Bestseller Lists and Product Variety
Author(s): Alan T. Sorensen
Source: The Journal of Industrial Economics, Vol. 55, No. 4 (Dec., 2007), pp. 715-738
Published by: Wiley
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THE JOURNALOF INDUSTRIALECONOMICS
VolumeLV
December2007
0022-1821
No. 4
BESTSELLERLISTSAND PRODUCT VARIETY*
ALAN T. SORENSEN+
Thispaperusesdetailedweeklydataon salesof hardcoverfictionbooks
to evaluatethe impactof the New YorkTimesbestsellerlist on salesand
product variety. In order to circumvent the obvious problem of
simultaneityof sales and bestsellerstatus, the analysis exploits time
lags and accidental omissions in the construction of the list. The
empiricalresultsindicatethat appearingon the list leads to a modest
increase in sales for the average book, and that the effect is more
dramaticfor bestsellersby debutauthors.The paperdiscusseshow the
additionalconcentrationof demandon top-sellingbooks couldleadto a
reduction in the privately optimal number of books to publish.
However,the data suggestthe opposite is true:the marketexpansion
effect of bestsellerlists appearsto dominateany businessstealingfrom
non-bestsellingtitles.
I. INTRODUCTION
THEPERCEIVED
IMPORTANCE
OFBESTSELLER
LISTS
is a salient feature of multimedia
industries.Weekly sales rankingsfor books of various genres are published in at
least 40 differentnewspapers across the U.S., and making the list seems to be a
benchmark of success for authors. In the movie industry, box office rankings
are watched closely by movie studios and widely reportedin television and print
media. Sales of music CDs are tracked and ranked by Billboard Magazine,
whose weekly charts are prominently displayed in most retail music stores.
Ostensibly, the purpose of bestseller lists is to simply report consumers'
purchases. However, there are a number of reasons why the conspicuous
publication of sales rankings may directly influence consumer behavior (in
addition to merely reflecting it). Bestseller status may serve as a signal of
quality: for example, bookstore patrons who are unfamiliar with a particular
author may nevertheless buy her current bestseller, thinking that its
popularity reflects other buyers' (favorable) information about the book's
quality.1 Publicized sales rankings would also directly affect consumer
*Iamthankfulto theHooverInstitution,wheremuchof thisresearchwasconducted,andto
NielsenBookScanfor providingthe book salesdata. The researchhas benefittedfromhelpful
conversationswith Jim King, Phillip Leslie, and Joel Sobel, among many others. Scott
Rasmussenprovidedexcellentresearchassistance.Any errorsaremine.
tAuthor'saffiliations:GraduateSchool of Business,StanfordUniversityand NBER, 518
MemorialWay, Stanford,California,94305-5015,U.S.A.
e-mail: [email protected].
Thereis an extensiveliteratureon quality-signaling
whenproducts'qualitiesareuncertain.
See, for example,(Milgromand Roberts[1986]).
? 2007 The Author. Journal compilation C 2007 Blackwell Publishing Ltd. and the Editorial Board of The Journal of Industrial
Economics. Published by Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK, and 350 Main Street, Maiden,
MA 02148, USA.
715
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716
ALANT. SORENSEN
behaviorin the presenceof social effects, with bestsellerlists servingas a
form of coordinatingmechanism.2For example, teenagerswho want to
listento musicthatis 'hot'can look to the Billboardchartsto findout whatis
popular, and people may favor movies at the top of the box-officecharts
becausethey want to be conversantin popularculture.In the specificcase
of books, bestsellerstatus also triggersadditionalpromotionalactivityby
retailers.
For the same reasonsthat bestsellerlists may directlyaffect consumers'
purchasedecisions,theymayalso causesalesto be morehighlyconcentrated
on the few bestselling products. This, in turn, could influence product
variety:if the additionalsalesaccruingto bestsellersas a directconsequence
of the publicationof the list come at the expenseof non-bestsellingproducts,
the optimal number of products to offer may decrease(relativeto what
would have been optimalin the absenceof a bestsellerlist). For example,if
publicizedbox officerankingscauseticketsalesto be moreconcentratedon
blockbusters,they may also make it unprofitableto incurthe fixedcosts of
producinga film whose popularityis expectedto be only marginal.3
This paper examinesthese issues in the context of the book publishing
industry,looking specificallyat the impactof the New YorkTimesbestseller
list on sales of hardcoverfictiontitles.The empiricalanalysisaddressestwo
questions.First, does being listed as a New YorkTimesbestsellercausean
increasein sales?Second,does the influenceof the bestsellerlist also affect
the number of books that are published, and if so, in which direction?
Obvious simultaneity problems make answering the first question a
nontrivialempiricalexercise;however, subtletiesin the constructionand
timingof the New YorkTimeslist can be exploitedto identifyits impact.The
resultssuggesta modestincreasein salesfor the typicalbestsellerwhenit first
appearson the list, andtheincreaseappearsto reflectan informationaleffect
ratherthan a promotionaleffect: the effects are concentratedin the first
week a book appears on the list, and they are most substantialfor new
authors. Regardingthe second question, the impact of bestsellerlists on
product variety is theoreticallyambiguous, since it depends on whether
marketexpansionor business-stealingeffectsdominate.Althoughthe data
are less than ideal for addressingthis question,I presentindirectevidence
suggestingthe business-stealingeffects of bestsellerlists are unimportant:
if anything,bestsellerlists appearto increasesales for both bestsellersand
non-bestsellersin similargenres.
Although this paperis the firstto explorethe impactof bestsellerlists on
product variety, similar questions have been previously addressedin a
numberof contexts.Earlytheoreticalworkemphasizedthetradeoffbetween
2See, e.g., (Beckerand
Murphy[2000],Banerjee[1992],and Vettas[1997]).
3 This line of reasoningwill be clarifiedin sectionII.
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BESTSELLERLISTS AND PRODUCT VARIETY
717
quantityand diversityin the presenceof scaleeconomies(Dixit and Stiglitz
[1977])and the effects of market structureon product variety (Lancaster
[1975]).Empiricalstudiesof productvarietyhave been undertakenfor the
radio broadcastingindustry (Berry and Waldfogel [2001], and Sweeting
[2005]),the music industry(Alexander[1997]),and for retaileyeglasssales
(Watson[2003]),to name a few examples.
In the following section, I outline a basic theoretical frameworkfor
understandinghow bestsellerlistscaninfluencethe numberof books thatget
publishedin equilibrium.Section3 providesa briefdescriptionof the book
industryand the dataset.The empiricalanalysis(Section4) proceedsin two
parts:first,the data areusedto identifyand quantifythe directimpactof the
bestsellerlist on sales;second,substitutionpatternsin the data areanalyzed
to determinethe likelydirectionof the list'simpacton productvariety.Some
broad implicationsof the results(as well as alternativeinterpretations)are
discussedin the concludingsection.
II. A SIMPLE THEORETICAL FRAMEWORK
Considera highlysimplifiedmodel in whicha singlepublisherchooses how
many manuscriptsto publish.4There are K manuscriptsunderconsideration. Printing and marketing a manuscriptrequires a fixed cost, F, in
additionto the (constant)marginalprintingcost c. Priorto publication,the
manuscriptscan be rankedin orderof expectedpopularity,with r beingthe
index of the rth-bestbook among the K alternatives.The marketprice of a
publishedbook,p, is takenas given,and the post-publicationpricedoes not
adjustto reflecta book's relativepopularity.5The expecteddemandfor the
rth best book is given by D(K) . 8(r; K), where D(K) can be interpreted as the
level of aggregatedemandfor books (whichmay dependon how many are
offered), and 0(r; K) is a function determininghow aggregatedemand is
allocated among books depending on their relative popularity (e.g., we
could have
O(r;K) - 1). Only books with positive expected profits
will be published,
so
the numberof books will be the maximumK*suchthat
-_
(p - c)D(K*)O(K*;K*) > F, as illustrated in Figure 1.
Now considerthe potentialimpact of publishinga bestsellerlist, so that
consumersobserve the sales ranks of the top L books. Suppose that the
4 The modelcould also applyto movie studios'decisionsabouthow manyfilmsto produce,
or to recordlabels'decisionsabout how manyartiststo sign.
5
Thisassumptionwouldbe absurdin mostcontexts,butin thiscaseit is at leastdescriptiveof
a curiouspracticein multimediamarkets.Pricesof books, movies, and CD's almost never
reflectthepopularityof the individualproducts.(Clerides[2002])providesdirectevidenceand
a thoroughdiscussionof pricingissuesin thebook industry.)Typically,'pricepoints'forbooks
and CDs aredeterminedbeforethey aremarketed,and subsequentadjustmentsareextremely
infrequent.Movieticketpricesareevenmorerigid:in the summerof 2002,for example,it cost
the sameamountto see 'Chicago'(whichwon the Oscarfor best pictureand was a box-office
success)as 'BoatTrip'(whichfloppedat the box officeandwasuniversallyridiculedby critics).
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ALAN T. SORENSEN
718
D
F/(p-c)
* K
L
L
K**K*
Figure 1
The publish/no-publish margin
aggregate consumer response to a bestseller list leads to an increased
concentrationof sales on bestsellers:letting O(r;L,K) denote the expected
book amongK alternativeswhena bestseller
marketshareof the rth-ranked
list of lengthL is published,and O(r;0,K) denote the marketsharewhen no
list is published, we assume that O(r;L,K) > ( < )O(r;0,K) if r < ( > )L. If
bestsellerlists have any direct impact on consumer behavior, the effect
would almostcertainlyhave this feature.The most obvious mechanismfor
this effect is informational:if consumers are uncertain about books'
qualities,and theybelievethat at least somepast purchasershadmeaningful
informationaboutthe books theypurchased,thenbestsellerstatuswouldbe
a signal of quality.Alternatively,social effectsmay lead to higherdemand
for bestsellers:consumersmay want to readwhateveryoneelse is readingin
the interestof keepingup with what is popular-e.g., they don't want to be
left out of the conversationwhentheygo to the cocktailparty.In the market
for hardcoverfiction books, an additionalmechanismpushessales toward
bestsellers: retailers routinely discount bestsellers and position them
prominentlyin theirstores.
If the overall level of demandD(K) is independentof any bestseller-list
effects,thenthe list unambiguouslyreducesthe numberof books thatcan be
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BESTSELLERLISTS AND PRODUCT VARIETY
719
D
I
I
F/(p-c)K
L
K* K**
Figure 2
List may increase overall level of demand
profitablypublished.Thisis illustratedin Figure1:the increasedsalesfor the
top L books come at the expense of the non-listed books, shifting the
publish/no-publishmarginto the left (from K* to K**).More realistically,
however,the publicationof a bestsellerlist couldincreaseoveralldemandin
addition to changing the allocation of demand across titles-i.e., the
additional promotion and information about bestsellers could attract
consumersthat otherwisewould not havepurchasedany book at all. In this
case, the impact of bestseller lists on the publish/no-publishmargin is
ambiguous. (Figure 2 illustratesa case in which more books would get
publishedin the presenceof a list, even though the list leads to a relatively
higherconcentrationof sales among bestsellers.)
This framework,while obviouslyoversimplified,illustratesthe principal
ideas underlyingthe empirical analyses to follow. Ideally, we want to
examinesales data to see if indeedO(r;L,K) > O(r;0,K) for r < L-that is,
to see if bestsellerlists causean increasein demandfor bestsellersrelativeto
non-bestsellers.In orderto say anythingaboutwhetherbestsellerlists affect
the numberof books that get published,we must then ask a much more
subtle question of the data: how are sales of relativelyunpopular(nonbestselling)books affectedby the publicationof bestsellerlists?Thatis, how
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720
ALANT. SORENSEN
does O(r;L,K)compareto 0(r;0,K)for r verycloseto
Unfortunately(but
K.6for
not surprisingly),the available data are inadequate
answeringthis
questiondirectly.Instead,we will look for indirectevidenceof substitution
between bestselling and non-bestsellingtitles, which would suggest the
potentialfor (and the likely directionof) productvarietyeffects.7
III. BACKGROUNDAND DATA
III (i). TheBookIndustry
In the U.S., the vast majorityof books are producedby a small numberof
largepublishinghouseslike RandomHouse and HarperCollins.8The odds
againsta manuscript'sbeingacceptedby one of thesepublishinghousesare
long, especiallyin the case of fiction. Thirtyper cent or fewer of available
manuscriptsin any given year are in print, and althoughninetyper cent of
published books are nonfiction, seventy per cent of the manuscripts
submitted to traditional publishers are fiction (Suzanne [1996]).9Most
successfulmanuscriptsare brokeredto publishersby literaryagents.These
agentsaretypicallyreluctantto takeon first-timeauthors,andtheirfeestend
to be steep (around 15 per cent of authors'royalties).However,using an
agent greatlyincreasesthe author'schances of success:unsolicitedmanuscripts (manuscriptsreceived 'over the transom') are estimated to have
fifteen thousand to one odds against acceptance(Greco [1997]).Manuscriptsare sometimessold to publishersby auction, but this method is the
exceptionratherthan the rule and is used primarilyby established,brandname authors.
The decisionto extenda contractto an authoris madeonly afterreviewof
the manuscript'squality and salability by several stages of editors. If a
manuscriptsurvivesthe reviewprocess, the publisheroffers the author a
contract grantingroyalty payments in exchange for exclusive marketing
rights as long as the publisherkeeps the book in print. Royalties average
seven percentof the wholesaleprice on hardcoverbooks by new authors,
6 Note that
the illustrationin Figure1 makesit appearthatsalesof the K'h-ranked
book and
theL + ls'-rankedbookareequallyaffected,whichneednot be thecase.Forinstance,it is quite
plausiblethatthe impactis a decliningfunctionof a book'srank.Moreover,only the effecton
books at the marginis relevantfor determiningthe numberof books that get published.
7 Becausethismodeldoesnot explicitlyspecifyconsumers'preferences,
it saysnothingabout
thewelfareeffectsof a reductionin productvariety.Thispaperwillbe deliberatelyagnosticon
this point,sincethe welfareeffectscouldplausiblygo eitherway:on theone hand,fewerbooks
could mean foregonesurplusfrom titles that would have appealedto readerswith diverse
tastes; on the other hand, fewer books could mean that less time is wasted readingbad
literature.
accountedfor over80 per
8 For thefirstquarterof 2003,the top sixpublishingconglomerates
cent of unit salesin adultfiction.
9Thesenumbersimplythat 10percent of fictionmanuscriptsget published.
?
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BESTSELLERLISTS AND PRODUCT VARIETY
721
and may increaseonce the book achievesa certainlevel of sales (Suzanne
[1996]).Upon the deliveryof a completedmanuscripta large portion of
expectedroyaltiesare given to the authorin the form of an advance;many
authorsnever receiveadditionalpaymentsbecauseadvancesoften exceed
royaltiesearnedfromactualsales.Publishersretaina largeshareof royalties
in escrow accounts to compensate for returnedbooks; booksellers may
returnunsold books to the publishersfor full price, and thereforereturn
ratesexceedingfifty per cent are common (Greco [1997]).
In spite of the difficulty that authors seem to face in getting their
manuscriptspublished,the book industrygeneratesan astonishingflow of
new books each year. Across all categories(fictionand nonfiction)and all
formats (hardcover,trade paper, and mass-marketpaper), over 100,000
titles werepublishedin the year 2000 alone. In adult fiction,the numberof
new books published(called'titleoutput'withinthe industry)has increased
dramaticallyoverthe past decade.The industry'stradepublication,Bowker
Annual,reportsthat title output for hardcoverfiction more than doubled
from 1,962 in 1990 to 4,250 in 2000. In contrast, the rate of increasewas
muchmoregradualpriorto 1990.In fact, Bowkerreportsthat the numberof
fictiontitlesin 1890was over 1,100,so title outputhad less than doubledin
the 100yearspriorto 1990(Bogart[2001]).
The dramatic increases in title output in the 1990's were roughly
concomitant with an increase in the concentration of sales among
bestsellers.From the mid-1980'sto the mid-1990's,the shareof total book
sales representedby the top 30 sellersnearlydoubled (Epstein[2001]).In
1994,over 70 percentof total fictionsaleswereaccountedfor by a merefive
authors:JohnGrisham,Tom Clancy,DanielleSteel,MichaelCrichton,and
StephenKing (Greco [1997]).1o
Publishersand authorsemploy a numberof marketingstrategiesin their
attemptsto achievethe kind of successenjoyedby thesetop-sellingauthors.
Publishers'marketingbudgetsmay rangebetweenten and twenty-fiveper
cent of net sales(Cole [1999]),and authorsareexpectedto appearpubliclyin
promotion tours. Book reviews are highly sought after but difficult to
obtain: tens of thousandsof books are publishedeach year in the United
States, but the New York Times(for example)reviewsonly one per day.
Publishers also may pay retail stores for shelf space or inclusion in
promotionalmaterials.Book marketersconcentratetheireffortson creating
a successfullaunch;retailstoresmay removelow-sellingnew releasesfrom
theirshelvesafteras little as one week (Greco [1997]).
10
Rosen [1981] provides a classic explanation for the presence of such 'superstars.' A critical
piece of the argument is that the convexity of returns results from the imperfect substitutability
of the products offered-i.e.,
reading several unremarkable books may not be a good
substitute for reading a single great one.
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722
ALANT. SORENSEN
In spite of all the resourcesspent on marketing,one of the best kinds of
publicity--appearing on a bestsellerlist-cannot be bought." Bestseller
lists have long played an important role in the book industry. Regular
publicationof bestsellerlists beganin 1895,whena literarymagazinecalled
TheBookmanstartedprintinga monthlylist of the top six best-sellingbooks.
The New YorkTimesBook Reviewbegan publishingits bestsellerlist as a
regularfeaturein 1942. Although many other prominentlists now exist,12
the New YorkTimeslist is generallyconsideredthe most influentialin the
industry(Korda [2001]).
III (ii). Data
The main dataset to be analyzedconsists of weeklynational sales for over
1,200 hardcoverfiction titles that were releasedin 2001 or 2002. The sales
datawereprovidedby NielsenBookScan,a marketresearchfirmthat tracks
book salesusingscannerdata froman almost-comprehensive
panelof retail
Additionalinformationabouttheindividualtitles(suchas the
booksellers.13
publication date, subject, and author information)was obtained from a
variety of sources, includingAmazon.comand a volunteerwebsite called
Table 1 reportssummaryinformationfor the books in the
Overbooked.org.
into
threesubsamples.
broken
data,
The overallsamplerepresentsa relativelylargefractionof the universeof
hardcoverfiction titles releasedin this time period,though it is likely to be
somewhatskewedtowardpopularbooks.14 Booksthatneversoldmorethan
50 copiesin a singleweek(nationwide)weredroppedfromthe sample,since
theirweeklysales numbersappearedto be mostly noise. Also, some books
are excludedfrom the empiricalanalysesif theirreleasedates weredifficult
to determine.'"In suchcases,the numberof books is reducedto 799. As will
11Occasionallyan authorhas testedthis propositionby purchasingnumerouscopies of his
own book, in hopes of pushingit onto a bestsellerlist. None of these attemptshas everbeen
for the perpetrator
trulysuccessful;the typicaloutcomehas beenconsiderableembarrassment
once the schemewas uncovered.
12Most majorU.S. newspaperspublishtheirownlocallist(sometimesin additionto theNew
YorkTimeslist), and a numberof nationallists competewith the New YorkTimeslist for
attention (e.g., Publishers Weekly, Wall Street Journal, USA Today, and BookSense).
13BookScancollects data throughcooperativearrangementswith virtuallyall the major
bookstorechains, most majordiscount stores (like Costco), and most of the major online
retailers(like Amazon.com).They claimto trackat least 80 percent of total sales.
14 In constructingthe set of candidatebooks to track,we had to firstlocate a book (and its
ISBN number)in orderto considerit. Obscure,slow-sellingbooksare(bydefinition)harderto
locate.
theexactweekof release.For most
15 Threesourcesof informationwereusedin determining
titles,Amazon.comliststheexactday of release.If not, BookScanreportsthemonthof release,
and 'eyeballing'the data usuallyrevealsan obviousreleasedate. If for any reasonthe release
andBookScanreleasedatesdidn'tmatch,and/or
datewasnot obvious-e.g., theAmazon.com
the releasedatewasn'tobviousfromlookingat the salesdata-the titlewasexcludedfromthe
samplewheneverthe resultsmightbe sensitiveto the accuracyof the releasedate.
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BESTSELLERLISTS AND PRODUCT VARIETY
723
TABLEI
SUMMARYSTATISTICS
All books with 26 + weeks of data (n = 1,217):
Min
Median
Max
Mean
Std. Dev.
68.85
83
1
1,443,345
360,133
24.28
49.64
.15
35,183
6,903
2.63
24.18
.36
111,444
24,453
Max
Mean
Std. Dev.
24.95
56
0
8,063
972
60
83
1
1,443,345
360,133
24.44
48.70
.14
51,396
10,227
2.38
24.52
.35
134,445
29,626
Min
Median
Max
Mean
Std. Dev.
10.95
1
0
20,963
2,108
4
1
25
40
0
93,507
18,438
4
4
29.95
83
1
1,443,345
360,133
34
30
25.05
41.33
.05
174,987
36,047
4.5
5.8
2.25
23.89
.22
222,530
50,058
2.4
5.3
10.95
1
0
87
50
List price
Releaseweek*
New author
Sales, first6 months
Max. one-weeksales
24.95
57
0
3,960
486
Books with reliablereleasedates (n = 799):
Min
Median
List price
Releaseweek*
New author
Sales, first6 months
Max. one-weeksales
10.95
1
0
87
50
New York Times bestsellers(n = 205):
List price
Releaseweek*
New author
Sales, first6 months
Max. one-weeksales
Week firstappeared
Weeks on list
*Forreleaseweek,the firstweekof 2001is week1, andthefirstweekof 2002is week53.
be shown in the next section, for nearlyall books the vast majorityof sales
occur in the first 4-6 months, and subsequent sales are relatively
uninformative.In light of this, only the first26 weeks of sales are included
in the samplefor any given book.
Data on bestseller-liststatuscome directlyfromthe New YorkTimes.The
analysis focuses on the New York Times list because it is a nationally
publishedlist (and the sales data are national), and because it is almost
universallyregardedas the most influentiallist in the industry.A majorityof
retail booksellers (including online bookstores) have special sections
devoted to New YorkTimesbestsellersand offer price discounts on these
titles,and authorsare sometimesofferedbonusesfor everyweektheirbook
appears on the New York Times list.
To constructits list, the New YorkTimessurveysnearly4,000 bookstores
eachweek,in additionto a numberof book wholesalerswho serveadditional
typesof booksellers(likesupermarkets,newsstands,etc.) The reportedsales
figures from these respondents are then extrapolated to a nationally
representativeset of salesrankingsusingstatisticalweights.Becausethe New
York Times list is constructedusing sampling methods, it often makes
'mistakes'--i.e., books that should have made the list in a given week
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724
ALANT. SORENSEN
(because their sales exceededthe sales of the book listed at rank 15) are
sometimes omitted, and the ordering of listed books sometimesdoesn't
reflectthe 'true'rankingof sales (as indicatedby the BookScandata).Also,
assemblingthe list takes time, so the printedbestsellerlist reflectsrankings
from three weeks prior. Both of these features of the New York Times
list-the mistakesandthe timelags-are criticalin the empiricalanalysisof
its impacton sales.
IV. EMPIRICAL ANALYSIS AND RESULTS
IV (i). Skewnessin BookSales
The most strikingpattern in the data is that book sales are remarkably
skewedin two importantways.First,the distributionof salesacrossbooks is
heavilyskewed.Figure3 plots total salesin the firstsix monthsagainstsales
rankfor the top 100books in the sample.Evenwhenlookingonly at the top
decile of books, the skewnessof the distributionis striking.Of the 1,217
books for which at least 26 weeks were observed,16the top 12 (1 per cent)
accountfor 25 percent of total six-monthsales,and the top 43 (3.5 percent)
accountfor 50 per cent. The 205 books that made it to the New YorkTimes
bestsellerlist accountfor 84 per cent of total sales in the sample.The most
popular book in the sample, SkippingChristmasby John Grisham, sold
more copies in its firstthreeweeks than did the bottom 368 books in their
firstsix months combined.
Second, book sales tend to be skewedwith respectto time for any given
title:thatis, salestendto be heavilyconcentratedin the firstfewweeksaftera
book's release.Of the 1,217books in the samplefor which26 or moreweeks
areobserved,898(73.8percent)hit theirsalespeaksometimein the firstfour
weeks. The median 'peak week' is week 2. Somewhat surprisingly,this
patternalso seems to hold for debut authors, for which one might expect
gradualdiffusionof informationand thereforean S-shapedsalespath. For
new authors, 112of 182books (61.5 per cent) peak in the first4 weeks,and
the medianpeak week is 4.
This second form of skewness is important to keep in mind when
interpretingthe models and results in the following sections. The steady
decayof a book's salesovertimeis the dominantpattern:withthe exception
of seasonaleffects(e.g., Christmas),any otherchangesin a book's salestend
to be second-orderrelativeto this decaytrend.17Essentially,the salespaths
16Books for
which the release date was questionable are not excluded here, since getting the
release date right isn't critical for this exercise.
17Decay-like sales patterns are not unique to the book industry; for example, Einav [2003]
reports similar patterns for movie box office sales and incorporates exponential decay directly
into his empirical model.
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BESTSELLERLISTS AND PRODUCT VARIETY
725
1500
U)
c.
0o
E
c( 1000
C,
C.)
-.
c 500.
cl)
1
100
Overallsales rank
Figure 3
Skewness of book sales
typicallyresembleexponentialdecay patterns.Eyeballingthe time path of
salesfor all the books in the sample,one rarelyobservesa book's sales 'take
off' after it hits the bestsellerlist; if anything,making the list appearsto
temporarilyslow the pace of decline.
IV (ii). Do Bestseller Lists Directly Affect Sales?
Theoryclearlysuggeststhat bestsellerlists may do morethan simplyreflect
consumerbehavior:the lists may directlyinfluenceconsumerbehavior,so
that a book's appearanceon the bestsellerlist has an independenteffect on
its sales.However,measuringsuch an effectis a difficultempiricalproblem:
the set of books that receivethe 'treatment'of being listed as bestsellersis
clearlynot random,and a naiveempiricalapproachwouldlikelyconfusethe
directionof causality.Thereis obviouslya correlationbetweenthe level of
sales and bestsellerstatus (by the verydefinitionof a bestsellerlist), but we
cannot infer from this correlationthat being listed as a bestsellercauses
highersales.
However,giventhe availabledata and the subtletiesin the constructionof
the New York Timeslist, there are at least two ways we can attempt to
identify the list's direct influence.One strategyis to exploit the so-called
'mistakes'that are sometimesmadein the list. As mentionedpreviously,the
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ALAN T. SORENSEN
726
TABLE 2
CHARACTERISTICS OF LISTED BOOKS VS. OMITTED BOOKS
Per cent of 'mistakes'
listing this genre (N = 75)
Per cent of bestsellers
listing this genre* (N = 44)
t-stat
(p-value)
Literature/Fiction
Mystery/Thriller
Romance
Science Fiction
Religion
Horror
51
51
21
12
1
3
64
36
11
7
0
2
- 1.39 (0.17)
1.53 (0.13)
1.47 (0.14)
0.96 (0.34)
1.00 (0.32)
0.13 (0.89)
Average list price:
24.98
24.54
Genre
1.09 (0.28)
*Onlybooks that were ranked 13-15 when they first appeared on the bestseller list are included in the bestsellers
group. Numbers are based on books' genres as listed on Amazon.com. Percentages add to more than 100
because books typically list more than one genre. Prices are publishers' list prices, from BookScan.
processusedto generatethe New YorkTimeslist is inexact.Althoughthe list
is by and largequite accuratewhen comparedwith the 'true'salesnumbers
availablefrom BookScan,it is not uncommonfor a bestsellingbook to be
missed-i.e., a book may not appear on the list even though its sales
exceededthe sales of listed books. In principle,these mistakes provide a
means of identifyingthe effect of appearingon the list, by servingas an
appropriatecontrol group. (Comparinglisted books to unlistedbooks is a
bad experiment,since whether a book is listed is a nearly deterministic
function of the dependentvariable;but comparinglisted books to books
that shouldhave been listed is, in principle,a valid experiment-as long as
the 'mistakes'are randomoccurrences.)
During the years 2001-2002, there were 182 instances in which a
hardcoverfictionbook was not listed as a New YorkTimesbestsellerwhen
in fact it should have been,'8representing109 differentbooks. (In several
cases,thereweremultipleweeksin whicha book shouldhavebeenon the list
but was not.) The majorityof these(roughly70%)werenarrowmisses:had
the books been listed, they would have been ranked 13-15 on the list. In
orderto constructa faircomparison,I focus on two setsof books:those that
werelisted at rank 13, 14, or 15 when they firstappearedon the New York
Timeslist (n = 44), andthosethat shouldhaveappearedat 13, 14,or 15when
theyweremistakenlyomitted(n = 75). Table2 summarizessomeobservable
characteristicsof the books in the two groups. If the omissions were not
random, but ratheran attempt by the New YorkTimesat 'editorializing'
the list, we might expect to see a differentsubjectcompositionamong the
omitted books. The distributionof subjectsand list prices appearsto be
mostly similarbetweenthe two groups, lending some confidencethat the
18To be precise,I willsaya book shouldhavebeenlistedif, fortheweekthatwasrelevantfor
generatingthelist,thebook'ssales(accordingto BookScan)exceededthesalesof thebook that
was listedat #15.
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BESTSELLERLISTS AND PRODUCT VARIETY
727
TABLE3
COMPARISONOFSALESCHANGES:LISTEDBOOKSVS. OMITTEDBOOKS
% A sales, firstweek appearingon bestsellerlist (n = 44)
% A sales, week in which mistakenlyomitted (n = 75)
Mean
Std. Dev.
- .076
- .227
.702
.157
Difference = .151. One-tailed t-test for Ho: difference is zero: t = 1.404, p = .084.
omissionsareindeedrandommistakes.Theonly notabledifferencesarethat
the genre'Literature& Fiction'is morelikely(and'Romance'or 'Mystery&
Thrillers'less likely) among books making the list than among omitted
books, butthesedifferencesarenot statisticallysignificant.Moreover,when
consideringthe overallcompositionof the list (notjust positions 13-15), the
proportionsof Romance and Mysterynovels match almost perfectlywith
the proportionsamong the omitted books, so it seems clear the omissions
werenot the resultof any bias againstparticulargenres.
Table 3 reportsa comparisonof salesfor the two groups.For books that
werepublishedfor the firsttimeon the New YorkTimeslist, salesdeclinedby
an averageof 7.6 per cent relativeto the previousweek.19For mistakenly
omittedbooks, sales declinedby an averageof 22.7 per cent. Taken at face
value,the differenceimpliesthat in the firstweek,beinglistedleadsto 19per
cent more sales than would have otherwise occurred. However, the
differenceis statisticallyimprecise:for significancelevels less than .08, we
would fail to rejectthe null hypothesisthat the list has no effectusinga onetailed test. The comparisonreportedin Table 3 does not control for any
covariates,but doing so has very little effect on the estimate.Using simple
linear regressionsto control for seasonal effects and time-since-release
effectsyields estimatesof the same approximatemagnitude.
Given the availabilityof panel data on book sales, a second strategyfor
identifyingthe effectof bestsellerlists is to use all the availabledata (notjust
'mistake'books) to measurethe week-by-weekchangesin sales associated
with changesin bestsellerstatus.Whereasthe resultsin Table 3 arebasedon
comparisonsacross books (with mistakenlyomitted books servingas the
controlgroupfor the listedbooks), this secondapproachis basedon withinbook variationover time (so the controlis effectivelythe samebook's sales
trajectoryprior to its appearanceon the list). Observingsales over several
weeksfor eachbook makesit possibleto controlfor book fixedeffects,thus
absorbingthe obviouslyendogenousdifferencesin saleslevelsfor bestseller
vs. non-bestsellertitles. Moreover,the time lag involved in the New York
Timeslist meansthat we have at least three'pre-treatment'observationson
19 Recall that the dominant pattern in sales over time is a steady decay: even for books
appearing on the bestseller list, it is rare for sales to increase from one week to the next.
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728
ALANT. SORENSEN
each bestsellerbeforeit hits the list. (Due to the timelag, the soonest a book
can appearon the list is at the beginningof its fourth week on bookstore
shelves.)In principle,therefore,the datacan be usedto estimatea modelthat
controlsfor book-specificdifferencesin the level of sales andbook-specific
differencesin sales trends observedprior to appearanceon the bestseller
list.20
An empiricalmodel of book sales over time must accommodate the
dominantdecay trendin sales over time (as describedin section IV(i)) and
allow for appearanceson the bestsellerlist to directlyaffectsales.Moreover,
goodness of fit is even more important than usual here, since the
counterfactualwe want from the model (how many units a book would
have sold if it hadn't appearedon the bestsellerlist in a certain week) is
essentiallya forecast.Giventheseconsiderations,one simpleapproachis to
model book sales as an autoregressiveprocessin which the autoregression
parameteris a functionof covariates:
(1)
?i,sit =
+
+itsalesit-1 8it,
?it =Xlt
withXi,a set of covariatesforbook i in weekt (includingbestsellerstatus)and
et, a demand shock that is independent(but not necessarilyidentically
distributed)acrossi and t. The autoregressivestructureis appealingin part
becauseit trackssalesquitewell,so thatthepredictionin anyperiodwillnever
be off by much. Moreover, this specificationfocuses the estimation on
changesin salesfor a givenbook ratherthan differencesin the level of sales
acrossbooks, and allowsfor book-specificdifferencesin the rateof decayin
sales (via book-specificconstants in it,). In other words, the model can
accommodateunobserved,book-specificheterogeneityin both the level and
trajectoryof sales.In orderfor any remainingendogeneityto be importantit
musttakea peculiarform.In particular,the timingof a book'sappearanceon
the bestsellerlistwouldhaveto correspondto weeksof idiosyncratically
high
demandin order to bias our estimateof the list's impact. Given that the
currentlist reflectssales from three weeks prior, such endogenoustiming
seemsveryunlikely.Althoughpublishersaresurelystrategicaboutwhenthey
releasebooks,it seemssafeto presumetheydon'tsystematicallyreleasebooks
exactlythreeweeksin advanceof anticipatedpositivedemandshocks.21
20 Though
implementedsomewhatdifferently,the empiricalstrategiesemployedhere are
similarin spiritto the analysisof Reinsteinand Snyder[2000],who exploitquirksin the timing
of moviecritics'reviewsto identifytheirimpacton box officesales.
21 Onepotentialcaveatis thatpublishersmaystrategically
avoidreleasingbooks at the same
time as major blockbusters,whose release dates are announcedwell in advance. (This is
discussedin more detailin section4.3 below.) However,even in this case it is not clearwhy
publisherswouldtimetheirreleasessuchthatthepositiveshockcomesexactlythreeweeksafter
release.
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BESTSELLERLISTS AND PRODUCT VARIETY
729
TABLE
4
REGRESSION ESTIMATES: BOOK SALES AND LIST STATUS
I
NYT-listed
NYT, week 1
II
III
.043
.030
(.015)
(.019)
.087
(.025)
.011
NYT, week 2+
(.020)
- .002
(.033)
NYT, 1s"week off
NYT-listed x rank
.004
(.004)
NYT-listed x [1-5]
.042
(.016)
.040
(.027)
.068
NYT-listed x [6-10]
NYT-listed x [11-15]
Oprah
GMA
Weeks out
R2
# books
# observations
IV
6.356
(.111)
.108
(.085)
6.286
(.111)
.100
(.083)
6.344
(.114)
.118
(.086)
(.053)
6.346
(.112)
.109
(.085)
-.064
(.022)
-.055
(.021)
-.066
(.024)
-.065
(.023)
.977
799
.978
799
.977
799
.977
799
19,024
19,024
19,024
19,024
Robust standard errorsin parentheses. All explanatory variables are interacted with lagged sales, as explained in
the text, and each specification includes week fixed effects and book fixed effects. The number of observations
does not equal the number of books times 25 because some weeks are missing.
Table 4 reportsestimatesof this model for four separatespecifications,
usingweeks2-26 for eachbook in the sample.All four specificationsinclude
a full set of week dummiesto control for seasonal variationin book sales
(thereis a largeincreasein sales in mid to late December,for example),as
well as a full set of book dummiesin
Column I reports the estimated
i/.for everyweek in whichthe book
coefficienton an indicatorthat equalsone
appearedon the New York Timesbestsellerlist. The point estimates are
statisticallysignificantat the 5 per cent level, and imply that sales decline
about 4 percentagepoints more slowly when a book is listed as a bestseller.
Given the many theoreticalreasonswhy appearanceson a bestsellerlist
shouldhave a directimpacton sales,it is not surprisingto finda statistically
significanteffect.Themoreinterestingquestionis perhapswhythe list has an
impact-i.e., what is the relevant mechanismby which list appearances
boost sales?One class of explanationsis based on the idea that bestseller
status is informative:for example,bestsellerappearancescould signalhigh
quality,or signalwhat otherpeoplearereading(andtherebyfacilitatesocial
coordination). Another possibility is that sales increases result from
promotionalactivitiesundertakenfor bestsellers(e.g., prominentplacement
on bookstore shelves). A rathermundaneexplanationwould be that the
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730
ALANT. SORENSEN
increasesaremerelypriceeffects,resultingfromthe automaticdiscountthat
many storesoffer on currentbestsellers.
The specificationsreportedin columnsII-IV of Table 4 attemptto shed
light on the source of the bestsellerlist's impact, and specificallytry to
distinguishwhetherinformationor promotionis drivingtheeffects.Column
II separates the effect of a list appearancebetween the first week and
subsequentweekson the list. If the salesincreasesaredrivenby information,
we shouldexpectthemto be concentratedin weekone, whenthe information
is new to consumers.On the other hand, if sales increasesmostly reflect
promotionalactivities,then the effects should be apparentas long as the
book remains on the list. The estimates are more consistent with the
information-shock explanation: they indicate an 8.7 percentage-point
change in the week the book first appears(statisticallysignificantat the 5
per cent level), with any effects in subsequentweeks (combined) being
statisticallyindistinguishablefrom zero. Moreover,a book's removalfrom
the list (which, accordingto the bookstoremanagerswith whom I spoke,
abruptlyterminatesany specialin-storepromotionalactivityfor the book)
has littleimpacton its sales:the coefficienton the dummyfor 'firstweekoff
is neither statisticallynor economicallysignificant.Given this result, it is
veryunlikelythat the measuredimpactof bestsellerstatusrepresentsa pure
priceeffect.
Just as informationaleffects should be concentratedin the week of a
book's first appearanceon the list, they may also be concentratedamong
books at the top of the list. Thereis obviously a big differencein the buzz
generated by a number-onebestseller than by a book barely breaking
through to the bottom of the list, and social effects in consumptionare
presumablystrongestfor thebooks at the verytop. By contrast,theeffectsof
in-storepromotionsshould be equallyimportantfor all books on the list,
because all of them are put on a special rack, and/or all of them are
discounted.ColumnsIII andIV of Table4 reportestimatesfromregressions
in whichthe impactof a list appearanceis allowedto dependon the book's
position on the list. Based on these estimates,thereis no evidencethat the
list'simpactis largerfor books at the top of the list:the interactionof the list
dummy with list rank is statistically indistinguishablefrom zero in
specificationIII, and the coefficientson list dummiesfor separategroups
(books 1-5 on the list, books 6-10, and books 11-15) are statistically
= 0.13, p-value = 0.88). If
indistinguishable in specification IV (F2,18114
anything,the point estimatessuggestlargereffectsfor books at the bottom
of the bestsellerlist.
In interpretingthese results, it is important to remember that the
information content of an appearanceon the list is quite low for some
authors-everyone knowsthatGrisham'snewestnovelwillmakethe list, so
there is no information shock when it first appears-and the estimates in
Table 4 report the average effect over all books that made the list. Indeed,
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BESTSELLER
LISTSAND PRODUCTVARIETY
731
it could be that for most books, appearingon the list has virtuallyno effect
on sales;only appearancesthat are surprisesmake a difference.The results
reportedin columnsIII and IV of the table, suggestingthat appearancesat
the top of the list have no more impact on sales than appearancesat the
bottom of the list, may simply reflectthat most top-rankedbestsellersare
books that wereexpectedto be there.
In orderto examinethis issuemorecarefully,the model was re-estimated
allowingfor book-specificheterogeneityin the effectof beinglisted.That is,
the model was estimatedwith
(2)
=
/it
fiNEWBSit
+ X~
t,
where NEWBSit is an indicator equal to one if book i appearedon the
bestsellerlist for the first time in week t. The most strikingfeatureof the
estimated#P'sis how they relate to the authors'histories.Figure 4 shows
kernel densities22for the estimated fi's separately for well-established
authors vs. newer authors.23For establishedauthors, the distributionis
centeredat - 0.02 and is approximatelysymmetric.For authorswho were
less well-established,the distributionhas a positive mode and a long right
tail, whichis consistentwiththe ideathatbestsellerstatushas an appreciable
impactonly for somebooks. Perhapsthe mostinterestingresultrelatesto the
small sampleof elevendebutauthorswho made the list. For these authors,
the estimated r;'sareconsistentlylargeandpositive:of the eleven,ninearein
the top quartile, and seven are in the top decile. The average value of
I3iamongthesenew authorsis 0.35 (comparedwith 0.05 for the remainderof
the authors).Althoughthe samplesize is obviouslysmallhere,the patterns
areclearlyconsistentwiththe hypothesisthat appearingon the bestsellerlist
only has an impactwhen the appearanceis informative.
The heterogeneityevidentin Figure4 is importantto keep in mind when
interpretingthe quantitativeresultsfrom Table 4. Taken at face value, the
estimatedcoefficientsimply a modest averageeffect of list appearanceson
sales, even though the specifiedautoregressivemodel allows the effect to
persist. For example, comparinga book that appearedonly once on the
bestsellerlist (in its fourthweekfromrelease)to a book that startedwith the
sameinitial sales but neverappearedon the list, and assuminga constant
22
Two outlierswereomittedin theestimationof thedensity.One,an extremenegativevalue,
was for a book that was announcedas a televisionbook club pick on the same day it first
appearedon the bestsellerlist, so the data have difficultydistinguishingthe two effects.The
other, an extremepositivevalue, was for a book with a 'mysteryauthor.'TheDiary of Ellen
Rimbauerwasnominallywrittenby a 'new'(butfictitious)authornamedJoyceReardon,andit
was rumoredthat the book was actuallywrittenby StevenKing. Sales spikedconsiderably
whenthe book firsthit the bestsellerlist, so its estimatedfli is verylarge.
23 The groups of authorswere split based on the numberof past adult fiction titles each
authorhad published.The top third(havingpublished35 or more titles)weredesignatedthe
'established'authors,and the remainingtwo thirdswerecalledthe 'newer'authors.
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732
ALAN T. SORENSEN
/i\
!
/
/
\
\
c/
\ kBeta
/\
0=
.\
Bet.8
-.4
--
-
established authors
newer authors
Figure 4
Heterogeneity in the impact of bestseller list on sales
equalto 0.7, the 8 percentage-pointincreasein week4 translatesto a 13.7per
cent differencein expected sales over the first 52 weeks.24However, the
averageeffectsseemto be maskingthe realstory:for establishedbestselling
authors, the list has no discernibleimpact on sales; but for new authors,
appearingon the list has a relativelydramaticimpact.(A one-timeincrease
in ) of 0.35 in week 4 would lead to a 57 per cent increasein sales over the
following 52 weeks.)
Note that even at theirlargest,the effectsof bestsellerlist appearancesare
smallwhencomparedto the effectsof the announcementvariablesincluded
in the regressionsreportedin Table4. The 'Oprah'indicatoris equalto one if
the book wasannouncedas a selectionfor OprahWinfrey'sbook clubin that
week,andthe 'GMA' indicatoris equalto one if the book was announcedas
the pick for the 'Good MorningAmerica'show'sbook club. The estimated
effectsof theseannouncementsareverylarge-they dwarfany effectof the
bestsellerlist. Theinfluenceof the announcementscouldderivefromvarious
24If So is initial
sales, then total sales over Tweeks is So ECT'
•1'. If increases by an amount
A in week r (but then reverts to its previous value and is otherwise constant), then the percentage
increasein salesis equalto A
;3t-
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BESTSELLERLISTS AND PRODUCT VARIETY
733
sources:the announcementscould simplyalerta relativelylargenumberof
consumersto the existenceof the book; they could serveas a qualitysignal;
or they could act as a coordinationmechanismby which a large number
of consumersagree to read the same book. (The latter mechanismis the
apparentobjectiveof the televisionbook clubs.)
Finally,althoughthe specificationsreportedin Table4 shouldadequately
control for unobservedheterogeneityrelated to books' varying levels of
sales, it is neverthelessuseful to construct a reality check for the key
estimated coefficients. For example, instead of regressing sales on an
interactionof laggedsalesand an indicatorfor 'firstweekon the list,'we can
interactlagged sales with an indicatorfor 'firstweek almost on the list,'
definedas any unlistedbook with sales greaterthan 90 per cent of the 15thrankedbestseller.If thecoefficientsreportedin Table4 aremerelyan artifact
of latentheterogeneityin salesdynamicsthatis correlatedwithdifferencesin
the level of sales, then the coefficienton this variablewould be positiveand
significant.In fact, runningthis regressionyields a coefficientestimateof
0.028witha standarderrorof .018,lendingsomeadditionalcredibilityto the
resultsreportedin the table.
IV (iii). Do Bestseller Lists Affect Product Variety?
The resultsof the previoussection indicatethat some books appearingon
the New YorkTimesbestsellerlistenjoya consequentincreasein sales.Butto
what extentare those extrasales 'stolen'from non-bestsellingtitles?As was
explainedin section II, whether (and in which direction)a bestsellerlist
influencesproductvarietydepends on whetherthe list has any impact on
books nearthe publish/no-publishmargin.If theconsumerwho buysa book
becausehe saw it on the bestsellerlist would otherwisehave bought a book
nearthe marginof profitability,then one can arguethat the list reducesthe
privatelyoptimal number of books by furtherconcentratingdemand on
bestsellers.However, it is also possible that the consumer would have
otherwiseboughtno book at all-in that case, sales are moreconcentrated
on bestsellers,but this comes at no expenseto non-bestsellingtitles, and the
numberof books publishedis unaffected.Moreover,it is also possiblethat
bestsellerlistsincreasedemandfor all books, for exampleby simplybringing
moreconsumersinto the bookstore.Bestsellersandnon-bestsellerscould,in
principle,be complementarygoods if consumersbuy multiplebooks when
they visit the bookstore.
The data availableare clearlyinsufficientto providea conclusiveanswer
to this question.The ideal experimentmight be one in which a large set of
consumersmakespurchasesin the presenceof a bestsellerlist, and another
set of consumers makes purchases without having any exposure to
the bestseller list (either through the media or at the retail outlet itself).
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734
ALANT. SORENSEN
TABLE5
SUMMARYOFLISTEDGENRES
Genre
Literature/Fiction
Mystery/Thriller
Romance
ScienceFiction
Religion
Horror
Per cent of books
listingthis genre
Per cent of bestsellers
listing this genre
68
39
11
6
2
2
44
56
19
9
3
3
addto morethan100becausebookstypicallylist
Basedon books'genresas listedon Amazon.com.
Percentages
morethanone genre.
The ubiquityof the New YorkTimeslist makesit virtuallyimpossibleto find
any group of book purchasersthat resemblessuch a control group.
What the available data can potentially reveal is indirect evidence of
substitutionbetweenbestsellersand non-bestsellers.Even this is difficult,
however, since the data contain no price variation that would enable
estimation of cross-priceelasticities. The only useable variation is time
variation in the subjectcomposition of the bestsellerlist. If substitution
betweennon-bestsellersand bestsellersis important,then-presuming that
books in the same genre are closer substitutes than books in different
genres-sales of non-bestsellingbooks should declinewhen the bestseller
list is comprisedof books in similargenres. For example, sales of a nonbestsellingdetectivenovel would decreasein a week when three detective
novels simultaneouslyhit the bestsellerlist.
In order to capture these kinds of substitution patterns, a variable
summarizingeach book's similarityto the currentset of bestsellerswas
constructedby comparingthe book's genre(s)(as listedby Amazon.com)to
the genresof all books on the currentbestsellerlist. Specifically,the pairwise
similaritybetweenbooks A and B is definedas
(3) sim(A,B) =
2 x (# of genressharedby booksA andB)
of
(number genreslistedforA) + (numberof genreslistedfor
B)"
Thismeasureis equalto one if the two books'genresareidentical,andzeroif
there is no overlap at all.25Book A's 'averagesubject similarity'to the
current bestseller list is then computed as 1/15 ~E1
5sim(A, r), where r
indexesthecurrentbestsellersby rank.Table5 showsthe relativefrequencies
of the genresin the samplefor all books and for bestsellersonly. As is clear
fromthe table,mysteriesand thrillersare the most commonbestsellers,and
25Amazon.comoften lists multiplegenresfor the same book:
e.g., 'Contemporaryfiction'
and 'Romance.'So sim(A,B)will be less than one unlessbooks A and B list exactlythe same
genres.
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BESTSELLER
LISTSAND PRODUCTVARIETY
week==53
week==54
week==55
week==57
week==56
week==58
week==60
week==61
<V
P
week==62
<V
week==63
O
week==64
O
Romance
O
ScienceFiction
(D1?
K
Literature
&Fiction
Q Mystery&Thrillers
•
week==59
735
week==65
week==66
week==67
week==68
week==69
week==70
week==71
week==72
week==73
week==74
week==75
week==76
Religion
Horror
week==77 week==78 week==79 week==80 week==81 week==82
week==83
week==84
week==85
week==86
week==87
week==88
Figure5
Variationin bestsellerlist compositionovertime
romance novels are represented more heavily among bestsellers than among
books overall. Importantly, there is substantial variation in the composition
of the bestseller list over time. Figure 5 shows a series of star graphs to
illustrate variation in list composition for a sample 36-week period. To the
extent that there is meaningful substitution between non-bestsellers and
bestsellers, we should observe that sales respond to changes in the average
similarity variable induced by variation in the list composition.
Table 6 reports estimates of a model analogous to the autoregressive
model of the previous section, but with similarity measures included in the
1,i.26 Instead of a business-stealing effect, the estimated coefficients seem to
suggest a complementarity between bestsellers and non-bestsellers. Weeks in
which the genre-composition of the bestseller list is close to a non-bestselling
26 Becausethe purposeis to evaluatethe potentialresponseof non-bestsellersto changesin
theirsimilaritywith bestsellers,only books that nevermade the bestsellerlist are used in the
estimation.Thetablereportsspecificationswithandwithoutsubjectfixedeffects;withoutthese
fixedeffects,the coefficientson the similaritymeasurescouldpartlyreflectaveragedifferences
in the prevalanceof certaingenreson the bestsellerlist, whichwould obviouslybe correlated
with salesin that genre.
? 2007 The Author. Journal compilation ? 2007 Blackwell Publishing Ltd. and the Editorial Board of The Journal of Industrial
Economics.
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ALAN T. SORENSEN
736
TABLE
6
REGRESSION ESTIMATES: BOOK SALES AND SIMILARITY TO CURRENT BESTSELLERS
I
Avg. Similarity:
All bestsellers
New on list
Weeks out
Week dummies?
Subject dummies?
R2
# books
# observations
.044
(.033)
.005
(.002)
II
-
.232
(.100)
III
.228
(.080)
.005
.003
(.002)
(.002)
IV
.400
(.102)
.002
(.002)
yes
no
yes
no
yes
yes
yes
yes
.945
.945
.951
.951
595
14,115
595
14,115
595
14,115
595
14,115
Robust standarderrorsin parentheses.All explanatory variables are interactedwith lagged sales, as explained in
the text. Avg. Similarity measures how similar a book is to the current set of bestsellers, based on the books'
listed genres. Only books that never made the bestseller list are included in the estimation. The number of
observations does not equal the number of books times 25 because some weeks are missing.
book's genretend to be good weeks for the non-bestseller.Consistentwith
the findingsin Table 4, the effect seems to be most pronouncedfor books
appearingon the list for the firsttime:whenthe similaritymeasureis defined
only for the subset of bestsellerswhose first appearancewas in the given
week, the positive effect of the similarityis stronger.This pattern could
plausibly reflectmultiple-bookpurchases:for example, weeks in which a
newromancenovelhit the bestsellerlist drawmorethanthe usualfractionof
romanceenthusiastsinto bookstores, and they may buy severalromance
novels when visitingthe store.
Although the estimated coefficients on the similarity measures are
statisticallysignificant,they should be interpretedcarefully.One caveat to
keep in mind is that publisherschoose theirreleasedates strategically.For
example, there is some evidence that publishersof non-bestsellingbooks
tendto avoidreleasedatesthatcoincidewiththe releaseof a blockbustertitle
in the samegenre.The regressionsin Table 6 may be biasedagainstfinding
business-stealing,because publisherschoose their books' releasedates to
activelyavoid business-stealing.That is, if book A were expectedto steal
business from book B, then book B's releasedate would be strategically
distant from book A's release, so that potential instances of businessstealingare less likely to be observedin the data. Anotherthing to keep in
mind when interpretingthe coefficients is that they represent(at best)
indirectevidenceof the substitutionpatternsbetweenbestsellingand nonbestsellingtitles,and the resultsmay not generalizeeasilyto othercategories
of books. For example,non-fictiontitles on similartopics are perhapsless
likely to exhibit complementarities.A consumer looking to buy a book
on investments,for instance,seemsmore likely to choosejust one (instead
? 2007 The Author. Journal compilation ? 2007 Blackwell Publishing Ltd. and the Editorial Board of The Journal of Industrial
Economics.
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BESTSELLERLISTS AND PRODUCT VARIETY
737
of buying many) than would a consumer looking for a suspense or
romancenovel.27
V.
DISCUSSION & CONCLUSIONS
Based on the sales data analyzedhere, it seemsclearthat appearingon the
New York Times bestseller list has a direct impact on a book's sales.
Estimatesbased on two alternativeidentificationstrategiesshow that sales
increasewhen a book appearson the list. However,the magnitudeof the
effectis modest,and appearsto reflectspikesin the salesof books that were
surpriseson the list (e.g., books by new authors).
Given the amount of attentionpaid to the New YorkTimeslist (and the
desperateschemesoccasionallyemployedby authorsto securea position on
the list), the estimatesof its impactmay seem surprisinglysmall. However,
the present analysis ignores two effects that could be quite significantto
authorsandpublishers.First,whileappearingon the bestsellerlist may have
only a modest immediateimpact on the book's sales, it may dramatically
increase the popularity of future books by the same author. Second,
paperbacksales may be influencedby whetherthe hardcoveredition was a
bestseller. (Indeed, paperback versions of books that were hardcover
bestsellerstypically announce that fact prominentlyon the front cover.)
Althoughtheseeffectscan't be measuredwith the availabledata, anecdotal
evidencesuggeststhey may be important.
Although there are a variety of reasons why the bestsellerlist might
directlyinfluencesales, the patternsin the data are most clearlyconsistent
with an information-basedexplanation. Retail-level promotions cannot
rationalizetwo of the study'scentralfindings:whereasretailers'promotions
persistfor the durationof a book's termon the list, the estimatessuggestthat
the impact of appearingon the list is transitory,with the bulk of the effect
realizedin the firstweek.Moreover,the impacton salesis most pronounced
among relativelyunknown authors (new authorsin particular),a pattern
that favorsinformationover promotionas an explanationfor the effect.
The impact of the list on sales (and the consequent increase in the
concentrationof demandon bestsellers)raisesthe interestingcounterfactual
question:'Wouldmore books be publishedif it weren'tfor bestsellerlists?'
Whether(and in which direction)the bestsellerlist shifts the publish/nopublish margindepends on the nature of substitutionbetweenbestselling
and non-bestsellingtitles-i.e., are the extra sales of bestsellersones that
27In unreportedanalysesusing sales data for nonfiction
titles, coefficientson similarity
measures like those in Table 6 indeed suggested a pattern of substitution rather than
complementarity.Nonfiction titles have the advantageof being able to categorizesubjects
muchmorefinelythanin fiction,but it was difficultto includenonfictiontitlesin the broader
analysisbecausethe coverageof nonfictionbestsellersin my samplewas sparse.
? 2007 The Author. Journal compilation ? 2007 Blackwell Publishing Ltd. and the Editorial Board of The Journal of Industrial
Economics.
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738
ALANT. SORENSEN
would have otherwisegone to less popular books? The resultshere offer
indirectevidencethat for hardcoverfiction, bestsellersand non-bestsellers
withinthe samegenremay in fact be complements:weeksin whichbooks of
a particulargenrefirstappearon the bestsellerlist tend to be strong-selling
weeks for non-bestsellersof the same genre. Although too indirectto be
conclusive,this resultsuggeststhat marketexpansioneffectsdominateany
business-stealingassociatedwithbestsellerlists,so thatbestsellerlistsmayin
fact increasethe numberof books publishedin equilibrium.
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