Framing Performance Information:
An Experimental Study of the Negativity Bias
Asmus Leth Olsen
Department of Political Science
University of Copenhagen
ajlo@ifs.ku.dk
www.asmusolsen.com
Paper invited to revise and resubmit at Public Administration Review
Paper presented at the 11th Public Management Research Conference
Madison, Wisconsin, June 20-23 2013.
Panel: Measurement and Methods in Public Management Research
Abstract
The negativity bias has important implications for how performance information is
likely to affect the attitudes and behavior of citizens, public employees, and policy
makers. I conduct a survey experiment with a large nationally representative sample
(n=3442) on how the negativity bias affects citizens’ evaluation of hospital services. In
line with the negativity bias, the study finds that framing performance information in
term of patient dissatisfaction lowers citizens’ evaluation of hospital services substantially compared with a logically equivalent patient satisfaction frame. Importantly, the
negativity bias is moderated by citizens’ alternative information sources on hospital services. Personal or professional experience from a hospital setting markedly diminishes
the negativity bias. The results provide an experimental basis for the further study of
the negativity bias in relation to performance information.
Keywords: Performance information; negativity bias; citizens’ satisfaction.
How strong is the negativity bias in citizens’ response to performance information and who
is affected by it? The negativity bias implies that individuals pay more attention to negative
information than positive information of the same magnitude (Lau, 1982, 1985; Baumeister
et al., 2001; Rozin and Royzman, 2001). Recently, the bias has gained interest among scholars
studying the effects of performance information on citizens (James and John, 2007; Boyne
et al., 2009). In the study of performance information, the negativity bias implies that citizens’ are asymmetrical in their response to good and bad performance by reacting mostly to
the latter. While the existing studies have pointed to some evidence of a negativity bias, the
few experimental studies in public administration on performance information found mixed
results (James, 2011b,a). As James (2011b, 414) has argued, “More investigation of possible
difference in magnitude of effect between information about good and bad performance is
merited...” In addition, recent interest has focused on how citizens’ alternative sources of
information might affect the strength of the negativity bias in response to performance information (Chingos, Henderson, and West, 2012; Johnsen, 2012). In short, the negativity bias
is expected to be important for how performance information affects citizens but it is still
unclear how important it is, and who is affected the most by it.
In this article, I analyze the negativity bias with an experimental study on citizens’ attitudinal response to performance information. The study is set in a health care setting with a
large nationally representative sample (n=3442). Performance information plays an increasingly important role in shaping the public’s view of national health care services. Importantly,
health care performance information is not only relevant to patients but will also function
as a source of information for the broader public for holding policy makers or health care
service providers accountable. With an equivalence framing experiment (Druckman, 2001), I
show how a negative framing of performance information has a markedly more negative effect
on citizens’ attitudes than a logically equivalent positive framing. This adds to our existing
understanding of the negativity bias in public administration but showing that strong asymmetrical responses to otherwise equivalent frames. This provides clear experimental evidence
2
for the relevance of the negativity bias for understanding the effects of performance information. In addition, I also find that citizens’ with alternative information sources are less
influenced by the negativity bias. This finding points to the importance of understanding who
exactly is affected by how the large amount of available performance information is framed.
The negativity bias is not just a theoretical puzzle or methodological technicality with
interest to performance measurement research. It is also a potentially very powerful bias
with large real-world implications. The growth of performance information will only increase
the importance of understanding the negativity bias because performance information, at
least at face value, makes the difference between “good” and “bad” even more salient (Espeland and Sauder, 2007; Bowman and Bastedo, 2009, 2011). As Behn (2003) notes: “Often,
however, existing or easily attainable data create an opportunity for simplistic, evaluative
comparisons”. If citizens are highly asymmetrical in their response to good and bad performance information, we can expect that this in turn shifts the behavior of those getting
measured. Some have linked the negativity bias to the general blame-avoidance observed
among political and administrative elites (Weaver, 1986; Hood, 2007; Hood and Dixon, 2010;
Carpenter and Krause, 2012). As Hood (2007, 199) notes a “key test of political power can
be said to be the ability to overcome or counteract negativity bias.” Here I show how simple
changes to the framing of performance information can dramatically shift citizens’ perception
of public sector performance. It stresses the importance of understanding how performance
information can evoke a negativity bias among citizens and from there on the rest of the
political-administrative system (Olsen, 2013a).
The remainder of the article is structured in the following manner. First, the theoretical
basis of the negativity bias is elaborated on for the case of performance information. In
addition, I outline how alternative informational sources is likely to moderate how the strong
the negativity bias is. Second, an experimental design is outlined along with the data in
the form of a large representative and diverse sample of Danish adults. The experiment
frames patient experience in terms of either satisfaction or dissatisfaction to see how it affects
3
citizens’ evaluation of a hypothetical hospitals performance. The experiment also randomizes
the numerical performance information provided in order to test if the negativity bias is
stable across varying numerical values.
Performance Information and the Negativity Bias
When providing citizens with performance information a rational model expects that citizens attend the information in a symmetrical manner. Prewitt (1987, 115) formulates this
baseline assumption by stating that: “When economic and social indicators are moving in
politically popular directions, political credit is claimed; when they are moving in unpopular
directions, political blame is assigned. Here, then, is a contribution of public statistics to
the workings of democracy.” In other words: good or bad performance should be given equal
weight. However, empirical work often point to that actual evaluations are asymmetrical due
to a negativity bias. The negativity bias implies that negative information has a stronger
impact than positive information of the same magnitude. Meta-reviews of the negativity bias
finds support of the bias across human perception, memory, decision making, and behavior
(Baumeister et al., 2001; Rozin and Royzman, 2001). Importantly, the bias can also be evoked
by framing information negatively compared with logically equivalent positively framed information, e.g. the cup is half empty vs. the cup is half full. Framing information positively
or negatively affects people because they process information in light of its valence; that is, a
positive framing evokes positive memories and associations, while a negative framing evokes
more negative memories (Levin and Gaeth, 1988).1
This idea has also found its way into political psychology which for a relatively long
time has focused on the asymmetrical effects of “positive” or “negative” information (Lau,
1982, 1985). In politics the negativity bias states that blame would be assigned to much
greater extend than credit would for good performance of a similar magnitude. Retrospective
1
Other point to that we tend to pay more attention and direct more cognitive capacities towards negative
information rather than positive. Hilbig (2009) provides evidence that the negativity bias proves a “sad,
thus true”-effect, in the sense that negatively framed information generally is hold to be more truthful than
positively framed information.
4
voting studies find that a worsening economy damages the incumbent to a greater extent
than an improving economy helps. Mueller (1973) found that US presidential popularity
was negatively affected by increasing unemployment while decreasing unemployment did
not affect popularity. In a similar manner, Kinder and Kiewiet (1979) found evidence of
decreasing voter support for the incumbent only as the unemployment rate went up. Bloom
and Price (1975) also found that a worsening of economic well-being affected incumbent
support negatively while improvements were not rewarded to the same extent.
In recent years this research has spread beyond the traditional measures of economic
performance indicators and into the more diverse measures of performance which often are
studied in public administration. Boyne et al. (2009) find evidence of a negativity bias in
the effect of municipal performance information on electoral support among English local
governments. James and John (2007), also in an English local government setting, find that
voters are primarily punishing poor performance and not rewarding good. These studies
have been successful in showing the negativity bias with real world observational data. The
only experimental studies of the negativity bias in a context of performance information find
more mixed results with one study finding zero effects (James, 2011b) and another study
clear support (James, 2011a). The experimental evidence for the relevance of the negativity
bias for understanding citizens’ responses to performance information is therefore still weak.
In addition, we still have no clear evidence of the extent to which the media is responsible for
the negativity bias. Soroka (2006) finds that negative economic performance is covered more
intensely in the media than positive economic performance of a similar magnitude. He also
finds that citizens response have an asymmetrical element independent of the news coverage.
Further evidence is therefore needed to tease out the extend to which citizens in their own
information-processing exhibit a negativity bias in their response to performance information.
Furthermore, the existing studies do not test the extend to which the framing of performance
information can affect citizens’ perceptions. Accordingly, here we take a step back to test if
logically equivalent negative and positive performance information have asymmetrical effects
5
on citizens’ attitudes toward public services. The prediction stemming from the negativity
bias will be that negative performance information should affect attitudes more negatively
than positive information, of the same magnitude, is able to affect attitudes in a positive
direction.
The Negativity Bias and Alternative Informational Sources
The second line of interest concerns who gets affected by the negativity bias when faced with
performance information. This question has yet to be answered in performance information
research. As noted by Hood (2007, 198): “But we have little direct survey evidence for
changing negativity bias (let alone a developed ’negativity bias index’). Nor are the causes of
negativity bias clearly established.” From a perspective of cognitive psychology, we can view
performance information as a heuristic for citizens in their attitude formation and choices
in relation to the public sector (Simon, 1955; Kahneman and Tversky, 1979; James, 2011b;
Johnsen, 2012). A heuristics approach will view performance information as an informational
shortcut replacing some of the pre-existing distorted, imperfect, and potentially biased ideas
floating around concerning public sector performance (Sniderman, Brody, and Tetlock, 1991).
One way to view performance information is thus as a means of lowering the cost of
obtaining information about public services. Suddenly, a number, label, set of scores, rank,
or tier can be associated with an organization’s performance. This will in particular be the
case for those with few or noisy alternative sources of information. As laid out by James
(2011b, 402), “Citizens may not have much of an idea about the overall performance of a
local public body only interacting with it on a case-by-case basis for a subset of services.”
Public sector performance is a complex concept to make inference about and most individuals
will recognize that their information is incomplete in most aspects. Citizens can draw on
multiple informal sources for performance information about the public sector. These include
media reports, personal experience, advice from family and friends, political debates, or
inference from visible traits of a particular organization such as its current users, facilities,
6
staff, or manager. Holzer and Yang (2004, 16) describes performance information as a source
of information replacing more vague and subjective ideas about performance in the public
sector, “Measurement helps to move the basis of decision-making from personal experience
to proof of measurable accomplishment or lack thereof.”
While some expect performance information to crowd out alternative sources of information about quality, it is just as likely that the effect of performance information will
be moderated by existing ideas about performance. Direct experience can be an important
source of information about services which potentially can affect how formal performance
information is used. Chingos, Henderson, and West (2012) found that mostly non-parents
with little alternative sources of information about school performance responded strongest
to accountability ratings on school performance. This prediction is seen as consistent with the
Receive-Accept-Sample model of opinion-formation (Zaller, 1992) which expect elite information (such as performance information) to mostly affect citizens with limited alternative
sources of information.
Along the same lines, we can also expect that personal experience or other informational
sources will moderate how citizens are affected by negative or positive performance information. Johnsen (2012, 139) speculates if the negativity bias works differently for “public services
where people in general have less direct experience”. Along the same lines, James (2011b,
414) has argued that the negativity bias may depend on how consistent the performance
information is with “personal experience or word of mouth”. Clearly, if prior information
about the quality of a public service is weak, new information should be given more weight
(Zaller, 1992). If citizens, generally respond stronger to negative information, we can suspect
that negatively framed performance information mostly will affect citizens with limited prior
information. We should therefore expect that alternative informational sources diminishes
the negativity bias of performance information.
7
Method and Data
I conducted a survey experiment to test for a negativity bias in citizens attitudes of public
services given performance information. While experiments are crucial for answering causal
question they are still infrequently used in public administration research (Margetts, 2011).
The is also the case for the question of the effects of performance information in relation to
the negativity bias where only two experimental studies have been made (James, 2011b,a).
The survey experiment is conducted in a hospital service performance information setting.
Hospital services are one of the public sector services in which performance information is
most widely used (Mannion, Davies, and Marshall, 2005; Propper et al., 2010). Importantly,
health care performance information is not only relevant to patients but will also function
as a source of information for the broader public for holding policy makers or health care
service providers accountable. However, most studies of health information deals with performance reporting to health professionals and/or to patients (Hibbard, Stockard, and Tusler,
2003, 2005; Lindenauer et al., 2007; Peters et al., 2007; Hibbard et al., 2009). We have little
knowledge about how the broader public responds to different ways of presenting patient
satisfaction measures.
The respondents for the experiment were recruited via YouGov’s Danish online panel
(n=3442). The data was collected between the 15th and 22th of October 2012. The study
was restricted to citizens between the age of 18 to 74 and was pre-stratified on gender, region,
age, and political party choice in order to achieve a representative sample of the Danish
population. Table 1 shows descriptive statistics for the sample. It highlight how diverse the
sample is on the most common socioeconomic variables such as age, gender, education and
geographical region. This is important in order to strengthen the external validity of the
findings across different types of citizens.
The diverse sample is also needed to test the additional expectation about alternative informational sources and performance information. In terms of alternative information sources,
I rely on two different indicators. The first is a dummy variable indicating if the respondent
8
Table 1 Descriptive statistics
Variable
Been to a hospital within the last year
Currently or previously work in a hospital
Gender (male)
Age (mean years)
Private employee
Vote for government/supporting parties
Education
High school or less
Vocational training
Short-cycle tertiary
Medium-cycle tertiary
Long-cycle tertiary
Geographical region
Capital area
Sealand
Southern Denmark
Middle Jutland
Northern Jutland
Pct.
17.8%
10.6%
49.8%
50.5 (SD=15)
32.6%
35.5%
18.4%
24.7%
12.9%
28.6%
15.5%
24.7%
15.3%
23.9%
23.8%
12.3%
Note: n=3443.
has been to a hospital within the last year. This indicates a personal experience with hospital
services which can be seen as an important alternative source of performance information.
The second indicator is a dummy variable indicating if the respondent either currently or
previously has worked at a hospital. This indicator captures any type of professional workrelated experience from a hospital setting. In summary, both indicators capture the extend to
which respondents potentially have alternative sources of hospital performance than through
the performance information they will be provided in the experiment.
An Experimental Research Design
In the experiment the basic setup is that respondents are asked to evaluate a hospital given
a single piece of performance information. The experiment randomized the performance information provided to respondents at two levels. An outline of the experiment is presented
in table 2. Overall the two levels of treatment constitutes an equivalence framing experiment in which respondents are assigned logically equivalent pieces of information (Tversky
and Kahneman, 1981; Levin, Schneider, and Gaeth, 1998; Rabin, 1998). In social and political psychology the negativity bias is often tested with similar types of equivalence framing
experiments (Druckman, 2001; Hilbig, 2009).2
2
More specifically we can place the case of framing experiments with performance information under the
general label of an outcome framing experiment which deals with numerical quantities (Soman, 2004).
9
The experiment itself took the following form: all respondents were provided with a short
factual note about how the Danish Health and Medicines Authority surveys patients experience with hospital services. Following this information they were asked to evaluate the
performance of an unnamed hospital. Hereafter the first randomization was introduced. Respondents were randomly assigned to two different conditions. The two conditions differed
in how the hospital’s performance was framed in terms of either (a) patient satisfaction
(n=1716) or (b) patient dissatisfaction (n=1726). Clearly, the satisfaction frame constitutes
the positive framing of performance information while the dissatisfaction frame stresses the
negative aspects of the performance information. Reporting various measures of patient satisfaction with hospital care is today common practice across most developed countries (Kravitz,
1998; Williams, 1994; Pope, 2009).
Table 2 Experimental design: Evaluation from patients’ satisfaction/dissatisfaction
Baseline question
Treatment
frame
Treatment wording
Numerical
treatment
Danish Health and Medicines
Authority repeatedly records
how patients have
experienced their treatment
at Danish hospitals. How do
you think the following
hospital is doing?
A: Satisfaction
At the hospital is x%
of the patients satisfied
with their treatment.
x ∈ U (75.0, 95.0)
B: Dissatisfaction
At the hospital is x% of
the patients dissatisfied
with their treatment.
x ∈ U (5.0, 25.0)
Note: Experiment conducted with YouGov’s Danish online panel (n=3442).
In order for the treatments to be logically equivalent, a second level of random assignment
is introduced in which respondents randomly are assigned various percentages of satisfaction/dissatisfaction. For the two different frames the numerical content was drawn from two
different uniform distributions. In a uniform distribution the values within an interval have
an equal probability of being drawn. In the satisfaction frame the uniform distribution ranged
from 75.0% to 95.0% satisfied patients. In the dissatisfied frame this interval was inverted
which provided a range of treatment values from 25.0% to 5.0% dissatisfied patients. For
10
instance some respondents have been given the treatment that “At the hospital is 10% of the
patients dissatisfied with their treatment”. At the same have other respondents been provided with the logically equivalent, but negatively framed information, that, “At the hospital
is 90% of the patients satisfied with their treatment”. That is, for each respondent which was
asked to evaluate a given rate of hospital satisfaction, I have a similar set of respondents evaluating logically identical, but negatively framed, percentages of hospital dissatisfaction. The
random assignment of percentages provided respondents with around 100 different treatment
values under each frame. The assignment of a large range of different values allow for testing
if the framing of performance is dependent on the numerical content of the frame. Numerical
content has been found to affect both citizens’ attitudes and the behavior of policy makers in
unexpected ways (Olsen, 2013b,c). By randomizing the numerical content, the results cannot
be driven by idiosyncratic artifacts of the numerical values respondents were given.
The respondents were asked to score their response on a slider scale ranging from ’very
bad’ (0) to ’very good’ (100). A picture of the response scale is provided in figure 1 below.
Figure 1 Screen caption of the exact response scale used in the experiment. The scale
varies from “very bad” (“Meget dårligt”, 0) to “very good” (“Meget godt”, 100).
Empirical Findings
First, we test the overall effect of framing patient experience in terms of satisfaction vs.
dissatisfaction. Under the satisfied frame citizens gave the unnamed hospital an average
score of 65.9. However, for the dissatisfied frame the average score was only 45.4. On average
citizens’ evaluated hospitals under the satisfied frame significantly better with an average
difference of 20.5 points which is a substantial and highly significant difference (t(3440) =
17.3, p < .001). The difference corresponds to about a one standard deviation change on
the dependent variable. Importantly, this effect is solely induced by reporting performance
11
of the logically equivalent magnitude as dissatisfaction instead of satisfaction with hospital
services. The fact that a simple change in the framing can induce a shift in evaluations of
this magnitude provides strong support for the negativity bias.
Next we observe how citizens’ respond to changes in the satisfaction/dissatisfaction percentage. In figure 2a two separate linear regression lines are plotted for respondents assigned
either the dissatisfaction frame or the satisfaction frame. At the lower x-axis the numerical
treatment value of the pct. of satisfied patients is shown. This is the numerical treatment
respondents received under the satisfaction frame. At the upper x-axis is the corresponding
treatment in terms of the percent of dissatisfied patients shown. In the plot itself filled dots
denote respondents which were treated with the satisfied frame (and a corresponding pct. on
the bottom axis) and the unfilled dots denote respondents for the unsatisfied frame (which
received a pct. on the top axis).
The difference in placement of the lines indicate the large main effect of the satisfaction/dissatisfaction framing reported above. For both frames there is a positive effect of
improvements in the pct. of satisfied/dissatisfied patients and citizens’ evaluation of hospital services (Satisfaction: β = 0.88, t(1714) = 10.65, p =< .001; Dissatisfaction: β = 0.99,
t(1725) = 10.20, p =< .001). In other words, citizens evaluate a hospital 0.88 points better for
1%-point more satisfied patients and increase evaluations with 0.99 points for each 1%-point
drop in dissatisfaction. Thus, if we compare the effect of changes in satisfaction/dissatisfaction
pct. to the overall framing effect, we see that a 20%-point improvement in the percentage of
satisfied/dissatisfied has the same effect as changing the overall framing from dissatisfaction
to satisfaction. In summary, the framing experiment shows evidence of a strong negativity
bias.
Interestingly the framing also affect response times for the questions. Specifically, respondents provided with the dissatisfied frame were about 4.7 seconds slower in their response
time than respondents under the satisfied frame (Dissatisfied frame: M = 25.1 seconds; Satisfied frame: M = 20.4 seconds). If trimmed means are used the difference is cut to about 1
12
Treatment B: Pct. Dissatisfied with Hospital Services
24%
23%
22%
21%
20%
19%
18%
17%
16%
15%
14%
13%
12%
11%
10%
9%
8%
7%
6%
5%
75
Satisfied
25
50
Dissatisfied
1
Citizens’ Evaluation of Hospital Performance
100
25%
75%
76%
77%
78%
79%
80%
81%
82%
83%
84%
85%
86%
87%
88%
89%
90%
91%
92%
93%
94%
95%
Treatment A: Pct. Satisfied with Hospital Services
Figure 2 OLS fitted lines for both the satisfied percentage and the dissatisfied percentage.
Dots with 95-% intervals represent the average treatment effect for the two frames.
13
second but still highly significant (p < .001). This is in line with the psychological research
which finds slower reaction times for processing negative information compared with positive
information (Baumeister et al., 2001, 342). Some attribute this to how negative information
initiates more complex and deeper processing (Rozin and Royzman, 2001). Here we take it
as a validation of that the framing experiment did evoke negative thoughts by describing
performance in terms of dissatisfaction.
Next step in the analysis is to see if alternative informational sources about hospital services mediates the effects of framing patients as dissatisfied or satisfied. In figure 3 coefficients
with 95%-CIs are shown for (1) if the respondent has been hospitalized within the last year,
or (2) if the respondent currently works or has worked at a hospital. The coefficients are
estimated via a set of OLS regression models controlling for other background variables. A
full output of the regressions are shown in table in the appendix.
Figure 3 shows an interesting pattern. For the satisfied frame the personal or professional
experience has no effect on hospital evaluations. Citizens with these types of experiences do
not evaluate hospitals any different from other respondents when asked about hospital performance via a satisfactions percentage. This indicates that alternative informational sources
about hospital performance do not affect performance evaluations when citizens are provided
with positively framed performance information.
However, for the dissatisfied frame we see clear differences for respondents with alternative informational sources. Here respondents with personal or professional experience respond
significantly different from everybody else. The positive coefficient for both groups indicate
that their average evaluation is more positive under the dissatisfied frame compared with people without such experience. That is, compared with individuals without alternative sources
of information, they respond less negatively to the performance information provided under the dissatisfied frame. The coefficient for the group with personal experience imply that
their evaluations is around 3.8 points more positive under the dissatisfied frame if compared
with citizens with no personal experience. For the group with professional experience from
14
10
8
6
4
2
0
−2
Citizens’ Evaluation of Hospital Performance
−4
Patient experience
Hospital work experience
Positive frame: Satisfaction
Patient experience
Hospital work experience
Negative frame: Dissatisfaction
Figure 3 Coefficient with 95%-CIs for separate subgroups with personal or professional
experience. Coefficients are obtained from linear regression model from in the appendix controlling for age, gender, education, employment sector, geograpical region, and party choice.
See model 4 in table in the appendix.
15
hospitals the effect is about 5.1 points more positive than for non-professionals.
If we view this finding together with the zero finding for the positive frame, it indicates
that alternative informational sources diminishes the negativity bias (in the form of framing
patient experience as dissatisfaction). That is, the distance in evaluations under the positive
and negative frame is reduced by having alternative sources of information and this effect
is driven by a less negative response under the dissatisfied frame. Importantly, if the results
were driven by a generally higher degree of satisfaction with hospital services among these
groups, then we should expect them to respond more positively under both frames. This would
indicate that patients and former or current hospital staff generally have a more favorable
view of hospital performance. This is however not what the findings indicate. That is, the
fact that alternative information sources only affect evaluations under the dissatisfaction
frame indicates that alternative information sources is likely to play a role in diminishing the
negativity bias.
Conclusion & Discussion
The analysis showed clear experimental evidence of the negativity bias in citizens’ evaluation
of performance information. Framing hospital performance in terms of a “dissatisfaction”
instead of “satisfaction” had a substantial negative impact on citizens’ evaluation of hospital
services. The effect was constant along a large interval of different numerical treatments for
the pct. of patients being either satisfied or dissatisfied. These findings provide experimental
support for the negativity bias found in observational data on citizens response to positive
and negative performance information (Soroka, 2006; James and John, 2007; Boyne et al.,
2009).
However, not all citizens were found to be equally vulnerable to the negativity bias.
Citizens which themselves recently had visited a hospital or which had working experience
from a hospital were less negatively affected by the dissatisfaction framing. These results
point to that performance information is most likely to induce a negativity bias for citizens’
16
which have no other sources of information about performance. The practical implication is
that more attention should be directed at how performance information to individuals with
few alternative information sources is framed. The research implication is that our empirical
models of the effects of performance information should factor in the degree of alternative
informational sources at the individual level.
At a more general level, the findings have highlighted that the experimental design can be
a simple but powerful tool for testing the scope and limits for how performance information
is evaluated by citizens. A stronger experimental basis for the central concepts underlying
the effects of performance information is important to improve future observational studies
done on this particular causal question.
A cautionary note on the limits of the findings should be made due to the hypothetical
low-incentive setting of the experimental design. Using a hypothetical treatment of an unnamed hospital does limit the extent to which such findings are applicable to settings with
more in-depth contextual information. Often performance information will be presented in
a more data rich context of news reports, government web pages, or official publications.
This beings said, the findings provide proof-of-concept of the importance of a negativity
bias for citizens’ response to performance information and how the effects are moderated by
alternative informational sources.
The finding of a strong negativity bias points to how arbitrary changes in the framing of performance information can have dramatic consequence for public opinion about an
important public service. The findings are therefore also in line with the research linking performance information, the negativity bias, and the blame-avoiding behavior of political and
administrative elites (Weaver, 1986; Hood, 2007; Hood and Dixon, 2010). It also highlight
how slight changes to the framing of citizen satisfaction measures can affect public opinion on the public sector (Oliver, 1980). It naturally raises the important questions of how
elites can affect public perception of performance via the framing of performance information
(Druckman, 2001). The presence of a dominant negativity bias will provide elites with strong
17
incentives to secure a positive framing of performance information.
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21
Table 3 APPENDIX: The Negativity Bias and Alternative Information Cues
Satisfaction frame
Model 1
Satisfaction pct.
0.88∗∗
(0.08)
Model 2
0.88∗∗
(0.08)
Model 3
0.88∗∗
(0.08)
Dissatisfaction frame
Model 4
0.96
(1.27)
Hospital work exp.
−1.66
(1.58)
0.90
(1.27)
−1.61
(1.58)
−9.04
(7.04)
−9.27
(7.05)
Gender
Age
Private employee
Government voter
Education
Intercept
Adj. R2
Observations
−9.24
(7.05)
0.06
1716
0.06
1716
0.06
1716
Model 2
Model 3
0.91
(1.28)
−1.20
(1.63)
−1.34
(1.00)
0.02
(0.03)
0.21
(1.09)
0.63
(1.02)
−1.42∗∗
(0.36)
−6.02
(7.42)
0.07
1703
−1.00∗∗
(0.10)
4.33∗∗
(1.47)
59.52∗∗
(1.57)
0.06
1727
−0.98∗∗
(0.10)
5.67∗∗
(1.82)
−0.99∗∗
(0.10)
4.34∗∗
(1.47)
5.68∗∗
(1.82)
59.40∗∗
(1.57)
58.77∗∗
(1.58)
0.06
1727
0.07
1727
OLS coefficients with standard error in parenthesis. Significance levels: ** p < 0.01, * p < 0.05
Model 4 also includes four regional dummies. These are excluded for presentational purpose.
22
Model 4
0.89∗∗
(0.08)
Dissatisfaction pct.
Patient exp.
Model 1
−1.00∗∗
(0.10)
3.75∗
(1.48)
5.06∗∗
(1.87)
−0.50
(1.16)
0.03
(0.04)
−2.40
(1.27)
1.67
(1.19)
−0.95∗
(0.41)
60.39∗∗
(2.93)
0.07
1710