A Statistical Analysis of Editorial
Influence and Author–Character
Similarities in 1990s New Yorker
Fiction............................................................................................................................................................

Katherine L. Milkman
Harvard University, Boston, MA 02138, USA
René Carmona and William Gleason
Princeton University, Princeton, NJ 08544, USA.......................................................................................................................................

Abstract

Correspondence:
Katherine L. Milkman
E-mail:kmilkman@fas.harvard.edu

We present a quantitative analysis of 442 pieces of fiction published between
5 October 1992 and 17 September 2001 in the New Yorker magazine. We address
two independent questions using the same data set. First, we examine whether
changes in the Executive Editor or Fiction Editor are associated with significant
changes in the type of fiction published at the New Yorker. Second, we examine
whether New Yorker authors write fiction more often than not about characterswith whom they share demographic traits. We find that changes in Fiction Editor
at the New Yorker are associated with numerous significant, quantifiable changes in
the magazine’s fiction and that these effects are greater than those associated witha change in the New Yorker’s Executive Editor. We also find that authors of New
Yorker fiction write significantly more often than not about protagonists who share
their race, gender, and country of origin and who are within or below their age
range. The same is true of secondary characters except in the case of gender.

.................................................................................................................................................................................

1 Introduction
This article employs the tools of statistics toanalyze various aspects of the fiction published in
the New Yorker magazine between 5 October 1992
and 17 September 2001. The analyses conductedseek to answer two very different questions about
New Yorker fiction from this era, both of which canbe addressed using the same data set. The firstquestion this article attempts to answer is whether
changes in Executive Editors or Fiction Editors
are associated with significant changes in the typeof fiction published at the New Yorker. The second
question is whether New Yorker authors write fiction
about characters who are similar to themselves.

The New Yorker is the most prestigious, widely
read and frequently published literary magazine in
the world. Its influence on America’s cultural scene
is generally agreed to be unparalleled among publications of its genre, and since the early twentiethcentury, aspiring as well as established writers haveviewed inclusion in its pages as one of the highest
honors attainable, offering the potential to launcha literary career. In the past decade, the magazine
has published an average of 52 pieces of fiction
annually, selected through a process that necessarily
involves subjective editorial judgment but isintended to be meritocratic. These stories are
viewed by literary critics and writers worldwide asa sample of the most compelling modern short

Literary and Linguistic Computing, Vol. 22, No. 3, 2007. ! The Author 2007. Published by Oxford University Press onbehalf of ALLC and ACH. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
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Advance Access Published on 2 May 2007305

K. L. Milkman et al.

fiction, although it should be noted that the
New Yorker does not necessarily publish a representative sample of modern fiction. However, inlight of its unique characteristics as a literary
magazine that has consistently published whatmost would argue to be the best contemporary
short fiction in a periodical, the New Yorker is an
ideal venue for analyzing modern short stories.
Of particular interest to the authors of thisarticle is the nature of the relationship between the
New Yorker’s editors and the magazine’s fictional
content. Since the submissions editors inspire andthe stories they subsequently choose to print
significantly shape the universe, at least domestically, of published short fiction in any given
month, year, or decade, gaining insight into what
type of influence, if any, editors exert over the
New Yorker’s fiction content seems a worthwhile
endeavor. For example, if the personal preferencesof editors—conscious or unconscious—or pressures
to satisfy particular audiences appear to influence
what fiction is printed in the New Yorker, an
exposure of trends in the fiction published bydifferent editors could lead to a better understanding of why New Yorker short fiction, andperhaps short fiction more generally, has been
dominated in recent years by particular genres ortypes of authors.
A second major unknown addressed in this article
is the extent to which authors write fiction aboutcharacters similar to themselves, a question that has
been raised time and again about authors and theirwork. Although literary criticism of individual pieces
often addresses the degree to which a writer has
drawn from personal experience, to our knowledge,
an attempt has never been made to analyze thisquestion with a large enough population to obtain
results with statistical significance. With the sizeable
amount of data available from 1990s New Yorker
fiction, it is possible to determine how often
New Yorker writers invent stories about characters
who resemble themselves, as well as which of theirown characteristics they most frequently project
onto their protagonists and secondary characters.
Essentially, this analysis will provide some insight
into how autobiographical modern New Yorker shortstories typically are.

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The era selected for study is of interest because it
is contemporary and because there were twodifferent Executive Editors at the New Yorker
during this time, as well as two different sets ofFiction Editors. In 1992, Tina Brown, the avantgarde editor of Vanity Fair, became the third
Executive Editor of the New Yorker, shaking up its
somewhat stilted image and drawing renewed
attention to the periodical.1 At the time, the
New Yorker’s Fiction Department was under the
stewardship of the magazine’s Deputy Editor,
Charles McGrath, a well-respected editor andwriter who had been with the New Yorker since
the early 1970s.2 Two other senior Fiction Editors,
Roger Angell and Daniel Menaker, largely shared
McGrath’s responsibilities and were essentially his
equals in the Fiction Department’s hierarchy.
Both of these men had been members of the
New Yorker staff for over 20 years by 1992,3,4 and thethree were considered to have fairly traditional
tastes in fiction, in part due to the time they spentat the New Yorker while it was under the stewardship of conservative Executive Editor William
Shawn. However, in early 1995, Bill Buford of theunconventional British literary magazine Granta
was tapped to take over the New Yorker Fiction
Department, and during his reign, the Department
was controlled by one rather than multiple editors.5
Buford’s tenure continued beyond the end of the era
studied in this article and weathered the 1998
transition to a new, less risqué Executive Editor,
when Pulitzer Prize winning author and New Yorker
staff writer David Remnick replaced Tina Brown.6
The period analyzed in this article allows thesignificance of changes in both fiction and executive editors to be assessed and ends just before
11 September 2001, in order to avoid dealing withany alterations in New Yorker content that may havebeen a result of the terrorist attacks.
The data set of New Yorker fiction referenced inthis article, which contains thirty-nine variables foreach of the 442 stories examined, was collected by
K.L. Milkman. The variables contained within thisdata set are listed in Table 1, and an explanationof the methodologies used in the compilation ofthis database is available upon request. Not every
variable was available for every piece of fiction,

Author–character similarities in New Yorker Fiction

Table 1 New Yorker fiction data set variables: all stories in issues between 5 October 1992 and 17 September 2001
Story

AuthorProtagonist

Secondary A

Secondary B

Date of publication
Double/single issue
Title
Exact location
US state location
Geographical region
Topics (up to seven)
Language (if translated)
Page length
1st or 3rd person

Name
GenderCountry of origin
Home region(s)Age range (decade)Race
Sexuality
Religion

GenderCountry of origin
Age range (decade)Race
Occupation
Sexuality
Religion

GenderCountry of origin
Age range (decade)Race
Occupation
Sexuality
Religion

GenderCountry of origin
Age range (decade)Race
Occupation
Sexuality
Religionand missing variables were excluded from all
analyses conducted.
In Section 2 of this article, we present an overview of past work on similar topics. In Section 3, we
attempt to answer the question of whether or notchanges in Executive Editor or Fiction Editor areassociated with significant changes in the type offiction published in the New Yorker. In Section 4, we
examine whether or not New Yorker authors write
fiction about characters who share their demographic characteristics. In Section 5, a summary of
the main conclusions resulting from this article ispresented.

2 Literature Review
Although a great deal of quantitative literary analysis has been conducted in the past, most notably by
Burrows (1987), Craig (2004), and Hoover (1998),
little if any analysis has been carried out using data
sets compiled by a human reader. The majority ofprevious computational studies of literature havebeen conducted on data sets collected by computer
programs, which are not sophisticated enoughto determine the demographic characteristics ofa protagonist, to classify secondary characters, or todetect story topics, among other things. Instead,past studies have focused on such subjects as
authorship attribution, word frequency, and translation. It is the hope of the authors of this article
that future computational studies of literature will
include work that relies on human readers to create
data sets containing attributes of fiction that are notdetectable using computer codes.7

3 Study 1: Editorial Shifts and1990s New Yorker Fiction
Even for those who have worked at the New Yorker
for decades, it is difficult to understand or explain
the impact of editorial changes on the magazine’s
fictional content. However, it is possible toanalyze various components of New Yorker fiction
to determine if and how they changed concurrently
with the editorial power shifts that took place
during the 1990s. Here we examine differences
in a sample of 442 stories, 120 of which were
published under Charles McGrath,8 who served as
the New Yorker’s highest ranking Fiction Editor
between 5 October 1992 and 10 April 1995, and322 of which were published under Bill Buford,
who served as Fiction Editor of the New Yorker
from 17 April 1995 through 17 September 2001.
We also examine differences in the same
sample of 442 stories published under successive
Executive Editors: 275 under Tina Brown, who
served as Executive Editor from 5 October 1992
until 3 August 1998, and 167 under David Remnick,
who took over as Executive Editor on 10 August
1998 and was still serving in that capacity on
17 September 2001.
In order to evaluate what changes in New Yorker
fiction were associated with different editorial
shifts that took place during the 1990s, we examine
the proportion of stories exhibiting various characteristics before and after editorial shakeups at
the magazine. The discussion that follows presents
our significant findings and refers readers to tables

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K. L. Milkman et al.

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70%

Percentage of stories
with third-person
narrators

A number of components of New Yorker fictionchanged significantly when there was a change in themagazine’s Fiction Editor but remained unchanged
when there was a switch in the New Yorker’s
Executive Editor. We begin by discussing significant
shifts in the proportions of stories with certain
characteristics that accompanied Bill Buford’s rise to
the helm of the fiction department but were notassociated with a change in the New Yorker’s
Executive Editor. First, we find that the proportionof stories published with third-person narrators (asopposed to first-person narrators) increased significantly under Bill Buford but was not statisticallydifferent under Tina Brown and David Remnick(Fig. 1 and Appendix Table 2). Similarly, there wasa significant increase in the percentage of fiction
published by male authors when Bill Buford took
over as Fiction Editor of the New Yorker, but the
proportion of fiction by men published in the
New Yorker was not significantly different undersuccessive Executive Editors (Fig. 2 and Appendix
Table 3). We also find that there was a significant
increase in the variance of the page length of
New Yorker fiction pieces published under Bill
Buford as compared with pieces published underCharles McGrath, whereas there was no significant
change in the variance in the length of fiction
published under successive Executive Editors of themagazine (Fig. 3 and Appendix Table 4).
The Fiction Editor transition we studied also
produced more shifts in the topics of New Yorker
fiction than a change in the magazine’s Executive
Editor. We examine the proportion of stories focusing on each of twenty-eight topics (topics wereselected for analysis in advance of the data collection stage) under each editor in our sample.9 Bill10
Buford’s rise to control over the New Yorker’s
fiction department was accompanied by seven significant shifts in the frequency of story topics, while
the change in Executive Editor we studied was only
accompanied by four significant shifts in thefrequency of story topics (Figs 4 and 5, Appendix
Tables 5 and 6).

60%

62%
50%

50%
40%

McGrath
Buford

30%
20%
10%
0%
Fiction editor

Fig. 1 Shift in percentage of stories with third-person
narrators published under different Fiction Editors
80%
Percentage of stories
by male authors

3.1 Results

In addition to examining significant shifts in the
proportion of stories with certain characteristics
that coincided with a change in the New Yorker’s
Fiction Editor, we examine shifts in the distributions of story characteristics. We find that the
distribution of story settings across the nine US
Census regions pictured in Fig. 6 shifted significantly when there was a change in the New Yorker’s
Fiction Editor but remained relatively unalteredunder the different New Yorker Executive Editors inour sample. In particular, the proportion of stories

70%
60%

70%
57%

50%
40%
30%

McGrath
Buford20%
10%0%Fiction editor

Fig. 2 Shift in percentage of male authors publishedunder different Fiction Editors
7.50

Variance in stories’
page lengths

in the Appendix for more detailed statisticalevidence supporting each of our results.

6.00

6.58
5.04

4.50
McGrath
Buford

3.00
1.50
0.00
Fiction editor

Fig. 3 Shift in variance of stories’ page lengths under
different Fiction Editors

Author–character similarities in New Yorker Fiction

set in the Middle Atlantic Region, which encompasses New York, increased dramatically when Bill
Buford took over for Charles McGrath in mid-1995,
but this statistic was not significantly affected by themid-1998 change in Executive Editor (Fig. 7 andAppendix Table 7). The distribution of characters’
religions also shifted significantly in the fiction
published after Bill Buford replaced Charles
McGrath at the helm of the New Yorker’s Fiction
Department, but there was no significant differencein the distribution of characters’ religions in thefiction published under David Remnick and Tina
Brown. Specifically, McGrath published fiction witha significantly higher percentage of Jewish charactersthan Buford (Fig. 8 and Appendix Table 8). We alsofind that the distributions of both protagonists’ ages
and characters’ ages differed significantly under

Percentage of stories

50%
45%
40%
35%

successive stewards of the Fiction Department in the
1990s but not under successive Executive Editors.
In particular, the proportion of protagonists andcharacters in their twenties increased significantly
when Bill Buford replaced Charles McGrath, butthese proportions remained relatively stationary
when David Remnick replaced Tina Brown (Figs 9
and 10 and A1ppendix Table 9).
Four patterns in our data contradict the paradigm that Fiction Editors exert more control than
Executive Editors over the fiction published at
the New Yorker. First, we find that statistically significant shifts in the distributions of characters’
nationalities occurred when David Remnick replaced
Tina Brown, but there was not a significant shift inthese underlying distributions when Bill Buford took
over for Charles McGrath. However, the proportion

47%
39%
35%

30%
25%
20%

35%
26%

25%

McGrath

21%
17%

15%

10%

10%

Buford

15%

10%

9%

Money

Religion

12%

5%

5%
0%
Sex

ChildrenTravelDrugs

Illness

Story topic

Fig. 4 Significant shifts in story topics under different Fiction Editors
45%

43%

Percentage of stories

40%
35%
30%

30%

28%

25%

Brown

19%

18%

20%
15%

10%

18%

Remnick

12%

10%
5%
0%
Marriage

Crime

NatureAging

Story topic

Fig. 5 Significant shifts in story topics under different Executive Editors
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K. L. Milkman et al.

Fig. 6 Breakdown of nine US Census regions
40%

37%

Percentage of stories

35%
30%
25%
20%

23%
18%

21% 21%

19%

17%

15%

13%

10%

8%

7%

5%

McGrath
Buford12%
5%0%
New England Middle Atlantic

East
N. Central

West
N. Central

South Atlantic,
E.S. Central &W. S. Central

Mountain &Pacific

US Census region(s)Fig. 7 Shift in distribution of stories set in each US Census region under different Fiction Editorsof characters from England decreased significantly
when Bill Buford replaced Charles McGrath, but
remained almost unchanged when David Remnick
replaced Tina Brown (Figs 11 and 12 and Appendix
Table 10).11 We also find that there were significant
shifts in the distributions of the races of characters
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and of authors when David Remnick replaced
Tina Brown at the helm of the New Yorker but
there were no shifts in these underlying distributions
when Bill Buford took over for Charles McGrath(Figs 13 and 14 and Appendix Table 11). Finally, weobserve a significant upward shift in the proportion

Author–character similarities in New Yorker Fiction

Percentage of characters

70%

63%

60%
49%

50%

42%

40%

McGrath30%

Buford

26%

20%
9%

10%

11%

0%
ChristianJewishOther

ReligionFig. 8 Shift in distribution of characters’ religions under different Fiction Editors

Percentage of protagonists ineach age group

35%
30%

30%

28%

25%

25%
20%
20%
15%

McGrath16%

15% 15%

Buford

11%

10%

10%

5%

5%

8%

7%

6%
3%

0%
< 20

20s

30s

40s

50s

60s

70s+

Age group

Fig. 9 Shift in distribution of protagonists’ ages under different Fiction Editors

Percentage of characters ineach age group35%
30% 30%

30%
25%

23%

20%
15%

14% 14%

McGrath

16%

16%
13%

10%

10%

5%

5%

Buford

11%
7%

5%5%

0%
< 20

20s

30s

40s

50s

60s

70s+

Age group

Fig. 10 Shift in distribution of characters’ ages under different Fiction Editors

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K. L. Milkman et al.

Percentage of characters

70%

63% 62%

60%
50%
40%

McGrath30%

Buford

20%
10%
10%

6%

14%

15%
6%

9%
7%

7%

0%
AmericaEnglandEurope(excluding UK)

Asia

Other

World region(s)Fig. 11 Distributions of characters from each country of origin under different Fiction Editors

Percentage of characters

70%
60%

64%

59%

50%
40%

Brown
Remnick

30%
20%
8% 7%

10%

15%

16%

11%
6%

8%

7%

0%
AmericaEnglandEurope(excluding UK)

Asia

Other

World region(s)Fig. 12 Shift distribution of characters from each country of origin under different Executive Editorsof heterosexual characters in the fiction publishedafter Remnick replaced Brown, while there was nochange observed following the Fiction Department’s
transition from McGrath to Buford (Fig. 15 andAppendix Table 12).

3.2 Discussion
The statistical analyses presented in this section
indicate that changes in the Fiction Editor of the
New Yorker were associated with changes in various
aspects of the magazine’s fiction during the era
studied and that changes in the New Yorker’s
Executive Editor were associated with fewer
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measurable changes in the fictional content of
the magazine. Overall, we find that underBill Buford:! fewer stories were published with third-person
narrators! more stories were published by male authors! there was more variance in stories’ page length! there were more stories about sex and fewer
about children, travel, drugs, money, religion,and illness! there was a shift in the geographic distributionof story settings, and more fiction was set in the
Mid-Atlantic region

Author–character similarities in New Yorker Fiction

100%

Percentage of characters

90%

84%

80%

79%

70%
60%
Brown

50%

Remnick

40%
30%
20%

7%

10%

5%

11%

5%

4%

5%

0%
Caucasian

Black

Asian

Other

Race

Fig. 13 Shift in distribution of characters of each race under different Executive Editors
100%
90%
Percentage of authors

80%

89%
78%

70%
60%
Brown

50%

Remnick

40%
30%
20%
10%

2%

0%
Caucasian

6%

7%

10%
4%

Black

Asian

5%

Other

Race

Fig. 14 Shift in distribution of authors of each race under different Executive Editors! there was a shift in the distribution of characters’
religions, and fewer characters in the fiction
were Jewish! the distribution of protagonists’ and characters’ ages shifted, and the proportion ofprotagonists and characters in their twenties
increased! there were fewer characters from England
Under David Remnick we find that:! there were more stories about marriage, nature,
and aging and fewer stories about crime! there was a shift in the distribution of characters’
countries of origin! there were shifts in the distributions of characters’ and authors’ races! more characters in the fiction were heterosexual
Additional tests we ran on the variables collectedwith respect to the question of how the fiction
published under successive editors differed yieldedno significant results but are available uponrequest. The results of the empirical analyses
presented herein demonstrate that a quantitative
Literary and Linguistic Computing, Vol. 22, No. 3, 2007313

Percentage of stories
with heterosexual
protagonists

K. L. Milkman et al.

100%
98%
98%
95%

95%

Brown
Remnick

93%
90%
Executive editor

Fig. 15 Shift in percentage of stories with heterosexual
protagonists published under different Executive Editors

study can help clarify the influence of different
editors’ preferences on a magazine’s content,
which might not otherwise be recognized orunderstood.
A significant question raised by the findings inthis section is why a change in the New Yorker’s
Fiction Editor would be associated with suchsignificant changes in the magazine’s fiction. There
are three plausible answers to this question: (1) thesubmissions the New Yorker received changed asa result of writers’ knowledge of who was making
selections at the magazine; (2) Charles McGrath
had significantly different tastes in fiction than Bill
Buford; and/or (3) our results are driven by generaltrends in short fiction in the 1990s. It is impossible
to disentangle whether explanation (1) or (2) is
responsible for our findings with the data available
to us, but this could be a fruitful area for future
research if the New Yorker would provide data
about the characteristics of unpublished manuscripts submitted to its Fiction Department. We feelsome confidence in ruling out explanation (3) for
our results given that general trends in fiction would
have manifested themselves in the analyses we
conducted of the relationship between New Yorker
fiction and a change in the New Yorker’s Executive
Editor, and they did not.
The findings from this study suggest that the
New Yorker’s Executive Editors have less influence
over the magazine’s fiction content than the New
Yorker’s Fiction Editors. However, it seems likelythat these same Executive Editors have moresignificant influence over some other aspect of the
New Yorker’s content. It would be interesting toconduct a similar study of another magazine oreven another division of the New Yorker to see how
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editorial changes at various levels impact other typesof magazine content. With respect to other literary
magazines, a greater focus on fiction vis-a-vis othercontent might translate into greater shifts in fictional content as a result of a change in Executive
Editor. It should also be acknowledged that the
influence of Brown and Remnick on the New
Yorker’s fiction arguably filtered through theirselections of fiction editors, and thus Brown’s
choice of Buford to replace McGrath could beviewed as her most significant way of exerting
influence over the magazine’s fiction.
Unfortunately, the size of the data set examinedin this study was insufficient for addressing questions about the effects of a change in Fiction Editor
under a single Executive Editor or the effects ofa change in Executive Editor served by a single
Fiction Editor. However, it would be interesting ina future study to see if Executive Editors perhaps
have a more significant impact on the New Yorker’s
fiction when their influence is not disturbed byshifts in Fiction Editors. It would also be interesting
to see if a change in Fiction Editor might havean even greater effect than detected in this study
if successive Fiction Editors were observed during a time period when there was no change in
Executive Editor.

4 Study 2: How Demographically
Similar are the Authors of
New Yorker Fiction and theCharacters they Invent?
It has long been assumed that much of fiction
contains elements of autobiography. However, it isdifficult to imagine how one might quantify theextent to which a piece of fiction is autobiographical in order to test this hypothesis. There are somecomponents of a work of fiction that can bemeasured, however, which may offer some insight
into the question of how closely an author hasmodeled her protagonist and secondary characterson herself. These measurable components areindicators of whether an author shares various
demographic characteristics with the protagonist

Author–character similarities in New Yorker Fiction

Percentage of stories

100%
84%
80%

72%
Protagonist ofsame gender

60%
40%

Protagonist ofdifferent gender

28%
16%

20%
0%
Male

FemaleGender of author

Fig. 16 Relationship between gender of author and protagonist

Percentage of stories

100%

93%

84%

80%

Protagonist
from same
country oforigin

69%

60%
40%
20%
0%

31%
16%
7%
Combined

AmericanProtagonist
from different
country oforiginNon-American

Country of origin of author

Fig. 17 Relationship between country of origin of author and protagonist

and secondary characters in a story. An examinationof the relationship between author and character
demographic characteristics at least begins toaddress the question of whether fictional characters
are crafted, to some extent, in their authors’ own
image.
Using the sample of 442 New Yorker shortstories whose characteristics we documented forthis article, we examine the rate at which New Yorker
authors write about characters who share various
aspects of their demographic profile. In the discussion that follows we present our significant findings
and refer readers to tables in the Appendix for
more detailed statistical evidence supporting each of
our results.

4.1 Results
We begin by discussing the demographic similarities
between New Yorker authors and their protagonists.
Consistent with the idea that authors write about
what they know, we find that the vast majority of

New Yorker fiction authors write stories aboutprotagonists of their gender (Fig. 16 and Appendix
Table 13). Interestingly, male and female authors do
this at differing levels—women write significantly
more often than men about protagonists of the
opposite gender. Similarly, we find that New Yorker
authors project their native nationalities on to
the protagonists they invent, the vast majorityof the time (Fig. 17 and Appendix Table 14) with
American authors writing about protagonists fromtheir country of origin at a significantly higher rate
than non-American authors.
In addition, we find that New Yorker authors
share their race with their protagonists significantly more often than not, although again, authors
from different racial groups vary in the frequency
at which they write about protagonists who share
their race (Fig. 18 and Appendix Table 15). There
was only enough data to statistically examine thestrength of the relationship between Caucasianauthors and the protagonists in their stories,
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K. L. Milkman et al.

Percentage of stories

100%

97%

89%

86%

80%

Protagonistof same
race

64%
60%
36%

40%
14%

20%

Protagonistof different
race

11%

3%
0%
Caucasian

Black

Asian

Hispanic

Race of author

Fig. 18 Relationship between race of author and protagonist

Percentage of stories

100%

88%

80%

Protagonist
younger than orsame age as
author

60%
40%

12%

20%

Protagonist older
than author0%

Fig. 19 Relationship between age of author and protagonist

and that relationship was extremely strong, withnearly all of the protagonists in stories written by
Caucasian authors sharing their author’s race.
However, the other data collected indicates that
there is also a strong relationship between the raceof Black, Asian, and Hispanic authors and theirprotagonists. Finally, we find that the vast majorityof New Yorker authors write about protagonists
who are within or below their decade age range,
or in other words, most protagonists are in an age
group that their author has experienced (Fig. 19
and Appendix Table 16).12 Slightly more than a fifthof protagonists in the fiction studied were within
the precise decade age-range of the author of thestory in which they appeared.
Next we turn to an examination of the demographic similarities between New Yorker authors
and their secondary characters. We find that New
Yorker authors are slightly less likely to write about
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secondary characters of their gender than of the
opposite gender (Fig. 20 and Appendix Table 17).
At first glance, this seems at odds with our findingthat New Yorker authors write about protagonists
who share their gender the vast majority of thetime. However, this pattern seems likely due to
New Yorker authors’ heavy reliance on heterosexual romantic plot lines,13 which require thecreation of characters of the opposite gender fromprotagonists.
We find that New Yorker authors share theirnationality with the secondary characters in theirstories significantly more often then not (Fig. 21
and Appendix Table 18). Again, this tendency isstronger among American authors who publish in
the New Yorker than among authors of othernationalities. We also find that New Yorker
authors project their race onto their secondary
characters the vast majority of the time (Fig. 22 andAuthor–character similarities in New Yorker Fiction

Percentage of stories

75%
60%

52%

48%

45%

Secondarycharacter ofsame gender

52%

48%

Secondarycharacter ofdifferent gender

30%
15%
0%
Male

FemaleGender of author

Percentage of stories

Fig. 20 Relationship between gender of author and secondary characters
100%
80%

Secondarycharacter fromsame countryof origin

81%

75%

59%

60%

41%

40%

25%

Secondarycharacter fromdifferent countryof origin

19%

20%
0%

Combined

American

Non-American

Country of origin of author

Fig. 21 Relationship between country of origin of author and secondary characters

Percentage of stories

100%

92%

90%

83%
Secondarycharacter ofsame race

80%
57%

60%

43%
40%
20%
0%

17%
10%

8%
Caucasian

Black

Asian

Secondarycharacter ofdifferent
race

Hispanic

Race of author

Fig. 22 Relationship between race of author and secondary characters

Appendix Table 19). Although very few stories inour sample were written by minorities, our data
allows us to detect statistically significant differencesin the rates at which both Caucasian authors andBlack authors write about secondary characters of
their race, and the limited data on Hispanic andAsian authors suggests that this pattern applies tothem as well. Finally, we observe that the secondary
characters in New Yorker fiction are almost always
in or below the same age range as the authorsof the stories in which they appear (Fig. 23 andAppendix Table 20). All of the demographic
similarities between authors and secondary
characters reported here are observed at lower
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K. L. Milkman et al.

100%
Percentage of stories

81%
80%

Secondarycharacter younger
than or same age
as author

60%

Secondarycharacter older
than author

40%
19%

20%
0%

Secondary character

Fig. 23 Relationship between age of author and secondary chacters

Percentage of stories

100%

94%

80%

90%
74%

69%

60%
40%

31%

26%

20%

CaucasianCharacterof different
race

10%

6%
0%

Character ofsame race

Black

Asian

Hispanic

Race of author

Fig. 24 Relationship between race of author and charactersfrequencies than the similarities between authors
and their protagonists, suggesting that authors
may be more prone to create protagonists in theirown image than secondary characters.
As mentioned earlier, with the exception of
Caucasian authors, there was insufficient data to
draw statistically supportable conclusions about
the relationship between the race of New Yorker
authors and their protagonists, and only for
Caucasian and Black authors was it possible todraw such conclusions about the relationshipbetween the race of authors and their secondary
characters. Consequently, the two sets of dataon protagonists and secondary characters werecombined for analysis under the broader headingof ‘characters’. This aggregated data allows us toconclude that Caucasian, Black, Hispanic, and Asianauthors all write about characters who share theirrace significantly more often than not (Fig. 24
and Appendix Table 21). The rates at which Blacks
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Literary and Linguistic Computing, Vol. 22, No. 3, 2007

and Hispanic portray characters of their own race
are extremely similar, yet considerably lower thanthose of Asian and Caucasian authors, who also
appear to write about members of their own races
at comparable frequencies.
Finally, we turn to an analysis of the settings
selected for the stories in this study. We find that
New Yorker authors not only write about characterswith numerous similarities to themselves; theyalso write about settings that are familiar to them.
Authors in our data set locate stories in theirnative world region significantly more often thannot (Fig. 25 and Appendix Table 22),14 andAmerican authors write stories set in their homeregion of the world significantly more often thannon-American authors.

4.2 Discussion
The data and statistical analyses presented inthis section offer convincing support for theAuthor–character similarities in New Yorker Fiction

Percentage of stories

100%

85%

80%
80%

73%

60%
40%
20%

20%

27%

15%

Same world
regionsettingDifferent
world region
setting0%
Combined

American

Non-American

Native world region of author

Fig. 25 Relationship between native world region of author and story setting

hypothesis that New Yorker fiction authors write
more often than not about protagonists who sharemany of their demographic characteristics. The
results also suggest that secondary characters have
many demographic similarities to the authors of thestories in which they appear, albeit fewer thanprotagonists. It is not surprising that New Yorker
authors of fiction write about what they know.
However, to our knowledge, the commonly held
hypothesis that authors of fiction write aboutcharacters who are similar to them has never beentested, making our study the first to provide a soundevidentiary basis for a widely held notion about
fiction.
Our results indicate that New Yorker authors
have a strong propensity to write about protagonists
who share their gender, race, and nationality,
and who are in or below their age range. Similarly,
they write more often than not about secondary
characters who share their race, their nationality,
and are within or below their age range. Our results
also suggest that New Yorker authors set theirstories in locales where they have lived significantly
more often than not. Perhaps more interesting
than any of these observations, however, is our
finding that New Yorker authors whose demographic characteristics make them minorities as
far as the magazine’s publishing roster is concerned(i.e. females, non-Whites, and non-Americans)are less likely than others to write about protagonists and secondary characters who share theirdemographic characteristics. Given the data available to us, it is not possible to determine if this isdue to a selection bias at the New Yorker, the choices
made by authors about what fiction to submit to the
New Yorker, or general tendencies among minority
authors to conform to fictional norms. It would beinteresting if a future study were able to disentangle
these alternative explanations for our results.
In addition to this potentially fruitful area for
future research, our findings suggest a number of
additional topics for future study. It seems thatfurther analysis of the relationship between the race,
sexuality, and religion of authors and their characters could yield interesting results. It might also
be worthwhile to look at the same aspects of fiction
studied in this article within the context of novels
and other sets of short fiction besides the stories
published by the New Yorker in the 1990s in order tounderstand whether or not the conclusions drawn
in this article apply to fictional literature as a whole,
short fiction as a whole, or some other, narrower
literary grouping.

5 Conclusions
In this article, an extensive quantitative analysisof two questions with regard to the 442 piecesof fiction published between 5 October 1992 and
17 September 2001 in the New Yorker magazine
yielded several interesting results. The analysis
presented in Section 3 examined shifts in the New
Yorker’s fictional content that were associated witha change in the magazine’s Fiction Editor as well as
shifts associated with a change in the magazine’s
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319

K. L. Milkman et al.

Executive Editor. We find that a change in the
New Yorker’s Fiction Editor is associated withnumerous significant shifts in the magazine’s fictional content. Specifically, we find that a change
in the New Yorker’s Fiction Editor is associatedwith shifts in such variables as narrative voice, theregional distribution of stories set in the US, thegender of authors, the distribution of characters’
religions, the variance in stories’ lengths, the age
distributions of protagonists and characters as awhole, the frequency of seven story topics, and the
proportion of British characters in New Yorker
short stories. Quantitative analyses reported on in
Section 3 also led to the conclusion that thechanges in New Yorker fiction associated with Bill
Buford’s replacement of Charles McGrath as the
New Yorker’s Fiction Editor were more pronounced
than the changes associated with David Remnick’s
replacement of Tina Brown as the magazine’s
Executive Editor. The shift in Executive Editor
studied in this article was only accompanied bychanges in the distribution of characters’ countriesof origin, authors’ and characters’ racial distributions, characters’ sexuality, and four shifts in topic
frequency.
As reported in Section 4 of this article,
quantitative analysis demonstrated that, in the vast
majority of cases, authors of New Yorker fiction
write about protagonists who share their race,
gender, and nationality and who are within or
below their age range. These findings are consistent
with the widely held notion that much of fiction
is autobiographical. We also find that, to a slightly
lesser degree, New Yorker authors invent secondary
characters who share their race and nationality
and who are within or below their age range.
However, most likely in deference to heterosexualplot considerations, authors write about secondary
characters who, slightly more than half the time,
do not share their gender. This finding suggeststhat New Yorker secondary characters are crafted
to resemble members of the demographic groupthat authors are most familiar with, but that
secondary characters are less likely than protagonists
to be autobiographical representations of an author.
Finally, we conclude that the majority of thetime New Yorker authors write stories set in their
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Literary and Linguistic Computing, Vol. 22, No. 3, 2007

home region(s). All of these findings point to theconclusion that the quantifiable components ofshort stories in the New Yorker are largely basedon what an author knows and that characters are,to a great extent, demographically similar to theauthors who invent them.
In addition to presenting specific findings, thisarticle demonstrates several ways in which statisticalanalysis can successfully be employed to addressquestions about literature. Although quantitative
methods have been used before to examine trends
in literature, the traditional approach has been to
examine questions that do not require content
analysis by a human reader. This article uses
variables derived both from reading stories andfrom outside sources to draw conclusions about
literature. It is the hope of the authors that future
research will further examine the questions raisedin this article by looking at other types of literature
and asking an aneven broader range of questions
about the impact of editors on fiction and thesimilarities between authors and the characters they
invent. However, care should always be taken insuch studies to select a data set with objective
variables in order to avoid the danger of computing
statistics influenced by the interpretation of ahuman reader.

References
Burrows, J. F. (1987). Computation Into Criticism:
A Study of Jane Austen’s Novels and an Experimentin Method. Oxford: Clarendon Press.
Contemporary Authors. (2004). Literature ResearchCenter. Gale Group Databases. [Internet] Princeton,
NJ: Princeton University Libraries. <http://galenet.
galegroup.com/servlet/LitRC?locID¼princetonu>(accessed 25 January 2004).
Craig, H. (2004). A Stylistic Analysis and Authorship
Studies. In Schreibman, S., Seimens, R., andUnsworth, J. (eds), A Companion to the Digital
Humanities. Oxford: Blackwell Publishing.
Fiction. (June 1992–September 2001). New Yorker.
Literature Online. (2004). [Internet] ProQuest
Information and Learning Company. <http://lion.
chadwyck.com/> (accessed 25 January 2004).

Author–character similarities in New Yorker Fiction

Hoover, D. L. (1998). Making Use of Statistical
Measures of Style, MLA Convention, San Francisco,
28 December 1998.

Notes
1 Gross, M. (1992). Tina’s turn: The New Yorker’s headtransplant, New York, 20–31.
2 Anderson, C. (1998). Flexibility is key in publishingcareers, says NYT book review editor, The Harvard
University Gazette. Cambridge, MA: Harvard
University. Online. 6 August.
3 Angell, R. (2004). Contemporary Authors, Literature
Research Center, Gale Group Databases, Princeton
NJ: Princeton University Libraries. Online. 3 January.
4 Untitled,
http://www.geocities.com/nhsclassof59/
menaker-Random-Hs-bio.html. Online. 3 January
2004.
5 New Yorker Names a Fiction Editor. (1994). New YorkTimes, C22.
6 Granatstein, L. (1998). Remnick: A new New Yorker,
Mediaweek, 22–23.
7 It should be noted that whenever data are collected bya human reader for a study, an element of subjectivity
is introduced. This is the danger of relying upon
people to create data sets about fiction rather thancomputers. However, it is the belief of the authors of
this article that if the set of variables being cataloged
are defined carefully enough to be largely objective
and if the data set collected is large enough, thisdanger can be limited sufficiently to cause little
if any variation in results from one experimenter
to the next.

8 Although Roger Angell and Daniel Menaker exerted
significant control over the New Yorker’s Fiction
Department during the early 1990s, from thispoint on Charles McGrath will be referred to as
the Fiction Editor who preceded Bill Buford because
McGrath held the highest title of the three.
9 It should be noted that many of the twenty-eight
topics coded as present or absent from a story arehighly correlated (e.g., marriage and family).
10 Census Regions and Divisions, (2003). Energy
Information Administration, 28 April 2003 <http://
www.eia.doe.gov/cneaf/electricity/page/co2_report/
fig1.html>.
11 This surprising decrease in the number of British
characters in New Yorker fiction printed under
Buford—a native of England—may be explained by
the Editor’s jaded attitude towards traditional British
writing. [Bill Buford, Telephone interview with
K.L Milkman (18 March 2004)].
12 The average age of the New Yorker authors in thissample was 49.8 years. Thus, the fact that authors were
found to write the vast majority of the time aboutprotagonists in or, to an even greater degree, below
their age range could be a coincidence based on thefact that the majority of characters, like the majority ofAmericans (73% according 2000 Census figures), were
under the age 50 as of 2000.
13 Around 96% of the characters in New Yorker fiction
during the era examined were heterosexuals.
14 In this article, the world regions classified are: theUnited States, Western Europe, Eastern Europe, Asia,
Middle East, Africa, North America (excluding theUnited States), and South America.

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K. L. Milkman et al.

Appendix of Statistical Results
Table 2 Tests of shifts in percentage of stories with third-person narrators under different editors
Null hypothesis

Sample size

Test

P-value

Proportion of stories with third-person narrators under McGrath (62%) " proportionof stories with third person narrators under Buford (50%)
Proportion of stories with third-person narrators under Brown (53%) ! proportionof stories with third person narrators under Remnick (55%)

McGrath: 120
Buford: 329
Brown: 283
Remnick: 166

Proportion

0.023

Proportion

0.364

Table 3 Tests of shifts in percentage of stories with male authors under different editors
Null hypothesis

Sample size

Test

P-value

Proportion of stories with male authors under McGrath (57%) ! proportion ofstories with male authors under Buford (70%)
Proportion of stories with male authors under Brown (67%) " proportion ofstories with male authors under Remnick (66%)

McGrath: 115
Buford: 321
Brown: 270
Remnick: 166

Proportion

0.005

Proportion

0.476

Null hypothesis

Sample size

Test

P-value

The variances in New Yorker fiction story length under McGrath (5.04) ! thevariances under Buford (6.58)
The variances in New Yorker fiction story length under Brown (5.78) ! thevariances under Remnick (6.74)

McGrath: 120
Buford: 321
Brown: 275
Remnick: 166

F-variance

0.045

F-variance0.1301

Table 4 Tests of shifts in variance of story length under different editors

Table 5 Proportion tests of shifts in frequency of story topics under different fiction editors
Topic% of stories on topic
printed by McGrath(Sample size:120)% stories on topic
printed by Buford(Sample size: 322)

Null hypothesis

P-valueChildrenDrugs
Illness
Money
Religion
Sex
TravelAdultery
Aging
AlcoholArt
Betrayal
Crime
Death

PM,children ¼ 36%
PM,drugs ¼ 10%
PM,illness ¼ 21%
PM,money ¼ 17%PM,religion ¼ 15%
PM,sex ¼ 35%
PM,travel ¼ 39%
PM,adultery ¼ 12%PM,aging ¼ 20%
PM,alcohol ¼ 9%
PM,art ¼ 17%
PM,betrayal ¼ 13%
PM,crime ¼ 20%
PM,death ¼ 38%

PB,children ¼ 26%
PB,drugs ¼ 5%
PB,illness ¼ 12%
PB,money ¼ 10%
PB,religion ¼ 9%
PB,sex ¼ 47%
PB,travel ¼ 25%
PB,adultery ¼ 17%
PB,aging ¼ 22%
PB,alcohol ¼ 8%
PB,art ¼ 13%
PB,betrayal ¼ 20%
PB,crime ¼ 13%
PB,death ¼ 39%

PM,children " PB,children
PM,drugs " PB,drugs
PM,illness " PB,illness
PM,money " PB,money
PM,religion " PB,religion
PM,sex ! PB,sex
PM,travel " PB,travelPM,adultery ! PB,adultery
PM,aging ! PB,aging
PM,alcohol " PB,alcoholPM,art " PB,art
PM,betrayal ! PB,betrayal
PM,crime " PB,crimePM,death ! PB,death

0.034
0.031
0.015
0.037
0.040
0.016
0.002
0.120
0.368
0.388
0.181
0.120
0.057
0.470
(continued)

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Literary and Linguistic Computing, Vol. 22, No. 3, 2007

Author–character similarities in New Yorker Fiction

Table 5 Continued
Topic% of stories on topic
printed by McGrath(Sample size:120)% stories on topic
printed by Buford(Sample size: 322)

Null hypothesis

P-value

Divorce
Education
FamilyHistory
Homosexuality
Love
Marriage
NaturePolitics
Race
Relationships
Violence
War
Work

PM,divorce ¼ 8%
PM,education ¼ 14%
PM,family ¼ 37%
PM,history ¼ 6%
PM,homosexuality ¼ 4%PM,love ¼ 28%
PM,marriage ¼ 28%
PM,nature ¼ 17%
PM,politics ¼ 7%PM,race ¼ 9%PM,relationships ¼ 58%
PM,violence ¼ 25%
PM,war ¼ 7%
PM,work ¼ 28%

PB,divorce ¼ 6%
PB,education ¼ 14%
PB,family ¼ 42%
PB,history ¼ 4%
PB,homosexuality ¼ 4%
PB,love ¼ 27%
PB,marriage ¼ 37%
PB,nature ¼ 13%
PB,politics ¼ 4%
PB,race ¼ 10%
PB,relationships ¼ 55%
PB,violence ¼ 21%
PB,war ¼ 8%
PB,work ¼ 22%

PM,divorce # PB,divorce
PM,educate " PB,educate
PM,family " PB,family
PM,history # PB,history
PM,homosexuality " PB,homosexuality
PM,love # PB,lovePM,marriage " PB,marriage
PM,nature # PB,nature
PM,politics # PB,politics
PM,race " PB,race
PM,relations # PB,relations
PM,violence # PB,violencePM,war " PB,war
PM,work # PB,work

0.241
0.487
0.202
0.240
0.467
0.4857
0.057
0.232
0.183
0.442
0.335
0.228
0.426
0.105

Table 6 Proportion tests of shifts in frequency of story topics under different executive editors
Topic% of stories
on topic printed
by Brown(Sample size: 275)

% stories on
topic printed
by Remnick(Sample size: 167)

Null hypothesis

P-value

Aging
Crime
Marriage
NatureAdultery
AlcoholArt
BetrayalChildren
Death
Divorce
Drugs
Education
FamilyHistory
Homosexuality
Illness
Love
Money
Politics
Race
Relationships
Religion
Sex
TravelViolence
War
Work

PT,aging ¼ 18%
PT,crime ¼ 18%
PT,marriage ¼ 30%
PT,nature ¼ 12%
PT,adultery ¼ 14%PT,alcohol ¼ 8%
PT,art ¼ 15%PT,betrayal ¼ 18%
PT,children ¼ 28%
PT,death ¼ 35%
PT,divorce ¼ 8%
PT,drugs ¼ 7%
PT,education ¼ 14%
PT,family ¼ 37%
PT,history ¼ 4%PT,homosexuality ¼ 5%
PT,illness ¼ 16%
PT,love ¼ 27%
PT,money ¼ 13%
PT,politics ¼ 5%PT,race ¼ 8%
PT,relationships ¼ 58%
PT,religion ¼ 12%
PT,sex ¼ 41%
PT,travel ¼ 29%
PT,violence ¼ 20%
PT,war ¼ 8%
PT,work ¼ 23%

PR,aging ¼ 28%
PR,crime ¼ 10%
PR,marriage ¼ 43%
PR,nature ¼ 19%
PR,adultery ¼ 17%PR,alcohol ¼ 8%
PR,art ¼ 12%PR,betrayal ¼ 19%
PR,children ¼ 31%
PR,death ¼ 42%
PR,divorce ¼ 4%
PR,drugs ¼ 5%
PR,education ¼ 14%
PR,family ¼ 45%
PR,history ¼ 4%PR,homosexuality ¼ 4%PR,illness ¼ 13%
PR,love ¼ 28%
PR,money ¼ 10%
PR,politics ¼ 4%PR,race ¼ 12%PR,relationships ¼ 51%
PR,religion ¼ 8%
PR,sex ¼ 48%
PR,travel ¼ 28%
PR,violence ¼ 26%
PR,war ¼ 7%
PR,work ¼ 25%

PT,aging " PR,aging
PT,crime # PR,crimePT,marriage " PR,marriage
PT,nature " PR,naturePT,adultery " PR,adultery
PT,alcohol " PR,alcoholPT,art # PR,art
PT,betrayal " PR,betrayal
PT,children " PR,children
PT,death " PR,deathPT,divorce # PR,divorce
PT,drugs # PR,drugs
PT,educate " PR,educatePT,family " PR,family
PT,history # PR,history
PT,homosexuality # PR,homosexuality
PT,illness # PR,illness
PT,love " PR,lovePT,money # PR,money
PT,politics # PR,politics
PT,race " PR,race
PT,relations # PR,relations
PT,religion # PR,religion
PT,sex " PR,sex
PT,travel # PR,travelPT,violence " PR,violencePT,war # PR,war
PT,work " PR,work

0.007
0.016
0.004
0.030
0.223
0.443
0.204
0.410
0.249
0.098
0.085
0.387
0.478
0.074
0.466
0.371
0.227
0.475
0.169
0.421
0.112
0.082
0.106
0.097
0.374
0.063
0.431
0.337

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K. L. Milkman et al.

Table 7 Tests of shifts in distribution of stories set in each US region under different editors
Null hypothesis
The distributions of story settings by US Region in New Yorker
fiction are the same under McGrath and Buford
Proportion of stories set in Middle Atlantic under McGrath (19%) " proportionof stories set in Middle Atlantic under Buford (37%)
The distributions of story settings by US Region in New Yorker fiction are thesame under Brown and Remnick
Proportion of stories set in Middle Atlantic under Brown (31%) " proportionof stories set in Middle Atlantic under Remnick (33%)

Sample size

Test

McGrath: 53
Buford: 147
McGrath: 53
Buford: 147
Brown: 124
Remnick: 76
Brown: 124
Remnick: 76

2

P-value

! GOF

0.028

Proportion

0.013!2 GOF

0.880

Proportion

0.478

Table 8 Tests of shifts in distribution of stories about characters of each religion under different editors
Null hypothesis
The distributions of characters’ religions in New Yorker fiction
are the same under McGrath and Buford
Proportion of Jewish characters under McGrath (42%) ! proportionof Jewish characters under Buford (26%)
The distributions of characters’ religions in New Yorker fiction are thesame under Brown and Remnick
Proportion of Jewish characters under Brown (27%) " Proportionof Jewish characters under Remnick (34%)

Sample size

Test

McGrath: 53
Buford: 102
McGrath: 53
Buford: 102
Brown: 99
Remnick: 56
Brown: 99
Remnick: 56

2

P-value

! GOF

0.041

Proportion

0.042!2 GOF

0.191

Proportion

0.214

Table 9 Tests of shifts in distribution of ages of protagonists and characters under different editors
Null hypothesis
The distributions of protagonists’ ages in New Yorker fiction are thesame under McGrath and Buford
Proportion of protagonists in their twenties under McGrath (15%) " proportionof protagonists in their twenties under Buford (25%)
The distributions of characters’ ages in New Yorker fiction are the sameunder McGrath and Buford
Proportion of characters in their twenties under McGrath (16%) " proportionof characters in their twenties under Buford (23%)
The distributions of protagonists’ ages in New Yorker fiction are the sameunder Brown and Remnick
Proportion of protagonists in their twenties under Brown (20%) " proportionof protagonists in their twenties under Remnick (25%)
The distributions of characters’ ages in New Yorker fiction are the sameunder Brown and Remnick
Proportion of characters in their twenties under Brown (20%) " proportionof characters in their twenties under Remnick (23%)

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Literary and Linguistic Computing, Vol. 22, No. 3, 2007

Sample size

Test

McGrath: 108
Buford: 296
McGrath: 108
Buford: 296
McGrath: 239
Buford: 666
McGrath: 239
Buford: 666
Brown: 247
Remnick: 157
Brown: 247
Remnick: 157
Brown: 545
Remnick: 360
Brown: 545
Remnick: 360

2

P-value

! GOF

0.011

Proportion

0.021!2 GOF< 10#3

Proportion

0.014!2 GOF

0.702

Proportion

0.134!2 GOF

0.547

Proportion

0.179

Author–character similarities in New Yorker Fiction

Table 10 Tests of shifts in distribution of characters’ countries of origin under different editors
Null hypothesis
The distributions of characters’ countries of origin in New Yorker
fiction are the same under McGrath and Buford
Proportion of characters from England under McGrath (10%) ! proportionof characters from England under Buford (6%)
The distributions of characters’ countries of origin in New Yorker fiction
are the same under Brown and Remnick
Proportion of characters from England under Brown (8%) ! proportionof characters from England under Remnick (7%)

Sample size

Test

P-value

McGrath: 214
Buford: 542
McGrath: 214
Buford: 542
Brown: 295
Remnick: 461
Brown: 295
Remnick: 461

2

! GOF

0.0796

Proportion

0.041!2 GOF0.0211

Proportion

0.350

Table 11 Tests of shifts in distribution of characters’ and authors’ racial characteristics under different editors
Null hypothesis
The distributions of characters’ races in New Yorker fiction are thesame under McGrath and Buford
The distributions of authors’ races in New Yorker fiction are thesame under McGrath and Buford
The distributions of characters’ races in New Yorker fiction are
the same under Brown and Remnick
The distributions of authors’ races in New Yorker fiction are
the same under Brown and Remnick

Sample size

Test

P-value

McGrath: 256
Buford: 656
McGrath: 101
Buford: 314
Brown: 562
Remnick: 350
Brown: 253
Remnick: 162

2

! GOF

0.1444!2 GOF0.3700!2 GOF

0.0315!2 GOF

0.0001

Table 12 Tests of shifts in proportion of stories about heterosexual characters under different editors
Null hypothesis

Sample size

Test

P-value

Proportion of heterosexual characters under McGrath (96%) ! proportion ofheterosexual characters under Buford (96%)
Proportion of heterosexual characters under Brown (95%) " proportion ofheterosexual characters under Remnick (98%)

McGrath: 221
Buford: 614
Brown: 493
Remnick: 342

Proportion

0.457

Proportion

0.035

Table 13 Tests of relationship between gender of author and gender of protagonist
Null hypothesis

Sample size

Test

P-value

Proportion of male authors who write about male protagonists(84%) ! proportion of male authors who write about female protagonists (16%)
Proportion of female authors who write about female protagonists(72%) ! proportion of female authors who write about male protagonists (28%)
Proportion of male authors who write about male protagonists(84%) ! proportion of female authors who write about female protagonists (72%)

228 stories

Binomial<10#3

144 stories

Binomial<10#3

Male: 228
Female: 144

Proportion

0.008

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K. L. Milkman et al.

Table 14 Tests of relationship between country of origin of author and country of origin of protagonist
Null hypothesis

Sample size

Test

P-value

Proportion of authors who write about protagonists from their countryof origin (84%) ! proportion of authors who write about protagonists
who do not share their country of origin (16%)
Proportion of American authors who write about protagonists from America(93%) ! proportion of American authors who write about protagonists
who do not share their country of origin (7%)
Proportion of non-American authors who write about protagonists fromtheir country of origin (69%) ! proportion of non-American authors whowrite about protagonists who do not share their country of origin (31%)
Proportion of American authors who write about protagonists from theircountry of origin (93%) ! proportion of non-American authors who write
about protagonists from their country of origin (69%)

375 stories

Binomial<10"3

243 stories

Binomial<10"3

132 stories

Binomial<10"3

American: 243
Other: 132

Proportion<10"3

Table 15 Tests of relationship between race of author and race of protagonist
Null hypothesis

Sample size

Test

P-value

Proportion of Caucasian protagonists in stories by Caucasians (97%) ! proportionof non-Caucasian protagonists in stories by Caucasians (3%)
Proportion of black protagonists in stories by blacks (86%) ! proportion ofnon-black protagonists in stories by blacks (14%)
Proportion of Asian protagonists in stories by Asians (89%) ! proportion of
non-Asian protagonists in stories by Asians (11%)
Proportion of Hispanic protagonists in stories by Hispanics (64%) ! proportionof non-Hispanic protagonists in stories by Hispanics (36%)

308 stories

Binomial<10"3

14 stories

Too little data
to use Binomial
Too little data
to use Binomial
Too little data
to use BinomialNA

28 stories
11 stories

NA
NA

Table 16 Test of relationship between age of author and age of protagonist
Null hypothesis

Sample size

Test

P-value

Proportion of authors who write about protagonists in or below theirdecade age range (88%) ! proportion of authors who write aboutprotagonists who are in an older age bracket than they are (12%)

368 stories

Binomial<10"3

Table 17 Tests of relationship between gender of author and gender of secondary characters
Null hypothesis

Sample size

Test

P-value

Proportion of male secondary characters in stories by males (48%) # proportionof female secondary characters in stories by males (52%)
Proportion of female secondary characters in stories by females (48%) # proportionof male secondary characters in stories by females (52%)
Proportion of male secondary characters in stories by males (48%) ! proportionof female secondary characters in stories by females (48%)

345 characters

Binomial<10"3

198 characters

Binomial<10"3Male: 345
Female: 198

Proportion

0.451

326

Literary and Linguistic Computing, Vol. 22, No. 3, 2007

Author–character similarities in New Yorker Fiction

Table 18 Tests of relationship between country of origin of author and country of origin of secondary characters
Null hypothesis

Sample size

Test

P-value

Proportion of authors who write about secondary characters from their countryof origin (75%) ! proportion of authors who write about secondary characters
who do not share their country of origin (25%)
Proportion of American authors who write about secondary characters from
America (84%) ! proportion of American authors who write about secondary
characters who do not share their country of origin (16%)
Proportion of non-American authors who write about secondary characters fromtheir country of origin (59%) ! proportion of non-American authors who write
about secondary characters who do not share their country of origin (41%)
Proportion of American authors who write about secondary characters from theircountry of origin (84%) ! proportion of non-American authors who write about
secondary characters from their country of origin (59%)

477 characters

Binomial<10"3

316 characters

Binomial<10"3

161 characters

Binomial<10"3

American: 316
Other: 161

Proportion<10"3

Table 19 Tests of relationship between race of author and race of secondary characters
Null hypothesis

Sample size

Test

P-value

Proportion of Caucasian secondary characters in stories by Caucasians(92%) ! proportion of non-Caucasian secondary characters in stories by
Caucasians (8%)
Proportion of black secondary characters in stories by blacks (57%) ! proportionof non-black secondary characters in stories by blacks (43%)
Proportion of Asian secondary characters in stories by Asians (90%) ! proportionof non-Asian secondary characters in stories by Asians (10%)
Proportion of Hispanic secondary characters in stories by Hispanics(83%) ! proportion of non-Hispanic secondary characters in stories
by Hispanics (17%)

388 characters

Binomial<10"3

21 characters

Binomial<10"340 characters

Too little data
to use Binomial
Too little data
to use BinomialNA

12 characters

NA

Table 20 Tests of relationship between age of author and age of secondary characters
Null hypothesis

Sample size

Test

P-value

Proportion of authors who write about secondary characters in or below theirdecade age range (81%) ! proportion of authors who write about secondary
characters who are in an older age bracket than they are (19%)

454 characters

Binomial<10"3

Table 21 Tests of relationship between race of author and race of characters
Null hypothesis

Sample size

Test

P-value

Proportion of Caucasian characters in stories by Caucasians (94%) ! proportionof non-Caucasian characters in stories by Caucasians (6%)
Proportion of black characters in stories by blacks (69%) ! proportion ofnon-black characters in stories by blacks (31%)
Proportion of Asian characters in stories by Asians (90%) ! proportion of
non-Asian characters in stories by Asians (10%)
Proportion of Hispanic characters in stories by Hispanics (74%) ! proportionof non-Hispanic characters in stories by Hispanics (26%)

696 characters

Binomial<10"3

35 characters

Binomial<10"3

68 characters

Binomial

0.001(continued)

Literary and Linguistic Computing, Vol. 22, No. 3, 2007

327

K. L. Milkman et al.

Table 21 Continued
Null hypothesis

Sample size

Test

P-value

Proportion of Caucasian characters in stories by Caucasians (94%) ! proportionof black characters in stories by blacks (69%)
Proportion of Caucasian characters in stories by Caucasians (94%) ! proportionof Asian characters in stories by Asians (90%)
Proportion of Caucasian characters in stories by Caucasians (94%) ! proportionof Hispanic characters in stories by Hispanics (74%)
Proportion of black characters in stories by blacks (69%) # proportion of Asiancharacters in stories by Asians (90%)
Proportion of black characters in stories by blacks (69%) # proportion of
Hispanic characters in stories by Hispanics (74%)
Proportion of Asian characters in stories by Asians (90%) ! proportion of
Hispanic characters in stories by Hispanics (74%)

Caucasian: 696
Black: 35
Caucasian: 696
Asian: 68
Caucasian: 696
Hispanic: 23
Black: 35
Asian: 68
Black: 35
Hispanic: 23
Asian: 68
Hispanic: 23

Proportion<10"3

Proportion

0.123

Proportion<10"3

Proportion

0.008

Proportion

0.443

Proportion

0.063

Table 22 Tests of relationship between native world region of author and world region of story setting
Null hypothesis

Sample size

Test

P-value

Proportion of authors who write stories set in their home region of theworld (80%) ! proportion of authors who write stories set outside of theirhome region of the world (20%)
Proportion of American authors who write stories set in America (85%) ! proportionof American authors who write stories set outside of the US (15%)
Proportion of non-American authors who write stories set in their home regionof the world (73%) ! proportion of non-American authors who write stories set
outside of their home region of the world (17%)
Proportion of American authors who write stories set in America (85%) ! proportionof non-American authors who write stories set in their home region of the world (73%)

383 stories

Binomial<10"3

241 stories

Binomial<10"3

142 stories

Binomial<10"3

American: 241
Other: 142

Proportion

0.005

328

Literary and Linguistic Computing, Vol. 22, No. 3, 2007