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Battle of the Sexes:

The Role of Gender in Survivor

Sophie Truscott*

Advised by Sara Hernández

MMSS Senior Thesis, Northwestern University

June 2017

Abstract

This paper utilizes data from the first 33 seasons of the American reality television show

Survivor to analyze the role of gender within competitive environments. Specifically, I use

contestant-level voting and performance data to model how gender impacts contestants’

voting decisions, both in eliminating other contestants throughout the game and voting for a

winner in the finals. I also observe potential differences in elimination order between male

and female contestants with similar performance attributes. I find that when the majority of

contestants voting against a contestant are female, the contestant is more likely to be male,

and vice versa. Furthermore, increasing the number of women voting against a contestant

increases the likelihood of the eliminated contestant being male. Additionally, I find that the

voting decisions of jurors in the finals do not seem to be affected by the juror’s gender. While

my paper finds some significant results regarding the role of gender in Survivor, I also

conclude that many of the most important variables impacting a contestant’s success in

Survivor are nuanced qualitative interpersonal factors not captured in my dataset that may

be quite difficult to measure quantitatively.

* I would like to thank my advisor, Professor Sara Hernández, for her guidance, support, and encouragement

throughout this process; our teaching assistant, Aniket Panjwani, for helping to provide structure and direction to

my analysis; Professor Joseph Ferrie for guiding our class through the thesis process; and Nicole Schneider for her

administrative assistance. Thank you also to Jeff Pitman of True Dork Times for generously sharing his dataset and

personal insights with me. Finally, thank you to my family and friends for their endless love and support.

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1 Introduction

While a number of researchers have conducted studies to examine the gender

differences in individuals’ competitive behavior, most studies utilize simple,

controlled tests to model competitive situations which often differ greatly from the

competitive situations that individuals encounter in real life. This paper aims to

minimize the impact of this environment discrepancy by studying the role of gender

within the reality television show Survivor, a game that more accurately models the

nuances of real world competition. Survivor is an American reality television series

in which contestants are stranded in a remote location and compete against each

other until one contestant remains. The game requires contestants to utilize their

social, strategic, and physical skills, and the $1 million prize provides a strong

incentive for players to play the game to the best of their abilities. As such, Survivor

acts a useful natural experiment to analyze how patterns of competitive behavior

differ between men and women.

There are, of course, drawbacks associated with using a televised reality

show to analyze human behavior. First, observed insights are likely to be impacted

by producers’ editing decisions intended to make the show as entertaining as

possible, and since each season’s 39 days are compressed into 13 hour-long episodes,

the majority of the footage from the game is not available to viewers. To overcome

the potential effects of these editing biases, this paper relies only on the most

objective observable variables on the show that are unaffected by editing decisions.

Second, game show contestants tend to share certain qualities; people who self-select

to participate in such competitions are usually more competitive, self-confident, risk-

seeking, and attention-seeking than the average person. This paper does not

formally account for this discrepancy, but it is an important point to keep in mind

when interpreting results. Finally, one might consider how a reality show’s

viewership might impact contestant’s behavior within the game—if contestants

know that their behavior in the game will be widely viewed, they may choose to

change their true behavior for fear of being viewed negatively by the show’s

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audience.1 There is, however, little evidence to believe that Survivor contestants

play the game with this mindset, and if nothing else, the $1 million prize acts as a

powerful incentive for contestants to act on their true intentions rather than

“playing a role” to create a particular image of themselves for viewers.

My analysis focuses on the role of gender within three competitive aspects of

Survivor: (1) the elimination of contestants at Tribal Councils, (2) the voting

decisions made by jurors at the Final Tribal Council, and (3) the elimination of

contestants with similar performance qualities. First, I examine how the gender of

the contestants voting against a contestant is related to the targeted contestant’s

gender; in other words, I consider if there is a gender-based influence on which

contestants eliminate male and female contestants. Second, I investigate how jurors

of different genders are affected by their relationships with each of the finalists;

specifically, I test whether there is a statistically significant difference between male

and female jurors’ propensity to forgive finalists for a personal betrayal. Finally, I

provide diagrams to observe potential differences in the elimination order of

similarly skilled male and female contestants.

Understanding how men and women behave in the face of competition can

help to identify how particular gender-based characteristics and behavior may help

or hinder individuals’ competitive performance. There are endless possible

applications for these insights, but perhaps the most relevant use of gender-based

competitive behavior patterns in today’s society is to help adjust the competitive

nature of the modern workplace to create a more even playing field for professional

men and women.

2 Literature Review

This section provides an overview on existing literature on the topics of reality

television and the relationship between gender and competition. I will also describe

where my research fits in among this existing work.

1 “Negative” in this case would likely differ from contestant to contestant. For example, some

contestants may worry about being perceived by viewers as domineering, while other

contestants may not want the audience to view them as passive.

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2.1 Existing Research on Gender and Competition

Since the intention of this paper is to investigate the relationship between gender

and competitiveness, using Survivor as a natural experiment, it is relevant to

examine existing literature and theories concerning gender and competition.

Lee, Kesebir, and Pillutla (2016) find that women view same-gender

workplace competition as less desirable than their male counterparts do, and in the

presence in same-gender competition, women’s relationships with one another often

suffer. Lee et al. cite gender socialization research that women are taught to value

equality and harmony while hierarchical competition is a critical aspect of

masculinity; therefore, in competitive workplace situations that create hierarchy,

men welcome competition while women shy away from competition, particularly in

situations that pit them against other women. On the other hand, Burow, Beblo,

Beninger, and Schröder (2017) find in an online experiment that women prefer to

enter competitions when their competitors are known to be women. Burow et al. find

that women who correctly or underestimated their task abilities were more likely to

display this behavior, while women who overestimated their abilities enter

competitions regardless of their opponents’ genders (women’s actual abilities,

however, did not affect their willingness to compete in different gender

environments). While these two studies from Lee et al. and Burow et al. may seem to

be at odds with each other, the studies together present a more thorough, complex

picture of gender and competition. When faced with same-sex competition that

yields hierarchical rankings, Lee et al. find that women shy away for fear of

disrupting equality with their female peers, but if women have to compete, Burow et

al. suggest that they prefer to do so among female competitors with whom they feel

less intimidated to reveal their true abilities.

Dato and Nieken (2014) conducted an experiment to investigate competitive

gender differences in games where players had the opportunity to sabotage their

opponents. Dato and Nieken find that men are more likely to sabotage their

opponents, which led to a higher probability of winning. However, since players in

the study incurred a cost when they chose to sabotage, men and women had the

same earnings on average. Dato and Nieken believe that men are generally more

status-seeking than women, so men are more willing to “invest” in victory by

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incurring a sabotage cost, even if doing so decreases their eventual earnings. In an

experiment with Swedish adolescents, Dreber, von Essen, and Ranehill (2014) find

that girls in competitive settings are more altruistic and risk-averse than boys.

Finally, Halladay (2017) conducted a two-stage experiment in which subjects

first play a game in teams and rate how positive, negative, and neutral their feelings

are toward each of their teammates. Then subjects play a second game against one

of their teammates from the previous round. Halladay finds that women in

competitive situations respond more strongly to negative emotions than men do;

women’s performance in the second stage was significantly higher when they

competed against an opponent toward whom they had negative feelings, whereas

male performance was unaffected by personal emotions toward an opponent.

It is important to note that Survivor is a self-selecting, coed game that

naturally attracts highly confident and competitive individuals, since the game

features a high-stakes $1 million reward and is broadcasted for an average of 16.2

million viewers per episode.2 As such, the studies from Lee et al. and Burow et al.

regarding women’s likelihood to enter into same- and mixed-gender competitions is

not wholly relevant to Survivor.3 More relevant to my analysis is Dato and Nieken’s

research into men and women’s propensity to sabotage in competitive situations and

Dreber et al.’s analysis of gendered altruism and risk-taking in competitions. While I

will not examine how contestants’ emotional opinions of one another affect their

personal performance in the game, Halladay’s research into the effect of emotions in

competition is certainly relevant to my examination of the effect of betrayal and

personal relationships on voting behavior at the Final Tribal Council.

2 Overall Survivor viewership average for all 34 seasons calculated from average episode

ratings per annual television season, as reported by CBS Entertainment. Average Survivor

viewership per episode for the 2016-2017 TV season was 10.32 million viewers per episode.

Source: The Nielsen Company. 3 Given the coed nature of Survivor, the research from Burow et al. suggests that women who

choose to compete in Survivor tend to overestimate their abilities. Since, however,

individuals’ self-confidence is not a factor in my analysis, this observation is not relevant to

my paper.

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2.2 Existing Research on Survivor and Game Shows

While the internet is home to hundreds of active fan communities, websites, and

blogs dedicated to quantitative and qualitative discussions of Survivor, there has

been relatively little academic research dedicated to the show. For my purposes, I

have identified three academic publications concerned with Survivor and one paper

on another reality game show, The Weakest Link, which employs a similar mode of

analysis to my own.

Mixon (2001) utilizes game theoretical notions of cartel behavior to analyze

contestants’ incentives and behavior from the first season of Survivor. Mixon

observed qualitative interpersonal factors across the 13-episode season as well as

the Tribal Council voting history to break the contestants into various voting

“cartels” and analayze their incentives and decisions. Mixon’s results are somewhat

difficult to extrapolate to Survivor gameplay more broadly as they focus on the

specific, unique personality traits and relationships of the first season’s contestants.

Nevertheless, Mixon’s paper demonstrates how Survivor can be used as a platform

for observing economic and game theoretical principles in a competitive setting.

Hedges’s (2014) paper examines how viewers perceive the gender identities of

contestants from four seasons of Survivor using Q-Methodology, a psychological

research method to capture individuals’ subjectivity. Her experiment finds that

viewers use more criteria to evaluate the masculinity of male contestants than the

femininity of female contestants. While Hedges’s focus on viewers’ perception of

gender differs from my focus on contestants’ perception of gender within the game,

her research reveals how both the game of Survivor and the show’s portrayal of

contestants can be useful in gender-based analyses.

Dilks, Thye, and Taylor (2010) analyze Survivor Tribal Council voting history

from the show’s first 17 seasons to test economic models of taste-based

discrimination and social identity theory. Dilks et al. assign contestants to either

“low status” (women, minorities, and elderly) or “high status” groups, and find that

low status contestants are more likely to be eliminated in early stages of the game—

when contestants have an incentive to eliminate less competent players—and high

status contestants receive more votes in later stages when competency is considered

a threat. My analysis will build upon this notion of voting discrimination in

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Survivor—specifically in terms of gender discrimination—by analyzing not only

which players types may be discriminated against but also by whom.

Although Antonovics, Arcirdiacono, and Walsh’s (2005) paper focuses on The

Weakest Link, a British reality television show, the principles they examine and the

identification strategies they employ bear resemblance to those utilized in this

paper. Antonovics et al. use contestants’ voting history to observe discrimination

between contestants of different genders and race. Their results do not find any

evidence of males discriminating against females or of whites discriminating against

blacks, but they do observe that women discriminate against men in early stages of

the game. Antonovics et al. substantiate the notion that voting patterns in reality

game shows can be extrapolated to identify patterns of social behavior.4

In a similar approach to Dilks et al. and Antonovics et al., my research aims

to use Survivor as a platform to quantitatively investigate gender-specific patterns

of social behavior in competitive environments. Both Dilks et al. and Antonovics use

contestants’ voting history to illuminate patterns of discriminatory decision-making.

I expand this approach by considering additional factors that might affect Survivor

contestants’ opinions of and relationships with one another, including the presence

of alliances and betrayals, and their perception of other’s abilities based on

performance within the game.

3 Survivor Overview and Strategies

Survivor first aired on CBS television network in May 2001. On May 24, 2017, the

most recent Survivor season (Season 34 – Game Changers) finished airing on CBS.

In May 2017 the show was renewed by CBS for the 2017-2018 television season.5

The show’s leadership has remained relatively unchanged since its inception;

4 It should be noted that contestants’ incentives in Survivor and The Weakest Link vary

rather significantly, since contestants in The Weakest Link compete as a team for the

entirety of the game, while Survivor contestants only compete as a team for half of the game

(which affects the team-centric mentality even in early stages of the game). Nonetheless,

Anthonovics et al. provide a foundation for evaluating voting differences amongst groups

that will be very useful in my analysis. 5 CBS Broadcasting Inc., CBS Entertainment. (2017, March 23). CBS Renews 18 Series for

2017-2018 Season [Press release]. Retrieved from https://cbspressexpress.com/cbs-

entertainment/releases/view?id=47362.

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notably, current creator/executive producer Mark Burnett and host/executive

producer Jeff Probst have worked on Survivor since the first season.

While numerous Survivor seasons have included a number of gameplay

twists, the basic format of the game has remained the same throughout the show’s

history.6 Each season, 16 to 20 contestants (known as “castaways”) are brought to a

remote location and divided into 2 to 4 tribes. The tribes live at separate camps with

limited resources but meet regularly to compete in reward or immunity challenges.

In reward challenges, the winning team receives amenities (often fire, food, fishing

supplies, blankets, or more extravagant gifts). In immunity challenges, tribes

compete to win immunity, thereby avoiding attending Tribal Council. The losing

tribe goes to Tribal Council, where one member of the tribe is voted out. After

approximately half of the contestants have been eliminated, the separate tribes

merge into one tribe, and the remaining contestants compete as individuals in

reward and individual immunity challenges. Contestants who win individual

immunity attend and vote at Tribal Council but are exempt from elimination. The

gameplay continues until 2 or 3 contestants remain (the “Final 2” or “Final 3”), at

which point the jury (composed of the last 7 to 10 contestants eliminated before the

finalists) vote for one of the finalists to win.7 The finalist who receives the most jury

votes at the Final Tribal Council is named the winner (“Sole Survivor”) and is

awarded the $1 million cash prize.

Fans of Survivor will likely note that the interpersonal complexities of the

game make it difficult to prescribe a singular rational gameplay strategy. With that

said, I identify a basic rational strategy for gameplay under the single assumption

that a contestant’s goal is to win the game, which necessitates that a contestant is

not eliminated before the finals. Rational strategy for contestants varies rather

significantly between the tribe and individual stages of the game. I will refer to the

6 The following is a non-exhaustive list of notable twists to the standard Survivor game

format: initial tribes divided by ethnicity, gender, age, or beauty/intelligence/physical

strength; players compete against family members; contestants from previous seasons return

to play in a new season. Table IV lists a number of these twists by season. 7 The use of the Final 2 or Final 3 depends on the season and is decided by the Survivor

producers. The Final 2 was the norm until the Final 3 was introduced in Survivor: China

(Season 13). Host Jeff Probst explained that a Final 3 prevents a universally respected

finalist facing off against an unlikeable finalist, which yields an anti-climactic Final Tribal

Council.

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initial stage of the game, in which contestants compete in tribes, as the “Pre-Merge

Stage” and the secondary stage of the game, in which contestants compete as

individuals, as the “Post-Merge Stage.” In the Pre-Merge Stage, contestants want

their tribe to be as strong as possible so they can win challenges. (Recall that

winning immunity challenges excuses the tribe from attending Tribal Council,

thereby guaranteeing that every contestant on the winning tribe is spared from

elimination that round.) As such, when a tribe attends Tribal Council, contestants

should vote to eliminate the weakest member of their tribe to increase their tribe’s

chance of winning future challenges. A contestant’s “strength” is usually evaluated

primarily by one’s performance in challenges (usually physical strength and agility),

although other skills, such as providing food and shelter around camp, may

contribute to the tribe’s overall strength and thus increase a contestant’s perceived

strength. In the Post-Merge Stage, in which contestants compete as individuals,

contestants should vote off the strongest challenge competitors in order to increase

their own chances of winning individual immunity challenges and thus

guaranteeing their progression in the game.

The jury component somewhat complicates this basic strategy. Since the jury

selects the overall winner, contestants may also consider who will accompany them

to the finals. There are no rules on the evaluation criteria used by jurors to vote for a

winner, but jurors generally vote for a winner based on their perception of the

quality of the finalists’ gameplay, as well as more personal and interpersonal

qualities such as their perception of the finalists’ integrity and the jurors’ personal

relationships with each of the finalists. Therefore in the Post-Merge Stage of the

game, particularly as contestants near the finals, contestants may consider

eliminating not only contestants who perform well in challenges but also contestants

who they believe would be likely to receive numerous jury votes at the Final Tribal

Council.

While these strategies and incentives may oversimplify a number of

contestants’ considerations throughout the game (including, but not limited to,

personal relationships/alliances, mistrust of contestants, and dislike of or annoyance

with contestants), this basic strategy will be useful in motivating some decisions in

my analysis later in this paper.

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4 Data

I compiled a relational database on every Survivor season excluding Season 34 using

data from two websites: Survivor Wiki and the True Dork Times.8, 9 The Survivor

Wiki data was retrieved directly from the website and includes demographic

information on all contestants and contestant-level voting and elimination history

for every Tribal Council and Final Tribal Council. The editor-in-chief of the True

Dork Times, Jeff Pittman, shared his entire dataset with me via email (some, but

not all, of the data in this dataset appears on the True Dork Times website). The

data from the True Dork Times includes contestant-level performance data from

every challenge and Tribal Council.

All data in my database is observed exclusively from Survivor episodes (in

other words, potential data from post-Survivor interviews, contestants’ social media,

etc. is not included). In an effort to avoid potential bias from editing decisions made

by Survivor producers, contestant data such as testimonials, airtime, and character

portrayal is not included in the database.

The database contains four main data tables: data by contestant, data by

Tribal Council, data by juror, and data by finalist. The data by contestant includes a

row for each contestant with columns for variables pertaining to each contestant. It

should be noted that this table treats returning players as unique players (i.e.

players who return to the game have a row for each gameplay), but the table

includes variables noting whether a contestant is a returning player. The data by

Tribal Council includes rows for each Tribal Council across every season; this data

can be thought of as panel data where the ordered Tribal Council within a season

(which can be used to track “stages” or periods of a season) is the individual

dimension and the season is the time dimension. The data by juror is a pooled cross

section that includes a row for each juror/finalist pair and includes variables

capturing whether the juror voted for the finalist at the Final Tribal Council and to

what degree the juror and finalist interacted throughout the game. The data by

8 Survivor Game Changers (Season 34) aired its finale on May 24, 2017, one week before the

submission of this thesis. Given this time constraint, this season was not included in any of

my analysis. 9 Survivor Wiki can be accessed at survivor.wikia.com; the True Dork Times can be accessed

at truedorktimes.com.

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finalist lists each finalist across seasons and includes variables concerning number

of jury votes receives and comparing the finalists’ jury votes to their overall

performance in the game.

Table I includes summary statistics for all Survivor contestants in the

sample, Table II includes summary statistics for Survivor winners, and Table IV

provides summary statistics for the first 33 Survivor seasons. Table III provides

summary statistics for contestants’ performance disaggregated by gender. The first

variable in Table III, “Overall Finish,” captures a contestant’s rank amongst the

other contestants within a given season, where the winner’s rank is coded as 1 and

each subsequent finisher’s rank increases by 1. The “Mean Percent Finish in

Individual Challenges” captures how contestants placed on average in individual

challenges. For example, in an individual challenge with five competitors, the

challenge winner’s percent finish would be coded as 1, the second-place finisher’s

percent finish would be coded as 0.8, the third-place finisher’s percent finish would

be coded as 0.6, and so on. These percent finishes are averaged for each contestant

across every individual challenge in which they participate to generate a

contestant’s mean percent finish. Note that individual challenges, with very few

exceptions, take place only after the tribe merge, so contestants who never

participated in an individual challenge are excluded from this statistic. “Votes

against Boot Percentage” counts at how many Tribal Councils a contestant cast a

vote against the contestant who was eliminated at that Tribal Council (known as the

“bootee”) and divides it by the total number of Tribal Councils the contestant

attended. This variable captures, at least to a certain degree, a contestant’s strategic

strength, as it measures how often the contestant effectively eliminated another

contestant; the higher a contestant’s score, the more often he or she voted for the

bootee. Since a rational contestant’s goal is to win the game, he or she must survive

every Tribal Council without being eliminated. A contestant’s best Tribal Council

strategy is therefore to ensure that the person who they vote for is the person

ultimately eliminated at that Tribal Council.10 Under this strategy, a higher “Vote

10 Contestants usually attempt to ensure this by speaking with their peers and alliance

before Tribal Council and either convincing others to vote for a particular contestant or

learning for whom the majority of tribe members are voting and voting the same way.

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for Boot Percentage” indicates that the contestant has more regularly achieved this

voting strategy.

5 Gender-Based Voting and Elimination Analysis

5.1 Identification Strategy

My first research question concerns the relationship between the gender of an

eliminated contestant and the gender(s) of the contestants who eliminate him or her.

I am interested to test how the expected gender of an eliminated contestant might be

affected by the number of female and male contestants voting against the

contestant.

Before laying out the model, it is useful to define a few terms. First, “bootee”

refers to the eliminated contestant at a particular Tribal Council. Second,

“elimination Tribal Council” refers to the Tribal Council at which a particular bootee

was eliminated. Third, an “eliminator” is a contestant who votes for the bootee at the

bootee’s elimination Tribal Council, thereby contributing to the bootee’s elimination.

Finally, an “eliminator group” is the group of all eliminators who vote for the bootee

at the bootee’s elimination Tribal Council.

The data for this model lists each contestant from all 33 seasons and

quantifies how many male eliminators (“Total No. Male Eliminators”) and female

eliminators (“Total No. Female Eliminators”) voted for the contestant at the

contestant’s elimination Tribal Council, as well as the percent of females in the

eliminator group (“Eliminator Group Gender”). The sample for this model excludes

all contestants who progressed to the finals (including winners), since these

contestants were not eliminated in a Tribal Council. Additionally, contestants who

were eliminated by means other than a traditional Tribal Council are excluded from

the sample.11 Finally, contestants who were eliminated while members of single-

gender tribes (as assigned by Survivor producers as a gameplay “twist”) were

excluded from this sample.12

11 Here, “traditional Tribal Council” refers to a Tribal Council that did not result in a tie (tie

votes require a re-vote) and did not involve the successful use of a hidden immunity idol.

Furthermore, contestants who quit or were medically evacuated from the game are not

eliminated by traditional Tribal Council, and are therefore excluded from this sample. 12 See Table IV for seasons with initial tribes divided by gender,

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Since my dependent variable (“Bootee is Female”) is a binary dummy

variable, I selected a conditional fixed effects logistic regression (“logit model”) to

test this question. My initial regression, Equation (1) captures the effect of the

number of female and male eliminators and the gender-mix composition of the

eliminator group on a bootee’s gender, including fixed effects for each Survivor

season:13

(Bootee is Female)i = β0 + β1(Total No. Female Eliminators)i + β2(Total No. Male

Eliminators)i + β3(Eliminator Group Gender)i + εi

As discussed earlier in Section 3, contestants’ voting incentives may vary

significantly between the pre- and post-merge stages of the game. Equation (2)

includes a dummy variable and interaction term for Post-Merge (coded as 0 if the

bootee’s elimination Tribal Council was before the merge, 1 if the bootee’s

elimination Tribal Council was after the merge) to account for the potential impact

of this strategic difference:

(Bootee is Female)i = β0 + β1(Total No. Female Eliminators)i

+ β2(Total No. Male Eliminators)i + β3(Eliminator Group Gender)i

+ β4(Post-Merge)i + β5(Total No. Female Eliminators * Post-Merge)i

+ β2(Total No. Male Eliminators * Post-Merge)i

+ β3(Eliminator Group Gender * Post-Merge)i + εi

5.2 Results

Table V shows that a higher percentage of female eliminators in an eliminator group

increases the likelihood that the group eliminates a male contestant. Furthermore,

the table shows that an increase in the number of female eliminators, holding all

else constant, increases the likelihood of a male being eliminated, although the effect

of changing the number of male eliminators is not statistically significant. The first

column shows that with the addition of one female eliminator, the log odds of the

bootee being female decrease by 0.282. This result is statistically significant at the

0.01 level. However, when the number of male eliminators is accounted for in the

second and third columns, we find that the effect of the number of male eliminators

13 Note that the variable Eliminator Group Gender in Equation (1) is expressed as the

percentage of female eliminators within a particular eliminator group.

(1)

(2)

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is not significant, although the effect of the number of female eliminators remains

significant when considered alongside the number of male eliminators. The fourth

column shows that a one percentage point increase in the share of females in an

eliminator group decreases the log odds of the bootee being female by 1.552; this

effect is statistically significant at the 0.01 level.

Table VI repeats the same logistic regressions as those in Table V but also

includes the interactions for each independent variable with a dummy for post-

merge. Since the correlation coefficients on neither the post-merge dummy nor the

interaction terms are significant, we cannot conclude that there is a significant

difference in the impact of eliminators’ genders on the bootee’s gender before and

after the merge.

5.3 Discussion

These results suggest that the more women who eliminate a contestant, the more

likely the bootee is to be male. Conversely, as the number of women who vote to

eliminate a contestant decreases, the bootee is more likely to be male. Furthermore,

as the share of women within an eliminator group increases, the bootee is more

likely to be male; so, similarly, increasing the share of men within an eliminator

group increases the likelihood of a female bootee. Importantly, though, my results

suggest that changing the absolute number of male eliminators, holding all else

constant, has no a significant effect on the bootee’s gender.

The statistically significant negative coefficient on the number of female

eliminators suggests that women on Survivor have a tendency to “band together”

and vote out a man; the more additional women who join a voting group, the more

likely the group’s target is a man. However, since the coefficient on the number of

male eliminators is statistically significant, the data suggest that adding an

additional male eliminator does not impact the gender of the bootee. In other words,

men on Survivor have historically been less inclined to “band together” with other

men and vote out a contestant of a particular gender. Based on this finding, male

Survivor contestants should be wary of large groups of women who frequently vote

together, as they are most likely to target male contestants. Women, however, have

less to fear from large groups of men who vote together.

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The “Eliminator Group Gender” variable, which describes the percentage of

female eliminators within a voting group, is perhaps the most intuitive variable for

measuring this gender effect. The coefficient on the variable suggests that an

eliminator group that is majority female is more likely to vote out a male contestant

and a majority-male eliminator group is more likely to eliminate a female.

The lack of statistically significant coefficients on the interaction terms in

Table IV suggests that the merge does not impact the effect of eliminators’ genders

on the bootee’s gender. This does not necessarily disprove the theory that players

display different strategic behavior before and after the tribal merge (see Section 3),

but it does suggest that the gender-based relationship between an eliminator group

and a bootee is unaffected by the presence of the tribal merge.

It should be noted that this mode of analysis does not consider the gender of

the entire tribe at each Tribal Council, only the genders of the bootee and the

contestant who vote to eliminate the bootee. Often at Tribal Council, there will be

contestants who do not vote for the bootee but are not eliminated (think of these

contestants as being outside of the “majority alliance” which is often synonymous

with the eliminator group). More thorough analysis of this question might index the

gender-mix of the eliminator group against the gender-mix of the entire tribe at the

stage of the game to better analyze how the gender makeup of a tribe impacts how

individual contestants choose to align themselves with other contestants by way of

their Tribal Council vote.

6 Gender Differences in Jurors’ Forgiveness

6.1 Identification Strategy

My second question is concerned with the differences in how male and female jurors

decide to vote at the Final Tribal Council. Specifically, I am interested in

constructing a measure of “betrayal” between finalists and jurors and analyzing how

forgiving male and female jurors are of finalists in the face of this betrayal. Given

the finding from Dato et al. (2014) that men are more likely to sabotage opponents in

competitions than women are, I hypothesize that men will be more forgiving of a

personal betrayal, since they are more likely to empathize with a contestant’s

decision to betray or sabotage an opponent. Additionally, Halladay’s (2017)

16

observation that women are more affected by negative emotions toward opponents

further supports the notion that a personal transgression between a juror and

finalist would more negatively impact a female juror’s likelihood of voting for the

finalist that that of a male juror. For my purposes, jurors’ forgiveness will be

measured by their likelihood of voting for a finalist in the Final Tribal Council based

on their personal and strategic relationship with the finalist and a binary variable

measuring whether the finalist voted to eliminate the juror at the juror’s elimination

Tribal Council.14

Before explaining the model I have developed to analyze this question, it is

important to provide some explanation and rationale for my question. In Survivor, a

“betrayal” is, in general, when a contestant is eliminated by contestants with whom

he or she has a close personal relationship and/or a strategic alliance. This betrayal

can take the form of a “blindside,” in which the eliminated contestant is tricked by

the eliminator group into believing another contestant is the target when the

eliminated contestant is, in fact, the intended elimination target. But often a

contestant’s elimination by his or her friends or allies does not have to be secretive

in order for the eliminated contestant to feel betrayed. This notion of betrayal

becomes important when jurors vote for a winner at the Final Tribal Council. Each

juror may choose to prioritize different factors when casting their vote—some jurors

may value finalists’ strategic or social strengths over their own personal

relationships or experiences with the finalists, some jurors might vote for a finalist

based on a prior alliance or personal relationship, and other jurors might choose to

vote against a contestant—by voting for another finalist, since jurors cannot abstain

from voting—because of a personal transgression or a lack of respect for that

finalist’s gameplay. In short, each juror has a unique and complicated rationale for

14 It should be noted that each contestant votes privately at Tribal Council, and while a

number of the votes are revealed to the contestants at the Tribal Council, contestants do not

know who cast which votes. (At the end of each episode, though, each contestants’ vote is

revealed to the television audience.) It is possible, although unusual, that a juror would not

know if a finalist had voted to eliminate him/her at the time of his/her elimination. However,

during the Final Tribal Council, each juror has the opportunity to question the finalists. If a

juror was unsure if a finalist had eliminated him/her, the juror would certainly ask the

finalist at the Final Tribal Council; given the nature of the Final Tribal Council questioning,

it would be extremely unlikely for a finalist to provide a false answer to this question.

Therefore, I am confident that, before casting their vote at the Final Tribal Council, every

Survivor juror knows if each finalist voted to eliminate him/her.

17

the vote they cast at the Final Tribal Council. This decision, though, is rarely devoid

of considerations of jurors’ personal relationships and experiences with each of the

finalists. I am interested in capturing a degree of betrayal between each juror and

finalist, and seeing how this level of betrayal affects whether or not a juror votes for

a particular finalist.

To investigate this question, I built a data table that includes a row for every

juror/finalist pair across all 33 Survivor seasons. I coded a dummy variable “Juror

Voted for Finalist” equal to 1 if the juror voted for the finalist to win at the Final

Tribal Council and 0 if the juror did not vote for the juror at the Final Tribal

Council. I also coded a dummy variable “Finalist Booted Juror” equal to 1 if the

finalist had voted against the juror at the juror’s elimination Tribal Council and 0 if

the juror had not voted against the juror when the juror was eliminated. Next, I

created a variable “% of Pre-Merge Game in Same Tribe” that calculates the

percentage of pre-merge Tribal Councils during which the juror and the finalist were

in the same tribe (in an attempt to quantify the amount of time the contestants

might have had to develop a closeness or fondness for one another before the merge).

Similarly, I created a variable “% of Same Votes at Shared Tribal Councils” that

calculates the percentage of Tribal Councils at which the juror and finalist had both

attended and cast the same vote (in an attempt to quantify the degree of an alliance

or strategic agreement between the juror and finalist). Finally, I created a

continuous variable “Finalist Booted Juror (Scaled)” which multiplies the “Finalist

Booted Juror” dummy variable with the stage of the game in which the juror was

eliminated, to allow us to account for the potential differences between how jurors

perceive elimination earlier or later in the game. Note that all jurors who were

eliminated by means other than a “traditional Tribal Council” are excluded from this

dataset.15

I identified a conditional fixed effects logistic regression (logit model) to test

this question since my dependent variable, whether or not a juror voted for a finalist

at the Final Tribal Council, is a dummy variable. The model aims to examine how a

15 Recall “traditional Tribal Council” refers to a Tribal Council that did not result in a tie and

did not involve the successful use of a hidden immunity idol. Furthermore, contestants who

quit or were medically evacuated from the game are not eliminated by traditional Tribal

Council, and are therefore excluded from this sample.

18

finalist’s elimination of a juror (“Finalist Booted Juror”) alongside variables

quantifying the closeness of the personal (“% of Pre-Merge Game in Same Tribe”)

and strategic (“% of Same Vote at Shared Tribal Councils”) relationships between

the juror and finalist affects a juror’s decision to vote for the finalist to win

Survivor.16 I also included variables that measure a finalist’s strategic strength

through the percentage of Tribal Councils at which he or she successfully voted for

the bootee (“% Vote for Bootee”) and challenge performance through his or her

average finish in individual challenges (“Mean Percent Challenge Finish”). Equation

(3) shows the model:

(Juror Votes for Finalist)i = β0 + β1(Finalist Booted Juror)i

+ β2(% of Pre-Merge Game in Same Tribe)i

+ β3(% of Same Vote at Shared Tribal Councils)i

+ β4(% Vote for Bootee)i + β5(Mean Percent Challenge Finish)i + εi

Equation (4) utilizes the same model but uses the “Finalist Booted Juror (Scaled)”

variable to account for the potential difference in the stage of a juror’s elimination:

(Juror Votes for Finalist)i = β0 + β1(Finalist Booted Juror Scaled)i

+ β2(% of Pre-Merge Game in Same Tribe)i

+ β3(% of Same Vote at Shared Tribal Councils)i

+ β4(% Vote for Bootee)i + β5(Mean Percent Challenge Finish)i + εi

6.2 Results

Table VII limits the sample to only female jurors (the gender of the finalists,

however, is unconstrained) and finds that a female juror are unlikely to significantly

consider a finalist’s role in her elimination when voting at the Final Tribal Council,

but that female jurors do consider a finalist’s performance in individual challenges.

Note that all of the correlation coefficients for “Finalist’s Mean Percent Challenge

16 These three variables together function to quantify a finalist’s betrayal of a juror. If a

finalist booted a juror, and the finalist and juror had spent a majority of the game on the

same tribe and had voted the same at the majority of Tribal Councils, the juror would be

likely to feel betrayed by the finalist’s role in his/her elimination. In other words, if Finalist

Booted Juror equals 1, as the variables % of Shared Tribes Pre-Merge and % Same Votes at

Shared Tribal Councils increase, the more betrayed by the finalist the juror is likely to feel. If

the finalist did not eliminate the juror, the juror is not likely to feel betrayed by the finalist;

in this case, it is still interesting to examine the effect of amount of tribes and votes shared

by the juror and finalist (i.e. the strength of the juror and finalist’s personal and strategic

interactions) on the juror’s decision to vote for the finalist.

(3)

(3)

19

Finish” are all positive and statistically significant at the 0.01 level, which suggests

that an increase in the finalist’s mean percent challenge finish by one percent

increases the log odds of a female juror voting for the finalist by at least 3.614. The

only other significant correlation coefficients are on the “Finalist Eliminated Juror”

variable in the first and second columns. The negative value of these coefficients

suggest that if the finalist eliminated a female juror, the log odds of the female juror

voting for the finalist decrease by 0.945 and 0.934, respectively. However, since

these coefficients are not significant in columns 3 and 4, it is difficult to conclude

that a finalist’s elimination of a female juror significantly impacts her decision to

vote for the finalist.

Table VIII repeats the same regressions as Table VII does but limits the

sample to only male jurors. In this table, the only statistically significant coefficients

are on the “Finalist Eliminated Juror” variable in columns 1, 2, 3, and 4. These

coefficients indicate that if a finalist eliminated a male juror, the log odds of the

male juror voting for the finalist in the Final Tribal Council decrease by at least

0.903. In other words, a male juror’s voting decision at the Final Tribal Council does

appear to be impacted by a finalist’s role in his elimination. Male jurors, however, do

not appear to be affected by when in the game the finalist eliminated them, since

none of the correlation coefficients on “Finalist Eliminated Juror (Scaled)” are

statistically significant.

Table IX displays the regression results for all juror/finalist pairs and

includes an interaction dummy for the juror’s gender, which allows us to observe

differences between male and female jurors. First, since none of the correlation

coefficients on any of the interaction terms are statistically significant, we cannot

conclude that male and female jurors differ in how the included variables impact

their decision to vote for a finalist; as such, in this instance there is no evidence for a

gender difference. However, the table can still be used to understand which

variables affect the decision of all jurors (regardless of gender) to vote for a finalist.

Note that the correlation coefficients on “Finalist Eliminated Juror” in columns 1

through 4 are negative and statistically significant, which indicates that a juror is

less likely to vote for a finalist if the finalist eliminated the him or her. Second,

notice that the correlation coefficients for the “Finalist’s Mean Percent Challenge

Finish,” are positive and statistically significant at the 0.05 level in all columns,

20

indicating that jurors are more likely to vote for a finalist with a better individual

challenge performance record. Among all the finalist qualities explored in this

section, it appears that a relatively strong individual challenge performance record

is the only quality that will significantly improve a finalist’s chance of receiving jury

votes and thus winning Survivor.

6.3 Discussion

I hypothesized that there is a difference in how male and female jurors view

betrayal and finalists’ game performance at the Final Tribal Council. My results,

however, fail to confirm statistically different reactions to betrayal by male and

female jurors. With that said, my results do suggest that all jurors are less likely to

vote for a finalist who previously eliminated them and are also likely to regard more

highly finalists with relatively strong performances in individual challenges.

It is possible that female and male jurors do differ in their propensity to

forgive finalists but that the variables in my model do not capture the aspects of a

betrayal that are meaningful to contestants. Furthermore, my results do not

indicate that male and female jurors definitely employ the same decision-making

strategies at the Final Tribal Council; further qualitative analysis might yield more

insight into possible differences between the decisions of male and female jurors at

the Final Tribal Council.

While regression results with only a few statistically significant correlation

coefficients can often be disappointing, these results nonetheless suggest something

important about juror’s Final Tribal Council voting behavior. Since jurors do not

appear to rely heavily on any if the variables used in these regressions to make their

vote at the Final Tribal Council, there must be a number of other factors that are

significantly important to jurors across seasons, some of which may be correlated

with a juror’s gender. Based on my results, I hypothesize that the most significant

factors impacting a juror’s vote at the Final Tribal Council are qualitative factors,

such as a finalist’s social abilities, strategic accomplishments, and perceived

integrity. More thorough analysis of the qualitative factors in Survivor episodes may

help to substantiate this hypothesis.

21

7 The Impact of Abilities on Elimination Order

7.1 Identification Strategy

In my final question, I investigate how male and female contestants of particular

ability types differ in their order of elimination. I analyze contestants’ ability across

two metrics used previously in this paper: (1) contestants’ mean percent finish in

individual challenges and (2) contestants’ vote for boot percentage at Tribal

Councils. In addition, I created a third variable, the simple sum of contestants’ mean

percent challenge finish and vote for boot percentage, to capture a contestants’

overall performance, both in competitions (generally associated with physical

strength) and Tribal Councils (generally associated with strategic and social

strength).

I decided to show contestants’ average overall finish by ability in bar charts.

“Mean Overall Finish” is a variable calculated by dividing a contestant’s order of

elimination by the total number of contestants that season. This variable provides a

way of representing a contestant’s order of elimination, but is better than simply

using a contestant’s boot order because it creates a more uniform metric across

seasons with varying numbers of contestants. To create the first table, displaying

contestants’ average overall finish by their ability in individual challenges, I first

divided all contestants with non-zero “Mean Percent Challenge Finish” scores into

quartiles.17 I then calculated the “Average Overall Finish” for male and female

contestants within each “Mean Percent Challenge Finish” quartile (see Figure I). I

repeated the same procedure based on contestants’ “Vote for Boot Percent” (see

Figure II) and the sum of contestants’ “Mean Percent Challenge Finish” and “Vote

for Boot Percent” (see Figure III). My objective here is to give an overview of how the

overall finish of male and female contestants of similar abilities differ. Note that

these charts do not explain a causal relationship between male and female

contestants’ abilities and their overall finish, since the charts show averages within

groups rather than correlations between variables.

17 Recall that not all Survivor contestants participate in individual challenges since the vast

majority of individual challenges take place after the tribal merge. Contestants who do not

compete in any individual challenges have a mean percent challenge finish value equal to 0,

and are thus excluded from this first chart.

22

7.2 Results & Discussion

Figure I shows the average overall finish of male and female contestants within

quartiles for their “Mean Percent Challenge Finish.” The total sample size for this

table is 392 contestants; 203 contestants were excluded from this sample because

they did not compete in any individual challenges. Note that the first quartile, or

“Q1,” is the group of contestants with the lowest “Mean Percent Finish” scores; these

contestants, on average, performed the worst in individual challenges relative to

their peers. The contestants in the fourth quartile, or “Q4,” represent the

contestants with the top 25% of “Mean Percent Finish” scores. Figure I shows that

women with challenge performances in the first and third quartiles progressed

further on average in the game than their male counterparts. Men and women

whose “Mean Percent Challenge Finish” falls in the second quartile progressed

equally far in the game, on average. But amongst contestants with the strongest

individual challenges (Q4), the average male contestant progressed further than the

average female. Among the four quartiles, women whose average challenge

performance fell in the third quartile of contestants progressed further on average

than other female contestants; the same is true of male contestants with average

percent challenge finish in the third quartile. However, women with the strongest

challenge performances (Q4) had the worst average overall percent finish among

women in each of the quartiles, whereas men with the worst challenge performances

(Q1) progressed the least on average compared to the average male contestant in the

other quartiles.

Figure II shows the “Mean Overall Finish” of male and female contestants

within quartiles by their vote for boot percent at Tribal Councils. The sample for this

chart includes all 595 Survivor contestants. In general, the chart shows that as both

male and female contestants’ “Vote for Boot Percentages” increase, the “Mean

Overall Finish” of the average contestant also increases. This is sensible, since

voting for the boot indicates that the contestant voted with the majority of his or

tribemates at that Tribal Council. If a contestant repeatedly successfully votes for

the boot, the contestant is likely part of a majority alliance; unless a majority

alliance decides to turn on one of its members, contestants within a majority alliance

should progress farther in the game than their tribe mates who are not included in

23

the majority alliance, since the alliance will eliminate these outlying contestants

before eliminating its own members. The table shows that male and female

contestants with the best “Vote for Boot Percentages” (Q3 and Q4) finished, on

average, in approximately the same place as each other. However, among

contestants with the worst Tribal Council voting histories (Q1 and Q2), the average

male contestant progressed further in Survivor than the average female contestant.

Finally, Figure III divides contestants into quartiles based on the sum of

their “Mean Percent Challenge Finish” and their “Vote for Boot Percent.”18 This

sample includes 392 contestants (the same sample size as Figure I) as it excludes all

contestants who never participated in an individual challenge. Women with scores

in the second, third, and forth quartiles had a higher average overall finish than

men in these quartiles. However, between men and women with scores in the lowest

quartile, the average male contestant progressed further than the average female

contestant.

On the whole, most of the average overall finishes between men and women

in the same ability quartile are relatively close, separated by at most two places in

Survivor.19 Based on this observation one might hypothesize that among contestants

with similar abilities, a contestant’s gender does not significantly impact how far he

or she will progress in Survivor.

8 Conclusions

This paper makes three primary insights about the role of gender within Survivor.

First, there is an inverse relationship between the average gender of the group who

votes to eliminate a contestant and the eliminated contestant’s gender. Second,

women are more likely to band together and vote out a man, while men who vote

together do not, as a rule, use gender as a criteria to identify a target. Third, male

18 The maximum sum of a contestant’s “Mean Percent Challenge Finish” and “Vote for Boot

Percent” is 2, since the maximum value for each variable is 1. 19 Among the calculations displayed in Figures I, II, and III, the greatest difference in mean

overall finish between men and women occurs amongst contestants whose mean percent

challenge finish falls in the first quartile (the difference between female and male average

overall finish here is 0.12). In seasons with 16 contestants, outlasting one additional

contestant adds 0.0625 to a contestant’s overall finish. Therefore, a difference in average

overall finish of 0.12 represents the progression of no more than two “places” in Survivor.

24

and female jurors do not appear to regard differently a finalist’s role in their

elimination when deciding their vote at the Final Tribal Council. Thus the most

valuable strategic finding from this paper serves as a warning to male Survivor

contestants: if female contestants are voting (and successfully eliminating

opponents) together, men would do well to heed the old adage, “if you can’t beat ‘em,

join ‘em,” and either align with or defeat this female group, as these women are

more likely to vote against male contestants than female contestants.

This paper’s most significant contribution to the body of academic work on

gender and competition is the insight that women who band together tend to target

male contestants. This pattern complements the finding from Burow et al. (2017)

that women prefer competing against other women than against men. Although all

women in Survivor have self-selected to compete in the game against both male and

female opponents, it may be the case that female contestants still feel more

comfortable competing amongst other women, and are thus more inclined to align

with other women and eliminate men.

The methods and results in this paper highlight both the interesting and

challenging aspects of using Survivor to evaluate patterns of social behavior. On one

hand, Survivor acts as a self-selecting multi-period game in which contestants

compete under conditions of deprivation for $1 million, yielding human decisions

that are generally unimpeded by the situational effects of more simplistic controlled

studies, including boredom and apathy. In this way, Survivor can be a platform for

studying a multitude of social and psychological phenomena including, but also far

beyond, behavioral gender differences. My results suggest that a purely quantitative

analysis of the objective components of Survivor may not fully capture the important

factors that influence contestants’ decisions. More thorough analyses of the role of

gender and other social science theories within Survivor might consider the

qualitative relationships and personalities of individual players alongside the type of

quantitative data I identified.

25

References

[1] Antonovics, Kate; Arcidiacono, Peter and Walsh, Randall. “Games and

Discrimination: Lessons from The Weakest Link”. Journal of Human

Resources, Fall 2005, XL:4, pp. 918-947.

[2] Burow, Norma; Beblo, Miriam; Beninger, Denis and Schröder, Melanie. “Why

Do Women Favor Same-Gender Competition? Evidence from a Choice

Experiment”. Discussion Paper #1662. DIW Berlin, German Institute for

Economic Research, 2017.

[3] Dato, Simon and Nieken, Petra. 2014. “Gender differences in competition and

sabotage”. The Journal of Economic Behavior and Organization, April 2014,

100, pp. 64-80.

[4] Dilks, Lisa M.; Thye, Shane R. and Taylor, Patricia A. “Socializing economic

theories of discrimination: Lessons from Survivor”. Social Science Research,

November 2010, 39:6, pp. 1164-1180.

[5] Dreber, Anna; von Essen, Emma and Ranehill, Eva. “Gender and competition

in adolescence: task matters”. Experimental Economics, March 2014, 14:1, pp.

154-172.

[6] Halladay, Brianna Noelle. “Gender, Competition, & Confidence with

Methodological Insights: Experimental Evidence”. PhD Dissertation.

University of California, Santa Barbara; Department of Economics, March

2017.

[7] Hedges, Carolyn D. “The Gender Factor of Survivor: A Q-Method Approach”.

The International Journal of Q Methodology, 2014, 37:1-2, pp. 2-22.

[8] Lee, Sun Young; Kesebir, Selina and Pillutla, Madan M. “Gender differences

in response to competition with same-gender coworkers: A relational

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pp. 869-886.

[9] Mixon, Franklin G., Jr. “Cartel (In)Stability on Survivor Island”. Journal of

Education for Business, November/December 2001, 77:2, pp. 89-94.

26

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27

TABLE II

N Percent / Mean (Std. Dev.)

Total Contestants 595

Contestant Sex

Female 296 0.50

Male 299 0.50

Contestant Race

White 446 0.75

Minority 149 0.25

Black 72 0.12

Hispanic 41 0.07

Asian 36 0.06

Contestant Age

In years 33.53 (10.35)

By group

< 20 years 5 0.01

20-29 years 269 0.45

30-39 years 170 0.29

40-49 years 98 0.16

50-59 years 41 0.07

60+ years 12 0.02

Contestant Residence

Northeast 138 0.23

Midwest 74 0.12

South 167 0.28

West 216 0.36

Source: Survivor Wiki

Descriptive Demographic Statistics for

Survivor Contestants (Seasons 1-33)

Note: N represents sample sizes for each population. For binary

variables, the percentage of indivudals in that population is

given. For continuous variables, the variable's mean is given

with its standard deviation in parentheses.

28

TABLE III

N Percent / Mean (Std. Dev.)

Total Winners 33*

Returning Players 6† 0.18

Winner Sex

Female 14 0.42

Male 19 0.58

Winner Race

White 26 0.79

Minority 7 0.21

Black 3 0.09

Hispanic 2 0.06

Asian 2 0.06

Winner Age

In years 31.21 (7.81)

By group

< 20 years 0 0.00

20-29 years 16 0.48

30-39 years 14 0.42

40-49 years 2 0.06

50-59 years 1 0.03

60+ years 0 0.00

Winner Residence

Northeast 13 0.39

Midwest 3 0.09

South 6 0.18

West 11 0.33

Source: Survivor Wiki

Descriptive Demographic Statistics for

Survivor Winners (Seasons 1-33)

Note: N represents sample sizes for each population. For binary

variables, the percentage of indivudals in that population is

given. For continuous variables, the variable's mean is given

with its standard deviation in parentheses.

† Sandra Diaz-Twine is counted once as a returning player (for

her second Survivor win).

* Sandra Diaz-Twine won both Survivor: Pearl Islands and

Survivor: Heroes vs. Villains. This table treats Sandra as a

unique player in each season.

29

TABLE IV Descriptive Performance Statistics of Survivor Contestants by Gender

Female Male

N Percent / Mean (Std. Dev.) N Percent / Mean (Std. Dev.)

Overall Finish* 9.87 (5.54) 9.28 (5.11)

Mean % Finish in Individual Challenges† 0.53 (0.14) 0.60 (0.18)

Votes Against Boot Percentage‡ 0.52 (0.31) 0.54 (0.29)

Winner 14 0.42 19 0.58

Finalist§ 29 0.57 22 0.43

Jury Member 117 0.45 144 0.55

* Categorical variable. Winner coded as 1, second-place finisher coded as 2, third-place finisher coded as 3, etc.

§ Constants who make it to the Final 2 or Final 3. Statistic here excludes winners.

Note: N represents sample sizes for each population. For binary variables, the percentage of indivudals in that population is

given. For continuous variables, the variable's mean is given with its standard deviation in parentheses.

† Captures how contestants placed on average in individual challenges. For example, in an individual challenge with five

competitors, the challenge winner’s percent finish would be coded as 1, the second-place finisher’s percent finish would be

coded as 0.8, the third-place finisher’s percent finish would be coded as 0.6, and so on. These percent finishes are averaged for

each contestant across every individual challenge in which they participate to generate a contestant’s mean percent finish.

Note that individual challenges, with very few exceptions, take place only after the tribe merge, so contestants who never

participated in an individual challenge are excluded from this statistic.

‡ Counts at how many Tribal Councils a contestant cast a vote against the contestant who was eliminated at that Tribal

Council (known as the “bootee”) and divides it by the total number of Tribal Councils the contestant attended.

30

TABLE V

Dependent Variable: Bootee is Female (Binary)

(1) (2) (3) (4)

Total No. Female Eliminators -0.282** -0.260**

(-3.06) (-2.71)

Total No. Male Eliminators 0.135 0.069

(1.67) (0.80)

Eliminator Group Gender (% Female)† -1.553**

(-3.28)

Fixed Effects for Season? Yes Yes Yes Yes

N‡ 411 411 411 411

* p<0.05, ** p<0.01, *** p<0.001

Source: Survivor Wiki

Note: t-statistics reported in parentheses. Data include eliminated contestants from seasons 1-33.

† Eliminator Group Gender equals the percentage of female eliminators in a particular eliminator group.

‡ Contestants who were eliminated by non-traditional means (in a Tribal Council with an initial tie vote or the

successful use of an immunity idol, by quitting the game, or by medical evacuation) are excluded from the

sample. Finalists (contestants who last until the Final Tribal Council) are also excluded from the sample.

Conditional Fixed Effects Logit Estimates of the Effect of

Eliminators' Gender on the Bootee's Gender

31

TABLE VI

Dependent Variable: Bootee is Female

(1) (2) (3) (4)

Total No. Female Eliminators -0.219 -0.170

(-1.75) (-1.27)

Total No. Male Eliminators 0.184 0.128

(1.61) (1.05)

Eliminator Group Gender (% Female)† -1.587*

(-2.25)

Dummy for Post-Merge‡ -0.587 -1.151 -0.597 -1.069

(-0.83) (-1.77) (-0.63) (-1.55)

Post-Merge * Total No. Female Eliminators -0.108 -0.128

(-0.57) (-0.65)

Post-Merge * Total No. Male Eliminators -0.0156 -0.0278

(-0.09) (-0.16)

Post-Merge * Eliminator Group Gender (% Female) -0.0720

(-0.08)

Fixed Effects for Season? Yes Yes Yes Yes

N§ 411 411 411 411

* p<0.05, ** p<0.01, *** p<0.001

Source: Survivor Wiki

Note: t-statistics reported in parentheses. Data include eliminated contestants from seasons 1-33.

† Eliminator Group Gender equals the percentage of female eliminators in particular eliminator group.

‡ Post-Merge equals 1 if bootee was eliminated after tribal merge.

§ Contestants who were eliminated by non-traditional means (in a Tribal Council with an initial tie vote or

the successful use of an immunity idol, by quitting the game, or by medical evacuation) are excluded from the

sample. Finalists (contestants who last until the Final Tribal Council) are also excluded from the sample.

Conditional Fixed Effects Logit Estimates of Effects

of Eliminators' Gender on Bootee's Gender, with

Interaction Term for Post-Merge

32

TABLE VII

Dependent Variable: Female Juror Voted for Finalist to Win (Binary)

Unscaled Elimination Scaled Elimination

(1) (2) (3) (4) (5) (6) (7) (8)

Finalist Eliminated Juror† -0.945* -0.934* -0.912 -0.843

(-1.99) (-1.96) (-1.90) (-1.72)

Finalist Eliminated Juror (Scaled)‡ -1.344 -1.366 -1.204 -0.980

(-1.39) (-1.41) (-1.16) (-0.92)

% of Pre-Merge Game in Same Tribe§ -0.117 -0.375 -0.186 -0.417

(-0.33) (-0.83) (-0.52) (-0.92)

% of Same Votes at Shared Tribal Councils¶ 0.328 0.670 0.212 0.627

(0.59) (0.97) (0.36) (0.84)

Finalist's Mean Percent Challenge Finish†† 3.871** 3.865** 3.869** 3.844** 3.651** 3.650** 3.640** 3.614**

(3.24) (3.23) (3.24) (3.21) (3.12) (3.11) (3.11) (3.09)

Finalist's Vote for Boot Percent‡‡ 0.164 0.189 -0.0288 -0.151 -0.00789 0.0413 -0.151 -0.324

(0.10) (0.12) (-0.02) (-0.09) (-0.01) (0.03) (-0.09) (-0.20)

Fixed Effects for Season? Yes Yes Yes Yes Yes Yes Yes Yes

N 221 221 221 221 221 221 221 221

* p<0.05, ** p<0.01, *** p<0.001

Source: Survivor Wiki and True Dork Times

Note: t-statistics in parentheses.

† Finalist Eliminated Juror is a binary variable equal to 1 if the finalist voted against the juror at the juror's elimination Tribal Council.

§ % of Pre-Merge Game Played Together gives the percentage of pre-merge Tribal Councils that the juror and finalist both attended.

‡‡ Finalist's Vote for Percent gives the frequency with which the finalist voted against the boot at Tribal Council.

Conditional Fixed Effects Logit Estimates for Effect of a Finalist's Elimination of a Female Juror on

the Female Juror's Decision to Vote for the Finalist to Win Survivor

‡ Finalist Eliminated Juror (Scaled) scales the previous variable by the stage of the game in which the juror was eliminated (a higher number means

the juror lasted longer in the game).

¶ % of Same Votes at Shared Tribal Councils takes the percentage of Tribal Councils the juror and finalist both attended at which the juror and finalist

voted against the same contestant.

†† Finalist's Mean Percent Challenge Finish gives the finalist's average finish in individual challenges (a higher number means the finalist performed

better on average in individual challenges).

33

TABLE VIII

Dependent Variable: Male Juror Voted for Finalist to Win (Binary)

Unscaled Elimination Scaled Elimination

(1) (2) (3) (4) (5) (6) (7) (8)

Finalist Eliminated Juror† -0.938* -0.965* -0.903* -0.926*

(-2.50) (-2.51) (-2.33) (-2.37)

Finalist Eliminated Juror (Scaled)‡ -1.333 -1.358 -1.225 -1.216

(-1.69) (-1.68) (-1.38) (-1.37)

% of Pre-Merge Game in Same Tribe§ -0.111 -0.239 -0.0450 -0.126

(-0.32) (-0.59) (-0.13) (-0.32)

% of Same Votes at Shared Tribal Councils¶ 0.163 0.334 0.134 0.231

(0.35) (0.61) (0.27) (0.39)

Finalist's Mean Percent Challenge Finish†† 1.958 1.966 1.932 1.921 1.927 1.930 1.909 1.904

(1.85) (1.85) (1.82) (1.81) (1.83) (1.83) (1.81) (1.81)

Finalist's Vote for Boot Percent‡‡ -0.888 -0.882 -0.917 -0.932 -1.089 -1.086 -1.125 -1.140

(-0.61) (-0.60) (-0.63) (-0.64) (-0.76) (-0.75) (-0.78) (-0.79)

Fixed Effects for Season? Yes Yes Yes Yes Yes Yes Yes Yes

N 270 270 270 270 270 270 270 270

* p<0.05, ** p<0.01, *** p<0.001

Source: Survivor Wiki and True Dork Times

Note: t-statistics in parentheses.

† Finalist Eliminated Juror is a binary variable equal to 1 if the finalist voted against the juror at the juror's elimination Tribal Council.

§ % of Pre-Merge Game Played Together gives the percentage of pre-merge Tribal Councils that the juror and finalist both attended.

‡‡ Finalist's Vote for Percent gives the frequency with which the finalist voted against the boot at Tribal Council.

Conditional Fixed Effects Logit Estimates for Effect of a Finalist's Elimination of a Male Juror on the

Male Juror's Decision to Vote for the Finalist to Win Survivor

‡ Finalist Eliminated Juror (Scaled) scales the previous variable by the stage of the game in which the juror was eliminated (a higher number means

the juror lasted longer in the game).

¶ % of Same Votes at Shared Tribal Councils takes the percentage of Tribal Councils the juror and finalist both attended at which the juror and finalist

voted against the same contestant.

†† Finalist's Mean Percent Challenge Finish gives the finalist's average finish in individual challenges (a higher number means the finalist performed

better on average in individual challenges).

34

TABLE IX

Dependent Variable: Juror Voted for Finalist to Win (Binary)

Unscaled Elimination Scaled Elimination

(1) (2) (3) (4) (5) (6) (7) (8)

Finalist Eliminated Juror† -0.957* -0.989** -0.928* -0.956*

(0.374) (0.383) (0.385) (0.388)

Finalist Eliminated Juror (Scaled)‡ -1.319 -1.361 -1.217 -1.232

(0.776) (0.798) (0.858) (0.859)

% of Pre-Merge Game in Same Tribe§ -0.121 -0.231 -0.053 -0.133

(0.337) (0.398) (0.334) (0.392)

% of Same Votes at Shared Tribal Councils¶ 0.129 0.306 0.132 0.245

(0.445) (0.525) (0.473) (0.554)

Finalist's Mean Percent Challenge Finish†† 2.316* 2.324 2.298* 2.291* 2.241* 2.248* 2.221* 2.217*

(1.045) (1.045) (1.047) (1.047) (1.036) (1.036) (1.037) (1.037)

Finalist's Vote for Boot Percent‡‡ -0.693 -0.678 -0.747 -0.771 -0.808 -0.784 -0.881 -0.902

(1.313) (1.313) (1.321) (1.322) (1.301) (1.301) (1.316) (1.318)

Dummy for Female Juror§§ -1.244 -1.268 -1.267 -1.251 -1.057 -0.995 -1.098 -1.036

(1.700) (1.719) (1.70) (1.724) (1.686) (1.705) (1.692) (1.710)

Female Juror * Finalist Eliminated Juror 0.126 0.167 0.113 0.196

(0.572) (0.579) (0.579) (0.587)

Female Juror * Finalist Eliminated Juror (Scaled) 0.161 0.185 0.204 0.397

(1.201) (1.216) (1.284) (1.304)

Female Juror * % of Pre-Merge Game in Same Tribe 0.0166 -0.139 -0.130 -0.279

(0.486) (0.583) (0.482) (0.581)

Female Juror * % of Same Votes at Shared Tribal Councils 0.264 0.401 0.124 0.393

(0.678) (0.813) (0.712) (0.861)

Female Juror * Finalist's Mean Percent Challenge Finish 1.512 1.501 1.527 1.504 1.443 1.432 1.452 1.431

(1.475) (1.477) (1.476) (1.478) (1.460) (1.461) (1.461) (1.464)

Female Juror * Finalist's Vote for Boot Percent 0.263 0.258 0.151 0.077 0.143 0.140 0.102 -0.011

(1.569) (1.57) (1.596) (1.61) (1.552) (1.555) (1.590) (1.609)

Fixed Effects for Season? Yes Yes Yes Yes Yes Yes Yes Yes

N 491 491 491 491 491 491 491 491

* p<0.05, ** p<0.01, *** p<0.001

Source: Survivor Wiki and True Dork Times

Note: standard errors in parentheses.

† Finalist Eliminated Juror is a binary variable equal to 1 if the finalist voted against the juror at the juror's elimination Tribal Council.

§ % of Pre-Merge Game Played Together gives the percentage of pre-merge Tribal Councils that the juror and finalist both attended.

‡‡ Finalist's Vote for Percent gives the frequency with which the finalist voted against the boot at Tribal Council.

§§ Dummy for Female Juror equals one if juror is female.

Conditional Fixed Effects Logit Estimates for Effect of a Finalist's Elimination of a Juror on the Juror's

Decision to Vote for the Finalist to Win Survivor , with Interaction Term for Juror's Gender

‡ Finalist Eliminated Juror (Scaled) scales the previous variable by the stage of the game in which the juror was eliminated (a higher number means the juror lasted longer

in the game).

¶ % of Same Votes at Shared Tribal Councils takes the percentage of Tribal Councils the juror and finalist both attended at which the juror and finalist voted against the

same contestant.

†† Finalist's Mean Percent Challenge Finish gives the finalist's average finish in individual challenges (a higher number means the finalist performed better on average in

individual challenges).

35

FIGURE I Male and Female Overall Mean Percent Finish

by Average Challenge Finish Quartiles

Source: Survivor Wiki and True Dork Times

Note: all contestants were divided into quartiles based on mean percent finish in challenges, then

average finish for men and women was computed within each quartile. A contestant's average

finish is calculated by dividing the order in which a contestant was eliminated by the total

number of contestants that season. For example, in a season with 16 contestants, the last place

(i.e. 16th place) finisher would have an overall percent finish of 0.0625. The season winner always

has an overall percent finish of 1.

0.620.64

0.720.68

0.74

0.64

0.77

0.63

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

Q1 Q2 Q3 Q4

Mean

Overa

ll P

erc

en

t F

inis

h

Challenge Mean Percent Finish, Quartiles

Male Female

36

FIGURE II Male and Female Overall Mean Percent Finish

by Vote for Boot Percent Quartiles

Source: Survivor Wiki and True Dork Times

Note: all contestants were divided into quartiles based on vote for boot percent at Tribal Councils,

then average finish for men and women was computed within each quartile. A contestant's

average finish is calculated by dividing the order in which a contestant was eliminated by the

total number of contestants that season. For example, in a season with 16 contestants, the last

place (i.e. 16th place) finisher would have an overall percent finish of 0.0625. The season winner

always has an overall percent finish of 1.

0.31

0.52

0.58

0.80

0.21

0.47

0.60

0.80

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

Q1 Q2 Q3 Q4

Mean

Overa

ll P

erc

en

t F

inis

h

Vote for Boot Percent, Quartiles

Male Female

37

FIGURE III Male and Female Overall Mean Percent Finish by Sum of

Mean Percent Challenge Finish and Vote for Boot Percent Quartiles

Source: Survivor Wiki and True Dork Times

Note: all contestants were divided into quartiles based on the sum of their mean percent finish in

challenges and vote for boot percent at Tribal Councils, then average finish for men and women

was computed within each quartile. A contestant's average finish is calculated by dividing the

order in which a contestant was eliminated by the total number of contestants that season. For

example, in a season with 16 contestants, the last place (i.e. 16th place) finisher would have an

overall percent finish of 0.0625. The season winner always has an overall percent finish of 1.

0.55

0.63

0.70

0.79

0.53

0.71

0.75

0.82

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

Q1 Q2 Q3 Q4

Mean

OV

era

ll P

erc

en

t F

inis

h

Challenge Mean Percent Finish + Vote for Boot Percent, Quartiles

Male Female