Discussion Paper No. 554

EMPIRICAL RESEARCH ON
THE DEMAND FOR INFLUENZA VACCINATIONAMONG THE ELDERLY

Yasushi Ohkusa

August 2001

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Empirical Research on the Demand for Influenza
Vaccination Among the Elderly

Yasushi Ohkusa
ISER, Osaka University

JEL Classifications: I11, I12, I18
Keywords: Influenza Vaccination, Actual Vaccination Rate, Conjoint Analysis, Joint Estimation, the Elderly as a High Risk Group
Correspondence:Yasushi Ohkusa, Institute of Social and Economic Research, Osaka University, 6-1 Mihogaoka Ibaraki Osaka, Japan
Tel:81-6-6879-8566

Fax:81-6-6878-2766e-mail:ohkusa@iser.osaka-u.ac.jp

Abstract
Purpose: This paper analyses what factors determine the demand for vaccination among
the elderly as a high risk group. Using the estimation results, this paper then evaluates
how legal requirements and/or subsidies aﬀect their demand.
Methods: Original data were obtained from two surveys, conducted by the author, given
to two groups: elderly people living with descendants, and elderly people living without
descendants. The surveys contained information about the elderly, the household, experience of influenza during the last season, immunization during this time, and a hypotheticalquestionnaire about immunization for purposes of Conjoint Analysis. Three estimations
are performed for actual behaviour, Conjoint Analysis and Joint Estimation, the latter
combining the first two estimations.
Results: Among estimation results, factors such as cost, number of immunizations, availability of immunization at night or on the weekend, and legal requirements heavily aﬀect
the demand for immunization. Experience of influenza and immunization in the preceding
season is one of the most important determinants. In addition, the superiority of the Joint
Estimation method is confirmed.
Conclusions: The estimation results imply that about 8.9 million elderly people will have ademand for vaccination if there is no cost and it is legally recommended. If the cost is 6000
yen (about US$50) and there is no legal recommendation, demand for vaccination will be
reduced to about 3.2 million elderly people. An increase in cost from free vaccination tojust 500 yen (about US$4) depresses demand from 1.6 million elderly people. On its own,
legal recommendations for vaccination can push up demand by 2.0 million elderly persons.

1Introduction

In Japan, the influenza epidemic of the 1997-98 season was the most severe since influenza
surveillance began in 1987. A total of 136,929 patients were clinically diagnosed with
influenza by sentinel clinics, the highest number ever recorded. There were approximately
50,000 pneumonia and influenza deaths during this season. Each year in the U.S., 10 to
50 million individuals become ill with ‘the flu’. In a typical year, approximately 20,000 ofthese individuals die from complications related to the disease.
To assess the severity of influenza epidemics, ‘excess mortality’, the number of deaths
actually recorded in excess of the number expected on the basis of past seasonal experiencehas been used as a major index (Serfling (1963), Assad, Cockburn and Sundaresan (1973),
Choi and Thacker (1981) for the US; and Tachibana, Kawaminami and Minowa (1999),
Tachibana and Minowa (1999) for Japan). Shindo, Ii, Ohkusa and Taniguchi (2000) proposed a new method to forecast the expected number of pneumonia and influenza deaths.
According to their method, excess mortality was 5,032 in January 1997, 2,820 in February
1998, and 6,798 in January 1999.
At present, vaccination is considered to be the best measure against influenza. Several studies have been conducted on the cost eﬀectiveness of influenza vaccination (Nichol,
Margolis, Wuorena and Sternberg (1994), Gross, Hermogenes, Sacks, Lau and Levandowski(1995), Levy (1996), Scott and Scott (1996)). Such discussions are useful where vaccination is compulsory. However, in most industrialized countries, including Japan, this is not
the case. Consequently, it is necessary to investigate what factors determine the demandfor vaccination. This aspect of influenza prevention has attracted little research attention so far, with the exceptions of Philipson (1996) and Mullahy (1999). Philipson (1996)
examined the demand for measles vaccination using survival analysis to analyse the timing of vaccination, both during the epidemic and non-epidemic seasons. Mullahy (1999)
estimated the demand for influenza immunization and the labour supply simultaneously,1and showed that during the influenza outbreak of 1998, the probability of receiving immunization increased according to the level of education, insurance, and higher opportunity
cost. However, Mullahy’s use of a linear probability model and the applicability of thesimultaneous estimation method are questionable.
The demand for influenza vaccination amongst adults less than 70 years old has already been investigated in Japan12) . In that study, two approaches, analysis for the actual
behaviour and conjoint analysis, were employed. In the actual behaviour analysis, pastexperience of influenza and the history of vaccination during the 1999-2000 season were
found to be influential in the decision to be vaccinated. In the Conjoint Analysis, it was
found that if the present vaccination fee of 6000 yen were to be waived, demand for vaccination would increase by 8.7 percentage points. If vaccination were provided at night andduring weekends demand would increase by 2.1 percentage points, and if provided as wellat workplaces and schools, it would increase by 3.2 percentage points.
The news of an influenza outbreak was also found to increase the demand for vaccination
by 6.6 percentage points. Higher income, however, was found to reduce the demand for
vaccination. This suggests that opportunity costs may be an influential factor in individuals’ decisions on vaccination. Habit formation, revealed in vaccination history, also plays
a quite important role in the demand for vaccination.
This paper extends the approach of the above study to the elderly, a high-risk group for
influenza. Initially, the survey was completed separately by two groups, elderly people livingwith descendants, and elderly people living not living with their descendants. In particular,
for elderly persons living with descendants, the hypothetical questionnaire for conjoint
analysis was answered by their descendants or family. Hospital patients and nursing homeresidents were excluded by the purpose of this paper. Following completion of the surveys,
actual behaviour, Conjoint Analysis, and Joint Estimation tests were performed. Joint
Estimation, which combines the first two methods, was found to be the most reliable
method13,14) .
2

2

Data

This paper is based on data from two separate surveys: elderly people living with descendants, and elderly people not living with their descendants.

2.1

Data for Elderly People with Descendants

The data for elderly people living with descendants was obtained from a survey conductedin May, 2001 in the Kanto (Tokyo, Kanagawa, Saitama, and Chiba prefectures) in eastern Japan, and the Kansai areas (Osaka, Kyoto, Nara, and Hyogo prefectures) in westernJapan. Of a total of 1300 questionnaires distributed, 1024 were completed and returned.
All these households voluntarily contracted with the survey firm to co-operate on various
surveys. The households that were surveyed are randomly sampled by a two-step strata,
but the decision to co-operate is, of course, not random. Therefore, particular attention
should be paid to the sampling bias caused by this type of sampling. As no unemployedpeople and few self-employed responded to the survey, there is a small bias to richer households. However, these biases would be controlled by using such information as explanatory
variables in the estimation rather than making simple comparisons about the average.
This sample contains 265 households and 338 elderly people. The questionnaire asks
about age, gender, chronic illness, household income, assets, home ownership, history of
vaccination, and experience of influenza during the last two seasons. In addition, it asks
hypothetical questions about vaccination which were used in the conjoint analysis. Though
hypothetical questions are completed by members of the elderly person’s family, such as
their descendants or housewives, they should reflect the elderly person’s opinion.

2.2

Data for Elderly People Not Living With Their Descendants

The data for elderly people not living with their descendants were obtained from a survey
conducted in June, 2001, covering the whole of Japan. Of the total 800 questionnaires
3

distributed, 737 were completed and returned. These elderly people also voluntarily contracted with the survey firm to co-operate on various surveys. It should be noted that there
is a sampling bias towards healthier elderly people, despite the fact that they are chosenrandomly. Moreover, the sampling rate for more than 70 elderly persons or living in therural area because they should be very few in the case of constant sampling rate. Of course,
these unequal sampling rates should be taken into account in the summary statistics or
estimation. While largely identical information is collected in the two surveys from the two
types of households, the complexity of the questionnaire is reduced for elderly people notliving with their descendants.

3

Summary Statistics

Table 1 summarizes the summary statistics for both types of households: the sample size is
338 in the group of elderly people with descendants, and 668 in the group of elderly people
not living with their descendants . The vaccination rates in the ’99/’00 season and ’00/’01
season are 7.5% and 16.1%, respectively. The percentage rates for experiencing influenza,
based on self-assessment, are 15.2
The significant diﬀerence in the experience rates of influenza based on each definition
cannot be found. Needless to say, this diﬀerence is partly due to the endogenous choicemade about whether to be vaccinated or not. Consequently, there is a selection bias whichcontaminates the true eﬃcacy of the data.
In the group of elderly people living with their descendants, vaccination rates and theexperience rate are almost the same. However, the experience rate for this group in the’99/’00 season based on a doctor’s diagnosis is only 3.0%, which is about half of the experience rate for elderly persons not living with their descendants. The outbreak of influenza inthat year emphasizes the diﬀerence between the two types of households. Of course, elderly
people make an endogenous choice whether or not to live with their descendants. Conse-4

quently, simple comparison of the two types of households is meaningless because there is aselection bias. However, to analyse this selection, information about the descendants wholive with the elderly people is required. Unfortunately, the collection of such information isvery diﬃcult and there are very few studies that examine this selection process explicitly.
On the other hand, many research studies ignore that bias15,16,17) . Consequently, this paper
does not deal with this selection process: that is, the decision whether or not to live with
their descendants is considered to be exogenous, at least for the vaccination decision.
Tables 2 and 3 contain the simple average of the responses to the hypothetical questions.
Note that the figure in Table 2, which represents the responses of elderly people livingwith their descendants, is not adjusted for the omitted attributes and thus is somewhat
misleading. Conversely, Table 3, which represents the responses of elderly people not livingwith their descendants, contains all the information from the hypothetical questionnaires.
While the questionnaires are diﬀerent for the two types of household, free vaccination
increases the demand by 5-10 times and it reaches about almost half. It is interestingthat the demand for vaccination is higher when it costs 500 yen (about US$4), than in
the case of free vaccination in the households not living with descendants. The eﬀect of alegal recommendation to be vaccinated is surveyed only in the case of elderly people notliving with their descendants. It increases the demand for vaccination by 20% points when
vaccination is free, but it has no substantial eﬀect when vaccination costs 2000 yen (about
$16).

4

Analysis of Actual Demand

The dependent variable takes two values: Ji = 1 if a person i received, Ji = 0 if they
did not. The independent variables are Spline function of Age f (Ai ), Gender (Gi = 1 if
female, Gi = 0 if male), dummy variable Ci taking the value of one if a person has a historyof chronic illness, household income (in logarithm) Hi , household net financial asset Ni ,5dummy variable Mi1 taking the value of one if a person owns a house, dummy variable Mi2taking the value of one if a person owns an apartment, a dummy variable Fi taking thevalue of one if a person suﬀered from influenza during the last season, and finally a dummy
variable Wi taking the value of one if a person received vaccination during the last season.
Empirical specifications are as follows:Ji∗ = α0 + αA f (Ai ) + αG Gi + αH Hi + αC Ci+ αN Ni + αM 1 Mi1 + αM 2 Mi2 + αF Fi + αW Wi + εiJi =(1 if Ji∗ > 0
0 otherwise(1)

For the purpose of estimation, the probit method with heteroscedasticity consistent is
adopted. The results of the estimation are presented in Table 4.
Though there are not so many significant variables in this table, vaccination in the lastseason is significant in both types of households. These marginal eﬀects are very high at
76-83% points. The experience of influenza in the last season is significant in elderly people
living with descendants and it rises 25% points. It is not significant in the group of elderly
people not living with their descendants. Almost all other variables like age or chronic
illness. On average, after taking into account other characteristics, the vaccination rateof elderly couples is 8.5%points less than for single elderly people, even in the group not
living with their descendants.

5

Conjoint Analysis

5.1

A Brief Explanation of Conjoint Analysis

This study utilizes a technique called Conjoint Analysis, which attempts to elicit people’s
preference for programme benefits. In diﬀerent hypothetical situations, respondents are
given various options and select their most preferred choice. Conjoint Analysis uses various

6

hypothetical situations and individual characteristics as independent variables, and choices
as dependent variables to estimate the statistical model. In addition, Conjoint Analysis
measures the change in utility.
In seeking measures of preference in the hypothetical contexts, approaches that havecommonly been used in health care include standard gamble and time trade-oﬀ. These
techniques tend to concentrate on obtaining preference values for health conditions, which
are then used as weights for the adjustment of life years in the calculation of qualityadjusted life years (QALYs). In addition, willingness-to-pay techniques have also beenused to obtain monetary valuations for programme benefits in the hypothetical context inhealth care. Conjoint Analysis, which uses peoples’ statements of how they would respondto diﬀerent hypothetical situations, is an alternative approach to obtaining preferences for
programme benefits. Such methods are increasingly being adopted in the health care sector.
Most studies pose questions on opinions on the introduction of new medical technologies,
such as in-vitro fertilization (Ryan (1999)), orthodontic services (Farrar and Ryan (1999)),
abortion technology (Ryan and Hughes(1997)), blood transfusion (Van der Pol and Cairns(1997)) and the use of MRI for the investigation of knee injuries (Bryan et al. (1998)). In
Japan, however, very few studies have employed the Conjoint Analysis method (Ohkusa(2000)).
Although Conjoint Analysis may be a better method than the conventional ones, there
remains a possibility of bias arising from two sources: hypothetical choice and hypotheticalscenarios. Ohkusa (2000) shows that both biases are fairly significant, particularly theconspicuous bias arising from the hypothetical scenarios. The analysis of these biases andthe method of controlling them is an important issue in future research.
For Conjoint Analysis, a probit estimation method with random eﬀect is used. Since thedependent variable takes the value of one or zero, probit estimation is a basic method. The
random eﬀects approach was chosen as it treats the individual eﬀects as being uncorrelatedwith other independent variables, and allows adjustment for the panel nature of the data.
7

For Conjoint Analysis, diﬀerent questionnaires were adopted for elderly people livingwith their descendants and for elderly people not living with their descendants. In thesurveys of elderly people living with their descendants, there are four attributes, which are
set as number of times immunized, cost, prevalence, and convenience, which refers to when
and where it is possible to be immunized. In addition, there are two to four conditions
attached to each attribution. These include:
Times: once or twice
Cost: free, 1500 yen (about US$12) per shot, or 3000 yen (about US$24) per shot
Prevalence: yes or no
Accessibility : vaccination at the medical institution during the day time, vaccination at the medical
institution at night or during the weekend, vaccination at an accessible place otherthan a medical institution, or vaccination at home.
Consequently, there are 48 hypothetical cases. Each respondent is asked 10 cases, in which
there are 5 patterns, and thus in total 50 cases are covered.
On the other hand, for elderly persons not living with their descendants, the hypotheticalquestions include would you like to receive an influenza vaccination if
Scenario 1: The law recommends immunization and the cost is 5000 yen (about US$40)
Scenario 2: The law recommends immunization and the cost is 2000 yen (about US$16)
Scenario 3: The law recommends immunization and the cost is 500 yen (about US$4)
Scenario 4: The law recommends immunization and it is free
Scenario 5: The law does not recommend immunization and the cost is 5000 yen (about US$40)
Scenario 6: The law does not recommend immunization and the cost is 2000 yen (about US$16)
8

Scenario 7: The law does not recommend immunization and the cost is 500 yen (about US$4)
Scenario 8: The law does not recommend immunization and the cost is free
At present the cost is about 3000 yen (about US$24 dollars) per shot, one shot is required
which can be received from a medical institution during the daytime, though there is
no legal recommendation to be immunized. Whether influenza is prevalent or not is asubjective question to the respondent. In the last season, it was seen to be of moderateprevalence.

5.2

Estimation using Conjoint Analysis

The dependent variable takes two values: Ji,j . under the jth hypothetical situation. The
independent variables are the variables in addition to those used in equation (1), prices Pi
under hypothetical situations (in logarithm), a dummy variable Rj1 which takes the valueof one if vaccination is available anytime, a dummy variable Rj2 which takes the value of
one if vaccination is available at an accessible place, Rj3 which takes the value of one if
vaccination is available at home, a dummy variable Kj which takes the value of one if
influenza is prevalent, and finally, Lj which takes the value of one if the law recommends
immunization.
Consequently, the empirical specification is∗Ji,j= βi + βP log Pj + βR1 Rj1 + βR2 Rj2 + βR3 Rj3 + βK Kj + βL Lj+ βA f (Ai ) + βG Gi + βH Hi + βN Ni+ βC Ci + βM 1 Mi1 + βM 2 Mi2 + βW Wi + βF Fi + βE Ei + εji
Ji,j =(∗
1 if Ji,j>00 otherwise(2)

where βi is a random variable following N (0, σβ2 ) and represents the individual eﬀect. For
estimation, the probit method with random eﬀect is used. With 10/8 hypothetical situa9

tions, the maximum size of j is 10/8, although some did not answer all questions. Therefore,
we were not able to observe 18 samples for each respondent. Estimation results are shownin Table 5.
The cost is significantly negative. When the cost is reduced from the current level of 6000
yen (about $24) in two shots, and made free of charge, the vaccination rate would increaseby 22-32% points. If the number of shots required is reduced from two to one, vaccination
rates rise by 14% points. If vaccination is available at any time, the rate wouldincreaseby 34% points, but vaccination at an accessible place or at home does not contribute to
an increase in the vaccination rate. Prevalence does not influence the rate. On the other
hand, if the law enforces immunization it increases by 9.5% points.
In other variables under the hypothetical question, the vaccination and experience of
influenza in the last season are positive and significant, as in Table 4. Moreover, themarginal eﬀect of the vaccination in the last season reached 43-62% points. The experienceof influenza in the last season is significant and its marginal eﬀect is about 12%points.
However, it is not significant amongst elderly people living with their descendants.

6

Analysis using Joint Estimation

So far, the actual behaviour and conjoint analysis have been estimated separately, butthese two data set have advantages and disadvantages. For example, although the actual
behaviour is objective and amenable to analysis, it does not contain information about theeﬀect of cost or other policy variables. In addition, it cannot be determined which individual
eﬀect would be the most important determinant of the behaviour. Joint estimation13,14) is
proposed to overcome the problems of using these two approaches. Specifically, on the one
hand, joint estimation uses the same coeﬃcients on the common variables as the other two
approaches, such as age, gender and chronic illness, and on the other hand, it estimatesthe eﬀect of the hypothetical situation. In this way, it excludes extreme responses to the10hypothetical situation and thus provides a more reliable estimator for cost and/or otherpolicy variables. The estimation equation is as follows:∗
Ji,j,k= γi + γP log Pj + γR1 Rj1 + γR2 Rj2 + γR3 Rj3 + γK Kj + γL Lj+ γA f (Ai ) + γG Gi + γH Hi + γN Ni+ γC Ci + γM 1 Mi1 + γM 2 Mi2 + γW Wi + γF Fi + γE Ei + γCJ CJk + εi,j,kJi,jk =(∗
1 if Ji,j,k>00 otherwise(3)

where k denotes whether this data comes from actual behaviour or Conjoint Analysis, andCJk is the dummy variable for Conjoint Analysis data. γi is a random eﬀect like βi in
Eq.(2), but is also included even in the actual behaviour data.
The estimation results are summarized in Table 6. This shows that the estimators for
hypothetical situations like cost are almost the same as in Table 5. In this sense, theestimates provided by Conjoint Analysis are as reliable as the Joint Estimation, at least in
this estimation.
Under Joint Estimation, the eﬀect of vaccination in the last season decreases to 3722% points, which is the lowest in the three estimation procedures. The past experienceof influenza is significant for both types of households and its impact is 12-19% points.
This figure is close to the actual behaviour estimate for elderly persons living with theirdescendants, and, conversely, it is close to the Conjoint Analysis estimate for elderly persons
not living with their descendants. Thus, the figure shows how the Joint Estimation method
avoids the extreme estimator.
In regard to chronic illness, the number of significant coeﬃcients in Joint estimation is
the highest of the three estimations. Cardiovascular disease is significant and positive inelderly people living with their descendants, and nervous, sensorium, and other chronic
illness are significant in elderly people not living with their descendants. This finding also
indicates the advantages of Joint estimation, as these are not significant in the estimation11

in the actual behaviour or Conjoint Analysis. In Need for Assistance increases the rate by
11%points, which is the same in Table 5.

7

Pooling Estimation for Both Types of Household

In addition, Joint estimation is applied to the pooled data for both household types. We
can discuss household types and demand forecasts via this method of pooling
Let t be the subscription for the household type. Then the estimation equation is:∗
Ji,j,k,t= ηi + ηP log Pj + ηR1 Rj1 + ηR2 Rj2 + ηR3 Rj3 + ηK Kj + ηL Lj+ ηA f (Ai ) + ηG Gi + ηH Hi + ηN Ni+ ηC Ci + ηM 1 Mi1 + ηM 2 Mi2 + ηW Wi + ηF Fi + ηE Ei + ηC Ck+ ηA f (Ai ) + ηG Gi + ηH Hi + ηN Ni
t
1
t
2
t
t
t
t
+ Zt (η t + ηPt log Pj + ηCt Ci + ηM
1 Mi + ηM 2 Mi + ηW Wi + ηF Fi + ηE Ei + ηC Ck )+ εi,j,kJi,jk ,t =(∗1 if Ji,j,k,t>0
0 otherwise(4)

where Zt is the dummy variable for elderly people not living with their descendants. This
specification means that the coeﬃcients of the hypothetical situation, other than cost, areassumed to be the same among the type of households, while cost and other characteristics
of households or elderly persons can diﬀer. Estimation results are summarized in Table
7. Note that the number in the column labelled ”elderly persons not living with theirdescendants” does not have the same meaning as in other tables and indicates the diﬀerence
between the coeﬃcients of elderly people not living with their descendants and those ofelderly people living with their descendants.
Table 7 shows that cost is more inelastic, i.e. its marginal eﬀect is -0.022 for elderly
persons not living with their descendants, and -0.036 for elderly persons living with their12descendants. Other coeﬃcients are very similar to the non-pooled estimations. For example, although the dummy variable for household types is not significant, there are significant
diﬀerences among types in chronic illness as with the Conjoint dummy variable. In particular, as in Table 6, elderly persons not living with their descendants showed a smaller gap
between actual behaviour and Conjoint Analysis than for elderly persons living with theirdescendants.

8

Simulation for Vaccination Demand

Finally, we attempt to simulate and forecast the vaccination demand. Incidentally, in 1998
there were 20.62 million elderly persons over 65 years of age, of which 11.22 million lived
with their descendants and 9.39 million did not live with their descendants.
Since there are many policy variables in the hypothetical questionnaire, to avoid anoverly complex presentation, the simulation relates only to the two factors which mostconcern policy: the cost of vaccination, and whether or not it is a legal requirement. Other
factors in the hypothetical situations are assumed to remain constant - it is assumed that
only one shot is necessary, which is given at a medical institution during the daytime, andthat there is no prevalence. These assumptions basically represent the current situation.
Other variables outside the hypothetical situation, such as age, gender and chronic illness,
are assumed to obey the actual distribution of the sample data, which means it is assumed
that the sample distribution is representative.
Simulation results are summarized in Table 8 with a 90% confidence interval. The tableshows that when cost is 6000 yen (about $48 dollars) and there is no legal recommendationfor vaccination, the number of elderly persons who were immunized is the lowest, 3.22
million. This is believed to be the current situation. When vaccination is free of charge andrecommended by the law, the number of elderly persons vaccinated increases dramatically
to 8.93 million, or about 40% of elderly persons.

13However, when the cost increases to 500 yen (about $4), 1.6 million elderly persons are
discouraged from immunization. On the other hand, if vaccination is recommended by law,even if the cost is not reduced, 2 million elderly persons are encouraged to be immunized.

9

Concluding Remarks

This paper examines the demand for influenza vaccination in Japan by elderly persons,which are a high-risk group. Original data were obtained from a survey conducted by theauthor. Three approaches, actual behaviour, conjoint analysis, and joint estimation, which
combines the first two approaches, were employed. The results provide many interesting
points and reliable policy recommendations.
However, some remarks should be noted on interpretation of the data. First, the researchbasis of the survey may be contaminated by the sampling bias. In particular, it does notsurvey inpatients and residents in institutions. In addition, even if they live at home, weak
and unhealthy elderly persons may not have been included in the survey. In general, it is
not well known whether this group is likely to be immunized or whether their demand for
vaccination has a low price elasticity. To answer this problem, the survey should be extend
to this group, but a mailing survey may not be appropriate for this purpose.
Secondly, even if joint estimation techniques are adopted, which can exclude extreme
responses to hypothetical situations, it is not clear how these could be successfully utilised.
A gap may remain between actual behaviour and the hypothetical situation. This can only
be resolved by conducting further surveys and analysis, which will provide more reliable
estimators. There remains a need for further research.

14

Acknowledgement

This paper is a combination of two independent research projects. The research forelderly persons not living with their descendants is one part of a research project financed by
the Japanese Ministry of Labour, Health and Welfare (2001 Scientific Research Grant, titled”The Research for the Forecast of Vaccination Demand for Influenza”, headed by Prof.
Yoshihiko Miura of Saitama Prefecture University). The other research project concerning
elderly persons living with their descendants is part of a project financed by the Japanese
Ministry of Education and Science (2000 Scientific Research Grant, titled ”Evaluation
for Economic Institution Based on Empirical Research for Household Behavior” (grant
number:12124207), headed by Prof. Fumio Hayashi of Tokyo University. I am gratefulfor the comments of the participants in the projects. I would also like to acknowledge
helpful suggestions from Kiyosu Taniguchi and Nahoko Shindo at the National Institutefor Infectious Diseases. In addition, I would like to acknowledge the assistance provided
by Kazuko Matsumoto.

15

References[1] Serfling, R.E.(1963), ” Methods for Current Statistical Analysis of Excess PneumoniaInfluenza Deaths,” Public Health Rep 78, pp.494-506.
[2] Assad, F., W.C. Cockburn, and T.K. Sundaresan (1973), ”Use of Excess Mortality
from Respiratory Diseases in the Study of Influenza,” Bulletin of WHO 49, pp.219233.
[3] Choi, K. and S.B. Thacker (1981), ”An Evaluation of Influenza Mortality Surveillance,
1962-1979,” I. Am J Epidemiology 113, pp.215-226.
[4] Tachibana, T., K. Kawaminami, and M. Minowa (1999), ”Excess Mortality from Influenza Epidemics in Japan, 1980-1994” (in Japanese), Journal of National Institutionof Public Health 46, 4, pp.263-273.
[5] Tachibana, T. and M. Minowa (1999), ”Excess Mortality from Influenza”(in Japanese),
Journal of National Institution of Public Health 48, 4, pp.291-297.
[6] Shindo, N., M. Ii, Y. Ohkusa, and K. Taniguchi (2000), ”A Newly Developed Japanese
Pneumonia and Influence Mortality Model and Statistical Analysis Influenza Excess
Mortality by Stochastic Frontier Estimation,” ISER DP No.501.
[7] Nichol, K.L., K.L. Margolis, J. Wuorena, and T.V. Sternberg (1994), ”The Eﬃciency
and Cost Eﬀectiveness of Vaccination against Influenza Among Elderly Perdescendants
Living in the Community,” The New England Journal of Medicine, pp.778-784.
[8] Gross, P.A., A.W. Hermogenes, H.S. Sacks, J. Lau, and R.A. Levandowski (1995),”The Eﬃciency of Influenza Vaccine in Elderly Perdescendants: A Meta-analysis andReview of Literature,” Annals of Internal Medicine 123, pp.519-527.

16

[9] Levy, E. (1996), ”French Economic Evaluations of Influenza and Influenza Vaccination,” PharmacoEconomics, IX, Supple.III, pp.62-66.
[10] Scott, G.W., and H.M. Scott (1996), ”Economic Evaluation of Vaccination Against
Influenza in New Zealand,”PharmacoEconomics IX, pp.51-60.
[11] Mullahy, J. (1999), ”It’ll Only Hurt a Second? Microeconomic Determinants of Who
Gets Flue Shots,” Health Economics 8, pp.9-24.
[12] Ohkusa, Y.(2001), ”An Empirical Analysis of Demand for Influenza Vaccination” (inJapanese), Journal of Japan Public Health 48, pp.16-27.
[13] Ben-Akiva and T. Morikawa. (1990), Estimation of Travel Demand Models from Multiple Data Sources,”Transportation and Traﬃc Theory, pp.461-476.
[14] Reed F.J., M.R. Banzhaf, and William H. Desvousges (2000), ”Willingness to Pay
For Improved Respiratory and Cardiovascular Health: A Multiple-Format, StatedPreference Approach,” Health Economics 9, pp.295-317.
[15] Ohtake, F. (1991),” Bequest Motives of Aged Household in Japan,” Ricerche Economiche 45, pp.2-3.
[16] Yashiro N., M. Ii, and Y. Terasaki (1997), ”Economic Analysis for Ageing (inJapanese),” Economic Analysis.
[17] Ohkusa, Y. (1999), ”Decision Making Process of Care Provision and Labor Supply ofThose Who Take Care at Home” (in: Japanese),Journal of Health Care and Society(inJapanese) 9, pp.101-121.
[18] Tolley, G., D. Kenkel, and R. Fabian (1994), Valuing Health Policy: An Economic
Approach, University of Chicago Press.

17[19] Ryan, M.(1999), ”Using Conjoint Analysis to Take Account of Patient Preferences
and Go Beyond Health Outcomes. An Application to In-Vitro Fertilization,” Social
Science and Medicine 48, pp.535-546.
[20] Ryan, M. and S. Farrar (1994), ”A Pilot Study Using Conjoint Analysis to Establish
the Views of Users in the Provision of Orthodontic Services in Grampian,” Health Economics Research Unit Discussion Paper No 07/94, Aberdeen, University of Aberdeen.
[21] Ryan, M and J. Hughes (1997), ”Using Conjoint Analysis to Assess Women’s Preference for Miscarriage Management,” Health Economics 6, pp.261-274.
[22] Van der Pol, M. and J. Cairns (1997), ”Establishing Patient’s Preferences for Blood
Transfusion Support: An Application of Conjoint Analysis” Journal of Health Services
Research and Policy 3, pp.70-76.
[23] Bryan, S., M. Buxton, R. Sheldon, and A. Grant (1998), ”The Use of Magnetic Resonance Imaging for the Investigation of Knee Injuries: A Discrete Choice Conjoint
Analysis Exercise,” Health Economics 7, pp.595-604.
[24] Ohkusa, Y. (2000), ”Conjoint Analysis for the Demand of Health Care related to the
Common Cold” (in Japanese), Journal of Health Care and Society 10, 1, pp.125-144.
[25] Hausman, J. (1978), ”Specification Test in Econometrics,”Econometrica 46, pp.12511271.

18

Table 1: Summary Statistics

Vaccination(’99/’00)
Vaccination(’00/’00)
Influenza Experience
Self Assessment(’99/’00)
Self Assessment(’00/’01)
Diagnosis(’99/’00)
Diagnosis(’00/’01)
Age
Female dummy
Respiratory Chronic Illness
Digestive Chronic Illness
Cardiovascular Chronic Illness
Nervous Chronic Illness
Muscular and Skeletal Chronic Illness
Unitary Chronic Illness
Endocrine Chronic Illness
Sensorium Chronic Illness
Other Chronic Illness
In Need for Assistance
Household Income (in log)
Net Financial Asset
Real Asset (Home)
Real Asset (Apartment)
Single Elderly Person
Elderly Couples
Elderly persons with Descendants
Other Household
Big City
Medium City
Small City
Rural
Farm Village

19

with
descendants.0757576.1449704

without
descendants.0752212.1617021.1615854.1397516.0304878.0310559
71.10853.4571429.121447.1111111.3333333.0981912.3436693.0723514.2609819.2325581.3229974.1525926.1101322.0681481.0368189
70.15875.4966079.0556309.1451832.256445.0257802.1478969.0597015.202171.2035278.0257802.0502035
5.121297
1412.687.8276798.0759837

5.99161676.615.8191214.0930233

1
.3747
.0310
.5504
.0439

.8680556.0208333.0083333
.2551
.1493
.4518
.1343
.0095

Table 2: Conjoint Analysis of Vaccination for Elderly Persons Livingwith Descendants (Simple Average)
Conditions
Number of Shots
Once
Twice
Cost per Shot
3000 yen1500 yenFree
Prevalence
No
Yes
Accessibility
Current
Night/Weekend
Other Places
At Home.3954545.3097913.2597701.3448276.4624697.2237237.4943274.3638743.3299663.3449367.375

20

Table 3: Conjoint Analysis of Vaccination for Elderly Persons Not
Living with Descendants
Legal recommendation

Cost (Yen)

Yes

5000
2000
500
Free
5000
2000
500
Free

No

21

Vaccination
Rate.0758929.4061433.7437186.6881188.1013216.4285714.6131805.4829268

Table 4: Estimation Results for Actual Behaviour
with descendants
Marginal
p
Eﬀect
value
Age
.0067135
0.562(Age - 70)1[Age ≥ 70]-.0170941
0.469(Age - 75)1[Age ≥ 75].0009228
0.978(Age - 80)1[Age ≥ 80].0123913
0.769(Age - 85)1[Age ≥ 85]-.0322048
0.463
Female du1[Age ≥ 90]-.0027124
0.937
Respiratory Chronic Illness-.0435962
0.440
Digestive Chronic Illness-.0059544
0.913
Cardiovascular Chronic Illness.0446591
0.255
Nervous Chronic Illness-.0131215
0.793
Muscular and Skeletal Chronic Illness -.0109033
0.767
Unitary Chronic Illness.0471642
0.451
Endocrine Chronic Illness-.0153965
0.692
Sensorium Chronic Illness-.0344334
0.447
Other Chronic Illness.0004998
0.990
In Need for Assistance
Influenza Experience.2780875
0.000
Vaccination Experience.7553172
0.000
Household Income (in log)
.0043047
0.593
Net Financial Asset-2.02e-06
0.846
Real Asset (Home).0788892
0.131
Real Asset (Apartment).0527810.639
Elderly Couples
Elderly persons with Descendants
Medium City
.0357897
0.692
Small City
.0058377
0.880
Rural

without descendants
Marginal
p
Eﬀect
value
.0101318
0.240-.013802
0.367.0183957
0.363-.01073241 0.592-.01073241 0.592.0366968
0.191.0378260.476.0035925
0.916.0519703
0.102.0374057
0.680.0440140.223.0884861
0.108.0998169
0.003.0073932
0.810-.067304
0.079-.0076047
0.907.0364175
0.357.8339464
0.000-.0037152
0.682-9.50e-06
0.273.016572
0.706.1109919
0.136-.0851013
0.070.0346669
0.688-.0203777
0.564-.0127894
0.675.0173722
0.684

Note: The likelihood ratio test for all coeﬃcients except for the constant term which is zerofor elderly persons living with their descendants are rejected at 1% significance level. Its log
likelihood is -82.791 and pseudo R2 is 0.3031. The likelihood ratio test for all coeﬃcients
except for the constant term which is zero for elderly persons not living with descendants
are rejected at 1% significance level. Its log likelihood is -151.54 and pseudo R2 is 0.3657.

22

Table 5: Estimation Results for Conjoint Analysis

Cost(in log)
Two Shots Dummy
Shot at Night or Weekend Dummy
Shot at Accessible Place
Shot at Home
Prevalence Dummy
Legal Recommendation
Age(Age - 70)1[Age ≥ 70](Age - 75)1[Age ≥ 75](Age - 80)1[Age ≥ 80](Age - 85)1[Age ≥ 85](Age - 90)1[Age ≥ 90]
Female dummy
Respiratory Chronic Illness
Digestive Chronic Illness
Cardiovascular Chronic Illness
Nervous Chronic Illness
Muscular and Skeletal Chronic Illness
Unitary Chronic Illness
Endocrine Chronic Illness
Sensorium Chronic Illness
Other Chronic Illness
In Need for Assistance
Influenza Experience
Vaccination Experience
Household Income (in log)
Net Financial Asset
Real Asset (Home)
Real Asset (Apartment)with descendants
Marginal
p
Eﬀect
value
-.02522089 0.000-.14276516 0.000.34337536
0.000.00893647
0.814-.04432643 0.224.00534984
0.891.0362586

0.164-.07922911.04564262.06334376-.08105918-.00112939-.09629074-.07912406.06743487-.10955807.11335961.07793709.02870001-.11174018-.04287212

0.091
0.387
0.317
0.261
0.990
0.452
0.391
0.306
0.150
0.148
0.422
0.711
0.150
0.561

.01551211.61963421.0066106.00001227-.12492886-.00025391

0.885
0.000
0.714
0.505
0.332
0.999

23

without descendants
Marginal
p
Eﬀect
value
-.03677605 0.000.09498724.00082562-.0103762.01283505.02290995

0.000
0.940
0.604
0.674
0.637

.0303149-.11666444.02229562.00888117.27131134-.00771539.06497005.00743586.05051267.19054796.11989901.11571933.43626562-.00585357.00001175-.08429571-.10049675

0.345
0.063
0.627
0.796
0.034
0.855
0.308
0.846
0.210
0.176
0.098
0.005
0.000
0.647
0.294
0.136
0.180

Elderly Couples
Elderly persons with Descendants
Other Household
Medium City
.15525582
Small City
.06028977
Rural.35386404
Farm Village

.
0.437
0.372
0.049

-.00468678.03410025-.16169356.03815038.06095812.01307271-.04240934

0.931
0.780
0.402
0.465
0.118
0.813
0.601

Note: The sample size of elderly persons living with their descendants is 144 individuals
and 1283 samples. The likelihood ratio test for all coeﬃcients except for the constantterm is zero in this case. The sample size of the group of elderly persons not living with
their descendants is 700 individuals and 2557 samples. The likelihood ratio test for all
coeﬃcients, except for the constant term which is zero, is rejected at 1% significance level.

24

Table 6: Joint Estimation among Actual Behaviour and Conjoint
Analysis

Cost(in log)
Two Shots Dummy
Shot at Night or Weekend Dummy
Shot at Accessibility Place
Shot at Home
Prevalence Dummy
Law Recommendation
Age(Age - 70)1[Age ≥ 70](Age - 75)1[Age ≥ 75](Age - 80)1[Age ≥ 80](Age - 85)1[Age ≥ 85](Age - 90)1[Age ≥ 90]
Female dummy
Respiratory Chronic Illness
Digestive Chronic Illness
Cardiovascular Chronic Illness
Nervous Chronic Illness
Muscular and Skeletal Chronic Illness
Unitary Chronic Illness
Endocrine Chronic Illness
Sensorium Chronic Illness
Other Chronic Illness
In Need for Assistance
Influenza Experience
Vaccination Experience
Household Income (in log)
Net Financial Asset
Real Asset (Home)
Real Asset (Apartment)with descendants
Marginal
p
Eﬀect
value
-.02438339 0.000-.13432005 0.000.33165383
0.000.01282752
0.723-.03727379 0.276.00990128
0.786.0075168.01453093-.05203914.0160218.05284339-.05539586.06417374-.09595305-.09708313.08529077-.07076987.08061989.146584.04467399-.04014177-.0361404

0.736
0.699
0.204
0.754
0.391
0.402
0.290
0.313
0.252
0.097
0.349
0.173
0.114
0.441
0.522
0.540

.16867374.36870903.00474678
7.209e-06-.0370723-.06185246

0.017
0.000
0.755
0.633
0.702
0.624

25

without descendants
Marginal
p
Eﬀect
value
-.03589957 0.000.09475876.00230228-.00958017.01699283-.02005843

0.000
0.812
0.596
0.538
0.615

-.00828369-.05999469.01960893.03668624.27624604-.01111679.08578814.04232854.06506282.15032881.1095157.12428474.22118837-.005132
6.338e-06-.07369098-.06226342

0.778
0.335
0.637
0.251
0.000
0.780
0.127
0.228
0.069
0.069
0.070
0.000
0.000
0.658
0.534
0.149
0.365

Elderly Couples
Elderly persons with Descendants
Other Household
Medium City
.0886554
Small City
.03478028
Rural.15519883
Farm Village
Conjoint Dummy.27317652

0.565
0.538
0.250
0.000

-.02452867.03124743-.22222303.02670482.05346023.0080299-.0707356.5945397

0.629
0.794
0.282
0.575
0.134
0.868
0.301
0.000

Note: The sample size of elderly persons living with descendants is 328 individuals and1590 samples. The likelihood ratio test for all coeﬃcients except for the constant term
is zero in this case. The sample size of elderly persons not living with descendants is
718 individuals and 3185 samples and its likelihood ratio test for all coeﬃcient except for
constant term is zero for the elderly persons living without descendants are rejected at 1%
significant level.

26

Table 7: Joint Estimation on the Pooled among Household Types
with descendants
Marginal
p
Eﬀect
value
-.02255168 0.000-.12342393 0.000.30599144
0.000.01249799
0.708-.0340722
0.282.00967683
0.774

Cost (in log)
Two Shots Dummy
Shot at Night or Weekend Dummy
Shot at Accessibility Place
Shot at Home
Prevalence Dummy
Legal Recommendation
Age.00797087(Age - 70)1[Age ≥ 70].01254973(Age - 75)1[Age ≥ 75]-.0488111(Age - 80)1[Age ≥ 80].01444977(Age - 85)1[Age ≥ 85].05020476(Age - 90)1[Age ≥ 90]-.05156333
Female dummy.0656891
Respiratory Chronic Illness-.08603337
Digestive Chronic Illness-.09906714
Cardiovascular Chronic Illness.07969082
Nervous Chronic Illness-.06638294
Muscular and Skeletal Chronic Illness .07496171
Unitary Chronic Illness.14671101
Endocrine Chronic Illness.04251697
Sensorium Chronic Illness-.03192429
Other Chronic Illness-.03353948
In Need for Assistance
Influenza Experience.30999771
Vaccination Experience.16526113
Household Income (in log)
.00451159
Net Financial Asset
5.266e-06
Real Asset (Home)-.03900807
Real Asset (Apartment)-.07345112

27

0.703
0.722
0.206
0.765
0.390
0.408
0.245
0.334
0.220
0.101
0.359
0.178
0.098
0.436
0.589
0.548
0.001
0.013
0.755
0.710
0.673
0.535

without descendants
Marginal
p
Eﬀect
value
-.01409002 0.003.09732786-.00592108-.0220246.06603176-.03394031

0.000
0.797
0.579
0.165
0.590

-.07540935.02372582.11901429-.04231704.34596825-.08564496-.05977958-.00063726.09709249.18539838.11277641-.04607038-.04014137-.00986377
1.557e-06-.03267661.0116132

0.238
0.827
0.191
0.468
0.001
0.212
0.570
0.992
0.161
0.069
0.064
0.648
0.593
0.596
0.929
0.758
0.933

Elderly Couples
Elderly persons with Descendants
Other Household
Medium City
Small City
Rural
Farm Village
Conjoint Dummy.58472371

0.709.08098421.02960441.13517834-.06888663.25660342

0.574
0.578
0.290
0.314
0.000

-.02373158.03512046-.22844841-.05477642.0238345-.12904885

0.643
0.772
0.275
0.718
0.711
0.346

.34807029

0.000

Note: The sample size is 1046 individuals and 4775 samples. The likelihood ratio test for
all coeﬃcient except for the constant term are zero.

28

Table 8: Forecast for Demand of Vaccination and its 95%CI (Million
Elderly Persons)
Legal Recommendation
Cost
Living with Descendants
Free
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
5500
6000
Living without Descendants
Free
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
5500
6000

No
Yes
Lower AverageUpper Lower AverageUpperboundbound bound
bound
2.687
1.472
1.366
1.307
1.265
1.234
1.209
1.188
1.170
1.155
1.141
1.129
1.117

3.006
2.005
1.909
1.854
1.816
1.786
1.763
1.743
1.726
1.711
1.697
1.685
1.675

3.344
2.274
2.169
2.110
2.068
2.036
2.010
1.989
1.970
1.953
1.939
1.926
1.914

3.617
2.134
1.996
1.919
1.865
1.824
1.791
1.763
1.739
1.719
1.700
1.684
1.669

3.985
2.803
2.684
2.616
2.569
2.532
2.503
2.478
2.456
2.437
2.421
2.405
2.392

4.366
3.129
3.003
2.931
2.880
2.841
2.809
2.783
2.760
2.739
2.721
2.705
2.690

3.624
1.589
1.421
1.328
1.265
1.218
1.180
1.148
1.122
1.099
1.078
1.060
1.043

3.967
2.113
1.943
1.847
1.781
1.731
1.691
1.657
1.628
1.603
1.581
1.561
1.543

4.318
2.386
2.203
2.100
2.028
1.974
1.930
1.893
1.862
1.835
1.811
1.789
1.769

4.592
2.277
2.065
1.946
1.865
1.803
1.754
1.713
1.678
1.647
1.620
1.596
1.574

4.947
2.913
2.710
2.594
2.514
2.452
2.402
2.361
2.325
2.294
2.266
2.241
2.219

5.301
3.231
3.018
2.897
2.812
2.747
2.694
2.650
2.612
2.579
2.549
2.523
2.499

29