Time or Consequences?
Uncovering the Separate Effects of Casualties and War Duration
on Popular Support for American Military Conflicts
Scott L. Althaus
University of Illinois Urbana-Champaign
Abstract: The mass public’s latent sensitivity to wartime deaths has been amply demonstrated in
experimental studies that place subjects in oversimplified information environments where
exposure to casualty information is guaranteed, and where the dependent variable is an
immediate response to being presented with such information. But it remains an open question
whether such casualty sensitivity manifests within complex information environments that
accompany real-world wars, and within realistically committed citizens who have already taken
a position on the rightness of an ongoing conflict. This study presents content analysis data from
both visual and textual news reports spanning World War I to the present and finds that while
Americans have been consistently exposed to vivid depictions of combat over the past 100 years,
the amount of casualty information reaching them has been fairly minimal, and did not mirror
actual levels or rates of wartime deaths. The study then reviews psychological barriers that work
against the potential for numerical casualty information to change attitudes about war. Finally,
the analysis turns to assessing the effects of American casualties on popular support for war with
a unique pooled data set consisting of the entire population of comparably-worded war support
questions asked in nine major American military conflicts from the Korean War to the present.
After controlling for elapsed time in conflict, the study finds that cumulative American war
deaths have a statistically detectable but substantively negligible impact on war support. Changes
in popular support for America’s wars appear to reflect a conflict’s duration in time far more
than its cost in lives. Americans in realistically complex information environments are neither
indifferent nor especially sensitive over the long term to the human costs of ongoing wars.
Instead, mass opinion appears strongly and consistently sensitive to how long these wars last.
Acknowledgements: The author thanks the Cline Center for Democracy at the University of
Illinois Urbana-Champaign for its extensive support of this project. Data on newsreel and CNN
coverage of war casualties and combat was collected in collaboration with Kate Conrad, Erin
Janulis, Omair Akhtar, Kristin Drogos, Bradley Bond, and Christopher Josey. Data on newspaper
coverage of war casualties and combat was collected in collaboration with Nathaniel Swigger,
Svitlana Chernykh, David J. Hendry, Sergio C. Wals, and Christopher Tiwald. Thanks also to
Ned and Linda O’Gorman for taping the CNN video during the invasion of Iraq, Dan Hallin for
graciously allowing us to use his Vietnam television data, as well as Linda Kaye of the British
Universities Film & Video Council and Jenny Hammerton of Film Images Limited (London) for
their invaluable assistance with the British Pathe newsreel holdings.
Paper prepared for presentation at the 2016 Annual Meeting of the
American Political Science Association, Philadelphia PA, September 1-4.
—PRELIMINARY DRAFT—
PLEASE DO NOT QUOTE WITHOUT PERMISSION FROM THE AUTHOR
Time or Consequences? — 2
Introduction
Casualties are the most visible consequences of military conflict, and there is no question that
casualties matter to leaders and citizens alike. Wars would be fought more frequently if leaders
thought their citizens were indifferent to the human costs of war. The question is how much
casualties matter, and under what circumstances. The answer is not at all clear.
Consider the puzzle of how Americans have responded historically when a thousand Americans
die in war. Nearly 1,000 American lives were lost in the opening week of the Vietnam War’s
1968 Tet Offensive. During that tumultuous week, Americans at home would see frantic fighting
to retake the U.S. embassy in Saigon, and watch television film of a South Vietnamese general
putting a bullet through the head of a Vietcong prisoner. Yet at the end of that intensely-followed
week in which so many died, the percentage of Americans back home who were calling the war
a mistake had risen only one percentage point from where it had been a month before.1
Decades later, Army Staff Sergeant Elvis Bourdon became the thousandth American to die the
Iraq War, killed in a Baghdad firefight on September 6, 2004. Over the eighteen months that
elapsed from the start of the invasion to the time of Staff Sergeant Bourdon’s death, the
percentage of Americans calling the Iraq war a mistake in Gallup polls had risen 15 percentage
points, from 23% in March 2003 to 38% in September 2004.
Six years after Staff Sergeant Bourdon’s death, Marine Corporal Jacob Leicht became the
thousandth American to die in the Afghanistan war, killed on May 29, 2010 while on a combat
patrol in Helmand province. Corporal Leicht died nearly nine years after the start of fighting in
Afghanistan, and over those nine years the percentage of Americans telling Gallup pollsters that
U.S. involvement was a mistake had risen 32 points, from 6% in January 2002 to 38% by July
2010.
The same thousand deaths were met with one, 15, and 32 percentage points increase in
opposition across these conflicts. These numbers appear to suggest that the American public has
become more averse to friendly losses. If so, this would represent a profound constraint for the
ability of leaders to use and sustain military conflict in furtherance of America’s strategic
interests abroad.
Most scholars studying this question believe that popular support for America’s wars is sensitive
to the number of American war deaths, but they disagree on the reasons why. They also disagree
on how casualty-sensitive the public might be, whether the public is becoming more sensitive to
casualties, and what implications the public’s apparent reticence about sustaining casualties
might have for the conduct of military operations. Enemies of the United States view the
American public’s seeming sensitivity to military deaths in a different way. In generating
1
In the Gallup poll released December 12, 1967, 45% of Americans said that the Vietnam War was a mistake. The
next Gallup poll, released on February 6, 1968—roughly a week after the start of the Tet Offensive—showed 46%
calling the war a mistake, a statistical tie with the December number. The next poll, released on February 27,
showed an increase of three percentage points to 49% calling the war a mistake. This change in opposition to the
war is not mirrored in the support trend, which remains stable over this three-poll period.
Time or Consequences? — 3
wartime deaths, they see opportunity to weaken the public’s resolve by forcing Americans to
confront continuous reminders of war costs and unsettling images of violent conflict.
But the research literature on casualty sensitivity rests on two important limitations that call the
validity of such conclusions into question. First, because it is so difficult to document the nature
or content of casualty information that reaches individuals outside of controlled laboratory
settings, this research literature has tended to model casualty effects as a simple product of
increasing numbers of casualties drawn from administrative sources, without considering
whether or how this casualty information actually reaches the American public. Second, this
research literature also tends to study opinion dynamics one war at a time, so that it remains
unclear whether similar patterns in opinion dynamics might be observed across wars with
different casualty rates.
The present study aims to reconcile the literature’s conflicting conclusions by clarifying how
casualty information has historically been communicated to American news audiences, and by
studying the effects of casualties across multiple major wars having varying levels of war-related
deaths. The paper first presents content analysis findings from New York Times coverage and
from both newsreel and (later) television news coverage of the five costliest American wars in
the last 100 years: World War I, World War II, Korea, Vietnam and Iraq. This analysis shows
that news attention to American war deaths is about the same in every major war, regardless of
casualty levels. It also shows that news depictions of combat and casualties have become
somewhat less graphic over the past 100 years. In short, news coverage of war casualties is
relatively insensitive to the actual occurrence of war-related deaths.
The paper then analyses all comparable public opinion data on war support covering every major
American military conflict since World War II. Comparing opinion trend data from wars
involving Korea, Vietnam, the Persian Gulf, Kosovo, Afghanistan, Iraq, Libya, and ISIS/ISIL,
this analysis shows that once a war’s duration is controlled for, the remaining effect of war
deaths on war support is substantively small and statistically negligible.
This study concludes that scholars and enemies alike are mistaken about the American public’s
apparent sensitivity to wartime deaths. Although the wars in Vietnam, Iraq, and Afghanistan
might appear to offer three seemingly different answers to the question of how much casualties
matter to popular support for war, this paper suggests that the popular response to casualties
occurred in pretty much the same way across all three wars: it goes down with the passage of
time, rather than as a response to the consequences of fighting. Conflict duration appears to be
the most important correlate of support dynamics in America’s wars.
The Scholarly Debate over Popular Sensitivity to Wartime Casualties
The casualty sensitivity debate was inaugurated in 1971 when the American Political Science
Review published an influential article showing that support for war goes down when friendly
casualties go up (Mueller 1971). This hypothesis has been embraced and modified in years since
by scholarship seeking to understand the dynamics of public support for war. Almost without
exception, proponents of this view have interpreted the relationship identified by Mueller in
causal terms: casualties—the consequences of engaging in combat—are thought to drive down
Time or Consequences? — 4
support for war.
Three Perspectives on the Mass Public’s Casualty Sensitivity
Three main perspectives about the impact of mounting combat deaths on the levels of popular
support for war have emerged in the scholarly literature.
1. Major impact. John Mueller’s germinal analyses of several wars (e.g., 1973, 2005)
supports the claim that war support dynamics are largely determined by the cumulative
number of friendly war deaths that occur during a conflict. This perspective has been
highly influential in the international relations literature (e.g., Eichenberg 2005;
Eichenberg, Stoll, and Lebo 2006; Larson 1996; Larson and Savych 2005), and traces
back to an earlier generation of thinkers who were concerned with the public’s reaction to
the unpleasant realities of war.2
2. Conditional impact: This perspective offers a reinterpretation of the major impact view
by suggesting that the relationship between cumulative war casualties and war support is
moderated by other variables like perceived chances of victory or recent trends in
casualty rates, so that a major impact is seen under certain conditions but not others
(Gartner 2008a; Gelpi, Feaver, and Reifler 2005).
3. Minor impact: This perspective challenges both the major impact and conditional impact
views by suggesting that casualties usually have a far smaller impact on war support than
other factors (e.g., Berinsky 2009; Althaus, Bramlett, and Gimpel 2012; Althaus and Coe
2011). This perspective suggests that any apparent relationship between casualties and
support is a spurious artifact that reflects the influence of some other factor, such as
levels of elite dissensus (e.g., Baum and Groeling 2010; Berinsky 2007) or perceived
chances of victory (Sidman and Norpoth 2012).
The trajectory of emphasis within the casualty sensitivity literature has moved from major
impact to conditional impact, all within a cost-benefit rationality framework. Recent voices have
begun to raise the visibility of the minor impact, but the relative importance of casualties to war
support remains an open question.
One reason why this debate remains unsettled is the combination of methods and data used to
study the question. Most of the support for the casualty sensitivity hypothesis comes from two
types of evidence: observational analysis comparing levels of war support and levels of war
casualties within a particular conflict, or experimental analysis (whether embedded in surveys or
conducted in labs) that present subjects with hypothetical casualty scenarios. Traditionally,
research on casualty sensitivity drew conclusions from statistical analysis of aggregate opinion
dynamics in real wars using observational data. A wide range of conclusions are drawn from
these regression analyses, in part because the uneven spacing of data points and the scarcity of
data from less recent conflicts make it difficult to apply more precise and determinative
estimation strategies. A more recent development is the use of experimental methods to gain
additional causal leverage of questions about casualty sensitivity. These experimental studies
typically explore how different types of casualty information affect popular support for military
2
For instance, Lasswell’s (1927) treatise on wartime propaganda offered the view that “The justification of war can
proceed more smoothly if the hideous aspects of the war business are screened from public gaze. People may be
permitted to deplore war in the abstract, but they must not be encouraged to paint its horrors too vividly” (98).
Time or Consequences? — 5
conflicts among research subjects giving opinions on hypothetical wars. In contrast to the varied
conclusions drawn from regression analysis of observational data, these experimental studies
tend to find strong and consistent evidence of the public’s sensitivity to wartime losses. The
varied conclusions drawn from these different approaches lead to conflicting conclusions about
the nature and importance of the public’s “casualty sensitivity” in shaping the dynamics of
popular support for military conflicts.
This standard mix of methodological approaches shares an important assumption: all of these
approaches assume that people possess timely and accurate information about the human toll in
ongoing wars. If national news outlets rarely cover wartime losses, or if few Americans pay
close attention to news about ongoing wars, Americans may lack accurate or timely information
about the human costs of war. If this is the case, then the latent tendencies toward casualty
sensitivity demonstrated so clearly in hypothetical scenarios should rarely be activated during
military conflicts. Something else would have to be responsible for changes in war support,
something other than accurate perceptions of wartime losses.
A related threat to validity is specific to studies tracing aggregate changes in war support and
casualty levels within a single conflict, as is typically done in the literature. Such studies tend to
find that support goes down as casualties go up. However, because war support and casualty
levels are both correlated with the passage of time, this research design is ill-suited to ruling out
alternative explanations. Analyzing those same data with a research design that eliminates this
confound might show something different, and as will be demonstrated below, it does.
The analysis that follows proceeds in four sections. First, the paper briefly reviews the question
of how accurate are popular perceptions of casualties in ongoing wars. Second, the paper
presents content analysis data from five major American wars of the last 100 years comparing
how friendly casualties have been covered in newspapers and in visual news (newsreels and,
later, television). Third, the paper reviews some insights from social psychology that suggest
most people’s support for ongoing wars should be fairly unperturbed over the long run by new
information about friendly losses. Fourth, the paper presents an analysis of the entire population
of comparable war support survey data across every major American military conflict that
happened since World War II. Pooling these data together across wars allows for a clearer test of
real-world casualty sensitivity by including conflicts with very high casualty levels (Korea and
Vietnam) and conflicts with very low casualties (Afghanistan, War on Terror) or no friendly
losses at all (Kosovo and Libya).
What Americans Don’t Know Can’t Change Their Minds
Berinsky (2007, 2009) and others (e.g., Cobb 2007; Boettcher and Cobb 2006, 2009) have
observed how few Americans possess accurate knowledge about American war losses in ongoing
conflicts overseas. The record of inaccurate perceptions stretches back to at least World War II,
when large proportions of Americans significantly underestimated or overestimated American
losses from the war (Berinsky 2007, 986).
Gallup asked Americans in January 1953 to guess how many U.S. soldiers had been killed in the
Korean War, which by that time was nearly at its end. The correct answer at the time of the
Time or Consequences? — 6
survey was around 29,750 killed in action. But only six percent of Americans stated correctly
that the real number was between 25,000 and 35,000. Nearly one in six believed fewer than
15,000 Americans had been killed in the war, while one in five thought more than 100,000
Americans had died. Even after two and a half years of intense fighting the American public was
nearly clueless about how many of its soldiers had died in the Korean War.3
A similar tendency is found in estimates of American casualties from the Vietnam War. In
December 1965 Gallup asked Americans to guess how many Americans had died in Vietnam up
to that point. Only 10% could say correctly that the number of American dead at that early stage
of the war was between 2,000 and 2,499 (the actual number at the time of the survey was around
2,100 American dead). One in five thought that fewer than 1,000 had died, while another one in
five thought that more than 3,000 had been lost. One in ten may have had an accurate sense of
American casualties in late 1965, but two and a half years later only one in a hundred could
correctly guess the actual level of war dead. By mid-June of 1967 around 13,800 Americans had
lost their lives in the Vietnam fighting, but only 1% of survey respondents ventured a guess of
between 13,000 and 14,000 killed in action. One in ten could be considered on target if we
broaden the range of “correct” responses to include anywhere from 11,000 to 15,000 war dead.
But a bullseye that is 4,000 lives wide encompasses the entire number of American dead in the
first six years of the Iraq War, so it hardly seems reasonable to give the American public a pass
in Vietnam for a level of casualties that amounts to the sum total of six years’ of involvement in
Iraq.4
Fast forward to the Iraq War, and the American public seems to be only somewhat more
informed about friendly losses. A CBS/New York Times poll from September 2005 found that
45% of Americans were unwilling to even offer a guess as to how many Americans had been
killed in Iraq up to that point. Another 43% of Americans selected a response that was close to
the actual number of deaths at the time of the survey (counting either “1,000-1,999” or “2,000 to
2,999”; as the actual number of battle deaths was at nearly 1,900 at the time of the survey). More
nuanced survey measures of casualty estimates suggest that the public’s perception of war deaths
was even less accurate than this (e.g., Cobb 2007; Boettcher and Cobb 2006).
Public perceptions of casualties in lower-intensity wars can be just as inaccurate. The 1999
Kosovo campaign was notable for the complete absence of American combat fatalities. A
majority of Americans nonetheless believed that at least some of their forces had been killed in
action. During the NATO air operations to stop Serbian forces in Kosovo, a PIPA survey (1999)
from May 13-17, 1999 found that 56% of Americans mistakenly believed that U.S. soldiers had
been killed in Bosnia during the last year. Of those who believed American lives had been lost,
the median number of perceived battle deaths was 20.
If there is a difference between the Korean and Vietnam Wars and now, it is for Americans to
appear somewhat more accurate in their perceptions about casualties. Yet this increased accuracy
is occurring in a period when casualty levels are orders of magnitude lower than in previous
3
This appears to have been the only survey from the Korean War that asked a question about casualty perceptions.
Survey is Gallup poll from January 1953, Roper number USGALLUP.53-510.QK09C.
4
These two Gallup surveys seem to have been the only Vietnam-era polls to ask about perceptions of casualty
levels. Roper numbers USGALLUP.747.Q04 and USGALLUP.721.Q08.
Time or Consequences? — 7
wars. But even today, most Americans lack accurate information about how many military
personnel are dying in ongoing wars. One reason for this lack of awareness stems from the ways
casualties are reported to Americans in the news.
News Reporting of War Costs
A recent pair of content analysis studies has documented how American newspaper coverage of
wartime casualties over the past 100 years has presented a selective view of the human costs of
war. From World War I through the Iraq War, not only were casualties rarely mentioned in New
York Times coverage—“on average across the five wars, only 2% of war-related stories mention
cumulative deaths, and less than 1% of stories mention numerical trends in American deaths”
(Althaus et al. 2011, :1073)—but this minimal volume of news coverage of casualties was
completely uncorrelated with the scale of wartime losses. Instead, newspaper coverage of
American casualties tended to become more common when a war was going badly, and less
common when a war was going well (Althaus et al. 2011). And rather than presenting war losses
in largely negative terms, this newspaper coverage frequently presented American wartime
deaths in ways that minimized, justified, or downplayed the human costs of war (Althaus et al.
2014).
Much less is known about how casualty information has been presented to the American public
through visual news media, which today are far more widely consumed than newspapers. To
remedy this gap, I organized a group of collaborators to systematically sample visual news
content from the same five American wars as the newspaper study had covered: World War I,
World War II, the Korean War, the Vietnam War, and the Iraq war. Although it is popularly
believed that Vietnam was the first war to be widely covered with news reports of moving
images, Americans had been voracious consumers of wartime visual news coverage through the
global newsreel system, which began in 1911 and closed up (in the United States) in 1967
(Althaus 2010). To compare newsreel depictions of casualties and combat with those shown on
television today, we analyzed CNN news coverage of the 2003 invasion of Iraq. The invasion
phase of the Iraq war was thought to be directly comparable to the type and intensity of combat
that occurred in the earlier wars being analyzed. This analysis contrasts newsreel coverage of the
four earlier wars to randomly-sampled minutes from CNN’s around-the-clock coverage of the
2003 invasion of Iraq. Details of the sampling strategy and content analysis protocols are
available in the methods appendix to this paper.5
Frequency of Casualty Imagery
Although newspaper coverage of wartime casualties tended to be fairly rare from World War I
through the Vietnam War (Althaus et al. 2011; Althaus et al. 2014), a slightly higher percentage
of war stories shown in newsreels presented images of casualties. On average across the five
5
Since the United States was the leading distributor of worldwide newsreel footage, it would be ideal to study the
contents of American newsreel film. Unfortunately, the data resources that would allow a systematic analysis of
American newsreel footage over the period from World War I through the Vietnam War do not yet exist (Murphy
1996). Our analysis therefore draws from the film libraries of the two longest-running British newsreel companies,
British Pathe and British Movietone. Since these companies were subsidiaries of American-coordinated newsreels,
their visual records of war should be nearly identical to the combat film available to American newsreel editors,
differing mainly in the likelihood of containing film showing British home-front activities generated exclusively for
British audiences.
Time or Consequences? — 8
wars, 6% of visual war stories included moving images of allied wounded, and 4% included
shots of allied dead. As shown in Figure 1, ANOVA analysis reveals there are no significant
differences between wars in the frequency with which allied dead were shown (F [4, 2017] = 1.3,
p = .26). Between war differences in depictions of allied wounded were significant (F [4, 2017]
= 3.4, p < .01), but pairwise comparisons showed that this is because allied wounded were
significantly less likely to be shown in in CNN’s Iraq coverage than in newsreel coverage of the
Korean War. No other differences between the other wars were found to be statistically
significant. In short, images of the dead and wounded were no more likely to be shown on
CNN's coverage of the Iraq invasion then they were in newsreel coverage all the way back to
World War I.
INSERT FIGURE 1 HERE
Intensity of Casualty Imagery
However, frequency of coverage is not the same as the emotional intensity of vividness of war
imagery. Images of war might be no more common on television news reports as they were in
newsreels, but the images themselves might be more graphic or vivid today than they used to be.
We examined this possibility by rating the visual intensity of moving images depicting wartime
dead and wounded. The graphical vividness of images of the wounded and dead was categorized
as light (intensity = 1), moderate (intensity = 2), or intense (intensity = 3; see methods appendix
for details). We found no evidence to support the idea that moving images of wartime casualties
have become more graphic over time. ANOVA analysis of the data in Figure 2 shows that the
average visual intensity for shots of wounded persons was almost identical across the five wars,
F (4, 145) = 0.2, p = .93. However, the average visual intensity for footage of dead persons was
significantly different across conflicts, F (4, 97) = 7.1, p < .001. Post hoc contrasts reveal that
shots of dead persons in CNN’s coverage of the Iraq invasion were significantly less visually
intense than comparable shots in newsreel coverage from both Vietnam and World War II. The
same patterns appear when looking at the frequency of visually intense shots of the dead and
wounded. Shots of moderate, intense, or graphic wounds appeared proportionally with the same
frequency across all five wars, F (4, 2017) = 1.4, p = .22, while shots of moderate or intense
death were more likely to appear in newsreel footage from World War II and Vietnam than in
CNN’s coverage of the Iraq invasion, F (4, 2017) = 7.1, p < .001. Although now broadcast live
and in color, news footage of combat at the dawn of the 21st century seems no more graphic than
that shown in the first half of the 20th century. In some respects, news images of war casualties
were presented somewhat less graphically during the 2003 invasion of Iraq than they were in the
pre-television era.
INSERT FIGURE 2 HERE
Frequency of Combat Footage
In addition to looking at casualty images in news film of war over the past 100 years, it is also
illuminating to look at representations of actual fighting in war, whether described in newspapers
or shown on screens. Even if casualty coverage is minimal, audiences might infer something
about the costs of war by reading about or watching how the fighting actually played out. Much
attention has been given to Vietnam as the first “living room war” (Arlen 1969), but Figure 3
shows that combat images appeared much more frequently in newsreel stories from the 1940s
Time or Consequences? — 9
and 1950s than in television reports from Iraq. One in four war-related newsreel stories from
World War II showed moving images of combat, as did 28% of newsreel stories about the
Korean War. By comparison, only 15% of sampled minutes from CNN’s coverage of Iraq
showed images of combat, and just 6% of newsreel stories from World War I showed moving
images of combat.
INSERT FIGURE 3 HERE
The trend for newspaper descriptions of combat activities is more straightforward. Figure 3
shows that detailed textual descriptions of war-fighting used to be fairly common in World War I
newspaper coverage, where two out of every five war-related stories described the fighting, but
gradually tapered off in frequency over the following century. Figure 3 shows that by the time of
the Iraq War, written depictions of combat were found in fewer than one in five war-related New
York Times articles.6 Overall, the general tendency in Figure 3 is for less news attention to
combat activities today than in the past.
Intensity of Combat Imagery
The graphical intensity of this combat coverage has also changed to some degree. Figure 4 shows
that the percentage of newspaper stories describing combat in moderately or intensely graphic
terms (that is, showing or describing the combatants as recognizable individuals, and presenting
either the initiation of an attack or its consequences—for more details, see the methods
appendix) has remained fairly stable since World War I, with an average of just more than one in
ten stories about combat describing that fighting in particularly graphic terms. In contrast,
Figure 4 shows that among visual war stories containing film of combat, scenes of intense
combat were shown more frequently in movie theaters than on television. In CNN’s Iraq
invasion coverage, 35% of sampled minutes depicting combat action contained a shot of
moderate or intense combat. By contrast, 60% of newsreel stories about Vietnam that showed
any combat action included shots of moderate or intense combat. The same was true of 56% of
newsreel stories featuring combat in the Korean War, 44% of newsreel stories showing combat
in World War II, and 35% of newsreel stories showing combat scenes during World War I. On
the visual side of news coverage, CNN’s depictions of fighting were no more graphic than
newsreel footage of combat sequences from World War I, but combat in both of these wars was
depicted less dramatically than in newsreel coverage of fighting in World War II, Korea, and
Vietnam.7
6
Unlike the CNN data for Iraq that only cover the initial invasion period, newspaper data for the Iraq War cover the
period from the 2003 invasion until late 2006. See (Althaus et al. 2011; Althaus et al. 2014) for additional details.
7
We also obtained a copy of Hallin’s (1986) dataset of Vietnam-era network news coverage in order to compare
television coverage of Vietnam combat to the data collected for this comparison of newsreel and CNN coverage.
Hallin’s Vietnam television sample contains network evening newscasts from 786 days within the 1965-1973
period. At least some film from Vietnam was shown on 512 of these days (65.2%), but film of combat operations
was aired on only 102 days (13.0%). Visual coverage of combat during the Vietnam War was therefore far from
being a daily occurrence: in Hallin’s data, combat footage from Vietnam was shown only about every eighth day or
slightly less than once a week. Newsreels were significantly more likely than Vietnam-era television news reports to
show both the dead and the wounded: 8.2% of newsreel stories showed the wounded compared to 4.5% of television
stories (t [4506] = 5.0, p < .001), while 4.7% of newsreel stories showed the dead compared to 3.2% of television
stories (t [4506] = 2.5, p < .01).
Time or Consequences? — 10
INSERT FIGURE 4 HERE
In summary, this brief tour of news data suggests two conclusions. First, news reporting of war
costs—whether of friendly casualties or of the fighting that produces them—seems to be no more
graphic or frequent today than in wars past (one exception is newspaper coverage of American
deaths in Iraq—see Althaus et al. 2014). So any apparently increased sensitivity to casualties in
contemporary conflicts relative to earlier wars would not appear to result from exposure to any
more graphic or extensive casualty reporting in the news than used to be the case. If anything,
recent wars appear to be covered more sedately in major news outlets than were wars of the
distant past. Second, there seems to be a fairly constant rate of (usually minimal) casualty
coverage across major wars, regardless of differences in actual casualty levels. This regularity
means that the amount of casualty coverage isn’t correlated with the rate of casualty occurrence,
although the graphic nature of casualty coverage in newsreels did tend to be higher in the wars
with higher overall casualty levels. When combined with the scarcity of historical newspaper
reporting on cumulative casualty counts or numerical casualty trends (Althaus et al. 2011;
Althaus et al. 2014), these conclusions suggest that news coverage of war tends to be fairly
information-poor on the sort of casualty count data that political scientists have used for decades
in statistical models testing the casualty sensitivity hypothesis.
The importance of this insight can be illustrated most vividly with a close look at casualty
coverage for the wartime event that is perhaps most closely associated with the casualty
sensitivity hypotheses: the rapid and precipitous loss of American resolve to fight in Korea
following China’s entry to the war.
Case Study: The Infamous Drop in Korean War Support Following China’s Entry
Secretary of State Dean Acheson later called the Army’s rout in the week following the start of
the Chinese offensive “the worst defeat of U.S. forces since Bull Run” (McLellan and Acheson
1980, 104). The Marines around the Chosin Reservoir fared somewhat better, but overall the
opening weeks of the Chinese offensive was a period of bad news for U.S. forces without
precedent in a war already known for its bad news. This particular incident weighs heavily in
Mueller’s (1973) path-breaking analysis of the relationship between casualties and war support,
because “The large drop in war support that occurred with the Chinese entry into the war is
associated, of course, with a corresponding jump in the cumulative casualties suffered” (59).
That the former should follow from the latter seemed rather obvious given the available data at
the time, and this episode stands out in the annals of the war support literature as especially
compelling evidence of the apparent relationship between battlefield losses and the American
public’s willingness to support ongoing military conflicts. But no study has yet looked at how
casualty information was being conveyed to American domestic audiences at the time, or
whether that reporting accurately conveyed the loss of life that American forces had suddenly
experienced.
It is important to underscore just how historically large the drop in popular support for military
action was. Two public opinion trends registered the impact of Chinese entry into the war and
give an important clue to the time structuring of this event’s impact. In NORC’s “right thing”
war support trend, 81% of Americans supported involvement in Korea on September 30th, two
Time or Consequences? — 11
weeks after the start of the UN counteroffensive that followed the Inchon landings. The next poll
on January 8th found only 55% support. Likewise, Gallup’s “not a mistake” war support trend
registered 65% support for the war in late August 1950, but just 38% support on January 5, 1951.
Since the news between late September and late November was uniformly positive as UN forces
advanced up to nearly the top of the Korean peninsula, the drop in support levels must have
occurred after the November 26th entry of China into the war, an event that occurred within five
weeks of the two post-event surveys. Gallup’s 27-point drop and NORC’s 26-point drop in
support over probably just five weeks mark this event as precipitating the single most rapid loss
of public confidence ever recorded in the history of survey research measuring support for war.
The official post-war records of the Department of Defense list a total of 312 combat-related
deaths that occurred in the two-week period from November 11 to November 25th, 1950. The
Chinese offensive began on November 26th, and the official record shows an additional 3,483
combat deaths registered in the week running from November 26th through December 2nd, and
another 593 combat deaths incurred between December 3rd and December 10th.
This sudden and dramatic surge in combat deaths has no parallel in New York Times coverage of
Korean War casualties. On December 1st the Times published a casualty list containing people
whose next of kin had been notified through November 24th. Including this list, the published
cumulative death toll since June 1950 was 5,307 killed in action and 4,611 missing in action, for
a combined potential death toll prior to the start of the Chinese offensive of 9,918 American
service personnel (in comparison, the post-war Department of Defense casualty file shows 9,516
KIAs incurred as of November 24th). A week later, another 760 persons dead or missing were
added to that number in a December 8th news report.8 In the following weeks, only another 480
were added to the death toll on December 15th and another 368 on December 27th. On this date,
the combined dead and missing was reported as 11,496, but one day later the combined total
dropped by 641 to 10,885 after an adjustment for MIAs now declared prisoners of war.
The first rise in reported war deaths clearly associated with the Chinese counter-offensive wasn’t
announced until nearly a month and a half after China entered the war. On January 6, 1951 a
three-paragraph article on page two quietly added 329 new deaths and 1,395 new MIAs to the
previous total. This was the first public indication (to careful observers of these numbers) that a
surge in casualties had occurred. The next announcement on January 18th added 1,155 new KIAs
and 1,599 new MIAs, reporting a combined total of 15,050 dead or missing. On February 2nd the
next casualty report added 436 to the number of KIAs and 1,555 to the total number of MIAs, for
a total of 17,041 dead or missing. The running total of publicly-reported war deaths of American
military personnel in Korea is shown in Figure 5, next to the actual total of war deaths contained
in the Department of Defense casualty file that is used in academic research to track casualty
trends.
INSERT FIGURE 5 HERE
8
The New York Times offered this explanation for the trickle of casualty information: “The new list did not include
many of the casualties suffered in the crushing Chinese Communist drive that opened a week ago last Sunday. The
department’s lists are based on notification to next of kin and it generally takes from two to three weeks for the
notices to reach relatives.” “U.S. Casualty Toll Climbs to 32,442.” New York Times December 8, 1950, p. 7
Time or Consequences? — 12
Figure 5 shows that although 4,592 new deaths occurred between November 26th and January 6th,
only 1,454 new deaths were made public between December 1st and January 6th, none at a rate
suggesting any deviation from the pre-offensive period. The only hint of sudden increase comes
in the MIA report on January 6th, with the main public announcement arriving on January 18th.
This news came nearly 10 days after the Gallup and NORC polls revealed a stunning decline in
popular American support for the conflict. That drop had occurred before the number of war
deaths was publicly known, so news about the number of war deaths associated with China’s
entry could not possibly have eroded American resolve to continue fighting in Korea. As noted
in a Times article dated March 15, 1951, up to that point in the conflict the “heaviest casualties of
the Korean War, 4,218, were reported on Aug. 25, eighteen days after the Communists struck.”9
No mention was made of any surge of casualties associated with China’s entry into the war,
because the number of war deaths that followed China’s entry was simply unknown until long
after the rapid drop in American war support had occurred.10
The sudden loss of American popular support for the conflict in Korea could not have resulted
from accurate information about the increased level of American casualties following China’s
entry into the war. It is difficult to know what, if not war deaths, explains the drop in support that
followed the Korean invasion.11 But even when war costs are accurately reported at the time they
actually occur, the psychological processes that structure how people process, store, and use new
information about ongoing wars should tend to shield people from any long-term corrosive effect
that casualty information might have on war support.
The Psychology of Evaluating War Costs
When placed in an information environment where attention is focused on the human costs of
war, surveys and experiments provide ample evidence that Americans are sensitive to casualties.
For example, an April 2004 Time/CNN/Harris survey on Iraq found that the 48% of American
approved of current military policy in Iraq. Those respondents were subsequently told that 600
U.S. soldiers had been killed in combat so far. This fact led half of that original 48% to reverse
9
Source: “U.S. Toll in Korea is 54,649 to Date.” New York Times March 15, 1951, p. 3. I have been unable to locate
such a report in the Times thus far. A September 6th Times article seems to be the first time that the paper reported
the official cumulative total. A check of the Los Angeles Times and Chicago Tribune for the same period revealed
the same story—not much cumulative casualty reporting before 9/5/50. All were reporting daily lists of region
casualties by name, but not aggregate numbers.
10
Indeed, although the first week of the November 26th Chinese offensive pushed the cumulative total of American
war dead to just above 13,000, the number of war dead known to the American public would only tip 13,000 in July
1951, eight months after that number of casualties had already been incurred.10 Partly the delay seems to have been
to deny the enemy useful information, but partly it can be explained by the thousands of Americans missing in
action from the turbulent early months of war. Even today, more than half a century after the war’s armistice, more
than eight thousand Americans whose bodies were never recovered are still listed as missing and presumed dead
(O'Brien n.d.).
11
One possibility is that the event shifted popular perceptions of the war’s eventual victory [CITES]. Another
possibility, perhaps more significant than any casualty numbers that wouldn’t come out until long after China
entered the war, was the front page announcement on December 13, 1950 that 160,000 men soon would be drafted
to begin training in January and February, representing an 8% increase in the size of America’s standing army.
[“160,000 Men Sought: Army’s Strength Would Rise to 1,250,000 by End of February.” NYT, December 13, 1950,
p. 1.]. This was followed by a call for 50,000 more draftees made public on January 12, 1951. [“Casualties Forcing
Army to Add 50,000 Draftees.” NYT, January 12, 1951, p. 3. ] It was clear by mid-December that the United States
was gearing for a much more prolonged fight than it had been expecting up to that point in the conflict.
Time or Consequences? — 13
their earlier opinion and oppose the military’s policy on Iraq.12
But there is good reason to expect that casualty sensitivity doesn’t work this way in less artificial
contexts. For instance, a survey from February 1966 found that 61% of Americans approved how
President Johnson was handling the war in Vietnam. But when asked if “you would approve of
continuing the fighting if it meant several hundred American soldiers would be killed every
week,” only 38% approved of Vietnam involvement under those terms.13 Based on this survey
evidence, it would seem that once several hundred American soldiers were dying each week, we
should see war support drop as precipitously as this evidence indicates. But even in February
1966, this did not happen. Because unbeknownst to those survey respondents, the weekly toll in
February 1966 was already averaging 125 American deaths per week. The rate of American
losses would reach over 200 per week by April of 1967. Yet in April of 1967 Gallup’s measure
of war support had fallen to just 50%, not the 38% suggested in the NORC question from the
previous year. It would seem in this case that Americans were less sensitive to real-world
casualties than to hypothetical casualties.
One reason why numerical information about the human costs of war is unlikely to translate into
opposition to military action is the psychological phenomenon known as the “identifiable victim
effect” (Slovic 2007; Small, Loewenstein, and Slovic 2007). In an extensive series of
experiments, Slovic found that people react more strongly to detailed information about a single,
personally-identifiable victim of tragic circumstances, than to statistical information about large
numbers of victims. This is because numerical information about the human toll of any tragedy
fails to engage the emotional centers of human information processing that motivate attitudinal
and behavioral change. These emotional pathways are much more likely to be engaged by
narrative information about a single identifiable casualty, leading to the startling conclusion that
“The more who die, the less we care” (Slovic 2010). The reason for this unusual relationship
seems to be that people rely on their intuitive feelings when assessing moral value of tragic
events. Intuitive feelings that something is “bad” turn out to be “insensitive to large losses of life
and thus mislead us in the face of natural disasters or human disasters associated with poverty,
disease, and violence” (Slovic and Västfjäll 2010, 387).
Although the identifiable victim effect has not been specifically tested in the context of assessing
war casualty information, its conclusions would appear to suggest that large numbers of
anonymous war deaths are unlikely to make as large an impression as news that a single, named
person has perished in battle. If the identifiable victim phenomenon applies to assessments of
wartime casualties, then we should observe that news about a single, local casualty should weigh
more heavily on assessments of war support than news about large numbers of national-level
casualties. The small number of studies testing this possibility provide strong support for this
conclusion (e.g., Gartner and Segura 1998; Althaus, Bramlett, and Gimpel 2012). But even when
the human costs of war hit closer to home, the impact of local casualty information will vary
depending on whether it produces attitude change or merely stands out temporarily in memory.
12
Survey is Time/CNN/Harris Interactive poll from April 8, 2004, Roper Center data set number
USHARRIS.Y040904.R26.
13
Survey is NORC/Stamford University Survey from February 1966, Roper Center study number
USNORC.031566.R18.
Time or Consequences? — 14
A second reason why casualty information should have a limited effect on war support is that
exposure to new attitudinally-relevant information rarely triggers fundamental reassessments of
long-standing attitudes like support for an ongoing war. Instead, the more typical effect of being
exposed to new information is what political psychologists call a “priming effect”: a temporary
increase in the likelihood that the new information will be remembered when a person is called
upon to make a judgment using the long-standing attitude (e.g., Krosnick and Kinder 1990; Price
and Tewksbury 1997). If new casualty information leads a person to fundamentally reassess
whether the war is justified, then any attitude change that results from deep introspection is likely
to be durable and predictive of behavior (e.g., Chen and Chaiken 1999; Petty and Wegener
1999). But when negative assessments of the war are merely a product of having local casualty
information temporarily accessible in long-term memory, the attitude change that results from
exposure to such information is likely to be short-lived without any lasting consequences on a
person’s underlying sense of whether a war is justified or not.
One study tested for this distinction by matching the hometown of record for Iraq War casualties
to the residences of tens of thousands of survey respondents who were asked about their support
for the conflict (Althaus, Bramlett, and Gimpel 2012). As shown in Figure 6 (reproduced here
from the original article), people living in areas that experienced local casualties tended to have
negative assessments about the war for up to two months after the losses occurred. But after two
months had passed, few remaining effects of the local casualty information could be detected.
INSERT FIGURE 6 HERE
A third reason for why the mass public might appear callous to the human costs of war stems not
from indifference but to a routine feature of human psychology: people tend to interpret new
information selectively in ways that protect existing attitudes (e.g., Evans 1989; Jonas et al.
2001; Nickerson 1998). In a path-breaking study, Gaines et al. (2007) used panel data of student
respondents collected in various waves over a 14 month period early in the Iraq War to assess
how war support was affected by rising numbers of American combat deaths. They found that
respondent perceptions of American losses rose in parallel to the rise in actual losses, but that the
students interpreted those losses selectively: supporters of the war saw the rising American
losses as justifiable sacrifices, while opponents saw the mounting casualties as yet more reason
to disapprove of the war. In short, the authors found compelling evidence that changes in beliefs
about casualty levels mattered far less than how people interpreted those casualties. And people
interpreted them in ways that justified and rationalized their already-existing support for (or
opposition to) the war. This is evidence of a defensive processing strategy that political
psychologists refer to as “motivated reasoning”: new information is taken in and interpreted in
ways that reinforce existing opinions (e.g., Kunda 1990; Lodge and Taber 2000; Taber and
Lodge 2006).
The Gaines et al. panel study has not been replicated (to my knowledge), so it is difficult to know
how far to take its findings. But it turns out that there is a good source of related but neglected
survey evidence from the Vietnam War that strongly supports the idea that new casualty
information tends to be rationalized away by the people who receive it, even when the casualties
in question are “identifiable victims.”
Time or Consequences? — 15
On rare occasions, surveys during the Vietnam war asked respondents if they personally knew
someone killed or wounded in the war, and also asked the same respondents whether they
supported or opposed the war. These surveys offer consistent and compelling individual-level
evidence that casualties either didn’t matter much to levels of Vietnam support, or increased
rather than decreased support for the war. This is just as the motivated reasoning argument would
predict.14
A Harris Survey from May 1967 found that 30% of Americans at that time personally knew
someone killed or wounded in Vietnam.15 This group was then asked “When he was killed
(wounded), did this make you feel more like supporting or opposing the war?” Among those
having direct contact with a war casualty, only 28% recalled that it made them more opposed to
the war. Of the remainder, 55% said that news of the casualty made them more supportive of the
war, and 17% weren’t sure whether it had any impact on their view of the war. In other words,
nearly three quarters of Americans who personally knew someone killed or wounded in Vietnam
said that news of the casualty either increased their support for the war or made no difference at
all. However, saying that news of a casualty increased a person’s support for the war does not
mean that it changed the person’s opinion about the war. Among those who said they became
more supportive after hearing about the casualty, 14% had indicated at the beginning of the
survey that they wanted the U.S. to “end the war and get out as quickly as possible,” while only
30% had said they preferred “fighting on to a total military victory,” with the remaining 53%
saying they preferred “fighting until we achieve a negotiated peace.” Instead of changing
opinions, the most plausible consequence was that news of the casualty reinforced whatever
opinion about the war that they already held (Klapper 1960; Zaller 1992; Gaines et al. 2007).
A similar pattern emerges in Gallup/Newsweek survey data from August 1969, during one of the
bloodiest seasons of the Vietnam War. At that time, the survey revealed that 52% of Americans
personally knew someone who had been killed or wounded in Vietnam. Yet as with the earlier
finding from 1967, respondents with a personal connection to a war casualty were significantly
more supportive of the war than those who had no personal connection to a casualty: 58%
supported U.S. involvement in Vietnam, compared to 52% support among those who had no
personal tie to a Vietnam casualty.16 Projecting to some 100 million persons claiming some kind
14
In contrast, Gartner (Gartner 2008b) finds that personal ties to Iraq War and 9/11 casualties were correlated with
higher levels of disapproval for President Bush. However, it is unclear how such personal ties were correlated with
support for the Iraq War or the Global War on Terror.
15
This comes from a May 1967 Harris Survey archived at the University of North Carolina’s Odum Institute for
Research in Social Science, study number s1735. The validity of this question has an unfortunate limitation: it was
asked only of respondents who indicated in a previous question that they knew someone who was currently serving
in Vietnam. Since someone killed in Vietnam could not have been serving at the time of the survey, this series of
questions almost certainly underestimates the number of people who knew someone killed or wounded in Vietnam.
16
These findings are from an August 1969 Gallup/Newsweek poll, Roper Center study number
USGALNEW.696988.Q16. This survey used a non-standard measure of Vietnam support, which reads as follows:
“would you please read carefully all the statements on this card. Which one of the statements comes closest to your
feelings about the war in Vietnam? (1) It was our right and duty to send troops to Vietnam to fight the communists.
(2) While we were justified in sending our troops to Vietnam, it would have been better if we had only sent military
aid and supplies. (3) Even though we had some reasons for sending our troops to Vietnam, everything considered we
should have stayed out. (4) We had no right or reason to send our troops to fight in Vietnam in the first place. (5)
No opinion.” This variable is scored so that lower values represent higher levels of war support. Mean support for
the war among those who knew a casualty was 2.31, among those who didn’t mean support was 2.44, t(2380) = –
Time or Consequences? — 16
of personal tie to the roughly 300,000 Americans dead or wounded at that point in the war,17 this
averages to around 300 persons claiming knowledge of each casualty. Those who said they
personally knew someone wounded or killed in the war could not therefore be intimates, but
mainly persons with weak ties to the casualties. This suggests that the elevated support for war
among this group could not have been merely a form of justification by a lost soldier’s
immediate family members who might be strongly motivated to rationalize the loss.18
One year later, another survey from August 1970 asked Americans to report their typical reaction
to casualty news from Vietnam: “When you hear that a young draftee has been killed in
Vietnam, do you personally tend to feel upset, or more that his death is unfortunate but part of
what war is all about?” This survey found that fully 45% of men and 29% of women reported
feeling that additional war deaths were unfortunate but not personally upsetting. Given that the
socially desirable response is to show sensitivity to war deaths, it is remarkable that such large
proportions of respondents were willing to reveal their indifference toward additional human
costs of a war that had already claimed more than 50,000 American lives. This level of
acquiescence is all the more remarkable at a time when over half of Americans personally knew
someone who had been wounded or killed in Vietnam.19
In summary, there is good reason to expect that the common psychological mechanisms that
structure how people make sense of new information in light of old attitudes should limit any
potential impact that news about wartime casualties might have on popular support for
America’s wars. Since news about such casualties seems to have been relatively uncommon in
news reporting of American wars over the past 100 years, the limited flow of information about
casualties reaching the American public should also insulate against negative repercussions of
rising casualty levels.
But if casualty reporting in the news is fairly steady regardless of casualty rates, if few
Americans have accurate information about current levels of war deaths, and if new information
about war deaths should tend to be rationalized away by people who already have developed
attitudes about a conflict, then what basis remains for expecting that casualties drive support for
war? Setting aside the important forms of evidence from experimental studies, which artificially
focus the attention of subjects on changes in casualty levels, the most extensive and compelling
support for the casualty sensitivity hypothesis comes from regression analyses of aggregate war
support data, usually in the context of a single war. And these studies all share a common
vulnerability: as many scholars have noted (e.g., Lai and Reiter 2005), casualties are correlated
2.92, p <.01. To simplify presentation of these findings, I combined responses to options 1 and 2 as a measure of
overall support for the war.
17
In early December the New York Times reported a total of 39,642 Americans dead and 259,828 wounded, Ralph
Blumenthal “American Deaths in Combat for the Week Drop to 70”, New York Times, December 5, 1969, p. 18.
18
As with the previous example, these findings suggest not that news of a casualty somehow changed people from
opponents to supporters of the war, but only that support was not lower among those who knew someone who died
or was wounded in Vietnam. The higher level of support among those who knew a Vietnam casualty is likely a
product of selection bias—those who knew a casualty may have had different propensities to support the war quite
apart from their connection to a dead or wounded serviceperson.
19
The casualty sensitivity question appeared in the Virginia Slims American Women’s Poll 1970, available at the
Roper Center as study USHARRIS.70VSF2.RF10B. The estimate that 52% of Americans personally knew someone
who died or was wounded in Vietnam came from an August 1969 Gallup/Newsweek poll, Roper Center study
number USGALNEW.696988.Q16.
Time or Consequences? — 17
with time.
Isolating the Effects of Casualties and Time
Observational studies supporting the casualty sensitivity hypothesis generally examine
relationships between casualties and war support in one conflict at a time. But as many observers
have pointed out, this standard approach makes it impossible to separate the effects of
cumulating war deaths from the effects of war duration. Except in the cases of wars without
friendly deaths, there will always be a positive correlation between the occurrence of combat
deaths and the passage of time in conflict. As so aptly stated by Chairman of the Joint Chiefs of
Staff General Martin Dempsey, “Time generally translates into casualties in my line of work”
(Martin 2013).
In data collected for this study and described in detail below, Pearson correlations between the
level of war support registered in a survey and the number of cumulative casualties from the start
of the war to the date of that survey tend to be strongly negative: –.63 for the Korean War, –.93
for Vietnam, –.83 for Afghanistan, and –.81 for Iraq. This is the sort of relationship that would
be expected if mounting battle deaths were driving support downward. However, there are two
difficulties that muddy the picture. First, although war support tends to go down as battle deaths
go up, this was not the case for Kosovo, Libya, or the Persian Gulf War. During Kosovo and
Libya, support went down even when U.S. forces sustained no combat deaths. During the Gulf
War, support went up as casualties mounted, and the correlation between support and cumulative
casualties is a healthy +.52. Partly this is because support had already dropped so much before
the fighting had even started: the onset of fighting was accompanied with a spike in war support
that was sustained through the end of the conflict.
The second difficulty is that the negative coefficients are just about as large when war support is
correlated with the elapsed number of weeks since the start of a conflict: –.61 for Korea, –.90 for
Vietnam,–.91 for Afghanistan and –.81 for Iraq (see Table 1). When elapsed time is used in place
of cumulative casualties, support trends for Kosovo start looking like those from other wars, with
a –.72 correlation between support and elapsed time. Support for the Persian Gulf War remains
positively correlated with elapsed time (.17), but this relationship is both small and
nonsignificant due to the support rally at the start of the air war.
INSERT TABLE 1 HERE
These simple correlations between support, elapsed time, and casualties fail to control for the
possibility that both casualties and time may have independent influences on war support.
Statistical techniques are available to parse out explanatory power among these rival factors, but
previous attempts to do this have nearly all been done within the context of a single war, when
mounting casualties and the passage of time are so hopelessly entangled that their effects become
difficult to sort out. One study that did attempt to control for elapsed time in conflict revealed an
apparently odd finding: Gartner and Segura’s reanalysis of Mueller’s Korea and Vietnam data
controlled for both and found that elapsed time had a more significant effect on support for war
than cumulative casualties (1998, 290, 294).
Time or Consequences? — 18
Within these observational studies, time is usually seen as a nuisance: time is so highly
correlated with casualties that their effects become impossible to sort out within a single war.
Expecting, following the lead of Mueller, that casualties must be the variable that matters,
researchers usually presume that time is a spurious relationship that can simply to ignored. This
study takes a different approach: pooling the entire population of war support measures from
every major American military conflict since World War Two, and leveraging the uneven tempo,
levels and rates of American deaths to disentangle the separate effects of time and consequences.
Data
Wars Included in the Analysis
Analyzing the dynamic properties of war support requires narrowing the range of relevant
military operations to major uses of force that lasted long enough for opinion dynamics to
emerge. The United States has fought several smaller-scale military operations that lasted just
days or weeks, and at a lower level of combat intensity than required to sustain widespread
popular attention. The comparable population of interest is therefore defined as major American
wars that occurred during the era of modern opinion polling, involving a significant expenditure
of American military resources in sustained combat operations lasting longer than two months.20
All nine such conflicts are analyzed in this study:





20
The Korean War began on June 25, 1950, when North Korea invaded South Korea. After
the lines of fighting solidified in 1951, armistice talks began and eventually produced a
cease-fire agreement that went into effect on July 27, 1953.
The Vietnam War is considered to have begun on August 7, 1964 when the U.S.
Congress passed the Gulf of Tonkin Resolution that authorized military force in
Southeast Asia, following an alleged North Vietnamese attack on a U.S. naval destroyer.
This marked the start of large-scale combat operations that continued until March 29,
1973, when the last American combat forces left Vietnam.
The Persian Gulf Crisis (encompassing Operation Desert Shield and Operation Desert
Storm) began on August 2, 1990, when Iraqi forces launched a surprise invasion of
neighboring Kuwait. An aerial campaign against Iraqi forces began on January 17, 1991,
and continued for several weeks until the awaited ground invasion took place on
February 25. Iraqi forces in Kuwait were quickly defeated and President Bush declared
the crisis over on February 28, 1991.
The Kosovo War began on March 23, 1999, when the Secretary General of NATO
initiated a military air campaign under the name Operation Allied Force to drive
Yugoslav government forces out of the province of Kosovo. American military aircraft
took a leading role in the NATO air campaign, which was suspended on June 11, 1999,
when Yugoslav forces began withdrawing from the Kosovo province.
“Operation Enduring Freedom” (OEF) was the umbrella title given by the American
government to a collection of military operations that were initiated in response to
This definition excludes the 1984 invasion of Grenada, in which major combat operations lasted roughly two
days, as well as the 1989 operation in Panama, where combat operations lasted about two weeks from start to finish.
It also excludes American military deployments to such places as the Quemoy and Matsu Islands (1958-65), the
Dominican Republic (1965-6), Somalia (1992-5), and Haiti (1994-5), which were primarily peacekeeping or
humanitarian missions rather than combat operations. Throughout the book, the term “war” in the common sense,
rather than the constitutional sense of Congressionally-authorized military action.
Time or Consequences? — 19



terrorist attacks on September 11, 2001 against the Twin Towers in New York City and
the Pentagon in Washington DC. OEF’s primary focus of became known as the
Afghanistan War, while a number of smaller but related operations in other parts of the
world came to be known as the Global War on Terror. Operation Enduring Freedom
began on October 7, 2001 with American and British strikes against Taliban forces in
Afghanistan. The American combat mission officially ended with the closing of NATO’s
International Security Assistance Force’s Joint Command on December 28, 2014, making
Afghanistan the longest sustained large-scale combat operation in American history. The
Global War on Terror continues at the time of this writing, encompassing a diverse set of
limited-scale special forces operations in a wide range of areas including the Horn of
Africa, the Trans-Sahara region of Africa, Central America, and the Philippines.
The Iraq War began on March 19, 2003, with an American bomb strike against the
reported location of Iraqi leader Saddam Hussein. American combat operations were
officially declared over on September 1, 2010, and the Iraq war was renamed “Operation
New Dawn” to mark the drawing down of active military operations by American forces.
The last American combat forces left Iraq on December 15, 2011, bringing the Iraq War
to an official end.
The Libya campaign was initiated by a multinational coalition on March 19, 2011 to
enforce a UN-sanctioned no-fly zone against the Libyan government during that
country’s civil war. Operation Unified Protector was the name adopted by NATO after it
took over the military operation a week later, and American forces provided extensive air
and naval support to the operation. The combat operation ended on October 31, 2011
when the UN Security Council voted to cease the air campaign following the death of
Libyan leader Muammar Gaddafi.
The ISIS campaign (“Operation Desert Resolve”) began on June 15, 2014 when President
Obama ordered US forces to be dispatched to the area of ISIS operations in Syria and
Iraq. Combat operations started with an air campaign that opened on August 8, 2014 and
soon evolved to include ground forces in special operations roles. At this time of this
writing, the ISIS campaign was still underway.
War Support
This project has compiled every comparably-worded war support question asked of a national
random sample of Americans from the Korean War to the present. War support questions
measure whether the conflict should have been started at all.21 Although there is no single,
authoritative way of measuring war support—slightly different questions produce slightly
different results—four common measures of war support are especially abundant in the historical
record:

21
“Right decision” questions, such as “Do you think the U.S. made the right decision or the
The war support measures used here tap what John Mueller called “generalized support for the war” (Mueller
1973, 43). There are important limitations to using any particular war support question as a definitive measure of
popular opinion about a conflict. Slight differences in the wording of a question can sometimes produce quite
different trends (e.g., Mueller 1973, 44-50). And any particular wording can be read in multiple ways: a person
might come to believe a decision to go to war was mistaken while still supporting American involvement in the
conflict (e.g., Berinsky and Druckman 2007). Therefore it is important to clarify that the mistake question and its
variants measure only support for being involved in the first place. Support for how the war is currently being
fought, who is leading the fight, or whether it should continue to be fought, is something else.
Time or Consequences? — 20
wrong decision in using military force in Afghanistan?” Those saying the U.S. made the
right decision are counted as supporting the conflict.

“Worth fighting” questions, like “As things stand now, do you feel that the war in Korea
has been worth fighting or not?” Those saying the war has been worth fighting are
counted as supporting the conflict.

“Not a mistake” questions, as with “In view of the developments since we entered the
fighting in Vietnam, do you think the U.S. made a mistake sending troops to fight in
Vietnam?” Those saying the war was not a mistake are counted as supporting the
conflict.

“Favor the war” questions, such as “Do you favor or oppose the U.S. war with Iraq?”
Those favoring the war are counted as supporting the conflict.
There are many variations within each type of question, but all share the same basic wording. In
all but one major American war since 1950, sufficient numbers of identically-worded war
support questions have been asked to permit the broadest possible comparison across and within
wars. The lone exception is the Global War on Terror, which has mainly been polled using leader
support rather than war support questions. These leader support questions follow the basic
pattern “Do you approve or disapprove or the way George W. Bush is handling the war on
terrorism?” While not strictly equivalent to war support trends, movement within these leader
support trends can be compared to the war support dynamics for every other major war following
World War II. In all analyses that follow, I ensure that pooled data findings are essentially the
same whether questions from the Global War on Terror are included or not.22
The distribution of data points is weighted toward more recent (hence, heavily-polled) and longer
wars. The largest number of data points come from the Iraq War (n = 678, 29 different trends,
45.2% of total data points), followed by the Global War on Terror (n = 488, 21 trends, 32.6% of
total), Afghanistan (n = 166, 13 trends, 11.1% of total), the ISIS campaign (n = 50, 12 trends,
3.3%), the Persian Gulf War (n = 33, 3 trends, 2.2%), the Korean War (n = 31, 4 trends, 2.1%),
Vietnam (n = 26, 2 trends, 1.7%), Libya (n = 18, 6 trends, 1.2%), and Kosovo (n = 9, 2 trends,
0.6%).23
War Deaths
All casualty data used in this analysis are of American deaths only. Trends in Korean War deaths
were derived from the Korean Combat Casualty File, 1950-57, while those for the Vietnam War
come from the Southeast Asia Combat Area Casualties File.24 Casualty data for all other
22
In the pooled war support dataset used in this analysis, about 23% of available data points are variants on the
“right decision” question, 14% are versions of the “worth fighting” question, another 12% are of the “not a mistake”
variety, 18% are “favor the war” variants, and the remainder are “presidential handling” questions for presidents
Bush (21%) and Obama (11%).
23
For this analysis, only opinion questions asked before the war started or while it was still underway are included.
Questions asked after the end of hostilities are excluded from this analysis.
24
The authors are grateful to Scott Gartner for making these data files available to us.
Time or Consequences? — 21
conflicts comes from official Department of Defense casualty files available from
https://www.dmdc.osd.mil/dcas/pages/casualties.xhtml.
The analysis uses three variables to model different sources of casualty effects. The cumulative
(or logged cumulative) count tracks American deaths from the start of each conflict until the
week that each survey was fielded. The marginal casualty trend (following Gartner 2008a) is
represented by changes in the number of American deaths that occurred in the most recent 30
days leading up to each survey relative to the previous 30 days (rising deaths = 1, falling deaths
= –1, and no change in deaths = 0). Finally, an interaction between the cumulative (or logged
cumulative) casualty count and the marginal casualty trend captures any remaining impact that
joint effects of marginal and cumulative casualty trends might have. To simplify comparisons,
casualty variables will be described as either cumulative or logged: cumulative war deaths refers
to the total number of war-related deaths at the time war support was measured, while logged
war deaths refers to the base 10 log of cumulative deaths.
Elapsed Time
The duration of each conflict is measured as the elapsed number of weeks from the start of a war
until the survey was in the field. Visual inspection of war-specific scatterplots between war
support and elapsed years in conflict revealed that initial periods of declining support were often
followed by a period of stability and then (if the conflict lasted long enough) a second period of
declining support. To capture this regular pattern between war support and war duration, elapsed
time in conflict is modeled quadratically with three terms: elapsed weeks, elapsed weeks
squared, and elapsed weeks cubed.
Additional Controls for Question Wording and House Effects
Because wording differences across war support measures can influence the apparent level of
support, unless otherwise noted all regressions include a set of five question wording dummy
variables (capturing five of the six main wording variants, with Gallup’s “not a mistake”
question as the reference category). Levels of apparent war support are also affected by
variations in sampling frames and interview procedures used by different survey organizations.
Unless otherwise noted, these “house effects” (Smith 1978) are controlled in all regressions with
a set of 15 dummy variables (capturing 15 of the survey houses collecting data, with the Gallup
Organization as the reference category). These coefficients are not of direct relevance to the
analysis and so are not shown in the tables below, but they are available upon request.
Findings
Assessing the relationships between war support, casualties, and elapsed time in conflict would
ideally employ econometric methods appropriate to time series analysis. However, the historical
record of comparable war support data does not lend itself to such methods. Data from each war
come from differently-worded questions, varied survey organizations, and multiple data series
with unevenly spaced (and often infrequently recorded) measures. Such diversity does not easily
conform to the strict requirements of econometric methods, and this is one reason why so much
research on war support shifted after Mueller to focus on presidential approval measures that
tend to be collected regularly over long spans of time, even if they are not direct measures of war
support.
Time or Consequences? — 22
Using the full population of comparably-worded historical data on war support requires a less
restrictive approach than standard econometric methods require. I therefore explore the data in
two different ways and assess the robustness of results across two different estimation strategies.
The first approach is to use multiple OLS regression with appropriate controls to examine
whether time or casualties better predict levels of war support. The second approach is to
examine changes in war support from Time 1 to Time 2 by differencing the measures of war
support from within each opinion trend, and then dividing those differences by the number of
elapsed weeks between measures of war support to yield an average change in war support per
week. The same transformation is applied to casualty data, allowing a clean comparison of
average change per week in casualty levels and war support that is purged of all the confounding
variables which must be explicitly controlled for in the first approach.
Since the theoretically important moderating variables relevant to the “conditional impact” and
“minor impact” perspectives are almost completely absent from the historical record of war
support data, the estimation strategy is designed to infer the average size of casualty effects.
Once elapsed time in conflict is controlled for, any remaining large correlations between
casualties and war support can be taken as evidence supporting the “major impact” perspective,
smallish or nonexistent correlations can be taken as evidence supporting the “minor effect”
perspective, and middling-level correlations indicate support for the “conditional impact”
perspective, assuming—as it does—that the impact of casualties on war support is sometimes
large and sometimes small.
The analysis proceeds by first modeling levels of war support before turning next to modeling
changes between Time 1 and Time 2 in war support measures within the 92 question trends
nested covering nine wars.
Modeling Levels of War Support Using the Pooled Dataset
Turning first to examine how support for war is separately affected by time and casualties, Table
2 shows that when entered in separate equations and without any additional controls, both
elapsed time and casualty dynamics appear to be significant predictors of war support levels.
However, elapsed time in a conflict (Model 1) accounts for more variance in support levels than
either cumulative (Model 2) or logged cumulative American deaths (Model 3). Although
cumulative deaths are negatively and significantly correlated with levels of war support, the
effects of casualty trends are insignificant in Model 2, contrary to the argument advanced by
Gartner (2008a). The set of three cumulative deaths variables together account for only 14
percent of variance in levels of war support. Turning to Model 3, higher levels of logged
cumulative deaths are likewise associated with lower support, and the trend variables in this
model are significant predictors of support levels.
INSERT TABLE 2 ABOUT HERE
The curious thing in Table 2 is why the logged number of cumulative deaths should have nearly
three times the predictive power as the simple count of cumulative deaths. One possibility,
originally advanced by Mueller (1973), is that casualties incurred early in a war somehow matter
Time or Consequences? — 23
more than later casualties. Mueller observed (1973, 59-63) that because support declined at a
faster rate in the earlier stages of the Korean and Vietnam wars than in the later stages, the public
seemed more sensitive to earlier casualties than to later casualties. For this reason, Mueller
concluded, logged deaths should provide a better fit to the support data than unlogged deaths
(and they did). However, disaggregating the Korea and Vietnam support data tells a different
story: the high negative correlation between logged casualties and support comes entirely from
the later stages of these wars, not the earlier stages. If we look only at measures of support from
Korea and Vietnam that were registered before the death toll in either conflict had reached
10,000, we find that the correlation between support and logged deaths is positive and
nonsignificant (r = .23, p = .49, n = 11). In contrast, the correlation between support and logged
deaths for survey data collected after 10,000 deaths had been reached in both wars is –.65 (p <
.001, n = 46).25 In the Korea and Vietnam data, the tight fit between logged deaths and levels of
support is driven more by what happened later in those wars than by any early sensitivity to
combat deaths.26
There remains a second explanation for the improved fit of logged deaths relative to unlogged
deaths: logging the number of war dead makes casualties more collinear with time (as vigorously
demonstrated by Gartner and Segura 1998). In the pooled support data from all nine wars,
cumulative deaths and elapsed weeks in a conflict are entirely uncorrelated with one another (r =
+.02, p = .50). In contrast, the correlation between logged deaths and elapsed weeks is a healthy
+.35 (p < .001).27 More important, the correlation between logged deaths and elapsed weeks
grows larger as time passes. In the Korea and Vietnam data analyzed by Mueller, the correlation
between elapsed time and logged casualties is only +.48 for support measures taken before the
first 10,000 deaths, but +.81 for support measures taken after the initial 10,000 deaths. This
suggests that the apparent superiority of logged casualties over the simple cumulative count
appears mainly to be a statistical artifact of its stronger association with elapsed time in conflict.
If logged casualties only appears to be a strong predictor of war support because of its covariance
with elapsed time, then entering both time and war deaths together should leave logged deaths
with about as little explanatory power as unlogged deaths. And to the degree that the relationship
25
In contrast, the relationship between support and elapsed time shows no such discontinuity. For Korea and
Vietnam support measures taken before 10,000 war deaths had occurred, the correlation between support and
elapsed time is –.60 (p = .06). For support measures taken after the initial 10,000 deaths, the correlation is –.67 (p <
.001). Once again, this shows that logging the casualty count obscures the unique contributions of elapsed time and
mounting deaths.
26
Feaver and Gelpi (2004, 136-9) use a similar division of earlier and later support data from the Vietnam to argue
that American casualty sensitivity increased following the Tet Offensive, which began in late January 1968. In the
data considered here, the correlation between weekly marginal deaths and weekly change in support Prior to Tet was
–.16 (p = .63, n = 11). From the start of Tet until the end of the war, that correlation was a slightly larger –.32 (p =
.28, n = 13). However, neither correlation approached even marginal levels of significance, suggesting again that the
relationship between casualties and support is not a primary determinant of support dynamics. More important,
although Feaver and Gelpi argue that “casualties had a much more corrosive effect on presidential approval” after
Tet, this claim ignores the fact that of the roughly 30-point decline in public support over the course of the Vietnam
War, two-thirds of that overall decline had already occurred before Tet (see Table 4.12 in Feaver and Gelpi 2004, :
137). Tet may have had an additional impact on undermining support, but as Mueller and others have already
pointed out, public support for the Vietnam War had collapsed long before Tet.
27
Logging the casualty count not only makes it more collinear with time but also makes it less collinear with the
raw casualty data, so that logged and unlogged measures of cumulative KIA correlate at only .52 across all six wars.
Time or Consequences? — 24
between support and unlogged war deaths is an artifact of the tendency for casualties to go up
over time, then controlling for both time and casualties should reduce the apparent impact of
cumulative war deaths beyond the already smallish predictive power revealed in Table 2. This is
precisely what happens, as shown in Table 3.
Table 3 reports OLS coefficients from the final models with simultaneous entry of all variables,
but also notes the incremental amount of variance accounted for when sequentially entering each
block of independent variables. To capture average levels of war support for different conflicts,
the first block consists of dummy variables for each war (omitting Iraq). Relative to Iraq, Korea,
Kosovo, and the Libya campaign were especially unpopular, while Afghanistan and the Global
War on Terror were relatively more popular. These eight war-specific dummies together account
for around 13% of variance in war support, confirming that the American public exercised
considerable discretion in its support for these nine wars (Jentleson 1992; Jentleson and Britton
1998).
INSERT TABLE 3 ABOUT HERE
The next block consists of the three variables capturing elapsed time at war, entered second on
the grounds that in every war considered here, the passage of time in conflict was theoretically
(and causally) prior to the occurrence of war dead among American combat forces. Time
accounts for fully 58% of variance in levels of war support, which shows that the dynamics of
war support are remarkably consistent across wars even though these conflicts vary considerably
in levels of average support.
Casualty variables form the third block, which are properly interpreted as the effects of casualties
purged of the effects of time. Table 3 shows that once elapsed time is taken into account, the
tally of war dead accounts for less than one percent of additional variance in war support. In the
case of cumulative deaths, the direction of significant casualty coefficients is opposite what
would be expected by the casualty sensitivity literature. In the cumulative deaths equation
(Model 2), the cumulative count is statistically insignificant while the marginal trend is
significant at conventional levels but in the wrong direction: rising numbers of recent casualties
are associated with (slightly) higher levels of war support. In the logged deaths equation (Model
3), not only is the marginal trend coefficient likewise in the wrong direction, so that rising
casualties predict higher support at lower levels of logged deaths, but the logged cumulative
casualty count is positively associated with war support levels. Not only are the substantive
effects of casualties small once elapsed time is controlled, but their relationship to support levels
makes no obvious theoretical sense.
Finally, a fourth block of independent variables controls for differences in survey procedures
across polling organizations—known as “house effects” to survey researchers—and differences
in how the survey questions measuring support were worded. These sets of controls accounted
for an additional nine percent of variance in war support.
Taken together, these four blocks of variables account for about 80% of variance in levels of
support for war, regardless of whether casualties are included in the models or not. The
importance of elapsed time in shaping support for war is seen not only in incremental
Time or Consequences? — 25
contributions to variance explained, but also in the consistent size and direction of the time
coefficients across models, which shows that the time coefficients are unaffected by the inclusion
of casualty variables. These findings are robust across a wide range of model specifications,
including models that drop support measures from each war in turn from the set of pooled data.
The same general pattern of relationships is observed with models run on data from each war in
isolation from the others, although obviously it is difficult to draw firm conclusions when using
models with large numbers of coefficients on data from the wars with smallish numbers of
observations.
In light of the odd behavior of the logged deaths variables from Model 3, the most appropriate
model for assessing what moves support for war seems to be Model 2, which accounts for both
time and (unlogged) cumulative war deaths. The reason why this model accounts for 80% of the
variance in war support can be seen in Figures 7 and 8, which present scatterplots of war support
by elapsed time in conflict, scaled here in elapsed years to simplify interpretation. “Unadjusted
support” data in Figure 7 shows the raw measures in the pooled dataset, while “adjusted support”
data in Figure 8 show the same measures after adjusting for question wording effects, house
effects, and war-specific average levels of support.28 The “adjusted data” are therefore purged of
question-, house-, and war-specific differences to reveal the underlying relationship between
support and elapsed time in conflict that is being modeled in Table 3. It is important to note that
the higher density of data points in the left side of the figure is not easily seen when displayed in
this way. Fully 88.5% of the war support measures in the pooled data set are captured in the
period from zero to nine elapsed years since the start of conflict (since most American wars have
historically been shorter than nine years). Support measures from nine years or later are from
Afghanistan, the War on Terror, and Iraq only.
Displaying the entire population of 1,499 comparable measures of war support from Korea
through ISIS (as of July 2016), this figure shows graphically what Table 3 demonstrated
statistically: the dynamics of war support in every one of America’s major wars during the past
66 years has been closely tied to elapsed time in conflict. The longer the conflict continues, the
lower support drops. Additional analysis (not shown here) reveals that war support declined at
about the same rate in each of these wars, which explains why the three time variables should
account for one-third of variance in Table 2 and more than half of variance in Table 3 after
controlling for average levels of support. As war marches on, levels of public support fall away
until in several cases they eventually bottom out against a “floor” of hard-core hawks. In the case
of Vietnam, which until recently had been the longest U.S. war on record, this floor effect kept
support from dropping much below 30% for the duration of the war. Our current longest war in
Afghanistan is charting new territory in this regard, but limitations in the available record of war
support make it difficult to discern whether support has bottomed out or still has additional room
28
All of these adjusted values use the average level of war support for Iraq and Gallup’s “not a mistake” question in
Model 1 as the reference point. For example, a value of 75 (meaning, 75% of the American population supports the
conflict) for the Afghanistan War that was from the “right decision” question mentioned earlier in text and that was
in a survey fielded by the CBS/New York Time organization would receive an adjusted score of 72.71 in the
following way: 75-16.05 (the reverse of the Afghanistan coefficient in Table 3 Model 1)+2.27 (adjusting for the
“right thing” question type)+11.49 (adjusting for the tendency of CBS/NYT surveys to have lower average levels of
support) = 72.71.
Time or Consequences? — 26
to fall.29
INSERT FIGURES 7 AND 8 ABOUT HERE
The evidence produced by this analysis consistently suggests that cumulative war deaths have
little effect on war support once time is taken into account, but this evidence is less than fully
satisfying. One weakness of the analysis is that a conventional OLS model is being used on data
that clearly have large amounts of serial autocorrelation. Replicating the analysis in Table 4 with
an OLS autoregressive model that includes lagged values of the support variables (not shown)
produces the same pattern of results, but even this additional robustness check is less than fully
satisfying given that the observations are so unevenly spaced in time. A second weakness is that
this approach necessarily pools all wars together, assuming that other differences among the wars
besides time and casualties can otherwise be ignored.30 It is possible that the American public
has become more sensitive to casualties in the post-Cold War era. If so, the pooled analysis could
obscure the possibility that Americans today may be sensitive to smaller changes in casualty
levels, or more sensitive to the same casualty levels. Within the pooled data, any such pattern
would tend to be washed out by the larger casualty levels of the Korean and Vietnam wars.
What is needed is a way to investigate whether these patterns obtain within the bloodiest wars
considered separately (Vietnam and Korea) and also within the separate set of lower-casualty
and more recent conflicts. There are too few data points from the Korean and Vietnam wars to
replicate the full model separately for each of these conflicts, but there remains another way to
study the same dynamic within wars by looking at what moves changes in support rather than
what predicts levels of support.
Modeling Changes in War Support Using the Pooled Dataset
A scarcity of data problem has long plagued the literature attempting to explain the dynamics of
public support for war. For many wars, and particularly for the high-casualty wars in Korea and
Vietnam, the data points are so few in number and spread so unevenly across time that their
dynamics cannot be studied using complex models or sophisticated methods for time series
analysis.
But there is an approach to analyzing the effects of casualties on war support within these data
that has been overlooked by previous scholarship: whether the number of war deaths that occur
between any two measures of war support is simply correlated with the direction of change in
war support from the first measure to the second. If the casualty sensitivity hypothesis is right,
then we should tend to see large drops in support when larger numbers of American personnel
have died in the period between support measures, and smaller drops in support when fewer
American deaths have occurred between t1 and t2 support measures. This approach is used
29
Afghanistan is of course the country’s longest war, but the polling record on Afghanistan support has been too
intermittent to conduct an analysis of support changes. Despite a spate of early polling in late 2001 and early 2002,
little subsequent polling was done on Afghanistan support until 2004 and 2005, and even now such questions are
asked so intermittently as to be unsuitable for such an analysis.
30
The findings could also be challenged on the basis that casualties should be entered first in the model, although
this would only make a difference to the allocation of explained variance if logged casualties were used. The
coefficients would be the same regardless, and the coefficients tell a clear story of minimal influence for casualties.
Time or Consequences? — 27
extensively in the literature on the dynamics of collective opinion (e.g., Page and Shapiro 1983;
Page, Shapiro, and Dempsey 1987; Page and Shapiro 1992; Shapiro 1998; Shapiro and Page
1988) and has been used to examine the impact of news coverage on public support for war
(Althaus and Coe 2011), but never has been used to examine the casualty sensitivity hypothesis.
This is, of course, what econometric methods would do with entire series if such methods could
be applied to these data. The novel aspect is both pooling data across wars and dropping
multivariate analysis altogether to make such comparisons appropriate to the small number of
unevenly-spaced observations that several of these wars contain. Doing so requires an important
correction is needed before marginal casualty rates can be compared to changes in public
support: the raw differences in support levels and the raw number of marginal casualties that
occurred between support measures need to be standardized into common time periods. The
solution used here is dividing both quantities by the number of intervening weeks between each
pair of support measures. Within each question trend, differencing the support measures and then
dividing that difference by the number of intervening weeks between the two measures of
support yields the average weekly change in percent supporting the war that occurred between
each pair of support measures. Likewise, dividing the number of war deaths that occurred in the
period between each pair of support measures by the number of weeks separating those support
measures yields the average number of war deaths per week that occurred between support
measures. This simplistic approach makes far fewer assumptions about the nature of the
underlying change process than required by the Kalman Filter or related approaches (e.g.,
Sidman and Norpoth 2012). Because differencing drops the first observation within each support
trend, the total number of change observations is reduced to 1,407.
Figure 9 plots the weekly average change in war support against the weekly average number of
American combat deaths for all wars combined. This figure shows that there is no detectable
relationship between these variables: the correlation between average change in support per week
and average American war deaths per week across all wars combined is –.01 (p = .58). Most of
the larger average changes in war support occur when casualties are relatively low, and of course
this is largely because few of these conflicts have suffered the high casualty rates of Korea and
Vietnam. It is therefore possible that the utter lack of correlation between changes in deaths and
changes in war support is simply an artifact of including the two high-casualty wars, which
includes every data point with more than 70 American deaths in an average week.
INSERT FIGURE 9 ABOUT HERE
Figure 10 therefore shows the same scatterplot when the observations from Korea and Vietnam
are removed (note the change in X-axis value, which had a maximum of 500 deaths per week in
Figure 9 but only 60 in Figure 10). Removing Korea and Vietnam makes the relationship
between marginal casualties and changes in war support even more clear-cut: the new correlation
between average change in support per week and average American war deaths per week across
all wars combined is now reduced to an utterly nonsignificant –.003 (p = .92). There simply is no
relationship here to be found. Regardless of how many Americans died in the interim, the most
common value for the differenced measure of war support is approximately zero.
INSERT FIGURE 10 ABOUT HERE
Time or Consequences? — 28
To ensure that this finding is no artifact of composition—the lion’s share of observations in
Figure 10 are from Iraq and the War on Terror—Table 4 reports correlations separately for each
conflict (except for Kosovo and Libya, which experienced no combat-related American deaths).
In seven of the nine conflicts, the correlation between average weekly deaths and average weekly
change in war support is statistically nonsignificant. For the ISIS campaign, the correlation is
significant and positive, but the (current) cumulative total of 19 American deaths places the
substantive importance of this correlation into question. Only for Afghanistan is there a
statistically significant negative correlation between these two variables.31 Squaring the
correlation, we can say that at best, American war deaths could potentially account for up to 3.6
percent of variance in American support for the war in Afghanistan. Yet this also seems unlikely,
as the higher-casualty and contemporaneous conflict in Iraq has a corresponding correlation of
just –.03.
INSERT TABLE 4 HERE
Table 5 presents the same data in a regression framework that adds war-specific dummy
variables. This analysis confirms that while the coefficients for average weekly change in death
toll from one measure of support to the next are all negatively signed (as expected by the
casualty sensitivity argument), none of these coefficients even remotely approach conventional
levels of statistical significance (respectively, the p values for the death toll coefficients are .25
in the first model, .30 in the second, and .45 in the third). Of the eight war-specific dummy
variables (leaving Iraq as the contrast category), only the Persian Gulf coefficient attains
statistical significance, and this is merely an artifact of the jump in war support that accompanied
the start of combat operations (and therefore preceded the occurrence of American combat
deaths). The same pattern of relationships holds when cases from the War on Terror are omitted
(on grounds that the war has lasted so long and has produced just 130 American deaths as of July
2016), and when cases from Korea and Vietnam are omitted (on grounds that their high average
casualty levels might obscure patterns in less-lethal wars). In short, this approach to analyzing
the entire population of war support measures from Korea to the present offers no supporting
evidence at all that the American public is sensitive to wartime casualties once conflict duration
is controlled for.
INSERT TABLE 5 HERE
Conclusion: Less Than Meets the Eye
This paper began with the observation that 1,000 American deaths appeared to be associated
with popular support for war in three different ways across three different conflicts: hardly at all
in Vietnam, moderately in Iraq, and to an extreme in Afghanistan. Although it might seem that
Americans must be more sensitive to the costs of war today than they were during Vietnam, this
study suggests otherwise. In all three wars, popular support dropped roughly 30 percentage
31
The correlation shown for Korea omits the two cases of sudden decline in war support following China’s entry. If
those two change cases are added in, the Korean correlation rises to a significant –.57. However, since the earlier
analysis has shown that the American public had no knowledge of these casualties at the time support declined, the
choice to omit them from this analysis seems warranted.
Time or Consequences? — 29
points over roughly eight years, even though over 50,000 Americans were killed over this period
in Vietnam, nearly 5,000 over eight years in Iraq, and nearly 1,000 American deaths in the first
eight years of the Afghanistan war.32
For many Americans, the most visible consequence of war should be the lost lives of so many
who were sent to do the fighting. Yet after controlling for the passage of time, this study found
that cumulative and marginal casualties registered little additional influence on levels of war
support. Contrary to a long line of observational and experimental studies, there is no consistent
evidence in this analysis that the rising toll of American war deaths exerts a major or even
conditional impact on levels of popular support for war in the United States.
This is not to say that Americans are indifferent to wartime losses. Quite the contrary, evidence
from laboratory experiments (e.g., Gartner 2008a; Gelpi 2010; Kriner and Shen 2012) and from
survey experiments (e.g., Gelpi, Feaver, and Reifler 2009) has abundantly demonstrated that the
American public appears quite sensitive to casualties when casualty information is made highly
salient and is presented in unrealistically simplified information environments that shield other
considerations from view. But the casualties that matter outside experimental settings are the
casualties that Americans have heard and cared about.
There is a straightforward way to reconcile findings from previous studies using hypothetical
casualty scenarios with this study’s evidence that the number of cumulative casualties
nationwide has no obvious impact on levels of war support: in the real-world settings, casualty
sensitivity is observed at the local level when local casualties are involved, but the effect does
not persist. These casualties have an immediate negative effect on war support among those who
live near the hometowns of those who have fallen. However, this negative effect appears to be
temporary (Althaus, Bramlett, and Gimpel 2012). Information about war casualties appears to
produce short-term priming effects rather than long-term attitude change, a relationship that is
inconsistent with the information updating perspective but in keeping with a long line of
literature in public opinion and political psychology.
The hypothetical scenarios presented to experimental subjects seem likely to be generating
priming effects too, rather than attitude change. If the negative effects of casualties do not
persist, then the ebb and flow of casualties over the course of a conflict should be unrelated to
the dynamics of war support. Casualty sensitivity at the local level is almost certainly a
contributing factor to loss of public support for wars, but it cannot be the main factor driving
support for war. The numbers of casualties across wars are too varied, while patterns of support
across wars are remarkably consistent over time.
This analysis suggests that time, rather than consequences, appears to be the main engine
propelling the dynamics of public support for war. Although the nine wars varied considerably in
their levels of average public support, levels of support for all nine wars declined in parallel as a
function of elapsed time in the fight. This result squares neatly with what is known about the
32
Gallup’s “not a mistake” trend from Vietnam started at 60% in September 1965 and dropped 31 points to 29% in
January 1973. Gallup’s “not a mistake” trend from Afghanistan started at 89% in November 2001 and dropped 29
points to 60% in November 2009. Gallup’s “not a mistake” trend for Iraq starts at 75% in March 2003 and dropped
34 points to 41% support in August 2010.
Time or Consequences? — 30
psychology of processing information about war casualties. It is consistent with the tendency for
American news coverage of war—whether newspaper or visual news reporting—to minimize
attention to (or concern about) wartime losses by the United States. This result is also consistent
with the now well-documented finding that local casualties can have an impact on local levels of
war support (e.g., Gartner, Segura, and Wilkening 1997). In part this is because such effects
appear to be short-lived (Althaus, Bramlett, and Gimpel 2012), and in part because the
geographic distribution of local casualties is limited to particular parts of the country (Kriner and
Shen 2010) and can only cumulate to affect national-level measures of war support when
casualty levels are particularly high.
While this finding directly contradicts the “major impact” and “conditional impact” perspectives
on casualty sensitivity, it is as yet unclear whether it is consistent with the main argument in
support of the “minor impact” perspective: that American opinion about war is more strongly
influenced by elite cues about the war than by war-related information (Berinsky 2009; Baum
and Groeling 2010; Zaller 1992).
Time is more highly correlated with changes in war support than casualties, but what to make of
time is at present an open question. Time has been recognized as a “nuisance” confound since
the earliest research on war support (e.g., Verba and Brody 1970). This paper shows that
casualties have much less of an impact than earlier research surmised, once proper controls are
made for both the accumulation of casualties and the passage of time.
Does this mean falling back on time as a sort of ill-defined placeholder, allowing us to describe
dynamics without permitting us to explain them? Perhaps. But if cumulative casualties don’t
trigger increased opposition to war, it is hard to understand what other cumulating variable
might. Some have suggested elite discourse, but this analysis suggests that war support always
falls in accord with time, regardless of the timing of elite disagreement. Economic costs of war
are surely not perceived accurately if the American public cannot even tell roughly how many of
its soldiers have been killed in the fighting. But it may be that time is, after all, an explanatory
factor. The duration of a war could serve meaningfully as an informative heuristic, quite apart
from any covariance it might share with other important variables.
There is insufficient space to give this latter topic the detail it deserves at present—that will have
to wait until my forthcoming book comes to light. But there is a clear policy implication if time
matters more than casualties: for support to be sustained, wars need to be finished quickly rather
than cleanly.
Time or Consequences? — 31
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Figure 1: Percentage of War Stories Showing Film of Allied Casualties 40
Allied Wounded
Percentage of War Stories
Allied Dead
30
20
10
9.7
6.8
5.6
2.5
5.6
1.6
3.1
4.6
3.9
3.9
0
WW1
WW2
Korea
Vietnam
Iraq
Note: All but Iraq are newsreel stories; Iraq is CNN coverage from the 2003 invasion period.
Time or Consequences? — 36
Figure 2: Mean Visual Intensity of Casualty Film 3.0
Film of the Wounded
Film of the Dead
Mean Visual Intensity
2.5
2.0
1.8
1.5
1.8
1.5
1.5
1.5
1.3
1.4
1.5
1.4
1.0
1.0
WW1
WW2
Korea
Vietnam
Iraq
Note: All but Iraq are newsreel stories; Iraq is CNN coverage from the 2003 invasion period.
Time or Consequences? — 37
Figure 3. Percentage of War‐Related News Stories Depicting Combat 100%
Visual News
Textual News
75%
50%
40%
33%
25%
25%
28%
24%
19%20%
15%17%
6%
0%
WW1
WW2
Korea
Vietnam
Iraq
Note: Figures are for newsreel coverage in WW1, WW2, Korea, and Vietnam, and for CNN
coverage during the Iraq Invasion.
Time or Consequences? — 38
Figure 4. Percentage of Combat Stories with Moderate or Intense Depictions of Combat 100%
Visual News
Textual News
75%
56%
50%
60%
44%
35%
35%
25%
11%
12%
16%
14%
5%
0%
WW1
WW2
Korea
Vietnam
Iraq
Note: Figures are for newsreel coverage in WW1, WW2, Korea, and Vietnam, and for CNN
coverage during the Iraq Invasion.
Time or Consequences? — 39
Figure 5: Comparison of Actual and Publicly Reported Combat Deaths in New York Times Coverage, July 1950 through March 1951 20,000
Actual Combat Deaths
Reported Combat Deaths
China Enters the War
15,000
10,000
5,000
25
-J
un
-5
0
9Ju
l-5
23
0
-J
ul
6- 5 0
Au
g
2 0 -5 0
-A
ug
3- 50
Se
p
17 -50
-S
ep
-5
0
1O
ct
1 5 -50
-O
ct
29 - 50
-O
c
1 2 t- 5 0
-N
ov
2 6 - 50
-N
ov
10 - 50
-D
ec
24 -50
-D
ec
-5
0
7Ja
n51
21
-J
an
4- 5 1
Fe
b
18 -51
-F
eb
4- 51
M
ar
18 -51
-M
ar
-5
1
0
Time or Consequences? — 40
Figure 6 Impact of Five Local Deaths When Exposure to Either Newspapers or Local Television News Varies from Zero to Seven Days per Week Note: This figure reproduced from Althaus, Scott L., Brittany Bramlett, and James G. Gimpel. 2012. "When war hits
home: The geography of military losses and support for war in time and space." Journal of Conflict Resolution no.
56 (3):382-412.
Time or Consequences? — 41
Table 1
Correlations between War Support and Cumulative Deaths, Logged Cumulative Deaths, and Elapsed Time in
Conflict
Support for War
Cumulative KIA
Korea
Vietnam
Persian
Gulf
–.63
–.93
+.52
Kosovo
…
War on
Terror
(GWB)
War on
Terror
(BHO)
Afghanistan
Iraq
Libya
ISIS
All Wars
Combined
–.94
–.56
–.83
–.81
…
–.01
–.28
n.s.
Log10 Cumulative KIA
–.62
–.79
+.54
…
–.78
–.53
–.89
–.71
…
–.00
–.59
n.s.
Elapsed Time
–.61
–.90
+.17n.s.
–.72
–.93
–.54
–.91
–.81
–.08
–.02
–.43
n.s.
N=
31
26
33
9
318
170
Note: Pearson correlation coefficients are significant at the p < .05 level unless noted.
166
678
18
50
1,499
Time or Consequences? — 42
Table 2
Separate Entry of Variable Blocks Predicting Support for War
Predicting Support for War
(1)
(2)
B
beta
–.24**
.00**
–.00**
–3.31
5.70
–2.92
B
(3)
beta
B
beta
Time
Elapsed Week of Conflict
Elapsed Week Squared
Elapsed Week Cubed
Cumulative Deaths
Cume KIA (1000s)
KIA Trend
KIA Trend×Cume. KIA
–.72**
–.11
.01
Logged Cumulative Deaths
Logged Cume KIA
KIA Trend
KIA Trend×Logged Cume. KIA
Constant
** p < .01 * p < .05
–6.38**
6.43**
–1.89**
71.56**
R2 =
N=
–.28
–.01
.01
.421
1,499
53.26**
.135
1,499
66.83**
.366
1,499
–.58
.38
–.32
Time or Consequences? — 43
Table 3
Simultaneous Entry of Variable Blocks Predicting Support for War, with Controls
Predicting Support for War
(1)
B
(2)
beta
(3)
B
beta
B
beta
War Dummies
Korea
Vietnam
Persian Gulf
Kosovo
Afghanistan
Libya
ISIS
War on Terror
Incremental R2 from Block=
–19.53**
–5.86**
.36
–20.87**
16.05**
–30.69**
–3.74**
7.49**
–.21
–.06
.00
–.12
.37
–.24
–.05
.26
.131**
–20.29**
–.21
–6.93
–.07
.39
.00
–20.68**
–.12
16.15**
.38
–30.53**
–.24
–3.76**
–.05
7.79**
.27
.131**
–23.75**
–.25
–7.68**
–.07
2.83*
.03
–16.81**
–.10
17.26**
.40
–27.30**
–.22
–2.99**
–.04
…
…
.131**
Elapsed Week of Conflict
Elapsed Week Squared
Elapsed Week Cubed
Incremental R2 from Block=
Cumulative Casualties
Cume KIA (1000s)
KIA Trend
KIA Trend×Cume. KIA
Logged Cumulative Casualties
Logged Cume KIA
KIA Trend
KIA Trend×Logged Cume. KIA
–.24**
.00**
–.00**
–3.22
4.38
–2.21
.580**
–.24**
–3.24
.00**
4.41
–.00**
–2.22
.580**
–.27**
–3.71
.00**
2.12
–.00**
–2.57
.580**
Time
–.04
.46*
.02
2.35**
2.32**
–.72*
Incremental R2 from Block=
Controls for House Effects and
Question Wording (not shown)
Incremental R2 from Block=
Constant
Final R2 =
N=
–.01
.02
.01
.087**
85.89**
.798
1,499
.20
.13
–.12
.002*
.008**
.086**
.086**
85.57**
.799
1,499
81.06**
.805
1,499
** p < .01 * p < .05
Note: Cells contain OLS regression coefficients from the final models. Control variable coefficients
estimating house effects and question wording effects not shown.
Figure 7
Unadjusted Support for War by Elapsed Years in Conflict
0
25
% Support
50
75
100
Time or Consequences? — 44
0
2
4
6
8
10
Elapsed Years in Conflict
12
14
16
0
% Support (Adjusted)
25
50
75
100
Figure 8
Adjusted Support for War by Elapsed Years in Conflict
0
2
4
6
8
10
Elapsed Years in Conflict
12
14
16
Note: “Unadjusted support” data in Figure 7 shows the raw measures in the pooled dataset, while “adjusted support”
data in Figure 8 show the same measures after adjusting for question wording effects, house effects, and warspecific average levels of support. The “adjusted data” are therefore purged of question-, house-, and war-specific
differences to reveal the underlying relationship between support and elapsed time in conflict. Fully 88.5% of the
war support measures in the pooled data set are in the span from one to nine elapsed years. Support measures from
nine years or later are from Afghanistan, the War on Terror, and Iraq only.
Time or Consequences? — 45
Change in % Support per Week
-10
0
10
Figure 9
Average Weekly Change in War Support by Average
Weekly Deaths Since Last Survey, All Wars Combined
0
100
200
300
Average Deaths Per Week
400
500
Time or Consequences? — 46
Change in % Support per Week
-10
0
10
Figure 10
Average Weekly Change in War Support by Average Weekly
Deaths Since Last Survey, Excluding Korea and Vietnam
0
20
40
Average Deaths Per Week
60
Time or Consequences? — 47
Table 4
Correlations between Average Weekly Change in War Support from T1 to T2 and
Average Weekly Change in Death Toll from T1 to T2
Correlation between
Avg. Weekly Change
in War Support and
Death Toll
Korea
Vietnam
Persian
Gulf
Kosovo
War on
Terror
(GWB)
War on
Terror
(BHO)
Afghanistan
Iraq
Libya
ISIS
All Wars
Combined
–.24 n.s.
–.20 n.s.
+.18 n.s.
…
–.01 n.s.
–.03 n.s.
–.19
–.03 n.s.
…
+.63
–.01 n.s.
Note: Pearson correlation coefficients are significant at the p < .05 level unless noted. Correlations cannot be calculated for Kosovo and Libya because the
average weekly death toll has a constant value of zero. If the two cases of sudden plunge in war support associated with the Chinese entry into the Korean War
are retained, the Korea correlation becomes a significant –.57.
Time or Consequences? — 48
Table 5
Predicting Average Weekly Change in War Support for War from
Average Weekly Deaths and Elapsed Time in Conflict
Average Weekly Change in War Support from T1 to T2
All Wars
Omitting
War on Terror
Omitting
Korea and Vietnam
Deaths
Average Weekly Change
in Death Toll from T1 to T2
–.002
–.002
–.006
.427
.322
.756*
–.214
.060
.194
–.063
–.193
.427
.322
.756*
–.214
.060
.194
–.063
…
…
…
.723*
–.258
.035
.150
–.106
–.187
.037
.037
.081
.007
1,407
.007
940
.007
1,356
War Dummies
Korea
Vietnam
Persian Gulf
Kosovo
Afghanistan
Libya
ISIS
War on Terror
Constant
R2 =
N=
** p < .01 * p < .05
Note: Cells contain OLS regression coefficients.