GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No.61

Decision to get influenza vaccination:
A behavioral economic approach

Yoshiro Tsutsui, Uri Benzion, Shosh Shahrabanic,and Gregory Yom Din

June 2009GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

Decision to get influenza vaccination:
A behavioral economic approach
Yoshiro Tsutsuia,* , Uri Benzionb,c, 1, Shosh Shahrabanic,2, Gregory Yom Dind,e, 3aGraduate School of Economics, Osaka University, 1-7 Machikaneyama, Toyonaka, Osaka,
560-0043 Japanb
Department of Economics, Ben Gurion University, Beer Sheva 84105, IsraelcEconomics and Management Department, Max Stern Academic College of Emek Yezreel,
Emek Yezreel 19300, IsraeldGolan Research Institute, University of Haifa, P.O. Box 97, Katzrin, 12900, IsraeleOhalo College, P.O .Box 222, Katzrin, 12900 Israel* Corresponding author. Fax: +81 6 650 5223
E-mail address: tsutsui@econ.osaka-u.ac.jp (Y. Tsutsui).
1
uriusa@gmail.com.
2
shoshs@yvc.ac.il.
3

rres102@research.haifa.ac.il.

JEL classification: I19
Keywords: Influenza, Inoculation, Health belief model, Survey, Time preference.
Abstract
The aims of this study were to identify predictors regarding people’s willingness to be
vaccinated against influenza and to determine how to improve the inoculation rate using ouroriginal large-scale survey in the USA in 2005. The main results are (a) a model of bounded
rationality explains vaccination behavior fairly well, i.e., people evaluate the costs and benefitsof vaccination by applying risk aversion and time preference, while the ‘status quo bias’ of
those who received vaccinations in the past affect their decision to be vaccinated in the future,(b) it is recommended to increase people’s knowledge regarding flu vaccination, but notregarding influenza illness, (c) reducing the vaccination fee may be ineffective in raising therate of vaccination.
1. Introduction
Influenza vaccination has been shown to be cost effective in reducing morbidity andmortality in the older adult population, and in reducing morbidity, work absenteeism, and useof healthcare resources among the healthy working adult population (Bridges et al., 2000; Leeet al., 2002; Nichol et al., 2003). Vaccine prevents influenza in approximately 70%−90% of
healthy adults under the age of 65 years and in 58% among persons over 60 years of age
1

(Bridges et al., 2000; Demicheli et al., 2000). Although influenza vaccination levels increased
substantially during the 1990s, further improvements in vaccination coverage levels are needed.
For example, in 2004, the estimated vaccination coverage among adults with high-risk
conditions aged 18−49 years and 50−64 years was 26% and 46%, respectively, substantiallylower than the Healthy People 2000 and Healthy People 2010 objectives of 60% (CDC, 2006).
Theoretically, the socially optimal rate of inoculation should be substantially higher
than an individual’s optimal rate because catching an infectious disease has great external
implications on the society. Thus, raising willingness to be inoculated is socially desirable,
even if it does not conform to an individual’s rational decision. In this paper, we investigate
whether or not propagation of information regarding influenza and its vaccine will raise
willingness to undergo inoculation.1 This is not known a priori; it could be that more
information will diminish people’s willingness because they may unreasonably fear influenza
and/or irrationally overestimate the effectiveness of flu inoculation. Of course the opposite
might be the case, that increased information will substantially promote inoculation. This paper
tries to identify what kind of information might be effective in raising the inoculation rate. In
this sense, our study follows a behavioral economic approach and focuses on the bounded
rationality of individuals. In addition to the effect of knowledge, we expected to find otherbehavioral factors, such as status quo bias and overconfidence effect, to affect the inoculation
decision. Status quo bias refers to the situation in which people tend not to change anestablished behavior unless the incentive to change is compelling (Samuelson and Zeckhauser,
1988). The overconfidence effect is the tendency of people to underestimate “real” objective
risks (Griffin and Varey, 1996).
To be useful in policy planning, analyses should consider comprehensive factors aswell as irrational aspects of human beings. First, bounded rationality means that people are
fairly rational and can be expected to make a decision on inoculation based on its benefits andcosts. In particular, it is hypothesized that beliefs regarding the probability of contracting theflu, the severity of the illness, and the effectiveness and side effects of the vaccine as well as
preferences involving time and risks play important roles in assessing the costs and benefits ofinfluenza inoculation. In addition, characteristics such as gender and age may explain thewillingness to be vaccinated. The present study analyzes comprehensive factors regarding
people’s willingness to be vaccinated against the flu, an approach that is possible because of
our original large survey conducted across the USA.
It is natural to suppose that people agree to be vaccinated based on perceived costs andbenefits which, in turn, depend on subjective beliefs regarding influenza and vaccination.
Indeed, the Health Belief Model (HBM) explains and predicts preventive health behavior interms of belief patterns that focus on the relationship of health behaviors and utilization ofhealth services (Rosenstock et al., 1988). According to the HBM, acceptance of an influenza
vaccination depends on the following predictors: perception of susceptibility to influenza,1

In an empirical study conducted in the USA, Wu (2003) found that people with more education,higher incomes, and better insurance coverage are more likely to get flu shots, as well as other types of
preventive medical treatments.

2

beliefs about the severity of influenza, perceived benefits of the vaccine in preventing
influenza, perceived barriers to accepting a vaccine, influence by cues (such as media
information or physician recommendation) to action, and degree of motivation for other healthbehaviors (Blue and Valley, 2002). Indeed, cited reasons for not receiving an influenza
vaccination were similar across studies with reference to perceived barriers, i.e., concern aboutside effects or vaccine safety, lack of effectiveness of the vaccine in preventing illness, andlack of awareness (Chapman and Coups, 1999a; Chen J.Y. et al. 2007). The HBM has beenused to explore a variety of health behaviors, including vaccination (Blue and Valley, 2002;
Chen J.Y. et al., 2007; Lau et al., 2008; Shahrabani et al., 2009). Our study, likewise, ispartially based on the HBM.
The paper is organized as follows. In section 2, we explain our model and methods,
Section 3 explains our survey, and Section 4 is devoted to the results. Section 5 summarizes
and concludes the study.
2. The model
2.1. Vaccination behavior model
2.1.1. The basic model
As traditional economics assumes, we assume that people make a decision about being
vaccinated based on perceived costs and benefits which, in turn, are based on beliefs regarding
the probability of infection, the severity of the illness, the effectiveness of the vaccine, and theside effects of the vaccine, as the HBM assumes. We also assume that the decision depends ona time discount rate because the benefits of vaccination will be realized in the future while its
costs are paid in the present.2 Thus, it is expected that people with a higher discount rate willbe less likely to receive the vaccination. High risk aversion may affect the decision to be
vaccinated in two opposing ways: while some will take the vaccine because they fear theconsequences of influenza illness, others may worry about its side effects and not be
vaccinated. Thus, the effect of degree of risk aversion is, a priori, ambiguous.
Although we agree that traditional economics explains inoculating behavior fairy well,
we also assume that rationality is bounded. Thus, we suppose that the decision to be vaccinatedis biased by several factors. Previous vaccination and illness experiences affect the decision, as
well as overconfidence and level of education. Socio-demographic background, economic
status, and health status are known to impact an individual’s decision to be vaccinated(Shahrabani and Benzion, 2006; Chen Y. et al., 2007). Thus, we include respondents’characteristics in our estimation.
Our model is that willingness of inoculation (WTINJ) depends on (a) beliefs regarding
costs and benefits of vaccination, (b) time discounting, risk aversion, and overconfidence2

See Shahrabani et al. (2008). Chapman and Coups (1999b) provide some evidence that individuals’

time preference patterns can explain preventive health behavior; in particular, monetary time
preferences predict whether people took flu shots.

3(perceived lower self-risk), (c) past experience with flu shots, preventive health, and influenza
illness, and (d) education level and socio-demographic attributes.
2.1.2 The dependent variable
The dependent variable, WTINJ, is the willingness to receive a flu shot within twelve
months, a categorical variable from 1 = certainly to 5 = certainly not.
2.1.3. The independent variables
Perceived benefits and costs of vaccination depend on perceptions of the (a) probabilityof infection (PROB), (b) seriousness of the illness (ILL), (c) degree to which family and friends
would be bothered or inconvenienced in case the respondent contracts influenza (BOTHER),(d) effectiveness of vaccination (EFFECT), (e) side effects of the flu shot (SIDE), and (f)financial cost of the flu shot (FEE). An increase in items (a)-(d) results in an increase inbenefits of the vaccination; an increase in items (e) and/or (f) results in an increase in costs.
The degree of time preference (DTP) is measured by whether or not a person is willing
to postpone pleasure. A person with a higher DTP (i.e., smaller time discount rate) is more
future oriented. A sample question for measuring risk aversion (RA) is “how high does theprobability of rain have to be for you to take an umbrella?” RA is a percent, defined as 100 lessthe answer to the question.3 Overconfidence (OVER) is evaluated by the answer to the question“does the statement ‘I will never be robbed’ hold true to you?” and is measured on a scale from
1 to 5.
Regarding past experience, we examine: (a) whether respondents ever received a flu
shot (EXINJ), (b) whether they were infected by influenza during the last two years (EXILL),(c) if they had periodic blood tests (TESTP), and (d) if they had blood tests because of asuspected illness (TESTS)4. Variable (a) is especially interesting because we expect that
individuals have status quo bias, i.e., those who previously received the flu vaccination willcontinue to receive an annual vaccination. We expect that variables (a), (c), and (d) will havepositive effects on WTINJ in the estimation of Eq. (1) with whole samples.
We also speculate that status quo bias makes people automatically choose to accept thevaccine without examining it fully so that, for such respondents, the explanatory power ofperceived benefits and costs of vaccination will be lower. To examine this hypothesis, weestimate Eq. (1) not only for whole samples but also for samples divided according to whether
or not they previously received the vaccination.
Regarding characteristics, we examine education level (EDUC), gender (MALE),
marital status (MARRY), anxiety regarding health (HEALTH), and age (AGE). Special attention
is paid to the level of education because it may represent acquired knowledge and the ability to
3

The variable may also be interpreted as an individual’s probability for action and precautionary

motivation.
4

Evidence indicates that preventive behaviors may be highly correlated with one another (Fukunaga etal., 1997).

4digest information, which may impact WTINJ. We include age because flu is especially
dangerous to elderly people. In addition to the elderly, unhealthy people are especiallyvulnerable to flu, thus we include HEALTH, which represents the respondent’s degree ofanxiety about his/her own health. We expect that those with higher anxiety levels will be more
motivated to take the vaccine.
2.1.4. Equation for the model
The equation for the model that examines factors affecting the willingness to be
vaccinated is as follows:
WTINJ = α + β 1 PROB + β 2 ILL + β 3 BOTHER + β 4 EFFECT + β 5 SIDE
+ β 6 FEE + γ 1 DTP + γ 2 RA + γ 3OVER + δ 1 EXILL + δ 2 EXINJ +δ 3 TESTP(1)+ δ 4TESTS + ε 1 EDUC + ε 2 MALE + ε 3 MARRY + ε 4 HEALTH + ε 5 AGE + u
The list of variables, their definitions, and a short description of the survey question for eachare presented in Table 15. The constant term is indicated by α and disturbance is marked by u.
2.2. How the subjective variables are influenced by exogenous variables
The variables representing costs and benefits in Eq. (1) are subjective variables that
may be affected by exogenous variables such as a respondent’s characteristics. Therefore, if
we want to draw policy implications from estimations of Eq. (1) and accurately evaluate theeffect of exogenous variables on WTINJ, we need to weigh their indirect effect via thesubjective variables. We used the equation below to analyze the effect of exogenous factors on
subjective variables that, in turn, affect the willingness to be vaccinated.
SBJVAR = ϕ 0 + ϕ 1 EXILL + ϕ 2 EDUC + ϕ 3 OVER + ϕ 4 HEALTH+ ϕ 5 MALE + ϕ 6 MARRY + ϕ 7 AGE + v(2)Here, SBJVAR stands for PROB, ILL, BOTHER, EFFECT, SIDE, and FEE.6 The
constant term is indicated by ϕ0 and disturbance is indicated by v.7 In cases when PROB is thedependent variable, we suppose that anxiety about one’s own health (HEALTH) has a positiveeffect on PROB, and that overconfidence (OVER) has a negative effect. We also suppose that
MALE has a negative effect because males are more overconfident than females (Barber andOdean, 2001), a tendency that might not be fully grasped by OVER. We further suppose that5

Expected signs for the coefficients of the factors are shown in Table 4. The signs indicate whether afactor is expected to positively or negatively affect the willingness to be vaccinated6

However, FEE turns out to be insignificant in the estimation of Eq. (1).

7Expected signs for the factors are shown in Table 5.

5

AGE has a positive effect on PROB because elderly people tend to be less healthy, a tendency
that might not be fully represented by HEALTH.
3. Our survey and data
The COE (Center of Excellence) project of Osaka University in Japan, which was
funded by the Japanese government, conducted a large survey in the USA in 2005. 8 We
extracted a sample of 12,338 subjects from the study based on region, gender, race, and age,balanced according to census division demographics. Questionnaires were sent to the subjects
in January and 4979 responses (40%) were received. The number of respondents from eachregion is shown in Fig. 1. The survey questionnaire comprised 102 questions including elevenon attitude toward influenza and flu vaccination.
The number of respondents in each category (gender, age, received flu shot in past, andhad been ill with influenza) is given in Table 2. Women comprised about 55% of the
respondents, over 60 years represented 23%, and those who received a flu shot in the past
constituted 59%. The percentage of those who had been vaccinated in the past was muchhigher (78%) among subjects over 60 years than among subjects 60 years and under (53%).
Table 3 summarizes the mean values of the variables used in this paper, as reported on
the returned questionnaires. The intention to be vaccinated is higher for the group that was
vaccinated in the past (3.40 for individuals 60 years and younger and 4.10 for individuals over
60 years) than for the group that was never vaccinated (1.68 for 60 years and younger and 1.78
for over 60). Mean values for perceived effectiveness of vaccination and degree of anxiety
about health are higher for the group that received the vaccine in the past than for those who
never received it, while the mean value for estimated side effects of the flu shot is higher forthe second group.
4. Results
4.1. Results of the decision model
Table 4 reports the results for Eq. (1) estimated by the ordered probit method. For theentire sample, most of the variables relating to costs and benefits of vaccination, including
perceived probability of infection (PROB), seriousness of illness (ILL), degree of bother tofamily and friends in case of illness (BOTHER), effectiveness of vaccination (EFFECT), andexpected side effects of flu shot (SIDE), are highly significant and have the expected positive
influence on the willingness to receive the flu shot (WTINJ). The only exception is the fee(FEE), for which the influence was negative but the p value was 0.33. Therefore, we conclude
that models such as the HBM, in which costs and benefits play an important role, areoverwhelmingly supported.

8

The questionnaire can be seen at http://www2.econ.osaka-u.ac.jp/coe/project/survey-0502e.pdf.

6

Risk aversion (RA) positively affects the decision to be vaccinated, implying that the
impact of perceived effectiveness of vaccination dominates the impact of its estimated side
effects. As expected, the degree of time preference (DTP) positively affects the decision to be
vaccinated, meaning that future-oriented people have greater intentions to take the flu shot.
These results suggest that a traditional economic approach assuming rational agents well
explains influenza vaccination behavior.
Regarding characteristics of the respondents, all age dummies are significantly positive,
with elderly people more willing to undergo vaccination, as expected. The degree of anxiety
about one’s health (HEALTH) is significantly positive, indicating that those with greater
anxiety have greater intentions to be vaccinated. Married people are more likely to get
vaccinated. Gender and education were not significant.
4.2. Examination of status quo bias
Among the variables concerning experience, previous flu illness (EXILL) was not
significant, which is an unexpected result. However, past flu vaccination (EXINJ) had asignificantly positive effect on WTINJ, clearly confirming the status quo bias. In addition,
having periodic blood tests (TESTP) was significantly positive, as was having had blood tests
when disease was suspected (TESTS), even though the significance was 20%. Status quo bias
is also seen in Table 3: those who were vaccinated in the past are much more willing to be
vaccinated than the average while those who were never vaccinated are much less willing than
the average.
We now examine if belief variables are less significant for those who were vaccinatedin the past than for those who were never vaccinated, a second hypothesis of the status quo
bias. In this estimation, the experience variables (EXINJ, EXILL, TESTP, and TESTS), weredeleted from Eq. (1). Results are given in columns 6-9 of Table 4 (headed ‘Vaccinated in past’and ‘Never vaccinated’). Focusing on the belief variables, PROB, BOTHER, and others
became insignificant among those who had been vaccinated in the past but remained
significant among those who were never vaccinated. So did the degree of time preference(DTP), which evaluates costs paid now versus benefits obtained in the future. These results
support our hypothesis.
In contrast, age dummies are more significant for those who were vaccinated in the pastthan for those who were never vaccinated. Although this result is not inconsistent with our
hypothesis regarding status quo bias since age is not ‘a perceived variable’, it is intriguing to
explore the reason behind it. One possible explanation is that this result does not reflect the
influence of having been vaccinated (EXINJ) but, rather, the causality of age on acceptance ofthe vaccination in the past. To examine this hypothesis, we regressed EXINJ over the
independent variables of Eq. (1).9 Results are shown in columns 10 and 11 of Table 4. (headed‘Dependent value is EXINJ’). While a couple of belief variables loose their explanatory power,

9

EXINJ, of course, is deleted from the explanatory variables.

7

age dummies have significant power to explain inoculation behavior in the past, supporting ourinterpretation.
In summary, results of the decision model confirm most of our hypotheses. Specifically,both traditional and behavioral economic approaches are valid for explaining inoculation
behavior. However, a direct effect of education level on the willingness to receive vaccination
was not found. Thus, we need to examine how education level indirectly affects willingness
via beliefs. This was done using Eq. (2) and is discussed in the next section.
4.3. Results of the subjective variable model
Results of the subjective variable model specified in Eq. (2) are summarized in Table 5.
All perception variables, except PROB, are ordered categories. Thus, Eq. (2) was estimated byordered probit except for PROB, which was estimated using ordinary least squares (OLS). All
the variables are highly significant, especially EXILL whose large positive coefficient suggests
representative bias. Health anxiety (HEALTH) also has a positive effect. On the other hand,overconfidence mitigates the subjective probability of infection. Women have a higher PROB
value than men; married individuals have a higher PROB value than unmarried. A higher age
results in a higher PROB, while higher education results in a lower PROB.
As to the estimations for belief variables, two comments are in order.
First, overconfidence does not directly affect vaccination behavior, as shown in Table 4,
but indirectly decreases WTINJ via PROB, as shown in Table 5. This fact would have been
overlooked if we did not estimate Eq. (2). Actually, consistent with our intuition,
overconfidence significantly decreases ILL, FEE, and SIDE, and significantly increases
EFFECT. In Table 5, the dummy variable representing gender (MALE) is negative, as is
overconfidence, in the estimation of PROB, ILL, FEE, and SIDE, suggesting that males are
more overconfident than females. This suggestion has been widely accepted; Barber andOdean (2001) used the male dummy as a proxy of overconfidence.
Second, the finding that AGE negatively affects PROB is somewhat unexpected but
probably because older people tend to accept more vaccines and think their probability ofcontracting influenza is lower. Effects of AGE on other dependent variables were consistent
with our expectations.
4.4. How to raise inoculation rate?
Our final goal was to find a means of raising the inoculation rate. Although a naturalsuggestion might be to lower the inoculation fee, this seems useless in the USA because thecoefficient of FEE is insignificant (Table 4).10 Thus, we examined a second possibility,
increasing public knowledge of the risks of influenza and the usefulness of the vaccine.
The coefficient of education (EDUC) is significantly negative in the estimation of
PROB (Table 5). In Eq. (1), PROB has a positive effect on the willingness to receive
10

However, for those who took vaccination, it is significant at the 10% level.

8vaccination but EDUC is not significant, implying that higher education lowers WTINJ.
Assuming that people with a higher education are better able to acquire and process
information, this result suggests that conveying more information to people may lower thevaccination rate.
To examine the effect of information on WTINJ, however, we need to measure its
effects not only on PROB but also on the other belief variables. Results show that thecoefficients of EDUC are negative in the estimation of PROB, ILL, and SIDE, but positive for
FEE and EFFECT. To measure the effect of EDUC on WTINJ through these belief variables,
we multiplied the marginal effects (ME) of EDUC in Table 5 by the ME of these variables in
Eq. (1), shown in Table 4. Since all equations except for PROB are estimated by ordered probit,
we calculated their effects as follows. First, the expectation of the dependent variable Y (whichtakes the value 1, 2, …, J) is defined as:J

EY ≡ ∑ jP( j )

(3)

j =1

where P(j) stands for the probability that Y takes on the value of j. The ME of an exogenous
variable (x) on the expected value is defined as:
J∂EY∂P( j ) 11
.
≡∑ j
∂x
∂x
j =1

(4)

Therefore, for example, the ME of SIDE, is calculated as follows:
5∂( EWTINJ)∂P( j ) of eq.(1) 7 ∂P( j ) of eq.(2) of SIDE=∑j×∑ j∂( EDUC) via SIDE j =1∂(SIDE)∂( EDUC)j =1(5)

The calculated values are shown in the bottom row of Table 5. They are negative for
PROB and ILL, while positive for EFFECT and SIDE.12 Considering that PROB, ILL, and
BOTHER are subjective perceptions regarding the illness, while FEE, EFFECT, and SIDE are
subjective perceptions regarding the vaccine, these results may suggest that increased
education on the illness will lead to a decrease in willingness to be vaccinated, while increased
education on the vaccine will raise this willingness.
To confirm this hypothesis, we calculated the sum of indirect effects of EDUC on
WTINJ through the perception variables. The sum of the indirect effects of EDUC throughperception variables concerning the illness (PROB, ILL, and BOTHER) is -0.0091, while thesum concerning the vaccine (EFFECT, FEE, and SIDE) is 0.0143, resulting in a cumulative11

For the variable PROB, the value of the estimated coefficient itself is the marginal value.

12

Those for BOTHER and FEE are insignificant.

9

positive effect of 0.0052.13 These results may be interpreted thusly: (a) people, on average,
believe influenza to be more serious than it really is and (b) they do not trust the vaccination to
the extent it deserves.
Indeed, as shown in Table 5, having had influenza (EXILL) lowers the subjective
severity of the disease (ILL), suggesting that the flu was milder than expected. To see the effectof having been vaccinated, we added a cross term of EXINJ and EXILL to Eq. (2) andestimated them (results not shown). The coefficient of the cross term is significantly positive
for EFFECT, suggesting that EXINJ raises EFFECT in people that experienced the flu.14
These results suggest that dissemination of information on the vaccine may promoteinoculation because people currently undervalue the effectiveness of vaccination. In contrast,
dissemination of information on the illness may decrease vaccination because people believe
influenza is more serious than it really is.
5. Summary and conclusions

The current study identified behaviors and beliefs regarding willingness to get a flu
shot. It shows that individuals who were more willing to be vaccinated had stronger beliefs (a)in the probability of contracting the disease, (b) that influenza is serious, (c) that the vaccine iseffective, and (d) that side effects of the vaccine are minor. These results are compatible with
the HBM approach, suggesting that people rationally assess costs and benefits before deciding
to be vaccinated.15
The current study is unique in that it uses survey questions to grasp preference factors
such as attitude towards risk and time preference. We found that individuals who are more
future oriented (i.e., have a lower discount rate) put greater emphasis on future benefits ofvaccination and lesser emphasis on immediate costs. Therefore, they are more willing to be
vaccinated. We also found that individuals with greater risk aversion are more willing to be
vaccinated, suggesting that the fear of influenza dominates the fear of side effects of thevaccine. Finding that costs and benefits as well as preferences have strong explanatory power
indicates that traditional economics is a powerful vehicle for understanding vaccination
behavior.

13

For a robustness check, we also calculated the effects (a) using the coefficients of Eq. (1) instead ofME, (b) using OLS estimates instead of estimates by ordered probit, and (c) aggregating only thestatistically significant paths instead of all paths. In all cases, results were similar.
14

Following the HBM approach, Blue and Valley (2002) and Nexoe et al. (1999) found that individuals

who had been vaccinated believed more strongly that influenza is a serious illness and that vaccination
provides health benefits than those who had not been vaccinated. Our results are consistent with theirfindings.
15

The high significance of ILL is consistent with the results of Blank et al. (2008). In their study, thelevel of influenza vaccination in five European countries was analyzed. Perception of the flu as aserious illness was determined to be one of the principal reasons for being vaccinated.

10Nonetheless, this paper argues that traditional economics is not sufficient to fully
understand vaccination behavior. We speculated that rationality is bounded and examined
whether overconfidence and past experience affect the behavior. We found that overconfidence
does not directly affect willingness to be vaccinated, but indirectly affects it through subjective
variables such as the probability of getting influenza, assessment of its severity, effectivenessof the vaccination, and assessment of side effects of the vaccination.
We confirmed status quo bias in that previously vaccinated individuals were more
willing to take the vaccine. Dividing the respondents into two groups according to whether or
not they had been vaccinated in the past, we confirmed our second hypothesis of status quo
bias that people who had been vaccinated in the past automatically chose to be vaccinatedagain without careful examination. The subjective probability of contracting influenza (PROB)
and the degree that the family would be bothered (BOTHER) became insignificant in the
regression using those who had previously been vaccinated, suggesting our hypothesis isaccurate.
Because influenza has large externalities on society, the optimal vaccination rate ofsociety is probably higher than the optimal rate of individuals. Thus, our final goal was to finda way to raise the rate based on our results. While policy instruments may include reduction of
the vaccination fee and dissemination of information regarding influenza and vaccination, ourresults suggest that reducing the fee will not significantly raise the vaccination rate.
Then, how about disseminating information? If rationality is bounded, and usually it is,advertisement strongly influences behavior. A problem is how to grasp the magnitude of
information. The variable EDUC may be a good proxy because acquisition and digestion of
information are easier for those with a higher education.16 Although schooling did not directly
affect willingness to be vaccinated in Eq. (1), it affected willingness indirectly throughsubjective variables such as PROB, EFFECT, and SIDE in Eq. (2). Closer inspection reveals
that dissemination of information on the illness does not raise the vaccination rate, but
information on the vaccination does.
References
Barber Brad M., Odean T., 2001. Boys will be boys: gender, overconfidence, and common
stock investment. The Quarterly Journal of Economics 116 (1), 261–292.
Blank P., Schwenkglenks M., Szucs T., 2008. Influenza vaccination coverage rates in fiveEuropean countries during season 2006/07 and trends over six consecutive seasons.
BMC Public Health 8, 272.
Blue C.L., Valley J.M., 2002. Predictors of influenza vaccine: acceptance among healthy adult
workers. AAOHN Journal 50 (5), 227–235.

16

White et al. (2008) studied relationships between health literacy (based on 28 health-related items in

the National Assessment of Adult Literacy) and preventive health practice in the USA. They found that
low literacy was associated with a decreased likelihood of flu vaccination in adults aged 65

11Bridges C.B., Thompson W.W., Meltzer M.I., et al., 2000. Effectiveness and cost-benefit of
influenza vaccination of healthy working adults: a randomized controlled trial. The
Journal of the American Medical Association 284 (13), 1655–1663.
CDC, 2006. Prevention and Control of Influenza: Recommendations of the Advisory
Committee on Immunization Practices (ACIP). Centers for Disease Control andPrevention, United States Department of Health and Human Services, available at:
http://www.cdc.gov/mmwr/preview/mmwrhtml/rr54e713a1.htm.
Chapman G.B., Coups E.J., 1999a. Predictors of influenza vaccine acceptance among healthy
adults. Preventive Medicine 29 (4), 249–262.
Chapman G.B., Coups E.J., 1999b. Time preferences and preventive health behavior:acceptance of the influenza vaccine. Medical Decision Making 19 (3), 307–314.
Chen J.Y., Fox S.A., Cantrell C.H., Stockdale S.E., Kagawa-Singer M., 2007. Healthdisparities and prevention: racial/ethnic barriers to flu vaccinations. Journal ofCommunity Health 32 (1), 5–20.
Chen Y., Yi Q. L., Wu J., Li F., 2007. Chronic disease status, self-perceived health andhospital admissions are important predictors for having a flu shot in Canada. Vaccine
25 , 7436–7440.
Demicheli V., Jefferson T., Rivetti D., Deeks J., 2000. Prevention and early treatment ofinfluenza in healthy adults. Vaccine 18, 957–1030.
Fukunaga I., Jitsunari F., Takeda N., Asakawa F., Maruyama Y., 1997. A study of healthbehavior of the elderly - correlation between participation in health examinations andhealth behavior. Japanese Journal of Hygiene 52 (2), 490–503.
Greene W.H., 2001. Econometric Analysis. Prentice-Hall Inc.
Griffin D.W., Varey C.A., 1996. Towards a consensus on overconfidence. Organizational
Behavior and Human Decision Processes 65 (3), 227–231.
Lau J., Kim J., Yang X., Tsui H.Y., 2008. Cross-sectional and longitudinal factors predicting
influenza vaccination in Hong Kong Chinese elderly aged 65 and above. Journal of
Infection, 56 (6), 460–468.
Lee P.Y., Matchar D.B., Clements D.A., Huber J., Hamilton J.D., Peterson E.D., 2002.
Economic analysis of influenza vaccination and antiviral treatment for healthy working
adults. Annals of Internal Medicine 137 (4), 225–231.
Nexoe J., Kragstrup J., Sogaard J., 1999. Decision on influenza vaccination among the elderly:
a questionnaire study based on the Health Belief Model and the Multidimensional
Locus of Control Theory. Scandinavian Journal of Primary Health Care 17 (2), 105–
110.
Nichol K.L., Mallon K.P., Mendelman P.M., 2003. Cost benefit of influenza vaccination in
healthy, working adults: an economic analysis based on the results of a clinical trial oftrivalent live attenuated influenza virus vaccine. Vaccine 21 (17-18), 2207–2217.
Rosenstock L.M., Strecher V.J., Becker M.H., 1988. Social learning theory and the HealthBelief Model. Health Education Quarterly 15 (2), 175–183.
Samuelson W., Zeckhauser R.J., 1988. Status quo bias in decision making. Journal of Risk andUncertainty 1, 7–59.
12

Shahrabani S., Benzion U., 2006. The effects of socio-economic factors on the decision to be
vaccinated: the case of flu-shot vaccination. The Israel Medical Association Journal(IMAJ) 8, 630–634.
Shahrabani S., Gafni A., Benzion U., 2008. Low flu shot rates puzzle - some plausible
behavioral explanations. The American Economist LII (1), 66–72.
Shahrabani S., Benzion U., Yom Din G., 2009. Factors affecting nurses' decision to get the flu
vaccine. European Journal of Health Economics 10 (2), 227–231.
White S., Chen J., Atchison R., 2008. Relationship of preventive health practices and healthliteracy: a national study. American Journal of Health Behavior 32 (3), 227–242.
Wu S., 2003. Sickness and preventive medical behavior. Journal of Health Economics 22,
675–689.

13

Table 1
Variables and their linkage to the questionnaire.
Variable

Definition

Question

Range of answers

Willingness to get flu shot

Question
no.
48

WTINJ

Do you intend to receive the flu shot in the next 12 months?

PROB

Subjective probability of illness

50

ILL

Severity of illness

44

Estimate your chance to be infected by flu during the next 12months.
How serious a disease do you think the flu is?

1 = yes, certainly; 5 = no, certainlynot0-100%

BOTHER

Bother to family when ill

47

EFFECT

Effectiveness of vaccination

49

SIDE

Side effects of vaccine

46

FEE

Cost of flu shot

DTP
RA

Value entered into
formula6 less response
Reported value

1 = extremely serious; 6 = little
influence
1 = extremely; 4 = hardly

7 less response

1 = perfectly; 5 = never effective

6 less response

1 = extremely serious; 7 = notserious1 = free; 6 = above $50

8 less response

45

How serious do you think the side effects caused by a flu shotare?
How much do you think a flu shot costs?

Time preference

2.5

I want to postpone joys for later.

6 less response

21

OVER

Risk aversion; threshold foractionOverconfidence

2.6

How high does the probability of rain have to be in order for you
to take an umbrella?
I will never be robbed.

1 = particularly true; 5 = not trueat all0-100%

EXINJ

Ever received a flu shot

41

EXILL

Infected during previous 2 years

TESTP
TESTS

When infected, to what extent do you bother your family andfriends?
How effective do you think the flu shot is?

5 less response

Reported value

100 less response
6 less response

Have you ever received a flu shot?

1 = particularly true; 5 = not trueat allno = 0; yes = 1

43Have you been infected by the flu during the last two years?

no = 0; yes = 1

Reported value

Periodic blood test

51.1

no = 0; yes = 1

Reported value

51.2

no = 0; yes = 1

Reported value

EDUC

Blood test because of suspected
illness
Education level

Did you undergo a blood test in the last 12 months as part of aperiodic test?
Did you undergo a blood test in the last 12 months because ofsuspected disease?
The highest level of education completed?

Reported value

MALE

Gender54

1 = grade school; 9 = doctoral
degreemale = 1; female = 0

MARRY

Marital status55

married = 1; other =0

Reported value

HEALTH

Anxious about health2.12

I have anxieties about my health.

6 less response

AGE

Age

57.1

Your birth year?

1 = particularly true; 5 = not trueat all
20-90

58.1

14

Reported value

Reported value

2005 less responseTable 2
Number of respondents in each category (total n = 4979).
Men

3506

Infected by flu during
last two years
Never
Yes
No
Yes
1159
2039
2940
4004
975

Men

1541

498

920

1311

1809

422

Women

1965

661

1119

1629

2195

553

Age≤60

1646

1860

2704

802

Age>60

251

908

1047

112

1604

435

2400

540

Total

2231

Women Age≤60 Age>60
2748

Never received flu
shot
Received flu shot

15

Vaccinated in past

Table 3
Mean values of the variables (standard errors in parentheses) amongst those who had been
vaccinated in the past and those who had never been vaccinated, by age.
Variable

Scale

Mean

Vaccinated in pastAge≤60 Age >60
3.40
4.10
(0.03)(0.04)

Never vaccinatedAge≤60 Age>60
1.68
1.78
(0.02)(0.06)

WTINJ

1-5

2.83
(0.02)

PROB

0-100

26.01
(0.35)

28.99(0.58)

19.20(0.73)

26.82(0.61)

23.10(1.58)

ILL

1-6

4.22
(0.02)

4.35(0.03)

4.81(0.04)

3.81(0.03)

4.09(0.09)

BOTHER

1-4

1.68(0.01)

1.73(0.01)

1.53(0.02)

1.73(0.02)

1.57(0.04)EFFECT

1-5

3.00(0.01)

3.11(0.01)

3.27(0.02)

2.77(0.01)

2.69(0.05)

SIDE

1-7

3.15
(0.02)

2.97(0.03)

2.90(0.05)

3.39(0.03)

3.80(0.11)FEE

1-6 (US$0-50)

3.38(0.02)

3.50(0.03)

3.23(0.04)

3.42(0.03)

3.16(0.09)

DTP

1-5

3.18
(0.02)

3.16(0.03)

3.28(0.04)

3.17(0.03)

3.17(0.09)

RA

0-100

40.66
(0.42)

39.63(0.67)

43.67(0.94)

39.73(0.77)

39.69(1.89)

OVER

1-5

2.60
(0.01)

2.56(0.02)

2.61(0.04)

2.61(0.02)

2.54(0.07)EXILL

0 (no)
1 (yes)0.19(0.00)

0.22(0.01)

0.09(0.01)

0.23(0.01)

0.11(0.02)TESTP

0 (no)
1 (yes)0.59(0.00)

0.61(0.01)

0.83(0.01)

0.45(0.01)

0.57(0.03)TESTS

0 (no)
1 (yes)0.04(0.00)

0.05(0.00)

0.04(0.00)

0.03(0.00)

0.03(0.01)

EDUC

1-9

4.80
(0.02)

5.02(0.04)

4.62(0.06)

4.75(0.04)

4.46(0.11)MALE

0 (female)
1 (male)MARRY

0 (unmarried)
1 (married)

0.42(0.01)

0.35(0.01)

0.39(0.02)

0.43(0.01)

0.39(0.02)HEALTH

1-5

2.94(0.01)

3.04(0.02)

3.12(0.04)

2.78(0.03)

2.70(0.08)

AGE

20-90

47.61
(0.25)

42.25(0.27)

71.56(0.26)

37.97(0.28)

68.74(0.41)

16

Table 4
Results of decision model Eq. (1), by ordered probit.
Dependent value is WTINJ
Dependent value is
Whole sample
Vaccinated in past Never vaccinated
EXINJ*
Expected Estimate P value Marginal Estimate P value Estimate P value Estimate P valuesigneffectPROB+
0.0029 0.0000
0.00350.0014 0.1820
0.0071 0.0000
0.0024 0.0080
ILL+
0.1685 0.0000
0.20420.2046 0.0000
0.1017 0.0000
0.1692 0.0000
BOTHER +0.1072 0.0000
0.12990.0458 0.1370
0.2239 0.0000 -0.0373 0.1870
EFFECT +0.4695 0.0000
0.56870.5299 0.0000
0.3601 0.0000
0.3473 0.0000
SIDE-0.1450 0.0000-0.1756 -0.1455 0.0000 -0.1142 0.0000 -0.1461 0.0000
FEE-0.0134 0.3300-0.0163 -0.0286 0.1100 -0.0015 0.9440 -0.0007 0.9640
DTP+
0.0287 0.0830
0.03480.0189 0.3670
0.0633 0.0210
0.0009 0.9640
RA?
0.0035 0.0000
0.00430.0022 0.0100
0.0062 0.0000
0.0011 0.1810
OVER-0.0019 0.9080-0.0023 -0.0121 0.5620
0.0125 0.6510 -0.0017 0.9300
EXINJ+
1.3024 0.0000
1.4861EXILL+
0.0296 0.5450
0.0359TESTP+
0.2582 0.0000
0.3108TESTS+
0.1192 0.2050
0.1455EDUC?
-0.0034 0.7410-0.00420.0067 0.6100 -0.0126 0.4670
0.0449 0.0000
MALE
0.0464 0.2190
0.05620.0403 0.4010
0.0213 0.7300
0.0296 0.5090
MARRY?
0.0114 0.8140
0.0138 -0.1375 0.0360
0.1350 0.0570 -0.2963 0.0000
HEALTH +0.0702 0.0000
0.08500.0813 0.0000
0.0605 0.0210
0.1099 0.0000
AGE40+
0.1312 0.0130
0.15980.2234 0.0020
0.0821 0.3000
0.0888 0.1430
AGE50+
0.2914 0.0000
0.35640.4496 0.0000
0.1138 0.2090
0.4109 0.0000
AGE60+
0.5343 0.0000
0.65300.6672 0.0000
0.3329 0.0010
0.7557 0.0000
Constant ?
-1.8001 0.0000
S.E.
S.E.
S.E.
μ1
2.4186 2.7137
1.3197 0.1996
2.1704 0.2328
μ23.5376 3.8410
2.2364 0.2012
3.5182 0.2405
μ33.9190 4.2257
2.5596 0.2022
4.2164 0.2496
μ44.5474 4.8599
3.2279 0.2047
4.7455 0.2659
Pseudo0.53390.28250.16740.1861
No. of observations
3809
2362
1534
3908

Note: μ indicates boundary values of categories for which standard errors (S.E.) are given instead of p values.
* In these columns, results of Eq. (1) exclude EXINJ, EXILL, TESTP, and TESTS.
17

Table 5
How subjective variables are determined, results of Eq. (2).
Dependent variable
PROB
Variable
Constant
OVER
EXILL
EDUC
MALE
MARRY
HEALTH
AGE

Sign
?
+
?
?
+
+

μ1
μ2
μ3
μ4
μ5
μ6
R2
No. of observations
ME of EDUC
Effect of EDUC
on WTINJ

ILL
Coeff
34.072-1.28918.7-0.704-2.149-2.4851.602-0.173

P value
0.000
0.000
0.000
0.000
0.002
0.006
0.000
0.000

BOTHER
Coeff

P value

Sign

Coeff

P value

Sign

?
?
?
+
+

-0.048-0.175-0.028-0.169-0.017
0.097
0.012

0.001
0.000
0.002
0.000
0.687
0.000
0.000
S.E.
-1.5
-0.7
0.38
0.83
1.18

?
?
?
+
?

0.014
0.041
0.01
0.204
-0.1
0.088
-0.01

0.338
0.348
0.292
0.000
0.025
0.000
0.000
S.E.
0.091
1.194
1.975

?
?
?
?
?

-1.661-0.875
0.205
0.65
0.995
0.136
4318
-0.704-0.002

0.022
0.193

FEE

Sign

4369
-0.037-0.007

-0.1
1.001
1.775

4336
0.007
0.0009

-0.04
0.005
0.014
-0.07
-0.01
0.021
-0.01
-1.2
-1.04
-0.54
0.593
1.678

0.017
0.012

EFFECT
Coeff

P value

Sign

0.003
0.909
0.124
0.032
0806
0117
0.000
S.E.
-1.02
-0.86
-0.37
0.77
1.866

+
?
?
?
?
?

0.004
4374
0.017
-0.0003

0.007

SIDE

Coeff

P value

Sign

Coeff

P value

0.044-0.2270.025-0.008
0.062
0.033
0.006

0.002
0.000
0.007
0.812
0.147
0.015
0.000
S.E.
-1.23
-0.06
1.61
2.602

?
?
?
+
+

-0.04
0.098
-0.02
-0.09
0.008
0.038
-0.000

0.007
0014
0.036
0.004
0.841
0.003
0045
S.E.
-1.3
-0.1
0.43
0.85
1.1
1.65

-1.422-0.244
1.425
2.407

0.009
0.011

4387
0.017
0.00972

0.006

-1.47
-0.29
0.261
0.674
0.925
1.466
0.003
4367
-0.028
0.005

0.013

Note: All equations were estimated by ordered probit except for PROB, which was estimated by ordinary least squares. Sign = expected influence or sign ofcoefficient. Coeff = coefficient. μ indicates boundary values of categories for which standard errors (S.E.) are given instead of p values. The marginal effectJ∂P( j )∂EY(ME) of EDUC was estimated as, where EY stands for the expectation of the dependent variable (ILL, BOTHER, FEE, EFFECT, or=∑j∂EDUC j =1 ∂EDUC
SIDE ). Note that ∂P( j ) / ∂EDUC is usually called marginal effect (Greene, 2001). The effect of EDUC on WTINJ is calculated as the product of marginal
effect (ME) of EDUC in this table and ME of the variable in Eq. (1), shown in Table 4.
18

721

19

GEO9 Pacific316

GEO8 Mountain247

GEO7 West South
Central368

GEO6 East South
Central835

GEO5 South Atlantic

GEO4 West North
Central

GEO3 East North
Central

GEO2 Middle Atlantic

1000
900
800
700
600
500
400
300
200
100
0
GEO1 New EnglandFig. 1
Number of responses by region.

935
699

541
317