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published online 9 June 2014American Sociological ReviewElizabeth Aura McClintock

Beauty and Status: The Illusion of Exchange in Partner Selection?

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American Sociological Review 1 –30© American Sociological Association 2014DOI: 10.1177/0003122414536391http://asr.sagepub.com

This article revisits the claim that individuals (generally women) of relatively high physical attractiveness barter their beauty to attract a partner of higher socioeconomic status. This beauty-status exchange model is popularized in the “trophy wife” stereotype that pretty women marry high-status men. Yet this popular focus overlooks the role of matching—selecting a partner with similar characteristics to oneself—that is well-documented in research on relation-ships (e.g., McPherson, Smith-Lovin, and Cook 2001). I unite the two literatures by modeling exchange and matching as simultaneous and competing processes that might vary in

relevance for different types of couples. Identi-fying the conditions under which couples exchange beauty and status provides insight into processes of social mobility and stratifica-tion. Additionally, beauty-status exchange is highly relevant to gender inequality and to sociobiological models of partner selection.

536391 ASRXXX10.1177/0003122414536391American Sociological ReviewMcClintock2014

aUniversity of Notre Dame

Corresponding Author:Elizabeth Aura McClintock, 810 Flanner Hall, University of Notre Dame, Notre Dame, IN 46556 E-mail: [email protected]

Beauty and Status: The Illusion of Exchange in Partner Selection?

Elizabeth Aura McClintocka

AbstractScholars have long been interested in exchange and matching (assortative mating) in romantic partner selection. But many analyses of exchange, particularly those that examine beauty and socioeconomic status, fail to control for partners’ tendency to match each other on these traits. Because desirable traits in mates are positively correlated between partners and within individuals, ignoring matching may exaggerate evidence of cross-trait beauty-status exchange. Moreover, many prior analyses assume a gendered exchange in which women trade beauty for men’s status, without testing whether men might use handsomeness to attract higher-status women. Nor have prior analyses fully investigated how the prevalence of beauty-status exchange varies between different types of couples. I use data from the National Longitudinal Study of Adolescent Health Romantic Pair Sample, a large (N = 1,507), nationally representative probability sample of dating, cohabiting, and married couples, to investigate how often romantic partners exchange physical attractiveness and socioeconomic status, net of matching on these traits. I find that controlling for matching eliminates nearly all evidence of beauty-status exchange. The discussion focuses on the contexts in which beauty-status exchange is most likely and on implications these results have for market-based and sociobiological theories of partner selection.

Keywordsmarriage, demography, gender, social stratification, sociobiology

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2 American Sociological Review

Partner selection is important in reproduc-ing group boundaries and social inequalities (Gordon 1964; Rosenfeld 2008). Matching on socioeconomic status compounds inequality: high-earners partner with other high-earners and low-earners with other low-earners (Blossfeld and Buchholz 2009; Schwartz 2010; Schwartz and Mare 2005). However, under a market model of partner selection, in which choices are relatively unconstrained by group boundaries and partners informally “exchange” personal attributes, individuals could leverage a desirable noneconomic trait to achieve socioeconomic mobility. This would undermine the tendency for partner selection to reproduce existing patterns of inequality. Under the beauty-status exchange model, physical attractiveness might enable class mobility for women, although such an exchange would ensure women’s economic dependency on their husbands.

Indeed, partner selection is closely related to gender inequality. Norms dictating that the man be older and more successful in his career reinforce power inequalities within marriage (Presser 1975). Rules prescribing these and other forms of marital hypergamy may be weakening, but related stereotypes that men prioritize a partner’s appearance and women prioritize a partner’s status remain strong. Such stereotypes are pre-feminist, ignoring women’s economic independence and their valuation of men’s physical attrac-tiveness. Although these stereotypes may be derived from both social structural and socio-biological theories, the sociobiological litera-ture is especially adamant that men will select long-term partners on the basis of youth and beauty, whereas women will select partners who are good providers (Buss 1990, 1998).

Beauty-status exchange accords with the popular conception of romantic partner selec-tion as a competitive market process, a con-ception widely accepted in both popular culture and academia. Under the market model of romantic relationship formation, individuals negotiate an informal exchange by trading their own assets for those of their partner. This market metaphor has been

applied to the exchange of socioeconomic status for other purportedly desired resources, such as race (Fu 2001; Kalmijn 1993; Qian 1997; Schoen and Wooldredge 1989; but see Rosenfeld 2005, 2010), homemaker skills (Becker 1991), youth (Coles and Francesconi 2007; England and McClintock 2009), and physical attractiveness (Burdett and Coles 2001; Carmalt et al. 2008; Elder 1969; Sassler and Joyner 2011; Stevens, Owens, and Schaefer 1990; Taylor and Glenn 1976; Udry 1977). In popular culture, the concept of a gendered beauty-status exchange, in which an economically successful man partners with a beautiful “trophy wife,” is commonplace.

In practice, the related exchange-based the-ory that individuals of low racial status but high socioeconomic status partner with those of high racial status and low socioeconomic status (status-caste exchange) also bolsters support for gender-stereotyped models of part-ner selection. Proponents argue that low-status white women partner with higher-status black men (Fu 2001; Kalmijn 1993; Qian 1997; Sch-oen and Wooldredge 1989) or that minority women exchange beauty and sexual access for white men’s income (Sassler and Joyner 2011). If this occurs, such couples would undermine racial boundaries but reinforce female eco-nomic dependency in marriage.

These market-based exchange models are difficult to reconcile with the consistent empir-ical finding that romantic partners tend to match on many dimensions. In contradiction to beauty-status exchange theory, economically successful women partner with economically successful men (Sweeney and Cancian 2004), and physically attractive women partner with physically attractive men (Murstein and Christy 1976).1 I attempt to resolve the empiri-cal paradox presented by the evidence of matching on physical attractiveness and on socioeconomic status with the conflicting lit-erature affirming beauty-status exchange. I argue that the positive individual-level correla-tion between physical attractiveness and socio-economic status, combined with a tendency for partners to match on both attractiveness and status, might be easily misconstrued



McClintock 3

as beauty-status exchange, particularly in regression models that do not fully account for matching.

That said, although prior research may rely on faulty empirical models, it is possible that beauty-status exchange occurs in some cou-ples. Although they are competing forces, matching and exchange are not mutually exclusive—some couples might match while others engage in cross-trait exchange. Prior research proposes that certain types of cou-ples, such as less-committed or interracial couples (Sassler and Joyner 2011), are espe-cially likely to engage in beauty-status exchange. My analysis examines the preva-lence of matching and exchange among a representative sample of young couples and considers whether any subgroups dispropor-tionately engage in beauty-status exchange. Considering the contexts in which beauty-status exchange might predominate provides nuanced insight into processes of partner selection and highlights the differential mean-ings that couples may attach to their relationships.

BACKgroundMatching in Partner Selection

The strongest force in partner selection is matching, or assortative mating (in marriage, endogamy): men and women select partners with characteristics similar to their own. Couples tend to be alike in education, age, race, and religion (Bereczkei and Csanaky 1996; Blackwell and Lichter 2004; Mare 1991; Rosenfeld 2008; Schoen and Cheng 2006; Schoen and Weinick 1993; Stevens 1991). Of most relevance to this study, there is strong evidence of matching on physical attractiveness (Berscheid et al. 1971; Carmalt et al. 2008; Feingold 1988; Kalick and Ham-ilton 1986; Murstein 1972; Murstein and Christy 1976; Stevens et al. 1990) and socio-economic status (Blackwell and Lichter 2004; Gardyn 2002; Mare 1991; Stevens 1991).

Prior literature suggests at least three important forces generating matching: (1)

homophily, that is, individuals’ preferences for similar (in-group) partners, (2) pressure from third parties to select similar partners, and (3) lack of contact with potential dissimi-lar (out-group) partners (Kalmijn 1998). These explanations are undoubtedly impor-tant and may suffice to explain matching on many traits, particularly traits for which pref-erences vary greatly between individuals. For these traits, such as religion, no one group is generally desired more than others. Instead, individuals want partners of their own group, whatever that may be. But for other traits, including income and physical attractiveness, more is often assumed to be better. For these consensually ranked traits, individuals may prefer partners who are superior to them-selves over partners who are their equals. Additionally, in the case of physical attrac-tiveness, it is not obvious that interested third parties would object to dissimilarity or that social-structural barriers prevent contact between more and less physically attractive individuals.

A competitive marriage market model might explain some degree of matching on consensually ranked traits, including physical attractiveness. In a competitive market, every-one may desire the most beautiful and wealth-iest partners, but individuals will discover that the most desirable partner they can attract is one of their own level of desirability (see Burdett and Coles 1997, 1999, 2001; Choo and Siow 2006; Loughran 2002). Still, match-ing on specific traits is not the only possible outcome. Individuals might exchange a high level of one desirable trait for a high level of a different desirable trait in a partner, engag-ing in cross-trait exchange while matching on total desirability.

I am interested in physical attractiveness and socioeconomic status because they are often thought to be consensually ranked traits, and existing theories of partner selection indi-cate that such traits might be either matched or exchanged. Despite the inherent tension between theories purporting beauty-status exchange and those arguing that couples will match on these traits, the theories are not




incompatible. Some couples might match while others exchange. Individuals might also seek approximate equality on both beauty and status but accept a limited tradeoff between the two—such as a slightly less-attractive partner with slightly higher status.

Co-occurrence of Desirable Traits

Analyses endorsing matching and those endorsing exchange generally overlook the co-occurrence of (un)desirable traits—that beauty and status are positively correlated within individuals. Perhaps partially because physically attractive individuals are treated preferentially, they enjoy improved school performance, greater occupational success, and higher earnings (Clifford and Walster 1973; Hamermesh and Biddle 1994; Haskins and Ransford 1999; Jackson, Hunter, and Hodge 1995; Langlois et al. 2000; Rosenblat 2008; Rosenblat and Mobius 2006; Singer 1964; Umberson and Hughes 1987; Wardle, Waller, and Jarvis 2002). Also, income may help individuals purchase goods and services that enhance attractiveness, such as dental care and gym memberships. Some of the beauty-status correlation might be explained by rater bias: for example, individuals thought to be of higher-status nations are rated more favorably (Kowner 1996).2

The co-occurrence of desirable traits encourages matching on multiple dimen-sions—couples who match on one trait tend toward similarity on related traits. This might lead to overestimating the strength of match-ing on any single dimension, considered alone (Kalmijn 1998). For example, if college grad-uates are (on average) better-looking than nongraduates, matching on college status facilitates matching on attractiveness. More subtly, because the within-individual co-occurrence of desirable traits might create a spurious between-partner cross-trait correla-tion of beauty and status, a bias toward observing men’s status and women’s beauty might cause matching to be misidentified as exchange. If couples match on college status and on attractiveness, college-educated men

would have prettier wives than less-educated men (and college-educated women would have handsomer husbands). Because physi-cally attractive men and women average higher socioeconomic status, partner match-ing on status or attractiveness (or on both traits) would create a positive correlation between women’s physical attractiveness and men’s socioeconomic status, and between men’s attractiveness and women’s status, even in the absence of beauty-status exchange.

In models that assume men value attrac-tiveness and women value status, couples would appear to engage in beauty-status exchange even when they match. For exam-ple, a high-earning man married to a pretty wife might be interpreted as beauty-status exchange by a researcher who only observes male status and female beauty—but if the man is handsome and his wife a high-earner, then the couple is matched on both traits. Most prior studies of beauty-status exchange assume a gendered importance of beauty and status, overlooking men’s attractiveness and women’s status and potentially misidentify-ing matching as exchange. Analyses assum-ing a stereotypically gendered importance of beauty and status could thus erroneously, and unintentionally, perpetuate these very stereo-types. Also, because most prior studies assume gendered exchange, it is unclear whether patterns of beauty-status exchange (insofar as they exist after controlling for matching) are gender-symmetric.

Evidence of Exchange

Despite strong evidence of matching on phys-ical attractiveness (Carmalt et al. 2008; Fein-gold 1988; Murstein 1972; Murstein and Christy 1976; Stevens et al. 1990) and on socioeconomic status (Blackwell and Lichter 2004; Gardyn 2002; Mare 1991; Stevens et al. 1990), prior studies argue that physical attrac-tiveness is traded for socioeconomic status (Bjerk 2009; Carmalt et al. 2008; Elder 1969; Taylor and Glenn 1976; Udry 1977). Specifi-cally, early studies of beauty-status exchange in partner selection propose a gendered



McClintock 5

exchange of women’s beauty for men’s status. Such studies find that both physical attrac-tiveness and education help a woman achieve upward mobility through marriage (defined as marrying a man of higher occupational status than her father [Elder 1969; Udry 1977]) and help her marry a man of high occupational status, in absolute terms (Taylor and Glenn 1976). However, these analyses exclude men’s physical attractiveness and therefore do not address how the co-occur-rence of desirable traits within individuals might encourage matching and might also generate the illusion of exchange.3 Control-ling for both partners’ physical attractiveness may not eliminate the relationship between female beauty and male status, but it should at least reduce this relationship substantially.

A recent article using the National Longi-tudinal Study of Adolescent Health (Add Health) Romantic Pair data, a nationally rep-resentative sample of young adult dating, cohabiting, and marital partners, finds that for both genders, personal assets including edu-cation predict having a physically attractive partner (Carmalt et al. 2008). The authors interpret this as evidence of cross-trait beauty-education exchange, but they fail to control for the partner’s education. Because educa-tion is strongly associated with physical attractiveness within individuals (beauty and education co-occur), it is unclear from this study whether education is exchanged to attain an attractive partner or whether the findings are spurious and result from match-ing. Therefore, I use the same data to evaluate patterns of beauty-status exchange while con-trolling for matching.

Some individuals certainly intend to trade high levels of one trait for high levels of a dif-ferent trait. Personal and online dating adver-tisements are often couched in deal-making language (Alterovitz and Mendelsohn 2009; Davis 1990; Harrison and Saeed 1977). These advertisements imply that some women hope to trade their physical attractiveness for men’s financial security, and some men hope to trade their socioeconomic status for a physically attractive but lower-status partner. But

experimental studies indicate that women (and men) will not compromise on physical attrac-tiveness (Li et al. 2002; Li and Kenrick 2006; Sprecher 1989). In practice, individuals might be unwilling to make the cross-trait exchanges that they anticipated making. Moreover, women and men pursuing a gendered beauty-status exchange in their advertisements might be overlooking the importance of class-based cultural compatibility. They may ultimately value cultural similarity more than similarity on tangible measures of status (income or degree attainment) (Kalmijn 1994), but they may realize the cultural importance of socio-economic status only after meeting status-disparate dates. For example, a man might attach little importance to a prospective wife’s income or educational attainment per se, but he might discover that the women with whom he is most interpersonally compatible are women of his own socioeconomic status.

Is More of a Good Thing Always Better?

Prior studies espousing beauty-status exchange implicitly assume that physical attractiveness and social status are consensu-ally ranked traits: that is, these traits can be measured and partners with higher levels are generally more desired (e.g., Elder 1969; Taylor and Glenn 1976). Socioeconomic sta-tus is an abstract concept, but it is often approximated by education, income, or occu-pation-tangible and quantifiable characteris-tics. Despite some inevitable subjectivity in evaluating physical attractiveness, prior stud-ies report high inter-rater consensus (Langlois et al. 2000; Murstein 1972). In the United States, there may be racial differences in ideal body shape (Cohn and Adler 1992; Lovejoy 2001; Webb, Looby, and Fults-McMurtery 2004), but assessment of facial attractiveness does not vary by race (Cunningham et al. 1995; Moss, Miller, and Page 1975). Attrac-tiveness and status are thus quantifiable.

However, neither men nor women report attaching much importance to either trait. For example, when asked to rank the importance




of 76 traits in a romantic partner, no measures of appearance or socioeconomic status made the top 10 (Buss and Barnes 1986). This and similar studies find that men value appear-ance more than women, whereas women value socioeconomic indicators more than men, but neither gender ranks physical attrac-tiveness or socioeconomic status highly (Furnham 2009; Howard, Blumstein, and Schwartz 1987; Nevid 1984). (This gender difference may be declining in recent cohorts [Buss et al. 2001; Regan and Joshi 2003].)

In contrast to self-reported preferences, experimental studies find that physical attrac-tiveness is highly valued by both genders, and that women also value men’s socioeconomic status (Li et al. 2002; Li and Kenrick 2006; Sprecher 1989). Similarly, recent speed- dating studies find that physical attractiveness and earnings potential are both strong predic-tors of attraction (Eastwick and Finkel 2008; Fisman et al. 2006; Luo and Zhang 2009). Consistent with these findings, women and men using an online-dating website valued physical attractiveness highly (Hitsch, Hort-acsu, and Ariely 2010). Evidence from speed-dating and online-dating studies is mixed regarding gender differences in the relative importance of attractiveness and earnings potential.

Arguably, “acted” preferences in experi-ments and real dating situations may be more genuine indicators of true preferences than are stated preferences (McClintock 2011)—and acted preferences indicate that when it comes to beauty and status in romantic part-ners, more is better. This is a necessary but not sufficient condition for exchange to occur. Exchange requires not only that these traits are valued, but also that they are substituta-ble. Yet individuals might be unwilling to compromise on one dimension (e.g., by accepting a homely partner), even when com-pensated on another dimension (if the homely partner were high-status). Indeed, acted pref-erences suggest that both genders consider physical attractiveness a necessity and disre-gard potential partners who fail their attrac-tiveness criteria (see Li and Kenrick 2006).

Union Status and Duration

Most studies purporting beauty-status exchange restrict their analysis to married couples. Only the most recent of these articles (Carmalt et al. 2008) uses dating, cohabiting, and married couples. I use these same data, the National Longitudinal Study of Adoles-cent Health Romantic Pair sample (2001 to 2002; N = 1,507). Insofar as commitment can be assumed to increase as couples move from dating to cohabitation to marriage, couples in more committed relationships are more simi-lar in race, education, and other traits (Black-well and Lichter 2004; Joyner and Kao 2005). Individuals may hold looser partner criteria for short-term relationships, and dissimilar couples may face higher dissolution rates (McClintock 2010; Wang, Kao, and Joyner 2006). If beauty-status exchange is a form of dissimilarity that increases instability, it would be most prevalent in dating couples, less prevalent among cohabiters, and least preva-lent among married couples. Likewise, beauty-status exchange would be less com-mon in relationships of longer duration. In addition to “winnowing” (the selective disso-lution of less similar couples), romantic part-ners may grow more similar over time due to shared lifestyle (e.g., eating habits, exercise, or encouragement to earn a higher degree).

However, entering into a beauty-status exchange is most rational if one anticipates marriage, because it is in marriage that eco-nomic resources are most often shared. If so, exchange would be most prevalent among married couples, because not all dating and cohabiting couples anticipate marriage. Cohab-iting couples would be especially unlikely to exchange beauty and status, because they are generally more egalitarian than married cou-ples (Brines and Joyner 1999) and are less likely to pool resources (Hamplova and Le Bourdais 2009). Indeed, some research sug-gests that patterns of exchange evident among married couples are not viable among cohabit-ers, including the exchange of housework for economic support (Brines and Joyner 1999) and the exchange of racial “caste” for



McClintock 7

socioeconomic status (Blackwell and Lichter 2000; but see Rosenfeld 2005). If my analysis produces evidence of beauty-status exchange, differences in its prevalence by union status (dating, cohabiting, married) might clarify whether it is a source of instability or a viable long-term strategy.

Interracial Couples

In a classic variant of cross-trait exchange in partner selection, individuals of low racial status but high socioeconomic status partner with those of high racial status but low socio-economic status (Davis 1941; Fu 2001; Kalmijn 1993; Merton 1941; Schoen and Wooldredge 1989). In practice, race-status (status-caste) exchange, like beauty-status exchange, is gender-stereotypical: lower- status white women are thought to partner with higher-status black men (Fu 2001; Kalmijn 1993; Qian 1997; Schoen and Wooldredge 1989). Although not entirely analogous to beauty-status exchange (individuals desire partners of higher status and greater beauty, but generally prefer partners of their own race [Yancey 2009]), race-status exchange theory suggests that interracial couples might be uniquely prone to engage in cross-trait exchange. Indeed, Sassler and Joyner (2011) find that minority women exchange beauty and sexual access for white men’s social sta-tus (but their test is incomplete because they omit men’s physical attractiveness).

However, just as beauty-status exchange conflicts with couple matching on beauty and status, race-status exchange conflicts with matching on race and status. Empirical sup-port for race-status exchange is mixed and controversial (Gullickson and Fu 2010; Kalmijn 2010; Rosenfeld 2005, 2010). It is beyond the scope of this article to test whether couples engage in race-status exchange. I do, however, consider whether patterns of beauty-status exchange might differ for interracial couples. Part A of the online supplement (http://asr.sagepub.com/supplemental) addresses beauty-status exchange among interracial couples in greater depth.

HyPoTHESES

Prior research demonstrates some support for the theory that individuals trade one desirable trait to obtain a partner with more of a differ-ent desirable trait, but in the case of beauty and status there is stronger evidence in sup-port of matching. I expect that patterns of beauty-status exchange found in earlier stud-ies are, at least in part, artifacts of matching on partner traits not controlled for in the model. However, prior research proposes that the prevalence of beauty-status exchange may differ for certain subgroups, such as interra-cial and less-committed couples.

Understanding the prevalence of beauty-status exchange is important for theoretical conceptions of partner selection and for ste-reotypes regarding the importance of beauty and status for women and men. Gendered beauty-status exchange is the most prominent example of cross-trait exchange in the “part-ner market,” especially in popular culture. Questioning the frequency of cross-trait exchange in a context in which it is generally assumed to occur does not challenge the mar-ket model in all contexts, but it weakens its status as the predominant paradigm for under-standing partner selection processes. Addi-tionally, if there is no evidence of gendered beauty-status exchange, this challenges the evolutionary model of partner selection and discredits common assumptions about wom-en’s and men’s priorities for partners. Finally, understanding the social contexts in which beauty-status exchange may occur provides insight into the varying meanings of young-adult romantic relationships.

dATA And MEASurESData

I use data from the 2001 to 2002 National Longitudinal Study of Adolescent Health Romantic Pair sample, a supplementary data-set to the 1994 to 2008 National Longitudinal Study of Adolescent Health (Add Health). Add Health is a nationally representative,




longitudinal survey of adolescents in grades 7 through 12 at the initial interview (Chantala 2006; Harris et al. 2009). In the third wave of data collection (Wave III), respondents, who were mainly in their early 20s, provided information on romantic relationships. From current relationships of minimum three-months duration, approximately 500 dating, 500 cohabiting, and 500 marriage partners were selected to complete a slightly modified version of the Wave III interview. These 1,507 couples (3,014 individuals) make up the Romantic Pair sample.

Women in the Romantic Pair sample aver-age about 22 years old and men average about 23.5 years. Except for Carmalt and colleagues (2008) who use these same data, prior analyses use small convenience samples (Elder 1969; Stevens et al. 1990) or datasets that measure only one partner’s attractiveness (Elder 1969; Taylor and Glenn 1976; Udry 1977). Respond-ents in the Romantic Pair data are equivalent in age to at least two of the samples used previ-ously—Carmalt and colleagues (2008) and Stevens and colleagues (1990). The samples used by Udry (1977) and Taylor and Glenn (1976) benefit from substantial age variation (25 to 40 years), but they measure only one partner’s physical attractiveness, and attrac-tiveness ratings were often made several years after marriage. Thus, although the Romantic Pair data may not be ideal due to the sample’s young average age and limited age-range (18 to 43 years), it improves upon datasets used in prior analyses. Like prior analyses, this study inevitably excludes single individuals; supple-mental analyses suggest that attractiveness and status promote partnering (see Part B in the online supplement).

Measures

Add Health interviewers rated respondents’ physical attractiveness using five levels: (1) very unattractive to (5) very attractive. An average of several observers’ ratings is ideal, but prior studies indicate high inter-rater reli-ability in evaluating attractiveness (Langlois et al. 2000; Murstein 1972). In the Add Health

data, interviewers’ ratings are positively and significantly correlated across waves: the cor-relation between Wave I and Wave III ratings (six to seven years apart) is .17 ( p < .001); between Wave II and Wave III ratings (five years apart), the correlation is .20 ( p < .001). When the same interviewer rated both part-ners, there is no evidence of unconsciously exaggerating their similarity. Indeed, the cor-relation of her and his physical attractiveness is slightly lower when both partners were inter-viewed by the same interviewer (not shown).

Interviewers also rated respondents’ grooming and personality attractiveness on five-level scales, yielding a three-item per-sonal attractiveness index (physical, groom-ing, personality). I primarily present models using the single-item measure of physical attractiveness, but results using the index are similar. Body mass index (BMI) is a ratio of height to weight approximating thinness or overweight. Self-rated health ranges from (1) fair or poor to (4) excellent.

Years of education refers to years of com-pleted education. Among respondents, 17 per-cent of men and 25 percent of women were enrolled in college full-time, but only in 11 percent of couples were both partners full-time students. Expected/completed college graduation status is a dichotomous variable indicating whether a respondent was a college graduate or enrolled in a four-year degree program versus having never attended college or having dropped out. This variable addresses right-censoring.

To measure socioeconomic status, I use the Duncan Socioeconomic Index (SEI; Ruggles et al. 2010), a measure of occupational pres-tige that I obtained from U.S. Census data from the Integrated Public Use Microdata Series (IPUMS; Minnesota Population Center 2013). I use both current and forecast (future) SEI. To create forecast SEI, I regressed Wave IV SEI (five years later; available only for original respondents) on race, age, educational attainment, current SEI, picture-vocabulary test score at Wave III, parental income, paren-tal education, and parental SEI. I estimated models separately by gender. I then used



McClintock 9

these regressions to forecast SEI for all respondents. I use current (actual) and fore-cast (future) income, generated in the same way as SEI, to measure personal income. For father’s occupational status, I use the Duncan SEI of the occupation that the respondent’s father worked in when the respondent was an adolescent. Working-class origins are deter-mined by whether a respondent’s father had a working-class occupation when the respond-ent was an adolescent, defined by having a Hollingshead score of four or lower (of seven possible levels). This distinguishes profes-sionals, lesser professionals, semi-profession-als, small business owners, and medium farm owners from technicians, small farm owners, farm workers, and skilled manual workers.

To measure race, respondents were asked which one race best describes them. Because of strong racial matching, I classify couples as both non-Hispanic white (51 percent), both non-Hispanic black (15 percent), both other race (includes Hispanic; 13 percent), and mixed-race (21 percent). Using only her or his race does not alter results. Age is couples’ age in years at the time of the interview. Union status equals dating, cohabiting, or married, and relationship duration is meas-ured in months. Pregnancy denotes whether the female partner is pregnant.

METHodologyAbsolute or Relative Measures of Desirability?

A methodological concern when investigating exchange in partner selection is whether to use absolute measures or relative measures, like the difference between partners’ endowments of a given asset. Regression models using dif-ference measures always incorporate equal information on both partners. But such models are not equivalent to models that use absolute measures and include all of both partners’ characteristics. In a difference model, the coefficient on the partner’s outcome variable is constrained to be one and absolute levels are assumed to be unimportant.

Although prior studies tend to focus on absolute measures (but see McNulty, Neff, and Karney 2008), beauty-status exchange theory is more applicable to relative meas-ures. Exchange theory predicts that when one partner possesses more of one asset, this will be offset by the other partner possessing more of a different asset: which partner has more of a given asset is best measured by the differ-ence in their endowments of that asset. The assumption that each partner’s absolute level of a given asset does not matter is also con-sistent with an exchange model. For example, a woman might marry a man who is less physically attractive than she is because the man has higher socioeconomic status than the woman—but neither partner is necessarily high or low in attractiveness or status, meas-ured in absolute terms. In my analysis I use both the absolute and the difference approach: if couples truly engage in cross-trait beauty-status exchange, evidence of exchange should be robust across both models and should be especially apparent in the difference models.

Are Log-Linear or Negative Binomial Models More Appropriate?

Researchers studying other aspects of partner selection, such as race-status exchange, often use log-linear or negative binomial regression models (e.g., Rosenfeld 2001, 2005, 2010). Negative binomial models are a generalization of log-linear regression models that relax assumptions about the distribution of data (Long 1997). These models are well-suited to studying partner selection because the depen-dent variable is the distribution of couples (Agresti 1996). This addresses an important weakness in prior analyses of beauty-status exchange: the hypothesis that physical attrac-tiveness is traded for socioeconomic status does not indicate either attractiveness or status as a dependent variable. As a result, some prior studies arbitrarily use socioeconomic status as the dependent variable (Elder 1969; Taylor and Glenn 1976; Udry 1977), whereas others use physical attractiveness (Carmalt et al. 2008) or use both measures (Stevens et al. 1990).




Log-linear and negative binomial regres-sion models are also particularly appropriate for identifying patterns of matching or exchange. Controlling for the distribution of beauty and status among partnered men and women, these models test which pairings are overrepresented in the data (e.g., whether beautiful low-status women are disproportion-ately paired with unattractive high-status men). Parameters directly model the tendency for partners to match (by having equal levels of a given trait) or exchange (when one part-ner has a higher level of one trait and the other partner has a higher level of another trait).

The Current Analysis

I begin by describing the co-occurrence of desirable traits at the individual and couple level to illustrate the importance of including complete information on both partners’ char-acteristics in regression models testing exchange theories. To search for evidence of exchange, I examine the differences between partners’ endowments of desirable traits. Finally, because it is couples who differ on a given trait that might engage in cross-trait exchange, I look for patterns of exchange among this subgroup of couples.

I next consider multivariate regression models, estimating models that ignore match-ing and the within-individual correlation of desirable traits and then adding variables to fully control for these forces. Consistent with prior work, I estimate models separately by gender. All models include age, race, preg-nancy status, relationship duration, and union status. Next, to test for exchange more directly, I estimate regression models using difference measures (his minus her level of each trait). Finally, I estimate negative bino-mial regression models, a generalization of log-linear models that are preferred when data are scarce and over-dispersion is prob-lematic (King 1989; Long 1997; Long and Freese 2006). In these data, relatively few couples have disparate levels of attractiveness and socioeconomic status—data are scarce in these cells. I use various measures and model specifications to test the robustness of results.

I primarily present results using the entire sample, but some key results are also pre-sented for subgroups suspected of engaging in beauty-status exchange—couples who dif-fer on attractiveness (N = 810) or college status (N = 242), minority-female white-male couples (N = 95), and couples involving a working-class-origin woman (N = 1,146) or a five-year or larger age gap favoring the man (N = 206). I also discuss models estimated separately by union status; models that inter-act the effects of interest with union status, relationship duration, class background, and race; and models restricted to couples in the top 25th percentile of the age distribution (women 23 to 40 years and men 24 to 43 years, N = 378), non-student couples (N = 1,341), couples interviewed by the same interviewer (N = 1,305), and minority-white couples (N = 209).

Although no one variable is missing for a large number of cases, many cases are miss-ing data on at least one variable. Dropping cases with missing data would reduce the sample size to 1,408 and would likely intro-duce bias (Acock 2005). I use the ICE (Impu-tation by Chained Equations) procedure in the statistical software program Stata 10.1 (Royston 2004) to impute missing data using switching regression, an iterative multivaria-ble regression technique. I show results from regression models estimated using the MIM procedure in Stata 10.1 (Royston 2004). Descriptive statistics and regression models calculated by dropping cases with missing data are similar to those using MIM (not shown).

rESulTSSample Characteristics

Table 1 presents summary statistics on all variables, by gender. Women are rated more physically attractive than men. This gender difference in physical attractiveness exists in the full Add Health sample (including among single individuals; see Table S3 in the online supplement), perhaps because women invest more in personal care and cosmetics. If women



McClintock 11

in prior cohorts were rated as more attractive than men, this may have created the illusion of exchange. In prior cohorts, many women mar-ried higher-status men simply because, on average, men had more education and higher occupational status than did women. Even if all couples sought equality in physical attrac-tiveness and status, men would have had higher status while women would have been

more attractive. But this would have resulted from gender differences in average endow-ments of attractiveness and status rather than from a beauty-for-status exchange. In con-trast, in these data women surpass men in educational attainment and occupational sta-tus (SEI). This gender reversal reflects changes in the general population—women now earn more college degrees than men (DiPrete and

Table 1. Mean Characteristics by Gender; National Longitudinal Study of Adolescent Health Romantic Pair Sample (2001 to 2002); N = 1,507

Women Men

Mean or

Proportion SDMean or

Proportion SD

Attractiveness Physical attractivenessa 3.63*** .86 3.44 .75 Personal attractiveness indexa 3.67*** .69 3.48 .62 Body mass index (BMI) 26.49*** 6.67 27.15 5.64 Self-rated healthb 2.92*** .87 3.10 .84Socioeconomic Status Expected college graduatec .27*** .22 Years of completed education 12.96** 1.96 12.74 1.98 Current Duncan socioeconomic index (SEI)d 28.66 27.34 28.31 24.89 Forecast Duncan socioeconomic index (SEI)d 46.94*** 8.74 39.69 11.73 Current income ($10,000s) 11.96*** 17.69 19.69 22.65 Forecast income ($10,000s) 29.46*** 12.91 46.83 13.09 Working-class social originse .76 .74 Demographic Characteristics Age (years) 21.85*** 2.37 23.48 3.30 Race White .59 .59 Black .17 .18 Other .24 .23 Pregnant .07 Relationship Characteristics Relationship duration (months) 38.66 26.66 38.29 26.76 Married .36 .36 Cohabiting .36 .36 Dating .28 .28

Note: Descriptive statistics use multiple imputed datasets to estimate missing values.aScored from 1 (very physically unattractive) to 5 (very physically attractive). The personal attractiveness index reflects respondents’ average rating on three measures of attractiveness: physical, personality, and grooming.bFrom (1) fair or poor to (4) excellent.cThis dichotomous variable indicates respondents who have completed a four-year college degree or are currently enrolled in a four-year degree program.dA measure of occupational prestige. Ratings range from 0 (low) to 96 (high). Forecast SEI is projected SEI at Wave IV when respondents will be in their late 20s.eI define individuals of working-class social origins as having a father who worked in an occupation with a Hollingshead rank of four or lower.*p < .05; **p < .01; ***p < .001; p-values indicate significant gender differences (two-tailed tests).




Table 2. Within-Individual Correlation of Desirable Characteristics; National Longitudinal Study of Adolescent Health Romantic Pair Sample (2001 to 2002); N = 1,507

Women Men

Correlation of Her Attractiveness and

Her Status

Correlation of His Attractiveness and His Status

Attractiveness EDUd GRADe SEIf INCg EDUd GRADe SEIf INCg

Physical attractiveness .127***a .155***a .175***a .065*a .166***a .156***a .132***a .096***a

Personal attractiveness index

.179***b .186***b .222***b .104***b .230***b .179***b .197***b .120***b

BMI, reverse-codedc .140***b .207***b .207***b .043b –.015b .071**b .011b –.028b

Self-rated health .200***a .191***a .167***a .102***a .172***a .166***a .124*a .102***a

Note: Descriptive statistics use multiple imputed datasets to estimate missing values.aCorrelations and significance tests involving one or more ordinal variables are calculated using Spearman’s Rho.bCorrelations and significance tests involving only interval variables are calculated using Pearson’s correlation.cBody mass index. Reverse-coded so that higher values indicate thinner (normatively more desirable) physiques.dEDU is years of completed education.eGRAD is expected college graduation status.fSEI is projected future occupational status.gINC is projected future income.*p < .05; **p < .01; ***p < .001 (two-tailed tests).

Buchman 2006), and single, childless, young women out-earn their male counterparts (Wiseman 2010). These gender differences in education and occupational status observed in the Add Health data are also evident among respondents of a similar age in the 2000 U.S. Census (author’s calculations; not shown).

Co-occurrence of Desirable Traits

Table 2 demonstrates that the various dimen-sions of “desirability” are strongly and posi-tively correlated within individuals. Because physically attractive individuals tend to be high in socioeconomic status (and vice versa), partner matching on one or both of these traits might generate the illusion of cross-trait between-partner exchange. The within-indi-vidual correlations between desirable traits are not strong enough to preclude exchange, but they are strong enough to generate spurious evidence of exchange. Additionally, when desirable traits co-occur, matching on any one trait encourages matching on related traits.

Between-Partner Similarity

Table 3 displays strong evidence of matching on physical attractiveness, education, and occupa-tional status (SEI). The correlations between her and his expected college graduation status (.575), years of completed education (.557), and SEI (.546) are especially strong. The between-partner correlation of attractiveness (.256) is similar in magnitude to equivalent correlations in all other samples known to the author (Barelds et al. 2011; Stevens et al. 1990). None of the statistically significant between-partner within-trait correlations are negative, as predicted by an exchange model in which high-status but homely individuals are paired with low-status but good-looking partners.

The various measures of one partner’s desirability are also positively and signifi-cantly correlated with other measures of the other partner’s desirability. For example, the correlation between her physical attractive-ness and his years of completed education is .186 ( p < .001). Prior studies present similar



13

Tabl

e 3.

Bet

wee

n-P

artn

er C

orre

lati

on o

f W

omen

’s a

nd

Men

’s C

har

acte

rist

ics;

Nat

ion

al L

ongi

tud

inal

Stu

dy

of A

dol

esce

nt

Hea

lth

Rom

anti

c P

air

Sam

ple

(20

01 t

o 20

02);

N =

1,5

07

Mal

e P

artn

er’s

Ch

arac

teri

stic

s

A

ttra

ctiv

enes

sS

ocio

econ

omic

Sta

tus

Fem

ale

Par

tner

’s C

har

acte

rist

ics

AT

TR

AC

TP

ER

BM

IH

EA

LTH

ED

UG

RA

DS

EI

INC

Att

ract

iven

ess

Ph

ysic

al a

ttra

ctiv

enes

s (A

TT

RA

CT

).2

56***a

.297

***a

.070

**a

.099

***a

.186

***a

.142

***a

.130

***a

.057

a

P

erso

nal

att

ract

iven

ess

ind

ex (

PE

R)

.306

***a

.386

***b

.050

b.1

23***a

.190

***b

.145

***b

.165

***b

.087

**b

B

MI,

rev

erse

-cod

edc

.134

***a

.134

***b

.266

***b

.138

***a

.182

***b

.191

***b

.164

***b

.112

***b

S

elf-

rate

d h

ealt

h (

HE

ALT

H)

.104

***a

.133

***a

.044

a.1

72***a

.182

***a

.157

***a

.145

***a

.111

***a

Soc

ioec

onom

ic S

tatu

s

Y

ears

of

com

ple

ted

ed

uca

tion

(E

DU

).1

43***a

.188

***b

–.00

6b.1

40***a

.557

***b

.456

***b

.450

***b

.308

***b

E

xpec

ted

col

lege

gra

du

ate

(GR

AD

).1

19***a

.150

***b

.058

*b.1

47***a

.499

***b

.575

***b

.481

***b

.295

***b

F

orec

ast

soci

oeco

nom

ic i

nd

ex (

SE

I).1

41***a

.176

***b

.048

b.0

99***a

.525

***b

.555

***b

.546

***b

.351

***b

F

orec

ast

inco

me

(IN

C)

.080

**a

.108

***b

.036

b.0

66*a

.358

***b

.333

***b

.373

***b

.330

***b

Not

e: D

escr

ipti

ve s

tati

stic

s u

se m

ult

iple

im

pu

ted

dat

aset

s to

est

imat

e m

issi

ng

valu

es.

a Cor

rela

tion

s an

d s

ign

ifica

nce

tes

ts i

nvo

lvin

g on

e or

mor

e or

din

al v

aria

bles

are

cal

cula

ted

usi

ng

Sp

earm

an’s

Rh

o.b C

orre

lati

ons

and

sig

nifi

can

ce t

ests

in

volv

ing

only

in

terv

al v

aria

bles

are

cal

cula

ted

usi

ng

Pea

rson

’s c

orre

lati

on.

c Bod

y m

ass

ind

ex. R

ever

se-c

oded

so

that

hig

her

val

ues

in

dic

ate

thin

ner

(n

orm

ativ

ely

mor

e d

esir

able

) p

hys

iqu

es.

* p <

.05;

** p

< .0

1; ***

p <

.001

(tw

o-ta

iled

tes

ts).




bivariate cross-trait associations as evidence that men exchange socioeconomic status for women’s attractiveness (e.g., Elder 1969). However, these same cross-trait correlations are evident within individuals, and couple-level within-trait correlations are generally stronger than cross-trait correlations, so matching may be a sufficient explanation.

The correlations in Table 3 might be mis-leading if a minority of couples engage in beauty-status exchange while the majority match on these traits. Importantly, couples who might engage in beauty-status exchange are identifiable: they differ on some measure of desirability (few couples differ on these items—for example, 84 percent have the same college graduation status). If beauty-status exchange is prevalent, couples who differ in status would have negatively corre-lated attractiveness ratings. Equivalently, couples who differ in attractiveness would have negatively correlated status. Only cou-ples with equal status or equal attractiveness (who cannot be engaging in beauty-status exchange) would have positively correlated traits.

Estimating correlations separately by com-binations of her and his status (or attractive-ness) indicates that matching is the dominant pattern for all groups (not shown). For exam-ple, the correlation of her and his physical attractiveness is .20 when both partners are not expected to earn college diplomas ( p < .001; 67 percent of couples belong to this group), .30 when she is expected to graduate from college but he is not ( p < .001; 11 per-cent of couples), .34 when he is expected to graduate from college but she is not ( p < .01; 5 percent of couples), and .25 when both are expected to graduate from college ( p < .001; 17 percent of couples). In contradiction to the beauty-status exchange model, the correlation of her and his attractiveness is positive regard-less of whether the couple differs on status. In fact, correlations are stronger when the couple differs on college status. Correlations of phys-ical attractiveness by categorical measures of her and his completed years of education and quartiles of her and his SEI are also positive for all groups.

Between-Partner Differences in Endowments

If cross-trait exchange occurs, then when part-ners possess an unequal amount of one par-ticular trait, this should be offset by an inequality in the other direction on some other trait. For example, if one partner is physically attractive and the other is not, the unattractive partner should compensate by possessing more of some other asset, such as education or high occupational status. In this case, the dif-ferences between the partners’ levels of these desirable traits (his minus her level) would be negatively correlated. Table 4 shows these correlations for the entire sample and for sub-groups thought to engage in gendered beauty-status exchange. None of the differences in the measures of socioeconomic status are signifi-cantly and negatively correlated with the dif-ference in physical attractiveness or with the personal attractiveness index. Table S5 in the online supplement presents equivalent corre-lations using differences (his minus her) in an expanded array of endowments.

I also examined average differences in endowments for couples who differ on status or attractiveness (not shown). If these couples engage in cross-trait exchange, then when one partner has an advantage on one trait, the other partner should have an advantage on another trait—but this is not the case in these data. For example, when women are advan-taged in college degree status (women are better-educated), women’s advantage in phys-ical attractiveness is .26 (women are more attractive). When partners have the same degree status, women’s advantage is .19. When men are advantaged in degree status, there is no gender difference in physical attractiveness. This is the reverse of the pat-tern predicted by exchange. For no measure of status does the difference in attractiveness change monotonically in the direction pre-dicted by beauty-status exchange.

Conventional Regression Models

Table 5 presents regression models examin-ing gender-stereotypical exchange, in which



15

Tabl

e 4.

Cor

rela

tion

of

the

Dif

fere

nce

(H

is M

inu

s H

er)

betw

een

Wom

en’s

an

d M

en’s

Ch

arac

teri

stic

s; S

tati

stic

ally

Sig

nif

ican

t N

egat

ive

Cor

rela

tion

s W

ould

Su

gges

t B

etw

een

-Par

tner

Cro

ss-T

rait

Exc

han

ge; N

atio

nal

Lon

gitu

din

al S

tud

y of

Ad

oles

cen

t H

ealt

h R

oman

tic

Pai

r S

amp

le (

2001

to

2002

)

Dif

fere

nce

(H

is M

inu

s H

er)

in P

hys

ical

Att

ract

iven

ess

Rat

ing

(DF

-AT

T)

and

in

Per

son

al A

ttra

ctiv

enes

s In

dex

(D

F-P

ER

)

Dif

fere

nce

(H

is

Min

us

Her

) in

S

tatu

s

En

tire

Sam

ple

Dif

fer

on

Att

ract

iven

essa

Dif

fer

on

Col

lege

Sta

tus

(GR

AD

)a

Min

orit

y-F

emal

e W

hit

e-M

ale

Cou

ple

bW

orki

ng-

Cla

ss-

Ori

gin

Fem

alec

Mal

e P

artn

er i

s 5+

Yea

rs O

lder

Mar

ried

C

oup

les

DF

-AT

TD

F-P

ER

DF

-AT

TD

F-P

ER

DF

-AT

TD

F-P

ER

DF

-AT

TD

F-P

ER

DF

-AT

TD

F-P

ER

DF

-AT

TD

F-P

ER

DF

-AT

TD

F-P

ER

DF

-ED

U–.

017

.027

–.01

9.0

22.1

05.1

52*

–.07

6–.

048

–.02

0.0

26–.

002

.073

.013

.092

*

DF

-GR

AD

.046

.070

**.0

60.0

76.1

23.1

71**

.148

.221

*.0

46.0

60.0

35.0

95.0

10.0

49D

F-S

EI

.034

.066

*.0

47.0

73.1

36*

.164

*.0

67.1

15.0

20.0

50.0

45.0

73–.

034

.023

DF

-IN

C.0

16.0

23.0

19.0

24.0

75.0

91.1

23.0

38–.

001

.006

–.01

1.0

07–.

052

–.02

9S

amp

le s

ize

1,50

781

083

824

295

1,14

620

654

3

Not

e: D

escr

ipti

ve s

tati

stic

s u

se m

ult

iple

im

pu

ted

dat

aset

s to

est

imat

e m

issi

ng

valu

es.

a It

is c

oup

les

that

dif

fer

on a

ttra

ctiv

enes

s or

sta

tus

wh

o m

igh

t p

ossi

bly

enga

ge i

n b

eau

ty-s

tatu

s ex

chan

ge. N

= 8

10 c

oup

les

hav

e d

iffe

ren

t le

vels

on

th

e fi

ve-l

evel

p

hys

ical

att

ract

iven

ess

rati

ng.

N =

838

cou

ple

s be

lon

g to

dif

fere

nt

quar

tile

s of

th

e p

erso

nal

att

ract

iven

ess

ind

ex. N

= 2

42 c

oup

les

hav

e d

iffe

ren

t ex

pec

ted

col

lege

gr

adu

atio

n s

tatu

s.b S

assl

er a

nd

Joy

ner

(20

11)

sugg

ests

th

at m

inor

ity

wom

en p

artn

ered

wit

h w

hit

e m

en a

re e

spec

iall

y li

kely

to

trad

e be

auty

for

sta

tus.

c Eld

er (

1969

) an

d T

aylo

r an

d G

len

n (

1976

) su

gges

t th

at w

omen

of

wor

kin

g-cl

ass

and

mid

dle

-cla

ss o

rigi

ns

are

esp

ecia

lly

like

ly t

o tr

ade

beau

ty f

or s

tatu

s. I

defi

ne

wor

kin

g-cl

ass

orig

ins

as h

avin

g a

fath

er w

ho

wor

ked

in

an

occ

up

atio

n w

ith

a H

olli

ngs

hea

d r

ank

of f

our

or l

ower

.* p

< .0

5; ** p

< .0

1; ***

p <

.001

(tw

o-ta

iled

tes

ts).

Cor

rela

tion

s an

d s

ign

ifica

nce

tes

ts a

re c

alcu

late

d u

sin

g P

ears

on’s

cor

rela

tion

.




women might use attractiveness to obtain high-status men, and models examining reverse-stereotypical exchange, in which men use attractiveness to obtain high-status women. Results are similar in otherwise equivalent models that use physical attrac-tiveness as the dependent variable (see Table S6 in the online supplement). The table pres-ents status as the dependent variable because more prior researchers have taken this approach. Models GS-1a, GS-2a, and GS-3a examine gender-stereotypical exchange (GS), using women’s physical attractiveness to pre-dict men’s socioeconomic status. Models RS-1a, RS-2a, and RS-3a examine reverse-stereotypical exchange (RS). Models GS-1b, GS-2b, and GS-3b add the male partner’s attractiveness and the female partner’s status to test whether any apparent exchange of attractiveness for status in Models GS-1a, GS-2a, and GS-3a was due to matching on attractiveness and to the within-individual correlation of attractiveness with status. Mod-els RS-1a, RS-2a, and RS-3a are equivalent for reverse-stereotypical exchange.

In Models GS-2b and GS-3b (but not in GS-1b), adding the male partner’s attractive-ness and the female partner’s socioeconomic status eliminates the apparent relationship between women’s attractiveness and men’s status. Fully controlling for both partners’ traits has the same effect in Models RS-2b and RS-3b. But there still remains evidence in Models GS-1b and RS-1b that both genders might exchange attractiveness for a more educated partner, when measured by com-pleted years of education (but not when meas-ured by expected college graduation status). Models including interactions to test for dis-proportionate pairings (e.g., whether attrac-tive low-status women disproportionately partner with unattractive high-status men) do not reveal evidence of beauty-status exchange (not shown).

Difference Measures

The models displayed in Table 6 use the dif-ference in physical attractiveness (his minus her rating) to predict the difference (his minus

her) in the various measures of socioeco-nomic status. This is arguably the most direct and compelling test of beauty-status exchange; I therefore present models for the entire sample and for subgroups thought to engage in beauty-status exchange. In no instance is there support for exchange. The beauty-status exchange model predicts that when one part-ner has more beauty, the other partner will have more status—if so, the differences in these attributes would be negatively related. Yet the differences in attractiveness and status are not related (and coefficients are generally positive). Models using the difference in sta-tus to predict the difference in physical attrac-tiveness likewise provide no evidence of beauty-status exchange (not shown).

Table 6 indicates that the difference in age is positively associated with the difference in years of completed education (but not with the difference in college degree). Respond-ents are in their early 20s and many are still finishing their education—until education is completed, an older partner may temporarily have more education. Given that men tend to be somewhat older than their partners, they often have a temporary advantage in com-pleted years of schooling. This might gener-ate the appearance of women dating up in education, when it is actually an artifact of the gendered age gap.

Negative Binomial Models

Table 7 presents results from three sets of negative binomial models. I use negative binomial models rather than log-linear mod-els because of significant over-dispersion. These models analyze the distribution of cross-tabulated data so they require categori-cal variables. Physical attractiveness is divided into four groups (“very physically unattractive” is combined with “physically unattractive”). Occupational status (SEI) is divided into four quartiles. Years of com-pleted education is divided into four levels: less than high school, high school graduate, some college, and four-year college graduate or higher. Tables using completed education and SEI are 4x4x4x4. The table using



17

Tabl

e 5.

Reg

ress

ion

Coe

ffic

ien

ts (

from

Lin

ear

Reg

ress

ion

Mod

els)

an

d O

dd

s R

atio

s (f

rom

Log

isti

c R

egre

ssio

n M

odel

s) f

rom

Reg

ress

ing

On

e P

artn

er’s

Sta

tus

on t

he

Oth

er P

artn

er’s

Ph

ysic

al A

ttra

ctiv

enes

s an

d S

tatu

s an

d o

n t

he

Fir

st P

artn

er’s

Ow

n P

hys

ical

Att

ract

iven

ess;

Nat

ion

al

Lon

gitu

din

al S

tud

y of

Ad

oles

cen

t H

ealt

h R

oman

tic

Pai

r S

amp

le (

2001

to

2002

); N

= 1

,507

Dep

end

ent

Var

iabl

e: M

ale

Par

tner

’s S

ocio

econ

omic

S

tatu

s (E

xam

ines

Gen

der

-Ste

reot

ypic

al E

xch

ange

; GS

)D

epen

den

t V

aria

ble:

Fem

ale

Par

tner

’s S

ocio

econ

omic

S

tatu

s (E

xam

ines

Rev

erse

-Ste

reot

ypic

al E

xch

ange

; RS

)

E

DU

GR

AD

SE

IE

DU

GR

AD

SE

I

M

odel

G

S-1

aM

odel

G

S-1

bM

odel

G

S-2

aM

odel

G

S-2

bM

odel

G

S-3

aM

odel

G

S-3

bM

odel

R

S-1

aM

odel

R

S-1

bM

odel

R

S-2

aM

odel

R

S-2

bM

odel

R

S-3

aM

odel

R

S-3

b

Ph

ysic

al A

ttra

ctiv

enes

s

H

er a

ttra

ctiv

enes

s.1

44***

.074

**1.

322***

1.08

5.1

00***

.021

.018

1.19

3*.0

64**

H

is a

ttra

ctiv

enes

s.0

73**

1.34

4***

.060

*.1

29***

.045

**1.

293***

1.05

1.1

22***

.044

*

Yea

rs o

f C

omp

lete

d E

du

cati

on

(ED

U)

H

er E

DU

.497

***

His

ED

U.5

13***

E

xpec

ted

Col

lege

Gra

du

ate

(GR

AD

)

H

er G

RA

D16

.318

***

His

GR

AD

16.3

48***

F

orec

ast

SE

I In

dex

Her

SE

I.5

18***

His

SE

I.4

87***

Not

e: M

odel

s ar

e es

tim

ated

usi

ng

mu

ltip

le i

mp

ute

d d

atas

ets.

Wit

h t

he

exce

pti

on o

f d

ich

otom

ous

vari

able

s, a

ll m

easu

res

are

stan

dar

diz

ed. A

ll r

egre

ssio

ns

con

trol

fo

r ra

ce, a

ge, r

elat

ion

ship

du

rati

on, p

regn

ancy

, an

d u

nio

n s

tatu

s.* p

< .0

5; ** p

< .0

1; ***

p <

.001

(tw

o-ta

iled

tes

ts).



18

Tabl

e 6.

Reg

ress

ion

Coe

ffic

ien

ts f

rom

Lin

ear

Reg

ress

ion

Mod

els

Reg

ress

ing

the

Dif

fere

nce

in

Cou

ple

s’ M

easu

res

of S

tatu

s (H

is S

tatu

s M

inu

s H

er

Sta

tus)

on

th

e D

iffe

ren

ce i

n P

hys

ical

Att

ract

iven

ess

(His

Ph

ysic

al A

ttra

ctiv

enes

s M

inu

s H

er P

hys

ical

Att

ract

iven

ess)

; Nat

ion

al L

ongi

tud

inal

S

tud

y of

Ad

oles

cen

t H

ealt

h R

oman

tic

Pai

r S

amp

le (

2001

to

2002

)

Dif

fere

nce

(H

is

Min

us

Her

) in

A

ttra

ctiv

enes

s an

d i

n A

ge

Dif

fere

nce

(H

is M

inu

s H

er)

in S

ocio

econ

omic

Sta

tus

En

tire

Sam

ple

Dif

fer

on P

hys

ical

A

ttra

ctiv

enes

sfM

inor

ity-

Fem

ale

W

hit

e-M

ale

Cou

ple

gW

orki

ng-

Cla

ss-

Ori

gin

Fem

aleh

Mal

e P

artn

er i

s

5+ Y

ears

Old

er

DF

-E

DU

cD

F-

GR

AD

dD

F-

SE

IeD

F-

ED

UD

F-

GR

AD

DF

- S

EI

DF

- E

DU

DF

-G

RA

DD

F-

SE

ID

F-

ED

UD

F-

GR

AD

DF

- S

EI

DF

- E

DU

DF

-G

RA

DD

F-

SE

I

DF

-AT

Ta

–.01

2.0

17.0

31–.

009

.016

.032

–.10

7.0

45.0

36–.

014

.016

.019

.024

.020

.059

DF

-AG

Eb

.158

***

.002

.054

.109

**–.

006

.035

.086

.013

.203

*.1

35***

–.00

7.0

50.0

83.0

16.0

87

Sam

ple

siz

e1,

507

810

951,

146

206

Not

e: M

odel

s ar

e es

tim

ated

usi

ng

mu

ltip

le i

mp

ute

d d

atas

ets.

Wit

h t

he

exce

pti

on o

f d

ich

otom

ous

vari

able

s, a

ll m

easu

res

are

stan

dar

diz

ed. A

ll r

egre

ssio

ns

con

trol

fo

r ra

ce, t

he

dif

fere

nce

in

age

, rel

atio

nsh

ip d

ura

tion

, pre

gnan

cy, a

nd

un

ion

sta

tus.

Mod

els

pre

dic

tin

g th

e d

iffe

ren

ce i

n p

hys

ical

att

ract

iven

ess

pro

du

ce e

quiv

alen

t re

sult

s.a H

is m

inu

s h

er p

hys

ical

att

ract

iven

ess

rati

ng

(AT

T).

b His

min

us

her

age

in

yea

rs (

AG

E).

c His

min

us

her

yea

rs o

f co

mp

lete

d e

du

cati

on (

ED

U).

dH

is m

inu

s h

er e

xpec

ted

col

lege

gra

du

atio

n s

tatu

s (G

RA

D).

e His

min

us

her

Du

nca

n S

ocio

econ

omic

In

dex

(S

EI)

.f C

oup

les

dif

fer

on p

hys

ical

att

ract

iven

ess

(n =

810

).g S

assl

er a

nd

Joy

ner

(20

11)

sugg

ests

th

at m

inor

ity

wom

en p

artn

ered

wit

h w

hit

e m

en a

re e

spec

iall

y li

kely

to

trad

e be

auty

for

sta

tus.

hE

lder

(19

69)

and

Tay

lor

and

Gle

nn

(19

76)

sugg

est

that

wom

en o

f w

orki

ng-

clas

s an

d m

idd

le-c

lass

ori

gin

s ar

e es

pec

iall

y li

kely

to

trad

e be

auty

for

sta

tus.

I d

efin

e w

orki

ng-

clas

s or

igin

s as

hav

ing

a fa

ther

wh

o w

orke

d i

n a

n o

ccu

pat

ion

wit

h a

Hol

lin

gsh

ead

ran

k of

fou

r or

low

er.

* p <

.05;

** p

< .0

1; ***

p <

.001

(tw

o-ta

iled

tes

ts).



McClintock 19

expected college graduation status is 2x2x4x4. Equations are as follows:

(1a)

(1b)

(2a)

(2b)

(3a)

(3b)

MPA is the male’s physical attractiveness, FPA is the female’s physical attractiveness, MEDU is his completed education, FEDU is her completed education, MGRAD is his expected college graduation status, FGRAD is her expected college graduation status, MSEI is his occupational status, FSEI is her occupa-tional status, matching on PA is matching on physical attractiveness (equal to one if MPA = FPA; equal to zero otherwise), matching on EDU is matching on completed education (one if MEDU = FEDU; zero otherwise), matching on GRAD is matching on college graduation status (one if MGRAD = FGRAD; zero other-wise), matching on SEI is matching on

occupational status (one if MSEI = FSEI; zero otherwise), PA-EDU exchange is gender-sym-metric exchange of physical attractiveness for completed education (one if MPA > FPA and MEDU < FEDU; also one if MPA < FPA and MEDU > FEDU; zero otherwise), PA-GRAD exchange is gender-symmetric exchange of physical attractiveness for expected college graduation status (one if MPA > FPA and MGRAD < FGRAD; one if MPA < FPA and MGRAD > FGRAD; zero otherwise), PA-SEI exchange is gender-symmetric exchange of physical attractiveness for occupational status (one if MPA > FPA and MSEI < FSEI; one if MPA < FPA and MSEI > FSEI; zero other-wise), FPA-MEDU exchange is gender-stereo-typical exchange of the female’s physical attractiveness for the male’s completed educa-tion (one if MPA < FPA and MEDU > FEDU; zero otherwise), FPA-MGRAD exchange is gender-stereotypical exchange of her physical attractiveness for his expected college gradua-tion status (one if MPA < FPA and MGRAD > FGRAD; zero otherwise), and FPA-MSEI exchange is gender-stereotypical exchange of her physical attractiveness for his status (one if MPA < FPA and MSEI > FSEI; zero other-wise). The remaining interactions (MPA x MEDU, FPA x FEDU, MPA x MGRAD, FPA x FGRAD, MPA x MSEI, and FPA x FSEI) account for within-individual correlation of physical attractiveness and status.4

For each measure of socioeconomic sta-tus, I first present a model testing for match-ing and for gender-symmetric exchange (either partner might trade attractiveness for status or vice versa). I next add a parameter modeling gender-stereotypical exchange (men offer status and women offer attractive-ness). All six models indicate a strong ten-dency toward matching, but only one set of models provides any evidence of beauty-sta-tus exchange. Specifically, Model 1a indi-cates gender-symmetric exchange of current educational attainment for attractiveness. There is no evidence of gender-stereotypical exchange—women are not significantly more likely to trade attractiveness for status than are men.

Log(P) = Constant + MPA + FPA + MEDU + FEDU + MPA x MEDU + FPA x FEDU + Matching on PA + Matching on EDU + PA-EDU Exchange

Log(P) = Constant + MPA + FPA + MEDU + FEDU + MPA x MEDU + FPA x FEDU + Matching on PA + Matching on EDU + PA-EDU Exchange + FPA-MEDU Exchange

Log(P) = Constant + MPA + FPA + MGRAD + FGRAD + MPA x MGRAD + FPA x FGRAD + Matching on PA + Matching on GRAD + PA-GRAD Exchange

Log(P) = Constant + MPA + FPA + MGRAD + FGRAD + MPA x MGRAD + FPA x FGRAD + Matching on PA + Matching on GRAD + PA-GRAD Exchange + FPA-MGRAD Exchange

Log(P) = Constant + MPA + FPA + MSEI + FSEI + MPA x MSEI + FPA x FSEI + Matching on PA + Matching on SEI + PA-SEI Exchange

Log(P) = Constant + MPA + FPA + MSEI + FSEI + MPA x MSEI + FPA x FSEI + Matching on PA + Matching on SEI + PA-SEI Exchange + FPA-MSEI Exchange



20

Tabl

e 7.

Coe

ffic

ien

ts f

rom

Neg

ativ

e B

inom

ial

Reg

ress

ion

s E

xam

inin

g P

atte

rns

of E

xch

ange

an

d M

atch

ing

wit

h R

esp

ect

to P

hys

ical

Att

ract

iven

ess

and

Soc

ioec

onom

ic S

tatu

s; N

atio

nal

Lon

gitu

din

al S

tud

y of

Ad

oles

cen

t H

ealt

h R

oman

tic

Pai

r S

amp

le (

2001

to

2002

); N

= 1

,507

Mea

sure

s of

Soc

ioec

onom

ic S

tatu

s

E

DU

GR

AD

SE

I

M

odel

1a

Mod

el 1

bM

odel

2a

Mod

el 2

bM

odel

3a

Mod

el 3

b

Mat

chin

g on

Yea

rs o

f co

mp

lete

d e

du

cati

on (

ED

U)a

1.28

3***

1.28

8***

Exp

ecte

d c

olle

ge g

rad

uat

e (G

RA

D)

1.50

2***

1.50

5***

For

ecas

t so

cioe

con

omic

in

dex

(S

EI)

b.6

85***

.687

***

P

hys

ical

att

ract

iven

essc

.832

***

.826

***

.664

***

.664

***

.671

***

.669

***

Gen

der

-Sym

met

ric

Exc

han

ged

Ph

ysic

al a

ttra

ctiv

enes

s fo

r E

DU

.524

***

.342

Ph

ysic

al a

ttra

ctiv

enes

s fo

r G

RA

D.1

70.1

34

P

hys

ical

att

ract

iven

ess

for

SE

I.2

11.1

20G

end

er-S

tere

otyp

ical

Exc

han

ged

Her

ph

ysic

al a

ttra

ctiv

enes

s fo

r h

is E

DU

.348

Her

ph

ysic

al a

ttra

ctiv

enes

s fo

r h

is G

RA

D.0

78

H

er p

hys

ical

att

ract

iven

ess

for

his

SE

I.1

6M

odel

Fit

Mod

el d

egre

es o

f fr

eed

om35

3619

2035

36

Res

idu

al d

egre

es o

f fr

eed

om22

122

045

4422

122

0

BIC

(lo

wer

val

ues

in

dic

ate

bett

er f

it)

1219

1222

407

411

1251

1255

Not

e: R

egre

ssio

ns

are

esti

mat

ed u

sin

g m

ult

iple

im

pu

ted

dat

aset

s. A

ll m

odel

s ac

cou

nt

for

wit

hin

-in

div

idu

al c

orre

lati

on o

f at

trac

tive

nes

s an

d s

tatu

s.a L

ess

than

hig

h s

choo

l, h

igh

sch

ool

grad

uat

e, s

ome

coll

ege,

or

fou

r-ye

ar c

olle

ge g

rad

uat

e or

hig

her

.b G

rou

ped

in

to f

our

cate

gori

es w

ith

div

isio

ns

at t

he

gen

der

-sp

ecifi

c 25

th, 5

0th

, an

d 7

5th

per

cen

tile

s.c V

ery

ph

ysic

ally

un

attr

acti

ve o

r u

nat

trac

tive

, ave

rage

, att

ract

ive,

or

very

ph

ysic

ally

att

ract

ive.

dIn

a g

end

er-s

ymm

etri

c ex

chan

ge, e

ith

er p

artn

er m

ay t

rad

e so

cioe

con

omic

sta

tus

for

the

oth

er p

artn

er’s

ph

ysic

al a

ttra

ctiv

enes

s. I

n a

gen

der

-ste

reot

ypic

al e

xch

ange

, th

e m

ale

par

tner

tra

des

his

soc

ioec

onom

ic s

tatu

s fo

r th

e fe

mal

e p

artn

er’s

ph

ysic

al a

ttra

ctiv

enes

s.* p

< .0

5; ** p

< .0

1; ***

p <

.001

(tw

o-ta

iled

tes

ts).



McClintock 21

MEASurEMEnTAlternative Measures

Evidence of matching is very robust to differ-ent measures, but evidence of beauty-status exchange is not robust. Partners’ levels of other desirable traits (grooming, personality, emotional supportiveness, self-rated health, current occupational status, and current and projected income) are generally positively correlated (partial results in Table 3). The dif-ferences (his minus her) in these other traits are predominately uncorrelated or positively correlated with the differences in attractive-ness and status, whereas the exchange model predicts negative correlations (see Table S5 in the online supplement). Regression models using alternative measures of socioeconomic status do not produce evidence of beauty-status exchange (see Table S7 in the online supplement); measures include “social mobil-ity” (an individual is socially mobile if her/his partner has higher occupational status than her/his father), current occupational status, and current income. Using the attractiveness index (physical, personality, grooming) pro-duces equivalent results to those using the single measure of physical attractiveness (partial results in Table 3).

Life Course and Measurement Bias

To test whether the young age of the Roman-tic Pair sample is biasing results, I estimated models using only the oldest quartile of cou-ples (women age 23 to 40 years and men age 24 to 43; results not shown). Beauty-status exchange is no more evident among these couples than among the entire sample. Still, even the oldest fourth of couples are young, making results difficult to generalize to older-age marriages or to remarriages. The young age of this sample is also problematic in that education and occupational status in the early 20s might be poor indicators of long-term economic prospects. To some extent, the mea-sure of expected college graduation status addresses this concern. Almost 85 percent of couples share the same expected college

graduation status, and there is no evidence of exchanging college status for beauty. Like-wise, the measure of forecasted SEI tests whether physically attractive individuals leverage their beauty to obtain partners with high future status. Actual SEI five years later (Wave IV) is available for the original respon-dents, and substituting it for projected SEI does not change results. This is also true when using forecasted and actual income. This allays concerns that measures taken at Wave III might be misleading. Finally, cur-rent college enrollment status might bias results if college students are particularly likely to match on attractiveness or status. However, only 11 percent of couples consist of two full-time students and excluding them does not change results.

SuBgrouP dIffErEnCESUnion Status

Conventional regression models (as in Table 5) estimated by union status (see Table S8 in the online supplement) indicate that the exchange effect in Models GS-1b and RS-1b is driven by dating couples: only for dating couples did one partner’s physical attractive-ness predict the other partner’s years of edu-cation. However, only when predicting women’s education did the difference by union status reach statistical significance. When the difference models (as in Table 6) are estimated by union status, or when inter-actions are added to test for differences by union status, there is still no evidence of beauty-status exchange for any group (not shown). Similarly, I estimated the negative binomial models by union status and also estimated models interacting the exchange and matching parameters with union status (not shown). The exchange effect in Model 1a (Table 7) is evident only for cohabiting and dating couples (the difference by union status is not statistically significant). Results do not vary significantly by relationship duration (divided into thirds) for any of the regression analyses.




Race and Class BackgroundElder (1969) and Taylor and Glenn (1976) propose social-class differences in women’s propensity to use beauty as a means of secur-ing a high-status husband, arguing that women of working-class origins would need beauty to compensate higher-status husbands for the women’s low class status.5 Udry (1977) also suggests differences between black and white respondents, although with-out elucidating the theoretical rationale for expecting racial differences. Elder (1969), Taylor and Glenn (1976), and Udry (1977) all use regression models similar to those shown in Table 5, so I estimated models like Model GS-1b, GS-2b, and GS-3b, adding the wom-en’s father’s occupational status and the inter-action of women’s attractiveness with their fathers’ occupational status (not shown). To test whether gendered beauty-status exchange varies by race, I estimated models like Model GS-1b, GS-2b, and GS-3b in Table 5, adding the interaction of women’s attractiveness and race (not shown). I did not find that beauty-status exchange varies by father’s occupa-tional status or that it is different for black versus white women.

Interracial CouplesSassler and Joyner (2011) find that couples with the most disparate earnings are minor-ity women partnered with white men, and that these women are more likely to be rated as “attractive” than are women in every other racial combination. However, it is impossible to conclude that minority women trade their beauty for white men’s status or income without knowing how attractive the men are. Sassler and Joyner’s analysis also fails to account for race-gender differences in earnings and for the full, five-level distri-bution of attractiveness. My analysis of average physical attractiveness rating, years of education, college status, age, and income by individual race and by couple race com-bination does not suggest racialized, gender-stereotypical cross-trait exchange (Table S2; see Part A in the online supplement for fur-ther discussion).

I also estimated regression models equiva-lent to those in Table 5 for couples in which either partner is a minority and the other is white, and for couples in which the female is a minority and the male is white (see Table S2 in the online supplement). There is some evi-dence of beauty-status exchange among minority-white couples when years of com-pleted education is used as the measure of status, but not when college graduation status or SEI is used. This is the same pattern dis-played in the larger population, so there is no evidence that white-minority couples differ from other couples in their propensity to engage in beauty-status exchange.

Age-Discrepant CouplesStereotypical trophy wives are not only prettier but also younger. When restricting the sample to the 206 couples in which the male partner is five or more years older, estimates from regression models equivalent to those in Table 5 (see Table S9 in the online supplement) support reverse-stereotypical exchange only when years of education is used to measure status, not when using college graduation status or SEI. Gender-stereotypical exchange is not evident for any measure of status. Altogether, there is weaker evidence of beauty-status exchange among age-discrepant couples than among the larger popu-lation (possibly due partly to reduced power resulting from the smaller sample size). Simi-larly, there is little evidence of women (or men) trading youth for status. In Table 6, the differ-ence in age (his minus her) is positively associ-ated with the difference in years of education, but this is simply because the younger partner temporarily has less education. For example, a college sophomore dating a college senior has two years less education, but this difference is presumably transient. Consistent with this inter-pretation, the difference in age is unrelated to the difference in expected/completed college graduation status.

Dissimilar CouplesI examined descriptive statistics (Table 4) and regression models (Table 6 and Table S10 in



McClintock 23

the online supplement) restricting the sample to couples who differ substantially on educa-tion (by at least one degree level), on occupa-tional status (by belonging to different quartiles), or on physical attractiveness (by one or more levels). It is the couples who dif-fer on socioeconomic status or on attractive-ness who might possibly exchange one trait for the other, but partnering patterns among these couples mirror those presented for the entire sample. Despite a difference on one dimension, these couples still tend toward matching on other dimensions.

SuMMAry of rESulTSI first began with bivariate analyses and found strong evidence of matching but no evidence of beauty-status exchange. Second, I estimated multivariate regression models that are similar to those used in prior analyses (by other authors) in that they do not account for between-partner matching or for within-individual correlation of desirable traits. In these models there initially appears to be an exchange of socioeconomic status for physi-cal attractiveness, but it is generally elimi-nated in a correctly specified model that accounts for matching and for within-individ-ual correlation of desirable traits (Table 5). Third, I estimated models using difference measures: these models provide a more direct test of beauty-status exchange than do the conventional multivariate regression models, but they produced no evidence of exchange (Table 6). Fourth, I estimated negative bino-mial models (a generalization of log-linear models), which are particularly well-suited to identifying patterns of matching and exchange. For only one measure of status is there any support for beauty-status exchange, although matching is evident for all measures (Table 7). Patterns of beauty-status exchange do not differ much (if at all) for interracial or age-discrepant couples or for couples who differ on beauty or status. The only possible form of beauty-status exchange is gender-symmetric exchange of current educational attainment for physical attractiveness, but it is

not robust to all model specifications or alter-native measures of status. Models estimated by union status suggest that this possible beauty-status exchange is limited to less-committed relationships.

dISCuSSIonUsing data on 1,507 couples in their early 20s in married, cohabiting, and dating relation-ships of minimum three-months duration, I revisit the question of how often romantic partners trade physical attractiveness for socioeconomic status, uniting the research on matching in partner selection with that on beauty-status exchange. Prior literature presents an empirical paradox, with some authors demonstrating widespread matching on status and on attractiveness and other authors argu-ing that individuals commonly exchange these traits (usually, that pretty women marry high-status men). It is certainly possible that some couples might match on attractiveness and status while other couples exchange these traits—the theories are not mutually exclu-sive. However, prior empirical support for beauty-status exchange suffers from method-ological limitations, leaving the prevalence and distribution of beauty-status exchange indeterminate.

There is a positive within-individual cor-relation of physical attractiveness and socio-economic status—individuals advantaged on one dimension tend to be advantaged on the other. Given this, matching on attractiveness and status creates a between-partner cross-trait correlation between her beauty and his status (and vice versa), even in the absence of beauty-status exchange. This correlation might be misconstrued as beauty-status exchange in miss-specified models, including the models used in prior analyses. Accord-ingly, I investigated how much evidence of exchange remains in correctly specified mod-els that fully account for matching and for within-individual correlation of desirable traits. Results of this analysis have implica-tions for general theoretical understandings of partner selection and for sociobiological




explanations that assume a gendered beauty-status exchange.

I found some support for beauty-status exchange, but it is not robust to alternative measures or model specifications. In conduct-ing a quantitative analysis, the question should not be whether a specific model can be found that supports a given theory, but whether that result is consistent across a range of reasonable models and measures (Leamer 1983; Rosenfeld 2005). Beauty-sta-tus exchange is only evident when status is measured by current years of education, the most transient measure of status advantage (compared to expected/completed college graduation status, projected socioeconomic status, and projected income). In the early 20s, when many individuals are still complet-ing school, a difference in years of completed education is often temporary and therefore not indicative of true socioeconomic advan-tage. Moreover, exchange of years of educa-tion for physical attractiveness was evident in the conventional multivariate regression models and the negative binomial models, but not in the difference models or the descriptive analysis. This limited evidence of beauty-status exchange is gender-symmetric (either partner might trade beauty for status) and is driven by less-committed couples (dating and perhaps cohabiting couples).

This analysis suggests that some prior sup-port for beauty-status exchange may have resulted from between-partner matching and the within-individual co-occurrence of desira-ble traits. In these instances, the apparent exchange effect is eliminated by accounting for these forces or by testing for exchange more directly (as in the difference models). The inconsistent support for beauty-status exchange in this analysis is not due to its inclu-sion of dating and cohabiting couples. Were this study limited to the 543 married couples, there would be absolutely no evidence of exchange, yet prior studies claim evidence of exchange using much smaller married-couple samples (Elder 1969; Taylor and Glenn 1976) of equally narrow age range (Elder 1969). Using the sample most comparable to prior

studies (married couples) refutes prior findings even more unequivocally.

Admittedly, some prior studies claiming evidence of gendered beauty-status exchange examine earlier cohorts in which women had greater incentive to use beauty as a means of social mobility. Still, even these early studies show an association between women’s socio-economic status and their physical attractive-ness and between women’s and men’s socioeconomic status (Elder 1969; Taylor and Glenn 1976; Udry 1977). Additionally, two of the studies focus on recent cohorts in which women enjoyed reasonably equal access to labor market opportunities (partnerships formed in 2001 to 2002 in Carmalt and col-leagues [2008]; partnerships formed in 1990 in Stevens and colleagues [1990]). Moreover, the absence of robust evidence for exchange in the current analysis cannot be entirely attributed to women’s new economic independence: the argument that women trade beauty for money out of economic necessity implies that modern women might use their labor market success to secure physically attractive men with poor labor market prospects (e.g., Press 2004), but this occurs infrequently, if at all.

It is not just social structural theories that predict women trade beauty for men’s socio-economic resources. The sociobiological model makes the same prediction and sug-gests little variation between cohorts. If mat-ing strategies are genetically programmed, they will change only across many genera-tions. But among the recent cohort of young adults in this study, partner choices are not driven by hypothetical evolutionary adapta-tions that cause men to value women’s beauty and women to value men’s breadwinning. That this gendered beauty-status exchange does not occur casts doubt on the sociobio-logical account of partnering. If evolutionary adaptations for beauty-status exchange exist, they are less impervious to social-structural counter-forces than is usually assumed in sociobiological models. An evolutionary adaptation that is so completely nullified by changing social conditions is an adaptation that may very well not exist.



McClintock 25

What little evidence there is for beauty-status exchange indicates that its prevalence declines with commitment, insofar as dating couples can be assumed to be least committed, compared to cohabiting and married couples, and married couples most committed. Beauty-status disparate couples, like interracial cou-ples, may undergo a winnowing process and advance less often to higher levels of commit-ment. Couples might think it would be a good idea to trade beauty for status, only to find that the status difference renders them incompati-ble. Indeed, much of the tendency for partners to be of similar socioeconomic status is driven by a desire for cultural compatibility (Bourdieu 1984; Kalmijn 1994). Thus, a beautiful but poorly educated woman might find she has too little in common with college-educated men for a viable companionate marriage. Likewise, a college-educated man might value a woman’s beauty more than her income, but if he requires that his mate shares his middle-class culture, this may preclude beauty-status exchange. Only the very affluent could trade wealth for beauty while still matching on edu-cation and thus ensuring cultural compatibil-ity. They may also be the only ones able to “afford” a more beautiful partner, given indi-viduals’ reluctance to compromise on beauty (Li et al. 2002; Li and Kenrick 2006; Sprecher 1989). There are not enough wealthy individu-als in these data to test that possibility.

Another explanation for the minimal evi-dence of cross-trait beauty-status exchange in these data is that social constraints limit oppor-tunities to meet status-disparate partners. About a quarter of respondents were enrolled in or recently graduated from college, which encourages educationally homogenous peer groups and limits contact with potential educa-tionally dissimilar partners. Obviously, select-ing a partner of one’s own educational level precludes trading education for other desired traits such as physical attractiveness. Given the strong association between education and occupational status, matching on education also limits individuals’ ability to trade occupa-tional status for physical attractiveness.

Unfortunately, the Romantic Pair sample is limited to young couples of a recent cohort,

preventing an examination of beauty-status exchange over the life course and between cohorts. Gendered beauty-status exchange may have been more prevalent in earlier cohorts when women had less access to employment, or it might be primarily a phe-nomenon of later-age marriages or remar-riages. Men who marry at older ages marry down more in age (England and McClintock 2009), possibly indicating a tendency to trade youth and beauty for socioeconomic status. In these data I find no evidence of stereotypi-cally gendered beauty-status exchange among the 206 couples in which the husband is at least five years older, but none of these men exceed middle-age. Beauty-status exchange may be uncommon among young couples partly because few young adults have sub-stantially older partners. Young couples might attach more importance to similarity or face greater structural constraints that ensure matching. Beauty-status exchange may be more common from the perspective of older adults, particularly older men who have amassed substantial wealth and frequently marry much younger wives.

Additionally, the young age of the Roman-tic Pair sample complicates measurement of status attainment. Education and occupational status in the early 20s can only estimate long-term economic prospects. To the extent pos-sible, I address this weakness by utilizing current and forecasted measures of status. Still, despite the age and cohort limitations, the Add Health Romantic Pair data improve upon datasets used in prior analyses, which often use small convenience samples (Elder 1969; Stevens et al. 1990) or measure only one partner’s attractiveness (Elder 1969; Tay-lor and Glenn 1976; Udry 1977). To my knowledge, the Romantic Pair dataset is the only representative, large, probability sample providing observer-rated attractiveness for both romantic partners.

The near absence of beauty-status exchange in these data does not imply the absence of competition over these traits. If other attributes are not traded for beauty, competition for the most physically attractive individuals would result in matching on




attractiveness. Thus, results are compatible with a limited version of the market model, in which “markets” are restricted to individuals of similar social status or in which social norms regarding beauty and status prohibit cross-trait exchange while permitting compe-tition within any one dimension. Alternatively, the beauty-status exchange model may fail if its implicit assumption that socioeconomic status is a consensually ranked trait is invalid. Many individuals might desire wealthy part-ners, but assuming that status is consensually ranked overlooks the importance of culture and compatibility in modern unions.

Still, although it was unreliable, there was some support for beauty-status exchange in this analysis. It may be most common in less-committed relationships, suggesting that beauty-status exchange generates instability (and early relationship dissolution) or is only pursued when relationships have a short time-horizon. Importantly, what evidence there was for beauty-status exchange was gender-symmetric, meaning that both men and women trade attractiveness for status. By failing to find evidence of gendered beauty-status exchange, this article challenges the relevance of evolutionary adaptations in determining partner selection. If such adapta-tions exist, they ought to be evident among young adults of prime reproductive age (regardless of whether these young adults are consciously pursuing reproductive mates). This article also demonstrates how the expec-tations researchers bring to a topic may bias their findings. Assuming that the importance of beauty and status is gendered may cause researchers to overlook men’s attractiveness and women’s socioeconomic resources and thus to misidentify matching as exchange. Finally, this article questions the preeminence of the market model of partner selection in sociological research. Competition doubtless occurs, and cross-trait exchange might be common in other populations and along dimensions other than beauty and status. But among the young adults in these data, there is strong evidence in favor of matching as the dominant paradigm for selecting partners, at

least with regard to physical attractiveness and socioeconomic status. In this context, social structural barriers, compatibility, and companionship may strongly influence part-ner choice.

dataThis research uses data from Add Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the National Institute of Child Health and Human Development, with cooperative fund-ing from 17 other agencies. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assis-tance in the original design. Persons interested in obtain-ing data files from Add Health should contact Add Health, Carolina Population Center, 123 W. Franklin Street, Chapel Hill, NC 27516-2524 (addhealth@unc .edu).

AcknowledgmentsThanks are due to Michael J. Rosenfeld, Paula England, Shelley J. Correll, Matthijs Kalmijn, Christine R. Schwartz, Jessica L. Collett, Erin Metz McDonnell, David Wood, and four anonymous ASR reviewers for their helpful comments on this article.

notes 1. Equivalent empirical paradoxes regarding other

combinations of traits are beyond the scope of this study.

2. Kanazawa and Kovar (2004) argue that insofar as physical attractiveness, intelligence, and status are heritable, beauty-status exchange would generate a correlation between attractiveness and status in off-spring. But I argue that this beauty-status exchange may not occur.

3. Stevens and colleagues (1990) include male attrac-tiveness and note strong similarity between spouses’ physical attractiveness and education. They refute the cross-trait exchange model due to a null find-ing, not because they specify the regression model correctly (they use the first partner’s education and physical attractiveness to predict one of these traits in the second partner, ignoring the second partner’s other trait). Carmalt and colleagues 2008 dismiss the null result in Stevens and colleagues, arguing that the sample size may be too small to reveal pat-terns of exchange that do in fact exist.

4. Excluding these parameters suppresses evidence of exchange. The cells representing exchange are sparse partly because these are the cells in which individuals violate the usual pattern of having simi-larly high (low) levels of attractiveness and status.



McClintock 27

5. These studies do not report whether the class dif-ference is statistically significant. These analyses also fail to control for matching, and thus do not consider the spurious appearance of exchange that is generated by matching.

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Elizabeth Aura McClintock is an Assistant Professor of sociology at the University of Notre Dame. She studies gender and inequality in the context of romantic and sexual relationships, particularly in partner selection and in negotiated outcomes within established relationships. Her research addresses how intimate relationships reflect, perpetuate, and potentially alter broader patterns of gen-der, class, age, and racial inequality.



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