 0 when the highest preference for education is lower than his or her own education level (i.e. strictly hypogamous). We also include those who stated more than one category. In analyses using this variable, we exclude participants in the lowest educational category (high school) to avoid biasing or overweighting our homogamy versus hypergamy results. Respondents in this category can technically only state a preference for the same educational level or higher. Thus 191 high- school females and 444 high-school males were excluded. I addition, we also exclude 61 females and 46 male participants who did not report their education level, generating a final sample consisting of 3,377 females and 2,759 males.

Minimum acceptable level of education. It is important to acknowledge that the study of positive educational assortment and hypergamous preference are problematic, which stems

63 from individuals’ educational preferences being correlated with their own level of education.

As a further robustness test, our final dependent variable operationalises our sample population’s minimum acceptable level of education in their preferred mate. The measure then includes all five educational categories available for selection, with our minimum option

(High School) taking the value of 1, up to the highest level (Postgraduate) of 5. The number of males who stated a minimum level of education totalled N = 4,808, with females numbering N = 4,440.

Empirical Analysis

Our empirical analysis employs probit estimates that control for factors such as age, sex, education level, duration of RSVP membership, and dependent offspring (Table 1). We also use an Epanechnikov kernel function to estimate a local polynomial regression, on the basis of which we graph the relation between the likelihood of stated preference at different ages differentiated by sex (Figs. 2 to 6). This regression, which is a typical linear smoother

(i.e. linear transformation of the weighted average of responses), provides an estimate of the dependent variable (for different ages) by weighting the proximity of the observations based on their distance from the densest group of observations for that age. Thus, outliers, for example, are given less weight than closely grouped observations. The weighting for each observation is then included in the least squares optimization, providing an aesthetic representation with a 95% confidence interval.

Results

We begin by exploring the share of RSVP members who state a preference, coding those who tick all five or no preference categories as 0 and all those who state a preference between one and four possible preferences as 1. We differentiate these individuals by sex to

64 establish the likelihood of males versus females stipulating an educational preference in a potential mate (at different ages). This (dependent) variable is used for both our non- parametric (Fig. 10) and parametric analyses (Table 6).

Fig 10. Percentage of sample stating an explicit educational preference

Note. The proportions of the sample stating a specific preference for partner educational level are broken out by sex (Panel a, female =15,194; male = 26,742). Panel a. The dashed lines with markers represent raw averages. The solid coloured lines

65 show the average of local polynomial smoothing for the Epanechnikov kernel function given a bandwidth of 5 (Panel b). The shaded area represents the 95% confidence interval.

Table 6: Regression Coefficients from Multivariate Models of Participant Educational Preference

Probit Independent SE 95% CI / variables Age 0.023 0.000 0.0164 [0.021, 0.025] 0.006

Female 0.426 0.000 0.0009 [0.394, 0.458] 0.109 dummy Join date -1.22e-0.5 0.070 6.72e-06 [-2.54e-05, 9.89e-07] -2.95e-06

Education High school -0.059 0.048 0.0298 [-0.113, -0.001] -0.011

Some college -0.022 0.464 0.0298 [-0.080, 0.037] -0.004 Degree 0.368 0.000 0.0239 [0.321, 0.415] 0.086

Postgraduate 0.684 0.000 0.0247 [0.636, 0.733] 0.181 Reproductive 0.060 0.002 0.0191 [0.023, 0.098] 0.015 market Note: Probit regression analysis: N = 36,519; Prob > 2 = 0.0000; Pseudo R2 = 0.0870. = regression coefficient; = p value; SE = robust standard errors; CI = confidence Interval; / = marginal effects. The reproductive market variable indicates those with no offspring and/or offspring currently living at home.

Fig. 11. Marginal effects of sex and member’s age on the probability of stating an explicit educational preference with effects of control variables reported in Table 1 taken out

When we break the sample down by both participant age and sex (Fig. 10. lower panel), however, across all age groups, females (as a percentage of their sex) are more likely than males to specify the educational level(s) desired in a potential ideal mate. Such sex difference is confirmed by our multivariate analysis (Table 6 and Fig. 11), which shows in

Table 1 that females are 10.9% more likely than males to stipulate an educational preference.

Longer registered members are also more likely than recently joined members to state a preference, as are older participants (p < 0.001), and, all else being equal, those with the highest two educational levels. Those who may no longer be participating in the reproductive market (i.e. with offspring no longer living at home9) are also more relaxed about specifying an educational preference for a potential online mate.

Figure 12 illustrates the sex differences in relation to the actual definitiveness or specificity of individual preferences (i.e. pickiness), using the subsample of participants that state a specific preference. Females in their reproductively fertile years (ages 18-40) exhibit more stringent or specific preferences than males, but the level of pickiness diminishes as fertility declines. Past 40–50 years of age, participant preferences in both sexes become more specific with age.

9 Those with no offspring or with offspring living at home (value = 1) versus those with offspring who are no longer living with them (value = 0).

Fig 12. Specificity of educational preference: Pickiness

Note. Panel a: The solid coloured lines show the average of local polynomial smoothing for the Epanechnikov kernel function given a bandwidth of 1. The shaded area represents the 95% confidence interval. The sample includes participants who state a specific preference for education. Preference specificity (pickiness) for partner’s educational level is differentiated by sex (female, N=3,629; male, N=3,249). Panel b: predicted levels of pickiness by sex and age obtained from an ordinary least squares (OLS) regression controlling for factors reported in Table 1.

Fig 13. Preference for educational homogamy or hypergamy in ideal partner by sex

Note. The solid coloured lines show the average of local polynomial smoothing for the Epanechnikov kernel function given a bandwidth of 1. The shaded area represents the 95% confidence interval. The sample includes participants who state a specific preference for education. The proportions of those stating a preference for homogamy or hypergamy are broken out by sex (female, N=3,377; male, N=2,759), and excludes participants with high school as their highest educational level.

Because females and males incur different reproductive costs (internal gestation, lactation, higher maternal than paternal post-partum investment) females’ relatively more specific educational preferences may be attributable to the evolutionary distal cause of their higher opportunity cost in reproductive investment. If the increased likelihood of females stating an educational preference and the higher specificity of this preference can really be explained by evolutionary psychological theories such as parental investment, then female preferences should drive them to seek a mate who is more able to provide (i.e. with a higher level of education). Hence, for parental investment theory to be normatively sound, female preference for resource proxies such as education should not be hypogamous (lower relative to one’s own) because such a mate would not compensate females for the disproportionate opportunity cost of their reproductive investment. In fact, as Fig. 13 shows, both females and

69 males exhibit an increasing preference for homogamous or hypergamous preference (with values closer to a strict preference of y = 1) across the years of peak fertility (between 18 and the early 30s). Both tendencies then diminish with age, although the reduction is far less pronounced for females. This outcome appears even stronger when compared against finding from the raw data that females (as a percentage of their sex) tend to have higher educational levels than males, with higher percentages in the top three education categories10.

Fig 14. Lowest minimum acceptable educational preference in ideal mate

Note. The solid coloured lines show the average of local polynomial smoothing for the Epanechnikov kernel function given a bandwidth of 1. The shaded area represents the 95% confidence interval. The shaded area represents the 95% confidence interval. The sample includes participants who has a specific preference for education. The proportions of those stating a minimum level of education in their ideal partner are broken out by sex (female, N=4,440; male, N=4,808).

The study of educational homogamy and hypergamy can be problematic, as an individual’s own education level might be inherently correlated with their preference. Figure

14 also (in part) reduces the clarity of the data in question by providing only binomial results.

As a further robustness check for our overall findings, in Figure 6 we operationalise sex differentiated educational preference to reflect only the lowest educational category in a

10 Diploma (F = 21.96%.M = 18.85%), degree (F = 31.95%, M = 27.11%), postgraduate (F = 22.70%, M = 19.67).

70 potential mate that is acceptable to participants, thereby providing an analysis of a new dependent variable that is not correlated with individuals own level of education. Our results again show that females are more likely (compared to males) to state a higher minimum preference for the level of education in their ideal mate (for all age groups in our sample). In our sample, the minimum preference increases for the women across the years of peak fertility

(18-30 years).

Discussion

Because both male and female populations exhibit homogenous and sex differentiated preferences for certain mate characteristics (Buss, 1994), deriving a more accurate and nuanced picture of these individuals (and their wider mate choice behaviour) requires understanding that they do not always have the opportunity to state their preferences. Our analysis overcomes this drawback by using a large data set from the RSVP web site, which allows its members to specify which characteristics they are seeking. Although the results of this large-scale analysis align with the predictions of parental investment and sexual selection theory (that females of all ages are more likely than males to specify the educational level desired in a mate), when we exclude indifferent participants or those with no preference, males and females differ in this specificity at different life cycle stages.

In particular, we find that females (relative to males) exhibit more explicit preferences during their reproductive years, although those with a lower parental investment burden

(offspring out of the house) tend to be less specific. These findings reflect females’ evolved adaptive preference for males with an ability and willingness to provide resources (Buss,

1991). The fact that preferences are not static is further underscored by male preference changes over the life cycle, with preference specificity surpassing female pickiness when males reach the peak of their career and earnings potential. Our study thus contributes to a

71 growing body of literature indicating that women demonstrate a variety of context-dependent shifts in mate preferences. Nonetheless, because our results are based on cross-sectional data, the question remains as to whether they can be replicated using longitudinal data that capture the same individuals multiple times throughout the life cycle.

Chapter 5: WHAT WOMEN WANT IN THEIR SPERM DONOR: A STUDY OF MORE THAN 1000 WOMEN’S SPERM DONOR SELECTIONS

Stephen Whyte, Benno Torgler & Keith Harrison

Economics and Human Biology (2016) 23, 1-9

Abstract

Reproductive medicine and commercial sperm banking have facilitated an evolutionary shift in how women are able to choose who fathers their offspring, by notionally expanding women’s opportunity set beyond former constraints. This study analyses 1546 individual reservations of semen by women from a private Australian assisted reproductive health facility across a ten year period from 2006 to 2015. Using the time that each sample was available at the facility until reservation, we explore women’s preference for particular male characteristics. We find that younger donors, and those who hold a higher formal education compared to those with no academic qualifications are more quickly selected for reservation by women. Both age and education as proxies for resources are at the centre of Parental Investment theory, and our findings further build on this standard evolutionary construct in relation to female mate preferences. Reproductive medicine not only provides women the opportunity to become a parent, where previously they would not have been able to, it also reveals that female preference for resources of their potential mate (sperm donor) remain, even when the notion of paternal investment becomes redundant. These findings build on behavioural science’s understanding of large-scale decisions and human behaviour in reproductive medical settings.

Introduction

Human females (like most mammals) bear a heavier burden in reproduction than their male counterparts. Women’s considerable physical investment of internal fertilization, months of gestation and possibly years of ongoing lactation all come at a substantial physical and resource cost. Because of this significant reproductive constraint of parental investment

(Trivers, 1972), women have evolved preferences for males with the ability and willingness to provide resources. This may be to partly offset, or compensate, for the opportunity cost of their heavy maternal burden. As such, women’s capacity to identify men with both the ability and willingness to provide resources to them and their offspring is of critical importance (Buss

& Schmitt, 1993). A growing global market place for human gametes has facilitated a change in how women are able to identify and choose who fathers their offspring. Firstly, women no longer need to pursue and secure possible mates themselves; they are readily available as cryogenically frozen gamete samples at their nearest invitro fertilization (IVF) facility.

Consequently, women no longer need a mate’s consent to pursue pregnancy, as male donors relinquish dissemination control of their sperm at the time of their donation. Women are also no longer bound by the constraints of proximity, social class, culture, or race when choosing a male to mate with. A woman’s choice is therefore no longer limited by the availability of certain genetic and environmental factors such as aesthetics, education, and income, in potential or available mates. The willingness of a male to provide resources or to paternally invest has also become redundant. Women can choose a male to father their children based purely on the suitability of his genetic fitness.

In most developed countries women (and men) with fertility problems, single women, and lesbians are all now able to freely access sperm from sperm banks and reproductive health facilities for the purpose of insemination. They are able to reserve sperm close to where they live, and the facilities they seek treatment from are able to source sperm from all over the world. This market place for life has been significantly driven by the commercialisation of the

74 sperm donation industry in countries where it is illegal. Private companies such as Xytex Cryo

International11 and European Sperm Bank USA12 supply a global market of reproductive health facilities and institutions. The commercialisation of the sperm donation industry and advances in (IVF) technology has notionally expanded women’s opportunity set for mating, far beyond previous historical and evolutionary constraints. Behavioural exploration of how this market for human gametes operates is important, not only for the future of reproductive medicine and the psychology of its patients, but also wider behavioural and evolutionary science.

Exploring the characteristics of women’s preferences in this market place, and how they notionally and quantitatively differ from more traditional mating settings like our social circles, speed dating, and in online dating (Lykken & Tellegen, 1993; Buss, 1989; Fisman et al., 2006; Lee & Niederle, 2015) is currently an under-researched field. Research into the characteristics of preferred sperm donors and the women that participate in these markets is relatively new (Ripper, 2008; Riggs & Russell, 2010; Whyte & Torgler, 2015). By utilizing data on donor gamete reservations from an Australian private practice fertility firm, we are able to explore the factors and characteristics preferred by women when choosing a sperm donor. The innovative element of our study is that we use the speed at which certain donor’s samples are reserved as our dependent variable to analyse women’s actual consumption decision of a mate. This provides a far more robust analysis, as the reservations are actual decisions, rather than a more commonly used instrument in behavioural sciences which records participants’ stated preference (Scheib, 1994; Leiblum et al., 1995). Using the difference between date of gamete arrival at the firm and date of reservation by the recipient, we are able to create an elapsed time variable to explore women’s preference for specific donor characteristics.

11 Xytex Cryo International Ltd. 1100 Emmett Street Augusta, Georgia 30904-5826 USA. 12 European Sperm Banks USA: Sperm Bank & Cryobank. 4915 25th Ave NE #204, Seattle, WA 98105, United States

While resources (as a signal of parental investment) has been a core theoretical construct in understanding how women decide with whom to mate (Trivers, 1972), this setting allows us to explore the decision making process free of such constraint. Key genetic and environmental factors such as a male’s age, aesthetic features (eye colour, hair colour, height and weight), occupation and education level, can be analysed and distinguished from any correlation with paternity. The exclusion of proxies for resources allows us to explore women’s true preference for certain genetic factors: the genetic factors that women know will be passed on to their future offspring.

Another interesting feature of the gamete market is that it is non-sequential in supply.

Traditional mate choice decisions usually entail humans making a “yes or no” decision about a possible partner at a single point in time, never knowing if another more suitable (or any) other option may materialise in the future. The sperm donor market is non-sequential, in that women have multiple options to choose from in real time, and the ability to attempt to maximise their preference set in a particular group of (mate) options (Whyte & Torgler,

2015).

Like many mate choice studies, this quantitative research seeks to ascertain the relevance and importance of the specific characteristics of males in the donation process. We question the key determinants or properties of donors, and whether certain traits increase reproductive success (i.e. do women prefer certain characteristics in men for reproduction when more formal constraints are relaxed). Our research aims to understand and demonstrate a tangible measurement of the female choice mechanism in a (notionally unbounded) non- sequential mating market. By exploring the timing of reservation of gametes from a commercial IVF facility, we aim to ascertain the factors at play in this large scale human mating decision.

Non-sequential multiple demographic factor evaluation of potential mates (sperm donor profile selection) deviates from how women and men historically have gathered information on and chosen who to mate with. Technological and human capital advancement in reproductive medicine (and to a lesser extent the conduit of the internet) is facilitating a fundamental shift in how the human species can make decisions about who to reproduce with.

The unique setting explored in this study allows the researchers to observe how people actually make decisions (choose) in a domain where they have little or no prior training or expertise, and furthermore in a domain with extremely high (economic, psychological and biological) stakes. To the best of the author’s knowledge this is the first ever economic analysis of actual female choice (not just preference) in a reproductive medical setting. While the inimitability of the data set used in this research means the findings presented are somewhat limited in scope, the researchers believe they represent a valuable contribution to what is sure to become a topic of burgeoning interest across a wide range of scientific disciplines.

Method

Data

This empirical research was conducted in conjunction with Queensland Fertility

Group13 (QFG). The data for this project were collated between December 2014 and June

2015. The donor information was generated from pre-existing non-identifiable data collected from individual donor profiles, readily available on a myriad of internet sperm bank websites

(for example Xytex & European Sperm Bank USA). Donor profile data generally includes (at a minimum) the donor’s date of birth, his marital status, occupation, ethnicity, blood type, physical attributes, and educational attainments.

13 Queensland Fertility Group, 55 Little Edward St Level 2 Boundary Court Spring Hill, Queensland 4000, Australia

The empirical data consists of the date of arrival for the donor’s semen sample and the date of reservation by the recipient at the QFG facility. As the sale of human tissue is illegal in Australia, women do not purchase gamete samples, rather they “reserve” the donation for use. To create a unique dependent variable of the elapsed time before reservation, the more recent date (the reservation date) was subtracted from date of arrival to give an “elapsed number of days” for each individual reservation. Only the first reservation by each different woman was included in the sample, as subsequent reservations are naturally correlated with the initial decision, and are often linked to reproductive medical procedures or ongoing semen storage decisions. As reservation numbers differ between donors (minimum one to maximum ten), each reservation in our sample represents a recipient’s14 unique choice of donor. The primary sample consisted of 1546 individual recipient reservations of semen across the time period 16th October 2006 to 22nd January 2015. Not all of these observations were able to be used in our analysis, as some profiles have missing values for some variables. This can be due to the fact that donor profiles are not globally standardised and differ by sperm bank. In addition, donors do not necessarily answer or provide information for all demographic factors.

QFG patients navigate the decision process in a standardized method across all facilities state-wide. Once a woman becomes a registered patient of the organisation they are given online access to QFG’s database of all currently available semen donor stock. Women are then free to look through the database for as long as they wish, and as many times as they wish, without constraint. Available stock (on average) numbers approximately 10-20 different donors at any one time. The order available donor stock is shown to recipients in the database is from longest to shortest time of storage at the organisation (date of sample arrival at QFG).

14 No female patient of QFG was identified at any point in this research by the QUT researchers. No demographics or identifying data was viewed or collated by the QUT researchers at any time. All female observations in this research were anonymous and consisted only of the date of reservation by the female, and the date of arrival of the sample at the facility.

Elapsed time of storage, from either the point of donation or at the QFG facility has zero bearing on the quality of the semen sample itself. And women are informed of this. The cryopreservation process of semen donation ensures that samples are frozen at a temperature of -196°C within approximately one hour of the donation being made. In fact, cryopreserved semen remains viable and has been successfully used well past several decades of storage, with no reduction in sperm quality (Cancer Council, 2014).

The individual number of unique male donors totalled 169 men. Donors’ ages ranged from 19 to 41 years with a mean of 26 years. Men who identified as married made up 5.76% of the sample, and 13.08% of these stated they currently have at least one child. 35.51% listed that they were currently a tertiary student of some kind, and 23.16% had stated that they had already attained some level of tertiary (education) accreditation.

175 150 125 100 75 Numberreservations of 50 25 0 0 20 40 60 80 100 120 140 Days

Figure. 15. Frequency of sperm sample reservation (90.82% of sample)

70 65 60 55 50 45 40 35 30 25 Numberreservations of 20 15 10 5 0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 Days

Figure. 16. Frequency of sperm sample reservation (49.48% of sample)

Figure 15 and 16 show the frequency of reservation of gamete donations based on number of elapsed days available at the QFG facilities before reservation. Figure 15 excludes

80 outliers that exceed 1000 days before reservation (90.82% of sample: N =1404). As such, more than nine out of every ten samples reserved by recipients are done so in less than five months from the date received. It is clear that there is strong demand for particular donor’s gametes as approximately one in every two samples is reserved in under a month (Figure 16 –

49.48% of sample).

Figure 17 and 18 represent the average elapsed time between consecutive reservations of a donor’s sample. Figure 17 is the complete sample (N = 1546) and shows a somewhat consistent average range of 10-18 days (9.60 days to 17.62 days) between reservations number one to number eight. And the elapsed reservation time average between the eighth and ninth, and ninth and tenth jumping significantly to 27.96 and 21.34, respectively.

30 27.96

21.34 20 17.62 17.52 16.66

13.12 12.13 11.45 11.74 9.60 10 Average numberofdays 0 d h h h n th th 1st 4t 6t 7t 8 9 o to 5th to to to to to 2 t to 10th t rd th th th th th 4 5 6 7 8 th 1s 2nd to 3rd 3 9

Figure. 17. Average days elapsed between reservations (N = 1546)

6.37 6

5.26

4 3.65 3.50 3.57 3.47 3.25 3.27 3.00 2.71 2 Average numberofdays 0 d h h h n th th 1st 4t 6t 7t 8 9 o to 5th to to to to to 2 t to 10th t rd th th th th th 4 5 6 7 8 th 1s 2nd to 3rd 3 9

Figure. 18. Average days elapsed between reservations (49.48% of sample)

As the supply in this market clears so quickly, we also look at those donors whose samples are reserved the fastest. In Figure 18 we present approximately half the sample

(49.48%: N = 765) which consists of all of the donors whose maximum available 10 samples were reserved in less than 32 days (one month). The average time for first reservation and first to second reservation are more than twice as fast as the full sample comparison in Figure

17. There also appears to be a consistent average trend between reservations from second to third, all the way to ninth to tenth, of approximately 3 days (2.71 days to 3.65 days).

Results

Ordinary least squares (OLS) regression was used to analyse the preferred determinants of choice for women when selecting a sperm donor using the number of days until reservation as a dependent variable (Table 7). We also report beta (standardized) coefficients so that all coefficients are in the same metric (standard deviation units), and thus

82 can be compared across variables. In all specifications we add dummies for the reservation order (e.g., whether it was the first reservation of person i, the second one of person i, etc.).

In specification (1) we start with four independent variables: the donor’s age, and their educational qualification using three dummy variables, namely tertiary degree, tertiary qualification but still studying, and still studying. All the rest of the donors are in the reference group. We observe that women have a preference for donors who are young. A sperm sample of a person age 20 is reserved on average 30 days sooner than one of a man age

30. Women also prefer men with a tertiary qualification. Having such a degree reduces the reservation time by more than 20 days. In particular those who have a degree and who are still studying are in high demand although it should be noted that the sample of such individuals is quite small. The number of observations of samples reserved where donor had both a tertiary qualification and was still studying was N = 35 (2.26% of sample).

Table 7. Factors influencing number of days until reservation

Dep Variable: N days until (1) (2) (3) (4) (5) reservation Age 2.951*** 2.587*** 2.990*** 2.667** 2.948** 0.708 0.683 0.927 1.305 1.328 (0.135) (0.118) (0.129) (0.124) (0.137) Degree -26.094*** -25.869*** -23.450*** -21.570*** -23.706*** 6.981 6.950 8.124 7.258 7.741 (-0.951) (-0.094) (-0.863) (-0.080) (-0.088) Degree & -45.947*** -53.684*** -25.236** -47.223** -45.569** still studying 9.342 10.614 10.319 20.213 22.793 (-0.666) (-0.778) (-0.032) (-0.057) (-0.055) Student -2.627 -3.919 -5.435 -11.112 -10.056 7.835 7.564 7.563 8.603 9.298 (-0.010) (0.015) (-0.021) (-0.464) (-0.043) Total Reserved -13.248*** -11.136*** -9.998*** 2.738 3.418 3.481 (-0.147) (-0.129) (-0.117) Height 1.814* 1.603 1.053 1.095 (0.085) (0.075) Weight 0.108 0.095 0.305 0.313 (0.010) (0.008) Number of Children -8.485 -6.901 10.691 11.404 (-0.042) (-0.037) Marital Status: -39.181** Married 17.548 (-0.077) Marital Status: 25.919 Divorced 21.740 (0.037) Marital Status: 4.531 No information 12.134 (0.019) Reservation Order Yes Yes Yes Yes Yes Dummies Skin Colour No No No Yes Yes Dummies Eye Colour No No No Yes Yes Dummies Hair Colour No No No Yes Yes Dummies Blood Type No No No Yes Yes Dummies

N 1189 1189 950 992 992 Prob > F 0.0000 0.0000 0.0000 0.0000 0.0000 R-Squared 0.1436 0.1639 0.1677 0.2479 0.2531 Notes: Robust std. err. are presented in italics, beta coefficient presented in parentheses. *, ** and *** represent statistical significance at 10, 5 and 1% levels, respectively. Reference group variables include: Hair colour: Auburn, black, blonde, blonde dark, blonde light, blonde medium, blonde strawberry, brown, brown dark, brown light and brown medium. Eye colour: Black, blue, blue green, blue hazel, brown, brown dark, brown light, brown medium, green, green brown and hazel green. Blood type consists of all ABO and Rh blood types. Skin Colour: Brown, dark, fair, medium and olive.

Next we control for the total number of reservations (1 to 10) as a proxy for attractiveness (see specification (2)) as not all semen donors realised the maximum 10

84 samples reserved by women. The effect size of both age and education remain, as does the statistical significance. The results indicate that the more popular the donor, the faster they reach the maximum reservation of ten samples. In specification three (3), we further restrict the data set to only those donors who had achieved their maximum of ten reservations. Thus, the sample size decreases to 950 observations. This variation mainly affects the group of people who have a degree and are still studying, which is possibly due to the low number of observations. In the next two specification we take better advantage of the available variables in our data set (see specifications (4) and (5)). This allows us to further investigate the robustness of our two key variables, age and education. First, we add donors’ height and weight, and number of children. We also now use dummies to control for further individual characteristics such as skin and hair colour and blood type. We do not report the coefficients of these variables, but rather note the F-tests for the joint significance of each group of variables. The F-tests indicate the usefulness of including eye colour, hair colour, and blood type in our specification. In specification (5) we include marital status. For some donors there is no information about their marital status, thus we build a dummy variable “No information” to avoid further reducing the number of observations.

Finally, in specification five (5), we include a marital status classification for those donors who are married, divorced, or chose to provide no information on their donor profile.

In this context we find that donors who are married are chosen for reservation faster than donors who state that they are single. Both marital status and number of children are used as possible proxies for signals of fertility and attractiveness. The number of children15 is positively correlated with a faster pick but the coefficient is not statistically significant. On the other hand, being married rather than single (reference group) reduced the reservation

15 We also ran additional specifications (that have not been included in the tables) that included a dummy independent variable for having a child. As with the number of children variable, these results were also significant at a 1% level.

85 time by 39 days (coefficient statistically significant at the 1% level). Height and weight are included as signals of genetic fitness of the donor, as research has shown that women exhibit a preference for taller men (Lynn & Shurgot, 1984).

Table 8. Interaction effects: Donor Age and reservation order

Dep Variable: N days until reservation (6) (7) Age -2.335* -2.078* 1.144 1.096 (-0.102) (-0.096) Reservation Order (1 to 10) -8.527 -9.289 7.745 7.721 (-0.201) (-0.239) Age x Reservation Order (1 to 10) 0.949*** 0.917*** 0.314 0.320 (0.630) (0.661) Degree -24.370*** -22.451*** 7.309 7.226 (-0.085) (-0.083) Degree & still studying -53.328*** -46.104** 15.137 20.971 (-0.066) (-0.056) Student -3.637 -10.704 7.772 8.508 (-0.014) (-0.045) Number of Children -4.403 -8.319 9.349 10.401 (-0.019) (-0.041) Height -0.043 1.927* 0.607 1.054 (-0.002) (0.089) Weight 0.035 0.055 0.269 0.304 (-0.002) (0.005) Total Reserved Yes Yes

Skin Colour No Yes Dummies Eye Colour No Yes Dummies Hair Colour No Yes Dummies Blood Type No Yes Dummies N 1088 992 Prob > F 0.0000 0.0000 R-Squared 0.2590 0.2590 Notes: Robust std. err. are presented in italics, Beta Coefficient presented in parentheses. *, ** and *** represent statistical significance at 10, 5 and 1% levels, respectively. Reference group variables are: Hair colour: Auburn, black, blonde, blonde dark, blonde light, blonde medium, blonde strawberry, brown, brown dark, brown light and brown medium. Eye colour: Black, blue, blue green, blue hazel, brown, brown dark, brown light, brown medium, green, green brown and hazel green. Blood type consists of all ABO and Rh blood types. Skin Colour: Brown, dark, fair, medium and olive.

Weight16 has the expected positive relationship but the coefficient is negative. Surprisingly, height also shows a positive correlation but the coefficient is only statistically significant in one specification (and only at the 10% level).

Looking at the standardised coefficient in specification (5) we observe that age has the strongest influence on the reservation speed. This result motivated us to look more closely at age. In Table 8 we present specification six (6) and seven (7), which further explores the interaction effect between donor age and reservation order on the speed of donor semen reservation. This interaction analysis explores whether the order of the reservation is influenced by the age of the donor. In both regressions younger donors are increasingly more attractive in later reservations. 8 6 4 2 Effects on Linear Prediction Linear on Effects 0 -2 1 2 3 4 5 6 7 8 9 10 Reservations

Figure. 19. Average marginal effects: age and reservation order

16 We also ran additional specifications (that have not been included in the tables) that included a dummy independent variable for BMI. This is weight, divided by height squared, however this independent variable was not statistically significant in any specification.

Table 9. Average Marginal Effects: age and reservation order

Age & reservation order dy/dx Std. Err. [95% Conf. Interval] 1st -1.161 0.927 -2.979 0.658 2nd -0.243 0.849 -1.909 1.422 3rd 0.674 0.887 -1.067 2.415 4th 1.592 1.029 -0.428 3.611 5th 2.509** 1.239 0.077 4.941 6th 3.426** 1.489 0.504 6.349 7th 4.344** 1.762 0.886 7.802 8th 5.261*** 2.049 1.241 9.282 9th 6.179*** 2.344 1.579 10.779 10th 7.096*** 2.645 1.905 12.287 Note: . *, ** and *** represent statistical significance at 10, 5 and 1% levels, respectively. N = 992

To get a better idea of the underlying relationship, we represent age by reservation order interaction, demonstrating the way that the age slope changes as a function of the reservation order (see Figure 19). Furthermore, we estimated the age slope at each level of reservation. This shows that the age slope increases as a function of reservation order. In fact, the slope was even negative for reservation 1 and 2. The graph visually depicts the age slope increasing linearly as a function of reservation order. We have also included the confidence interval with respect to each age slope, illustrating that the age slope is statistically significant in later reservations (reservation five to 10).

Discussion

Our multivariate analysis demonstrates women’s preference for younger and tertiary educated men, particularly for those continuing with further study. Education is certainly a proxy for resources and paternal investment, but as previously discussed, sperm donation is not a setting in which parental investment is possible. Even before Trivers (1972) identified

“parental investment” as the driving force behind women’s preferences, science had researched and identified women’s penchant for resources (not all of which are economic or material) as a reproductive strategy. In fact, the “cumulative weight of scientific evidence

88 supports the hypothesis that human females have evolved species-typical psychological mate preferences for males who display cues to resources and resource acquisition” (Buss, 1991, p.406). Favoured proxies for resource acquisition such as education (Regan, 1998, Townsend

& Wasserman, 1998) can also be signals of a male’s genetic fitness. Therefore, women’s preference for tertiary educated sperm donors is more likely to represent a decision based on the genetic payoff. Bearing children with higher intelligence or children who are more willing to learn naturally reduces maternal investment and cost for the mother involved in the short run. And in the long run possibly the realisation of resource gains from more productive offspring. Education has previously been shown to be deemed “very important” for recipients when choosing a donor (Leiblum et al., 1995, pg.11). Sperm donation profiles do not carry information regarding a donor’s income, but they definitely allude to resource acquisition or potential future earnings. Without information on the recipient it is impossible to establish if females’ preference for tertiary educated sperm donors is driven by assortative homophily

(Skopek et al., 2010) or possibly even hypergamy on the part of the recipient (Whyte and

Torgler, 2014). As women seeking IVF are often in, or post, peak fertility (late 20’s to early

30’s, or older), it would not be unreasonable to expect female preference for assortative and aspirational mating based on their own educational attainment. Women in their late twenties and early thirties are more likely to have accrued a significantly higher level of education, than their younger (donor) male counterparts.

In a market where supply rapidly clears, younger male donors do not necessarily realise a fitness bonus in their earlier reservation, but in later reservation of semen, older men are definitely not preferred to younger males. This interaction effect between donor age and the speed at which his remaining gametes are reserved again significantly favours the younger donor. The age of the donor, while not relatively important in the preliminary reservation, becomes a highly significant factor between the fifth and tenth (final reservation). Our

89 research poses an interesting question: when the issue of “actual” paternal investment by males becomes redundant thanks to reproductive medicine, why is age of the preferred mate

(donor) the only significant change in women’s preferences. One hypothesis may be that in a reproductive setting, women are choosing younger men on the assumption that they will have more viable sperm (Kidd et al., 2001) than their older counterparts, even though a male’s sperm counts and motility or probability of reproductive success is unlikely to significantly vary in the current study’s observed donor population age range (19-41 years). This cognitive bias towards youth is further accentuated based on the knowledge that current semen cryopreservation techniques ensure no reduction in semen quality or viability across decades of storage.

Limitations

The authors acknowledge several limitations of the study. Firstly, while the donor semen market does offer a greater diversity of choice in a non-sequential setting, it could be argued that it is a quasi-unbounded market, as available donors are not infinite. Availability of supply (Van den Broeck et al., 2013; Yee, 2009) is at times compromised for a range of social, medical, financial, legal, or even logistical reasons. We are also unable to collect information on participants’ (both donor and recipient) personality or preference for personality, which has previously been shown to impact sperm donor choice (Whyte &

Torgler, 2015).

Not all donor profiles used in this study had completely homogenous demographic variables. It is of little surprise that donor profiles are incorporating increasing amounts demographic data, as women seek additional information (Porter & Bhattacharya, 2008;

Wingert et al., 2005) to assist their choice, and to forecast their potential for reproductive success. US research indicates that the number of donor insemination (DI) programs offering

90 open-identity sperm donors is also increasing (Scheib & Cushing, 2007). It is still unclear whether this is indicative of women wanting ongoing contact with their donor, or just increased information to assist in the child’s development. However, it is clear that women are requesting and are being provided with more information in their choice of donor than ever before.

Healthcare professionals also influence a recipient’s preferences and decisions, either deliberately or unknowingly (Almeling, 2006). Both the recipient’s specific medical requirements and the type of IVF procedure chosen also impact women’s decision making.

The recipient’s age and fertility affects the probability of reproductive success, and thus naturally influences the speed and desire to select a donor. Women’s fertility is cyclical and as such not linear like our time dependent variable. Furthermore, pre-existing family arrangements (e.g. siblings), or social pressures may also guide preference and choice, and influence timing of reservation.

This study does also not delineate sexuality on the part of the recipient, and whether this has an influence on the decision making process. The literature acknowledges that less attention has been paid to the specific dynamics inherent in lesbian donor conception, and how lesbian couples navigate these processes (Nordqvist, 2010; 2012). However, with respect to sperm donation decisions and sexual preference, research has shown that the only major difference being “that heterosexual women begin DI attempts on average at an older age”

(Leiblum et al., 1995, pg.11). Both groups are also alike in their information requests, “about the donor, principally health variables and medical history” (Jacob et al., 1999, pg.203).

The question also arises as to whether women in the process of IVF and DI are disproportionally risk-seeking in their attitudes, or at least non-reflective of the broader community base in relation to their perception of risk. A study by Reading (1989) found that

91 although none of the women in his study reported being given a success rate greater than

50%, they each believed their own chances to be higher, and did not consider the actual probability of success. One in every two women stated that “the decision to undergo treatment was not affected by probability estimates” (Reading, 1989, p.107). The fact that there may be

“misconceptions in the community about the effect of age on natural fecundity and the outcome of fertility treatment” (Maheshwari et al., 2008, p.1041) may actually mean that women participating in IVF and DI procedures are more representative than first thought.

While there exists a body of scientific literature on how men advertise in traditional mating settings like personal ads and dating websites (Waynforth & Dunbar, 1995;

Pawlowski & Dunbar, 1999; Pawloski & Koziel, 2002), exploration of how men advertise via sperm donation profiles (Riggs & Russell 2010) is limited. It is also unclear if males are cognitively aware of developing female preferences in this new and unique setting when they compile their donation profiles.

It is also uncertain what impact sexuality has on the way men present and provide information in their donor profiles, and the effect this has on information asymmetry. Studies have shown marked differences “between heterosexual and gay/bisexual donors with the latter being significantly more likely to desire contact with children born of their donations”

(Ripper 2008, pg. 313). It is also unclear how this influences the flow of information and formation of donor profiles when sexuality is not a requested or stated preference.

Finally, and most importantly, the authors would like to acknowledge that not all donation reservations that occurred at QFG during the time frame stated were included in the sample. Our sample did not include some international and domestic donors that were excluded because: 1) for whatever reason their samples were of a standard that meant they were restricted by the treatment procedure that they could be used for, 2) there was a lack of

92 the necessary demographic characteristics to conduct an adequate analysis for the purpose of this study, and 3) the donor had been specifically ordered for one recipient only and were not available for wider general usage (selection).

Conclusion

Research exploring the decision making process of women in reproductive medical settings is in its infancy. Our study provides important insight into the characteristics preferred by women when choosing a sperm donor in a formal medical setting. We suggest that future studies explore the role of personality in choice, and aesthetic symmetry in selection (assortative mating), two areas that have been widely explored in a range of other human mating settings. It is important to understand the role and influence of behavioural tendencies shaped in previous or redundant environments, and its impact on this particular mate selection (reproductive medicine) scenario (Simpson & Gangestad, 1992).

While on the surface it would be easy to assume females show a preference for particular (alpha) male(s). But our research shows that women do not necessarily gravitate towards a specific male, rather groups of genetic and phenotypic traits signalling genetic fitness in males. Male quality (their dna) is well defined in this setting, and it would appear that unlike in real life supply is highly elastic, meaning choices (to an extent) are not necessarily constrained by rationing or pricing. But the reality is that while price is

(somewhat) homogenous across the industry, supply is significantly constrained globally. It is for this very reason that the use of elapsed time as our dependent variable is apt for this study.

Time, is the most valuable commodity for women with finite reproductive viability, who are wishing to maximise in their choice of male donor characteristics.

There is no doubt that “human mating defies simple characterization” (Buss, 2000, p.47). This is primarily due to the fact that decisions in one particular mating scenario or

93 event “may not necessarily reflect choices on subsequent occasions or opportunities” (Roberts

& Little, 2008, p.309). However, whether in traditional human mating settings or in regard to sperm donation decisions, women in the aggregate consistently exhibit preferences for signals of paternal investment and the future provision of resources (i.e. educational attainment).

The social sciences have been able to build an understanding of the importance of particular factors (and humans preferences for them) in the decision making process. That female mate choice preference for age is reversed in a reproductive medical setting is an important finding for behavioural science, and particularly for reproductive medicine and psychology. As mate choice is one of the largest economic decisions humans can make, the study of IVF and DI choices by women provides unique insight into the female choice mechanism, and the developing impact that reproductive technology is having on sex difference in mate preference. As advances in assisted reproductive medicine gather pace, along with demands for these processes from both men and women, ongoing and increased behavioural research is warranted in this field.

Chapter 6: ONLINE SPERM DONORS: THE IMPACT OF FAMILY, FRIENDS, PERSONALITY AND RISK PERCEPTION ON BEHAVIOUR

Stephen Whyte, David, A. Savage & Benno Torgler

Reproductive Biomedicine Online (2017) forthcoming.

Abstract:

As informal sperm donation becomes more prevalent worldwide, understanding donor psychology and interactions is critical in providing effective policy, equitable legislative frameworks, and frontline health support to an ever-growing number of global participants. We analyse data of informal sperm donors who were members of the connection website PrideAngel to identify the role and impact of several factors (such as kinship, social networks, personality, and risk perception) on behaviour. A key strength of the study is the ability to analyse various factors such as the level and history of informal donation, risk concerns, number of women to whom donations are informally made, and the number offspring generated. Our results indicate donors who have also been active in formal clinical settings (compared to those who exclusively donate informally) donate to more women in the informal market and realise more offspring. Donor’s sexual orientation also affects activity. From a personality perspective, conscientiousness provides comparative advantage. It is possible this characteristic provides positive externalities, as more conscientious men maybe more efficient or organised in a market that requires increased cooperation and communication. The importance of kin and social networks appears to only

impact frequency of donation, possibly representing a time constraint (or opportunity cost).

Introduction

The current global climate in sperm donation can best be described as “one that is in transition” (Daniels, 2007, p.124). This is because the internet is facilitating a change in the way women choose the (biological) father of their offspring. The internet and the development of “connection websites” constitute a new setting in which men and women can increase information flow, reduce financial burdens or barriers to sperm donation, and negotiate their own individual donation and parenting arrangements. Sperm donors and the women (and couples) who seek their gametes are no longer bound by logistical or national boundaries (Jadva et al., 2015), by cultural, social, financial, or even sexuality based barriers that historically have excluded them from donor insemination opportunities (Acker, 2013).

Donors and recipients are stepping away from the (medical and legally) regulated setting of clinical donation, to find each other through connection websites and web forums (Whyte &

Torgler, 2016a). Yet, the factors driving men to participate in these informal donation processes are not as clear. It is thought that in many instances the implementation of donor identity legislation has resulted in a contraction in the number of formal (clinic) donors (Riggs

& Russell, 2010), and a movement of men towards informal donation settings (Bossema et al.,

2014). While the media has recently brought wider social visibility to informal donation

(Acker, 2013) it is unclear how many recipients and donors are currently participating globally in the informal market (Woestenburg et al., 2016), or how many offspring are being realized annually. Of the studies to date exploring two of the internet’s largest global

96 connection websites (Free Sperm Donors Worldwide (FSDW) and PrideAngel), registered sperm donor web profiles number in excess of 2000 and 5000 men, respectively (Riggs &

Russell, 2010; Freeman et al., 2016). Research has proposed that informal donors are demographically diverse, with primarily altruistic and procreative motivations (Yee, 2009;

Riggs & Russell, 2010; Freeman et al., 2016; Woestenburg et al., 2016). Previous research has also found that more successful informal donors tend to exhibit personality traits that are more cooperative (Whyte & Torgler, 2015) and introverted (Whyte & Torgler, 2016a). As behavioural research has only really begun in the last decade, little else is known about informal donors’ psychology or behaviour.

Technology (the internet) has facilitated a fundamental change in how men and women choose whom to have children with. Humans are no longer constrained by logistical propinquity in mate choice. In developed economies, online dating, dating apps, social media, and the wider internet is now a socially accepted global platform for meeting a partner (Whyte

& Torgler, 2017a). This cyberspace human mating conduit is also being utilised as a compensatory mechanism for decreases in the availability of the global supply of clinically donated gametes. Beginning in the early 2000s, a global online market has developed for sperm donation outside of regulated donor insemination (DI) clinics and sperm banks.

Changes in cultural and social norms, same-sex (lesbian, gay, bisexual, intersex and questioning) equality, gender equity, changes in family structure, the increasing delay of fertility decisions, and the breaking down of previous stigmas attached to donation have all been contributing factors in the growth of connection websites, and wider informal donation.

Legislative frameworks regarding donor anonymity are also gradually moving towards mandatory identity accessibility for donor-conceived children at 18 years of age. In the United

Kingdom, where PrideAngel is based, this (the abolition of donor anonymity) occurred in

2005. But for many men and women private donation arrangements (free of regulatory oversight) are their preferred choice.

There are significant advantages for men and women who participate in informal donation practices. Firstly, formal donation restricts information transfers about, and for both parties. For women, formal settings mean a reliance on the clinical provider to deliver vital demographic, genetic, aesthetic and personality information on their choice of donor (Nelson

& Hertz, 2016). Furthermore, women have also been constrained by the availability of commercial supply, thus limiting choice (Whyte et al., 2015; Whyte et al., 2016). Donors, on the other hand, receive limited information on the recipient and any resulting offspring, effectively relinquishing all control of their gametes at the point of donation. The informal donation process reverses this relationship by enabling donor choice and ongoing cooperation.

It enables donor and recipient autonomy (from the regulator) through increased information transfers to both parties on demographics, personality, and motivations of both donors and recipients based on their needs and preferences. For women, access to current and ongoing paternal medical information can be a significant factor in deciding on a donor (Acker, 2013).

For donors, information on the actual (procreation) outcomes of their donation may be the very impetus for their participation. Informal donation can also provide men with the opportunity for different forms of ongoing contact and bonding with recipient and offspring

(Bossema et al., 2014); something that anonymous formal donation cannot.

Formal donation can also be a significant financial burden for women and couples.

Clinical DI treatment can cost thousands of dollars with no guarantee or greater probability of success for recipients. Informal interactions incur no such direct treatment costs (Ravelingien et al., 2016). For donors, clinical screening processes based on genetic, demographic, marital status or sexual orientation caveats restrict access to donation markets. Informal donation then normatively creates a more competitive market by increasing the available donor pool

(supply), and reducing financial (and opportunity) cost for women and couples seeking donors (increased demand). Most importantly, one-to-one interactions allow men and women to freely negotiate post-partum parenting and interaction arrangements before any resulting offspring. Such arrangements can be more adaptable, appropriate or relevant for donors and recipients than the particular legal framework currently in place in the domestic market in which the donation takes place.

Despite the positive benefits to the individuals involved, non-clinical settings do however open donors up to the possibility and risks of “social disapproval” (Bossema et al.,

2014), something that has historically never been an issue for sperm donors, and something that may result in significant psychological harm. Ongoing misalignment of donor/recipient attitudes or objectives, or a lack of positive consequences or outcomes from informal participation may actually mean some men cease donation all together. Such psychosocial needs of donors are largely neglected in DI research (Daniels, 1998; Van den Broeck et al.,

2013). Research into the relationship between male personality traits and the large scale decision setting that is informal sperm donation is also extremely limited (Bossema et al.,

2014; Whyte & Torgler, 2015).

The historical anonymity of sperm donation has meant that research into both formal and informal donors is problematic. Both formal donation by anonymous donors (no demographic information made available for researchers), and informal donation by men in markets with no regulatory oversight (no third party regulator collecting donor information) have made it difficult to assess the practice and scope of the donors in question (Harper et al.,

2017). This is reflected by Van den Broeck et al.’s (2012) systematic review of 29 studies into formal sperm donor demographics, attitudes, and motivations, which showed a mean sample size of only 147 participants (range 17 to 1428), and a median sample of just 52 observations.

Studies with larger observations (N = 1546) have since been published looking into recipients

99 favoured demographic characteristics of clinical donors (Whyte et al., 2016), as well as the demographics, motivations and preferences of online donors (N = 383) (Freeman et al., 2016).

Research has shown an increase in the number of men looking to donate in informal settings (Bossema et al., 2014) and studies have also started to explore donor motivations and attitudes finding differences across sexual orientations (Ripper, 2008; Riggs & Russell, 2010;

Freeman et al., 2010). Informal market research into recipient’s donor preferences has shown that women prioritize donors’ internal values (like reliability and openness) over their external characteristics like aesthetics, education or occupation (Whyte & Torgler, 2015). And those informal donors with less extroverted and lively personalities, who are more intellectual, shy, and systematic, realize more offspring via informal donation (Whyte & Torgler, 2016a).

The notion that paternal identity and (or) ongoing interaction with offspring are motivating or inhibiting factors for online donors (Ripper, 2008; Riggs & Russell, 2010) warrants researchers to develop a greater understanding of the impacts of donors’ pre-existing kinship, social networks, and risk perceptions towards informal participation. How these factors impact informal donor behaviour and decision-making is something that is yet to be explored. This study aims to build on the limited previous research by exploring the influence of factors widely used in economics, psychology and sociology, but until now have been previously untested in this setting. The study explores factors such as kinship, social networks, personality, and risk perceptions, and their impact on male interactions and behaviour in the informal donation market. We utilise a unique data set of 112 informal donors’ survey responses collected between November and December 2016 from registered members of the connection website PrideAngel. The studies aim being, to develop a more nuanced understanding of the factors that impact male behaviour in the informal market for sperm donation.

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Method

Data collection

Data collection for the research was conducted via a 42 question online survey using the Queensland University of Technology (QUT) Key Survey software17. More than 7000 registered donor members of PrideAngel were mass emailed an invitation to participate in the survey on Tuesday 14th November 2016. The survey consisted of a range of generic sociodemographic questions (age, income, education, height, weight, marital status, sexual orientation, weekly work commitments.), as well as questions on individuals history of informal and formal behaviour, three 0-100 self-rate scales for happiness, health and risk perception, and finally a Mini-marker Big 5 personality test (Saucier, 1994). The survey was open for one month and closed on the 13th December 2016. Donors were also targeted via advertising through PrideAngel social media on Twitter, Facebook, and LinkedIn.

Participants were incentivised with 10 free message credits (£10 GBP in value) for their participation. Half of this payment was funded by the researchers, and the other half was generously donated by Erika Tranfield, CEO of PrideAngel (Whyte et al. 2017).

Table 10. Donor descriptive statistics

Siblings, Cousins & Friends N Mean SD Number of siblings 105 2.28 2.05 Number of cousins 102 13.21 15.53 Number of close friends 104 6.47 9.24

Informal donation behaviour N Mean SD Number of monthly donations 105 4.84 19.64 Number of years donating (total) 107 3.19 3.20 Number of women donated to (total) 105 15.24 38.40 Number of offspring from donation (total) 99 6.39 13.75

Personal FactorsA N Mean SD Happiness 111 73.91 18.88 Health 111 85.86 13.56 Risk Perception 104 32.54 28.70

17 Queensland University of Technology (QLD), Brisbane, Queensland, Australia.

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Big 5 Personality TraitsB N Mean SD Extroversion 105 4.66 0.70 Conscientiousness 105 5.45 0.83 Agreeableness 105 5.61 0.73 Emotional Stability 105 5.67 0.65 Openness 105 4.87 0.95

Importance of Kin, Networks & ReligionA N Mean SD Family (Immediate) 110 78.58 28.88 Family (Extended) 110 71.40 26.50 Friends 109 69.81 20.35 Neighbours 109 38.11 26.91 Religion 107 29.21 36.18 Note: Due to QUT Human Research Ethics clearance restrictions on this project, respondents were able to skip questions they did not wish to answer; as such the N is not consistent for all responses. A). Denotes 100 point scale question: Example: “All things considered, on a scale of 0 – 100, how important is your extended family in your life?” B) Denotes 7 point lickert scale questions.

Fig. 20. Distribution of donor age

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Note: Self rated risk perception measure, “Not risky at all” = 0 to “Extremely risky” = 100

Fig. 21. Distribution of donor risk perception towards informal donation

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Table 11. Factors impacting number of average monthly informal donations (Nbreg)

(1) (2) (3) (4) Age 0.008 0.006 (0.012) (0.112) 0.030 0.016 Height -0.049 -0.048 (0.196) (0.158) -0.187 -0.131 Education 0.158 0.027 (0.130) (0.083) 0.602 0.074 Income 0.068 -0.008 (0.047) (0.031) 0.257 -0.023 Marital Status (Single) 0.437 0.244 (0.315) (0.238) 1.607 0.653 Non-heterosexual -1.513*** -1.114*** (0.486) (0.161) -3.910 -2.246 Weekly hours worked 0.009 -0.028 (0.009) (0.037) -0.034 -0.078 Formal donation -0.574 -0.213 (0.380) (0.254) -1.955 -0.561 Happiness -0.023* -0.009 (0.013) (0.008) -0.089 -0.025 Health 0.041*** 0.015* (0.014) (0.008) 0.156 0.040 Extraversion 0.435 -0.023 (0.301) (0.199) 1.683 -0.062 Conscientiousness 0.002 0.170 (0.210) (0.148) 0.010 0.463 Agreeableness -0.143 -0.233 (0.258) (0.208) -0.552 -0.636 Emotional Stability 0.235 0.359 (0.216) (0.251) 0.907 0.978 Openness -0.404*** -0.197 (0.154) (0.130) -1.560 -0.539 Family (Immediate) 0.019*** 0.020*** (0.007) (0.007) 0.060 0.055 Family (Extended) -0.025*** -0.023*** (0.008) (0.006) -0.079 -0.063 Friends -0.001 0.005 (0.007) (0.009) -0.002 0.015 Neighbours -0.010** -0.008* (0.005) (0.004) -0.030 -0.023 Religion 0.006 0.005 (0.004) (0.003) 0.018 0.013 N 105 104 105 104 Prob > X2 0.0761 0.0320 0.0034 0.0001 Pseudo R2 0.0466 0.0451 0.1017 0.1413

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Table 12. Factors impacting number of years donating informally (Nbreg)

(1) (2) (3) (4) Age 0.028*** 0.025*** (0.008) (0.009) 0.078 0.067 Height 0.171* 0.103 (0.092) (0.093) 0.475 0.270 Education 0.145 0.155* (0.096) (0.082) 0.402 0.406 Income -0.007 -0.020 (0.021) (0.022) -0.018 -0.051 Marital Status (Single) 0.186 0.260 (0.183) (0.190) 0.506 0.663 Non-heterosexual -0.067** -0.689** (0.090) (0.320) -1.525 -1.483 Weekly hours worked -0.051* -0.041 (0.027) (0.033) -0.141 -0.108 Formal donation 0.347* 0.481*** (0.181) (0.174) 1.027 1.382 Happiness -0.004 -0.001 (0.513) (0.005) -0.013 -0.004 Health 0.002 -0.007 (0.007) (0.006) 0.007 -0.019 Extraversion -0.143 -0.134 (0.183) (0.178) -0.433 -0.351 Conscientiousness 0.087 0.052 (0.124) (0.117) 0.264 0.136 Agreeableness -0.228 -0.225 (0.164) (0.174) -0.692 -0.587 Emotional Stability 0.256 0.284* (0.164) (0.107) 0.775 0.741 Openness -0.072 0.005 (0.117) (0.107) -0.219 -0.013 Family (Immediate) 0.003 0.008 (0.004) (0.005) 0.009 0.020 Family (Extended) -0.011** -0.009* (0.005) (0.005) -0.033 -0.024 Friends -3.4e -05 0.008 (0.005) (0.005) -0.001 0.020 Neighbours -0.004 -0.003 (0.004) (0.004) -0.012 -0.009 Religion 0.004 0.004 (0.003) (0.003) 0.011 0.010 N 107 107 107 105 Prob > X2 0.0000 0.2865 0.2055 0.0000 Pseudo R2 0.0646 0.0145 0.0180 0.0927

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Table 13. Factors impacting total number of women informally donated to in lifetime (Nbreg)

(1) (2) (3) (4) Age 0.011 0.017 (0.158) (0.017) 0.104 0.142 Height 0.506*** 0.629*** (0.163) (0.184) 4.690 5.263 Education 0.097 0.025 (0.092) (0.111) 0.895 0.209 Income -3.2e -04 0.022 (0.043) (0.048) -0.003 0.184 Marital Status (Single) -0.225 -0.104 (0.305) (0.340) -2.136 -0.882 Non-heterosexual -1.188*** -1.407*** (0.456) (0.376) -7.983 -8.179 Weekly hours worked -0.152*** -0.123 (0.055) (0.062) -1.410 -1.032 Formal donation 1.025*** 0.901*** (0.358) (0.319) 11.994 9.196 Happiness 0.010 0.001 (0.009) (0.010) 0.133 -0.011 Health 0.013 0.001 (0.012) (0.010) 0.165 0.049 Extraversion -0.469 -0.319 (0.300) (0.306) -5.960 -2.669 Conscientiousness 0.595*** 0.452** (0.219) (0.210) 7.554 3.784 Agreeableness -0.188 -0.177 (0.302) (0.305) -2.391 -1.479 Emotional Stability 0.379 -0.001 (0.325) (0.329) 4.815 -0.012 Openness 0.091 0.247 (0.267) (0.197) 1.151 2.066 Family (Immediate) 0.003 0.007 (0.008) (0.007) 0.042 0.057 Family (Extended) -0.002 -0.005 (0.010) (0.007) -0.026 -0.042 Friends 0.002 0.010 (0.011) (0.008) 0.023 0.087 Neighbours -0.003 -0.004 (0.010) (0.007) -0.040 -0.031 Religion 0.002 -0.003 (0.008) (0.006) 0.034 -0.022 N 105 104 105 104 Prob > X2 0.0000 0.0032 0.9984 0.0000 Pseudo R2 0.0651 0.0215 0.0005 0.0817

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Table 14. Factors impacting number of offspring from donation (Nbreg)

(1) (2) (3) (4) Age 0.016 0.006 (0.016) (0.018) 0.054 0.016 Height 0.347** 0.407** (0.155) (0.163) 1.181 1.177 Education 0.206** 0.189* (0.102) (0.110) 0.702 0.547 Income -0.048 -0.030 (0.038) (0.045) -0.164 -0.086 Marital Status (Single) -0.184 -0.334 (0.322) (0.337) -0.640 -1.003 Non-heterosexual -1.428*** -1.800*** (0.477) (0.511) -3.405 -3.406 Weekly hours worked -0.180*** -0.151*** (0.053) (0.059) -0.612 -0.436 Formal donation 1.336*** 1.261*** (0.306) (0.307) 6.285 4.927 Happiness -0.004 -0.129 (0.010) (0.009) -0.020 -0.037 Health 0.016 0.002 (0.012) (0.011) 0.078 0.004 Extraversion -0.158 0.107 (0.281) (0.289) -0.756 0.310 Conscientiousness 0.870*** 0.586*** (0.210) (0.200) 4.161 1.694 Agreeableness -0.715** -0.361 (0.288) (0.339) -3,42 -1.044 Emotional Stability 0.359 0.201 (0.305) (0.326) 1.717 0.581 Openness -0.141 -0.112 (0.207) (0.189) -0.676 -0.324 Family (Immediate) -0.003 0.006 (0.007) (0.007) -0.020 0.016 Family (Extended) -0.004 -0.005 (0.008) (0.007) -0.023 -0.016 Friends -0.002 0.013 (0.011) (0.009) -0.012 0.037 Neighbours -0.012 -0.012 (0.009) (0.008) -0.069 -0.035 Religion 0.009 0.008 (0.007) (0.006) 0.052 0.022 N 99 99 99 99 Prob > X2 0.0000 0.0000 0.4706 0.0000 Pseudo R2 0.0909 0.0369 0.0103 0.1201

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Table 15. Factors impacting risk attitudes to informal donation (OLS)

(1) (2) (3) (4) Age -0.210 -0.127 (0.295) (0.303) -0.075 -0.045 Height 6.121* 5.670* (3.226) (3.380) 0.192 0.178 Education -2.584 -3.303 (1.949) (1.993) -0.114 -0.146 Income 0.474 -0.075 (0.812) (0.848) 0.069 -0.011 Marital Status (Single) -5.681 0.075 (6.183) (6.501) -0.097 0.001 Non-heterosexual -10.191 -10.854 (6.692) (7.244) -0.141 -0.150 Weekly hours worked -0.264 0.127 (0.921) (1.100) -0.030 0.014 Formal donation -8.942 -4.467 (6.547) (7.070) -0.147 -0.073 Happiness 0.177 0.012 (0.149) (0.191) 0.117 0.008 Health -0.193 -0.222 (0.209) (0.192) -0.093 -0.106 Extraversion -10.009** -9.700** (4.203) (4.330) -0.246 -0.238 Conscientiousness -2.112 -1.510 (3.920) (4.019) -0.061 -0.044 Agreeableness -2.180 -7.937 (4.625) (4.804) -0.056 -0.202 Emotional Stability -7.149 -6.004 (5.047) (6.143) -0.162 -0.136 Openness 0.530 3.506 (3.074) (3.098) 0.017 0.114 Family (Immediate) 0.226 0.318** (0.148) (0.139) 0.206 0.289 Family (Extended) -0.142 -0.146 (0.150) (0.150) -0.132 -0.136 Friends 0.012 0.134 (0.158) (0.154) 0.009 0.095 Neighbours 0.035 0.055 (0.122) (0.123) 0.032 0.052 Religion 0.100 0.116 (0.098) (0.092) 0.126 0.146 N 104 104 104 104 Prob > F 0.0537 0.0024 0.4171 0.0004 R2 0.0921 0.1263 0.0508 0.2928

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Multivariate Analysis

In our empirical analysis we apply ordinary least squares (OLS) and negative binomial regression models. We present five different tables (see Tables 11 - 15), exploring 5 different key parameters of informal donor behaviour. We examine how often informal donations are made in an average month, how many years they have been donating, how many different women they have donated to, how many offspring they have from informal donation in total, and finally, we look at donors’ risk perception towards informal donation . For the first four variables (Tables 11 - 14), we apply a negative binomial model because the outcomes of interest are a count variable. Because of over-dispersion we employ a negative binomial model instead of a Poisson model. As risk perception is a self-rated scale from 1 to 100 we employ OLS (see Table 15). In each regression table we apply the same 4 specifications to our five different dependent variables. In specification 1 we first include personal factors (or information) that is more easily observable or most typically communicated by informal participants on connection websites and forums, namely age, height, education, income, marital status, sexual orientation, weekly work commitments, and formal donation history. In specification two we introduce more internal factors, namely self-evaluated data on health and happiness and the BIG5 personality measures. In specification three we analyse the importance donors place on family (immediate and extended), friends, neighbours and religion, and the impact of those factors on donor behaviour. And finally in specification four we provide a complete model by including all variables in the specification. Models (1 - 4) are displayed incrementally to provide a more nuanced picture of factors at play in relation to the variable of interest. It is a way of testing the robustness of key factors. The full specification allows for the incorporation of more factors but the overall number of observations decreases and the large number of independent variables reduces the degrees of

109 freedom. However, key results obtained in the reduced specifications are also visible and robust in the full specification.

Results

Total participants numbered 112 individuals with a mean age of 43 and a SD of 10.29 years (see Figure 1). Just over half of the sample was between the ages of 35 and 50, with a minimum age of 22 years and a maximum age of 66 years. These age groups are comparable with previous studies into online sperm donor’s demographics (Freeman et al. ,2016; Whyte

& Torgler ,2016b; Woestenburg et al., 2016).

With respect to marital status, 38% (43) of participants were in some form of committed relationship (married 31% (35), de facto (living together) 4% (5), engaged 2.68%

(3)). Not in a relationship: Single 40% (45), Divorced 14% (16), Separated 5% (6) and

Widowed 0.89% (1) (61%, (68)). Relationship status figures do not sum to 100% as some participants did not provide a response on this question. The sexual orientation of participants was predominantly heterosexual (82% (92)). Other sexual orientations represented were homosexual (9% (10)), bisexual (6% (7)), asexual (2% (2)) and other (0.89% (1)).

Only five participants had an educational level of Grade 12 (final year of Secondary school) or lower. Over half (54% (60)) held some form of post-secondary qualification

(Undergraduate, Bachelors or prevocational qualification). Those with a postgraduate qualification (Graduate qualification, Masters or PhD) made up 33% (36) of the sample, and

8% (9) had completed their doctoral studies. Just over half of all participants (56% (61)) worked between 35 and 50 hours a week, with the most common range of hours worked being

35 to 40 hours (28 donors).

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In relation to religious affiliation the two largest groups comprised those who identified as Christian (38% (41)) and atheist (37% (40)). Other answers provided were

Buddhism (5% (5)), Islam (2% (2)), other (11% (12)) and “do not wish to answer” (7% (7)).

Less than one in every three donors (31% (34)) had previously donated in a formal

(clinical) sperm donation setting. Of the 99 donors who provided a response, 35% (35) had yet to realise any offspring from informal donation, with the mean number of offspring being

6.39 children per male (SD = 13.75, min 0 to max 95).

Table 10 shows the sample size (N), mean and SD for the data on donors’ siblings, cousins and close friends. It also provides the participants’ self-reported informal donation behaviour, including their average number of monthly donations, number of years they have been donating for, total number of women donated to, and number of offspring realised from donation. Further Table 10 provides several self-rated scales (0-100) for participant’s happiness, health and evaluation of risk involved in informal donation participation. Figure 2 provides a graphical representation of the risk perception data. Table 1 also provides data on donors BIG5 Personality traits scores. Other self-rated 0-100 scales reflecting the level of importance participants placed on family (immediate), family (extended), friends, neighbours, and religion are also summarized in Table 10.

Our multivariate results begin with the exploration of factors that impact average monthly number of informal donations by participants. In Table 11, specification four we see that donors who place a higher importance on their immediate family make more informal donations in an average month. On the other hand, those who place a higher importance on their extended family make fewer donations in an average month. The negative correlation remains even though importance of the immediate family is a positive relationship with

111 number of average monthly donations. Moreover, in an average month, informally donating non-heterosexuals (relative to heterosexuals) make on average 22.46% less donations.

In the complete model (presented in Table 12, specification 4) older informal donors show a longer history of donation relative to younger donors. Heterosexual donors (compared to non-heterosexuals), and those who have a history of donating in formal settings are also more likely to have been donating informally for longer.

Table 13 reports the results looking into factors that impact the total number of women donated to across the donor’s lifetime. We see that taller donors (relative to shorter donors), and those who have a history of donating in clinical settings have higher lifetime number of informal donation recipients. Thus, height is a dominant force in the selection process. From a personality perspective, more conscientious donors also appear to realise more donations.

While non-heterosexual donors (compared with heterosexuals) realise less donations (on average). Obviously, the number of offspring realised (Table 14) is driven by female choice

(Table 13). Thus, characteristics such as height, having a history of clinical donation, being heterosexual, and conscientiousness remain dominant forces. We perform a robustness check to explore if this result is being driven by the contribution of donors with a history of formal donation. After excluding those who had donated formally, we still observe a positive conscientiousness coefficient, which remains statistically significant at the 1% level. These findings add weight to the theory that men who exhibit more organised and efficient personality types realise more offspring in informal donation settings. Beyond that, the variable weekly hours worked which was statistically significant in specification (1) but not in specification (4) in the selection process (Table 15) is now statistically significant on offspring in both specifications.

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Fig. 22a. Predicted margins for total number of women donated to

Fig. 22b. Predicted margins for number of years informally donating

Finally, in Table 15 we identify the factors at play for risk attitudes. Overall, we observe the trend that those who place higher importance on their immediate family are more

113 concerned about the risk of informal donation. Donors with extroverted personality characteristics seemed to be less risk concerned, while taller informal donors are more risk concerned.

To better understand the signalling effect of donating in the clinical (and therefore formal) setting, we interact the variable with age, and construct a specification including the number of women who choose the donor and number of years donating (see Figure 22a and

22b). The slope difference between those who are active in the formal versus only the informal setting is statistically significant at the 5% level. In figure 22a we see that older men with formal donation experience appear to informally donate to significantly greater numbers of women than do men who exclusively donate informally; this group has somewhat consistent values across age groups (gap is increasing with age).

Moreover, we also observe differences on the number of years donating when comparing those who are also active in the formal setting and those acting only in the informal setting (Figure 22b). Here we see that younger, exclusively informal donors appear to have been donating longer relative to those who have a history of formal donation.

However, for men over the age of 34 this trend is reversed, and donors who have previously donated in a clinical setting have a history of participating in informal donation for longer periods of time. For men who have never donated in a clinical setting, the number of years participating in the informal market appears to be somewhat consistent across all age groups

(approximately between 2 – 4 years).

Discussion

Our personality findings on donor behaviour build on previous findings of the importance of internal factors for donor success in informal settings (Whyte & Torgler, 2015).

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These findings lend weight to the argument that male motivation to participate in the informal market maybe driven by cooperative or complementary behaviour (Whyte, 2018): more conscientious donors realise more offspring and donate to more women, and donors with higher levels of emotional stability participate in the informal market for longer. As informal donors and recipients coordinate their own DI behaviour free of any regulatory oversight, it makes sense that those donors who are more efficient or organised in nature are able to more successfully navigate the logistical, communicative and cooperative hurdles of informal DI.

Such skills increase donor productivity from the increased number of women who receive donation, as well as number of offspring.

As with previous research, our findings show a distinct difference in heterosexual and non-heterosexual informal donor behaviour (Ripper, 2008; Riggs & Russell, 2010; Freeman et al, 2010). Our study however quantifies this difference, finding that non-heterosexuals on average donate less regularly, donate to less women, realise less offspring, and have not been donating as long compared with the heterosexuals in our sample. None of these findings imply that heterosexuals are better, preferred, or more suited to informal donation. It could rather be that non-hetero donors may invest more psychologically, emotionally, and financially in the women and couples that they donate to, as they are seeking a more cooperative and ongoing interactive arrangement with their recipient than their heterosexual counterpart. The opportunity cost of such increased investment would align with the findings outlined above, and suggests donors would have an upper limit on the number of recipients or couples with whom they could cooperate and participate with. This may also be problematic for heterosexual couples who are looking for less engaged donors or those who prefer less future contact with offspring. As such, heterosexual recipients and couples may knowingly or even subconsciously gravitate towards a heterosexual informal donor. Differences in informal

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DI behaviour based on sexual orientation, while extremely interesting, are not definitive and deserve to be the topic of future research.

Research has shown the impact of domestic or reproductive status, personality, and age on network size (Dunbar & Spoors, 1995). However, very little is known about the interplay between sperm donation and the social networks of the men that participate in DI

(Mohr, 2014). Our findings on the impact of donor family (immediate and extended), friends, neighbours, and religion, appears to imply donors’ perceived importance of all groups as more of an opportunity cost or constraint on informal participation rather than a driving or motivational basis for donation. Placing greater importance on immediate family translates to increased number of monthly donations, but also to greater feelings of risk in relation to informal participation. All other family and neighbour factors appear to restrict, reduce, or play no part in informal behaviour. Further research is warranted into the links between donor family and social networks and their impact on relationship formation pre-donation. Such research could provide vital insight into post-partum donor engagement, recipient and couple psychological support, and offspring psychological and sociological development.

From an evolutionary sense there is little surprise that increased donor height and education translates to increased popularity for recipients and resulting offspring. Mate choice research across a wide range of cultures has shown that women have evolved adaptive preference for cues of genetic fitness and proxies for resources (Buss, 1989). Higher education has also been indicated in recipients’ preferences in clinical donation settings

(Whyte et al., 2016).

The findings relating to informal donors who have a history of formal donation are probably the most interesting finding for clinical donor recruitment and future policy. Our results show that older donors who have previously formally donated have been informally

116 donating longer, donate to more women, and realise more offspring. This would imply that the informal market is essentially being driven by older men who are either current formal donors, or who have left the clinical environment all together. It is unclear as to why this is the case. A history of clinical donation may provide donors with information on exactly what they wish to achieve from donation (eg. increased recipient information, recipient interaction, ongoing offspring contact), and thus push them towards informal participation. They may also be disillusioned with formal procedures or processes, or simply find informal donation more convenient and less constrained in practice. As formal and informal donors do not differ on their risk perception toward informal donation, this may imply that donors with formal history do not believe that the medical and legal regulatory frameworks in their domestic market are necessary (or even beneficial) for DI participants. From a demand perspective, having been active in the clinical/formal donation market may provide a quality signal to the potential recipients searching for a donor.

The study is not free of limitations. Firstly, participants for the current study were recruited via a single informal donation or connection website (PrideAngel). While these types of online conduits barely number double digits worldwide, it is unclear exactly how many men are using such websites globally, and as such the size of the total population of informal donors is currently indistinguishable. Donors are also free to move between (and use multiple) online platforms for online donation, which may further exacerbate or confuse estimates on the total market size and the degree to which our study’s sample is representative of current market participants.

Secondly, as is the nature of online surveys, all data collected are self-reported, which may create a self-selection bias towards those who have decided to willingly provide their information for the study. This also creates issues in regards to the difference between stated preference and actual behaviour. Research has shown that knowingly or subconsciously, an

117 individual’s behaviour in online mate choice settings does not always align with their prior stated preference (Whyte & Torgler, 2017b). This is something that can only be alleviated by studies of actual informal donor and recipient behaviours (choices) and outcomes. Academic literature on such (in sperm DI settings) is virtually non-existent (Whyte et al., 2016).

As PrideAngel is a UK based connection website, it would not be unreasonable to assume that the majority of its online participants are also UK or at the very least EU based.

This research may then provide a study of a Western, educated, industrialised, rich and democratic population participating in informal donation. Thus, further research is warranted into more socio-economically diverse populations, or those in developing economies.

Just as DI and IVF technology has made rapid and dramatic advances across the last four (or more) decades, so has the way donor and recipients go about their decision making in donation settings. Hence, it may be difficult to compare studies of donor populations from the

1980s and 1990s to those of the current day (Pennings, 2016). This does not mean that early behavioural research has become invalid, rather that the ecology, sociology, and psychology of sperm donation needs to keep pace with the rapid technological changes (the internet) assisting those involved in all forms of DI (donors, recipients and couples, healthcare professionals, commercial operators, and connection websites). Research into the motivations, risk perception, and personality of informal donors could provide clinical practice and policy makers with a much clearer picture of how best to support (both formal and informal) donors, and how to encourage safe health practices in DI behaviour. Future research that focuses on donor personality (Whyte & Torgler, 2016a) and psychosocial needs (Daniels, 1989; Van den

Broeck et al., 2013) can assist counselling and psychological support services in building a more comprehensive understanding of donor’s behaviours beyond the simplicity of the

“altruistic and pro-creative” motivations previously outlined.

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It is also evident that this study analyses only one side (donors) of a two-sided market.

Future research should not just focus on donor behaviour in isolation but also the interaction between both parties (recipients and couples). Of course this will only be possible by using longitudinal (panel) data that begins before the point of gamete donation, something that continues to be problematic for the study of both informal donors’ and recipients’ psychology.

Finally, as this study and previous research (Ripper, 2008; Riggs & Russell, 2010) has shown, non-heterosexual informal donor behaviour differs significantly from that of heterosexual informal donors. Future research should aim to further develop this understanding with the aim of informing policy makers and healthcare professionals, particularly for those currently in the regulated DI sector (Harper et al., 2017). As non- heterosexual men (in most cases) face the biological reality of not being able to procreate or conceive with their husband or partner, sperm donation can be a logical and rewarding option for those wishing to pursue fatherhood with willing and physically able women and couples.

Also as who and what makes up a family continues to evolve changing attitudes and norms, so too will the developing understanding of non-hetero-donors. Just as same-sex marriage equality has recently gained global prominence, so too may the developing role of non- heterosexual donors in informal DI markets worldwide.

In conclusion, normatively, human reproduction is both a biological and social process that falls within the broad contexts of medicine, markets, technology, cultural and social norms, and personal life history (Hertz et al., 2015). As such, an empirical analysis that investigates a range of factors impacting informal donor behaviour can provide a more apt description of the psychology employed by informal donation participants. As informal sperm donation becomes more prevalent worldwide, understanding the impact and importance of family, personality and risk perception to informal donor market interactions is critical in

119 providing effective policy, equitable legislative frameworks, and frontline health and psychological support to an ever-growing number of global participants.

Chapter 7: SUMMARY AND CONCLUSIONS

Summary of Findings

In Chapter 2, 3 & 4, I studied human mate choice behaviour in the online dating market using a novel data set from the Australian dating website RSVP.

From the descriptive and multivariate analysis in Chapter 2 I show that more highly educated participants are more likely to contact those with homogenous levels of education.

And that the likelihood of positive assortment increases with age. A preference for hypogamous behaviour is more accentuated in younger males when compared to females.

And females across all ages groups are more likely to seek higher education (hypergamy) in a mate, particularly across the years of peak fertility. In Chapter 3 my study of the difference between stated preference and actual (contact) behaviour showed that individuals perception of their ideal (preferred) mate bears little resemblance to the individuals they actually contact.

Rather, participants age, education level and personality type are far greater predictors in understanding increases in stated preference characteristics matching the actual characteristics of the person they contact. Interaction effects analysis also show that females (compared to males) across reproductive age are more likely to exhibit contact behaviour that matches their original stated preference. And that those with lower (higher) levels of education are more

(less) selective when younger, and vice versa. In Chapter 4 I explore sex differences in

120 specificity of preference for education in a mate. I find that for those that a state a preference, females (compared to males) state a more defined preference during their years of maximum fertility, and then become more relaxed with age. Overall the major contribution of these three studies is the use of an unprecedentedly large cross-sectional data set of mate choice decision making to explore actual stated preference and decision outcomes. The studies have empirically quantified factors that impact positive and negative assortment. They have shown deviations from individuals stated preference and their actual behaviour are common and widespread. Further, and most importantly the first section of this thesis has contributed significant empirical evidence to the growing body of literature demonstrating females

(compared to males) exhibit context-dependent shifts in mate preferences.

In Chapter 5 & 6 this thesis explored male and female behaviour in the large-scale decision setting of the market for sperm donation. The Chapter 5 study provides the first ever analysis of characteristics of sperm donors that females have actually selected to use in IVF and DI decisions. I find that females in this setting prefer males with proxies for resources (ie. higher education), however, in contrast to the overwhelming evidence from the scientific mate choice literature (Buss & Barnes 1986; Buss, 1989; 1991; Buss & Schmitt 1993), the study finds that females exhibit a preference for younger males to father their offspring.

Chapter 6 explored the behaviour and characteristics of males who choose to donate sperm in the informal or online market. The findings show little to no relationship between the importance donors place on kin and social networks, and their online donation success. It also shows that men who exhibit more paternal characteristics like conscientiousness and cooperation have a competitive advantage in the cyber donation world.

Overall, the five studies I present in this thesis have contributed empirically to the behavioural economics and behavioural science literature in regards to stated preference and

121 choice, positive assortment, sex differentiated specificity of preference, recipient and sperm donor psychology and behaviour, and the factors impacting nonbinary gender identification.

Limitations

While each individual chapter states the limitations of the study in question, I reiterate some short comings and provide additional clarification of the studies included in this thesis.

Chapter 2, 3 & 4: Online dating

First and foremost, while these three studies utilise one of the largest ever samples of online daters behaviour used in empirical peer reviewed analysis, it must again be pointed out that this group is taken from only one dating website, available in only one country

(Australia). As such individual behaviour may in part reflect the cyber platform being used, and more widely the social norms and behaviours of the self-selected participants involved.

Information asymmetry, strategic behaviour and self-deception are also important factors to consider in the context of these three studies. It is important however to remember that for the majority of participants, an individuals’ end game or pay-off is that of a face-to- face meeting. For this reason any deception and strategic behaviour in cyber dating markets that does occur, is often minute and extremely difficult to detect (Ellison, Heino & Gibbs,

2006; Guadagno, Okdie & Kruse, 2012; Toma, Hancock & Ellison, 2008).

Chapter 5 & 6: Sperm donation in clinical and informal markets

Just as in the previous online dating study, it is important to reiterate that the clinical sperm donation study was conducted in conjunction with only one reproductive medical organisation. And even though participants decisions were taken from a state-wide sample

122 spanning more than a decade’s worth of observations, such individual behaviours may reflect the procedures and structures of the organisation in which the study was conducted.

Further, unlike other mate choice decision settings, the decision process of women choosing a sperm donor may be significantly influenced by a current mate (eg. husband, defacto, same-sex partner, etc) who is jointly participating in the process with their partner.

As such, selection decisions of sperm donors may in part reflect a satisficed amalgam of the recipient and her partners preferences.

The question also arises as to the rationality and representativeness of the females involved in sperm donation decisions. As those seeking offspring through assisted fertility processes are often pursuing medical assistance because they are unable to conceive naturally, their risk preferences in this setting may not be representative of the general population.

Certainly, research has shown that women involved in IVF overestimate the probability of their possible success in conceiving (Reading 1989).

It must also be acknowledged that the online study provided utilises participants from only one connection website (PrideAngel). Little is known about the total number of males and females participating globally in the online market for reproductive tissue donation.

While several websites claim to have membership in the thousands, it is currently unclear exactly how many active participants comprise the market. It is also important to note that the studies data capture instrument (namely an anonymous online survey) is problematic in not creating a self-selection bias towards those who willingly chose to provide information for the study.

Directions for future research

123

The work presented in this thesis offers both theoretical and applied economics, and the behavioural sciences more broadly, a range of unique findings and knowledge to use, incorporate and build on.

Probably the simplest and most interesting future extension to the online dating research conducted in this thesis is to track or observe individuals online dating behaviour across extended periods of time (ie. Longitudinal data, as opposed to cross sectional data).

Such analysis would provide insight into both individuals learning effects based on market forces, by exploring how malleable preferences are once individuals garner more complete information. As the cyber dating world provides a low-cost mate search conduit tracking changes in individual’s behaviour across their search can provide an empirical understanding how individuals update their prior, and how this impacts their actual decision process and outcome. Such research would also provide more definitive results on how the elasticity and specificity of preference develops or changes for both sexes with age.

Positive assortment research in online dating could further benefit through extensions that incorporated and explored participant paternal and maternal biological and sociodemographic characteristics. While such studies are possible using longitudinal marriage market data and local and federal birth records, merging kin (biological) data to explore search behaviour may provide insight into the development and growth of homogamous mate choice decision making as we age.

The work presented in this thesis that explores human behaviour in clinical sperm donation settings provides two clear directions for future research. Firstly, as many recipients and donors do not go through the IVF or DI process alone, exploration of the impact of

“significant others” in the donation decision making process is extremely important. While historically mate choice decisions were made by the individual (who to mate with),

124 reproductive technologies have now allowed couples to seek third parties with which to find, obtain or share gametes. Understanding of the role and impact of partner mate’s preferences and behaviour on individual recipient or donor psychology is currently limited at best.

Secondly, while commercial sperm donation is a global market, little is known as to the impact such transnational trade has on female mate choice preference. Like many oligopolistic markets choice for the consumer is often constrained or limited. What this means for female psychology in this new and burgeoning mate choice setting is currently unclear.

Regarding the study of informal sperm donation market participation, the most obvious and immediate economic research question is “How big is this market”? Little is known about participants of connection websites and web forums that facilitate gamete donation. Empirically, arguably the most important future research should first establish the size of the current marketplace before studying its future growth. As a follow on to this study, delineation between clinical and informal participation would be a natural second step.

Establishing the difference between clinical and informal participation would be one of the first steps in unlocking and understanding why this market has seen such rapid growth across the last decade.

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Appendices: Statement of Author Contributions

Chapter 2: THINGS CHANGE WITH AGE: EDUCATIONAL ASSORTMENT IN ONLINE DATING

Statement of Contribution of Co-Authors for Thesis by Published Paper

The authors listed below have certified that:

1. they meet the criteria for authorship in that they have participated in the conception, execution, or interpretation, of at least that part of the publication in their field of expertise; 2. they take public responsibility for their part of the publication, except for the responsible author who accepts overall responsibility for the publication; 3. there are no other authors of the publication according to these criteria; 4. potential conflicts of interest have been disclosed to (a) granting bodies, (b) the editor or publisher of journals or other publications, and (c) the head of the responsible academic unit, and 5. they agree to the use of the publication in the student’s thesis and its publication on the QUT’s ePrints site consistent with any limitations set by publisher requirements.

Things change with age: Educational assortment in online dating

Personality and Individual Differences (2017) 109, 5-11

Contributor Statement of Contribution

Stephen Whyte Has equally contributed to all aspects of this paper, including research, analysis and writing. 1st January 2018

Benno Torgler Has equally contributed to all aspects of this paper, including research, analysis and writing. Principal Supervisor Confirmation

I have sighted email or other correspondence from all Co-authors confirming their certifying authorship.

Benno Torgler QUT Verified Signature 9th March 2018

Name Signature Date

136

Chapter 3: PREFERENCE VS. CHOICE IN ONLINE DATING

Statement of Contribution of Co-Authors for Thesis by Published Paper

The authors listed below have certified that:

Preference vs. Choice in online dating Cyberpsychology, Behavior and Social Networking (2017) 20(3), 150-156

Contributor Statement of Contribution

Stephen Whyte Has equally contributed to all aspects of this paper, including research, analysis and writing. 1st January 2018

Benno Torgler Has equally contributed to all aspects of this paper, including research, analysis and writing.

Principal Supervisor Confirmation

I have sighted email or other correspondence from all Co-authors confirming their certifying authorship.

Benno Torgler QUT Verified Signature 9th March 2018

Name Signature Date

137

Chapter 4: DO WOMEN KNOW WHAT THEY WANT? SEX DIFFERENCES IN ONLINE DATERS EDUCATIONAL PREFERENCES

Statement of Contribution of Co-Authors for Thesis by Published Paper

The authors listed below have certified that:

Do women know what they want? Sex differences in online daters educational preferences

Psychological Science (2018) forthcoming

Contributor Statement of Contribution

Stephen Whyte Has equally contributed to all aspects of this paper, including research, analysis and writing. 1st January 2018

Benno Torgler Has equally contributed to all aspects of this paper, including research, analysis and writing. Ho Fai Chan Has equally contributed to all aspects of this paper, including research, analysis and writing.

Principal Supervisor Confirmation

I have sighted email or other correspondence from all Co-authors confirming their certifying authorship.

Benno Torgler QUT Verified Signature 9th March 2018

Name Signature Date

138

Chapter 5: WHAT WOMEN WANT IN THEIR SPERM DONOR: A STUDY OF MORE THAN 1000 WOMENS SPERM DONOR SELECTIONS

Statement of Contribution of Co-Authors for Thesis by Published Paper

The authors listed below have certified that:

What women want in their sperm donor: A study of more than 1000 womens sperm donor selections

Economics and Human Biology (2016) 23, 1-9

Contributor Statement of Contribution

Stephen Whyte Has equally contributed to all aspects of this paper, including research, analysis and writing. 1st January 2018

Benno Torgler Has equally contributed to all aspects of this paper, including research, analysis and writing. Keith L. Harrison Has equally contributed to all aspects of this paper, including research, analysis and writing. Principal Supervisor Confirmation

I have sighted email or other correspondence from all Co-authors confirming their certifying authorship. Benno Torgler QUT Verified Signature 9th March 2018

Name Signature Date

139

Chapter 6: ONLINE SPERM DONORS: THE IMPACT OF FAMILY, FRIENDS, PERSONALITY AND RISK PERCEPTION ON BEHAVIOUR

Statement of Contribution of Co-Authors for Thesis by Published Paper

The authors listed below have certified that:

Online sperm donors: The impact of family, friends, personality and risk perception on behaviour

Reproductive Biomedicine Online (2017), 35(6), 723-732.

Contributor Statement of Contribution

Stephen Whyte Has equally contributed to all aspects of this paper, including research, analysis and writing. 1st January 2018

Benno Torgler Has equally contributed to all aspects of this paper, including research, analysis and writing. David A. Savage Has equally contributed to all aspects of this paper, including research, analysis and writing.

Principal Supervisor Confirmation

I have sighted email or other correspondence from all Co-authors confirming their certifying authorship. Benno Torgler QUT Verified Signature 9th March 2018

Name Signature Date

140