Naming the Gender Binary:

Aesthetics, Conventions, and Symbolic Boundaries

Charles Seguin1

Department of Sociology

University of North Carolina @ Chapel Hill

Draft – Please Do Not Circulate Without Permission

1 Direct correspondence to Charles Seguin, Department of Sociology, CB 3210, University of North Carolina at Chapel Hill, seguin@email.unc.edu. I acknowledge the support of a National Science Foundation Graduate Research Fellowship while this research was being conducted. I thank Alison Appling, Bart Bonokowski, Neal Caren, Philip Cohen, Shane Elliot, Sarah Gaby, Brandon Gorman, John Levi Martin, Sally Morris, David Rigby, Carolyn Schulte, and Ashton Verdery.

AbstractNew children’s names are constantly introduced, and old names are continually rising or falling in popularity, yet these names continue to maintain a rigid separation between genders. The gender binary in children’s names is one case of a more general puzzle: the specific content of boundary markers is constantly shifting, yet the symbolic boundary itself often remains. I use computational techniques to develop a quantitative measure of names’ gender aesthetics—a measure of whether a name shares aesthetic features with predominantly boys’ or girls’ names. I then use this measure, and a dataset of US names from 1880-2009, to analyze the mechanisms which reproduce the gender boundary in children’s names. Results suggest that the symbolic boundary is maintained largely through aesthetic heuristics, such as girls being given names ending in a schwa, or short vowel sound (e.g. the final syllable in Emma), rather than contemporaneous institutionalization processes. When aesthetic boundary crossing does occur, it is much more likely with girls’ than boys’ names.

Introduction Gender scholars have shown that gender is characterized by the “gender binary,” a rigid symbolic boundary in which people are understood as either male or female. The gender binary is enacted through “doing gender”, a set of behaviors through which people perform their gender (West and Zimmerman 1987). Objects too are considered gendered, and a large part of doing gender is making the “right” choice of cultural objects to mark off one’s gender. In the US context, babies are given names that conform to the gender binary, being generally unambiguous in the gender that they connote, and androgynous names are rare and unstable (Barry and Harper 1982; Lieberson, Dumais, and Baumann 2000). Names thus mark the gender of the child, and represent a bright symbolic or aesthetic boundary. Like other symbolic boundaries, however, there is a puzzle: the specific content of boundary markers is constantly shifting, yet the symbolic boundary itself remains. How is it that new names are constantly introduced, and are continually rising or falling in popularity, while they continue to maintain a rigid separation between genders?

Sociological work on the adoption of cultural objects, such as names, suggests that the meaning and aesthetic appeal of cultural objects emerges largely from contemporaneous interaction and social influence (Godart and Mears 2009; van de Rijt et al. 2013; Salganik, Watts, and Dodds 2006). According to these models, we would expect that the gender of names emerges as a result of convention or institutionalization. That is, people will choose names whose gender they already know, whether through prior experience with people given that name, or its role in major cultural works such as religious texts (e.g. the name Mary) or major cultural works (e.g. Tiffany). The gender of names might also follow a Schelling process wherein as more and more children of one gender are given the name, fewer and fewer children of the other gender are given the name (Lieberson et al. 2000). Another possibility, is that the gendered meaning of names resides in aesthetic heuristics which reduce the cognitive complexity of naming decisions (Brashears 2013), such as the spelling or phonology of the name, which culturally competent parents can apply to even novel names (Barry and Harper 2000; Lieberson and Mikelson 1995). Parents may thus be competent consumers of culture, with prior cultural aesthetic heuristics, themselves the result of past social interaction, held cognitively, or “in their heads” (Lizardo 2006; Vaisey 2009; Vaisey and Lizardo 2010). Thus, this literature suggests two competing theories, one based on more contemporaneous meaning making, the other on socially constructed aesthetic properties of names.

There is little work quantitative work on how the content of symbolic boundaries is reproduced over time, largely owing to difficulties in measuring aspects of culture on a wide scale. Here, I follow recent work arguing that new computational methodology may offer some solutions to quantitative measurement in culture (Bail Forthcoming; Lee and Martin 2014; Rossman and Schilke 2014), developing a machine learning classifier which measures the gender aesthetics of US given names based on their spellings and phonology. I use this classifier, and the Social

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Security Administration’s data on names from 1880-2008, to analyze the long term maintenance of the gender boundary in US names. Results broadly suggest that the symbolic boundary is maintained via aesthetic heuristics, such as girls being given names ending in a schwa, or short vowel sound (e.g. Aleesia), rather than contemporaneous institutionalization processes. There is a nuance, however, in that patterns of aesthetic boundary crossings show that girls are given names with male aesthetics much more often than boys are given names with girls’ aesthetics—consistent with much gender scholarship (e.g. Kane 2006), parents seem more concerned that their boys not be given a “feminine” sounding name than vice-versa. When boys’ names do cross aesthetic boundaries, they generally have another signal of gender such as being a prominent male biblical figure (e.g. Noah).

Gender as Symbolic BoundaryIn the contemporary US context, gender is a system of interactions and practices that marks people according to a binary male/female distinction (Ridgeway and Smith-Lovin 1999; West and Zimmerman 1987). Similar to race and other binary classifications, categorization then comes to serve as an enduring basis for inequality (Ridgeway 2011; Tilly 1999). Gender is different from race and other boundaries, however, in that gender boundaries must be maintained in spite of constant interaction between individuals of different genders in households and kin networks (Ridgeway and Smith-Lovin 1999). Despite the massive changes in the social and economic structural bases of gender inequality over centuries, cultural beliefs about gender persist, and inscribe gender inequality into new socioeconomic structures (Ridgeway 2011). Thus the cultural and symbolic content and markers of gender boundaries underlies structural inequalities, but compared to the rich literatures of racial, ethnic, and class boundaries, relatively little work has been done on the cultural markers of gender boundaries (see e.g. Lamont, Beljean, and Clair 2014).

The symbolic boundaries of gender are constituted through a number of interactional cues, such that gender identification is continually achieved through interaction (West and Zimmerman 1987). Such boundaries are also maintained through legislation, which upholds a binary idea of gender, through defining gender legally as being marked through genitalia (Westbrook and Schilt 2014). Sex categorization, or the act of categorizing individuals as either male or female, occurs automatically, and although actors view it as natural, and based on secondary sex characteristics, generally occurs through other gendered social cues such as hairstyles, fashion (including color), and names (Kessler and McKenna 1978). Names are so clearly gendered that researchers are able to take it for granted that names signal gender in audit studies (e.g. Gaddis 2014).

While we know that such cues are used in interaction to maintain gender boundaries, we still know fairly little about how such cues are constructed. How is it that specific cultural objects come to be regarded as feminine or masculine, in the absence of any essentially masculine characteristics of those objects? This is especially puzzling in the context of languages, such as English, which do not have a grammatical gender.

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Convention, Aesthetics, and the Adoption of Cultural ObjectsName, like other cultural objects, mark social differences (Bourdieu 1984; Simmel 1957). Black names, for instance, became more distinctive with the beginning of the Black Power movement, which encouraged pride in Black culture (Fryer and Levitt 2004; Lieberson and Mikelson 1995). Immigrants that are more assimilated into their host societies tend to have less distinctive names (Gerhards and Hans 2009; Sue and Telles 2007). Names mark off gender perhaps the most distinctly: androgynous names (names that are popular for both boys and girls) are very rare, and unstable, tending to either become wholly unpopular, or become single-gendered over time, Leslie is one example (Lieberson et al. 2000)2. Views on marital surname change are a powerful predictor of other gender attitudes (Hamilton, Geist, and Powell 2011).

First names are ideal for a study of the cultural processes that create and maintain gender markers since names are not subject to many of the economic forces that shape the popularity of other cultural objects. Names are not marketed by business interests, nor do names have monetary costs, or in the US, any important legal restrictions. Names thus provide an empirical example of cultural objects that formal organizations do not intentionally promote (Lieberson and Bell 1992).

Observing that names mark off gender distinctions raises the question of how names become and remain gendered. Studies of cultural objects suggest that their meaning is not known to adopters a priori, but rather worked out in social interaction (Childress and Friedkin 2012; Tavory and Swidler 2009). Objects, for instance, are judged differently based on the social status and structural position of their producers (Cattani, Ferriani, and Allison 2014; Rossman, Esparza, and Bonacich 2010; Sgourev and Althuizen 2014). As such there is a cumulative advantage process: as objects become more popular, they are often seen as more desirable, and popularity is largely decoupled from any property of the object as such (Godart and Mears 2009; Salganik et al. 2006). Applying this literature to the gender of names, we might expect that names become gendered through a similar social convention process. This process seems to explain other culture markers of gender, such as the colors pink and blue denoting girls’ and boys’ gender (Paoletti 2012). Thus, Jennifer, for instance, might be considered a girls’ name because parents have experience with other girls and women named Jennifer, and no experience with men or boys named Jennifer. As Lieberson and colleagues put it: “the problem for the boy named Sue is not the inherent nature of the name, but rather that not enough boys have the same name” (Lieberson et al. 2000:1251).

Another possibility is that names have properties that denote a name’s gender to parents—a gender aesthetic. To turn the “boy named Sue” problem on its head, we can ask about a boy named Aleesia? Aleesia is a rare name, so few would have direct experience with its gender connotations. Yet, those familiar with American naming conventions will have no trouble identifying it as a girls’ name. Lieberson and Mikelson show that a gender aesthetic exists for rare names, since experimental subjects are capable of identifying the gender of rare African 2 Dominique is a rare exception, remaining androgynous throughout its rise and fall in popularity

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American names with considerable accuracy (1995). This gender aesthetic is governed by linguistic features of names, such as the suffix “ella,” which mark the gender of a name. This gender aesthetic is held in “people’s heads” as a heuristic to identify gender: experimental subjects asked to classify the gender of names are less accurate, and take longer, when names have atypical phonology for their gender (Cassidy, Kelly, and Sharoni 1999:371–373). Aesthetic taste should not be seen as asocial, but rather the product of past socialization (Bourdieu 1984).

Yet, aside from rare African American names, there is no research systematically examining how a gender aesthetic relates to the structure and dynamics of naming practices. This is likely because we have not, until recently, had methods capable of measuring a gender aesthetic on a wide scale, a point I will return to later on. An aesthetic is a “second order” convention or social influence process, in that the aesthetic rules are social constructions, created by the adherence of past parents naming practices. The existence of a gender aesthetic in names, however, would challenge accounts that cultural objects are chosen with respect to convention, or social influence, operating at the level of individual cultural objects. Rather it would suggest that consumers of culture carry with them a set of more systematic set of conventions and naming rules that structure “tastes” for names (Lieberson 2000), and therefore perhaps also other cultural objects.

Analytical Strategy

My analytical strategy is to analyze nearly the entire field of US baby names, and conduct a number of analyses describing how aesthetics work to reproduce the gender binary. I first develop a quantitative measure of the gender aesthetics of names. I then conduct a series of analyses using this measure, to assess if and how it is related to the maintenance of the separation of names into boys’ and girls’ names. I analyze the popularity of names which cross the aesthetic boundary (i.e. names with aesthetic features similar to names of the other gender), whether new names conform to the preexisting aesthetic, names which switch genders (e.g. Courtney). Next, I look at systematically at when aesthetics fail to predict the gender convention of names, both at the level of individual names, and during periods when the aesthetic seems to have the weakest effect on name gendering. While these analyses do not observe the micro-level processes of naming decisions, I argue that they provide evidence for a view of culture in which prior held aesthetic heuristics play a strong role in stabilizing cultural fields, while still allowing for significant change and innovation in individual cultural objects.

Data I use data from the US Social Security Administration3. These data contain all US baby names for children with a social security card, with at least 5 occurrences in a given year from 1880-2009, and the number of boys and girls given that name each year. These data contain 86,917 unique names, given to roughly 315 million children born over this period. 54,152 (62%) of

3 Available on the SSA website: http://www.ssa.gov/oact/babynames/limits.html

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these names were girls’ names (at least 80% of the children with this name were girls), 29,927 (34%) were boys’ names (at least 80% of the children with this name were boys), the remaining 2,838 (3%) names were classified as androgynous. I dropped all observations from 1960, as the data are not accurate for this year. Some people born before 1937 did not obtain a social security card, and before 1986 children who died before receiving a social security card were not included (Twenge, Abebe, and Campbell 2010:20).

Measurement

Constructing a Measure of Gender AestheticsThe qualities that make some cultural objects more aesthetically pleasing than others have been notoriously hard to measure. In part this is due to an inherent contingency in the popularity of cultural objects produced through social influence (Godart and Mears 2009; Salganik et al. 2006). Even culture industry experts often disavow any predictive ability, arguing that “all hits are flukes” (Bielby and Bielby 1994). In part the difficulty in measuring aesthetics emerges because cultural objects are complex assemblages characterized by interactions between their component parts. In cinema for instance, Oscar winning performances are the result of complex interactions between an actor and their supporting cast. Measuring the appeal of these performances thus requires detailed network measurement (Rossman et al. 2010). Even Rossman and colleagues’ detailed network measures, however, use actors’ past successes, and thus do not measure aesthetic appeal directly from properties of the cultural object itself (in this case an acting performance). One proposed solution to measuring culture is through “big data” techniques (Bail Forthcoming; Lee and Martin 2014; Rossman and Schilke 2014). “Big data” is a somewhat inexact and faddish umbrella term; here I refer to techniques for the statistical modeling of the meaning of texts drawn from the intersection of computer science and statistics known as machine learning. I use a machine learning classifier to measure names’ gender aesthetics based on features of their spelling and phonetics.

Throughout I make a distinction between a name’s gender convention and a name’s gender aesthetic. Convention refers to the extent to which the name is given to children of one gender, or another; for example a name like Noah is given overwhelming to male children, and thus will be considered to have a male gender convention. As a short-hand I sometimes refer to names with female (male) gender conventions as girls’ (boys’) names, which is not to reify the distinction but to avoid excess verbiage. A name’s gender aesthetic on the other hand, refers to the similarity of the phonology or spelling of the name with names of one gender convention. Thus Noah shares the schwa ending with, primarily, girls’ names, and thus I would say it has a female aesthetic. I sometimes use aesthetic as a shorthand for gender aesthetic, although there are numerous other aesthetic features of names.

Names’ gender conventions are calculated from the observed proportion of boys and girls given the name. While in principle the proportion male or female is a continuous quantity, most names

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are given to predominantly one gender, so that it makes sense to represent a name’s gender as an indicator variable—each name is either a boy’s, girl’s, or androgynous name. I call a name a girl’s (or boy’s) name if the proportion of children given that name is 80% female (male). Roughly 3% of names do meet these criteria, and I classify them as androgynous.

The Components of the Gender Aesthetic—Spelling and Phonology

In a literate society a person’s name might be first encountered either as it is written, or as it is spoken. Hence, there are two possible major systematic components to a gender aesthetic in names: spelling and phonology. In other words, girls and boys names are both spelled and pronounced differently. For example, in the US context, girls names are much more likely to end in what linguists refer to as a schwa: the sound of the a in about (Cassidy et al. 1999; Lieberson and Mikelson 1995). Different spellings can also mark the genders of homophone names such as Frances and Francis, Tony and Toni, or Charlie and Charli. The three letters a, e, and i are the last letter in the majority of girls’ names, but rare as boys’ names’ suffixes (Barry and Harper 2000).

Drawing on this prior work, I developed a set of linguistic feature sets of names which can be used to predict the gender of names. I began with features from both the spelling and phonology of names. For both spelling and phonology I focused on the prefix and suffix of the name. I measured prefix and suffix phonology via the double metaphone algorithm (Philips 2000). I found, however, that including phonology did not improve the classifier. Phonology does matter, but it is generally captured by spelling. For instance, including the long e sound in the suffix of a name is redundant once the spelling (i, ie, ee, or y excluding ay) of the suffix is included. The reverse, however, is not true: spelling is not reducible to phonology. For example, the single i suffix is relatively more common in girls’ as opposed to boys’ names than the y prefix (recall Toni vs Tony). In the rare cases where names are spelled the same, but pronounced differently by gender, there was no way to systematically measure the different phonologies (e.g. Dominique is pronounced like Dominick for boys, and rhymes with Monique for girls). In less data-rich applications it might make sense to collapse spelling to phonology, however explicit measures of phonology did not improve the classifier in this application. Thus in practice the systematic components of phonology are captured through spelling, and to avoid additional complexity I did not include explicit measures of phonology in my final classifier.

The features of names that I selected for the final classifier are: 1) the first letter of the name, 2) the last letter in the name, 3) the first pair of letters in the name, 4) the last pair of letters in the name, 5) selected three letter name suffixes, and 6) selected four letter name suffixes. I dropped very rare letter pairs from the features—any pair of letters that were present in less than 20 names. Dropping rare features generally helps to reduce overfitting, since rare features tend to be idiosyncratic. Substantively, it makes sense to remove rare features since their very rarity suggests they are not part of any recognized aesthetic. I selected features from the three and four letter suffixes by looking at the most common suffixes three and four letter suffixes, and

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subjectively assessing whether they were a meaningful suffix, beyond that captured in the last two letters (for example, the line in Caroline is a suffix not well represented by ne). I included the following additional suffixes: lyn, lin, sha, lee, son, elle, lynn, ette, etta, line, eigh in the featureset.

Machine Learning Classification

I then use a machine learning classifier to classify the gender convention of names according to these features (see e.g. Bird, Klein, and Loper 2009). Features, such as the presence of an a at the end of the name, can be thought of similarly to independent variables in a standard regression framework. The gender of the name is then predicted based on the similarity of its features to names of one gender or another. I use a Bernoulli naïve Bayes classifier. Naïve Bayes is very similar to logistic regression with the exception of the “naïve” assumption that features (independent variables) are uncorrelated. Here the naïve assumption would be, for example, that a name with the last letter a would be no more or less likely to have ah as its ending than any other name—clearly a false assumption. Thus, like any other model, strictly speaking, the Naïve Bayes classifier is “wrong.” However, the classifier is useful. Surprisingly, naïve Bayes works especially well in applications where features have perfect, or near perfect dependence (Rish 2001; Zhang 2004), as in the present application where, for example, if a name ends in one letter, it cannot end in another. Despite these reasons to prefer the naïve Bayes classifier, I perform numerous qualitative and quantitative checks, discussed below, to validate the classifier. The Bernoulli model represents features as binary occurrence variables, and is thus preferred, as in the present application, when features do not repeat (Manning, Raghavan, and Schütze 2008).

More formally, for the Bernoulli naïve Bayes classifier, the probability of a name y being in class (gender aesthetic classification) c is computed as:

P (c|y )∝ P(c ) ∏1≤k ≤ ny

P( tk∨c)

Where P(t k∨c) is the conditional probability of feature t koccurring in a name of class (gender aesthetic) c and ¿ t 1 , t 2 , t3 , …t ny>¿are the features of name y. P ¿ ) is the prior probability of any name being on class c, in this case estimated as the base rate of names of that class (gender) in the population of names. The naïve Bayes classifier then assigns each name the to the most likely (maximum a posteriori) class cmap :

cmap=argmaxc∈C P̂(c∨ y )=argmaxc∈C P̂(c ) ∏1≤k ≤ ny

P̂( tk∨c)

Note that the technical discussion above closely follows Manning et al (2008:258–259).

Validating the MeasureMachine learning methods are decontextualized, which can limit the accuracy of classifiers when assessing the meaning of texts. Sarcasm, for instance, is almost entirely context dependent, and

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almost impossible to detect with automated methods. In the case of measuring the aesthetic of names, however, making decontextualized judgments is an advantage of automated methods. Consider, for instance, the name Noah. Human coders familiar with US naming conventions would be likely to know that Noah is both a name given overwhelmingly to boys, and a central male figure in the Abrahamic religions. Human coders would have to separate this contextual information from aesthetic features of the name, such as the schwa (“ah” sound) at the ending, which Noah shares predominantly with girls’ names. While coders might be able to do this with a systematic set of coding rules, this would be little different from the machine classification process. Thus, while the machine classifier does not capture many of the contextual nuances, as in many other applications (Lee and Martin 2014), this decontextualization is exactly what is needed to remove prior preconceptions from the measurement of a gender aesthetic.

Predictions of the naïve Bayes classifier is treated as a measure of the gender aesthetic of a name. That is, if the classifier predicts, based on the features of that name, that it is a girls’ name, we would say that the name has a female aesthetic. As in the case of Noah above, a name’s aesthetic, and the actual gender of the children given that name, need not match. It is important to point out that, although the classifier uses the gender conventions of some names to predict the gender aesthetics of other names, the measurement is not circular. If the phonology and spelling of names did not vary systematically by gender, the classifier would perform no better than random chance on out-of-sample predictions. Moreover, in out-of-sample predictions, the gender convention of any given name has no bearing on how the classifier treats its aesthetic. Rather what this measure captures is that aesthetics are endogenous (Kaufman 2004), that is defined by aesthetics of other similar objects, as in say when an attractive skirt length is defined in reference to the length of popular skirts in the prior year (Kroeber 1919; Lieberson 2000:93–95).

Following best practices in machine learning, the classifier is validated principally on its ability to correctly classify names whose gender it does not already “know,” that is via out-of-sample prediction (Kohavi 1995; Watts 2014:338–339). Here I rely on aesthetic features of names, such as the spelling of a name’s suffix, to predict the gender convention of a name. Thus the observed match between predication and actual convention of a name, the accuracy of the classifier, is a measure of the extent to which the aesthetic features match with gender conventions. The classifier might fail to accurately predict the convention of a name if the classifier is not accurately representing a name’s aesthetic, or if the name’s aesthetic does not match its gender convention. To establish the validity of the measure, I started with all unique names in the social security data from 1880-2008, I then dropped androgynous names, and very rare names, leaving 65,229 individual names (see definitions above). I then trained a naïve Bayes classifier on a random sample of 55,229 names, and tested on the remaining 10,000 names. The accuracy of this classifier was 85%. That is, the classifier correctly classified the gender convention of a name that it hadn’t “seen” before via the features of its prefix and suffix given above, 85% of the time. This accuracy is similar, although somewhat better, than the 80% accuracy reported in Cassidy et al for a list of informant generated names (1999). Upon revision of this manuscript, I

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will attempt a more stringent form of prediction, forecasting, and use the classifier developed on these names to predict the gender of new names from more recent years that I have not seen or analyzed: 2010-2014, as a form of “pre-registered” analysis, to demonstrate that the measure is not substantially over-fitted to training data (Casey, Glennerster, and Miguel 2011; Simmons, Nelson, and Simonsohn 2011).

Accuracy as a measure of classifier performance can be misleading. Best practices in machine learning are to report a “confusion matrix.” The confusion matrix includes the classifier’s precision, recall, and F1 measure for each category (male/female). Precision refers to the fraction of a class identified by the classifier that is correctly identified. Thus, precision for girls’ names would refer to the number of actual girls’ names identified by the classifier divided by the total number of girls’ names identified (both true and false positives) by the classifier. Recall refers to the fraction of a class that is identified by the classifier. For girls’ names this would be the total number of girls’ names correctly identified by the classifier, divided by the total number of girls’ names. Another way to understand these metrics is that precision penalizes false positives, while recall penalizes false negatives4. The F1 measure is defined as the harmonic mean of precision and recall:

F1=2∙ Precision ∙ RecallPrecision+Recall

The F1 measure penalizes classifiers which perform poorly in either precision or recall, for example, a classifier that always predicted every name was a girl’s name would have an accuracy of around 64% since there are more unique girls’ names than boys, but an F1 measure of zero for the boys’ name class, since its recall would be zero for boys’ names. The confusion matrix below shows that the classifier is fairly well balanced in its performance on different metrics. The one major discrepancy being that the classifier has higher precision (.91) for girls’ names, than boys’ names (.76). This means that when the classifier identifies a name as a girls’ name, it is more likely to be correct than when it identifies a boys’ name. This is likely because boys’ names generally do not have as strong aesthetic markers as girls’ names do. This can be seen by looking at the strongest gender marking suffixes of names, for example: 97% of names ending with a are girls’ names, while 83% of names ending in an o are boys names; o is also less useful since it is much rarer ending than a5. Substantively, as I discuss later on, this pattern probably arises because parents are more willing to give their girls male sounding names than they are willing to

4 Researchers sometimes favor precision or recall for substantive reasons, for a fire alarm for example, recall would be more important than precision, and there are variants of F measures that favor either precision or recall. For this application, however there is no reason to favor either precision or recall.

5 Rare suffixes tend to be associated with boys names, sometimes quite strongly. However, they are less useful because they are so rare. E.g. 36 out of the 40 names ending with q are boys’ names. The suffixes t, m, k, z, u, g, c, b, f, v, w, x, p, j, and, q are all more common in boys’ than girls’ names, but together they account for only slightly less than 5% of total names.

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give their boys female sounding names. Aside from this discrepancy in performance, which is not terribly large in any case, the classifier performs similar to its 85% accuracy score. In later analyses I break accuracy out by period and popularity.

Table 1: Confusion Matrix for Aggregate Prediction Classifier

Precision Recall F1-score N Female 0.91 0.85 0.88 6411 Male 0.76 0.84 0.80 3589 Average/Total 0.85 0.85 0.85 10000

I checked the performance of the naïve Bayes classifiers against a support vector machines classifier (SVM) (Cortes and Vapnik 1995) trained on the same data and features. The two classifiers have similar levels of accuracy, 84% accuracy for the SVM classifier as opposed to 85% for naïve Bayes. F1 measures were also similar, with SVM having .87 and .78 for male and female, as opposed to .88 and .80 for naïve Bayes. Thus, naïve Bayes appears to perform slightly better than SVM, but both classifiers are rather similar. Indeed the classifiers agree in their out-of-sample predictions 95% of the time.

The most informative features of the classifier also help to validate the measure. Most informative features are those features which the classifier relies on most heavily to classify names. In a well behaved classifier most informative features are those that most reliably differentiate between categories. The most informative features from the naïve Bayes classifier accord with central markers of name aesthetics that have been identified in prior research. For instance, the most informative feature for marking boys’ names is ending with the letter n. A number of the most informative features marking girls’ names are schwa endings (e.g. sha). The most informative also include aesthetic features that are not, to my knowledge, identified in prior work, but are nevertheless intuitive. The prefix gw for instance, is the single most informative feature for identifying girls names. The social security data shows that there are 53 unique names beginning with gw, such as Gwendolyn, Gwen, and Gwyneth; all of which are overwhelming given to girls.

Although the naïve Bayes makes binary classifications, it also gives a predicted probability for the category it assigns. This can be treated as a measure of the classifier’s certainty. Looking at the cases where the classifier is most, and least, sure can also help to establish to validity of the measure6. The classifier is the most certain that Gwenetta is a girls’ name, while the classifier assigns the highest probability to Huck and Buck being boys’ names. Below I have a random sample of names that the classifier assigns over 98% predicted probability for both male and female classifications. Because most names are rare, the sample contains mostly names that are 6 In general the predicted probability of naïve Bayes classifiers is biased towards certainty as a result of the cumulative impact of many redundant features in long texts for example. For the present application, this is less of an issue since there are relatively few redundant features in any given name.

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not likely to be familiar. In table 2 below, the names gender conventions matches their gender aesthetic, except Genever, likely a modification of Jennifer, which as I discuss later on shares aesthetic features with boys’ names, especially the er suffix.

Table 2: Names with Highest Predicted Gender Predictability

Girl Sounding Names Boy Sounding Names Annalysia Rad Lianette Armando Aleesia Jerion Gloriana Kelvon Marvette Genever Yalena Guster Zamauria Muhamed Raeana Jermon Ishara Erron Samaia Dahmir Blasa Leonord Malavika Dartagnan Aranza Aeryk Larasha Isidoro Stasha Maksim Lura Dominyck Latya Jahrod Deedra Rockford Girtha Odus Shaylyn Kasir

As a final test of the measure’s validity, I checked its performance against human coders on a random sample of 100 rare names (names with between 20-30 children total in the SSA data). I had five human coders give their best guess for the gender of these names. Three of the coders were PhD students in sociology, one an ecology PhD, all were college educated. These coders were raised in different parts of the United States (rural South, rural Northeast, urban Northwest, Miami, and Denver). The human coders correctly classified the gender convention of an average of 86% of the names (accuracies ranged from 79-90%), while the naïve Bayes classifier was 85% accurate. Thus, the naïve Bayes classifier appears to be roughly as accurate in predicting gender conventions as an average individual human coder.

Looking at names where the majority of human classifications differed from the naïve Bayes classifier reveals no strong patterns. In some cases the human coders seem to have relied on convention rather than aesthetics to make accurate predictions when the machine classifier was wrong: for example, human coders noticed that Jeramiha was probably derived from or was a misspelling of Jeremiah, and coded it male, even though the a suffix is almost exclusively a feature of girls’ names. Given that humans draw on knowledge of both aesthetics and

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conventions, while the machine draws only on aesthetics, the machine’s performance relative to humans in measuring aesthetics as such is likely underestimated here.

Based on these results, it is reasonable to conclude that the classifier is both valid and reliable. Perhaps more sophisticated classifiers could achieve higher accuracy in predicting the gender of names. However, my efforts to improve the classifier with additional features, such as phonology, did not improve the classifier’s accuracy, nor was a classifier built on dramatically different assumptions (SVM), more accurate. It seems reasonable, therefore, to say that roughly 85% of names conform to a gender aesthetic. In later analyses I show that when the aesthetic and observed gender of a name do not match, this is generally, although not always, for reasons other than poor measurement of the aesthetic.

Summary of the Measure

The measure of name aesthetics is a binary classification of names into those which share aesthetic properties of primarily either boys’ or girls’ names. These aesthetic properties are the name’s prefix and suffix. While the classifier greatly reduces the complexity of a names sound and spelling, I have taken several steps to ensure its validity. Face validity of the classifier is established by a match between the suffixes and prefixes that the classifier identifies as most informative, and those identified in prior work. Looking at a random sample of names that the classifier identifies as strongly matching a girls’ or boy’s aesthetic lends further face validity. The classifier is more systematically validated by out-of-sample prediction of the gender conventions of names. The classifier is able to predict the gender convention of names with roughly 85% accuracy, similar to that of humans. As I show in later analyses the classifier is also able to predict the gender conventions of new names, based solely on information from naming conventions in the past. I now turn to analyses using this measure as an independent variable to systematically assess the mechanisms that reproduce the aesthetic and structure name evolution.

Mechanisms of Aesthetic Reproduction

Mechanism One: “Natural Selection” Do names whose gender aesthetic match their gender convention survive longer than mismatched names? Lieberson and colleagues (2000) show that androgynous names (names with no clear gender convention) do not survive as long as names with a clear gender convention, a similar result for names with an aesthetic-convention mismatch would identify an evolutionary process that could maintain the gender aesthetic.

I consider names that disappear entirely from the SSA dataset, names which are not given to at least five children in a year, to have “died.” I drop names that either switch genders or are androgynous (for this analysis I adopt a stricter measure of androgyny: less than 90% majority gender). 82,775 names remained after dropping androgynous names. They were in the SSA data for a mean time of 16.5 years, with a standard deviation of 25.3. To the first decimal point,

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names whose gender aesthetic matched, or did not match, their gender convention both had the same mean survival time of 16.5 years. That is names that sound or are spelled like names from the other gender, do not differ in their survival times.

This finding of no difference in survival times is true also when broken out a number of ways. When their gender aesthetic match or do not match their gender convention girls’ names survive a mean number of 15.6 and 15.8 years; boys’ names survive a mean number of 18.2 and 17.5 year respectively. Neither of these substantively small differences is statistically significant. Breaking survival times out by whether names are right censored (appear until the last year of the data) or left censored (appear in the first year of the data) does not change the no-difference finding. Breaking out names by their relative popularity does not change this result either.

Thus there appears to be no substantive difference in the survival times of names based on whether their aesthetics match their gender convention. Once names appear in the dataset their survival is agnostic with respect to their gender aesthetics. Thus, the gender aesthetic is not maintained through a survival of the fittest type process—although this analysis does not consider relative success/popularity which I address later.

Mechanism Two: “Unnatural Selection” Following around 1950, the number of new names given to children began to increase dramatically, as shown in figure 3 below (see also: Twenge et al. 2010). These new names embodied all types of fads, from place names like Dakota, or inventions like the name Nevaeh (Heaven spelled backwards). While names that were once tremendously popular like Mary and Bertha became unpopular. Names became, as Lieberson puts it, subject to fashion and change (2000).

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Figure 3: Names per thousand Births and Total Names 1950-2008

Note: Both series are generated from the Social Security data. Names with less than five children born in a year are thus not counted, nor are the births of children with these rare names.

Did the gender aesthetic survive this increasing diversity of names? To establish the durability of the gender aesthetic I trained a naïve Bayes classifier to predict the gender of recent names using information only from old names. Specifically, I trained the classifier on all names that were given to at least 10 children in the years from 1880-1950. I then used that classifier to predict the gender of names that first appear in the social security data from 1990-2008. I used the same feature sets as described in the section above. This split yielded 21,163 “old” names to train the classifier, and 33,659 recent names to test the classifier. The accuracy of the classifier was 80%, not much shy of the accuracy of the original classifier which was trained and tested on representative random samples. The confusion matrix is shown below.

Table 3: Confusion Matrix for New Name Prediction Classifier

Precision Recall F1-score N Female 0.86 0.81 0.84 20859 Male 0.72 0.79 0.75 12800 Average/Total 0.81 0.80 0.80 33659

Thus generally, despite the massive influence of new names, they seem to follow the same “rules” as names of a generation ago. This mechanism could be called “unnatural selection,” in that new names are “born” fitting into their environment. It is striking how little the aesthetic has

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changed over this period, especially in light of how much turnover there has been in specific names. In later analyses I look at the, limited, ways in which the gender aesthetic has changed.

Mechanism Three: Gender-Conforming Names are More PopularUp until now, I have been treating names interchangeably, but of course names vary dramatically in their popularity. One way a gender aesthetic could be reinforced is if names whose gender convention did not match their gender aesthetic were less popular. I analyzed the total popularity in terms of the number of children that were given the name over the entire period. Overall, there is little difference in the popularity of names whose gender convention match, or do not match, their gender aesthetics. Conforming names each had a yearly mean of 208 children sharing the name, while non-conforming names had a yearly mean of 231 children. In both the cases the point estimate of the mean is not particularly meaningful since it is swamped by the standard deviation (1773 and 1505 respectively). The median number of children was 12 and 11 respectively (the large difference between mean and median popularity reflects the massive skew in the popularity of names). Thus, the popularity of names overall seems mostly agnostic with respect to whether their gender convention matches their gender aesthetics.7

This overall equality however masks an important difference in how aesthetics influence the popularity of names by gender—both boys’ and girls’ names are more popular when they have male aesthetics. Conforming boys’ names have a mean popularity of 283, while non-conforming boys’ names have a mean popularity of 207. Conforming girls’ names have a mean popularity of 160, while non-conforming girls’ names have a mean popularity of 247. See figure four below.

Additional analyses (not shown) show that the difference in gender aesthetic matching is roughly stable over time. That is, roughly 77% of girls born each year have names with girl’s aesthetics8, and 87% of boys born each year have names with boy’s aesthetics, and these proportions do not change much over time.

7 Androgynous names, those that are neither given to greater than 80% girls or greater than 80% boys, are less popular than other names, their mean yearly popularity is 151. 8 This may be confusing, since girls’ names with boys’ aesthetics are more popular, but more girls have names with girls’ aesthetics. This is because there are far fewer girls’ name with boys’ aesthetics than with girls’ aesthetics.

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Figure 4: Popularity of Boys’ and Girls’ Name by Aesthetics

To more systematically analyze this difference I estimated a negative binomial models, ideal for skewed count variables, with fixed effects for year estimated directly as dummy variables (Allison and Waterman 2002). I included the gender convention of the name (a dummy for boys’ name), the gender aesthetic of the name (a dummy for whether the name had a boys’ aesthetic), and a variable defined as the multiplication of the other two variables (this variable can be thought of as an interaction term, or a separate categorical dummy for boys’ names that also have boys’ aesthetics). The results of the first model show that, net of baseline popularity differences between boys’ and girls’ names, and net of time trends, names with a boys’ aesthetic are more popular. The second model shows that the popularity effect of the boys’ aesthetic is not statistically stronger for either boys’ or girls’ names.

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Table 4: Negative Binomial Regression on Name Popularity by Aesthetics (Year Level Fixed Effects)

Coef S.E. Coef S.E.

Boy’s Name 0.22* 0.099 0.26 0.16 Boys’ Aesthetic 0.34*** 0.092 0.36** 0.11 Boy’s Name X Boy’s Aesthetic

-0.06 0.2

Constant 4.4*** 0.1 4.4*** 0.1 p<.05 *, p<.01 **, p<.001 *** N=86,917 names

Thus, names do not seem to be punished for not conforming to gender aesthetics per se, but rather the male aesthetic seems to be more valued by parents for both girls’ and boys’ names. One alternative explanation would be that when parents of girls’ give their girls a rare name, they want it to be gender-typed, so that rare names are chosen by aesthetic, but popular names are chosen by convention. This logic, however, makes little sense for explaining the pattern in boys’ names. It appears then, consistent with much gender scholarship, that parents value the male aesthetic more highly than the female aesthetic.

Careful readers might object that perhaps if a name is very popular as a girls’ name, it casts doubt on whether it can really be said to have a male aesthetic. Consider, however, the example of Jennifer. Jennifer is the most popular girls’ name that the classifier assigns a male aesthetic. The classifier picks up on the er suffix: 77% of the names in my data with an er suffix are boys’ names (1075/1387), and this ratio does change substantially over time (it ranges from 70% to 90%). Many of these boys’ names (e.g. Christopher, Alexander, Tyler) are also quite popular, indeed 11 boys’ names with the er prefix were given to over 100,000 boys, while only four girls names (Jennifer, Amber, Heather, Esther) were that popular among girls. Substantively we can note that the er prefix has active connotations which is associated with men in many gender stereotypes, and are encoded in names accordingly (see e.g. Whissell 2001). Thus, I would argue that Jennifer is best understood as a popular girls’ name with a boys’ aesthetic.

Mechanism Four: Androgynous Names Become Gender Typed via Aesthetics Lieberson and colleagues point out that androgynous names are unstable; these names generally soon disappear from use, or become overwhelmingly given to a single gender of children (Lieberson et al. 2000). Names also switch genders: some names were once boys’ names by convention, but later became girls’ names, or vice-versa. Because these names are very rare, the switching of name gender cannot be a primary mechanism of aesthetic reproduction. I argue however, that we can learn a lot about gender aesthetics by looking at these names. When a name with an androgynous convention becomes gender-typed, or when a name becomes favored by another gender, how do gender aesthetics influence this process?

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I identified 129 names that either switched genders, or were androgynous names that became gender-typed. I defined names that switched gender as names that either were solidly one gender for at least 5 years, then switched to become solidly another, or names that were androgynous for 10 years before becoming solidly one gender9. I found that 69 names switched from male to female, 13 names from androgynous to female, 37 names from androgynous to female, and 10 names from female to male. This is consistent with prior work that shows that girls’ names generally do not become boys’ names, since parents are very reluctant to give boys a name that doesn’t conform to their gender: even the 10 names that do switch from girls names to boys names are not very popular (Lieberson et al. 2000).

Figure 1: Switching Names

I then trained a naïve Bayes classifier on the remaining names to get a measure of these switching names’ gender aesthetics. Overall, these names tended to switch to the gender which matched their aesthetic (see figure 2 below). That is, 46/82 of names which became girls names matched the girls aesthetic, and 29/47 of names that switched to male (e.g. Theo, Ashton) were

9 More specifically I identifying “switching” names as meeting one of two sets of criteria, first names had to had to be given to children of one gender at least 90% of the time and given to at least 10 children a year for 5 consecutive years. Preceding that they had to either 1) have had a period of 5 consecutive years where they were given to children of at least 90% the other gender and given to least 10 children each year; or 2) they had to have a period of 10 consecutive years where they were given to at least 50% the other gender and given to at least 10 children.

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those that matched a male aesthetic. Overall, the switching names initially matched their aesthetic only 42% of the time, meaning their ‘final’ gender matched 58% of the time. Thus the tendency of names switching gender is to moderately reinforce the gender aesthetic. Because names that switch are very rare, comprising roughly one or two tenths of a percent of the total names, unstable names cannot be a primary mechanism through which gender aesthetics are maintained.

Figure 2: Gender Aesthetics of Switching Names

Looking closer at the names that switch from boys’ to girls’ names reveals a stronger aesthetic pattern, 49 out the 82, or 60% of names that switch this way end in the long e sound. Restricting to names that were fairly popular for both girls and boys (at least 5,000 births of each), 21/28 or 75% of switching names end in the long e sound. See figures A.1-A.3 in appendix A for time series figures of the most popular switching names.

Thus the majority of names which switch from androgynous or girls’ names to boys’ names have a long e suffix. This suggests the possibility that the switching of names gender is embedded in more macro aesthetic change. As the figure below shows, the long e suffix becomes more androgynous until the 1950s, and then begins to be more heavily associated with girls names following the 1950s when a lot of these names switch.

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Figure: The Sex Ratio of the Long e Suffix

To return to the Schelling model proposed by Lieberson and colleagues, although the Schelling model is more about how names switch genders than why specific names switch genders, the aesthetic similarity of these names raises doubts about whether the Schelling model applies. It would appear that names switch according to their place in a shifting aesthetic, rather than to more the contingent dynamics of the names sex ratio. To take a closer look at the Schelling model, I displayed the time series of the 27 most popular names that switch from being androgynous or boys’ names to being girls’ names in an appendix (see figures A.1, A.2, and A.3). It is in these names that we should expect the strongest evidence of the Schelling model, that is we should see that girls born with these names drive out boys being born with these names. Looking at figure A.1 however, we see that for eight of the nine most popular of these names the name peaks in popularity for boys after it has become a majority girls’ name, in most cases many years after. Looking at figures A.2 and A.3 shows that a similar pattern holds for the next 18 most popular of these names. If girls were driving boys out of these names, we would expect to see declines in popularity in the years following when the name becomes majority girl births, rather it appears from these time series that, if anything, girls contribute to the popularity of the name for boys. More formal analyses support this interpretation as well. The year to year correlation between boy and girl births is positive in 27/29 names, and the mean of these correlations is .55. Regressions of the first difference of boys births on the first difference of girls

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births and a linear time trend show that generally popularity grows weakly for boys when it grows for girls: the coefficient on girls births is positive in 25/29 of these regressions, and significant for 8/25. There was not a single name for which growth in girls born significantly predicted declines in the number of boys born.

Thus in the entire period from 1880-2009 there does not appear to be a single name that we could call “smoking gun” evidence for the Schelling model. That is, there is a not a single name that was once popular as a boy’s name, but that began a precipitous decline in popularity shortly after the name began to be given to girls. Admittedly, a number of other factors are undoubtedly influencing the dynamics of these names, and so I suspect that if we were able to isolate out all the other factors affecting the popularity of these names, we would see a Schelling type effect. Nevertheless, these results suggest that the Schelling is limited at best in its explanatory power.

Summary and Accounting of MechanismsHow does the name aesthetic endure and reproduce itself? First, names that match the aesthetic are no more popular than other names generally. Although names with a male aesthetic are more popular, that result can’t explain the reproduction of the gender aesthetic. Names are no more or less likely to “go extinct” whether their aesthetics conform or not. Some names do switch their gender convention, and they generally switch such that their gender convention and aesthetics match, and most names that switch are part of a general aesthetic movement. However, switching is far too rare to explain much of the reproduction of the gender aesthetic. Rather, what seems to account for most of the reproduction of the aesthetic is unnatural selection—that new names overwhelmingly conform to the gender aesthetics of previous names.

Sources of Aesthetic Diversity and Change

How do Non-Conforming Names Become Popular?If there are strong pressures on boys’ names not to share the aesthetic properties of girls’ names, how do a few of these names still become popular? I identified boys’ names that were both popular, and that the classifier identified as having a girls’ name aesthetic. I chose the ten most popular names. I chose only ten because there are so few popular misclassified boys’ names that even choosing as many as ten took me to the 217th most popular name Maurice. Specifically, I chose the ten most popular boys’ names that were classified as having girls’ aesthetics with at least .85 predicted probability. Table 5 below shows these names, and the ten most popular girls’ names that were classified as boys’ names with at least .85 probability. Even these names are limited in their deviation from gender aesthetics: there are no equivalents to Gwenetta or Buck among these names.

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Table 5: 10 Most Popular Names with Non-Conforming Aesthetics

Boys Name Total Children Girls Name Total Children Joshua 1,114,087 Jennifer 1,449,907 Lawrence 447,500 Helen 1,010,967 Eugene 374,877 Karen 945,275 Noah 230,397 Sharon 701,429 Shane 179,556 Kathleen 693,810 Elijah 171,442 Frances 586,497 Charlie 166,199 Rachel 544,605 Isaiah 144,550 Heather 522,316 Jeremiah 134,745 Joan 478,098 Maurice 131,195 Doris 461,292

Five of the boys’ names that have unconventional gender aesthetics end in a schwa, either ending in a (Joshua), or ending in ah (Noah, Elijah, Isaiah, Jeremiah), which is very rare for male names (97% of names ending a are girls’ names, 91% of those ending in ah are girls’ names)10. These five names are also male figures in the Old Testament Bible, where male names are very different from 19th and 20th century gender aesthetics11. Another name, Charlie, is a diminutive form of Charles which is a common boy’s name. Eugene is a very old Greek name shared by four Catholic popes. Thus, seven of ten of these popular names have a strong signpost of their gender. Maurice is a variant of Morris and often given the nickname of Mo or Moe, all of which have male aesthetics. The remaining two seem to be failures of the classifier to accurately assess its gender aesthetic. While the first two letters of Lawrence, la, are overwhelmingly given to girls names, they are generally pronounced differently than they are in Lawrence. More importantly, however, most names that end in ce are girls’ names, however, names ending with nce, are gender neutral (52% of such names are boys’ names). Thus Lawrence may not actually have a strongly female gender aesthetic. Shane may also be an artifact, although most names ending in ane are girls names, they end on a different sound. Of the ten most popular names ending in ane six are boy’s names and rhyme with Shane (e.g. Kane and Duane), and only Jane and Mary-Jane are rhyming girls’ names. Popular girls’ names ending with ane are often like Jeane and Diane and don’t rhyme with Shane. The similar sounding suffix ain is also heavily male. While the classifier fails to correctly classify the aesthetic of these two names, note that these are the only two of the 217 most popular boys’ names that fall into this category, and that attempts to include phonetic measures in the classifier did not materially improve the classifier. Excluding these two artifacts, we can say that all of the eight most popular aesthetically mismatched boys’ names have clear gender signposts from other conventions.

10 Lieberson and Bell (1992: 520) find that Joshua is the only name in the 100 most popular boys’ names in New York State from 1973-85 to end in a schwa. 11 New Testament names in their current translation (for example John, Luke, Mark, and Mathew) do appear to follow current gender aesthetics, and indeed are likely to have been at least part of the origin of the gender aesthetic. Another possibility is that gendered grammars from Latin and other languages influenced the aesthetic.

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A comparison to the most popular girls’ names with boys’ aesthetics makes clearer that boys’ names generally require a clear gendered signpost in a way that girls’ names do not. First note that the girls’ names are much more popular than the corresponding boys’ names, only Joshua is popular enough that it would appear on the girls’ names list (Joshua is the 24th most popular boys’ over this period). Of the girls’ names Rachel is the only name in the bible. Frances and Joan are forms of popular boys’ names (Francis and John). Kathleen appears to be an artifact of the classifier: although names ending in en are generally boys names, the smaller group of names ending in een are generally girls’ names. Thus, girls’ names with a male aesthetic do not generally require a gender signpost to become very popular.

Thus, it would appear that boys’ name are used more strictly to denote the gender of children. Boys’ names with a girl’s gender aesthetic tend to be less popular, and those few boy’s names that do become popular generally have some external signpost that types them as a boys’ name.

How Does the Aesthetic Change?Overall, the aesthetic is very durable. It has changed somewhat over this period however, as we saw with the long e suffix. How and why does the gender aesthetic change? An analysis shows that the gender aesthetic is stronger, that is the gender convention of names is more predictable from their spelling and phonology, during periods when names are more diverse.

I first calculated the Gini coefficient for name popularity by year. The Gini coefficient is a measure of inequality, or concentration, so here it is measuring how concentrated children are in the most popular names. The Gini coefficient is scale-invariant, meaning that it can be used to compare inequality in populations of different sizes (see e.g. Allison, Long, and Krauze 1982), which is important here since earlier years have lower populations. The Gini calculation yielded a time series of the inequality in name popularity. While other measures of inequality could also be used, the time trend in the Gini is very similar to other inequality measures (see: Twenge et al. 2010). I then calculated the percentage of names, by year, that were correctly classified by the naïve Bayes classifier. This gave me a time series measure of the strength of the gender aesthetic—the extent to which names’ gender conventions are predictable from their spelling and phonology. In principle, changes in this metric could reflect artifacts from the classifier as much as genuine changes in the strength of the gender aesthetic. I therefore validate the findings from my analysis with more substantive results later on.

Throughout this period, names are both highly predictable and highly unequal. The Gini ranges from .83 to .94, a very high level of inequality (the Gini’s theoretical maximum is 1, and the Gini for US wealth is around .8). The accuracy of the naïve Bayes classifier never dips below .81. As figure 5 below shows, the two time series vary inversely (r=-.6). When names are more concentrated in the most popular names, the gender aesthetic is not as predictive. Basic time series analysis confirms the visual impression from figure 5 that these series vary inversely, a first-order auto-regressive model (not shown) shows the relationship is significant and negative (p<.01).

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Figure 5: Name Diversity and Aesthetic Strength

The naïve Bayes classifier suggests that names spellings and phonology became less predictive, reaching a trough in the 1940s and 1950s, roughly describing a U shape over the period of 1880-2009. Yet this measurement is fairly abstract, what did this mean substantively? Figure 6 below shows the percentage of names by gender for the three most common suffixes. The short a sound with suffixes a or ah; the long e sound with suffixes y (excluding ay), i, ie, and ee; and the n suffix. Together these suffixes describe 60% percent of total names. I investigated rarer suffixes, but they were generally stable. As figure 6 shows, the a suffix is generally specific to girls names throughout the entire period. The n suffix begins as mostly in boys names, becomes nearly evenly split between girls’ and boys’ names in the early 1950s, and then again becomes a boys’ suffix. The long e suffix begins as a girls’ suffix, later becomes a near even split between boys’ and girls’ names, and then returns to being a girls’ name suffix. These substantive changes in the gender aesthetic mirror the drop in predictability from the previous figure, since just at the time that predictability is dropping, these suffixes contain less information about the gender of names. Take for example, names ending in the n suffix, if we observe a name with an n suffix in 1950 we don’t know much about its gender since only 55% of names ending with n were boys’ names during 1950. We can be much more certain however, that a name with an n suffix is a boy’s name in 2008, when 70% of names ending in n were boys’ names. Thus, this aesthetic marker contains more information about the gender of a name over time.

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Figure: Gender Composition of Name Suffixes

What explains the relationship between inequality in popular and aesthetics? Immigration is one possibility; perhaps immigrants bring novel names, but also conform more strongly to the American gender aesthetic (e.g. Maria or Ramiro). However, as Twenge and colleagues show, the increasing recent diversity in names occurs net of immigration, and also occurs at roughly the same pace in states with the lowest immigration rates as the highest (2010). Furthermore recent immigration from Mexico might explain an increase in the a and o prefixes, but it is the n and long e suffixes which show the most meaningful change. Rather the increasing diversity of names seems to reflect parents’ desire to give children more unique names (Lieberson 2000; Twenge et al. 2010). Thus, as names became more diverse and subject to fashion, they became less diverse in their aesthetics. While there are more novel names today than ever, they also conform to gender aesthetics as much as they ever have.

Conclusions How is it that new names are constantly introduced, and are continually rising or falling in popularity, while they continue to maintain a rigid separation between genders? To answer this, I first constructed a quantitative measure of the gender aesthetics of baby names. I then used this measure to analyze how the gender aesthetic is reproduced over time, and how it structures the popularity of names. I found that 1) Names tend to remain in use for the same amount of time

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whether their gender matches their aesthetic or not, i.e. there is no evidence for a survival of the fittest or “natural selection” process; 2) the gender of names introduced from 1990-2008 are predictable from aesthetic rules of names from 1880-1950, thus even novel names reproduce the logic of naming practices from 40-128 years ago; 3) names with male aesthetics tend to be more popular than those with female aesthetics for both male and female names, suggesting that the male aesthetic is generally more valued; 4) names whose aesthetic does not match the gender of children given the name, are more likely to “switch” genders and become solidly a name of the other gender (e.g. Theo switches from being given to boys and girls roughly equally, to being given to almost entirely boys) 5) popular boys’ names with female aesthetics, generally have another strong signal of their male gender, either through being a central male figure in a dominant religion (e.g. Noah), or through being a diminutive form of a popular boys’ name (e.g. Charlie); 6) As naming practices become more diverse, they also conform more tightly to a gender aesthetic.

Many prominent theories of various aspects of culture stress that meanings emerge from contemporaneous interaction—meanings are constructed “on the fly”. Meanings and aesthetic appeal are thus contingent, sometimes radically so, so that even the success of “superstar” cultural objects that are orders of magnitude more popular than their peers, can be traced to what are essentially accidents of history (Ginsburgh and Ours 2003; Godart and Mears 2009; van de Rijt et al. 2013; Rossman and Schilke 2014; Salganik et al. 2006). In this view then, the important aspects of cultural evolution and reception are social or public, and not held in people’s heads. The evidence in this paper however, suggests that the role of contemporaneous interaction may be overstated. If convention, or social influence, determined the fate of names, we would expect that 1) the gender of names would not predictable from features of the names themselves; 2) new names would not be “born” matching a gender aesthetic. The opposite, however, occurs: new names follow the convention of the old names, and there are extremely few moments of uncertainty over the gender of names (Lieberson and Mikelson 1995). In the rare cases that uncertainty over a names’ gender occurs, contra Lieberson et al (2000), it generally resolves along the lines of aesthetics, and shifts in the gender of originally boys’ names are mostly part of a larger aesthetic shift.

If the cultural rules that decide a name’s aesthetic are in people’s heads then these patterns make sense—parents choose names based on socially patterned tastes (Lieberson 2000) which conform to existing gender aesthetics. The very complexity of naming practices however, initially seems to cast doubt on this explanation, there are some 86,917 unique names and it is highly implausible that parents can hold all this complex knowledge in their heads (Martin 2010). It becomes plausible however, that parents hold a much small set of gender naming rules, as a sort of compression heuristic (Brashears 2013), which can be used to recall the gendered meaning of names. For instance, 97% of names ending in a are girls’ names. Thus the relatively simple rules that define a gender naming system still leave room for a considerable diversity of cultural objects (D’Andrade 2001). The relative simplicity of these rules also allows for considerable

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innovation and change to occur, while maintaining both the aesthetic rules, and the symbolic boundaries it defines, intact. This should not be taken to mean, of course, that a name’s gender aesthetic is somehow natural or essential, rather gender aesthetics should be seen as a second-order convention wherein the properties of names are constructed as gendered, rather than the individual names themselves. At its most abstract this suggests that social construction processes operate as much through prior cultural material that is stored cognitively, as they do through more contemporaneous interactive public meaning-making processes.

Swidler (1986) argues that in “unsettled times” explicit ideologies guide action, more so than in settled times. Lizardo and Strand argue similarly that “institutionally ill-defined environments” implicit culture stored as bodily dispositions will be more likely to guide action (Lizardo and Strand 2010:218–219). This argument finds a rough analog in the history of US naming practices, when naming practices are more diverse, and more in institutional flux, in terms of the creation of new names and the preference for more unique and newer names, names adhere more rigidly to a gender aesthetic. Perhaps there is a general relationship between the complexity of a field of cultural objects and the extent to which they are ordered through aesthetic considerations. Future work should explore whether in times where conventional patterns of action are in flux, whether people look more to pre-existing meaning systems, such as aesthetics and ideologies to guide action.

Finally, these findings are consistent with findings in the sociology of gender. Cultural beliefs about gender tend to value men over women (Ridgeway 2011). Likewise, objects and practices that are considered feminine are devalued, and men and boys who adopt these practices are subject to social censure or worse (Cahill 1989; Kane 2006; Pascoe 2011; Schrock and Schwalbe 2009:281). Reflecting the greater value placed on what is seen as masculine, in some contexts, it is acceptable or encouraged for women to adopt masculine practices or objects. The structure of name popularity is consistent with these beliefs, where boys’ names with girls’ aesthetics are much less popular, and girls’ names with boys’ aesthetics are more popular. Thus although aesthetics mark gender boundaries, the patterns of boundary crossing appear to reflect the gender hierarchy.

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Appendix 1

Figure A.1

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Figure A.2

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Figure A.3

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