Gender Inequality in New Media: Evidence from
Wikipedia∗
Marit Hinnosaar†
April 26, 2019

Abstract
Media is critical for gender equality. I analyze Wikipedia, one of the prominent
examples of new media. Using data from a survey and a randomized survey experiment,
I study why women are less likely to contribute to Wikipedia, the implications of
the gender gap on Wikipedia’s content, and what can be done about it. I find that:(1) gender differences in the frequency of Wikipedia use and in beliefs about one’s
competence explain a large share of the gender gap in Wikipedia writing; (2) the gender
gap among contributors leads to unequal coverage of topics; (3) providing information
about gender inequality has a large effect on contributions.
JEL codes: L86; L82; J16; H41
Keywords: Gender, Media, Internet, Public goods

1

Introduction

In most traditional media outlets, men vastly outnumber women, and several major media
organizations have taken steps to change that. Examples include Bloomberg introducing
gender quotas for news stories1 and BBC for panel shows.2 At the global level, the United∗

I’m grateful to Ainhoa Aparicio, Gabriele Camera, Matthias Doepke, Martin Dufwenberg, Ruben Durante,
Avi Goldfarb, Shane Greenstein, Toomas Hinnosaar, Eliana La Ferrara, Ryan McDevitt, David McKenzie,
Mario Pagliero, Andrea Prat, Stephan Seiler, Alex Tetenov, and Michael Zhang, as well as seminar audiencesat the European University Institute, ZEW Conference on the Economics of ICT, Digital Economy Workshop,
ICT Paris Conference, and Economics of Media Bias Workshop in Cologne for valuable comments.
†
Collegio Carlo Alberto and CEPR, marit.hinnosaar@gmail.com.
1
Posetti, Julie. 2015. “The Bloomberg News recipe for newsroom transformation.” World News Publishing
Focus by World Association of Newspapers and News Publishers, March 17. http://blog.wan-ifra.org/
2015/03/17/the-bloomberg-news-recipe-for-newsroom-transformation.
2
BBC. 2014. “BBC TV boss: No all-male panel shows.” February 9. http://www.bbc.com/news/
entertainment-arts-26107011.

1

Nations is engaged in an initiative to increase female presence in the media.3 One reason to
introduce these measures is that historically rigid institutions, work arrangements, and hiring
policies in the media might otherwise inhibit women to be heard. In this paper, I analyze a
new media source, which is not restricted by these institutions, but which nevertheless faces
similar issues.
I analyze Wikipedia, which is a widely used online encyclopedia. It is the fifth mostpopular website in the world.4 Although anyone can write and edit Wikipedia articles, thereis evidence that the majority of Wikipedia writers are men. The Wikimedia Foundation,
which owns Wikipedia, views the lack of female contributors as a problem and has set itself
goals to increase their share.5
Why does it matter who contributes to Wikipedia? Who contributes to its production
matters if a gender gap among contributors leads to biased content. There is some evidence
that Wikipedia’s coverage of topics is gender biased. In particular, popular media has voiced
concerns that Wikipedia has less information about female than male public figures,6 writers,7and scientists.8 For example, in 2018 a female Nobel Prize winner didn’t have a Wikipedia
page.9 More generally, Reagle and Rhue (2011) document that biographies of women are
more likely to be missing from Wikipedia compared to Encyclopedia Britannica than thoseof men.
Coverage of public figures in Wikipedia is important because they are role models.
Literature provides evidence that role models among other things influence educational
decisions and career choices, which affect earnings potential and overall economic outcomes.
For example, using a randomized natural experiment in India, Beaman et al. (2012) find that
exposure to women in local government eliminates the gender gap in educational attainment.
In a field experiment, Porter and Serra (2017) find that exposure to female economics alumni
3

http://www.unesco.org/new/en/communication-and-information/crosscutting-priorities/
gender-and-media/global-alliance-on-media-and-gender/about-gamag/, accessed November 15,
2017.
4
The popularity of Wikipedia is measured by the web traffic measurement company Alexa Internet(http://www.alexa.com/siteinfo/wikipedia.org, accessed November 15, 2017).
5
BBC. 2014. “Wikipedia ’completely failed’ to fix gender imbalance.” BBC interview with Jimmy Wales,
August 8. http://www.bbc.com/news/business-28701772.
6
Cohen, Noam. 2011. “Define Gender Gap? Look Up Wikipedia’s Contributor List.” New York Times,
January 30. http://www.nytimes.com/2011/01/31/business/media/31link.html.
7
Flood, Alison. 2013. “Wikipedia bumps women from ‘American novelists’ category.” The Guardian,
April 25. https://www.theguardian.com/books/2013/apr/25/wikipedia-women-american-novelists.
8
Donald, Athene, and Frank Norman. 2013. “Using Wikipedia to inspire the next generation of womenscientists.” The Guardian, July 25. https://www.theguardian.com/science/occams-corner/2013/jul/
25/wikipedia-next-generation-women-scientists.
9
Cecco, Leyland.
2018.
“Female Nobel prize winner deemed not important enough for
Wikipedia entry.” The Guardian, October 3. https://www.theguardian.com/science/2018/oct/03/
donna-strickland-nobel-physics-prize-wikipedia-denied.

2

increases female students’ likelihood of choosing economics as a major. Exposure to female
teachers or professors has been shown to increase female students’ likelihood of taking mathand science courses (Carrell et al., 2010; Lim and Meer, 2019).10 More generally, Beaman
et al. (2009) find that exposure to female leaders weakens stereotypes about gender roles andreduces gender bias. Even though there is no evidence that specifically the lack of detail in
Wikipedia about female role models affects economic decisions, more broadly, literature has
shown that media affects economic decisions (for an overview see DellaVigna and La Ferrara(2015)) and literature has also shown that availability of information specifically on Wikipedia
affects outcomes in the real world (Thompson and Hanley, 2017; Hinnosaar et al., 2017).
In this paper, I study gender inequality in Wikipedia. My goal is to learn why women areless represented and how to change that. Specifically, the questions I ask are the following:(1) Why are women less likely to contribute to Wikipedia? (2) What are the implications of
this gender gap on Wikipedia’s content? (3) What can be done about it? The analysis also
sheds some light on gender inequality in traditional media. It highlights the importance ofrepresenting diverse opinions and informs about measures to achieve that.
To answer these questions, I conduct a survey and a randomized survey experiment with1,000 participants on Amazon Mechanical Turk.11 This sample is representative of potential
editors of Wikipedia. In fact, 22% of the survey respondents have edited Wikipedia. However,there are gender differences—the percentage of Wikipedia editors is almost twice as large
among male respondents than female. The survey allows me to study why individuals do
or do not contribute to Wikipedia. The goal of the survey was to understand which factors
influence Wikipedia editing behavior and how the gender of editors affects editorial input.
The goal of the experiment was to test whether providing information about gender inequality
in Wikipedia changes editing behavior, specifically, whether the information provided to thetreatment group (1) increases the likelihood of editing Wikipedia articles about women and(2) increases the likelihood that survey participants, especially women, edit Wikipedia in the
future.
It has been puzzling that women contribute less to Wikipedia than men. There are nolarge gender differences in the amount of free time.12 The gender gap in Internet usage issmall, and there are no large gender differences in online behaviors such as social networking,
online news consumption, communication, and e-commerce.13 Moreover, women contribute
10

Similarly, racial minority students have been shown to benefit from taking courses with racial minority
instructors (Fairlie et al., 2014).
11
The randomized survey experiment is registered at the AEA RCT Registry with the number AEARCTR0000500.
12
Aguiar and Hurst (2007) document that in the US, men and women allocate about equal time to leisure.
13
Of US adults, 87 percent of men and 86 of women used the Internet in 2014 according to the Pew
Research Center (“The Web at 25.” 2014, Pew Research Center, February 28. http://www.pewinternet.

3

no less than men to another example of online public good provision, writing user reviews for
products and services.14 Based on the survey, I find that almost half of the gender gap inhaving edited Wikipedia is explained by gender differences in two characteristics: frequency
of Wikipedia use and belief about one’s competence.
The fact that most contributors are men could lead to biased coverage in Wikipedia. I
look at the articles about human beings. In the survey experiment, I find that women arealmost three times as likely as men to contribute to Wikipedia articles about women. I
analyze whether the distribution of editorial input reflects demand for the articles among
readers. However, I find no evidence that articles about women have lower demand thanthose about men.
What can be done about gender inequality in Wikipedia? From a randomized survey
experiment, I find that providing information about gender inequality in Wikipedia almost
doubles the likelihood of contributing to articles about women. However, the treatment ofproviding information decreases men’s intention to contribute to Wikipedia and has no effecton women. That is, the treatment leads editors to redirect their editorial input and equalizes
intended participation among men and women but decreases overall intended participationin Wikipedia editing. Because there are other ways to increase overall contributions to
Wikipedia, the informational treatment can still provide a useful way to decrease genderinequality.
The paper contributes to a number of different areas of research including media bias,
user-generated content, and gender differences. Literature in media economics has studied
the sources of media and editorial bias.15 In the case of traditional media, for example,
Enikolopov et al. (2011) and Durante and Knight (2012) have provided evidence how certain
news outlets give more media coverage to their favored politicians. In this paper, I findthat the coverage of public figures in Wikipedia also depends on editors’ characteristics.
org/2014/02/25/the-web-at-25-in-the-u-s). In the UK in 2014, 82 percent of men and 81 percent of
women between 16 and 74 years old used the Internet daily according to Eurostat. In the same sample in theUK, more women participated in online social networks (62 percent of women versus 58 of men), and moremen read online news (64 percent of men versus 55 of women). In other activities, such as sending emails,
making online purchases, and doing online banking, the differences are even smaller. (“Information Society
Statistics,” Eurostat. http://ec.europa.eu/eurostat/web/information-society/data/database.)
14
Chen et al. (2010) found that women contribute more to online movie ratings. Surveys by Pew ResearchCenter found no gender difference in writing user reviews (“Health Online 2013,” 2013, Pew Research Center. http://www.pewinternet.org/files/old-media//Files/Reports/PIP_HealthOnline.pdf; “Online
Product Research,” 2010, Pew Research Center. http://www.pewinternet.org/files/old-media//Files/
Reports/2010/PIP%20Online%20Product%20Research%20final.pdf).
15
When analyzing the sources of media bias, the literature has concentrated mainly on traditional media,
studying demand-side factors (e.g. Gentzkow and Shapiro (2010); Sen and Yildirim (2015)), the role ofadvertising (e.g. Di Tella and Franceschelli (2011); Sun and Zhu (2013); Beattie et al. (2017)), and media
capture, where government and other special interest groups try to control the content of media (for overviews
see Enikolopov and Petrova (2015); Strömberg (2015)).

4

This is somewhat surprising, considering that it should be more difficult to gain control
over Wikipedia than over a single news outlet as anyone is free to edit Wikipedia. Bias in
Wikipedia has been studied in the political articles by Greenstein and Zhu (2012, 2014).
The current paper is the first to comprehensively analyze the sources of gender inequality incoverage in Wikipedia.
There is an emerging small branch of literature in economics that studies contributions to
new media and user-generated content (for an overview, see Luca (2015)). The current paper
is most closely related to the studies on contributions to Wikipedia. Most notably, Zhang
and Zhu (2011) studied the relationship between contributions to Wikipedia and group size.
Aaltonen and Seiler (2015) studied the impact of the cumulative growth of content on newcontributions. Algan et al. (2013) conducted online experiments with current Wikipedia
editors to test whether pro-social motives explain their contributions to Wikipedia, and Gallus(2017), using a field experiment, estimated the impact of symbolic rewards on Wikipedia
editor retention. None of these papers examined the role of gender in contributions, which isthe subject of this paper.16
Finally, the paper is also related to the literature on gender differences in self-assessment,
contributions, and competition.17 Gender differences in self-assessment have been related
to the gender differences in various outcomes, including that women are less likely to give
advice in strategic settings (Cooper and Kagel, 2016) and contribute their ideas in group-worksettings (Coffman, 2014). The results presented in this paper are consistent with the findingsof Coffman (2014), who shows that a big part of the gender differences in contributing
ideas is driven by the gender differences in beliefs about competence. Editing Wikipedia
resembles a competition where losers’ contributions are deleted by fellow editors, while
winners’ contributions remain. The literature on competition has shown that women are lesslikely to choose to compete (Niederle and Vesterlund, 2007) and work in competitive settings(Flory et al., 2015).18 The current paper provides evidence from another competitive setting
where women are less likely to choose to participate.
16

In the case of open-source software and related Q&A communities, studies have shown that majority ofcontributors are men (David and Shapiro, 2008; Vasilescu et al., 2014), women’s contributions are valued less
(Bohren et al., 2018; Terrell et al., 2017), and women self-promote their skills less (Murciano-Goroff, 2018).
17
For overviews see Croson and Gneezy (2009); Niederle and Vesterlund (2011); Niederle (2017). Amongother things the literature has found that men are more (over-)confident than women and are more likely toover-estimate their probability of success at a given task (see, for example, Beyer (1990); Beyer and Bowden(1997)). The gender differences in self-assessment have been associated with the tendency of women to
attribute success more externally, taking less credit for their performance (see, for example, Feather (1969);
Deaux and Farris (1977); Beyer (1998)).
18
The literature has also shown that women perform worse than men in competitive environments evenwhen they perform similarly in non-competitive environments (Gneezy et al., 2003). The gender difference inthe willingness to compete is reversed in matrilineal societies (Gneezy et al., 2009) and no gender difference
is found when competing against one’s own previous performance (Apicella et al., 2017).

5

The remainder of the paper is organized as follows. Section 2 describes Wikipedia’s editing
process and presents basic empirical facts about gender inequality in Wikipedia. Section 3
describes the survey and the randomized survey experiment. Section 4 presents empiricalresults that answer three questions. First, why are women less likely to edit Wikipedia
compared to men? Second, how does the gender gap in participation affect biographies on
Wikipedia? Third, how does the treatment of providing information affect gender inequality
in Wikipedia? Section 5 concludes.

2

Background and basic empirical facts

2.1

Wikipedia’s editing process

Wikipedia is a free-access Internet encyclopedia. As of November 2017, it contains about 47
million articles, including 5.5 million articles in English-language Wikipedia.19 Wikipedia is
owned by the Wikimedia Foundation, a non-profit foundation. It is written by volunteers.
Anyone can create Wikipedia articles and edit almost any of its existing articles. There
are two types of editors: anonymous and registered. Registration is not required for mostediting activities. However, to create new articles or to upload images, editors have to register.
For each article, Wikipedia records who edited the article, when, and how. In the case ofanonymous editors, their computers’ IP addresses are recorded.
Wikipedia’s editing community is large. According to Wikimedia Statistics,20 as of
September 2017, about 6.7 million registered editors had contributed to the English-language
edition of Wikipedia. Most editors made only a few edits, but about 1.2 million editors hadedited at least 10 times.
Most editors don’t receive monetary rewards or derive career-related benefits from contributing to Wikipedia (Algan et al., 2013; Gallus, 2017).21 This is in contrast to open-source
software, where developers could be motivated by career concerns (Lerner and Tirole, 2002).
Most Wikipedia editors are anonymous or use pseudonyms and each article is a work productof many editors, which makes it difficult for the editors to use their experience on Wikipedia
to directly enhance their career prospects. Typically, there is also no personal direct benefit
from having better quality Wikipedia articles. Again, this is in contrast to open-source
software developers benefiting from improving their own software (Hertel et al., 2003).
19

https://meta.wikimedia.org/wiki/List_of_Wikipedias, accessed November 15, 2017.
http://stats.wikimedia.org/EN/TablesWikipediaEN.htm, accessed November 15, 2017.
21
The exception is paid editing, which is generally discouraged by Wikipedia; since 2014 all contributors
are required by the Wikipedia terms of use to disclose any paid editing. Source: https://en.wikipedia.
org/wiki/Conflict-of-interest_editing_on_Wikipedia.
20

6

2.2

Basic empirical facts about gender inequality in Wikipedia

In this section, I describe basic empirical facts about gender inequality in Wikipedia, which
provide a broad motivation for the following analysis.
There exists evidence that the majority of Wikipedia editors are men. The main sources
about the characteristics of Wikipedia editors are the surveys conducted by or in close collaboration with the Wikimedia Foundation. For example, in the 2018 Wikipedia Community
Engagement Insights survey, 85% of English Wikipedia editors who reported their gender
identified as male.22 In earlier surveys, also more than 80 percent of the editors were men.23
Similar evidence has been found in other studies involving Wikipedia editors. For example,
in an experimental study conducted among Wikipedia editors, 90% of the sample were men(Algan et al., 2013). In a survey conducted among top editors of Wikipedia medical articles,
82% of the respondents were men (Heilman and West, 2015). However, less is known about
the differences in the male and female editors’ editing behavior.
Here, I report basic empirical facts about the editing behavior of registered editors ofEnglish language Wikipedia who have reported their gender when registering their Wikipedia
username. The caveat of the dataset is that this is a selected sample of editors who voluntarily
report their gender. We might be worried that especially women, being afraid of hostility or
discrimination, are less likely to publicly reveal their gender. On the other hand, the dataset
provides the most detailed information about editors’ behavior. I describe their editing
behavior of articles about human beings. Concentrating on biographies provides a clear way
to study the differences in the editorial input, by analyzing which editors edit articles about
men or women. Details of the data are provided in appendix A.1.
Figure 1 presents the average percentage of contributions to biographies of women vsmen by the gender of Wikipedia editor. The contributions are measured in the number of
biographies edited. Figure 1 shows that female editors (compared to male editors) direct
relatively more (31% compared to 17%) of their editorial input towards biographies of women.
The difference is significant according to t-test and non-parametric Wilcoxon rank-sum test,
both with p-values less than 0.001. Additional analysis in appendix A.2 shows similar resultswith alternative outcome measures and alternative samples.
The above shows that in this sample, editors tend to contribute to different articles. This
22

Community Engagement Insights 2018 Report, https://meta.wikimedia.org/wiki/Community_
Engagement_Insights/2018_Report/Contributors.
23
Wikimedia Foundation. 2011. “Editor Survey,” April. http://upload.wikimedia.org/wikipedia/
commons/7/76/Editor_Survey_Report_-_April_2011.pdf; Wikimedia Foundation. 2011. “Second
2011 Wikipedia Editor Survey,” December. https://upload.wikimedia.org/wikipedia/commons/8/
84/December_2011_Wikipedia_Editor_Survey_topline.pdf; Glott, Ruediger; Schmidt, Philipp; Ghosh,Rishab. 2010. “Wikipedia Survey: Overview Results”, March. http://web.archive.org/web/20100414165445/http://wikipediasurvey.org/docs/Wikipedia_Overview_15March2010-FINAL.pdf.

7

Editors' contributions to biographies
80

83

40

Biographies of women
Biographies of men

31
17

0

20

Percent

60

69

Male editors

Female editors

Figure 1: Editors’ contributions to biographies of women vs men, by gender of the editor
Note: The average (over editors) percentage of contributions to the biographies of women vs men,by the gender of Wikipedia editor. Contributions are measured in the number of biographies edited.
Calculated based on registered editors who have reported their gender and edited biographies. There
are 65,398 such male and 9,419 such female editors. The difference is significant according to t-test
and non-parametric Wilcoxon rank-sum test, both with the p-value less than 0.001.

raises the question whose contributions are more likely to correspond to readers’ demand. Is
the allocation or editorial input proportional to the readership? To answer this, let’s look at
the Wikipedia viewership (see appendix A.3 for further details). Each day, there is a large
share of Wikipedia articles that noone reads. On a typical day, the percentage of biographiesof men that noone reads is 26% which is larger than that of women (only 16%). Over alonger time period, almost all articles have readers.
Figure 2 describes the distribution of biographies by the average number of views per day(for details of the calculations see appendix A.3). It shows that biographies of men receive
fewer views than those of women. For example, 23% of biographies of men and only 13% ofbiographies of women have on average less than one view per day. On the other end of the
distribution, only 2% of biographies of men and 5% of those of women receive 100 or moreviews per day. Appendix A.3 provides further evidence showing that biographies of women
receive relatively more views compared to those of men.

3

Survey and experiment

The survey together with the randomized survey experiment was administered in September
2014. The survey had the following structure: (1) background socioeconomic questions
8

Biographies by the average number of views per day
61

23

21

20

Percent

40

60

61

13

13
5

0

2
<1

1 - 9.9

10 - 99.9 100 and more

Biographies of men<1

1 - 9.9

10 - 99.9 100 and more

Biographies of women

Figure 2: Biographies by the average number of views per day
Note: Calculated based on page views in September 2014 of 784,178 biographies of men and 140,193
biographies of women in the English language Wikipedia. Details of the calculations are in appendix
A.3.

including gender, age, education, work status, and children; (2) questions about previous
Wikipedia use and editing; (3) randomized treatment providing information about genderinequality in Wikipedia shown only to the treatment group; the control group was shown
information about Wikipedia that was not related to gender; (4) hypothetical situation where
one is asked to edit Wikipedia; specifically, respondents were asked to choose a Wikipedia
page of a person and find some information from the Internet that is missing from the
Wikipedia page; note that the respondents were not asked to actually edit Wikipedia; (5)questions about Wikipedia editing, including respondents’ beliefs about their competence
and the likelihood of editing Wikipedia in the future; and (6) questions about charitablegiving, volunteering, writing user reviews, and free time.
In conducting the randomized survey experiment, I follow the approach taken by Kuziemko
et al. (2015), who studied how information about inequality changes reported preferences forredistribution in a similar research design on Amazon Mechanical Turk. Amazon Mechanical
Turk has recently gained popularity as a platform for experiments in economics (for otherexamples, see Horton et al., 2011). Methodological papers have evaluated the platform’s
suitability for surveys and experiments (see, for example, Mason and Suri, 2012), and I
followed their advice.

9

3.1

Randomized survey experiment

The goal of the experiment was to test whether providing information about gender inequality
in Wikipedia changes editing behavior. Individuals were randomly assigned to either a treatment or a control group with equal probabilities. The information provided to the treatmentgroup is a quote from the Wikipedia page, “Gender Bias on Wikipedia”24 : “Wikipedia
has been criticized by some academics and journalists for having only 9% to 13% female
contributors and for having fewer and less extensive articles about women or topics important
to women.” The control group read the following information about Wikipedia, which has
nothing to do with gender bias: “Wikipedia started in 2001. English-language Wikipedia hasover 4.5 million articles.”After reading the information, respondents were asked to imagine a hypothetical situationin which they edit a person’s Wikipedia page. Respondents were asked to look at Wikipedia
articles and find some relevant information from the web that is missing from a Wikipedia
article. Note that respondents were never asked to actually edit Wikipedia. Respondents
were only asked to report the Wikipedia page they would choose and the information theywould hypothetically add to the page. Specifically, the respondents were asked the following
questions: (1) “Which human being would you choose? Please write here the name of thehuman being.” (2) “Please provide a link to the Wikipedia page of the human being, the pagethat you would choose to edit. Please make sure that it is a valid link to the page of the humanbeing on English language Wikipedia; it must start with: http://en.wikipedia.org/wiki/”(3) “Please write here the text that you would add to the page.” (For the screenshot of the
instructions and questions, see figure B.1 in online appendix B.) In the end, they were also
asked how likely they are to edit Wikipedia in the future.
Limitations. Survey experiments are commonly used in various fields in economics tostudy the impact on stated preferences, willingness to pay, and intentions. But the method
raises a concern that the actual behavior might be different from reported intentions. To
alleviate the concern, I design the survey experiment to resemble the Wikipedia editing
process. Answering the survey question about which page you would edit requires much more
effort than just picking a name. A respondent has to go to Wikipedia, choose an article,
search on the web for relevant information missing from the article, and report that in thesurvey. The instructions only stopped short of asking to actually add the information to the
Wikipedia article, and this should be the least time-consuming part. Therefore, the data on
the reported choices of pages which to edit can be considered reasonably reliable. However,24

The Wikipedia page, “Gender Bias on Wikipedia”, was from August 24, 2014: http://en.wikipedia.
org/w/index.php?title=Gender_bias_on_Wikipedia&oldid=622670577

10

the data on the survey respondents’ intentions to edit Wikipedia in the future is only about
their reported intentions and might not correspond to their actual behavior.

3.2

Data collection

The survey was posted on Amazon Mechanical Turk. Amazon Mechanical Turk is an Internet
marketplace, where workers perform tasks, including answering surveys and participating inexperiments. The survey was described as a 20-minute research study on Wikipedia, whichpays the participant $1.50. The payment gives an effective hourly wage close to the average
effective hourly wage on Amazon Mechanical Turk according to Kuziemko et al. (2015).
To ensure high-quality responses, several measures were taken based on advice fromprevious studies that used Amazon Mechanical Turk. First, only workers with a good trackrecord were allowed to take the survey. The exact qualification criteria were that a worker
must have completed and approved at least 50 tasks, and his or her approval rate must be
95 percent or higher. Second, only workers from the US were eligible to take the survey. I
chose to limit the set of possible respondents to those in somewhat similar environments
because the survey asked about volunteering and writing user reviews. The location waschecked and restricted by Amazon itself. According to computer IP addresses of the workers
who completed the survey, a very small percentage came from locations outside of the US.
Third, respondents were told that payment would be contingent on completing the survey
and providing a survey code visible only after finishing. The respondents were not allowed
to skip any questions. Fourth, respondents were told that they can start the survey only
once. The main concern was that they reach the treatment page multiple times and see
both the treatment group’s and the control group’s information. One respondent reached thetreatment page twice, but by chance, he received the same treatment both times, so I still
include his responses in the sample.
The survey stayed open until 1,000 workers reported that they had completed it. Survey
attrition is analyzed in appendix C. Of the 1,000 Amazon Mechanical Turk workers, 28 did
not provide a valid survey response: four workers could not be matched to any survey response(because they either had not started the survey or did not provide enough information tomatch to the survey response), three started but did not complete the survey, and 21 did
not provide a Wikipedia biography of a human being (because they either misunderstoodthe question or ignored the instructions, for example, chose a fictional character instead of ahuman being). After excluding these respondents, 972 remained.
Quality of the data about the choices of pages which to edit. The main response
variables about Wikipedia editing are hypothetical, which raises a concern about the reliability11

of the data. As discussed in subsection 3.1, the questions that guide through the Wikipedia
editing process are designed to be detailed enough, requiring some effort, to make the data
about the page choices reasonably reliable. In the following, let’s assess the quality of theanswers.
In the hypothetical editing situation, respondents were essentially asked to do two things:(1) to look up and provide a Wikipedia biography of their choice, and (2) to provide somerelevant information that is missing from the page. It is clearcut how to evaluate whether ananswer was a biography in Wikipedia. Indeed, by the construction of the main sample, all of
the 972 respondents provided a biography of a human being.
The evaluation of the quality of the second part of the answer is not straightforward. To
get some idea of whether the respondents followed instructions and provided information,
the responses are categorized to added information or not. Note that this classification is
subjective. Let me illustrate it. Here are some examples of answers that were categorized as“not adding information”: about Albert Einstein, “his hair was unconventionally eccentric”;
Grace Hopper “is an inspiration to male and female computer scientists alike”; about J. R.
R. Tolkien, “one of the best linguists of the 20th century”. Answers that were too general or
ambiguous were also categorized as “not adding information”, for example, about Sojourner
Truth, “something more about her family”; about Persius, “I would shorten the scope ofwhat is accredited to him”; about Nicola Tesla, “I would expand the section about his role inthe development of electricity”. Most of the answers were classified as “adding information”.
These provided some specific facts, often about personal life, family, and relatives, or details
about their work and careers. For example, Abraham Lincoln “created the Secret Service
hours before his assassination”; Albert Einstein “had a pet parrot named Bibo”. As said
above, the classification is subjective and some of the answers categorized as “not providing
information” could nevertheless improve Wikipedia.
According to this classification, about 85 percent of the responses provided some information. While the percentage of good responses is rather high, it is subject to the caveat thatthe decision criteria to evaluate the quality is subjective. Therefore, all the following analysis
in the main part of the paper will use the sample of all the 972 respondents that provided abiography in Wikipedia. However, appendix D repeats the analysis restricting the sample tothese 85 percent of the responses categorized as providing information and the results arevery similar to the ones from the main sample.

12

3.3

Gender and other characteristics

Table 1 presents characteristics of the 972 respondents who completed the survey and compares
their characteristics to those from other surveys about Internet users, Wikipedia users, andWikipedia editors. Column 1 presents demographic characteristics of the survey participants:
49.5 percent of the sample is female, 53.9 percent has at least a college degree, 57.7 percent
is employed full-time (works at least 35 hours per week), and 9.9 percent is unemployed.
Although the population on Amazon Mechanical Turk excludes individuals under 18-years-old,the sample is rather young, only 35.5 percent is at least 35 years old. Altogether, the sample
is similar to other studies using Amazon Mechanical Turk, including Kuziemko et al. (2015).
Table 1: Survey respondents’ characteristics and comparison with other surveys
Current survey
All
Only
respondents editors

FemaleAt least 35 years old
College degree
Employed full-time
Unemployed
Use Wikipedia daily
Use Wikipedia at least weekly
Has edited Wikipedia
Observations

(1)
0.495
0.355
0.539
0.577
0.099
0.277
0.811
0.219
972

(2)
0.315
0.286
0.596
0.592
0.085
0.460
0.906
1.000
213

Comparison surveys
Internet Wikipedia Wikipedia
users
users
editors
Pew 2010 Pew 2010 WF 2011
(3)
(4)
(5)
0.514
0.485
0.135
0.628
0.536
0.452
0.344
0.445
0.700
0.513
0.539
0.452

1756

437

984

Note: Each cell presents the mean value of a binary characteristic. Columns 1 and 2 presentcharacteristics of the survey in the paper. In column 1, the sample includes all valid survey
responses. In column 2, the sample is restricted to those who have edited Wikipedia in the past. For
comparison, columns 3–5 present results from other surveys: in columns 3–4, Pew Research Center’sInternet and American Life Project 2010 survey, where Pew survey provided weights are used tocorrect for the response bias; in column 5, Wikimedia Foundation’s Wikipedia Editors Survey 2011,
where the sample is restricted to editors from the U.S.

Column 1 of table 1 also shows that 21.9 percent of the survey sample has edited Wikipedia
previously. How close is this percentage to the share of Wikipedia editors in a random sampleof Internet users? It might be not too far off because a back-of-the-envelope calculationsuggests that in 2010 17 percent of Internet users had edited Wikipedia. The back-of-theenvelope calculation is subject to caveats, because it combines information from two different

13

surveys, according to which in 2010: 53 percent of U.S. Internet users used Wikipedia25 and33 percent of Wikipedia users had edited Wikipedia.26
Column 2 of table 1 restricts the sample to those who have edited Wikipedia previously.
Of those who have edited, only 31.5 percent are women. Recall that the original sample
of survey respondents was gender-balanced (see column 1), but the editors’ sample is not.
Hence, the survey also provides evidence of gender imbalance among Wikipedia editors.
To understand how representative the current survey sample is of potential and actual
Wikipedia editors, table 1 also presents demographic characteristics of Internet users (column
3), Wikipedia users (column 4), and Wikipedia editors (column 5). The data on Internet
users and Wikipedia users is from Pew Research Center’s Internet and American Life Project,
May 2010 survey27 and the data on Wikipedia editors is from the Wikimedia Foundation’s
Wikipedia Editors Survey 2011,28 where the sample is restricted to editors from the U.S. Whileanyone can edit Wikipedia, Internet users and Wikipedia users are the most likely potential
editors. The sample in this survey (column 1) is similar to the Internet and Wikipedia users
in terms of gender and employment while being younger and more educated. Those whohave edited Wikipedia in the current survey (column 2) compared to Wikipedia editors in
the Wikimedia Foundation’s survey (column 5) are somewhat younger and more likely to befemale, somewhat less likely to have a college degree and less likely to be full-time employed.
Table 2 presents characteristics of men and women in the control group separately bygender. The treatment group is excluded here to make sure the responses are not affectedby the treatment, because some of the questions, specifically those that were not included
in table 1, were asked after treatment. The table also reports p-values from the t-test forwhether the difference between men and women is significantly different from zero.29 In termsof demographic characteristics, there are significant differences between men and women onlyin employment: 65 percent of men and 46 percent of women are employed full-time.
Table 2 shows that there are several significant differences between men and women interms of Wikipedia-related behaviors and attitudes. First, men are almost twice as likely tohave edited Wikipedia. The difference is statistically significant with the p-value of the t-test
25

Zickuhr, K. and Rainie, L. 2011. “Wikipedia, past and present”, Pew Research Center. http://www.
pewinternet.org/2011/01/13/wikipedia-past-and-present/, accessed January 23, 2019.
26
Glott, Ruediger; Schmidt, Philipp; Ghosh, Rishab. 2010. “Wikipedia Survey: Overview Results”, March. http://web.archive.org/web/20100414165445/http://wikipediasurvey.org/docs/
Wikipedia_Overview_15March2010-FINAL.pdf.
27
Pew Research Center’s Internet and American Life Project, May 2010 survey dataset is available from:http://www.pewinternet.org/dataset/may-2010-cell-phones/, accessed Jan 22, 2019.
28
Wikimedia Foundation’s Wikipedia Editors Survey 2011 dataset is available from: https://dumps.
wikimedia.org/other/surveys/editorsurvey2011/, accessed Jan 22, 2019.
29
Note that as all the reported characteristics are binary variables, the t-test and the corresponding
non-parametric Wilcoxon rank-sum test give the same results.

14

Table 2: Summary statistics of survey respondents in the control group, split by gender
Men Womenp-value
Demographic characteristics
At least 35 years old
0.320 0.382
0.154College degree
0.514 0.536
0.611
Works at least 35 hours per week
0.653 0.459
0.000
Unemployed
0.089 0.116
0.322
Wikipedia
Use Wikipedia daily
0.359 0.176
0.000
Use Wikipedia at least weekly
0.838 0.742
0.009
Has edited Wikipedia
0.313 0.163
0.000
Likely to edit Wikipedia
0.147 0.094
0.077
Believe: more competent than other editors
0.085 0.043
0.059
Believe: at least as competent as other editors 0.761 0.670
0.025
Other
Donated to charity
0.475 0.554
0.081
Volunteering at least one hour per week
0.332 0.365
0.447
Wrote user review in previous six months
0.517 0.545
0.540
Leisure at least 3 hours per weekday
0.741 0.734
0.852
Observations
259
233
Note: The sample is restricted to the control group. Columns 1 and 2 present the mean values ofthe survey respondents’ binary characteristics separately for men and women. Column 3 presents
p-values of the t-test for whether the difference between men and women is significantly different
from zero. Likely to edit Wikipedia is an indicator variable that takes value one if answered “verylikely” or “quite likely” to the question “How likely or unlikely is it that you will actually edit
Wikipedia over the next 30 days?” Believe: more competent than other editors is an indicatorvariable that takes value 1 if answered “more knowledgeable/competent” to the question “Think of
the Wikipedia article of the human being that you chose. On the whole, do you think that you are
more or less knowledgeable and competent to edit the article than other people who will edit it in
the future?” Believe: at least as competent as other editors is an indicator variable that takes value
1 if answered either “more knowledgeable/competent”, “somewhat more”, or “same” to the samequestion above.

less than 0.001. The difference is not as large as reported in other surveys (see subsection2.2), but it is still sizable. Second, men use Wikipedia more often — they are twice as likely
than women to use Wikipedia daily.30 Third, a larger share of women believe they are lesscompetent than other editors to edit Wikipedia. Fourth, a larger share of men intend to edit
Wikipedia in the future, however the gender difference is rather small, only five percentage
points. Note that there are no significant differences between men and women in several
other activities like in writing user reviews, in volunteering, and in the amount of free time.
30

Note that the frequency of Wikipedia use could depend on both the frequency of using encyclopedias
and preference for Wikipedia over other sources, such as Encyclopedia Britannica.

15

However, women are significantly more likely than men to give to charity.
Note that this survey is unlikely to be subject to the caveats of women being less likely torespond or less likely to reveal their gender. First, the participation in the survey is genderbalanced. Hence, there is no bias generated by possible non-response. Second, unlike the
Wikipedia editing data (subsection 2.2), where editors publicly report their gender and could
be afraid of hostility and discrimination, in this survey the participants are anonymous andthere is no reason for women to underreport Wikipedia editing. Moreover, in the responses,
while there are gender differences in having edited Wikipedia, there are no gender differences
in several other behaviors.

4

Empirical results

This section provides empirical results that answer three questions. First, why are womenless likely to contribute to Wikipedia? Second, what are the implications of the gender gap
in participation on the information available on Wikipedia? Third, how does the treatmentof providing information about gender inequality in Wikipedia affect editing behavior?

4.1

Why are women less likely to edit Wikipedia?

To answer the question of why women contribute less to Wikipedia, I use the survey data
restricting the sample to the control group in the survey experiment. I present two setsof results. First, I analyze which characteristics and behaviors are correlated with editing
Wikipedia. Second, I analyze how much of the gender difference in Wikipedia editing is
explained by the gender differences in these characteristics. I use two measures for Wikipedia
editing: (1) whether the respondent has edited Wikipedia in the past and (2) whether the
respondent reports that he or she is likely to edit Wikipedia in the future.
Table 3 presents results from logit regressions where the dependent variable, an indicatorfor editing Wikipedia, is regressed on gender and other characteristics. These include
demographic characteristics (indicators for being 35 or older, having a college degree, being
unemployed, and working at least 35 hours per week), behaviors and attitudes toward
Wikipedia (indicators for using Wikipedia daily, using Wikipedia at least weekly, and belief
about one’s competence compared to other Wikipedia editors), and other behaviors (indicatorsfor donating to charity in the past year, volunteering at least one hour per week in the past
year, writing a user review in the past six months, and having at least three hours of leisuretime per weekday).
In columns 1–2, the dependent variable is an indicator for whether the respondent has

16

Table 3: Estimated role of survey respondents’ characteristics in Wikipedia editing
Has edited Wikipedia
Likely to edit
Coef.
Marginal
Coef.
Marginal
estimates
effects
estimates effects
(1)
(2)
(3)
(4)
Female-0.669*** -0.106***-0.148-0.014(0.245)(0.038)(0.317)(0.030)At least 35 years old-0.104-0.017-0.184-0.017(0.251)(0.040)(0.322)(0.030)
College degree
0.429*
0.068*
0.310
0.029(0.238)(0.037)(0.310)(0.029)
Unemployed-0.055-0.0090.105
0.010(0.411)(0.065)(0.597)(0.056)
Works at least 35 hours per week
-0.345-0.0540.192
0.018(0.256)(0.040)(0.338)(0.032)Use Wikipedia daily
1.025*** 0.162*** 1.156*** 0.109***(0.254)(0.038)(0.331)(0.031)
Use Wikipedia at least weekly
0.475
0.075
0.063
0.006
(0.358)(0.056)(0.468)(0.044)Believe: more competent than other editors
0.782*
0.124*
0.414
0.039(0.415)(0.065)(0.479)(0.045)
Believe: at least as competent as other editors 0.743**
0.117** 1.946*** 0.184***(0.297)(0.046)(0.614)(0.059)Donated to charity
0.238
0.038
0.387
0.037
(0.253)(0.040)(0.336)(0.032)
Volunteering at least one hour per week
-0.146-0.0230.345
0.033(0.256)(0.040)(0.322)(0.030)
Wrote user review in previous six months
0.552**
0.087**
0.294
0.028(0.236)(0.037)(0.306)(0.029)
Leisure at least 3 hours per weekday-0.025-0.0040.094
0.009(0.260)(0.041)(0.338)(0.032)Log-likelihood-238.921-156.098
Observations
492
492
Note: Columns 1 and 3 present coefficient estimates from logit regressions and columns 2 and 4 thecorresponding marginal effects from the same regressions. In columns 1-2, the dependent variable is anindicator for whether the individual has edited Wikipedia in the past. In columns 3-4, the dependent variable
is whether the individual reports he or she is likely to edit Wikipedia in the next 30 days, specifically, thevariable takes value one if the respondent answered that he or she is either “very likely” or “quite likely”
to the question: “How likely or unlikely is it that you will actually edit Wikipedia over the next 30 days?”
Explanatory variables are described by table 2. The sample is restricted to the control group. Standard errors
are in parentheses. *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant
at the 10 percent level.

17

edited Wikipedia in the past. Column 1 presents coefficient estimates and column 2 thecorresponding marginal effects. Estimates show that those who use Wikipedia daily are 16
percentage points more likely to have edited Wikipedia. Those who have written a user
review in the past six months are 9 percentage points more likely to have edited Wikipedia.
Those with a stronger belief about one’s competence compared to other potential editors
are also more likely to have edited. Note that the gender difference in the belief about
competence could be due to women being less competent or due to women underestimating
their competence. The survey data does not allow these to be distinguished.
In columns 3–4, the dependent variable is an indicator for whether the respondent reports
he or she is likely to edit Wikipedia in the next 30 days. Specifically, the variable takes value
one if the respondent answered that he or she is either “very likely” or “quite likely” to thequestion: “How likely or unlikely is it that you will actually edit Wikipedia over the next
30 days?” The only characteristics that are statistically significantly associated with theintention to edit in the future are daily use of Wikipedia and belief about one’s competence.
Appendix B presents table B.2 with the same regressions estimated separately for men andwomen.
Let’s address the role of the editors’ gender in past and intended editing. The estimatedmarginal effects in column 2 in table 3 imply that women are 11 percentage points less likely
to have edited Wikipedia in the past even controlling for other factors. But according to theestimated marginal effects in column 4, gender doesn’t significantly affect the intention toedit in the future. This implies that the observed gender gap in the intention to edit (seetable 2) is explained by other characteristics. Note that the two editing variables differ not
only in terms of measuring past behavior vs future intent but also in terms of frequency ofediting. Specifically, the past editing measure is cumulative (“have you ever edited”), while
the future intent measures intent to edit in the next 30 days. As most editors make less thanten edits, a person who edits once a month is already a frequent Wikipedia editor. That
gender plays a different role in the case of these two editing measures is consistent with the
explanation that the two measures capture different parts of the distribution of editors (moreor less frequent editors) and that only in the case of less frequent editing, gender conditionalon other factors plays a significant role.
To learn how much of the gender difference in Wikipedia editing (both past and intended)is explained by gender and how much comes from differences in attitudes and behaviors
toward Wikipedia, I decompose the gender difference in editing Wikipedia using Oaxaca

18

(1973) decomposition, which was extended to non-linear equations by Fairlie (2005).31 For the
explanatory variables, I include indicators for daily Wikipedia use, at least weekly Wikipedia
use, and belief about one’s competence compared to other editors.
Table 4 presents the results from the non-linear decomposition. In the first column, thedependent variable is an indicator of whether the respondent has edited Wikipedia in the past.
The difference in the fractions of men and women having edited in the past is 15.0 percentage
points. Gender differences in the frequency of Wikipedia use and the belief about one’s
competence explain a large share, namely 43 percent of the gender gap in past editing. In thesecond column, the dependent variable is whether the individual is likely to edit Wikipedia
in the next 30 days. The gender difference in the intention to edit is 5.2 percentage points.
Gender differences in the same characteristics explain 79 percent of the gender gap in theintention to edit Wikipedia in the future.

4.2

The effects of the gender gap on Wikipedia content

In this subsection, I study whether the gender gap among editors could lead to gender inequality in the biographical information available on Wikipedia. Recall that in the hypotheticalsurvey experiment, each survey respondent picked a biography to edit. I test whether womenare more likely than men to choose to edit biographies of women. In the analysis in this part,the sample is restricted to the control group.
Table 5 presents the share of respondents, separately by gender, who chose to edit a
biography of a woman. The table also presents p-values from the t-test of whether thedifference between the choices of men and women is significantly different from zero. The
first row shows that 31 percent of female respondents chose to edit a biography of a woman,and only 11 percent of male respondents did that. The difference is statistically significant
with the p-value from the t-test less than 0.001. Note that 31 percent of female respondents
31

The decomposition for the non-linear equation Y = F (X β̂) can be written as
 M

W
N
X F (X M β̂ P ) N
X F (X W β̂ P )iiȲ M − Ȳ W = 
−
M
W
N
N
i=1
i=1
 M
  W

M
W
N
N
X F (X M β̂ M ) N
X F (X M β̂ P )
X F (X W β̂ W ) N
X F (X W β̂ P )
i
i
i
i
+

+
−
−
M
M
W
W
N
N
N
N
i=1
i=1
i=1
i=1

where Ȳ j is the average probability of the binary outcome variable Y by gender j = M, W , N j is the sample
size by gender, F is the cumulative distribution function of the logistic distribution, Xij is a row vector of
explanatory variables of individual i of gender j, β̂ j is a vector of coefficient estimates for gender j, and β̂ P isa vector of coefficient estimates from a pooled sample of both genders. The term in the first brackets captures
the part of the gender gap in editing that is explained by the gender differences in explanatory variables.

19

Table 4: Decomposition of gender gap in Wikipedia editing
Dependent variable:
Has edited Wikipedia Likely to edit(1)
(2)
Total gap
0.14970.0523
Explained gap
0.06420.0412
Explained percentage
42.9
78.8
Observations
492
492
Note: Each column presents a separate decomposition. In column 1, the dependent variable isan indicator for having edited Wikipedia in the past. In column 2, the dependent variable is anindicator for whether the individual reports he or she is likely to edit Wikipedia in the next 30 days,
defined in the same way as in table 2. There are four explanatory variables: an indicator for daily
Wikipedia use, an indicator for at least weekly Wikipedia use, Believe: more competent than othereditors, and Believe: at least as competent as other editors, defined in the same way as in table 3.
In the rows, Total gap is the difference between the average outcome variable of men and women;
Explained gap is the difference between the outcome variable of men and women that is explainedby the differences in these explanatory variables; Explained percentage is the percentage of the Total
gap that is explained by the differences in these explanatory variables. The sample consists of thesurvey respondents in the control group.

choosing to edit a biography of a woman is not a small percentage, because they had to choose
from the existing biographies in Wikipedia and only 15.2 percent of those are biographies ofwomen. Rows 2 - 7 of table 5 present the statistic by survey respondents’ education, age,
and whether they have edited Wikipedia in the past. In all the groups, female respondents
are more likely to edit biographies of women. The last rows of the table restrict attention to
subgroups of biographies (as defined in table B.1): first, lawyers, politicians, scientists, etc.
(group Professionals); and second, writers, painters, composers, etc. (group Culture). A large
gender difference remains in both groups.
Next, I test whether gender has a statistically significant impact on the likelihood ofchoosing to edit biographies of women versus biographies of men, even when controlling forother characteristics of the respondent. Table 6 presents estimates from a logit regression
where the dependent variable is an indicator for whether the respondent chose to edit anarticle about a woman. The explanatory variables included are an indicator for being atleast 35 years old, having a college degree, being unemployed, working at least 35 hours per
week, using Wikipedia daily, using Wikipedia at least weekly, and having edited Wikipedia in
the past. Column 1 presents coefficient estimates and column 2 the corresponding marginal
effects. The results show that women were 21 percentage points more likely than men to editan article about a woman versus a man; no other included characteristic has a significant
impact.
As in subsection 2.2, we could ask whether the allocation of editorial input of the survey
20

Table 5: Likelihood of choosing to edit a Wikipedia article about a woman vs a man, by thegender of survey respondent
Chose an article about a woman
t-test Obs.
Male respondents Female respondents p-value
All respondents in the control group
Total
0.112
0.305
0.000
492
Subsets of respondents by demographic characteristics
Has edited Wikipedia
0.148
0.395
0.003
119
Hasn’t edited Wikipedia
0.096
0.287
0.000
373
College
0.128
0.272
0.004
258
No college
0.095
0.343
0.000
234
Less than 35 years of age
0.084
0.270
0.001
172
At least 35 years of age
0.125
0.326
0.000
320
Subsets of respondents by type of Wikipedia articles
Biographies: Professionals
0.076
0.205
0.015
165
Biographies: Culture
0.123
0.279
0.016
159
Note: A unit of observation is a survey respondent. Each survey respondent chose a Wikipedia article
either about a man or a woman. Columns 1 and 2 present the mean of the indicator variable thatthe chosen article was about a woman, separately for male and female survey respondents. Column
3 presents p-values of the t-test for whether the difference between male and female respondents issignificantly different from zero. Column 4 presents the number of observations. In the first row, thesample consists of the survey respondents in the control group. In the following rows, the sample
is further restricted by the survey respondents’ demographic characteristics that are listed in theleftmost column. In the last two rows, the sample is restricted to the subgroups of biographies:group Professionals includes lawyers, politicians, scientists, etc; and group Culture includes writers,
painters, composers, etc; the exact definition of groups is in table B.1.

respondents corresponds to readers’ demand? Appendix E.1 shows that there is no evidence
that the biographies of women chosen by the survey respondents have lower viewership thanthose of men. There is evidence that images drive viewership in Wikipedia (Nagaraj, 2017).
Table E.2 in appendix E.1 shows that while pages with more images do indeed get moreviews, then even controlling for the number of images on the page, biographies of women still
get more views.

4.3

How to reduce gender inequality in Wikipedia?

There are many potential ways to reduce gender inequality in Wikipedia. I look at only oneof these, which is providing information. The main reason to concentrate on this one is that
it is cheap and simple to implement.
This section presents evidence from a randomized survey experiment on the impact
of providing information about gender inequality in Wikipedia. First, I look at how the21

Table 6: Estimated role of survey respondents’ characteristics on the likelihood of choosing
to edit a Wikipedia article about a woman vs a man
Chose an article about a woman
Coefficients
Marginal effects(1)(2)
Female1.368***
0.206***(0.261)(0.037)At least 35 years old-0.308-0.046(0.253)(0.038)
College degree-0.109-0.016(0.239)(0.036)
Unemployed-0.325-0.049(0.415)(0.062)Works at least 35 hours per week
-0.165-0.025(0.252)(0.038)Use Wikipedia daily
0.138
0.021
(0.292)(0.044)Use Wikipedia at least weekly
0.055
0.008
(0.305)(0.046)
Has edited Wikipedia
0.460
0.069
(0.282)(0.042)Log-likelihood-230.888
Observations
492
Note: Estimation results are from a logit regression. Column 1 presents coefficient estimates andcolumn 2 presents estimated marginal effects. Dependent variable is an indicator for whether the
respondent chose to edit an article about a woman. The explanatory variables are the characteristicsof the survey respondent. The sample is restricted to the control group. Standard errors are inparentheses. *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant
at the 10 percent level.

treatment affected respondents’ answers regarding their choice of which Wikipedia article toedit. Specifically, I’m interested in the gender of the person whose biography they chose toedit. Second, I look at whether the treatment affects answers about their intention to edit
Wikipedia in the future.
4.3.1

Choice of pages to edit

Table 7 presents the effect of the treatment on the answers about the choice of the Wikipedia
article respondents would edit. Column 1 shows that the treatment is associated with a
94 percent increase in the share of women’s biographies. Note that in the control group,
the share of women’s biographies is only 20 percent. With the treatment, it gets close to22

40 percent. The inclusion of demographic characteristics (column 2) decreases the scaled
treatment effect from 94 to 89 percent.
Table 7: The effect of treatment on the likelihood of choosing to edit a Wikipedia article
about a woman
Total
Male respondents Female respondents
(1)
(2)
(3)
(4)
(5)
(6)
Treated
0.190*** 0.180*** 0.129*** 0.129*** 0.232*** 0.234***(0.029)(0.028)(0.034)(0.034)(0.044)(0.044)
Covariates
No
Yes
No
Yes
No
Yes
Control group mean
0.203
0.203
0.112
0.112
0.305
0.305
Scaled treatment effect
0.937
0.885
1.156
1.154
0.760
0.767
Observations
972
972
491
491
481
481
Note: Each column presents estimates from a separate linear probability model. Dependent variable
is an indicator for whether the respondent chose to edit an article about a woman. Covariates include
demographic characteristics from table 2. The sample includes both the control and treatmentgroup of the survey respondents. The sample is restricted to men in columns 3-4 and women incolumns 5-6. Scaled treatment effect measures the percentage change of the share of women’s pages.
Standard errors are in parentheses. *** Significant at the 1 percent level. ** Significant at the 5
percent level. * Significant at the 10 percent level.

The scaled treatment effect is larger for men than women as shown in columns 3-6.
Column 4 shows that the treatment in the case of men is associated with a 115 percent
increase in the share of women’s biographies. Column 6 shows that the treatment in the caseof women is associated with a 77 percent increase in the share of women’s biographies. The
larger treatment effect for male respondents reflects the fact that the starting point for men
was lower. In the control group, only 11 percent of male respondents would edit biographies
of women compared to 31 percent of female respondents.
Overall, the treatment redirects editorial input and almost doubles the likelihood ofcontributing to an article about a woman. Appendix E.2 describes how the treatment affects
the allocation of editorial input in terms of readership. There is no evidence that the treatment
makes the allocation significantly worse or better in terms of readership.
4.3.2

Participation

Table 8 presents the effect of the treatment on the answers about the intention to edit
Wikipedia in the next 30 days. Column 1 shows that the treatment is associated witha 35 percent decrease in the intention to edit Wikipedia. When including demographic
characteristics (column 2), the decrease is 37 percent. That the treatment decreases theintention to edit in the future is somewhat unexpected. Columns 3-6 look at the effect23

of the treatment on men and women separately, and this helps to clarify the finding. We
see that the treatment is associated with a decrease in the intention to edit in the case ofmen (columns 3-4), and it has no effect on women (columns 5-6). Namely, column 3 showsthat the treatment in the case of men is associated with a 62 percent decrease in intended
editing. When including demographic characteristics (column 4), the decrease is 64 percent.
To analyze the robustness of the finding, I restrict the sample using three characteristics:
Wikipedia use, beliefs about competence, and writing user reviews. The results from therestricted samples are presented in table B.3 in appendix B and are similar to those from themain sample.
Table 8: The effect of treatment on the intention to edit Wikipedia in the future
Total
TreatedCovariates
Control group mean
Scaled treatment effectObservations(1)-0.043**(0.019)No
0.122-0.351972(2)-0.045**(0.019)Yes
0.122-0.367
972

Male respondents
Female respondents
(3)
(4)
(5)
(6)
-0.091*** -0.093*** 0.006
0.003(0.027)(0.027)(0.027)(0.027)
No
Yes
No
Yes
0.147
0.147
0.094
0.094
-0.618-0.636
0.068
0.028
491
491
481
481

Note: Each column presents estimates from a separate linear probability model. Dependent variable
is an indicator for whether the individual believes he or she is likely to edit Wikipedia in the next
30 days, defined in the same way as in table 2. Covariates include demographic characteristics
from table 2. The sample includes both the control and treatment group of the survey respondents.
The sample is restricted to men in columns 3-4 and women in columns 5-6. Scaled treatment effectmeasures the percentage change of the share of respondents who believe it is likely they will edit
Wikipedia. Standard errors are in parentheses. *** Significant at the 1 percent level. ** Significant
at the 5 percent level. * Significant at the 10 percent level.

The information that the majority of Wikipedia editors are men, leads men to reduce theirintended editing effort, but it does not change the behavior of women. The informational
treatment in this paper can be considered a moral reminder and also a peer informationintervention (information about what peers typically do). Note that it’s less costly for men
to react to the informational treatment than for women. Men can reduce the gender gap
among editors by editing less, and this is easy to do in terms of effort; but for women to
reduce gender inequality, the solution is more costly, they should edit more. That women
don’t report an increase in intended editing is in line with the literature that has found thatpeer information intervention moves behavior closer to the peer group average.
The treatment, emphasizing the importance of equality, was intended to motivate women
to contribute. But it also discouraged men from contributing. More generally, the results
24highlight a potential problem with a policy that motivates a minority to contribute while
discouraging the majority—the majority group is larger, and therefore, the overall effecton the quantity is likely to be negative. The implication is that policies which encouragecontributions from specific groups while potentially discouraging others, should be combinedwith those that increase overall contributions.

5

Conclusions

Using survey data and experimental evidence, I study the reasons why women are less likely
to contribute to Wikipedia, the implications of this gender gap, and what can be done about
it. The findings are important for a widely used reference source like Wikipedia and there
are wider implications for both traditional and new media.
I find that men and women tend to contribute to different articles. This implies thatthe gender gap among contributors leads to an unequal coverage of topics. Moreover, I
find that almost half of the gender difference in having edited Wikipedia is explained bygender differences in two characteristics: frequency of Wikipedia use and belief about one’s
competence. Combining the difference in Wikipedia use with the finding that men and womencontribute to different topics, it points toward possible equilibrium effects. If a media channel
covers fewer topics that women are interested in, then women use it less frequently and areless interested in contributing. On the other hand, those who contribute to Wikipedia may
over time start to use Wikipedia even more and grow more confident about their ability tocontribute.
The results of the paper suggest that providing information to the editors about genderinequality can alleviate the gender gap in contributions. The informational treatment changes
the allocation of editorial input. However it comes at the cost of decreasing the editorial inputof men. The result provides an example where encouraging gender equality can partially
backfire. Wikipedia has set a goal to increase the share of female editors. One way toachieve this is by discouraging male editors. However, this might not be desirable, especiallyconsidering the finding by Aaltonen and Seiler (2015) that past contributions tend to motivate
further contributions in Wikipedia. The implication for Wikipedia and other forms of media
is that it is important to balance the efforts of attracting new contributors and keeping thecurrent ones.
The paper highlights a trade-off between equality and quantity. The treatment generated
opposing effects on the benefits from Wikipedia to the readers. On one hand, increasing
equality of contributions is likely to be beneficial to the readers—readership statistics (insection 2) suggest that biographies of women have more readers than those of men and25

hence, re-directing editorial input is likely to be beneficial. On the other hand, decreasing
the quantity of contributions decreases Wikipedia’s benefit to the readers. The effects on thecosts of providing Wikipedia are even more complicated. For example, how do the payoffs
of Wikipedia editors depend on fellow editors and the readership of articles; do the editors
form correct expectations about the readership? Quantifying the costs and benefits provides
avenues for future research.
The paper analyzed gender differences in editing biographies. Literature on competition
has found that gender differences are eliminated when competing on behalf of others (Cassar
et al., 2016). Contributing to Wikipedia to alleviate the gender gap in biographies could
be interpreted as competing on behalf of others. If the result on competition extends
to Wikipedia editing, we would expect that in editing more neutral articles compared tobiographies, women might be even less likely to participate.
This paper has concentrated on gender, but more broadly it highlights the importanceof representing diverse opinions. Similar issues arise in the case of other demographic
characteristics. For example, it has been noted that information about black history is lacking
in Wikipedia and the Wikimedia Foundation is trying to engage new editors in order tochange that.32
The analysis in this paper faces limitations. First, using the survey experiment, insteadof actual choices, raises concerns. Although the survey experiment resembled Wikipedia
editing, participants were never asked to actually edit Wikipedia. It is reassuring that thequality of the answers is rather good (as described in subsection 3.2). Second, the survey
experiment used only one specific informational treatment. It remains for future research toanalyze whether other treatments are more effective in increasing women’s contributions to
Wikipedia.

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31

A

Online Appendix: Data overview and analysis supporting subsection 2.2

A.1

Dataset of Wikipedia editing histories

The dataset is constructed from editing histories of the English-language edition of Wikipedia.
To analyze editing by the gender of the editor, I restrict attention to the editors who havereported their gender when registering their Wikipedia username. I analyze their editing
behavior of articles about human beings. In particular, I’m interested in whether they edit
articles about men or women.
To learn whether a Wikipedia article is about a human being, I use information from
Wikidata, which is a database managed by the Wikimedia Foundation. From Wikidata, I also
learn the gender and year of birth of the human being. I include in the sample all the humanbeings in Wikidata as of July 2014 who were born in any year from 1000 to 2000 and who
were either male or female. There are almost one million such human beings. When there
was more than one birth year in the database, the earliest was taken (there were 1,445 suchhuman beings in the database). I exclude all human beings whose gender is not determined
to be either male or female in Wikidata (either gender is missing, it is not male or female,or the person has more than one gender in the database), there are 41 such cases. Afterexcluding those, we are left with the Wikidata sample of 924,371 human beings.
From the Wikidata database, I also obtain information about the professions of the humanbeings. Of the sample of human beings selected above, 490,023 have at least one profession in
the Wikidata database. I categorize all the professions that are held by at least 1,000 humanbeings in the sample, which is 88% of all the professions held by these people, into threegroups. The first group I call Professionals, this includes lawyers, politicians, and scientists
among others. The second group I call Culture, where I include writers, painters, composers,
etc. The third group called Athletes and entertainers consists mostly of athletes but also
includes actors and singers. The exact list is in Appendix B in Table B.1.
As of July 2014, 259,638 registered editors of English-language Wikipedia had reported
their gender when registering their Wikipedia username. Of those, 14.4 percent reported theirgender as female. The number of editors who reported their gender is not small; however, themajority of registered editors have not reported their gender. This is not surprising because
Wikipedia requires almost no information to register, no real name or even an email address.
The editors who report their gender tend to be more active, with a larger number of edits.
Note that for the analysis here it is not important that male and female editors report theirgender at equal rates. For example, it might be that women are less likely to report their32gender for fear of harassment. For this analysis that is not a problem. What is important isthat editors don’t systematically report the wrong gender. Since the default option is not
specifying one’s gender, I would not expect that they are massively reporting the wrong
gender.
In this appendix, I analyze the behavior of all the editors who reported their gender andwho have edited English-language Wikipedia articles about human beings in the Wikidata
sample. There are 74,817 such editors. They have edited 866,431 articles about the humanbeings in the Wikidata sample.
Summary statistics Table A.1 presents summary statistics of the Wikipedia editors. 12.6
percent of editors are women. Women contribute somewhat longer texts than men, both per
article and in total per year.
Table A.1: Summary statistics of Wikipedia editors, split by gender
Male editors
Female editors
Percentiles
25th 50th 75th 25th 50th 75th
Number of biographies edited, per year
1
2
4
1
1
3
Length of text added, per year22
169 1169 26
188 1247
Length of text added, per biography
13
80
294
16
98
474
Observations
65398
9419
Note: A unit of observation is an editor. Columns 1-3, present the 25th, 50th, and 75th percentile
for male editors, and columns 4 - 6, for female editors. Length of text added measures in charactersthe length of text added to biographies in Wikipedia. Sample of editors includes all the editors whoreported their gender and who have edited the English language Wikipedia articles about humanbeings in the Wikidata final sample.

Changes over time in the number of active editors and the share of active female editors
are depicted in Figure A.1. By active, I mean that the editor has edited at least one article
during a given year. In 2013, 10.4 percent of the active editors were female. The percentage
of female editors among active editors has increased from 3.7 percent in 2002 to a peak of
11.5 percent in 2011. Changes in the percentage of female editors mirrored changes in the
total number of active editors. The number of active editors increased monotonically from 81
in 2002 to 27,600 in 2011 and was 22,600 in 2013.

A.2

The effect of the gender gap on Wikipedia content: evidence
from Wikipedia editing histories

In this part, I simply document that Wikipedia female editors direct relatively more of
their editorial input to biographies of women. Table A.2 presents the average (over editors)
33

12

30

0

2

4
6
8
10
Percentage of female editors

Number of active editors, thousands
102002002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Year
Number of active editors

Perc. of female editors

Figure A.1: Number of active Wikipedia editors (left y-axes) and percentage of active female
editors (right y-axes), in 2002 - 2013
Note: The dataset is restricted to the registered editors of Wikipedia who have reported their genderand edited at least one article in a given calendar year.

percentage of contributions to biographies of women from contributions to all biographies.
An observation here is an editor. In column 2, the average percentage is calculated over maleeditors and, in column 3, over female editors. Contributions can be measured in multiple
ways. In rows 1-3, contributions are measured in the length of text added.
The first row of Table A.2 shows that the average percentage of text added to biographies
of women is 31% in the case of female editors and 17% in the case of male editors. To
make sure that the gender difference does not come only from celebrities like actors, athletes,and pop stars, rows 2 and 3 restrict attention to subgroups of biographies. In row 2, thesample includes lawyers, politicians, scientists, etc. (group Professionals); in row 3, it includes
writers, painters, composers, etc. (group Culture); the exact list of the groups is presented inAppendix B, Table B.1. A large gender difference remains in both groups. Rows 4-6 measurecontributions by the number of biographies edited. The measure gives similar results.
Figure A.2 presents the percentage of text added to biographies of women from text
added to all biographies, separately by male and female editors. Excluding the beginning of
Wikipedia when the number of editors was very small, the relative share of contributions
to biographies of women has increased, but the difference between male and female editors
remained large.
Figure A.3 presents the same statistics for the same subgroups of biographies as above.
The trends are similar to aggregate statistics.

34

Table A.2: The average (over editors) percentage of contributions to the biographies of womenvs men, by the gender of Wikipedia editor% biographies of women t-test Wilcoxon test Obs.
Men
Women
p-valuep-value
Total length of text added
16.6
31.4
0.000
0.000
66112
– Subset of biographies: Professionals 8.0
16.7
0.000
0.000
26459
– Subset of biographies: Culture
15.7
31.7
0.000
0.000
22665
Number of biographies edited
17.1
31.1
0.000
0.000
74817
– Subset of biographies: Professionals 8.3
15.9
0.000
0.000
30635
– Subset of biographies: Culture
15.7
31.5
0.000
0.000
26360

0

Percentage of biographies of women
10
20
30

Note: A unit of observation is a Wikipedia editor. In rows 1 - 3, the average percentage ofcontributions to the biographies of women is measured in terms of the total length of text added.
In Columns 1 and 2, present the means separately for male and female Wikipedia editors. Column
3, presents the p-value of the t-test for whether the difference between male and female editors is
significantly different from zero. Column 4, presents the p-value of the corresponding Wilcoxon
rank-sum test. Column 5 presents the number of observations used in each test. In rows 1 - 3, theaverage percentage of contributions to the biographies of women is measured in terms of the total
length of text added. In rows 4 - 6, it is measured in terms of the number of biographies edited.
Sample includes 65,398 male and 9,419 female editors. In rows 1–3, the sample is restricted toeditors who have added (instead of only deleted) text to biographies. In rows 2 and 5, the sample isrestricted to the biographies of lawyers, politicians, scientists, etc (Professionals). In rows 3 and 6,
the sample is restricted to the biographies of writers, painters, composers, etc (Culture). The exact
definition of these groups of professions is in Table B.1.

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Year
Male editors

Female editors

Figure A.2: Average (over Wikipedia editors) percentage of contributions to the biographies
of women vs men, in 2002 - 2013
Note: The percentage is calculated as the average (over Wikipedia editors) percentage of the total
length of text added to the biographies of women vs men.

35Percentage of biographies of women
0
10
20
30

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Year
Male editors; biographies: Professionals
Female editors; biographies: Professionals
Male editors; biographies: Culture
Female editors; biographies: Culture

Figure A.3: Average (over Wikipedia editors) percentage of contributions to the biographies
of women vs men, by gender of editor and profession of the person in the biography, in 2002 –2013
Note: The percentage is calculated as the average (over editors) percentage of the total length oftext added to the biographies of women in the total length added to all biographies. The sample ofMale editors; biographies: Professionals is restricted to male editors and a subgroup of biographiesthat includes lawyers, politicians, scientists, etc; the sample of biographies: Culture includes writers,
painters, composers, etc; the exact definition of the subgroups of biographies is in Table B.1.

A.3

Is the allocation of editorial input proportional to the readership?

This section analyzes whether the allocation of editorial input to the Wikipedia articles
about men and women is proportional to the readership. Each day, there is a large share of
Wikipedia articles that noone reads. I will start by restricting attention to those. Second, I
will look at all the biographies.
I use data on Wikipedia viewership statistics. The dataset includes for each Wikipedia
page the number of page views in September 2014 for more than 900,000 biographies in
Wikipedia..33 Page views are from a time period outside the editing histories in the sample.
On average, the number of page views is rather stable across months, and there is no reason
to believe that September 2014 is somehow different from other time periods.
First, I simply document that on a typical day, the percentage of biographies of men thatno one reads is larger than that of women. Table A.3 presents the daily statistics regarding
the percentage of biographies in Wikipedia that no one reads. On a typical (median) day in
September 2014, no one read 26 percent of the biographies of men versus only 16 percent of
the biographies of women.34 Note that over a longer period of time almost all the articles
33
34

The data on page views is downloaded from: http://dumps.wikimedia.org/other/pagecounts-raw/
The median is taken over the days, and it is calculated separately for the biographies of men and the36have readers. The above statistics could be interpreted that at the lower tail of the readership
distribution, biographies of men have fewer readers than biographies of women. We see thisalso when we look at the whole distribution of readership.
Table A.3: The daily percentage of biographies in Wikipedia that noone readMedian Min Max Obs
Biographies of men
25.8
18.6 28.4 30
Biographies of women
16.2
11.9 18.6 30
Note: A unit of observation is a day. The sample consists of daily data of page views from September
2014 for 784,178 biographies of men and 140,193 biographies of women in Wikipedia.

Figure A.4 presents the cumulative distribution of the number of page views of biographiesof men and women, where the page views are divided by the number of editors in the sample
who edited the page. As confirmed by the Kolmogorov-Smirnov test in Table A.4, biographies
of women receive more page views per editor.
1

Cumulative Probability

.8

.6

.4

.2

0
0

200
400
600
800
Number of page views divided by number of editors per biographyc.d.f.: biographies of men

1000

c.d.f.: biographies of women

Figure A.4: Cumulative distribution functions of the number of page views divided by the
number of editors per biography
Note: The sample includes page views data from September 2014 for 734,767 biographies of men
and 131,664 biographies of women in Wikipedia. The number of page views divided by the numberof editors is constructed based on the pages and editors in the sample of historical editing data from
Wikipedia. The sample excludes biographies that the editors in the sample did not edit.
biographies of women.

37

Table A.4: Kolmogorov-Smirnov test of whether the samples of the number of page views
divided by the number of editors of the pages of men and women are drawn from the same
distribution
Test statistic p-value Observations
One-sided hypothesis test
0.14140.000
866431
Two-sided hypothesis test
0.14140.000
866431
Note: The number of page views divided by the number of editors is constructed based on thepages and editors in the sample of historical editing data from Wikipedia. Page views data from
September 1, 2014. The null hypothesis is that the samples are drawn from the same distribution.
In row 1, the alternative hypothesis is that the c.d.f. of the pages of women is larger than the c.d.f.
of the pages of men. In row 2, the alternative hypothesis is that the samples are drawn from two
different distributions. The sample excludes biographies that the editors in the sample did not edit.

Appendix E.1 repeats the analysis for survey data, and the results are similar. Overall,this seems to imply that demand side cannot be the only reason for gender inequality in
Wikipedia coverage. Instead, gender inequality in coverage seems to originate, at least
partially, from the supply side of Wikipedia production.

38

B

Online Appendix: Additional tables and figures

Figure B.1: Survey instructions of Wikipedia hypothetical editing

39

Table B.1: Professions of human beings in Wikidata

Profession

Numberof people
football player
105871
actor
42096
politician
40084
sportsperson
23040
baseball player
20423
writer
15515
cricketer
15448
lawyer14812
painter
12746
ice hockey player
11751
composer11692
priest11242
film director
9794
basketball player
8561
singer8122
journalist
7619
poet
7273
screenwriter
6807
musician6325
diplomat
6214
Australian-rules footballer
6122
judge
5767
officer5509
mathematician
5352
linguist5229
photographer
4750
tennis player
4498
conductor
4428
boxer
4305
rugby league player4222
physicist
4197
film producer
4192
economist
3922
author3485
architect3425
anthropologist
3082
bicycle racer
3065
basketball coach3048
golfer2919
Table continues on next page
40

Category
Athletes & entertainers
Athletes & entertainers
Professionals
Athletes & entertainers
Athletes & entertainers
Arts & literature
Athletes & entertainers
Professionals
Arts & literature
Athletes & entertainers
Arts & literature
Professionals
Athletes & entertainers
Athletes & entertainers
Athletes & entertainers
Professionals
Arts & literature
Athletes & entertainers
Athletes & entertainers
Professionals
Athletes & entertainers
Professionals
Professionals
Professionals
Professionals
Arts & literature
Athletes & entertainers
Arts & literature
Athletes & entertainers
Athletes & entertainers
Professionals
Athletes & entertainers
Professionals
Arts & literature
Professionals
Professionals
Athletes & entertainers
Athletes & entertainers
Athletes & entertainers

Profession

Numberof people
explorer
2856
singer-songwriter
2841
chemist2651
botanist2585
film actor
2432
rugby union player
2374
astronomer
2271
television actor
2164
motorcycle racer
1961
television presenter
1924
sculptor
1915
historian1904
theologian
1672
model
1606
philosopher
1594
biologist
1580
computer scientist
1546
physician1515
pianist1457
field hockey player
1452
voice actor
1409
entrepreneur
1282
alpine skier
1281
swimmer1255
librarian1211
soldier1159
civil engineer
1075
badminton player
1064
Table continued from previous page

41

Category
Professionals
Athletes & entertainers
Professionals
Professionals
Athletes & entertainers
Athletes & entertainers
Professionals
Athletes & entertainers
Athletes & entertainers
Athletes & entertainers
Arts & literature
Professionals
Professionals
Athletes & entertainers
Professionals
Professionals
Professionals
Professionals
Arts & literature
Athletes & entertainers
Athletes & entertainers
Professionals
Athletes & entertainers
Athletes & entertainers
Professionals
Professionals
Professionals
Athletes & entertainers

Table B.2: Estimated role of survey respondent’s characteristics for Wikipedia editing, by
genderHas edited Wikipedia
Men
Women
(1)
(2)
At least 35 years old-0.0780.038(0.061)(0.048)
College degree
0.127**-0.001(0.055)(0.049)
Unemployed-0.0600.019(0.112)(0.072)Works at least 35 hours per week
-0.095-0.014(0.062)(0.049)Use Wikipedia daily
0.170*** 0.148***(0.055)(0.052)Use Wikipedia at least weekly
0.079
0.059
(0.091)(0.066)
Believe: more competent than other editors
0.146
0.109
(0.091)(0.095)
Believe: at least as competent as other editors
0.076
0.150**(0.071)(0.062)
Donated to charity
0.042
0.041
(0.061)(0.054)
Volunteering at least one hour per week
-0.022-0.030(0.063)(0.050)
Wrote user review in previous six months
0.089
0.098**(0.055)(0.049)
Leisure at least 3 hours per weekday
0.003-0.031(0.062)(0.054)Log-likelihood-144.593-90.465
Observations
259
233

Likely to edit
Men
Women
(3)
(4)
-0.0490.005(0.046) (0.039)
0.050
0.003
(0.043) (0.040)-0.0460.043(0.119) (0.060)-0.0440.066(0.047) (0.042)
0.127*** 0.080*(0.042) (0.044)
0.076-0.017(0.086) (0.049)
0.052-0.044(0.058) (0.089)
0.266** 0.122**(0.111) (0.061)
0.078
0.010
(0.048) (0.042)
0.008
0.047
(0.047) (0.039)
0.045
0.012
(0.043) (0.038)-0.0250.056(0.044) (0.048)-87.920 -63.547
259
233

Note: Each column presents estimated marginal effects from a separate logit regression. In columns 1 and 2,
the dependent variable is an indicator of whether the individual has edited Wikipedia in the past. In columns
3 and 4, the dependent variable is whether the individual is likely to edit Wikipedia in the next 30 days,
specifically, the variable takes value one if the respondent answered that he is either “very likely” or “quite
likely” to the question: “How likely or unlikely is it that you will actually edit Wikipedia over the next 30
days?” The sample in columns 1 and 3 is restricted to men in the control group; in columns 2 and 4, it’s
women in the control group. Believe: more competent than other editors is an indicator variable that takes
value 1 if answered “More knowledgeable/competent” to the question “Think of the Wikipedia article ofthe human being that you chose. On the whole, do you think that you are more or less knowledgeable andcompetent to edit the article than other people who will edit it in the future?” Believe: at least as competent
as other editors is an indicator variable that takes value 1 if answered either “More knowledgeable/competent”,“Somewhat more”, or “Same” to the same question above. Standard errors are in parentheses. *** Significant
at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent level.

42

Table B.3: Differences in the effect of treatment on the likelihood of editing Wikipedia: by
Wikipedia use, believed competence, and whether writes user reviews
Wikipedia use
Competence
Writing reviews
(1)
(2)
(3)
(4)
(5)
(6)
Treated-0.048** -0.049** -0.064** -0.066** -0.058** -0.058**(0.022)(0.022)(0.026)(0.026)(0.028)(0.029)Covariates
No
Yes
No
Yes
No
Yes
Control group mean
0.136
0.136
0.161
0.161
0.146
0.146
Scaled treatment effect -0.353-0.357-0.395-0.406-0.396-0.398Observations
788
788
691
691
511
511
Note: Each column presents estimates from a separate regression. Dependent variable is an indicator
for whether the individual believes he is likely to edit Wikipedia in the next 30 days, defined in
the same way as in Table 2. Covariates include demographic characteristics from Table 2. The
sample includes both the control and treatment group of the survey respondents. In columns 1-2,
the sample is restricted to those who use Wikipedia at least weekly. In columns 3-4 the sample isrestricted to those who believe they are at least as competent and knowledgeable as other people
who will edit Wikipedia in the future. In columns 5-6 the sample is restricted to those who wrote auser review during the past six months. Scaled treatment effect measures the percentage change ofthe share of respondents who believe it is likely they will edit Wikipedia. Standard errors are inparentheses. *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant
at the 10 percent level.

43

C

Online Appendix: Survey attrition

The survey had an overall attrition rate of 19%.35 This section tests whether attrition is
random or depends on demographic characteristics and treatment status.
98% of the individuals who dropped out, did that on the page of the survey that displayed
the task of choosing the Wikipedia page which to edit and finding information what to add tothe page. This is probably where the participants realized that the task is more difficult thanthey expected. Two percent of those who dropped out did that before seeing the treatment
page. None dropped out on the treatment page. Altogether, this is likely to indicate thattreatment itself was not a reason for dropping out.
Table C.1 shows that although attrition is not random, it doesn’t depend on treatment
status nor gender. However, as could be expected, people who use or edit Wikipedia, aremore likely to finish the survey. Table C.1 presents estimates from linear regressions36 of
the form: F inishedSurveyi = Constant + βV ariablei + εi , where the dependent variable
F inishedSurveyi is an indicator of whether person i finished the survey. Each row of thetable presents the estimate of coefficient β and corresponding p-value from a regression where
the V ariablei of the regression is listed in the first column. In each regression, the sample
includes only those respondents who have remained in the survey to answer the question; incase of treatment status, the sample includes those who have remained in the survey until
they were assigned the status. The first row of the table shows that gender has no statisticallysignificant impact on finishing the survey. The last row shows that treatment status has
no statistically significant impact either. The table also shows that attrition depends on
demographic characteristics and those who use Wikipedia more are more likely to finish thesurvey. Specifically, people who have at least a college degree are more likely to finish thesurvey; and those who are older or unemployed are less likely to finish the survey. People
who use Wikipedia at least weekly or who have edited it, are more likely to finish the survey.
To test it further that the attrition does not depend on treatment status, Table C.2 presents
estimated marginal effects from logit regressions, where the dependent variable is an indicator
for finishing the survey. It is regressed on the treatment status and demographic characteristics(in columns 1-2) and treatment status interacted with the demographic characteristics (incolumn 2). Treatment status has no significant effect on finishing the survey. According to
the specification in column 2, there are four characteristics that matter for attrition: those
who have edited Wikipedia before are 13 percentage points more likely to finish the survey,
35

When counting the number of people who dropped out, I exclude 2 responses that don’t have uniqueAmazon Mechanical Turk IDs. When calculating the attrition rate, I include only the 972 valid completed
responses and the uncompleted responses.
36
Estimates from analogous logit regressions are available from the author. The results are very similar.

44

Table C.1: Ability of covariates to predict whether respondents finish the survey
Coefficient p-value Observations
Female-0.0260.252
1203
At least 35 years old-0.0580.014
1203
Has college degree
0.045
0.050
1203
Works at least 35 hours per week
0.036
0.112
1203
Unemployed-0.097
0.007
1203
Use Wikipedia daily
0.026
0.307
1202
Use Wikipedia at least weekly
0.056
0.046
1202
Has edited Wikipedia
0.117
0.000
1202
Treatment group
0.028
0.210
1202
Note: Each row presents estimates from a separate linear regression of the form F inishedSurveyi =Constant + βV ariablei + εi , where the V ariablei is listed in the first column. A unit of observation
is a survey respondent. In each regression, the sample includes only those respondents who haveremained in the survey to answer the question; in case of treatment status, the sample includes
those who have remained in the survey until they were assigned the status. Number of observations
is 1203 for demographic characteristics and 1202 for Wikipedia related questions and treatment
status.

those who have a college degree are 5 percentage points more likely to finish it, and those
who are older (at least 35 years old) or unemployed are less likely to finish it.
Table C.3 shows that conditional on finishing the survey, assignment into a treatmentgroup was random in terms of most characteristics. The table presents estimates fromlinear regressions37 of the form: T reatmentGroupi = Constant + βV ariablei + εi , where thedependent variable T reatmentGroupi equals one if person i was assigned into the treatmentgroup and zero if he was assigned into the control group. Each row of the table presents
the estimate of coefficient β and corresponding p-value from a separate regression where
the V ariablei of the regression is listed in the first column. In each regression, the sample
includes only those respondents who finished the survey. The table shows that conditional on
finishing the survey, assignment into treatment group was random in terms of the demographic
characteristics, attitudes towards Wikipedia, the frequency of Wikipedia use, charity, writinguser reviews, and free time. From the 14 outcomes, one is significant at the 10 percent level.
Those who have edited Wikipedia before, conditional on finishing the survey are less likely
to be in the treatment group, with p-value equal to 0.08. Note that the question was asked
before treatment, hence, the treatment couldn’t affect the answers.
The treatment provided information about Wikipedia and was designed to change
Wikipedia editing behavior. Two concerns were that the treatment could lead to differential attrition or change the answers to the survey questions. From Table C.1 we saw that37

Estimates from analogous logit regressions are available from the author. The results are very similar.

45

Table C.2: Ability of covariates to predict whether respondents finish the survey(1)(2)
Female-0.002-0.027(0.023)(0.032)At least 35 years old-0.048** -0.066**(0.023)(0.031)
Has college degree
0.030
0.051*(0.023)(0.031)
Works at least 35 hours per week
0.005
-0.013(0.025)(0.035)
Unemployed-0.078** -0.085*(0.035)(0.047)
Use Wikipedia daily-0.012-0.031(0.028)(0.038)Use Wikipedia at least weekly
0.031
0.010
(0.028)(0.038)Has edited Wikipedia
0.130*** 0.132***(0.036)(0.047)
Treatment group
0.030-0.065(0.022)(0.070)Treatment group * Female
0.051(0.047)
Treatment group * At least 35 years old
0.042(0.046)
Treatment group * Has college degree-0.045(0.046)
Treatment group * Works at least 35 hours per week
0.040
(0.051)Treatment group * Unemployed
0.010(0.070)Treatment group * Use Wikipedia daily
0.042(0.056)
Treatment group * Use Wikipedia at least weekly
0.051(0.057)
Treatment group * Has edited Wikipedia-0.003(0.074)Log-likelihood-568.966 -566.551
Observations
1202
1202
Note: Each column presents estimated marginal effects from a separate logit regression. Dependent
variable is an indicator of whether the respondent finished the survey. Standard errors are inparentheses. *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant
at the 10 percent level.

46

there is no evidence that the treatment lead to differential attrition. Table C.3 showed thatin terms of most characteristics the randomization into treatment was successful. In any case,when analyzing the survey responses, I restrict attention to the control group, to make surethat the treatment does not affect the answers.
Table C.3: Ability of covariates to predict treatment status, conditional on finishing thesurveyCoefficient p-value Observations
Female
0.043
0.180
972
At least 35 years old
0.012
0.725
972
Has college degree
0.030
0.352
972
Works at least 35 hours per week
0.034
0.302
972
Unemployed-0.016
0.762
972
Use Wikipedia daily
0.011
0.757
972
Use Wikipedia at least weekly
0.059
0.147
972
Has edited Wikipedia-0.067
0.083
972
Believe: more competent than other editors-0.002
0.977
972
Believe: at least as competent as other editors-0.016
0.648
972
Donated to charity-0.0350.274
972
Volunteering at least one hour per week
-0.0180.592
972
Wrote user review in previous six months-0.0100.763
972
Leisure at least 3 hours in a weekday
0.016
0.664
972
Note: Each row presents estimates from a separate regression of the form T reatmentGroupi =Constant + βV ariablei + εi , where T reatmentGroupi equals one if person i was assigned into thetreatment group and zero if he was assigned into the control group, the V ariablei is listed in thefirst column. A unit of observation is a survey respondent. In each regression, the sample includes
only those 972 respondents who have finished the survey.

47

D

Online Appendix: Analysis with the restricted sample

Table D.1: Repeating Table 5 with the restricted sample. Likelihood of choosing to edit a
Wikipedia article about a woman vs a man, by the gender of survey respondent
Chose an article about a woman
t-test Obs.
Male respondents Female respondents p-value
All respondents in the control group
Total
0.115
0.317
0.000
417
Subsets of respondents by demographic characteristics
Has edited Wikipedia
0.154
0.382
0.010
99
Hasn’t edited Wikipedia
0.098
0.303
0.000
318
College
0.121
0.303
0.001
225
No college
0.108
0.333
0.000
192
Less than 35 years of age
0.069
0.284
0.001
146
At least 35 years of age
0.137
0.336
0.000
271
Subsets of respondents by type of Wikipedia articles
Biographies: Professionals
0.095
0.217
0.049
134
Biographies: Culture
0.085
0.267
0.007
134
Note: A unit of observation is a survey respondent. Each survey respondent chose a Wikipedia
article either about a man or a woman. Columns 1 and 2 present the mean of the indicator variable
that the chosen article was about a woman, separately for male and female survey respondents.
Column 3 presents the p-value of the t-test for whether the difference between male and female
respondents is significantly different from zero. In the first row, the sample consists of the survey
respondents in the control group whose answers were categorized as providing information as
described in subsection 3.2. In the following rows, the sample is further restricted by the survey
respondents’ demographic characteristics that are listed in the leftmost column. In the last tworows, the sample is restricted to the subgroups of biographies: group Professionals includes lawyers,politicians, scientists, etc; and group Culture includes writers, painters, composers, etc; the exact
definition of groups is in Table B.1.

48

Table D.2: Repeating Table 6 with the restricted sample. Estimated role of survey respondents’characteristics on the likelihood of choosing to edit a Wikipedia article about a woman vs aman
Chose an article about a woman
Coefficients
Marginal effects(1)(2)
Female1.361***
0.210***(0.280)(0.040)At least 35 years old-0.393-0.061(0.274)(0.042)
College degree-0.056-0.009(0.261)(0.040)
Unemployed-0.279-0.043(0.444)(0.068)Works at least 35 hours per week
-0.106-0.016(0.275)(0.042)Use Wikipedia daily
0.084
0.013
(0.322)(0.050)Use Wikipedia at least weekly-0.039-0.006(0.322)(0.050)Has edited Wikipedia
0.426
0.066
(0.305)(0.047)
Log-likelihood-199.394
Observations
417
Note: Estimation results are from a logit regression. Column 1 presents coefficient estimates andcolumn 2 presents estimated marginal effects. Dependent variable is an indicator for whether the
respondent chose to edit an article about a woman. The explanatory variables are the characteristicsof the survey respondent. The sample is restricted to the survey respondents in the control groupwhose answers were categorized as providing information as described in subsection 3.2. Standard
errors are in parentheses. *** Significant at the 1 percent level. ** Significant at the 5 percent level.
* Significant at the 10 percent level.

49

Table D.3: Repeating Table 7 with the restricted sample. The effect of treatment on thelikelihood of choosing to edit a Wikipedia article about a woman
Total
Male respondents Female respondents
(1)
(2)
(3)
(4)
(5)
(6)
Treated
0.195*** 0.187*** 0.130*** 0.132*** 0.247*** 0.250***(0.031)(0.030)(0.037)(0.037)(0.048)(0.048)
Covariates
No
Yes
No
Yes
No
Yes
Control group mean
0.211
0.211
0.115
0.115
0.317
0.317
Scaled treatment effect
0.926
0.888
1.136
1.149
0.779
0.788
Observations
823
823
418
418
405
405
Note: Each column presents estimates from a separate linear probability model. Dependent variable
is an indicator for whether the respondent chose to edit an article about a woman. Covariates
include demographic characteristics from Table 2. The sample includes both the control andtreatment group of the survey respondents but is restricted to those whose answers were categorized
as providing information as described in subsection 3.2. The sample is restricted to men in columns
3-4 and women in columns 5-6. Scaled treatment effect measures the percentage change of the shareof women’s pages. Standard errors are in parentheses. *** Significant at the 1 percent level. **Significant at the 5 percent level. * Significant at the 10 percent level.

Table D.4: Repeating Table 8 with the restricted sample. The effect of treatment on theintention to edit Wikipedia in the future
Total
Male respondents
Female respondents
(1)
(2)
(3)
(4)
(5)
(6)
Treated-0.039* -0.041** -0.078*** -0.081*** 0.002-0.002(0.021) (0.021)(0.029)(0.029)(0.029)(0.030)Covariates
No
Yes
No
Yes
No
Yes
Control group mean
0.115
0.115
0.133
0.133
0.095
0.095
Scaled treatment effect -0.337-0.358-0.587-0.6110.017-0.025Observations
823
823
418
418
405
405
Note: Each column presents estimates from a separate linear probability model. Dependent variable
is an indicator for whether the individual believes he is likely to edit Wikipedia in the next 30
days, defined in the same way as in Table 2. Covariates include demographic characteristics from
Table 2. The sample includes both the control and treatment group of the survey respondents
but is restricted to those whose answers were categorized as providing information as described insubsection 3.2. The sample is restricted to men in columns 3-4 and women in columns 5-6. Scaled
treatment effect measures the percentage change of the share of respondents who believe it is likely
they will edit Wikipedia. Standard errors are in parentheses. *** Significant at the 1 percent level.
** Significant at the 5 percent level. * Significant at the 10 percent level.

50

E

Online Appendix: Additional empirical analysis

E.1

Is the allocation of editorial input proportional to the readership: evidence from the survey

The evidence from the Wikipedia editing histories showed that pages of men receive relatively
fewer readers than the pages of women. A similar exercise with the survey data confirms theresults. The analysis uses survey respondents stated choices of the pages they would choose
to edit.
Figure E.1 presents the cumulative distribution of the number of page views of the
Wikipedia pages of men and women, where the page views are divided by the number ofsurvey respondents editing the page. The measure is constructed only based on the editors
and pages from the control group in the survey.
1

Cumulative Probability

.8

.6

.4

.2

0
0

20000
40000
60000
80000
100000
Number of page views divided by number of editors per biographyc.d.f.: biographies of men

c.d.f.: biographies of women

Figure E.1: Cumulative distribution functions of the number of page views divided by the
number of editors from the survey editing the article, by gender (of the person in the article)
Note: Page views data is from September 2014 for the first week after the survey was conducted.
The number of page views divided by the number of editors is constructed based on the editors andarticles from the control group in the survey.

As partly confirmed by the Kolmogorov-Smirnov test, presented in Table E.1, the c.d.f.
of the pages of women seems to be larger than that of the pages of men. In Table E.1, thenull hypothesis is that the samples are drawn from the same distribution. The first row of
the table presents the test statistic and p-value from the one-sided hypothesis test, where
alternative hypothesis is that the c.d.f. of the pages of women is larger than the c.d.f. of thepages of men. The one-sided test rejects the null hypothesis at the 10 percent significancelevel. The second row of the table presents the test statistic and p-value from the two-sided
hypothesis test, where the alternative hypothesis is that the samples are drawn from two51

different distributions. The two-sided test does not reject the null hypothesis.
Table E.1: Kolmogorov-Smirnov test of whether the samples of the number of page views
divided by the number of editors of the pages of men and women are drawn from the same
distribution
Test statistic p-value Observations
One-sided hypothesis test
0.13390.082
411
Two-sided hypothesis test
0.13390.147
411
Note: A unit of observation is a Wikipedia page. The sample is restricted to pages that were chosen
by the survey respondents in the control group. The number of unique pages is smaller than thenumber of survey respondents because some pages were chosen more than once. The null hypothesisis that the samples are drawn from the same distribution. In row 1, the alternative hypothesis isthat the c.d.f. of the pages of women is larger than the c.d.f. of the pages of men. In row 2, thealternative hypothesis is that the samples are drawn from two different distributions.

Past literature has shown that images drive viewership in Wikipedia. Table E.2 presents
results from a regression where the dependent variable is the logarithm of page views andexplanatory variables are the number of images on the page and an indicator whether it is abiography of a woman. The analysis is subject to the caveat that the data on images is froma later period (January 2019) than the data on page views (September 2014). The results
show that even controlling for the number of images on the page, biographies of women still
have more views.
Table E.2: Dependent variable: logarithm of page views
Number of images
0.089***(0.007)
Biography of a woman 0.541**(0.212)
Constant
8.070***(0.119)Observations
492
Adj. R-squared
0.268
Note: A unit of observation is a survey respondent. The sample is restricted to the survey respondents
in the control group. Dependent variable is the logarithm of views of the survey respondent’s chosen
Wikipedia page. Page views data is from September 2014 for the first week after the survey was
conducted. Data on images is from January 2019.

52

E.2

In terms of total readership, does the treatment improve theallocation of editorial input?

In this section, I compare the allocation of editorial input in the treatment and control group.
The goal is to learn whether in the treatment group compared to the control group more
editorial input is allocated to the pages with a larger or smaller number of readers. The
analysis uses survey respondents stated choices of the pages they would choose to edit.
Figure E.2 presents the cumulative distribution of the number of page views of the
Wikipedia pages in the control and treatment groups, where the page views are divided by the
number of survey respondents editing the page in control and treatment groups respectively.
As confirmed by the Kolmogorov-Smirnov test in Table E.3, the distributions look similar. In
Table E.3, the null hypothesis is that the samples are drawn from the same distribution. The
first row of the table presents the test statistic and p-value from the one-sided hypothesis test,
where alternative hypothesis is that the c.d.f. of the pages in the control group is smaller
than the c.d.f. of the pages in the treatment group. The second row presents the results fromthe one-sided hypothesis test, where the alternative hypothesis is that the c.d.f. of the pages
in the treatment group is smaller than the c.d.f. of the pages in the control group. The thirdrow presents results from the two-sided hypothesis test, where the alternative hypothesis isthat the samples are drawn from two different distributions. All three tests cannot reject
the null hypothesis that the samples are drawn from the same distribution at the 10 percent
significance level.
1

Cumulative Probability

.8

.6

.4

.2

0
0

20000
40000
60000
80000
100000
Number of page views divided by number of editors per biographyc.d.f. of control groupc.d.f. of treatment group

Figure E.2: Cumulative distribution functions of the number of page views divided by the
number of editors from the survey editing the article, by control and treatment group
Note: Page views data is from September 2014 for the first week after the survey was conducted.
The number of page views divided by the number of editors is constructed based on the editors andarticles from the survey.

53

Hence, we can conclude that although the treatment moved the allocation of editorial
input towards the pages of women, this didn’t make the allocation worse nor better in termsof the readership.
Table E.3: Kolmogorov-Smirnov test of whether the samples of the number of page views
divided by the number of editors of the pages in control and treatment groups are drawn
from the same distribution
One-sided hypothesis test: control group smaller
One-sided hypothesis test: treatment group smaller
Two-sided hypothesis test

Test statistic
0.0269-0.0468
0.0468p-value
0.744
0.408
0.735

Observations
818
818
818

Note: A unit of observation is a Wikipedia page. The sample includes pages chosen by the survey
respondents either in the treatment or control group. The number of unique pages is smaller than
the number of survey respondents because some pages were chosen more than once. The null
hypothesis is that the samples are drawn from the same distribution. In row 1, the alternative
hypothesis is that the c.d.f. of the pageviews of the pages chosen in the control group are smaller
than the c.d.f. of the pages in the treatment group. In row 3, the alternative hypothesis is that the
samples are drawn from two different distributions.

54