[table]captionskip=0pt

# Investigating writing style as a contributor to gender gaps in science and
technology††thanks: Correspondence to<EMAIL_ADDRESS>We thank Dr. Rebecca
Levitan and Dr. Martin Chodorow for guidance and feedback on an earlier
version of this work. We released an open-source Python package to compute
linguistic features used in this paper, available here:
https://pypi.org/project/stylometer. Financial support from the National
Science Foundation (grants 1932689 and 1932596) is gratefully acknowledged.
Any errors are those of the authors alone.

Ekaterina Levitskaya  New York University<EMAIL_ADDRESS>Kara Kedrick
University of Minnesota<EMAIL_ADDRESS>Russell J. Funk  University of
Minnesota<EMAIL_ADDRESS>
###### Abstract

While universalism is a foundational principle of science, a growing stream of
research finds that scientific contributions are evaluated differently
depending on the gender of the author, with women tending to receive fewer
citations relative to men, even for work of comparable quality. Strikingly,
research also suggests that these gender gaps are visible even under blinded
review, wherein the evaluator is not aware of the gender of the author. In
this article, we consider whether gender differences in writing styles—how men
and women communicate their work—may contribute to these observed gender gaps.
We ground our investigation in a previously established framework for
characterizing the linguistic style of written text, which distinguishes
between two sets of features—informational (i.e., features that emphasize
facts) and involved (i.e., features that emphasize relationships). Using a
large, matched sample of academic papers and patents, we find significant
differences in writing style by gender; women use more involved features in
their writing, a pattern that holds universally across fields. The magnitude
of the effect varies across fields, with larger gender differences observed in
the social sciences and arts humanities and smaller gaps in the physical
sciences and technology. Subsequently, we show that gender differences in
writing style may have parallels in reading preferences; papers and patents
with more informational features tend to be cited more by men, while those
with more involved features tend to be cited more by women, even after
controlling for the gender of the author, inventor, and patent attorney. Our
findings suggest that formal written text is not devoid of personal character,
which could contribute to bias in evaluation, thereby compromising the norm of
universalism.

##  1 Introduction

In his classic essay, “The Normative Structure of Science,” sociologist Robert
K. Merton identified universalism as a foundational principle of the
scientific enterprise, one that distinguishes science from other competing
systems of knowing. According to Merton and Storer’s [1973, p. 270]
formulation, universalism holds that the evaluation of scientific
contributions “is not to depend on the personal or social attributes of their
protagonist; his race, nationality, religion, class, and personal qualities
are as such irrelevant.” The value of universalism is manifested perhaps most
concretely in the practice of double-blind peer review, wherein the identities
of both those making scientific claims and those evaluating them are obscured
from one another (Bornmann, 2011).

While scholars have long observed that adherence to the principle of
universalism is far from universal (Mulkay, 1976; Cole, 1992; Long and Fox,
1995), the growing availability of large-scale databases is creating
opportunities for unprecedented insight into processes of scientific
evaluation (Teplitskiy et al., 2018; Dondio et al., 2019; Lane et al., 2021),
including the barriers that inhibit objective assessments. Recent literature
in particular has raised considerable concern about the role of gender in
scientific evaluation (Moss-Racusin et al., 2012; Reuben et al., 2014;
Oliveira et al., 2019; Card et al., 2020a). There is growing evidence to
suggest that the contributions of women are devalued relative to men (Joshi,
2014; Sarsons, 2017), thereby undermining the imperative of universalism, and
ultimately weakening the scientific enterprise by suppressing diverse
perspectives. Prior work has shown, for example, that relative to men, women’s
contributions are awarded less grant funding from prestigious sponsors
(Oliveira et al., 2019; Kolev et al., 2020), less likely to be granted patent
protection (Jensen et al., 2018), and also receive fewer citations from other
academic papers (Larivière et al., 2013). These gaps appear to be driven at
least in part by homophily, as men tend to cite other men over women, even in
fields with higher female representation, such as the social sciences
(Potthoff and Zimmermann, 2017; Dion et al., 2018; Ghiasi et al., 2018).

Yet much remains unknown about the role of gender in scientific assessment.
Prior scholarship has highlighted differences in field, topic choice, or
research quality as playing an important role in explaining observed gaps
between men and women (Leahey, 2007; Key and Sumner, 2019). While these
factors are undoubtedly important, studies have also shown that even when
accounting for field, topic choice, and quality, men’s and women’s research is
evaluated differently (Hengel, 2017; Hengel and Moon, 2020). Studies further
report gender gaps even in the presence of double-blind review (Kolev et al.,
2020; Mahajan et al., 2020), suggesting that conscious or unconscious bias
based on the identity of the author is likely not the determinative factor(van
der Lee and Ellemers, 2015; Hospido and Sanz, 2019; Card et al., 2021, 2020b).

In this article, we aim to contribute to the systematic understanding of
gender differences in the evaluation of scientific and technical work using a
linguistic lens. Specifically, we draw on a prior framework, developed by
Biber [1988], which contrasts linguistic features characteristic of a high
information density (“informational” features) with linguistic features
associated with more affective, interactional content (“involved” features).
This framework allows us to take a systematic approach to feature selection
and also to examine features which have been overlooked as indicators of
variation in the previous literature on gender gaps, such as the use of
determiners or pronouns. Biber [1988] found in a corpus of epistolary writing
that women used more “involved” features (e.g. increased use of pronouns) for
the purpose of building a relationship with the audience, while men used more
“informational” features (e.g. increased use of cardinal numbers in texts) for
the purpose of conveying the factual information. Using this framework, we
conduct a large-scale analysis of gender differences in scientific and
technical writing, leveraging two distinctive document types—academic articles
and patented inventions—which collectively span all major fields of science
and technology.

Several noteworthy findings emerge from our study. We document that even in
such restricted genres as academic writing and invention descriptions, female
authors tend to use more “involved” features than male authors, a pattern that
holds across all scientific fields and patent subcategories. In the patent
data specifically, we find that the gender of the lawyer appears to have more
impact on the writing than the gender of the inventor, suggesting both the
importance of attorneys in crafting patent text and the universal nature of
the patterns we observe, even across individuals with very different
professional training. Finally, we show that work that uses a higher number of
“involved” features is more likely to be cited by female authors.

Our study builds on, but also extends, a small but growing stream of research
in the Science of Science on gender and language (c.f., (Fortunato et al.,
2018)). First, while existing literature has identified a number of
differences in men’s and women’s scientific writing (Lerchenmueller et al.,
2019; Kolev et al., 2020; Kim et al., 2022), the choice of features have not
been motivated by a linguistic framework, thereby complicating the
interpretation of the results and making comparisons across studies difficult.
Second, existing research has been done within particular fields and on
particular kinds of text (e.g., scientific papers), and therefore little is
known about whether the patterns observed thus far will generalize across
diverse fields and document types (such as patent data), which have diverse
norms.

Our study also adds to the linguistics literature. First, previous research on
gender differences in language utilization has largely been done on informal
or conversational text, typically focusing on speech patterns, and using
corpora like emails (Thomson et al., 2001), essays (Mulac et al., 2006),
social media (Garimella and Mihalcea, 2016), blogs (Pennebaker, 2011). There
has been far less research on gender differences in formal writing, and the
work that has been done has relied on relatively small samples (Argamon et
al., 2003; Biber, 1988). Second, we study a new source of text data—patented
inventions—that has not been studied in previous linguistic analyses. Patents
represent a more restricted genre in terms of stylistic expression than
academic papers, therefore, finding stylistic differences by gender is
especially notable.

##  2 Background

###  2.1 Related Work

In this section, we briefly review prior related work on gender and writing
style, beginning with general research in linguistics, and then turning to
more recent literature specifically in the science and technology context.
Subsequently, we provide an overview of the linguistic framework that guides
our selection of features for the empirical analysis.

In the linguistics literature, interest in research on language and gender
started in the 1970s, beginning with work by R. Lakoff (“Language and Woman’s
Place”), who noted “a discrepancy between English as used by men and as used
by women” (Lakoff, 1973, p. 76). Since that time, attention to gender has
grown, and gender issues are now a major topic in many different subfields
(McElhinny, 2014; Wodak and Benke, 2017; Tripp and Munson, 2021). While the
literature is large and diverse, several key and well-documented findings are
particularly important in the context of our study. First, there are
consistent patterns of different language use by men and women, as evidenced
by research in both speech and writing (Mulac et al., 2006; Newman et al.,
2008; Pennebaker, 2011). Second, men tend to focus on relaying factual
information, while women tend to focus on building a relationship with a seen
or unseen audience in their communications (Biber et al., 1998; Pennebaker,
2011; Argamon et al., 2003).

Notwithstanding this progress, as noted above, most linguistic literature on
gender differences in writing has been done on informal text, typically using
corpora of emails, essays, social media, daily conversations, or blogs
(Thomson and Murachver, 2001; Mulac et al., 2006; Garimella and Mihalcea,
2016; Pennebaker, 2011). While the style of writing represented in these sorts
of corpora are increasingly ubiquitous, they are also unique in that they tend
to be less structured and therefore allow for more freedom of expression than
other genres. By contrast, scientific and technical writing—the kind
represented in scientific papers and patented inventions—tends to be much more
formal, with strong norms governing the style of presentation (Argamon et al.,
2003). These norms may overshadow the personal stylistic preferences of
individual authors, and if so, could minimize the extent of observable gender
differences in the written text of papers and patents. In short, whether
papers and patents use systematically different language depending on the
gender of the authors remains difficult to predict given established findings
from the linguistics literature.

To date, a small number of studies in the Science of Science have begun to
address this gap in knowledge. Lerchenmueller et al. [2019] found that male
authors (first or last male author on an academic paper) were more likely to
present their findings positively than female authors (first or last female
author on an academic paper), and the positive representation was found to be
associated with a higher number of citations. Kolev et al. [2020] examined the
text characteristics of grant applications and found differences by gender in
the use of topic-specific, “narrow,” words versus “broad”words, and further
demonstrated that the use of such words may have an impact on decision making
in blinded grant application review process. Most recently, Kim et al. [2022]
analyzed a corpus of U.S. PhD dissertations to understand how gender is
encoded in the research language, and whether gendered language has an impact
on future careers of male and female researchers. They made a distinction
between explicit references to gender in text (through words like “women”,
“girls” or “man”, “boy”, etc.) and research associated with gender (e.g.,
words like “war” and “machine learning” are found to be predominantly
associated with male authors, while words like “parenting” and “children” are
found to be predominantly associated with female authors). They found that
explicit references to women as subjects of study in dissertations increase
the likelihood of becoming a faculty advisor, while doing research
traditionally associated with women decreases those chances, suggesting an
implicit gender bias in the evaluation of research.

These studies add important new insight to the understanding of gender
differences in writing, and suggest that the patterns documented in previous
work on informal corpora may indeed extend to formal written text. However, as
our overview above highlights, this emerging literature has tended to focus on
a relatively ad hoc set of features, the selection of which has not been
informed by an underlying linguistic framework, which complicates the
interpretation of the reported findings. For example, while relative to men,
women may be less likely to describe their work as novel, it is possible that
the observed differences may reflect deeper variation by gender in the use of
adjectives and adverbs.

In light of this overview of prior work, a gap is evident. On the one hand,
linguistics literature has mostly used informal text for the study of gender
differences in writing and smaller samples of formal written text. On the
other hand, the analysis of gender differences in writing in the Science of
Science literature has not been motivated by a linguistic framework, which
complicates the interpretation of the results and makes comparisons across
studies difficult. In this study, we aim to address this gap by examining a
larger sample of formal writing, encompassing two different types of texts
(academic papers and invention descriptions), and applying a set of systematic
features that have been found in previous linguistic studies to be indicative
of differences in male and female communication styles.

###  2.2 Framework

We draw on a previously established linguistic framework, known as
“Informational versus Involved Production,” outlined by Biber [1988], which
characterizes the co-occurrence of certain linguistic features based on their
communicative function. While the linguistics community has developed a number
of potentially applicable frameworks, we chose Biber’s because it was created
using a diverse set of corpora, including non-fiction, and was intended to
capture universal features of English language; therefore, we anticipate that
it would likely capture useful variation across a broad range of scientific
and technological fields, encompassing potentially diverse norms of
composition. We specifically chose the “informational”/”involved” dimension of
Biber’s framework, as studies note (Argamon et al., 2003) that men tend to
focus more on relaying factual information (“informational”), while women tend
to focus on building a relationship with an audience in their communications
(“involved”). Based on a corpus of e-mail communication, speech, and blogs,
Pennebaker [2011] stipulated that “males categorize their worlds by counting,
naming, and organizing the objects they confront” (informational aspect),
while women “personalize their topics” (involved aspect).

Biber outlines two factors that influence the choice of individuals to use a
specific linguistic mode: the production circumstances of real-time
constraints (speech or conversation) versus editing possibilities (academic
writing), and the primary purpose of the writer (informational versus
involved). With respect to the production circumstances, given the type of
corpus that we are exploring in this study (formal written text), we can hold
these constant, as both papers and patents are produced in settings where
editing is possible and likely (i.e., the authors are under no real-time
constraints, unlike the case of a conversation). With respect to the primary
purpose of the writer (informational versus involved), (Argamon et al., 2003)
noted that formal written text is intended for a “broad unseen audience.”
Biber found that letters written by women and addressed to women were more
involved and letters written by men and addressed to men were less involved
(Biber et al., 1998). He also found that, overall, letters written by women
are more involved than letters written by men. Even though in formal written
text there is no specific addressee, and academic papers and patent
descriptions are intended for a “broad unseen audience”, our goal is to
understand if we are still able to observe the variation by the gender of the
writers.

In addition to Biber’s framework [1998], the specific set of “involved” and
“informational” features used in our study was informed by the follow-up work
of Pennebaker [2011] and Argamon et al. [2003]. We chose three distinctive
features for “involved” and “informational” writing modes (Table 1). Pronouns,
questions, and non-phrasal coordination (“and” connector) are highly
correlated as linguistic features and are reflective of the “involved” writing
style. The use of pronouns indicates interaction between the writer and the
reader—the writer assumes that the reader knows what they are referring to
with a pronoun, as the reader is following the writer’s story in a text;
pronouns “present things in a relational way” (Argamon et al., 2003, p. 7). We
consider questions to be an important involved feature as well, as they point
to the presence of a hypothetical addressee in a formal written text, and are
therefore a marker of interaction (Biber, 1986; Marckworth and Baker, 1974).
Non-phrasal coordination (“and”) is used “to string clauses together in a
loose, logically unspecified manner, instead of integrating the information
into fewer units” (Biber, 1988, p. 106), and thus is more associated with a
narrative type of communication (“involved” aspect). For “informational”
writing mode indicators, we use past tense, determiners, and cardinal numbers.
We use past tense as an “informational” feature, to indicate a “disconnection”
with the audience, absence of interaction in the narrative. The other two
“informational” features—determiners and cardinal numbers—are taken from
Argamon et al. [2003] who used the Balanced Winnow algorithm to identify
features that are strong indicators of either male or female writing. Argamon
and colleagues note that, based on their examination of texts, “the use of
determiners reflects that male writers are mentioning classes of things, in
contrast to female writers, who are personalizing their messages” (Argamon et
al., 2003, p. 12). The greater use of cardinal numbers (quantification) is
also noted as a strong male indicator in both the work of (Pennebaker, 2011)
and (Argamon et al., 2003).

##  3 Data and methods

###  3.1 Corpus

As noted previously, we draw on two different corpora: (1) abstracts of
academic papers from Web of Science111Web of Science:
https://clarivate.com/webofsciencegroup/solutions/web-of-science/ (“WoS data”)
and (2) abstracts of patents from PatentsView (“PatentsView
data”)222PatentsView: http://www.patentsview.org/. Both databases have been
widely used in prior research in the Science of Science, and provide generally
representative coverage of scientific papers and patented inventions,
respectively (Funk and Owen-Smith, 2017; Birkle et al., 2020; Jaffe and
Trajtenberg, 2002).

For the WoS data, we excluded document types that are not typically meant for
the publication of scientific findings (e.g., book reviews, letters, music
scores, and bibliographies). We further limited our attention to articles that
included full-text abstracts, and that were written in English333The WoS data
do not include accurate information on the language of paper abstracts. We
identified English language abstracts using a commercial machine translation
API.. For the PatentsView data, we limited our attention to utility patents,
which encompass roughly 90% of the patents granted by the United States Patent
and Trademark Office (USPTO), and have therefore been the central focus of
prior work (Jaffe and Trajtenberg, 2002). All patents granted by the USPTO are
written in English, and more than 90% (going back to 1976) include full-text
abstracts.

For the purposes of our study, we further narrow our focus to solo-authored
papers and patents (i.e., we exclude papers and patents written by teams). Our
motivation for doing so was to allow for a clearer identification of the
relationship between gender and writing style, which would be more complex in
case of teams, particularly those of mixed gender. Note that in the patent
data, we also narrowed our sample to include only those inventors with a
single lawyer, in order to more cleanly control for gender. In addition, we
require that abstracts have more than 100 words, in order to normalize
frequency counts per 100 words, which is consistent with prior research in
linguistics (Biber, 1988). Additional details on the procedure used to narrow
down our samples from the population of articles and patents, including the
number of observations lost at each stage, are given in Figure 4 in the
Supplementary Materials.

Women are underrepresented in the population of both authors and inventors.
For perspective, in our data, roughly 19.6% of authors and 5.4% of inventors
are women.444This is an estimate for academic papers with one author and for
patents with one inventor, where the gender of the author/inventor was
known/assigned. Moreover, men and women tend to publish in different areas,
and their relative representation changes over time. Therefore, to help
facilitate comparisons, we subset the WoS and PatentsView data by building a
matched sample of papers and patents written by male and female authors and
inventors. To do so, we identified, for each academic paper written by a
single female author in a particular WoS subject area (e.g., “Sociology”,
“Cell Biology”, “Mathematics”) and in a particular year, a corresponding
academic paper written by a single male author in the same subject area and
year. We follow a similar procedure for patents, using National Bureau of
Economic Research (NBER) patent subcategories to proxy for the field of
technology, and the grant year of the patent as the year of publication.

For the WoS data, we subset the corpus to years starting in 1991, as abstracts
are not well represented in earlier periods (Figure 1). After matching, most
of the abstracts in our final analytical sample are from the Social Sciences,
followed by Life Sciences and Biomedicine. (Figure 5 in the Supplementary
Materials shows the distribution of fields in the original Web of Science
corpus.) This is consistent with prior work, as women are well represented in
the social sciences and biomedical domains (Ceci et al., 2014; Su and Rounds,
2015). For the PatentsView data, our analysis begins in 1976, which is the
first year in which machine readable patent records are available from the
USPTO. After matching, most patents come from the “Others” NBER category,
which includes “Miscellaneous” patent types. (Figure 6 in the Supplementary
Materials shows the distribution of categories in the original PatentsView
corpus.)

Gender is assigned using author and inventor names. For papers, author gender
is coded based on first and (when available) middle names, as reported in the
Web of Science database, using the genderize.io application programming
interface (API). For patents, we assign inventor gender by using a coding
provided by the United States Patent and Trademark Office, as included with
the PatentsView data. More details about the gender assignment can be found in
the S1 section of Supplementary Materials.

###  3.2 Linguistic Measures

For each paper and patent in our sample, we constructed a series of measures
tracking the utilization of informational and involved features. We use
pronouns, questions, and non-phrasal coordination (“and” connector) as
indicators of the “involved” style of writing, and past tense, determiners,
and cardinal numbers as indicators of “informational” style of writing. Table
1 gives an overview of the specific features of interest; more details about
the computation of these features are included in the S2 section of the
Supplementary Materials.555We also released an open-source Python package to
compute these linguistic features, available here:
https://pypi.org/project/stylometer.

We define three separate measures: involved rate, informational rate, and
Involved-Informational Ratio. Because our corpus includes academic writing and
invention descriptions, there is already a high utilization of informational
features by both female and male authors; therefore, in addition to involved
and informational rates, we are also interested in seeing the relative rate at
which writers use involved features in relation to informational features
(Involved-Informational Ratio). We define the involved and informational rates
as the sum of three involved (or informational) linguistic features divided by
the total number of tokens in a given abstract, and normalized to units of 100
words (Biber, 1988). We define the Involved-Informational Ratio as the
involved rate divided by the informational rate.

$\textrm{Involved Rate}=\frac{N_{pron}+N_{and}+N_{q}}{N_{tokens}}\times 100$
(1)

$\textrm{Informational Rate}=\frac{N_{det}+N_{past}+N_{num}}{N_{tokens}}\times
100$ (2)

$\textrm{Inv.-Inf. Rate}=\frac{\textrm{Involved Rate}}{\textrm{Informational
Rate}}$ (3)

To better understand the relationship between author gender and writing style,
we estimated a series of linear regression models, predicting the utilization
of informational and involved features in paper and patent abstracts. For
models of both papers and patents, predictor variables included the gender of
the author (or inventor), the year of academic paper (or patent grant), and
the principal scientific (or technological) field. For models of patents, we
also included as a predictor the gender of the lawyer, as patent attorneys
often take part in writing and editing the patent text. More complex models
with additional predictors (e.g., number of citations) yielded substantively
similar results to those presented below.

###  3.3 Citation measures

The possibility that men and women write in systematically different ways
naturally raises questions about the relationship between writing styles and
the reception of intellectual products. Specifically, differences in how men
and women present their research in written text may help account for some of
the disparities in rates of citation documented in prior work (Caplar et al.,
2017; Potthoff and Zimmermann, 2017; Dion et al., 2018; Dworkin et al., 2020;
Zurn et al., 2020).

To evaluate this possibility, we conducted a series of analyses in which we
decomposed the number of citations made by future papers and patents to those
in our sample, according to the gender of the citing author or inventor. We
emphasize that citation is merely a proxy for (unobservable) reading
preferences—authors may cite prior work without reading it; scientists and
inventors probably only cite a fraction of the prior work that they do read;
and citation need not correspond to a favorable reading of the cited work.
Keeping those caveats in mind, for each sample paper and patent, we computed
two related measures: (1) citations from papers with a female (male) first
author, (2) citations from papers with a female (male) last author. For each
measure, we excluded self-citations, i.e., those made by the author of the
sample paper to his or her own work. In addition, to facilitate
interpretation, we transform each measure into a rate per 100 citations.
Subsequently, we estimated a series of regression models, wherein the three
measures are our outcomes of interest, and the predictors are the gender of
the author or inventor (and lawyer for patents) and the ratio of informational
and involved features. For papers and patents cited 0 times, we impute a rate
of 0, and add an imputation indicator as a covariate. As with our prior
regression, we control for the field and year of academic paper/patent.

##  4 Results

###  4.1 Writing

####  4.1.1 Writing Examples

Before presenting our quantitative results, we first highlight several
examples from our data that illustrate the differences between the
informational and involved styles. Beginning with papers, consider the
following excerpts from two abstracts, one written by a female author (1) and
the other written by a male (2). Both papers were published in the same field
(Business & Economics), in the same journal (_Financial Management Journal_),
during the same time period (1990s).

(1)

_Why do companies in the United Kingdom pay scrip (i.e., in stock) dividends?
Are tax savings the sole motive for this option? If so, are all tax-loss
companies offering this option? Or is this option driven by other motives,
such as cash savings, signaling, and agency conflicts? In this study, I
provide some insights into the motivation for scrip-dividend payment by
comparing the operational performance and other characteristics of all
companies that distributed scrip dividends with those of a control group of
non-scrip-paying, but otherwise similar, firms. Scrip dividends are offered by
an increasing number of companies in the United Kingdom as an option whereby
shareholders are able to choose between receiving dividends in cash or the
equivalent in the form of shares (scrip). Companies stress tax savings when
they offer this option because, unlike cash dividends, the scrip option is not
subject to a payment of the advanced corporation tax (ACT)_.

(2)

_The rich variety of securities innovations in recent years continues to
intrigue academicians and practitioners alike. The array of innovative
securities includes a variety of equity-linked debt securities. On January 28,
1991, the Republic of Austria publicly offered $100 million principal amount
of a new equity-linked debt security called stock index growth notes (“SIGNs”)
in the United States. The SIGNs were scheduled to mature in approximately 5.5
years. They would make no payments of interest prior to maturity. If the value
of the S &P 500 is below 336.69 on the maturity date, the holder will receive
$10, the initial offering price. If the value exceeds 336.69, the holder will
get $10 plus $10 multiplied by the percentage appreciation in the S&P 500
above 336.69. SIGNS may thus be characterized as a package consisting of (i) a
5.5-year triple-A-rated zero coupon note plus (ii) a 5.5-year European call
option, or warrant, on the S&P 500 with a strike price of 336.69_.

Although both abstracts address similar topics, they are presented in
dramatically different ways. Abstract (2), written by the male author, is
striking for its heavy utilization of cardinal numbers (e.g., “$100 million”,
“5.5 years”, “above 336.69”), which are mostly avoided by the female author.
Abstract (1), by contrast, stands out for its greater utilization of
“connector words” (e.g., “and”) and questions, the latter of which are not
used at all in Abstract (2). The female abstract reads a lot like a story,
building a relationship with the reader through the use of pronouns, present
tense, asking questions, and by stringing the clauses together with “and” in a
loose, storytelling-like manner; the male abstract is focused on relaying the
factual information, via the use of numbers and determiners, and past tense.
Figure 2 shows the full text of both abstracts, zoomed out, with informational
and involved features highlighted in different colors (blue for informational,
yellow for involved).

While the gender differences highlighted in the cases above are somewhat
unusual in degree, the general pattern is consistent across papers and patents
in our larger sample. Figure 3 show the distribution of the Involved-
Informational Ratio, stratified by author gender, for paper and patent
abstracts, respectively. In both figures, we observe a clear pattern wherein,
as the Involved-Informational Ratio increases, so too does the share of female
abstracts (as indicated in the figure, the observed gender differences are
statistically significant). Note in the patents, the number of bins is smaller
because we observe a narrower range of Involved-Informational Ratio values for
patents, perhaps reflecting the more constrained style of the genre.

####  4.1.2 Analyses

Table 2 shows the breakdown of informational and involved features for
academic papers and patents, respectively. The results there suggest that, in
absolute terms, informational features are used significantly more often in
both text sources, by both men and women. This distribution seems plausible
given the nature of our corpus (i.e., scientific articles and patents are
generally concerned with communicating information), and is also consistent
with prior work; Biber [1988], for example, situated academic prose closer to
the informational dimension, and Argamon et al. (2013) noted the “greater
quantification inherent to most non-fiction genres.” Table 3 also shows the
mean and standard deviation of involved and informational features combined
and the Involved-Informational Ratio in the academic papers and patents
samples.

The results of our regression analysis are shown in Tables 4 (papers) and 5
(patents)666We evaluated our regression models for multicollinearity; all
variance inflation factors were below the conventional threshold of 10,
suggesting little multicollinearity among the covariates.. Beginning with
papers, we find, as expected, a statistically significant association between
author gender and the Involved-Informational Ratio of paper abstracts
($\beta=0.02;p<0.001$, Model 2); relative to men, women tend to use a higher
ratio of involved to informational features in their scientific writing.
Holding all other variables at their means, the predicted Involved-
Informational Ratio (based on Model 2 of Table 4) is about 6.7% greater for
female than male authors. Models 4 and 5, also of Table 4, help to unpack this
finding by estimating separate regressions predicting the utilization of
involved and informational features, respectively. We find that relative to
men, women use involved features at a significantly greater rate
($\beta=0.23;p<0.001$, Model 4), and informational features at a significantly
lower rate ($\beta=-0.18;p<0.001$, Model 6) in their writing. This suggests
that the differences we detect between men and women in the Involved-
Informational Ratio is likely driven by both the numerator and the
denominator.

While the patterns we observe appear to hold universally across fields of
science and technology, we find heterogeneity in the magnitude of the effect.
Figure S3 in the Supplementary Materials shows the predicted gender
differences by field. The differences are greater in the Social Sciences and
Arts & Humanities and less pronounced in Physical Sciences and Technology,
which may be explained by the fact that the former fields generally permit
greater freedom of stylistic expression and higher use of involved features
than the latter fields, which have more restricted stylistic rules (Argamon et
al., 2003). Full regression results for each field are presented in S4 section
(for academic papers) and in S5 section (for patents) of the Supplementary
Materials.

Turning next to patents, we find similar patterns. There is a statistically
significant association between inventor gender and the Involved-Informational
Ratio of patent abstracts ($\beta=0.00;p<0.01$, Model 2); relative to men,
women tend to use a higher ratio of involved to informational features in
their technical writing. Once again holding all other variables at their
means, the predicted Involved-Informational Ratio (based on Model 2 of Table 5
is about 2.3% greater for female than male inventors. In Model 2 of Table 5,
we evaluate the relationship between the gender of the patent lawyer and the
Involved-Informational Ratio of the patent. Here, we find results that are
similar to those we observed for inventor gender, with a statistically
significant association between lawyer gender and Involved-Informational Ratio
($\beta=0.01,p<0.01$, Model 2). The magnitude of the association, however, is
larger than what we observed for inventor gender; holding all other variables
at their means, the predicted Involved-Informational Ratio (based on Model 2
of Table 5) is about 5.1% greater for patents with female than male attorneys.

Following our analytical strategy with papers, in Models 4 and 6 (Table 5), we
run separate regressions predicting the rate of utilization for informational
and involved features, respectively. Here, the findings are a bit more
complex. We find that women use both informational ($\beta<0.14,p<0.001$,
Model 4) and involved ($\beta=0.41,p<0.001$, Model 6) features at a
significantly greater rate in their writing than men, suggesting that the
association we observe between gender and the Involved-Informational Ratio
(Model 2) is driven by the numerator. Female lawyers use involved features in
their writing at a rate that is statistically indistinguishable from men
(Model 4), but statistically fewer informational ones ($\beta=-0.62,p<0.001$,
Model 6).

###  4.2 Citation

Next, we turn to our analysis of gender differences in citations, the results
of which are shown in Tables 6 (academic papers) and 7 (patents). To simplify
the presentation, in this section, we only report models predicting citation
as a function of the rate of informational and involved feature utilization
(i.e., we do not report the results of models that use the Involved-
Informational Ratio as a predictor). We found substantially similar results,
however, using the ratio measure.

Overall, we observe significant gender homophily in citation patterns. Papers
written by female authors receive a greater share of citations from papers
with a female first author ($\beta=4.33,p<0.001$) and papers with a female
last author ($\beta=3.57;p<0.001$). The opposite pattern holds for sample
papers written by men.

Taking into account the linguistic features, we see a consistent and
statistically significant pattern across the outcome measures: papers with
higher rates of utilization of involved features in their abstracts tend to be
cited at a higher rate by papers with a female first author
($\beta=0.25,p<0.001$, Model 1) and papers with a female last author
($\beta=0.29,p<0.001$, Model 3). More substantively, we can interpret the
coefficient estimates as indicating that for each unit increase in the rate of
involved features used, the rate of citation by papers with a female first and
last author is predicted to increase by 0.25 and 0.29, respectively. When we
consider the use of informational features, however, the pattern flips
($\beta=-0.08,p<0.001$, Model 1 for citation by papers with a female first
author; $\beta=-0.06,p<0.001$, Model 3 for citation by papers with a female
first author). Papers with higher rates of utilization of involved features
are cited at a lower rate by papers with a male first author
($\beta=-0.18,p<0.001$, Model 2) and papers with a male last author
($\beta=-0.30,p<0.001$, Model 4). We find that the opposite holds for
utilization of informational features, although only in models predicting
citation by papers with a male first author ($\beta=0.03,p<0.01$, Model 2).

Turning to patents, we find results that are generally similar, but much
weaker. While patents that use more informational features are less likely to
be cited by patents with a female first ($\beta=-0.07,p<0.001$, Model 1) or
last ($\beta=-0.07,p<0.001$, Model 3) inventor, none of the remaining
coefficients tracking the utilization of informational or involved features
are statistically significant. These results are not surprising, considering
that patent descriptions, as a type of text, allow much less stylistic
expression than academic papers do.

##  5 Discussion

The norm of universalism in science and technology stipulates that the
evaluation of scientific and technological contributions should be based on
impersonal criteria (Merton and Storer, 1973). Nevertheless, growing evidence
suggests that gender gaps in scholarly assessment remain both prevalent and
widespread (Moss-Racusin et al., 2012; Reuben et al., 2014; Oliveira et al.,
2019; Card et al., 2020a). In this study, we examined variation in writing
style by gender in scientific and technical texts, and considered the role
this variation plays in the citation patterns. To do so, we developed an
approach motivated by a larger linguistic framework, examining differences
between “involved” and “informational” writing styles.

Based on our sample of single-authored academic papers and patents, we find
differences in the writing styles of female and male authors, with female
writers using involved features more frequently in their work (features
associated with more affective, interactional content), a pattern that holds
across all scientific fields and patent subcategories. These differences are
greater in the Social Sciences and Arts & Humanities and less pronounced in
Physical Sciences, likely because the former fields allow for more stylistic
expression. In the patent data, we find that the gender of the lawyer is more
predictive of this pattern, suggesting that lawyers have a greater influence
on the editing of the patent description. The same pattern holds in the
citation analysis, where papers with higher rates of utilization of involved
features in their abstracts tend to be cited at a higher rate by papers with a
female first author and papers with a female last author.

Our work is not without limitations. Importantly, our study inferred the
gender of authors and inventors based on their names, which offers only a
rough proxy. Moreover, our gender assignments were binary, corresponding to
sex-based categories (female and male), which oversimplifies the true
diversity of gender identities among authors and inventors. We also limited
our attention to single-authored papers and patents and patents with a single
lawyer. While this approach helps to make the interpretation of our results
clearer, more work needs to be done to determine whether and how our findings
apply to teams. Further work on analyzing writing styles of mixed-gender teams
could help provide a more nuanced understanding of the observed pattern.
Finally, our study is based on observational data, and therefore our results
should not be interpreted as causal. As such, future behavioral studies,
involving experimental assessment of the response of reviewers to highly
involved and highly informational texts based on the reviewer’s gender, would
complement our corpus study of academic papers and patents.

Notwithstanding these limitations, our study makes several contributions.
First, our study adds to the Science of Science. Specifically, we propose an
analysis with a set of linguistic features which is motivated by a an
involved/informational framework, established in the linguistic research as
indicative of differences in male and female communication styles, which
allows us to take a systematic view on understanding gender differences in
scientific and technical writing.

Second, our study adds new insights to the linguistics literature.
Specifically, we use a larger sample of formal written text and utilize a new
source of data (patent descriptions) which to our knowledge has not been used
for the purposes of the analysis of gender differences in writing before. We
also released an open-source Python package to compute the linguistic features
used in this paper for future use of researchers, available here:
https://pypi.org/project/stylometer.

Finally, our study also has several natural policy implications. In
particular, our findings suggest that writing in science and technology is not
completely devoid of personal markers and, therefore, could constitute a
potential source of bias during the evaluation processes. However,
establishing rigid guidelines for communication styles in science and
technology may not be the right solution. Instead, we suggest that in order to
mitigate bias in evaluation processes, the diversity among the contributors
should be matched by the same diversity among the evaluators. For example,
previous researchers noted the need to increase the number of female reviewers
(Kolev et al., 2020), as well as to encourage citation diversity (Zurn et al.,
2020) and continue raising awareness of language bias in established
institutions, structures, and processes, including academia (Clements and
Petray, 2021). Such measures could help to uphold the norm of universalism in
scientific contributions.

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  * Zurn et al. (2020) Perry Zurn, Danielle S. Bassett, and Nicole C. Rust.  The citation diversity statement: A practice of transparency, a way of life.  _Trends in Cognitive Sciences_ , 24(9):669–672, 2020.  ISSN 1364-6613.  doi: https://doi.org/10.1016/j.tics.2020.06.009.  URL https://www.sciencedirect.com/science/article/pii/S1364661320301649.

Figure 1: Distribution of papers and patents by year and by field. These
panels represent data used in the analytical sample. Figure5 in the
Supplementary Materials shows the distribution of categories in the original
Web of Science corpus, and Figure 6 in the Supplementary Materials shows the
distribution of categories in the original PatentsView corpus.
Table 1: Involved and informational linguistic features. Examples of
“involved” features (pronouns, non-phrasal coordination, and questions) and
“informational” features (determiners, verbs in past tense, and cardinal
numbers) used in the analysis.

Involved | Informational
---|---
Pronouns | Determiners
(personal, possessive, wh-pronoun) | (predeterminers, wh-determiner, determiners)
_“it”, “theirs”, “whose”…_ | _“both”, “a lot”, “which”, “the”, “this”…_
Non-phrasal coordination | Verbs in past tense
_“and”_ | (verb, past tense; verb, past particle)
Questions _“?”_ | Cardinal numbers

Table 2: Informational and involved feature breakdown in academic papers and patents. The data represents the academic paper and patent samples used in the analysis. | Academic papers
---|---
| Informational features
| numbers | determiners | past tense
| mean | STD | p-value | mean | STD | p-value | mean | STD | p-value
female | 1.52 | 1.80 | <0.01 | 9.93 | 3.07 | <0.01 | 4.50 | 2.46 | <0.01
male | 1.54 | 1.83 |  | 10.14 | 3.08 |  | 4.45 | 2.40 |
| Involved features
| questions | “and” | pronoun
| mean | STD | p-value | mean | STD | p-value | mean | STD | p-value
female | 0.03 | 0.19 | 0.13 | 3.16 | 1.49 | <0.01 | 1.60 | 1.53 | <0.01
male | 0.03 | 0.19 |  | 2.94 | 1.47 |  | 1.58 | 1.51 |

| Patents
---|---
| Informational features
| numbers | determiners | past tense
| mean | STD | p-value | mean | STD | p-value | mean | STD | p-value
female | 1.30 | 2.23 | 0.77 | 15.91 | 3.71 | <0.01 | 4.39 | 2.09 | <0.01
male | 1.29 | 2.15 |  | 15.44 | 3.76 |  | 4.47 | 2.13 |
| Involved features
| questions | “and” | pronoun
| mean | STD | p-value | mean | STD | p-value | mean | STD | p-value
female | NA | NA | NA | 2.75 | 1.50 | <0.01 | 0.33 | 0.62 | 0.52
male | NA | NA |  | 2.60 | 1.43 |  | 0.34 | 0.60 |
Table 3: Distribution of involved and informational features in academic papers and patents. This table shows descriptive statistics regarding the presence of involved and informational features combined in the academic paper and patent samples used in the analysis. | Academic papers
---|---
| Involved (normalized) | Informational (normalized) | Ratio (normalized)
| mean | STD | p-value | mean | STD | p-value | mean | STD | p-value
female | 4.78 | 2.14 | <0.01 | 15.96 | 4.04 | <0.01 | 0.33 | 0.21 | <0.01
male | 4.55 | 2.09 |  | 16.13 | 4.02 |  | 0.31 | 0.20 |

| Patents
---|---
| Involved (normalized) | Informational (normalized) | Ratio (normalized)
| mean | STD | p-value | mean | STD | p-value | mean | STD | p-value
female | 3.08 | 1.60 | <0.01 | 21.60 | 4.07 | <0.01 | 0.15 | 0.10 | 0.01
male | 2.94 | 1.53 |  | 21.20 | 4.09 |  | 0.15 | 0.13 |

Figure 2: Writing samples. Excerpts from two academic paper abstracts, one
written by a female author (1) and the other written by a male (2). Both
papers were published in the same field (Business & Economics), in the same
journal (_Financial Management Journal_), during the same time period (1990s).
Blue represents informational features, orange represents involved features.
Why do companies in the United Kingdom pay scrip (i.e., in stock) dividends?
Are tax savings the sole motive for this option? If so, are all tax-loss
companies offering this option? Or is this option driven by other motives,
such as cash savings, signaling, and agency conflicts? In this study, I
provide some insights into the motivation for scrip-dividend payment by
comparing the operational performance and other characteristics of all
companies that distributed scrip dividends with those of a control group of
non-scrip-paying, but otherwise similar, firms. Scrip dividends are offered by
an increasing number of companies in the United Kingdom as an option whereby
shareholders are able to choose between receiving dividends in cash or the
equivalent in the form of shares (scrip). Companies stress tax savings when
they offer this option because, unlike cash dividends, the scrip option is not
subject to a payment of the advanced corporation tax (ACT). I find that there
are no significant differences in the scrip and non-scrip-paying firms’ tax
exposures. Compared to the control sample, firms that issue scrip dividends do
not report a higher proportion of irrecoverable ACT in their accounts, nor do
they generate a high proportion of their earnings overseas, earnings on which
the ACT cannot be claimed. My results also show that this option is not driven
by cash shortages, nor is it a substitute for external finance and/or a cut in
cash dividends. However, I find that firms that pay scrip dividends are, on
average, large, and have high dividend yield, suggesting that the cash saved
is substantial. However, these firms already have high cash-flow balances and
low growth opportunities. The overall results suggest that firms in the UK do
not appear to be making the optimal financial choice for their dividend
policies. Like stock dividends and dividend reinvestment plans in the US.
scrip dividends provide both firm and shareholders with many benefits.
Companies are able to retain cash without altering their payout policies or
raising new funds in the capital markets; thus, companies that use this option
can save on borrowing costs, underwriting fees, other issue costs, and avoid
negative signals of new equity. At the same time, they provide their
shareholders with the opportunity to increase their holdings without incurring
any transaction costs. In addition, the UK institutional setting and relevant
tax code provisions allow companies to derive tax benefits from this option.
Unlike cash dividends, this option is not subject to ACT. This tax is first
paid when a company declares cash dividends and is deducted from its
corporation-tax liability nine months after the accounting year-end if
dividends are paid from domestic earnings and if taxable profit is higher than
gross cash dividends. If these two conditions are not met, surplus ACT is
carried in the accounts and set off against preceding or immediately following
periods. For companies with no previous or foreseeable future taxable profit,
ACT is written off as a loss against reserves. Thus, companies with
prospective ACT loss are more likely to pay scrip, rather than cash,
dividends. However, scrip dividends are not costless. Firms incur
administrative costs in Petting up and running the scheme, such as the costs
of advertising the option, preparing and mailing the scrip-dividend
prospectus, and printing and allocating the new shares. Moreover, scrip
dividends impose costs on the firm’s shareholders. This option is not offered
to foreign investors. For some domestic investors, it is taxed differently
from cash dividends. While the tax credit associated with cash dividends can
be claimed by all investors, the tax credit on scrip dividends can only be
claimed by tax-paying individual investors. Individuals who have reliefs and
allowances in excess of their income, as well as corporate investors, forgo
the tax credit when they opt for scrip dividends. Thus, corporate and
nontaxable individual investors are likely to prefer cash dividends;
individual shareholders taxed at a higher rate of income tax may opt for scrip
dividends for which the firm issues additional shares. However, such
additional shares could result in a dilution of control for shareholders who
opt for cash dividends, and a loss in future dividend increases if scrip
dividends limit the scope for such increases. Why, then, do companies issue
scrip dividends? One possibility is that individual tax-paying investors
request this option from their companies, which suggests that companies are
subject to monitoring by atomistic shareholders while large shareholders, such
as corporate investors, are passive owners. I suggest a number of areas for
further research that will assess the extent to which the granting of this
option is driven by nonfinancial considerations. The rich variety of
securities innovations in recent years continues to intrigue academicians and
practitioners alike. The array of innovative securities includes a variety of
equity-linked debt securities. On January 28, 1991, the Republic of Austria
publicly offered $100 million principal amount of a new equity-linked debt
security called stock index growth notes (”SIGNs”) in the United States. The
SIGNs were scheduled to mature in approximately 5.5 years. They would make no
payments of interest prior to maturity. If the value of the S&P 500 is below
336.69 on the maturity date, the holder will receive $10, the initial offering
price. If the value exceeds 336.69, the holder will get $10 plus $10
multiplied by the percentage appreciation in the S&P 500 above 336.69. SIGNS
may thus be characterized as a package consisting of (i) a 5.5-year triple-A-
rated zero coupon note plus (ii) a 5.5-year European call option, or warrant,
on the S&P 500 with a strike price of 336.69. The value of each SIGN can be
expressed as the sum of (i) the value of the zero coupon bond component plus
(ii) the value of the call option component plus (iii) the value resulting
from interest on the SIGNs not being taxable until the maturity date plus (iv)
the reduction, if any, in transaction costs vis-a-vis acquiring a comparable
zero coupon bond and purchasing call options on the S&P 500 separately plus
(v) the value, if any, attributable to creating an investment alternative that
is not otherwise available to the investors who purchased the SIGNs. On
January 28. 1991, the Republic of Austria had outstanding an issue of zero
coupon bonds maturing July 17, 1995, that was yielding 8.05% per annum
semiannually compounded. Based on this yield, the zero coupon bond component
of each SIGN was worth approximately $6.48. The call option component, valued
using the Black-Scholes model modified to account for dividend-paying stocks,
was estimated to be $2.30. The value of the tax arbitrage created through the
introduction of SIGNs equals the sum of (i) the tax benefit resulting from the
investor’s ability to defer income taxes on the amortization of the zero
coupon bond component ($0.11 per SIGN) plus (ii) the tax benefit resulting
from the investor’s ability to defer income taxes that would otherwise be
incurred on the option component under the mark-to-market rules ($0.09 per
SIGN). The total value of the tax arbitrage is $0.20 per SIGN, just two
percent of the public offering price per SIGN. I compared the relative
transaction costs associated with (i) purchasing 1,000 SIGNs ($0.02 per SIGN),
and (ii) purchasing a Treasury strip with $10,000 principal amount and a
portfolio that replicates the call option component of each SIGN ($0.16 per
SIGN). The present-value after-tax transaction cost savings are thus roughly
$0.14 per SIGN. The creation of a new security can benefit investors if it
creates new risk-return combinations. Call options on the S&P 500 with a time
to expiration of 5.5 years are not available on any of the options exchanges.
An investor who wished to duplicate the return stream of the SIGNs could try
to purchase a customized option in the over-the-counter market or employ a
dynamic replication strategy. The SIGNs were sold in small denominations
primarily to retail investors, who would not have had access to the over-the-
counter market on account of the small transaction size. Dynamic replication
would be prohibitively expensive for at least some retail investors because of
transaction costs. The SIGNs were sold to investors at a price of $10.00.
Based on the four estimated component values. the implied value attributable
to creating a new investment alternative is $0.88 per SIGN. However, this
value also includes the effect of any mispricing that might have occurred. The
predicted value of the SIGNs was 8.80% below the market price on January 28,
1991. At the end of February 1991, the predicted value was only 1.60% below
the market price. At the end of March 1991, the predicted value was $10.03,
which slightly exceeded the $10,00 market price. Thereafter, the predicted
value and the market price approximate one another. This price behavior
suggests that investors may have initially overvalued the SIGNs but that the
mispricing was eliminated within roughly one to two months. It also suggests
that the value of the innovation should be attributed primarily to the value
of the tax arbitrage and to the reduction in after-tax transaction costs it
made possible. The Republic of Austria used approximately 30% of the $9.50 net
proceeds from the sale of the SIGNs issue to hedge its contingent S&P 500
liability, leaving proceeds net of hedging costs amounting to $6.65. These net
proceeds exceed the $6.48 value of the bond component of each SIGN. The profit
of $0.17 per SIGN effectively reduced the Republic of Austria’s cost of
issuing 5.5-year zero coupon debt to 7.56%, resulting in a savings of 49 basis
points per annum. FemaleMaleInvolvedInformational Figure 3: Distribution of
academic paper and patent abstracts by gender and by Involved-Informational
Ratio. Number of academic papers/patents by Involved-Informational Ratio bins
by gender, as percentage of total.

Table 4: Involved-Informational Ratio, involved rate, and informational rate for female and male authors. Regression results for predicted outcomes if author is female, with the following dependent variables: Involved-Informational Ratio, involved rate, informational rate. These results represent data across all fields. Detailed regression results by each field are presented in S4 section of the Supplementary Materials. | DV: Inv.-Inf. Ratio | DV: Involved rate | DV: Informational rate
---|---|---|---
| (1) | (2) | (3) | (4) | (5) | (6)
Author is female (1 = Yes) | $0.02^{***}$ | $0.02^{***}$ | $0.23^{***}$ | $0.23^{***}$ | $-0.18^{***}$ | $-0.18^{***}$
| $(0.00)$ | $(0.00)$ | $(0.01)$ | $(0.01)$ | $(0.01)$ | $(0.01)$
Constant | $0.31^{***}$ | $0.14^{***}$ | $4.55^{***}$ | $2.70^{***}$ | $16.13^{***}$ | $19.90^{***}$
| $(0.00)$ | $(0.01)$ | $(0.00)$ | $(0.15)$ | $(0.01)$ | $(0.29)$
Field fixed effects | No | Yes | No | Yes | No | Yes
Year fixed effects | No | Yes | No | Yes | No | Yes
N | $512520$ | $512520$ | $512520$ | $512520$ | $512520$ | $512520$
r2 | $0.0025$ | $0.1271$ | $0.0030$ | $0.1692$ | $0.0005$ | $0.0991$
${}^{***}p<0.001$; ${}^{**}p<0.01$; ${}^{*}p<0.05$

Table 5: Involved-Informational Ratio, involved rate, and informational rate for female and male inventors. Regression results for predicted outcomes if an inventor or patent lawyer is female, with the following dependent variables: Involved-Informational Ratio, involved rate, informational rate. These results represent data across all patent categories. Detailed regression results by each patent category are presented in S5 section of the Supplementary Materials. | DV: Inv.-Inf. Ratio | DV: Involved rate | DV: Informational rate
---|---|---|---
| (1) | (2) | (3) | (4) | (5) | (6)
Inventor is female (1 = Yes) | $0.00^{**}$ | $0.00^{**}$ | $0.14^{***}$ | $0.14^{***}$ | $0.42^{***}$ | $0.41^{***}$
| $(0.00)$ | $(0.00)$ | $(0.02)$ | $(0.02)$ | $(0.05)$ | $(0.04)$
Lawyer is female (1 = Yes) | $0.02^{***}$ | $0.01^{**}$ | $0.02$ | $0.02$ | $-1.21^{***}$ | $-0.62^{***}$
| $(0.00)$ | $(0.00)$ | $(0.03)$ | $(0.04)$ | $(0.09)$ | $(0.09)$
Constant | $0.15^{***}$ | $0.21^{***}$ | $2.94^{***}$ | $3.15^{***}$ | $21.27^{***}$ | $17.54^{***}$
| $(0.00)$ | $(0.02)$ | $(0.01)$ | $(0.21)$ | $(0.03)$ | $(0.52)$
Field fixed effects | No | Yes | No | Yes | No | Yes
Year fixed effects | No | Yes | No | Yes | No | Yes
N | $32106$ | $32106$ | $32106$ | $32106$ | $32106$ | $32106$
r2 | $0.0013$ | $0.0297$ | $0.0021$ | $0.0219$ | $0.0080$ | $0.0989$
${}^{***}p<0.001$; ${}^{**}p<0.01$; ${}^{*}p<0.05$

Table 6: Involved and informational rates for female and male authors by
citation patterns. Regression results for predicted outcomes if author is
female, with the following dependent variables: rate of citations from papers
with a female, male first author and with a female, male last author. These
results represent data across all fields.

| (1) | (2) | (3) | (4)
---|---|---|---|---
|  DV: Rate of citations from papers with a female first author |  DV: Rate of citations from papers with a male first author |  DV: Rate of citations from papers with a female last author |  DV: Rate of citations from papers with a male last author
Involved rate | 0.25*** | -0.18*** | 0.29*** | -0.30***
| (0.02) | (0.02) | (0.02) | (0.02)
Informational rate | -0.08*** | 0.03** | -0.06*** | -0.01
| (0.01) | (0.01) | (0.01) | (0.01)
Author is female (1 = Yes) | 4.33*** | -4.33*** | 3.57*** | -3.64***
| (0.07) | (0.08) | (0.07) | (0.08)
Paper is cited 0 times (1 = Yes) | -29.11*** | -51.19*** | -25.80*** | -55.35***
| (0.08) | (0.08) | (0.08) | (0.08)
Constant | 27.16*** | 53.38*** | 23.12*** | 59.00***
| (0.20) | (0.22) | (0.19) | (0.22)
Field fixed effects | Yes | Yes | Yes | Yes
Year fixed effects | Yes | Yes | Yes | Yes
N | 512520 | 512520 | 512520 | 512520
r2 | 0.3084 | 0.4720 | 0.2748 | 0.5244

  * •

_Notes:_ Robust standard errors are shown in parentheses; p-values correspond
to two-tailed tests.

  * •

*p<0.1; **p<0.05; ***p<0.01

Table 7: Involved and informational rates for female and male inventors by
citation patterns. Regression results for predicted outcomes if inventor or
patent lawyer is female, with the following dependent variables: rate of
citations from papers with a female, male first author and with a female, male
last author. These results represent data across all fields.

| (1) | (2) | (3) | (4)
---|---|---|---|---
|  DV: Rate of citations from patents with a female first inventor |  DV: Rate of citations from patents with a male first inventor |  DV: Rate of citations from patents with a female last inventor |  DV: Rate of citations from patents with a male last inventor
Involved rate | 0.02 | 0.20+ | 0.04 | 0.12
| (0.05) | (0.11) | (0.05) | (0.11)
Informational rate | -0.07*** | 0.04 | -0.07*** | 0.04
| (0.02) | (0.04) | (0.02) | (0.04)
Inventor is female (1 = Yes) | 2.78*** | -3.09*** | 2.75*** | -3.20***
| (0.16) | (0.34) | (0.16) | (0.34)
Lawyer is female (1 = Yes) | 0.31 | 0.21 | 0.03 | 0.50
| (0.33) | (0.69) | (0.33) | (0.68)
Patent is cited 0 times (1 = Yes) | -5.02*** | -41.99*** | -5.52*** | -41.42***
| (0.13) | (0.29) | (0.14) | (0.29)
Constant | 5.13*** | 42.39*** | 5.41*** | 42.25***
| (0.48) | (1.09) | (0.50) | (1.08)
Field fixed effects | Yes | Yes | Yes | Yes
Year fixed effects | Yes | Yes | Yes | Yes
N | 32106 | 32106 | 32106 | 32106
r2 | 0.0589 | 0.2963 | 0.0575 | 0.2964

  * •

_Notes:_ Robust standard errors are shown in parentheses; p-values correspond
to two-tailed tests.

  * •

*p<0.1; **p<0.05; ***p<0.01

## Supplementary Materials

Figure 4: Identification of the analytical sample Figure 5: Distribution of
papers included in the full WoS data by field

Figure 6: Distribution of patents included in the full PatentsView data by
category

### S1. Gender coding

In this appendix, we describe our procedure for coding the gender of paper
authors and patent inventors and lawyers. For papers, we coded author gender
based on author first and (when available) middle names, as reported in Web of
Science, using the genderize.io application programming interface (API). While
similar approaches have been widely used in prior work, we recognize that
author names offer only an imperfect proxy for gender, and this limitation
should be kept in mind when interpreting the results of our study. Given a
name, the genderize.io API returns the most likely gender and an associated
probability score. The genderize.io API makes gender suggestions based on
worldwide data from hundreds of millions of social media profiles; as such,
the service makes a significant improvement over prior approaches, which are
typically based on records from the United States Social Security
Administration or similar government entities, and that therefore perform
poorly on international samples. For our study, we used a probability cutoff
of 0.9 for assigning gender; gender suggestions below that level were set to
“unknown” and excluded from the study. When possible, we assigned gender by
looking across the academic papers of an author. For example, if the same
author had published one paper under the name “J. Smith” and the other under
the name “John Smith”, we would assign the gender of the author as “male” in
both cases, using the name from the latter paper to propagate a gender to the
second.

For patents, we assign inventor gender by using the coding provided by the
United States Patent and Trademark Office, via its Patents View database.
Similar to our approach for papers, the Patents View database assigns gender
based on inventor names. Details on the algorithm are given in (Toole et al.,
2021). Patents View does not assign gender to patent attorneys. Therefore, we
assigned gender ourselves, using an approach analogous to the one described
above for papers.

### S2. Linguistic feature construction

Linguistic features are computed using spaCy library in Python (Honnibal and
Montani, 2017). Tags in spaCy are based on OntoNotes 5 version of the Penn
Treebank tag set (Weischedel et al., 2013).

Tag | Source | Description | Feature | Dimension
---|---|---|---|---
CD | spaCy | Cardinal number | Number | Informational
PDT | spaCy | Predeterminer (e.g. both, etc.) | Determiner | Informational
WDT |  | Wh-determiner (e.g. which, etc.) |  |
DT |  | Determiner |  |
VBD | spaCy | Verb, past tense | Past tense | Informational
VBN |  | Verb, past participle |  |
PRP | spaCy | Personal pronoun | Pronoun | Involved
PRP$ |  | Possessive pronoun |  |
WP |  | Wh-pronoun |  |
WP$ |  | Possessive wh-pronoun |  |
AND | string match | Connector “and” | Connector | Involved
? | string match | Question mark | Question | Involved

S3. Gender differences by field in academic papers. The regression
coefficients (predicted gender differences) are derived from Table 4. The
x-axis represents Web of Science subject categories (fields).

S4. Gender differences in writing style by field for academic papers (full regression results). Regression models in this table replicate those of Table 4, but separately by WoS research area. ${}^{***}p<0.001$; ${}^{**}p<0.01$; ${}^{*}p<0.05$ | Physical Sciences
---|---
| DV: Inv.-Inf. Ratio | DV: Involved rate | DV: Informational rate
| (1) | (2) | (3) | (4) | (5) | (6)
Author is female (1 = Yes) | $0.0049^{***}$ | $0.0046^{***}$ | $0.1170^{***}$ | $0.1136^{***}$ | $0.2085^{***}$ | $0.2145^{***}$
| $(0.0012)$ | $(0.0011)$ | $(0.0136)$ | $(0.0134)$ | $(0.0311)$ | $(0.0303)$
Constant | $0.2314^{***}$ | $0.1668^{**}$ | $3.6371^{***}$ | $3.2202^{***}$ | $17.1514^{***}$ | $19.0286^{***}$
| $(0.0008)$ | $(0.0540)$ | $(0.0096)$ | $(0.6325)$ | $(0.0220)$ | $(1.4339)$
Field fixed effects | No | Yes | No | Yes | No | Yes
Year fixed effects | No | Yes | No | Yes | No | Yes
N | $62358$ | $62358$ | $62358$ | $62358$ | $62358$ | $62358$
r2 | $0.0003$ | $0.0544$ | $0.0012$ | $0.0484$ | $0.0007$ | $0.0576$
| Technology
| DV: Inv.-Inf. Ratio | DV: Involved rate | DV: Informational rate
| (1) | (2) | (3) | (4) | (5) | (6)
Author is female (1 = Yes) | $0.0096^{***}$ | $0.0101^{***}$ | $0.1404^{***}$ | $0.1444^{***}$ | $0.0162$ | $0.0018$
| $(0.0016)$ | $(0.0015)$ | $(0.0179)$ | $(0.0169)$ | $(0.0384)$ | $(0.0371)$
Constant | $0.2491^{***}$ | $0.1602^{***}$ | $3.8901^{***}$ | $2.8286^{***}$ | $17.3202^{***}$ | $19.2278^{***}$
| $(0.0011)$ | $(0.0134)$ | $(0.0127)$ | $(0.1478)$ | $(0.0272)$ | $(0.3239)$
Field fixed effects | No | Yes | No | Yes | No | Yes
Year fixed effects | No | Yes | No | Yes | No | Yes
N | $43675$ | $43675$ | $43675$ | $43675$ | $43675$ | $43675$
r2 | $0.0008$ | $0.1160$ | $0.0014$ | $0.1176$ | $0.0000$ | $0.0811$
| Life Sciences & Biomedicine
| DV: Inv.-Inf. Ratio | DV: Involved rate | DV: Informational rate
| (1) | (2) | (3) | (4) | (5) | (6)
Author is female (1 = Yes) | $0.0185^{***}$ | $0.0186^{***}$ | $0.2046^{***}$ | $0.2052^{***}$ | $-0.1583^{***}$ | $-0.1571^{***}$
| $(0.0010)$ | $(0.0010)$ | $(0.0096)$ | $(0.0090)$ | $(0.0220)$ | $(0.0211)$
Constant | $0.2893^{***}$ | $0.1160^{***}$ | $4.1138^{***}$ | $2.2907^{***}$ | $15.9629^{***}$ | $19.3331^{***}$
| $(0.0007)$ | $(0.0134)$ | $(0.0068)$ | $(0.1259)$ | $(0.0156)$ | $(0.2943)$
Field fixed effects | No | Yes | No | Yes | No | Yes
Year fixed effects | No | Yes | No | Yes | No | Yes
N | $149541$ | $149541$ | $149541$ | $149541$ | $149541$ | $149541$
r2 | $0.0023$ | $0.1007$ | $0.0030$ | $0.1230$ | $0.0003$ | $0.0892$
| Arts & Humanities
| DV: Inv.-Inf. Ratio | DV: Involved rate | DV: Informational rate
| (1) | (2) | (3) | (4) | (5) | (6)
Author is female (1 = Yes) | $0.0275^{***}$ | $0.0276^{***}$ | $0.2787^{***}$ | $0.2779^{***}$ | $-0.3846^{***}$ | $-0.3874^{***}$
| $(0.0018)$ | $(0.0017)$ | $(0.0177)$ | $(0.0176)$ | $(0.0302)$ | $(0.0285)$
Constant | $0.3860^{***}$ | $0.3551^{***}$ | $5.7840^{***}$ | $4.3604^{***}$ | $16.4391^{***}$ | $14.6273^{***}$
| $(0.0013)$ | $(0.0835)$ | $(0.0125)$ | $(0.8482)$ | $(0.0214)$ | $(1.3787)$
Field fixed effects | No | Yes | No | Yes | No | Yes
Year fixed effects | No | Yes | No | Yes | No | Yes
N | $64317$ | $64317$ | $64317$ | $64317$ | $64317$ | $64317$
r2 | $0.0037$ | $0.0556$ | $0.0038$ | $0.0232$ | $0.0025$ | $0.1148$
| Social Sciences
| DV: Inv.-Inf. Ratio | DV: Involved rate | DV: Informational rate
| (1) | (2) | (3) | (4) | (5) | (6)
Author is female (1 = Yes) | $0.0283^{***}$ | $0.0282^{***}$ | $0.2959^{***}$ | $0.2949^{***}$ | $-0.2911^{***}$ | $-0.2899^{***}$
| $(0.0010)$ | $(0.0010)$ | $(0.0097)$ | $(0.0095)$ | $(0.0173)$ | $(0.0169)$
Constant | $0.3482^{***}$ | $0.2189^{***}$ | $4.9205^{***}$ | $3.2420^{***}$ | $15.5635^{***}$ | $16.0783^{***}$
| $(0.0007)$ | $(0.0530)$ | $(0.0069)$ | $(0.5159)$ | $(0.0122)$ | $(0.9211)$
Field fixed effects | No | Yes | No | Yes | No | Yes
Year fixed effects | No | Yes | No | Yes | No | Yes
N | $192629$ | $192629$ | $192629$ | $192629$ | $192629$ | $192629$
r2 | $0.0042$ | $0.0453$ | $0.0048$ | $0.0587$ | $0.0015$ | $0.0499$

S5. Gender differences in writing style by field for patents (full regression results). Regression models in this table replicate those of Table 5, but separately by NBER category. ${}^{***}p<0.001$; ${}^{**}p<0.01$; ${}^{*}p<0.05$ | Computers & Comm
---|---
| DV: Inv.-Inf. Ratio | DV: Involved rate | DV: Informational rate
| (1) | (2) | (3) | (4) | (5) | (6)
Inventor is female (1 = Yes) | $0.0004$ | $0.0004$ | $0.0578$ | $0.0578$ | $0.3038^{**}$ | $0.3034^{**}$
| $(0.0026)$ | $(0.0026)$ | $(0.0412)$ | $(0.0411)$ | $(0.1064)$ | $(0.1054)$
Lawyer is female (1 = Yes) | $0.0102^{*}$ | $0.0093$ | $0.0373$ | $0.0356$ | $-0.7366^{***}$ | $-0.6249^{**}$
| $(0.0051)$ | $(0.0051)$ | $(0.0799)$ | $(0.0802)$ | $(0.2065)$ | $(0.2057)$
Constant | $0.1363^{***}$ | $0.1314^{***}$ | $2.6626^{***}$ | $2.6891^{***}$ | $20.9118^{***}$ | $20.9487^{***}$
| $(0.0019)$ | $(0.0178)$ | $(0.0296)$ | $(0.2812)$ | $(0.0766)$ | $(0.7211)$
Field fixed effects | No | Yes | No | Yes | No | Yes
Year fixed effects | No | Yes | No | Yes | No | Yes
N | $5580$ | $5580$ | $5580$ | $5580$ | $5580$ | $5580$
r2 | $0.0007$ | $0.0156$ | $0.0004$ | $0.0121$ | $0.0037$ | $0.0311$
| Electrical & Electronic
| DV: Inv.-Inf. Ratio | DV: Involved rate | DV: Informational rate
| (1) | (2) | (3) | (4) | (5) | (6)
Inventor is female (1 = Yes) | $0.0076^{**}$ | $0.0076^{**}$ | $0.2715^{***}$ | $0.2716^{***}$ | $0.8669^{***}$ | $0.8668^{***}$
| $(0.0026)$ | $(0.0026)$ | $(0.0436)$ | $(0.0435)$ | $(0.1176)$ | $(0.1156)$
Lawyer is female (1 = Yes) | $0.0053$ | $0.0048$ | $0.0183$ | $0.0034$ | $-0.6147^{*}$ | $-0.5997^{*}$
| $(0.0054)$ | $(0.0054)$ | $(0.0910)$ | $(0.0914)$ | $(0.2455)$ | $(0.2431)$
Constant | $0.1340^{***}$ | $0.1366^{***}$ | $2.7003^{***}$ | $2.9902^{***}$ | $21.2449^{***}$ | $22.2131^{***}$
| $(0.0018)$ | $(0.0120)$ | $(0.0312)$ | $(0.2046)$ | $(0.0843)$ | $(0.5441)$
Field fixed effects | No | Yes | No | Yes | No | Yes
Year fixed effects | No | Yes | No | Yes | No | Yes
N | $4632$ | $4632$ | $4632$ | $4632$ | $4632$ | $4632$
r2 | $0.0021$ | $0.0186$ | $0.0083$ | $0.0235$ | $0.0128$ | $0.0558$
| Mechanical
| DV: Inv.-Inf. Ratio | DV: Involved rate | DV: Informational rate
| (1) | (2) | (3) | (4) | (5) | (6)
Inventor is female (1 = Yes) | $-0.0012$ | $-0.0012$ | $0.1130^{*}$ | $0.1129^{*}$ | $0.9326^{***}$ | $0.9312^{***}$
| $(0.0025)$ | $(0.0025)$ | $(0.0456)$ | $(0.0454)$ | $(0.1149)$ | $(0.1133)$
Lawyer is female (1 = Yes) | $0.0104$ | $0.0100$ | $0.0477$ | $0.0711$ | $-1.1050^{***}$ | $-0.8692^{***}$
| $(0.0057)$ | $(0.0057)$ | $(0.1033)$ | $(0.1042)$ | $(0.2603)$ | $(0.2601)$
Constant | $0.1445^{***}$ | $0.1516^{***}$ | $2.9765^{***}$ | $3.2065^{***}$ | $21.5424^{***}$ | $22.2138^{***}$
| $(0.0018)$ | $(0.0100)$ | $(0.0326)$ | $(0.1816)$ | $(0.0822)$ | $(0.4531)$
Field fixed effects | No | Yes | No | Yes | No | Yes
Year fixed effects | No | Yes | No | Yes | No | Yes
N | $4536$ | $4536$ | $4536$ | $4536$ | $4536$ | $4536$
r2 | $0.0008$ | $0.0129$ | $0.0014$ | $0.0195$ | $0.0180$ | $0.0552$
| Drugs & Medical
| DV: Inv.-Inf. Ratio | DV: Involved rate | DV: Informational rate
| (1) | (2) | (3) | (4) | (5) | (6)
Inventor is female (1 = Yes) | $0.0034$ | $0.0040$ | $0.1690^{**}$ | $0.1702^{**}$ | $0.0993$ | $0.0615$
| $(0.0069)$ | $(0.0068)$ | $(0.0588)$ | $(0.0583)$ | $(0.1651)$ | $(0.1464)$
Lawyer is female (1 = Yes) | $0.0333^{**}$ | $0.0047$ | $0.1021$ | $0.0420$ | $-2.0064^{***}$ | $-0.2036$
| $(0.0112)$ | $(0.0117)$ | $(0.0950)$ | $(0.0995)$ | $(0.2667)$ | $(0.2495)$
Constant | $0.1689^{***}$ | $0.2024^{**}$ | $3.0063^{***}$ | $3.2853^{***}$ | $20.3865^{***}$ | $18.1602^{***}$
| $(0.0050)$ | $(0.0775)$ | $(0.0426)$ | $(0.6605)$ | $(0.1195)$ | $(1.6570)$
Field fixed effects | No | Yes | No | Yes | No | Yes
Year fixed effects | No | Yes | No | Yes | No | Yes
N | $3056$ | $3056$ | $3056$ | $3056$ | $3056$ | $3056$
r2 | $0.0030$ | $0.0405$ | $0.0031$ | $0.0332$ | $0.0182$ | $0.2395$

S5 (continued). Gender differences in writing style by field for patents (full regression results). Regression models in this table replicate those of Table 5, but separately by NBER category. ${}^{***}p<0.001$; ${}^{**}p<0.01$; ${}^{*}p<0.05$ | Chemical
---|---
| DV: Inv.-Inf. Ratio | DV: Involved rate | DV: Informational rate
| (1) | (2) | (3) | (4) | (5) | (6)
Inventor is female (1 = Yes) | $-0.0004$ | $-0.0004$ | $0.0743$ | $0.0739$ | $-0.0344$ | $-0.0371$
| $(0.0070)$ | $(0.0069)$ | $(0.0607)$ | $(0.0607)$ | $(0.1777)$ | $(0.1660)$
Lawyer is female (1 = Yes) | $0.0125$ | $0.0101$ | $-0.0121$ | $0.0143$ | $-1.4362^{***}$ | $-1.2652^{***}$
| $(0.0118)$ | $(0.0118)$ | $(0.1017)$ | $(0.1036)$ | $(0.2974)$ | $(0.2831)$
Constant | $0.1725^{***}$ | $0.2024^{***}$ | $3.0471^{***}$ | $2.8248^{***}$ | $19.8252^{***}$ | $16.3100^{***}$
| $(0.0051)$ | $(0.0379)$ | $(0.0439)$ | $(0.3318)$ | $(0.1285)$ | $(0.9070)$
Field fixed effects | No | Yes | No | Yes | No | Yes
Year fixed effects | No | Yes | No | Yes | No | Yes
N | $2524$ | $2524$ | $2524$ | $2524$ | $2524$ | $2524$
r2 | $0.0004$ | $0.0464$ | $0.0006$ | $0.0185$ | $0.0092$ | $0.1501$
| Others
| DV: Inv.-Inf. Ratio | DV: Involved rate | DV: Informational rate
| (1) | (2) | (3) | (4) | (5) | (6)
Inventor is female (1 = Yes) | $0.0056^{***}$ | $0.0056^{***}$ | $0.1542^{***}$ | $0.1537^{***}$ | $0.2651^{***}$ | $0.2603^{***}$
| $(0.0017)$ | $(0.0017)$ | $(0.0293)$ | $(0.0293)$ | $(0.0699)$ | $(0.0691)$
Lawyer is female (1 = Yes) | $0.0084^{*}$ | $0.0079^{*}$ | $-0.0074$ | $0.0198$ | $-0.7232^{***}$ | $-0.4835^{**}$
| $(0.0036)$ | $(0.0037)$ | $(0.0623)$ | $(0.0630)$ | $(0.1487)$ | $(0.1487)$
Constant | $0.1495^{***}$ | $0.1559^{***}$ | $3.1071^{***}$ | $3.1456^{***}$ | $21.8632^{***}$ | $21.8371^{***}$
| $(0.0012)$ | $(0.0085)$ | $(0.0209)$ | $(0.1451)$ | $(0.0499)$ | $(0.3427)$
Field fixed effects | No | Yes | No | Yes | No | Yes
Year fixed effects | No | Yes | No | Yes | No | Yes
N | $11778$ | $11778$ | $11778$ | $11778$ | $11778$ | $11778$
r2 | $0.0014$ | $0.0065$ | $0.0024$ | $0.0087$ | $0.0031$ | $0.0312$