Effectiveness of Negative Campaigning in U.S. Senate Elections
Author(s): Richard R. Lau and Gerald M. Pomper
Source: American Journal of Political Science, Vol. 46, No. 1, (Jan., 2002), pp. 47-66
Published by: Midwest Political Science Association
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Effectiveness of
in
U.S.
Senate
Negative
Campaigning
Elections
RichardR. Lau RutgersUniversity
GeraldM. Pomper RutgersUniversity
The characterof elections critically
affects the dialogue of democracy.
Thisarticleexamines thatdialogue in
143 U.S. Senate elections, 1988-1998,
in whichan incumbentsought reelection.We go beyond previousresearch
on the impactof campaign spending
to focus on the characterof the conthe impactof
test itself,particularly
negative campaigning.Campaign
strategies are endogenous to the
campaign itself,requiringplausible
instrumentsand two-stage statistical
techniques to produce reliableestimates. Ouranalyses combine informationon the relative"tone"of U.S. Senate campaigns withan original
aggregate data set and ANESsurvey
data. We ask a simple question:how
effective is negative campaigningin
helpingto get candidates elected?
Ourresultsprovideno straightforward
answer.Generallyspeaking, but dependent on the opponent'sstrategy,
negative campaigningis relatively
effectiveforchallengers, whilepositive
campaigningis moreeffectivefor
incumbents.Overall,our resultsdo
provideclear evidence thatthe campaign "matters."
emocracy is a dialogue between putative leaders and citizens.
Campaigns provide the most obvious and the loudest forums for
this dialogue. Candidates try to persuade voters to cast a ballot
and to support their cause. Voters respond by coming to the polls and selecting their preferred candidates. The quality of the dialogue can wane,
however, if candidates speak poorly or if voters close their ears.
Concern for the democratic dialogue in the United States often turns
to negative campaigning. American politics over the past two decades has
experienced a dramatic rise in its use, particularly in political advertising
(Ansolabehere and Iyengar 1995; Jamieson 1992; Johnson-Cartee and
Copeland 1991;West 1993)-or at least a dramatic increase in the number
of political observers decrying its use. Although empirical evidence about
the type of political advertisements actually used during this period (much
less earlier) is hard to come by, the limited available data do indicate a noticeable increase-at least at the presidential level (Geer 1998a; Kaid and
Johnston 1991;West 1993).
Negative campaigning may be on the rise because it is generally presumed that these methods, particularlyin the form of televised advertising,
are an unusually effective means of campaigning, a real advantage to those
candidates who have the backbone to employ it. Social psychological
theory provides severalreasons why negative information ought to be more
persuasive than comparable positive information (Kanouse and Hanson
1972). Lau (1982,1985) groups these reasons into two main categories. The
first is perceptual: negative information may be more likely than comparable positive information to be noticed and processed, thereby having the
opportunity to get its message across. The second reason is motivational,
based on the greater survival benefits resulting from avoiding costs rather
than approaching gains. Less formally, some negative campaigning may be
RichardR. Lau is Professorof Political Science, RutgersUniversity,89 George Street,
New Brunswick, New Jersey08901 ([email protected]).Gerald M. Pomper is
Boardof GovernorsProfessorof PoliticalScience,RutgersUniversity,89 GeorgeStreet,
New Brunswick,New Jersey08901 ([email protected]).
An earlierversion of this articlewas presentedat the 1998 annualmeeting of the American Political Science Association in Boston. We thank Erlinda Mazeika and Grace
Mumoli for coding most of the Senate campaign data;CharlesFranklin,Alan Gerber,
and Jerry Hagstrum for sharing various other information with us; and Stanley
Feldmanand MarkKamletfor statisticaladvice. This article was completed while the
first authorwas a fellow at the Centerfor the Studyof DemocraticPoliticsat Princeton.
AmericanJournalof PoliticalScience,Vol. 46, No. 1, January2002, Pp. 47-66
?2002 by the MidwestPoliticalScienceAssociation
47
RICHARD R. LAU AND GERALD M. POMPER
48
advantageousto candidates (particularly"untested"challengers) if it helps convey the impression that they are
"tough enough" to be a leader.
Politicians use negative campaigning not because
they are evil, or meanspirited then, but because they believe that it helps them to win elections. Prominent Republican and Democratic consultants agree:
Voterswill tell you in focus groups that they don't
like negativeads, but they retainthe informationso
much better than the positive ones. The point is:
People like dirty laundry. Why do tabloids sell?
(Roger Stone, Republican consultant, quoted by
Colford 1986, 104).
Candidatesengagein negativecampaigningbecause
it works.No matterhow much people say they dislike it, negativecampaignscontinue to move voters
from one column to the other (Susan Estrich,
Dukakis's1988 campaignmanager[1993, 11A]).
And yet conventional wisdom is not always right. If
we systematicallyexamine the evidence in the existing research literature,the inordinate power of negative political campaigning to persuade is-at best-unproven (see
Lau et al. [1999] for a comprehensive review). There are
but ten published papers on the subject, and none of
these studies involve data with a representative population sample. Only three of these studies reported effects
of any magnitude, and of these, two were counterto the
intentions of the candidate who sponsored the negative
advertisements. One would have to conclude from the
availableevidence that the effectiveness of negative campaigning is at best a researchhypothesis, and quite possibly a myth.
Part of the problem is the methodological difficulty
of finding campaign effects of any type (Bartels 1993).
Indeed, there is a considerable amount of researchin political science which argues that the context in which the
election is held (e.g., the state of the economy, the popularity of the incumbent President), rather than the campaign itself, is what really matters (e.g., Lewis-Beck and
Rice 1992; Rosenstone 1983). Of course these macrolevel explanations were put forth only after several major
attempts to determine the influence of political campaigns had failed to detect any influence at all (see
Holbrook [1996] for a recent review of this literature).
More recently researcheshave been able to provide convincing evidence that political campaigns do have some
detectable effects, although the typical study looks for
fairly specific, and therefore fairly limited, effects.
We would argue that the ability of negative cam-
paigning to affect the vote decision has rarely been put
to a convincing test. This article attempts a fuller analysis. We will address a very simple research question in
the context of U.S. Senate elections: how effective is
negative campaigning in winning elections? To answer
this simple question, however, we must do some complex investigation. We attempt here to define negative
campaigning, to develop an appropriate data set, to resolve problems of endogeneity in the crucial equations,
and finally to provide the appropriate statistical tests to
conduct the analyses.
theAnalysis
Framing
Defining
Negative
Campaigning
We must be careful to distinguish between negative campaigning and unfair campaigning, since "observersoften
define negativity as anything they do not like about campaigns" (West 1993, 46). The two categories are often
confused; in effect, the directional meaning of negativestatements in opposition to a person or program-is too
often equated with the evaluative meaning-statements
that are normatively disvalued. But the two are not necessarily linked. In this article we restrict our meaning to
direction. Negative campaigning is talking about the opponent-his or her programs, accomplishments, qualifications, associates,and so on-with the focus, usually,on
the defects of these attributes. Positive campaigning is
just the opposite: talking about one's own accomplishments, qualifications,programs, and so forth. Candidates
can lie (or more generously,stretch the truth) about their
opponents in negative campaigning, but they can do the
same about themselves in what we call positive campaigning. The motivation is the same either way, and it is
unclear why distortion would be any more likely in one
form than the other.'
In fact, some prominent researchers recommend
avoiding the term "negative"in describing political advertisingbecause is has become so affectivelyloaded. For
example, Jamieson and others (e.g., Bartels et al. 1998;
Jamieson, Waldman, and Sheer 1998), now prefer a
three-fold categorization: advocacy ads, attack ads, and
comparative ads (which contrast the sponsoring candidate to his or her opponent on one or more dimensions).
1Whetherin
practiceclaimsthat aremade about an opponent during a political campaign are generally less truthful than claims
made about oneself, is unclear;but in principle,by definition, this
cannot be assumed.In fact, Jamieson,Waldman,and Sherr(1998)
reportthat at the presidentiallevel at least,attack(negative)advertisementsare moretruthfulthat advocacy(positive) ads.
EFFECTIVENESS OF NEGATIVE CAMPAIGNING IN U.S. SENATE ELECTIONS
We have not adopted this new terminology, however, because all campaigns, if not all advertisements, are comparativeto some degree. We are trying to measure the nature of the campaign as a whole, not just the political
advertisements associated with it, and any di- or trichotomous classification is going to be misleading. As
described below, we develop a continuous measure of a
campaign's "tone" which describes the relative negativism of the campaign.
Locating
NegativeCampaigning
inSenateRaces
The existing literature on campaigning is limited by its
typical focus on presidential elections. From a research
perspective, presidential campaigns are poor venues for
exploring campaign effects. Of all political figures, incumbent presidents and their high-profile challengers
are the most well-known figures, and thus the least likely
to be "redefined"by any obviously partisan attacks from
an opponent. Moreover, the presidential campaign gets
so much "free"coverage from the national media-coverage that candidates can only partially influence-that
candidate-sponsored advertisements, speeches, and rallies comprise only a small proportion of the total information available about the competitors.
As an alternative,we believe that statewide elections
are a much more promising opportunity for exploring
campaign effects (see Franklin [1991] for similar arguments). On the one hand, statewide races are prominent
enough to attract, and thus require,substantial campaign
funds. There are real campaigns to study. And on the
other hand, statewide candidates tend to be less wellknown than presidential candidates, and these races get
little coverage in the national media and substantiallyless
coverage than the presidential campaign even in the local
media. As a result, the respective campaigns can influence more of the readily available information about the
candidates. And, for statistical utility, there are sixtyseven elections for Senator for every quadrennial presidential contest.
We will examine the effects of negative campaigning
in U.S. Senate elections. Of late there has been an upsurge of research on Senate elections (e.g., Abramowitz
1988; Campbell and Summers 1990; Carsey and Wright
1998; Franklin 1991; Gerber 1998; Kahn and Kenny 1999;
Westyle 1991). Although incumbents typically win reelection, many face stiff electoral challenges. Indeed,
when incumbent Senators sought reelection over the past
three decades (i.e., from 1970 through 1998), they were
49
defeated about 19 percent of the time. Thus the outcome
of many Senate campaigns is not a foregone conclusion
Previous researchon Senate elections has considered
various influences, both national and local. National factors include the popularity of the incumbent president
and the state of the national economy. Among local factors, an acknowledgedinfluence is the "quality"of the candidate challenging an incumbent (Squire 1992). Money is
certainly important, and campaign spending figures are
readilyavailable.However,there is a debate about the relative importance of campaign spending for incumbents
and challengers, with the conventional view that money
matters much more to challengers (Abramowitz 1988;
Jacobson 1978) currently under attack (Gerber 1998;
Green and Krasno 1988). The analytic problem, as all participants in this debate recognize, is that campaign spending, the crucial independent variable,is essentially a function of the independent variable, the election outcome.2
In technical terms, this is known as endogeneity in the
spending equations. We include the most important factors from this prior researchin our own analyses.
We would argue that one important variable has
been missing from these analyses. Any candidate, even
one blessed with a large campaign war chest and other
advantages, can campaign wisely or poorly. We do not
pretend to measure all of the strategic decisions that any
candidate must make during an election campaign. We
can, however, measure one important strategic decision
which surely shapes many particular campaign behaviors: Should I emphasize my own abilities, accomplishments, and policy stands; or should I concentrate on attacking my opponent on these grounds? Individual
campaigns will include both approaches, but they will
combine them in different proportions. Simply put, candidates will vary in their relative use of negative campaigning. We seek to examine the effect of that variation.
DataandMethod
A campaign goes beyond its televised political advertisements, the most common focus. Candidates engage in
many activities-they give speeches, conduct rallies,
2Whenan incumbentseemsparticularlyvulnerable,it will be
easierforchallengers
to raisemoney,andincumbents
willtypically
face"highquality,"
well-financed
successwill
opponents.Electoral
be positivelycorrelated
withchallenger
spending.Butthissimple
equationworksjust the oppositefor incumbents.If the election
looksclose,supporterswillbe particularly
likelyto contributeto
the incumbent;but if the electionlookslikea landslide,the incumbentwill cut backon fund-raisingactivities,generatinga
negativecorrelationbetweenlikelyelectoralsuccessandincumbent spending.
50
distribute literature,and meet with local opinion leaders,
editors and other elites to seek endorsements (Shaw
1999). There is reason to believe that candidates are consistent across these different venues in the themes they
stress (certainly their campaign managers want them to
be consistent), but there is no guarantee that they are. To
examine the effects of the campaign more broadly, we
need a more comprehensive view. We rely on estimates
of the nature of the campaign gathered from newspaper
accounts of the election campaigns. These accounts include descriptions of political advertisements broadcast
by the different candidates, but they also include reports
of a wide variety of other campaign activities. As such,
our data are second-hand, relying on the judgments of a
number of political experts (i.e., political reporters) in
each state.
These estimates come from two different sources.
For the 1988 and 1990 elections, we employed data gathered by Franklin (1992).3 For the remaining years-1992
through 1998-we coded stories about the Senate elections that appeared in any newspaper covered by the
Nexis database during the last eight weeks of the campaign. Following Franklin, each statement coming from
either major party campaign was coded as positive or
negative, and policy-based or person-based.4The percent
negative statements coming from the two campaigns
vary,on average,between a low of 29 percent in 1990 to a
high of almost 43 percent in 1994. We refer to these data
as measures of the campaign's"tone."5
3He coded every story about the Senate election appearingin the
newspaperwith the largestcirculationin the state during the last
four weeks of the campaign.Each statementby one of the major
party candidates(or a spokespersonfor one of those candidates)
was coded as to whetherit talkedabout the candidatesthemselves
(and was therefore "positive,"by our definition), or was talking
about the opponent (and was therefore"negative").These statements were further categorized as to whether they were policybased or person-based.See Lauand Pomper(2001a) for examples.
4In the data reportedbelow we rely on an overall measureof the
percentnegativestatementscoming from each campaign,ignoring
the policy-based or person-based distinction. We repeated all
analyses with separate measures of these two types of negative
campaigning,but no clear pattern of coefficients resulted across
both aggregateand individual-levelanalyses,and for greaterparsimony we only present data from the overall measure. Our data
were coded by severalresearchassistants.To check on coding reliability the authors independentlyexamined a random 10 percent
sample of the total articlescoded and found a very respectable92
percentcoding agreement.
5Wewere able to generatemeasuresof the campaign'stone in 191
of the 206 Senateelections over this period.We found too few stories for some elections (particularlythose in Alaskaand Hawaii)to
reliablyestimatethe campaign'stone, and these are droppedfrom
the data set, as arethe six elections in which an incumbentwas not
facing a majorparty opponent. We also drop forty-fouropen-seat
contestsand focus on those electionswhereincumbentswereseeking reelection.
RICHARD R. LAU AND GERALD M. POMPER
TheCampaign
Context:
Problems
ofCausation
andEndogeneity
We cannot evaluate the impact of negative campaigning
in isolation, nor treat it simply as one of several predictor
variables (along with party, incumbency, job performance evaluations, candidate spending, and so on) that
independently help explain the outcome of Senate elections. Campaigns are dynamic processes, requiring attention to contextual variations. The problem with treating
some critical variables-candidates' spending-as if they
were exogenous (that is, not themselves influenced by the
dependent variable) are well known in the literature
(Abramowitz 1988; Gerber 1998; Green and Krasno
1988; Jacobson 1978). We treat spending as endogenous
to the outcome of the election and devise two-stage estimation procedures to control for the statistical problems
this endogeneity causes. We follow Gerber (1998) in developing the necessary instruments for spending.6
Campaign strategies are equally problematic. They
are not chosen randomly, nor are they devised without
any knowledge of the likely outcome of the election
(Haynes and Rhine 1999; Lau and Pomper, 2001b). To
begin with, we certainly assume that candidates generally
choose the campaign strategy they believe will give them
the best chance of winning the election, although there
will inevitably be a great deal of uncertainty associated
with this choice. At the outset of the campaign, candidates who expect to lose may attack their opponent out
of desperation, or because they feel they have "nothing to
lose."Candidateswith less money to spend than their opponents may "go negative"because they believe they get
"more bang from the buck"with such a strategy.If we ignore the problem of endogeneity, we get strange results.
Using our aggregate data we regressed the percentage of
the two-party vote received by the Democratic candidate
on a simple measure of the relativeuse of negative campaigning by the two candidates. If life were simple and
"going negative"an effective campaign strategy,as many
practitioners seem to believe, then the more negative
candidate should get more votes, and our measure of the
relativenegativism of the two campaigns should be posi6Gerberemployed three instrumentsfor challengerspending:the
total amount spent by both candidatesin the previousSenateelection in the state (which is positively relatedto currentspending),
the voting age population of the state (negativelyrelatedto spending), and a dummy variable indicating whether the challengeris
wealthy (as coded from preelection coverageof the campaign in
CongressionalQuarterly).We followed this procedure,which resulted in a first-stage regressionpredicting 19 percent of the explainablevariancein challengerspending. Gerberemployed only
the first two variablesas instrumentsfor incumbent spending.We
added one more:whetherthe incumbentwas a member of the Finance Committee (see Romer and Snyder 1994). The first stage
equation for incumbent spendinghad an R2of almost .56.
EFFECTIVENESS OF NEGATIVE CAMPAIGNING IN U.S. SENATE ELECTIONS
tivelyassociatedwith electionoutcome.In fact,the coefficientis negative,a whopping-.30 (s.e. = .03; adjusted
R2= .39). Either"goingnegative"is a horriblyineffective
campaignstrategy,or somethingelse is goingon.
That"somethingelse,"most obviously,is thatalmost
all candidateshave a reasonablyaccuratesense of how
likely they are to win well before the campaignbegins.
Whatwe areseeingin the -.30 correlationis, to some degree,that candidateswho expectto lose, no matterhow
chooseto attacktheir
they campaign,disproportionately
opponents.7By addinga measureof expectedoutcome
to the basicregression,we may controlfor the effectsof
expectedlosersgoing negative,and providea much less
biasedestimateof the effectsof campaigntone on election outcome. When we do this, the effect of negative
tone is reduced,yieldinga coefficientof-.11 (s.e. = .03,
adjustedR2= .61). It still appearsthat attackingthe opponent is a bad campaignstrategy,but not nearlyas bad
as in our initialestimate.
We still havenot controlledfor all of the endogeneity. Candidatesdo not devisecampaignstrategiesat the
beginning of campaignsand never revisit those decisions. Candidateswho find themselvesdoing less well
than they had hoped at the outsetof the campaign(e.g.,
an incumbentwho findshimselfrunningbehind)might
switchfrom an initiallypositivestrategyto one that involves more negativecampaigning.Justlike candidate
spending,then, campaignstrategiesare to some degree
endogenousto the campaignitself, and requirea more
thoroughtwo-stageanalysis.
We considered a number of variablesas possible
first-stageinstrumentsfor campaigntone, includingthe
projectedclosenessof the race,dummyvariablesfor region, party,candidategender,electionyear,and dummy
variables for the polling firm and media consultant
workingfor each candidate.8To avoid"overfitting,"we
7Indeed,we createda measureof the projectedoutcome of the race,
and this measurecorrelates-.63 with our simple measureof relative campaign negativism. Clearly,candidateswho expect to lose
disproportionately"go negative"(see Lau and Pomper,2001b, for
more detail).
8The National
Journalpublishes these data in the issue a week or
two before each election-or did, until 1998. We are particularly
indebted to JerryHagstrom,who gatheredthese data for the Journal over the past two decades, and shared his 1998 data with us,
even though it ultimatelywas not published in the magazine.We
createddummy variablesfor all polling firms and all media consultants who, during the 1988-1998 period, worked for at least
three differentcandidatein at least threedifferentstates.Theselast
dummy variablesare designed to capturethe differentialproclivities of the various campaign consultants to recommend "going
negative"in seemingly similar situations across elections. These
data, along with our measures of campaign tone and candidate
quality, are available from our web site, Http://www.polisci.
rutgers.edu/people/RickLau/.
51
retainedonly those possiblepredictorvariableswith coefficientsapproximatelyas large (or larger)than their
standarderrors.The details of these first-stageregressions are presentedin the appendix.As with spending,
we estimatedseparatefirst-stageregressionsfor incumbentsand challengers.The R2sof thesefirst-stageregressionswere.47 and .32,respectively.
Variable
Definition
andData
Webringtwo distinctsets of datato bearon the central
question of the effectivenessof negativepolitical campaigningin Senateelections,one an originalaggregate,
state-leveldata set createdfrom standardsources,the
second individual-level survey data collected by the
ANES.We gather data and develop unique variables
whicharecombined
specifyingcampaigncharacteristics
with both the aggregateand individualleveldatasets.
We want to examine the effectivenessof negative
campaigningin achievingthe end its practitionersdesire-winning the election.Becausemost priorresearch
on Senateelectionshas used aggregatedata to measure
the electoralsuccessof incumbentsseekingreelection,we
begin our analysisby regressingthe proportionof vote
receivedby the incumbent(in our aggregate,state-level
dataset) on a seriesof predictorswhichpastresearchhas
foundto be important,includingseveralnationalfactors
that could influenceall Senateelections occurringin a
particularyear,and variouslocal factorsthat areunique
to each state-including candidate"quality,"
spending,
andour indicatorsof the natureof the campaign.In particular,the incumbentsupportequationincludesthese
independentvariables:
Presidential
the averageapprovalratingof
Popularity,
the Presidentfrom all Gallup polls conducted
duringJuly,August,and Septemberof the election year,minus 50. Thisvariableis multipliedby
-1 if the incumbentsenatoris of a differentparty
fromthat of the incumbentPresident.If popular
Presidentshelp candidatesfrom their own party
(that is, if Senateelections are in part referenda
on the President'sjob performance),this variable
will be positivelyrelatedto incumbentvote.
MidtermElection,which equals+1 when an incumbent of the President'spartyis seeking reelection
during an off-year,equals -1 if an incumbent
fromthe oppositepartyof the President'sis seeking reelection, and is 0 otherwise. Becausethe
President'spartyusuallyloses seats in Congress
during off-year elections, this variable should be
negatively related to incumbent vote.
RICHARD R. LAU AND GERALD M. POMPER
52
State Partisanship, the percentage of people in the
state who identify with the incumbent's party,
minus the percent who identify with the challenger's party. This variable should be positively
related to incumbent vote percent.9
Changein StatePer CapitaDisposableIncome(PCDI),
as reported by the Bureau of Economic Activity.
This objective indicator of the nature of the state's
economy should have a positive coefficient, assuming citizens are more likely to support incumbents when economic times are good, but are
more likely to prefer a change in their political
leaders (irrespective of party) when economic
times are bad.
Incumbent Scandal, Incumbent Controversy, and
Incumbent Health, dummy variables indicating
whether the incumbent had been involved in a potentially illegal scandal, an equally damning (but
not illegal) controversy,or if the incumbent's ability to perform the job had been questioned for
health reasons. These data were coded by reading
the pre-election issue of CongressionalQuarterly
each year.'0Although these three distinct dummy
variables have too little variance to have much
power in any statistical analysis, we have retained
them all to maintain some continuity with past research.Each of these variablesshould be negatively
relatedto percent vote receivedby the incumbent.
Challenger Governor, a dummy variable which
equaled 1 if the challenger were the current or recently retired governor of the state, and equaled 0
otherwise.
ChallengerMajor OfficeHolder equaled 1 if the challenger held some lesser statewide office (e.g.,
Lieutenant Governor) or was the mayor of a major city in the state, and was 0 otherwise.
9It is conventionalto rely on measuresdevelopedby Wright,
Theirdata
Erikson,andMcIver(1985)of state-levelpartisanship.
comefromsurveysconductedbetween1974and1982-at bestsix
yearsbeforeanyof the electionsconsideredhere.Weupdatedthe
Wright,Erikson,andMcIverdataby creatingourowncumulative
datasetfromallANESsurveysconductedbetween1988and 1996
(includingthe SenateElectionstudy).This cumulativedataset
containedon averageover 500 casesper state,with the actual
samplesvaryingbetweena lowof 153forHawaiito a highof 1847
Wethencreated"updated"
forCalifornia.
partisanship
figuresfor
eachstateby averagingtogetherour figuresfrom 1988to 1996
withWright,Erikson,andMclver'sfrom 1974to 1982.Ournew
statepartisanshipdatacorrelate.92 with the originaldatapresentedbyWright,Erikson,andMcIver.
0lWeemploydataoriginallycodedby Gerber(1998),who was
kindenoughto sharehis datafor the 1988,1990,and 1992elecelectionswithus.Theauthorscodedthedatafromtheremaining
tion years.All of these data are availablefrom our web site.
Challenger House, a dummy variable indicating
whether the challenger was a member of the
House of Representatives.
ChallengerMinor OfficeHolderwas a dummy variable
indicating whether the challenger was the mayor
of a small town or city, a state legislator, or held
some other elective office.
Incumbentand ChallengerSpending,the natural logarithm of spending (in constant 1988 dollars) per
capita (plus 1, so all logarithms will be positive
and defined). We employ instruments for spending resulting from the first-stage analyses described above. Incumbent Spending should have
a positive coefficient, Challenger Spending a
negative coefficient.
Incumbentand ChallengerNegativism,the percentage
of all statements attributed to representatives of
each campaign that attacked the opponent
(rather than talking about their own candidate).
Again, the actual variables used are first-stage estimates, resulting from the analyses reported in
the appendix. If negative campaigning is effective,
the Incumbent Negativism variable should predict positively, while the Challenger Negativism
variable should have a negative sign.
The range and mean of all variables are shown in Table 1.
I VariableMeans and Ranges
TABLE
for Aggregate Level Data
Low
High
Mean
42.4
VotePercent
Incumbent
-15
Presidential
Popularity
0
Midterm
Election
-29.2
StatePartisanship
-7.9
StateChangein PCDI
Scandal
0
Incumbent
0
Incumbent
Controversy
0
IncumbentHealthProblems
0
Challengera Governor
0
ChallengerMajorOfficeHolder
0
Challengerin House
0
ChallengerMinorOfficeHolder
0.0
Incumbent
Spending(2SLS)
0.0
ChallengerSpending(2SLS)
0.3
IncumbentNegativism(2SLS)
.0
WeightedInc.Negativisma
22.5
ChallengerNegativism(2SIS)
.2
WeightedChal.Negativisma
88.1
19
1
31.7
11.4
1
1
1
3
3
3
3
1.9
1.3
64.9
133.2
66.4
73.6
59.70
3.52
.49
2.61
4.91
.03
.10
.03
.03
.18
.15
.22
1.08
.75
31.28
33.81
44.52
34.21
Note: N = 143.
a Negativism
byspending.
multiplied
EFFECTIVENESS OF NEGATIVE CAMPAIGNING IN U.S. SENATE ELECTIONS
Any aggregate-level analysis of an individual decision is arguably misspecified. A more definitive test of
the effectiveness of negative campaigning should be provided by individual-level data, using the ANES's Senate
Election Study data for respondents in states where an
incumbent was facing a major party challenger in the
1988-1992 period. The aggregate-level data on the nature of the campaign were merged with this survey data,
which contained individual-level measures of many of
the conceptual variables used as predictors in the aggregate analysis. When data from two different levels of
analysis are combined, the resulting analyses are usually
referredto as "cross-level."Our data set includes seventyfour elections in which the incumbent was seeking reelection. The dependent variable is vote for the incumbent (coded 1) rather than the major party challenger
(coded 0). The handful of respondents who voted for
some other candidate are excluded from the analyses.
Individual-level predictors include
Approval of the President'sJob Performance,the standard item, with respondents who answer "Don't
Know"placed at the middle of the scale. Approval
is scored high when the incumbent President is a
Republican, low when he is a Democrat. Because
there were Republican presidents between 1988
and 1992, the variable should also have a positive
coefficient in the analyses.
National Economy Worse,the standard retrospective
judgment, with respondents who say"Don't
Know" placed at the middle ("Stayed about the
same") of the scale. "Worse"is scored high when a
Democrat is the incumbent senator, and scored
low when a Republican is the incumbent; again,
this variable should predict positively.
Party Identification and IdeologicalIdentification,the
two familiar seven-point scales, with respondents
claiming no such identification placed at the midpoint of each scale. These variables were coded
such that Republican and conservative identifiers
are scored high when there was a Republican incumbent senator, while Democratic and liberal
identification are scored high when the incumbent is a Democrat. Each variable should predict
positively.
PolicyPreferences,a count of the number of ten different government programs for which respondents
wanted more spending, minus the number of programs for which respondents wanted less spending. This variablewas reversedwhenever there was
a Republican incumbent, so that it should predict
positively in all elections.
53
Campaign Exposure,included in several models, is a
scale combining self-proclaimed interest in politics, and the frequencyof reading about (or watching) politics in the newspaper and on television.
Variablesmeasured at the state level which were used
in the analysis of the survey data included:
Changein StatePer CapitaDisposableIncome(PCDI),
the same variable used in the aggregateanalyses.
Incumbent Scandal, Incumbent Controversy,and Incumbent Health, and four dummy variables measuring challenger quality, all defined as above.
Incumbent and Challenger Spending, and Incumbent
and Challenger Negativism, were defined in exactly the same manner as in the aggregate analyses, except that the two campaign tone variables
were expressed as proportions rather than percentages.
The analysis also included standard demographic controls for age, education, income, race, and gender, measured at the individual level; and a dummy variable for a
midterm election. All variables except those involving
spending and campaign tone were rescaledto have a onepoint range; all of these control variables (except the dichotomous dummy variables) were centered around 0.
Results
Aggregate
Analysis
We begin our exploration of the effectiveness of negative
campaigning by considering our aggregate, state-level
data set. Much of the previous research on Senate elections has employed such data. Table 2 reports four separate analyses, the first without any of our measures of
campaign tone, the others with different indicators of incumbent and challenger negativism.
Although we are primarily concerned with the effects of the campaign tone variables,we will briefly comment on our base model, which excludes them. To begin
with the national factors, presidential popularity does
not seem to be much of a factor when incumbent senators are seeking reelection, but being an incumbent of
the president'sparty seeking reelection during a midterm
election is a definite detriment, costing the incumbent
two to three points. This effect is statistically significant,
one-tailed. Among the local factors, statewide partisanship clearly affects the incumbent's reelection chances.
For every point that the number of partisans of the
RICHARD R. LAU AND GERALD M. POMPER
54
TABLE2
PercentVote for IncumbentSenator, State-Level,1988-1998
Base Model
B
S.E.
Constant
Presidential
Popularity
Election
Midterm
StatePartisanship
StateChangein PCDI
Scandal
Incumbent
Incumbent
Controversy
IncumbentHealthProblems
Challengera Governor
ChallengerMajorOffice
Challengerin House
ChallengerMinorOffice
Incumbent
Spending
ChallengerSpending
IncumbentNegativism
ChallengerNegativism
WeightedInc.Negativism
WeightedChal.Negativism
WeightedInc.NegativismX
WeightedChal.Negativism
AdjustedR2
StandardError
*p < .05
**p< .01
61.80***
.10
-2.69
.11*
-.12
- .60
-1.90
-5.97
-9.05*
-.46
-4.43
.38
7.97*
-10.75
2.80
.06
1.38
.05
.30
4.11
2.67
4.51
4.57
2.36
2.48
1.84
3.78
6.79
B
Model1
S.E.
72.92***
.05
-1.67
.09*
-.34
-.04
.02
-2.48
-6.67
.98
-2.12
1.18
6.75*
-7.65
-.17**
-.16*
3.99
.06
1.25
.05
.27
3.65
2.48
4.11
4.16
2.12
2.28
1.66
3.36
6.07
.06
.08
B
Model2
S.E.
61.28***
.07
-2.07
.09*
-.28
.74
.12
-1.61
-5.78
1.03
-2.18
1.16
11.85**
-2.81
2.45
.05
1.21
.04
.27
3.59
2.42
4.13
4.16
2.09
2.26
1.62
3.68
7.27
-.13*
-.17
.06
.09
B
Model3
S.E.
64.13***
.05
-1.90
.09
-.34
.18
-.62
-3.42
-7.08
.73
-2.26
1.18
11.33**
1.22
-.25*
-.31*
.003
.24
6.92
.31
6.59
.30
6.66
3.45
.06
1.27
.05
.28
3.78
2.56
4.55
4.46
2.19
2.35
1.69
3.86
8.27
.11
.15
.002
.30
6.64
***p< .001
Note:Tableentriesare2SLScoefficients. N= 143.
incumbent's party exceeds that number of partisans of
the challenger's party, the incumbent gets about a tenth
of a point boost at the polls. On the other hand, state
change in disposable per capita income did not even have
its predicted sign. This variable was not statistically significant, however, and we have no good explanation for
this finding. Controlling on other variables in the equation, only one of the candidate-specific factors has a significant direct influence on the outcome of the election,
in part because their influence is partially indirect, acting
through the candidates' ability to raise money (and in
subsequent equations, on their influence over the type of
campaign they choose to run). Money matters, of course,
and by our estimates matters a bit more (about a third
more) for challengers than for incumbents. But our figures are very much in line with those reported by Gerber
(1998), and far less than the huge differences reported by
investigators who may not have sufficiently controlled
for the endogeneity of candidate spending. 1
For present purposes the more interesting findings
are reported in Models 1-3, which include indicators of
llThe OLS (rather than 2SLS) coefficients for incumbent and
challengerspending are 5.54 and - 12.70, respectively,indicating
(incorrectly,we would argue along with Gerber) that challenger
spending is more than twice as importantas incumbent spending.
the nature of the campaign'stone. If attacking the opponent is an effective campaign strategy,then the campaign
tone variablesought to have a positive sign for the incumbent and a negative sign for the challenger.The results of
Model 1 indicate that attacking the opponent could indeed be an effective strategy for challengers. The coefficient for overall challenger negativism is -.16, indicating
that challengers can improve their performance at the
polls by 1 percent by increasing their attackson their opponent during the campaign by about 6 percent. On the
other hand, attackingthe opponent is a particularineffective strategyfor incumbents to follow. The -.17 coefficient
is roughly the same magnitude as that for challengers,but
opposite in effect: for every 6 percent of their campaign
pronouncements incumbents used attacking their opponent, they did about 1 percent worseat the polls. To phrase
these results positively, incumbents who campaigned on
their own accomplishments, abilities, and issue stands did
significantly better at the polls than incumbents who
chose instead to attack their opponent.12
12Sinceevery statementis coded as either positive or negative,the
percent negativism of a campaign equals 100 minus the percent
positivism.Thus if we had createda measureof the relativepositivism of the two majorparty campaigns,the -.17 coefficientwould
simply reverseto +.17. As one anonymous reviewerpoints out to
EFFECTIVENESS OF NEGATIVE CAMPAIGNING IN U.S. SENATE ELECTIONS
The campaign tone variables in Model 1 are simply
the percentage of all statements coming from each
candidate's campaign which attacked the opponent.
Operationalizing the crucial independent variables in
terms of raw percentages is straightforward and easy to
understand, but makes the implicit assumption that all
candidates were equally able to get their messages across
to the voters, a dubious assumption at best. Model 2 employs weightedmeasures of campaign tone, the same raw
percentages used in Model 1, multiplied by the campaign
spending variable. This weighting should magnify the effects of the campaign for candidates with a lot of campaign resources to spend, while minimizing the effects of
the campaign among those with relatively few resources
to help them get their message across. Operationalizing
the crucial independent variables in this manner makes
greater intuitive sense. As it turns out, however, it makes
little practical difference, as the two crucial campaign
tone coefficients remain essentially unchanged.
The previous two models treated the campaign tone
variables as if they had simple linear effects. That is, these
models assumed that the effect of negative campaigning
by either candidate were independent of the level of
negative campaigning by his or her opponent. This assumption seems overly restrictive, and again does not
match our intuitions. Model 3 relaxesthis assumption by
including an interaction term between Weighted Incumbent Negativism and Weighted Challenger Negativism.
us, the interpretationof the marginaleffectiveness(or ineffectiveness) of negative campaigningpresentedhere in the text is based
cruciallyon our model of candidatedecision making.If candidates
know preciselyhow effectivedifferentcampaignstrategieswill be
in their specific state, and choose accordingly,then our resultsreflect the relativelygreaternumber such races where "going negative"is the best strategyfor challengersto follow, whereasstaying
positive is the better strategyfor the largemajorityof incumbents,
and it would be wrong to present our results as if all challengers
would do 1 percentbetter at the polls for 6 percentmore negative
campaigning,and so on. In such a world of preciseknowledge (or
anything approachingit), no candidatein their right mind would
listen to what political scientistswould have to say about the relative effectivenessof differentcampaignstrategies.We suspecthowever,that ambiguity and uncertainty,ratherthan precision, characterizethe world of campaigndecision making.The question,we
think, comes down to whetherthereis a singledistributionof campaign effectsthat all campaignsare a part of; or whetherthere are
two (or more) distributionsof campaigneffects (one where negative campaigningis more effective,one where positive campaigning is more effective), and candidates know which distribution
they arein. Our intuitionstell us that if there aremultipledistributions of campaigneffects,there is so much overlapbetween them
that for all practicalpurposes,they might as well be modeled as a
single distribution.The argumentfor uncertaintyand a single distribution of campaign effects is particularlystrong for Model 3,
where all campaign effects are conditional on what the opponent
does, and all campaign decisions must be made (at least in the
short run) without preciseknowledgeof how the opponent is going to campaign.
55
As shown in the last columns of Table 2, this interaction
term is positive and much greaterthan its standard error,
although it does not achieve conventional levels of statistical significance (p < .20).13 The interaction suggests
that negative campaigning by the incumbent becomes
more effective (or less ineffective) at higher levels of
negative campaigning by the challenger. Alternately,
negative campaigning by the challengerbecomes less and
less effective, the more the incumbent chooses to attack
the challenger.
Our initial findings, then, paint a very mixed picture
of the usefulness of negative campaigning. It would appear that attackingthe opponent is a more effective strategy for challengers than for incumbents. We may be distorting the picture by including all available elections in
which the incumbent was seeking reelection during the
1988-1998 period, however.Westyle (1991) in particular
points to the competitiveness of the election (which he
calls "intensity")as an important variable in determining
what factors influence voter's decisions. Some incumbents are so popular, and some states are so dominated
by one of the political parties, that the Senate election is
never really competitive. In such cases the campaign,
whatever its nature, probably has little effect on the outcome of the election. But if we limit our sample to competitive races, negative campaigning may prove to be a
more effective strategy,particularlyfor incumbents.
We divided our elections into two groups according
to CongressionalQuarterly'sfall projections, those where
each candidate was judged to have at least some chance
of winning, and those that were largely noncompetitive
(considered to be "Safe"for one of the parties), and repeated the analyses in these two groups. The results for
the campaign tone variables are reported in Table 3,
separately for competitive and noncompetitive races.
They do not conform to our expectations; indeed, the
case for the efficacy of negative campaigning becomes
even weaker.In competitive races, negative campaigning
by incumbents still hurts their reelection chances, while
negative campaigning by challengershas almost no effect
on their reelection chances. In noncompetitive races, on
the other hand, negative campaigning helps incumbents
a little (never significantly) and helps challengers a lot.
Of course "help"must be interpreted generously in the
latter case, because the incumbent won all but one of the
sixty-two noncompetitive races by an average margin of
almost 32 percent.
Controlling on the competitiveness of the election
thus makes negative campaigning appear to be an even
13This interaction was
nearly identical in magnitude but highly
significant in the OLS results (p < .001). This is the one place
where the loss of efficiencydue to the two-stageestimationprocedure seemed to noticeablyaffectthe results.
RICHARD R. LAU AND GERALD M. POMPER
TABLE3
PercentVotefor IncumbentSenator,State-Level,1988-1998,
Controllingon Competitivenessof Election
Elections
81 Competitive
IncumbentNegativism
Challenger Negativism
Weighted Inc. Negativism
Weighted Challenger Negativism
Weighted Inc. Negativism X
Weighted Challenger Negativism
S.E.
-.14*
-.02
.07
.08
Model3
B
Model2
S.E.
B
S.E.
-.09
-.02
.06
.09
-.14
-.07
.12
.15
Model 1
B
.001
.002
Elections
62 Noncompetitive
Model 1
B
IncumbentNegativism
Challenger Negativism
Weighted Inc. Negativism
Weighted Challenger Negativism
Weighted Inc. Negativism X
Weighted Challenger Negativism
.08
-.35***
S.E.
B
Model2
S.E.
Model3
B
S.E.
.06
.07
.09
-.45***
.07
.11
.02
-.52**
.15
.20
.002
.004
*p < .05 **p< .01 ***p< .001
Note:Allequationsincludethe same variablesas those showninTable2.
less effective strategy than in did initially, at least when
the campaign has a chance to affect the outcome of the
election. We do have real concerns that aggregation bias
may be distorting these findings, however, and turn immediately to a cross-level analysis of survey data for a
further test.14 We want to make one additional point before moving to the individual level data, however. Notice
that the adjusted R2 reported in Table 2 increases appreciably between the base model and those models including measures of the campaign's tone, from .24 to about
.30. This dramatic increase substantiates our claim that it
is not just money, but how candidates choose to spend
their campaign funds, that matters.
'4Beforemoving on we should mention severaladditionalanalyses
that were conduced but are not reportedin Table2. We followed
the guidelines of Abramowitz(1988) and Gerber(1998) in creating a dummy variablefor a "celebrity"challenger,but this variable
alwayshad the wrong sign and neverapproachedstatisticalsignificance.We also createda measureof the "issueproximity"of the incumbent to the mean ideology of that state'selectorate,but this
measurehad no effect whatsoeverin any analysis.When we break
the overall measuresof campaign negativism into separatemeasures of policy-based and person-based negativism, it is policybased attacksby the incumbent which stand out as the strongest
(negative)effects.It would appearfrom these data that attackinga
challenger'sissue stands,ratherthan running on one's own record,
is a particularlyineffectivecampaignstrategyfor an incumbent.
Cross-Level
SurveyAnalyses
Table4 presents the results of three increasingly complex
models of vote for the incumbent senator, now based on
individual-level survey data from seventy-four Senate
elections between 1988 and 1992 for which we have campaign and survey data. These analyses conceptually replicate Models 1-3 from the aggregate analysis, with individual-level measures replacing their aggregate-level
equivalent whenever possible.15The dependent variable
is vote for the incumbent rather than the challenger.We
continue to employ two-stage estimation procedures for
candidate spending and campaign tone, using the same
15The one
exception to this statement is an individuallevel measure of Approval of the Incumbent Senator'sJob Performance,
which was considered in lieu of the three dummy variablesconcerning the incumbent that were employed in the aggregate-level
analyses. A comparison of models employing these alternative
measuresled us to stronglysuspect that this standardmeasure of
incumbent job approval was also somewhat endogenous to the
vote decision. Ratherthan trying to develop a set of instruments
for this item and employing two-stageproceduresyet a fifth time,
we simplyused the same dummyvariablesabout IncumbentScandal, Controversy,and Health Problemsemployed in the aggregate
analyses.These three variablescertainlywould have been the primary instrumentsin a first-stageregressionpredictingIncumbent
SenatorJobApproval.
EFFECTIVENESS OF NEGATIVE CAMPAIGNING IN U.S. SENATE ELECTIONS
TABLE4
57
Vote for IncumbentSenator,Individual-Level,
1988-1992
B
Constant
ApprovePresident'sJob Perf.
Midterm
Election
NationalEconomyWorse
Changein PCDI
PartyIdentification
IdeologicalIdentification
PolicyPreferences
IncumbentScandal
IncumbentControversy
IncumbentHealthProblem
Challengera Governor
ChallengerMajorOfficeHolder
Challengerin House
ChallengerMinorOfficeHolder
IncumbentSpending
ChallengerSpending
IncumbentNegativism
ChallengerNegativism
WeightedIncumbentNegativism
WeightedChallengerNegativism
WeightedIncumbentNegativismX
WeightedChallengerNegativism
-2*LogLikelihood
ModelChi-Square
Cox&SnellPseudoR2
PercentCorrectlyClassified
Model1
S.E. Exp(B)
3.12***
1.07***
-.35
.35
-1.05**
2.59***
1.69***
1.85***
.37
.37
-1.31***
-1.22
.03
.29
.84***
-.01
.01
-2.50**
-1.93***
.61
.18
.25
.23
.36
.19
.27
.33
.27
.27
.41
.70
.21
.23
.18
.37
.64
.63
.78
2.92
.71
1.42
.35
13.30
5.42
6.36
1.45
1.44
.27
.30
1.03
1.33
2.32
.99
1.01
.08
.14
B
Model2
S.E. Exp(B)
1.76***
1.07***
-.29
.36
-.96**
2.59***
.88*
1.32**
.50
.17
-1.22***
-1.13
-.03
.25
.86***
.54
.75
.49
.18
.24
.23
.36
.18
.37
.44
.28
.34
.41
.70
.21
.23
.18
.41
.77
2.92
.75
1.43
.38
13.29
5.40
6.30
1.66
1.19
.30
.32
.97
1.28
2.37
1.71
2.14
-2.09*** .57
-1.77*
.74
.12
.17
B
2.31***
1.07***
-.26
.33
-.87*
2.59***
1.69***
1.85***
.23
.28
-1.56***
-1.38
-.07
.26
.85***
.46
1.01
.59
.18
.25
.23
.36
.19
.27
.33
.32
.34
.46
.72
.26
.23
.18
.41
.78
2.92
.77
1.39
.42
13.33
5.42
6.36
1.26
1.33
.21
.25
.93
1.29
2.35
1.58
2.73
-3.97*** 1.22
-3.25** 1.12
.02
.04
2.42
68.85
4.23
2373.34
1037.77***
.32
78.2%
2375.30
1035.82***
.32
77.6%
Model3
S.E. Exp(B)
2372.23
1038.89***
.32
78.0%
< .001
*p< .05 **p< .01 ***p
Note:Tableentriesaretwo-stage
coefficients.The
alsoincluded
anda "Pseudo
controls
Mills
Inverse
Ratio"
logisticregression
equations
demographic
variable(see text). N = 2682.
first-stage estimates employed in the aggregate-level
analyses. We will focus on the campaign tone variables,
but note in passing that the aggregate- and individuallevel analyses provide very similar results for the other
variables in the equations, with the individual level results generally proving somewhat stronger.For example,
presidential popularity, measured at the national level,
was not significant in the aggregate analyses but was
highly significant (measured at the individual level) in
the survey analyses. State levels of partisanship was just
significant in the aggregate level but party identification
was highly significant (measured at the individual level)
in the survey results. State-based Change in Per Capita
Disposable Income (measured at the aggregatelevel in all
analyses) was never significant in the aggregate analyses
but always proved significant in the survey data. The
most dramatic difference between the aggregate- and individual-level analyses is that, controlling for the other
variables in the equations, neither Incumbent Spending
nor Challenger Spending were ever significant in any
model considered employing the survey data.
Turning to the campaign tone variables, the results
of the individual-level analyses are strikingly similar to
those from the aggregate analysis. When all available incumbent reelection races are considered, negative campaigning hurts the incumbent but helps the challenger.
This is true whether the campaign variables are entered
in a simple additive fashion in Model 1, weighted by
spending in Model 2, or include an interaction term between Incumbent Negativism and Challenger Negativism in Model 3. The most realistic model is certainly the
last. At the individual level, the interaction term just
misses conventional levels of significance (p < .08). It
suggests that negative campaigning by the incumbent
becomes more effective (or rather, is no longer ineffective) at higher levels of negative campaigning by the
58
challenger. Similarly, negative campaigning by the challenger becomes less and less effective, the more the incumbent chooses to attack the challenger.
Models 2 and 3 weight the campaign tone measures
by spending because it is reasonable to assume that a
campaign strategy will have a larger effect when it is
backed by more campaign resources. At the aggregate
level, this is the only way we can try to control for the
probability of exposure to the campaign. At the individual level there are more precise measures available.In
particular, it is reasonable to expect that the campaign
will have greater effects upon those who are most interested in and/or pay the most attention to the campaign,
who are consequently most likely to be exposed to it. We
therefore tested additional models that included a measure of campaign exposure, and the interaction between
it and our various campaign tone variables.When all incumbent reelection races are considered, as in Table 4,
these interaction terms never approached conventional
levels of significance. Consequently we do not report
these results (but again they are available from the authors upon request). Campaign exposure will prove to
have a stronger effect in more restrictiveanalyses below.
We also considered the effects of the competitiveness
of the elections by controlling on Congressional Quarterly's fall projections, taking advantage of the greater
sample size available from the survey data to distinguish
between highly competitive races, those where one candidate (the incumbent) was the clear favorite but there
was at least some chance for an upset (that is, breaking
the "competitive"races from the aggregate analyses into
two groups), and races which were largely noncompetitive and considered to be "safe"for one of the parties;and
repeated the analyses in these three groups. The results
for the crucial campaign tone variables are shown in
Table 5. None of the campaign negativism variables
proved statisticallysignificant in any of the models tested
among the noncompetitive races. These results contrast
with the aggregate results, where the only place that
negative campaigning appeared to be efficacious was in
these noncompetitive races. In more competitive races,
the electoral prospects of challengers improved when
they attacked their opponent, particularlyin moderately
competitive elections. The electoral prospects of incumbents are not significantly influenced by the negativism
of their campaigns in highly competitive races, but those
prospects are significantly decreasedin moderately competitive races by increased negativism. This analysis better isolates those situations when incumbents should
avoid going negative.
We have now presented a large number of different
models, in different subsets of the data, and the reader
RICHARD R. LAU AND GERALD M. POMPER
may be left wondering just what exactly is the effect of
negative campaigning in Senate elections. There is no
simple answer to this question, but Table 6 and Figure 1
present our best guess at the answer if we are willing to
limit the question to highly competitive races, where the
campaign has the best chance of making a real difference.
We only present the results of the most realistic Model 3,
which weighs the campaign tone variables by spending,
and which includes an interaction term between the two
weighted campaign tone variables.We also add Campaign
Exposure and the interaction between it and campaign
tone to the equation. If "going negative" is an effective
campaign strategyin highly competitive Senate elections,
the effect of Weighted Incumbent Negativism should be
positive, particularly among those who are widely exposed to the campaign (i.e., the interactionbetween Campaign Exposure and Weighted Incumbent Negativism),
while the effect of Weighted Challenger Negativism
should be negative,particularlyamong those most highly
exposed to the campaign-i.e., the interaction term.
Even restricting our attention to highly competitive
elections, the simple effects of weighted campaign negativism by the incumbent (now interpreted as the effect of
campaign negativism among those paying very little attention to the campaign) are significantly negative, i.e.,
counter to the intentions of the incumbent. The effect of
Weighted Incumbent Negativism is significantly better
for the incumbent among those highly exposed to the
campaign, but still the combined effect (-5.62 + 3.65 =
-1.97) is negative. As we have seen all along, challengers
are helped by going negative, and helped a little more
(p < .08) when voters are highly exposed to the campaign
(-6.89 - 2.82 = -9.71).
These simple effects of campaign tone are qualified
interaction between Weighted Incumbent Negathe
by
tivism and Weighted Challenger Negativism, however.
This interaction term is crucial to understanding what
the model is telling us, and we will spend some time
sketching out its implications. The top portion of Figure
1 illustratesthe meaning of the interaction effect at varying levels of incumbent and challenger negativism for
those low in campaign exposure, while the bottom half
of the figure considers the same situations for those
highly exposed to the campaign. The dependent variable
in the figure is the predicted probability of voting for the
incumbent, holding all control variables at their mean or
mode. This undoubtedly maximizes the apparent impact
of the campaign, for we are thus estimating its effects on
independent moderates who might have no strong prior
predisposition toward either party's candidate. The
analysis assumes a challenger who held some major
statewideoffice (but not governor),and an incumbent
EFFECTIVENESS OF NEGATIVE CAMPAIGNING IN U.S. SENATE ELECTIONS
59
PercentVote for IncumbentSenator, IndividualLevel, Controllingon Competitiveness of Election
VeryCompetitiveElections (N = 1000,27 elections)
TABLE5
B
IncumbentNegativism
ChallengerNegativism
WeightedInc.Negativism
WeightedChallengerNegativism
WeightedInc.NegativismX
WeightedChallengerNegativism
Model1
S.E.
-.74
-2.49
B
Model2
S.E.
Model3
B
S.E.
-3.56
-5.63**
2.85
2.22
1.94
1.63
.13
-2.83*
1.65
1.36
7.27
4.56
ModeratelyCompetitiveElections (N = 576, 16 elections)
Model1
IncumbentNegativism
ChallengerNegativism
WeightedInc.Negativism
WeightedChallengerNegativism
WeightedInc.NegativismX
WeightedChallengerNegativism
B
S.E.
-3.42*
-5.04*
1.62
2.61
B
Model2
S.E.
-3.10*
-4.40
1.47
2.36
Model3
B
S.E.
-13.19
-9.03
9.72
5.24
19.23
18.32
NoncompetitiveElections (N = 1106,31 elections)
Model1
IncumbentNegativism
ChallengerNegativism
WeightedInc.Negativism
WeightedChallengerNegativism
WeightedInc.NegativismX
WeightedChallengerNegativism
B
S.E.
.40
-2.75
1.26
1.53
B
.12
-2.01
Model2
S.E.
1.17
1.64
Model3
B
S.E.
1.68
-.94
3.36
2.71
-4.20
8.44
*p< .05 **p< .01 ***p< .001
as thoseshowninTable2.
Note:Allequations
includethesamevariables
facing no scandal, controversy,or major health problem.
We assume that candidates have "typical"campaign resources, which in these competitive elections means
roughly equal resources.
Under these circumstances, the top half of Figure 1
illustrates the only story we could logically expect: no
matter how negatively the candidates campaign, the
campaign doesn't matter much to those who are barely
exposed to it. There are some predicted differences resulting from the campaign, to be sure, but the incumbent is always predicted to have a fairly strong majority.16As illustrated in the bottom of Figure 1, however,
16If we
play around with the presumed candidatespending levels
in whichthe challengeris prewe cancreatea fewcircumstances
votes-but only
dictedto receivethemajorityof theuncommitted
a few.
this is not the case when people are paying attention to
the campaign. Now there are many situations in which
our model predicts that the challenger will receive the
majority of the uncommitted votes-in fact, virtually all
those where the challenger is more negative than the incumbent. These results, finally, provide some support
for the conventional wisdom about the effectiveness of
negative campaigning. Our model predicts that whichever candidate is more negative than his or her opponent will win a majority of the uncommitted voters, in
highly competitive elections, when resources are roughly
equal, and when voters pay a great deal of attention to
the campaign. Rare circumstances indeed-but these are
the only ones we have found where negative campaigning is as effective for both challenger and incumbent as
most political observers seem to believe.
60
RICHARD R. LAU AND GERALD M. POMPER
TABLE
6 Votefor IncumbentSenator,Individual-
RacesOnly
Level,HighlyCompetitive
Model3
S.E.
B
1.05
-.06
Constant
PseudoIMR
.94*
Age
-.74
Education
.90*
Income
Family
.05
White
-.16
Male
1.28***
ApprovePresident'sJob Perf.
-.05
Midterm
Election
.58
NationalEconomyWorse
-1.33
Changein PCDI
2.93***
PartyIdentification
2.04***
IdeologicalIdentification
2.29***
PolicyPreferences
.35
Incumbent
Scandal
.34
IncumbentControversy
-1.74**
IncumbentHealthProblem
-2.24*
Challengera Governor
Holder
-.46
Office
ChallengerMajor
.72
in
House
Challenger
.93
ChallengerMinorOfficeHolder
-.28
Incumbent
Spending
4.10*
ChallengerSpending
-5.62*
Incumbent
Weighted
Negativism
WeightedChallengerNegativism -6.89**
-1.08
CampaignExposure
3.65**
WeightedInc.Neg. X Exposure
X
-2.82
Chal.
Weighted
Neg. Exposure
WeightedIncumbentNegativismX
WeightedChallengerNegativism 10.64
-2*LogLikelihood
ModelChi-Square
Cox&SnellPseudoR2
Classified
PercentCorrectly
*p< .05
**p< .01
1.79
.38
.46
.72
.47
.30
.17
.29
.63
.40
.93
.30
.45
.54
.38
.54
.66
.93
.46
.47
.51
1.08
1.81
2.90
2.83
1.12
1.31
1.62
5.55 41728.54
933.57
555.24***
.39
79.6%
***p< .001
coefficients.
Note:Tableentriesaretwo-stage
logisticregression
N = 1000.
Discussion
We started this project with some skepticism that negative political campaigning really helped the candidates
who utilize it. Moreover, knowing the methodological
difficulties facing any researcher trying to detect campaign or media effects of any stripe, we feared that our
data would not be powerful enough to detect significant
effects. But our results surprised us: not only did they
prove to be reasonably powerful, but we found at least
some situations in which both an incumbent and a chal-
lenger would be helped by attacking the opponent-at
least in the context of U.S. Senate elections during the
last decade of the twentieth century. Our basic findings
were replicated across both aggregate and individuallevel data sets. We might add that our individual-level
analyses are the only use of representative survey data
(that we know of) to address the question of the relative
effectiveness of negative campaigning.
Still, our skepticism about the inordinate power of
negative campaigning seems to have been justified, and
our findings clearly should not be read as validating or
encouraging negative campaigning, either normatively or
empirically. A full accounting of the evidence suggests
that, as often as not, attacking the opponent is a counterproductive campaign strategyto follow. All else equal, our
results suggest that incumbents typically would have won
more votes than they actuallydid had they run more positive campaigns. Challengers tend to benefit from these
tactics, but victory is still unlikely unless challengersboth
outspend and out-attack the incumbent-and few incumbents will give them this kind of free ride.17 More
typically,the challenger'sgains from a negative campaign
can be offset by an incumbent's response. When the total
interactivecampaign efforts are considered,there is only a
limited effect on the ultimate vote.
There are several important methodological challenges that could be raised about some of our analyses,
and we want to address them here. Any time individual
level processes are studied with aggregate data, certain
red flags must be raised. But the individual-level survey
data, which provided comparable results, handle most of
these issues. Survey data also have their limitations, of
course, most notably in over-reporting turnout and, to a
lesser extent, over-reporting support for the winning
candidate. But the aggregate data provide more reliable
readings on actual levels of candidate support, and again
provide very comparable findings.
There could be a more basic problem, however. It is
possible that our measure of the campaign's tone, based
on a coding of secondary reports from a variety of political experts in each state, is too gross or indirect or noisy
to accurately reflect the true nature of each candidate's
campaign. Communications researchersmight be more
comfortable with the term "intermediated negative messages" (Haynes and Rhine 1999) rather than "campaign
tone" to describe the nature of our data, although we
stick with the latter because it has been used previously
'7Moreover the aggregate data clearly suggest that negative campaigning by the challenger is only relatively effective in noncompetitive races. In other words, negative campaigning by the challenger is most effective in reducing a rout to only a landslide.
EFFECTIVENESS OF NEGATIVE CAMPAIGNING IN U.S. SENATE ELECTIONS
FIGURE1
Effectsof CampaignNegativism, MedianSpending by Both Candidates
LowCampaign Exposure
1(:
c)
I 0.9E
..
?-
-
0.8-
--.
-._
'
IncumbentNegativism
`f--7?=---?
.. ..
0
|
`` ``
0.7-
o0
-------
0.5-
EL
0.4
Low
Mod. High
.----------------Average
------ - Mod. Low
?: 0.6-
._
-1
High
----
-Low
1
High
High
Average
ChallengerNegativism
HighCampaign Exposur
10.9 -
-_ 0.8E
(
0
a)
0
>
0.7-
IncumbentNegativism
0.6-
NN
High
'N.N
0.5-
----
Mod. High
------------------Average
---------Mod. Low
0.40.2
-. 0.3._
-------
0
-Low
0.20.1 0
Low
Average
ChallengerNegativism
in political science (Ansolabehere et al. 1994; Finkel and
Geer 1998). Whatever its label, because this measure is
used in both the aggregate- and individual-level analyses,
it could be distorting all of them.
We do not believe such criticism is justified. In another article utilizing this same campaign tone data (Lau
and Pomper, 2001b), we examine seven factors that theoretically should be associated with the use of negative
campaigning by political candidates.Although the details
High
High
are beyond the scope of the current article, we find empirical support for each of those seven hypothesized factors, at least at the level of zero-order associations. These
results can be viewed as providing strong constructvalidity for our measure of campaign tone. Certainly there is
evidence that the media exaggeratehow negative political
campaigns actually are (Kaid, Tedesco, and McKinnon
1996), so we may want to discount some the level of
negative campaigning revealed in our data. But unless
62
this bias works differently for coverage of the incumbent
and the challenger, it should not affect our conclusions.
Moreover, the campaign as reported by the media, while
not identical to the campaign conducted by the candidates, is nevertheless in no small part the campaign that is
experiencedby the public. We would agree that our measure of the nature of the campaign is not ideal; unfortunately, good evidence on the nature of political campaigns is simply very hard to come by, particularlywhen
looking at multiple campaigns in multiple locations
across different election years. Other researchers with
similar research designs (e.g., Ansolabehere et al. 1994;
Franklin 1991) rely on data much like ours.
Would our results be the same if we had direct measures of campaign advertising,ratherthan the broaderbut
more indirect measure utilized here? We cannot say for
sure, but we suspect they would be, particularlyif that direct measure of campaign advertising included measures
of how often each different ad were aired (Goldstein
1997). We believe that it is plausible to argue that data
such as ours may in fact be a bettermeasure of the advertising campaign than a direct coding of the ads produced
by the candidates, if there is no information about how
often the advertisementswere aired during the campaign.
Goldstein (1997, 7) reports at least one instance from the
1996 presidential campaign when a simple coding of the
ads produced by one of the candidatesprovides a very distorted view of the actual nature of the advertising campaign seen by voters.
So let us accept our results as provisionally valid. Do
they provide, were political consultants to ask our advice,
sufficient information to devise a successful Senate campaign?As noted above, we find little support for the conventional wisdom that negative campaigning is inordinately effective. Besides this, however, our methodology
is too crude to provide anything like detailed advice. We
only have broad summaries of the strategies that were
employed by the different candidates during the last
months of the campaigns. Tracking more subtle shifts
and variations in what a candidate does over time-and
we know such variation exists (e.g., Roberts and Alvarez
1996)--is beyond what our data can detect.
Nonetheless, we may be able to suggest why negative
campaigning would appear to be used much more often
than its relative effectiveness would seem to justify. (See
Lau et al. [1999] for a complete review of the existing evidence.) Any candidate must first secure and mobilize his
or her base of supporters, but then try to win the majority of independent voters. Our results suggest that negative campaigning by challengers can be fairly effective
with independents (at least in competitive races)-but it
can be effectively countered among independents by a
like response from the incumbent. Any campaign con-
RICHARD R. LAU AND GERALD M. POMPER
sultant with a sense of the relative effectiveness of negative campaigning similar to what our data suggest could
rationally advise a challenger to attack the incumbent
(and rationally advise an incumbent to respond, if attacked) with just those independent voters in mind. Even
in this day of cable TV, segmented publics, and webbased communications, any campaign message is a relatively blunt instrument, reaching those likely to be
swayed by it, and those likely to be turned off or to simply ignore it. Negative campaign appeals may be used as
often as they are with this particular audience in mind.
And since few consultants are scientists, with the inclination or incentives to conduct systematic research, there
may be little attention to the overall effect of any campaign strategy on the electorate as a whole.
Although the benefits of negative campaigning are
relatively small, the costs could still be considerable, not
simply in campaign dollars, but in the more important
effects on the dialogue of democracy. Regardlessof how
effective negative campaigning is, its use may serve to depress turnout (although the evidence here is mixed: see
Lau et al. [1999] for a recent review; and Lau and Pomper
[2001a] for the estimates calculated from these same
data). Normatively,while we do not join all the criticisms
made, we too would object to negative campaigning that
involves personal attacks unrelated to the governing
capabilities of candidates or distorts their records and
issue positions. Nothing in our data suggests this sort of
campaigning would be effective-or that it is very widespread.
And despite the widespread disapproval of negative
campaigning, there may be, ironically,positive featuresto
it. West (1993, 51) found in his study of advertisements
from 1952 to 1992 that "the most substantive appeals actually came in negative spots,"and "in recentyearsdomestic performance and specific policy statements more than
personal qualities have been the object of the negative
prominent ads."Likewise,Geer (1998b) shows that issues
are more commonly stressedin negative ratherthan positive advertisements.Indeed, one could make the case that
negative campaigning is good for democracy. Mayerpresents a trenchant position along these lines, arguing that
"any serious, substantive discussion of what a candidate
intends to do after the election can only be conducted by
talking about the flaws and shortcomings of current policies."Furthermore,he points out that voters need to know
not only about candidates'claimed merits but also about
"the abilities and virtues they don't have; the mistakes
they have made; the problems they haven'tdealt with; the
issues they would prefer not to talk about; the bad or unrealisticpolicies they have proposed" (Mayer 1996, 441).
The concerns about negative campaigning to some
extent reflect a dim appraisal of the electorate. Voters
EFFECTIVENESS OF NEGATIVE CAMPAIGNING IN U.S. SENATE ELECTIONS
must be protected from candidates, this line of argument
goes, because they know little about politics and seem to
care even less, they are easily discouraged, unable to distinguish truth from lies, and likely to be fooled by misrepresentations of the past and exaggerations about future consequences. Our data provide absolutely no
support for such a sweeping statement. The benefits and
costs of negative campaigning are much more subtle,
more nuanced, and the electorate seems reasonably able
to detect and respond to at least some of those nuances.
There are neither fixed rules for campaigners nor immutable preferences held by voters. It is one more reason to
believe, with Key,that "the electorate behaves about as rationally and responsibly as we should expect, given the
clarity of the alternativespresented to it and the character of the information availableto it" (1966, 7).
Surely, campaigning can be improved. Scholars like
us would probably express a preference for campaigns
conducted by exemplarycandidatesthrough civil debates,
in which statements were always disinterested, and truth
was alwaysunvarnished.That is to say,we would probably
express a preferencefor campaigns as the model academic
seminar. Yet we know that even academia does not now,
and probably never did, resemble Plato'ssymposia, and it
is vain to think that the exuberant world of democratic
TABLEA-1
63
politics will fit that model any better.Let us urge that campaigns be fair,that statements be accurate,that discourse
be relevant,that politicians be respected, and that citizens
be interested. And let us encourage researchers to continue studying campaigns, in a multitude of different settings and with a variety of different methods. But let us
not discourage relevant criticism of an opponent because
of any unwarranted fears that such attacks are inordinately persuasive, or that they inevitably discourage citizen engagement with the political system. The republic
still stands.
ManuscriptsubmittedFebruary18, 2000.
Final manuscriptreceivedMay 12, 2001.
Appendix
Any 2SLSanalysiscan only be successfulif the first stageregressions prove relativelyrobust. Our analysisrequiredinstruments for four variables: incumbent and challenger
spending, and incumbent and challenger campaign tone.
The full first-stagepurgingregressionsfor per capitaspending are shown in TableA-1. We basicallyreplicatedGerber's
FirstStage Regressionsfor CandidateSpending
B
LaggedSpending
VotingAge Population
Incumbenton FinanceComm.
ChallengerRich
Candidate'sParty(Republican)
Presidential
Popularity
Midterm
Election
StatePartisanship
StateChangein PCDI
IncumbentScandal
IncumbentControversy
IncumbentHealthProblems
Challengera Governor
ChallengerMajorOffice
Challengerin House
ChallengerMinorOffice
Constant
R2 FullPurgingEquation
R2 Just ExogenousVariables
R2 Instrument
Regressedon
ExogenousVariables
*p < .05 **p< .01
Note.N = 156.
***p< .001
Incumbent
S.E.
.30***
-.09***
.56**
.17
.06
-.00
-.25
2.15***
-.05
- .68
.68*
-.39
.61
.42
.21
.05
1.57***
.04
.02
.18
.21
.08
.01
.18
.64
.03
.48
.28
.50
.49
.23
.24
.20
.28
.54
.16
.27
B
Challenger
S.E.
.10***
-.04*
.19
.48**
-.02
.00
.04
.91
.02
-.08
.72***
.50
1.60***
.52**
.62**
.02
.13
.03
.02
.14
.17
.06
.01
.14
.50
.03
.38
.22
.39
.38
.18
.19
.16
.22
.38
.24
.63
RICHARD R. LAU AND GERALD M. POMPER
64
TABLEA-2
First Stage Regressions for CampaignNegativism
B
ProjectedClosenessof Race
FemaleCandidate
Candidate'sParty(Republican)
DemocraticMediaConsultant1
DemocraticMediaConsultant2
RepublicanMediaConsultant1
2
RepublicanMediaConsultant
3
RepublicanMediaConsultant
4
Media
Consultant
Republican
5
RepublicanMediaConsultant
DemocraticPollster1
DemocraticPollster2
DemocraticPollster3
DemocraticPollster4
DemocraticPollster5
RepublicanPollster1
RepublicanPollster2
RepublicanPollster3
NortheastState
Mid-Atlantic
State
Presidential
Popularity
Midterm
Election
StatePartisanship
StateChangein PCDI
IncumbentScandal
IncumbentControversy
IncumbentHealthProblems
Challengera Governor
ChallengerMajorOffice
Challengerin House
ChallengerMinorOffice
Constant
1.77***
-.88
-.72
-8.30
**p< .01
.49
6.37
1.64
8.08
13.49*
-9.02
6.97
-7.71
-9.16
6.99
6.73
7.06
3.67
7.64
8.13
4.27
17.86**
7.15
12.74**
15.50***
-.30*
3.25
13.39
-.72
19.27*
10.70*
-2.93
6.53
6.39
6.40
-1.81
2.62
5.02
4.69
.14
4.05
12.18
.68
8.44
5.09
8.86
8.93
4.24
4.34
3.66
7.99
R2 FullPurgingEquation
R2 Just ExogenousVariables
R2 Instrument
Regressedon
ExogenousVariables
*p< .05
Incumbent
S.E.
.47
.21
.45
B
Challenger
S.E.
-.44
-3.19
3.49*
.49
3.86
1.51
14.67*
14.54*
-19.13
-23.07*
7.06
6.41
12.36
10.35
-12.83
3.22
6.66
3.28
9.36
8.88
32.39**
11.95
11.11**
4.92
6.64
11.27
6.52
4.22
.09
-2.97
22.68
.73
2.97
5.43
-4.92
8.37
13.43***
6.08
1.26
36.91***
.20
5.50
11.84
.64
7.99
4.80
8.37
8.34
4.10
4.12
3.39
6.51
.32
.07
.22
***p< .001
Note:N = 143. Equationsalso includeyeardummies.
(1998) procedures,with the addition of one furtherinstrumental variable in the incumbent spending equation, a
dummyvariableindicatingwhetherthe incumbentis on the
FinanceCommittee.As shown in TableA-1, all instrumental
variableshad their expected signs in the first stage regressions, and all but one werestatisticallysignificant.
Following Bartels'(1991) recommendations, the table
lists the explainedvariance (R2) from three separateequations, the first from the full purgingregressionpresentedin
the table, the second when only the exogenous variables
from the second-stage regressionare used as independent
variables,and the lastwhen the instrumentitself (that is, the
predictedscorefrom the first stageequation)is regressedon
those same exogenousvariables.If the first R2(from the full
purgingregression)is relativelylow, and/or most of the explained variance is due to the exogenous variables rather
than the instrumentalvariables(i.e., the second R2is almost
as large as the first,or the third R2is very close to 1.0), then
the 2SLSprocedureis doing a poor job of adequatelyrepresenting the true model in a more efficient manner. There
are no hard and fast rules for determining the success of a
first-stageregression,but our intuitions suggestthat the in-
EFFECTIVENESS OF NEGATIVE CAMPAIGNING IN U.S. SENATE ELECTIONS
strumentfor IncumbentSpendingis excellent,while the instrument for ChallengerSpendingis adequate.
A number of possible instrumentalvariableswere considered for the campaign tone equations based on prior
theory about negativecampaigning,includingthe Projected
Closenessof the Race (the closer the race is expected to be,
the more negativewe expected the campaign'stone to be);
Candidate'sGender(female candidateswere expected to be
less negative, all else equal); and Candidate's Party
(Republican'swere expected to be somewhat more negative). Of these variables,the one which seems least likely to
be independent of the errorterm in the main regressionis
the ProjectedCloseness of the Race.To amelioratethis potentialproblem,we createda measureof the ProjectedOutcome of the election by regressingthe actual outcome on
threevariablesthat were all clearlyprior to the currentelection: the outcome of the past two electionsfor this same seat
(or just the most recent outcome, for incumbents seeking
their second term), the incumbent's party, and change in
per capitadisposableincome in the statefrom the yearprior
to the election. Projected Closeness of the Race was then
operationalized as the absolute value of the differencebetween the predicted scores from the Projected Outcome
equation (which in principle rangesfrom 0 to 100), minus
50. This variablewas then reversedsuch that elections expected to be "close"were scoredhigh.
Almost all major party Senatecandidatesrun very professional campaigns, employing both a media consultant
and a pollster.These consultantshave major input into the
nature of the candidate'scampaign,and it is reasonableto
expect their recommendations to differ (based on individual personalities,past experience,and so on) even when
facing very similar campaign situations. Thus we also created dummy variables for each consultant (of consulting
firm) that workedfor at least three differentcandidatesin at
leastthree differentstates.Wewill not pretendthat theory is
strong here, although some (but certainlynot all) of these
consultants have reputations for running very negative
campaigns. Preliminary analyses included all of these
dummy variables,but because degreesof freedomwere not
overly abundant, we eliminated all media consultant and
pollster dummy variablesthat were not at least as large as
their standard errors. Finally in an attempt to capture regional norms we also included dummy variablesfor region
of the country, and in a similar manner eliminated after
preliminaryanalysesregionaldummies which provedto be
statisticallytrivial. The full first stage purging regressions
are shown in TableA-2.
The R2from the purging equations for campaign tone
for the incumbent and the challengerare a very respectable
.47 and .32. While the explained variance is noticeably
smallerfor the challenger'scampaigntone than the incum-
bent's,virtuallyall of the former is explainedby the instrumental variables.In contrast,the instrumentalvariablesexplain a little more than half of the explainablevariance in
the incumbent's campaign tone. Thus the two campaign
tone variablesareabout equallywell representedby their respectiveinstrumentalvariables.
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