DP2018-01

Factors Affecting Participation in
Health Checkups:
Evidence from Japanese Survey Data

Riko NOGUCHI
Junyi SHEN

January 16, 2018

Factors affecting participation in health checkups: Evidence fromJapanese survey data
Riko Noguchi*
Graduate School of Economics, Kobe University

Junyi Shen
Research Institute for Economics and Business Administration, Kobe University
School of Economics, Shanghai University

Abstract
Multiple factors influence individuals to get health checkups. This study uses Japanese survey
data to investigate key determinants of the health checkup decision. Relevant personal attributes
and lifestyles are identified. The results indicate that the influence of these factors varies
according to the type of health checkup. We also examine the impact of an individual’s time
preference on his/her health checkup behavior. The results suggest that hyperbolic discounters
are more likely than non-hyperbolic discounters to seek health checkups, which indicates thatthe effect of time preference on health checkup behavior differs among the different types oftime discount structures.

Keywords: health checkup; health behavior; time preference; hyperbolic discounting; Japan* Corresponding author: Riko Noguchi, Graduate School of Economics, Kobe University, 2-1
Rokkodai-cho Nada-ku, Kobe, Hyogo, 657-8501, Japan. E-mail: riko.noguchi@stu.kobe-u.ac.jp

11. Introduction
Japan is a rapidly aging society. In 2015, 26.7% of the Japanese population was 65 years ofage or older. Relative to most other countries in the world, this is an extremely high figure. (Forcomparison, the rate is 21.2% in Germany, 19.1% in France, 17.8% in the United Kingdom,
14.8% in the United States, 13.4% in Russia, and 9.6% in China.) Under such circumstances, avariety of social problems such as increasing medical expenses and elder-to-elder nursing care(i.e., elderly persons caring for the elderly) have emerged.
As part of the approach to dealing with these problems, efforts to prevent disease andpromote health among the insured and their dependents have received increasing emphasis.
According to Japan’s Ministry of Health, Labor and Welfare in Japan, the implementation ofdata-driven health plans to reduce medical expenses is progressing. These plans are regarded as
an effort by health insurers to become more efficient and effective businesses by using a large
amount of electronically managed information that tracks such events as medical checkups.
At the same time, a mechanism that allows individuals to make efforts to improve theirown health is also needed. For instance, appropriate support for individuals who are in “frail”
health (somewhere between full health and being in need of care) should be provided. This isespecially true for those who are in a stage of declining physical function due to aging.
Appropriate support in such cases can be expected to delay the need for nursing care andprevent serious illness.
Getting routine health checkups to prevent illness and maintain good health is considered
one of the most effective measures to alleviate many of the problems associated with aging. The
most familiar health checkup program in Japan is composed of the Specific Health Checkup(SHC) and Specific Health Guidance (SHG). These two elements are part of an insurance
system that was instituted in 2008 for all public health insurance members from age 40 to 70
years. SHC is a health examination intended to prevent lifestyle diseases, which have been
increasing rapidly in recent years in Japan. SHG is the support provided to those who are judged
likely to develop lifestyle diseases based on the results of their health checkups. Figure 1
provides the implementation rate of SHC, the percentage of SHG operations, and the percentageof persons completing SHG from 2008 to 2014. As shown in the figure, although theimplementation rate for SHC has been increasing year by year, it is still low. On the other hand,
while the percentage of individuals following SHG is decreasing, the proportion of those whocomplete the guidance is increasing.

2

(%)
60
50
40
30
20
10
0

2008

2009

2010

2011

2012

2013

2014

Figure 1. The implementation rate of Specific Health Checkup (blue), the percentage of Specific Health Guidance
operation (red), and the percentage of persons completing Specific Health Guidance (green)

Through a health checkup and the medical report that follows, an individual is able toassess his/her health condition and take remedial steps if necessary. For those whose results are
not good, changes in lifestyle activities (e.g., eating habits, exercise, etc.) are likely needed.
Even for those whose examinations indicate no health problems, simply knowing their status
can be useful. The very act of deciding whether to get a health checkup would appear to berelated to one’s awareness of maintaining health and preventing disease.
In the current study, we aim to identify those factors that affect an individual’s participationin health checkups. While a number of studies have been conducted on this issue in Japan (e.g.,
Iwasaki et al., 2003; Funahashi et al., 2013; Takahashi et al., 2008; Mitsuhashi et al., 2006),
most have focused on socio-demographic factors. 1 At the same time, several studies on time
preference have reported that an individual’s time preference can be associated with his/herhealth-related behaviors (e.g., Kang and Ikeda, 2016; Ikeda et al., 2010). Thus, it is consideredthat time preference may be an important factor affecting one’s decision to get a health checkup.
In this paper, we consider the interaction between hyperbolic discounting and health behaviors (e.g.,smoking, drinking, engaging in physical exercise, etc.) as well as the individual’s social
demographic factors. Moreover, given that the cancer mortality rate in Japan is high and many

individuals can benefit from aggressive cancer screening, we also analyze those factors
affecting participation in cancer screenings.
The structure of this paper is as follows: In Section 2, we provide a detailed review of the1

More details are provided in Section 2.

3

literature dealing with factors related to health checkups and time preference. Section 3 explains
our analytical methods and describes the data. Estimation results are presented and discussed in
Section 4. Finally, we report our conclusions in Section 5.

2. Literature review
2.1 Factors affecting participation in health checkups
A number of studies have examined factors affecting the decision to seek a health checkup.
Looking at differences between those who elected to have a health examination and those who
did not, Iwasaki et al. (2003) reported that socio-demographic factors, lifestyle factors, and an
individual’s medical history were important factors influencing the checkup decision. Funahashi
et al. (2013) found that men who did not have a medical examination had lower householdincomes than those who had an exam. They also found that among the men who had not had a
medical examination, few were self-employed or were regular employees. In addition, it has
been found that the availability of support from the people close to an individual affects theindividual’s health checkup rate. 2 Mitsuhashi et al. (2006) analyzed the relationship between
participation of the elderly in medical examinations and their social support. They found thatthose elderly who had medical checkups had a greater connection to society through, forexample, their membership in a neighborhood association or having close friends and relatives.
Educational level also appears to affect health checkup behavior. Yoshida et al. (2008)
showed that a low educational level was related to the non-consultation of health examinationsin the case of men. Differences in the behavior of men and women with respect to their
participation in health examinations has also been reported. Men who did not receive medical
examinations had a weak relationship with those familiar to them, a feature not seen in women(Funahashi et al., 2013). Concerning the effect of age, Mitsuhashi et al. (2003) reported thatone’s subjective health condition affected the health examination decisions of the elderly.

2.2 Time preference and health-related behavior
In the studies mentioned in the preceding section, factors such as personal attributes andlifestyle were examined; in general, the time preference of the individual was not aconsideration. However, several studies (e.g., Kang and Ikeda, 2016; Ikeda et al., 2010) suggestthat time preference, especially hyperbolic discounting, affects health-related behaviors. We
pursue this idea here.
The time discount factor of the hyperbolic type is shown below:
2

Takahashi et al. (2008) reported that there is a connection between mental support and good healthbehavior.

4𝑓𝑓(𝑡𝑡) = (1 + 𝑘𝑘𝑘𝑘)−𝛾𝛾/𝑘𝑘(1)where 𝑡𝑡 ≥ 0, 𝛾𝛾 ≥ 0, 𝑘𝑘 > 0. Here, 𝛾𝛾 and 𝑘𝑘 are constants, and 𝑡𝑡 represents delay in gain. 3

The time discount rate 𝜌𝜌(𝑡𝑡) can then be defined as −𝑓𝑓 ′ (𝑡𝑡)/𝑓𝑓(𝑡𝑡). That is,𝑓𝑓′ (𝑡𝑡)𝜌𝜌(𝑡𝑡) = − 𝑓𝑓(𝑡𝑡) =𝛾𝛾.
1+𝑘𝑘𝑘𝑘(2)

Unlike exponential discounting, hyperbolic discounting is a time-inconsistent model of
discounting. It indicates that people have higher discount rates for very short horizons but havelower discount rates for longer horizons (Thaler, 1981). Frederick et al. (2002) showed that thehyperbolic type discount function is a better approximation of the time discount function thanthe exponential type.
With respect to the effect of hyperbolic discounting on health-related behavior, Kang and
Ikeda (2016) reported that respondents who were less patient (i.e., respondents with hyperbolic
discounting) tended to have worse health-related attributes. In addition, Ikeda et al. (2010)
found that time discounting was associated with body weight via hyperbolic discounting. That is,
individuals with hyperbolic discounting are more likely to have high body weight. Further,
Yamane et al. (2003) stated that socio-emotional status affected impulsivity in intertemporalchoice.
Regarding the connection between time preference and checkup-seeking behavior, Fang
and Wang (2015) used United States data to show a close relationship between the two.
However, in their study they did not individually control for the possibility that the act of getting
health checkups may be influenced by health-related behavior such as smoking and exercising.
Accordingly, in the current study we investigate the determinants of getting health checkups byconsidering the interaction between hyperbolic discounting and health-related behavior.

3. Methods
3.1 Methodology
To examine the determinants of health check-up behavior, the following specification isconsidered:
𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑗𝑗 = 𝛼𝛼𝑗𝑗 + 𝛽𝛽𝑀𝑀 𝑀𝑀𝑗𝑗 + 𝛽𝛽𝐵𝐵𝐵𝐵𝐵𝐵 𝐵𝐵𝐵𝐵𝐵𝐵𝑗𝑗 + 𝛽𝛽𝐴𝐴 𝐴𝐴𝑗𝑗 + 𝛽𝛽𝐼𝐼 𝐼𝐼𝑗𝑗 + 𝛽𝛽𝑆𝑆 𝑆𝑆𝑗𝑗 + 𝛽𝛽𝑃𝑃 𝑃𝑃𝑗𝑗 + 𝛽𝛽𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑗𝑗+𝛽𝛽𝑓𝑓𝑓𝑓 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑗𝑗 + 𝛽𝛽𝑝𝑝𝑝𝑝 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑗𝑗 + 𝛽𝛽𝑤𝑤 𝑤𝑤𝑗𝑗 + 𝛽𝛽𝑢𝑢 𝑢𝑢𝑗𝑗 + 𝛽𝛽𝐶𝐶 𝐶𝐶𝑗𝑗 + 𝛽𝛽ℎ𝑜𝑜 ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 + 𝛽𝛽ℎ𝑒𝑒 ℎ𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒ℎ+𝛽𝛽𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑗𝑗 + 𝛽𝛽𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑗𝑗 + 𝛽𝛽𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑗𝑗+𝛽𝛽𝐻𝐻𝐻𝐻 hyperbolic dummy𝑗𝑗 + 𝛽𝛽𝑆𝑆𝑀𝑀𝐻𝐻𝐻𝐻 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆・hyperbolic dummy𝑗𝑗+𝛽𝛽𝑆𝑆𝑃𝑃𝐻𝐻𝐻𝐻 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆・hyperbolic dummy𝑗𝑗 + 𝛽𝛽𝐷𝐷𝑅𝑅𝐻𝐻𝐻𝐻 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷・hyperbolic dummy𝑗𝑗 + 𝜇𝜇𝑗𝑗
3

We refer to Kang and Ikeda (2016) and Morimoto (2009).

5(3)

where CHECKUPi (i = 1, 2, 3) is the dependent variable; specifically, CHECKUP1 is theprobability of getting health checkups, CHECKUP2 is the number of health checkups
undertaken, and CHECKUP3 is the probability of having a cancer screening. M is male, BMI isbody mass index, A is age, I is income, S is household size, P is population size of the area inwhich the respondent lives, self is self-employed, full is full-time employed, part is part-time
employed, w is housewife, u is unemployed, C is a college graduate dummy, hospital indicates
whether the respondent has been hospitalized within the past year, and health is a subjective
health anxiety dummy. SMOKING is a binary variable for the frequency of smoking, SPORT isa binary variable for the frequency of participating in sports, DRINKING is a binary variableindicating the frequency of drinking alcohol, hyperbolic dummy is a binary variable indicating
whether or not the respondent is a hyperbolic discounter. In addition, 𝛼𝛼 is the constant termand 𝜇𝜇 is the error term. 𝛽𝛽𝛽𝛽 are coefficients of each variable and j is the respondent index.

To analyze the impact of being a hyperbolic discounter or non-hyperbolic discounter on thehealth checkup behavior of a respondent, we create terms for the interaction of HD and the
respondent’s health-related behavior (i.e., SMOKING・hyperbolic dummy, SPORT・hyperbolic
dummy, and DRINKING・hyperbolic dummy). 4 With the addition of these interaction terms to
the regression models, the coefficients of SMOKING, SPORT, DRINKING represent the
influence of these factors on the health checkup behavior of non-hyperbolic discounters, while
the coefficients of interaction terms will indicate the impact on the dependent variable of
SMOKING, SPORT, and DRINKING for hyperbolic discounters.

3.2 Data
The data used in this study were obtained from a questionnaire survey entitled “Preference
Parameters Study” that was conducted in four countries by the Global Centers of Excellence(Global COE) program at Osaka University: in Japan from 2004; in the U.S. from 2005; in India
from 2009; and in rural and urban areas in China from 2006 and 2007. The key question, “Didyou participate in the following health checkups in the last year?” was asked in the 2011questionnaire in Japan; therefore, we use the answers to this question for the dependent
variables in our regressions and use the other answers from the 2010 questionnaire for the
independent variables.5

4
Morimoto (2009) used interaction terms to clarify the relationship between hyperbolic discounters andtheir consumption behavior.
5
We used the data for one explanatory variable (i.e., hospitalization history) from the 2011 questionnaire
because this question asked for the respondent’s hospitalization history during the past year.

63.2.1 Questions regarding health checkups
We treat CHECKUP1, CHECKUP2, and CHECKUP3 as the dependent variables. Basedon responses to Q1 and Q2 as presented in Table 1, CHECKUP1 equals 1 if the respondent had
any health checkups or cancer screenings within the past year (i.e., if the respondent selectedany of the choices except 6 in Q1 and 7 in Q2), and 0 otherwise. CHECKUP2 indicates thenumber of health checkups taken; its value is assigned by combining a simple count of thenumber of checkups indicated by the respondent in Q1 and the number of cancer screenings
indicated in Q2. 6 CHECKUP3 indicates the behavior of respondents who received any cancer
screenings in addition to other health checkups. For those who had at least one health checkup(i.e., CHECKUP1 = 1), CHECKUP3 is coded 1 if the respondent checked any cancer screenings
in Q2, and 0 otherwise. We run Probit models for CHECKUP1 and CHECKUP3 and Ordinary
Least Squares (OLS) regression for CHECKUP2. The same independent variables are used ineach of these three regressions.
Table 1. Questions about health checkups (Q1, Q2)
Q1) Have you had any health checkups (excluding cancer examination, prenatal check, dental checkup, andmedical treatment) within the last year? (Indicate ALL That Apply)
1

Health check organized by local municipality

2

Health check organized by your employer or labor union of your employer

3

Health check organized by your school

4

Medical checkups (other above 1-3)

5

Other checkups

6

I haven’t taken any health check in the last year.

Q2) Have you had any cancer examinations in the last year? (ALL That Apply) Note: This can include thehealth check you checked in Q1.
1

Stomach cancer examination

2

Lung cancer examination

3

Uterine cancer examination

4

Breast cancer examination

5

Colon cancer examination

6

Other cancer examinations

7

I haven’t taken any cancer examination within the last one year.

3.2.2 Questions regarding time preferenceTime preference is assessed from responses to Q3 and Q4 as presented in Table 2. Q3 andQ4 are questions about intertemporal choices. We use it to measure time preference and to6

We run OLS regression on CHECKUP2 to check the robustness of our Probit regression on
CHECKUP1.

7

classify a respondent as a hyperbolic discounter or a non-hyperbolic discounter. In Q3, we elicit
time preference based on the respondent’s choices in the following situation: “Let’s assume you
have two options to receive some money. You may choose Option A, to receive ¥10,000 today,
or Option B, to receive a different amount in seven days. Compare the amounts and timing in
Option A with Option B and indicate which you would prefer for each of the 9 alternatives.” In
Q4, the respondent is asked to make choices in the following scenario: “Let’s assume you have
two options to receive some money. You may choose Option A, to receive ¥10,000 in ninety
days, or Option B, to receive a different amount in ninety-seven days. Compare the amounts andtiming in Option A with B and indicate which you would prefer to receive for each of the 9alternatives.” As can be seen, respondents were asked to make choices regarding the receipt ofmoney at a point in time close to the present (Q3) and the receipt of money at a relatively distantpoint in time (Q4). Respondents seeking higher interest rates for transactions close to thepresent time (Q3) were considered to be more current-oriented.
Table 2. Questions about time preference (Q3, Q4)
Q3) Let's assume you have two options to receive some money. You may choose Option A, to receive ¥10,000 today; or
Option B, to receive a different amount in 7 days. Compare the amounts and timing in Option A with B and indicate
which you would prefer to receive for each of the 9 choices.
Option A (receive today)

Option B (receive in 7 days)

annual interest rate(%)

10000

9980

-10

10000

10000

0

10000

10019

10

10000

10076

40

10000

10191

100

10000

10383

200

10000

10575

300

10000

11917

1000

10000

19589

5000

Q4) Let's assume you have two options: to receive ¥10,000 in 90 days or receive a different amount in 97 days. Compare
the amounts and timing in Option A with B and indicate which you would prefer to receive for each of the 9 choices.
Option A (receive in 90 days)

Option B (receive in 97 days)

annual interest rate(%)

10000

9980

-10

10000

10000

0

10000

10019

10

10000

10076

40

10000

10191

100

10000

10383

200

10000

10575

300

10000

11917

1000

10000

19589

5000

8

Following Ikeda and Kang (2015) and Morimoto (2009), the time discount rate (DR) is
measured as the mean of the interest rate at the point at which there is a shift in the preferred
option from A to B. 7,8 By comparing a respondent’s time discount rate from Q3 (i.e., DR1) andhis/her time discount rate from Q4 (i.e., DR2), we classify the respondent as either a hyperbolic
discounter (if DR1 > DR2) or a non-hyperbolic discounter (if DR1≦DR2). Accordingly, we
create a hyperbolic dummy as a binary variable coded 1 if the respondent is a hyperbolic
discounter and 0 otherwise.

3.2.3 Questions regarding other variables
The following health-related variables are also created. SPORT is a dummy variable thattakes the value 1 if the respondent exercises at least once a week and 0 otherwise. SPORT
indicates whether the respondent has frequent exercise opportunities. SMOKING is a dummy
variable with a value of 1 if the respondent smokes at least sometimes and 0 otherwise. Thisvariable signifies whether the respondent has a habit of smoking cigarettes. DRINKING is also adummy variable; it takes the value 1 if the respondent sometimes drinks alcohol and 0 otherwise.
This variable indicates whether the respondent has a habit of drinking alcoholic beverages. 9
The following variables, some of which have been used in previous studies, are also
included in the regressions: gender, BMI, age, annual income, household size, population,
occupational status, college graduate, hospital, and health anxiety. For gender, a male dummy iscreated and added to the model. BMI is defined as weight in kilograms divided by the square ofheight in meters and expressed in units of kg/m2. Annual income was set by the respondent’s
answer to the question, “What was your approximate salary in 2009 (including business income
if you are self-employed)?” Age is determined by the respondent’s birth year information.
Household size is defined as the number of family members, which is used as an indicator of the
respondent’s support from the people around him/her. In previous research (e.g., Takahashi et al.,2008), the presence of supportive family members and/or familiar friends or having connections
to the local community was deeply involved in the decision to have a medical examination.
7

For example, if a respondent chooses Option A for an interest rate from -10% to 10% and then shifts to
Option B for a rate from 40% to 5000%, his/her time discount rate is calculated as 25%—the mean of
10% and 40%
8
We exclude from the analysis respondents whose answers went back and forth between Option A andOption B and whose time discount rate was a negative value.
9
We checked the relationship between discount rate (i.e., DR1 and DR2) and health behavior (i.e.,
SMOKING, SPORT, and DRINKING) and found that both DR1 and DR2 significantly increase theprobability of smoking and drinking and decrease the probability of doing physical exercise, confirmingthat respondents with a high time discount rate have one or more bad habits in their health-related
behavior.

9

Population indicates the size of area in which the respondent lives. It takes the value of 4 if the
respondent lives in a government designated city with a population of more than 700,000, 3 if
he/she lives in a city with a population of 100,000 to 700,000, 2 if he/she lives in a city with apopulation less than 100,000, and 1 if he/she lives in a town or village with a population lessthan 50,000. Regarding occupational status, we create dummies for self-employed, full-time,
part-time, housewife, and unemployed. A college graduation dummy is added to represent
educational level, based on responses to the following: “Please indicate the highest level of
education (or equivalent) completed by you. If you are still in school, check the level you are innow.” Finally, to indicate the health condition of the respondent, we include in the model the
hospitalization history of the respondent during the past year as well as a subjective assessmentof the respondent’s health anxiety. The questions for creating the above variables are provided in
the Appendix. Table 3 summarizes the descriptive statistics for all of the variables.
Table 3. Descriptive statistics for all variables used in the regressions
Variable

Mean

Std. Dev.

Min

Max

taking health checkups

0.77810.41550

1

number of health checkups taken

1.3306

0.89140

4taking cancer screening

0.52290.499501

male

0.46830.499001

age

51.2317

13.1483

21

77

income22.9779

18.0922099

household3.48151.44151

12sel- employed0.12500.33070

1

full-time

0.38170.48590

1

part-time

0.18050.38460

1

housewife

0.14070.347801unemployed

0.07540.264001college graduate

0.22480.417501

population size

2.83030.8945

1

4

BMI

22.6239

3.194014.473158.5938

hospital

0.07480.263001health anxiety

3.18801.04541

5hyperbolicity dummy

0.14840.355601

smoking

0.23380.4233

0

1

sport

0.3727

0.483601drinking

0.52930.49920

1

10

4. Results
4.1 Results for getting health checkups
The marginal effects obtained from our Probit and OLS regressions are reported in Table 4.
With respect to having health checkups, being male has a significant and negative coefficient,indicating that males are less likely than females to participate in health checkups. The
coefficients of both age and income are significant and positive, implying that these two
attributes raise the probability of getting health checkups. It is clear that the greater the age, thehigher the risk of getting sick, which leads to a greater acceptance of health checkups amongolder people. The results also indicate that respondents with larger families are more likely to
get health checkups. Taken together, our results reveal the same tendencies shown in previous
studies: that men often do not undergo medical checkups and high-income earners are more
likely to seek health consultations (Funahashi et al., 2013), and that the larger the family size,
the more positive the impact on the individual’s decision to get health checkups (Mitsuhashi etal., 2006; Takahashi et al., 2008). Regarding occupational status, a respondent who isself-employed, employed part-time, unemployed, or a housewife is less likely to have healthcheckups. In contrast, full-time employed respondents are more likely to get health checkups.
The sign of the college graduate dummy is significant and positive, indicating that the higher
the level of education, the more likely it is that the respondent will get a medical examination.
This result is consistent with the results of earlier studies (Yoshida et al., 2008). The effect of
having been hospitalized in the past year is significant, as well, with respondents in thissituation having a higher probability of getting health checkups. 10
Representing the key factor in this study, the hyperbolic dummy coefficient is significant
and positive, which suggests that individuals with a hyperbolic discounting function are more
likely than non-hyperbolic discounters to get health checkups. As for the interaction terms, thecoefficient of the DRINKING_ hyperbolic dummy is significant and negative, indicating that in
the group of hyperbolic discounters, the influence of drinking alcohol on getting healthcheckups is smaller than that in the group of non-hyperbolic discounters. On the other hand,
SPORT, DRINKING, and SMOKING represent the influence of these health-related variables on
the probability of getting checkups in the group of non-hyperbolic discounters. Probit regression
results thus suggest that in the group of non-hyperbolic discounters, respondents who have thehabit of engaging in sports and drinking alcohol are more likely to get health checkups but
smokers are less likely.

10

Hospitalization does not include overnight medical checkups.

11

Table 4. Marginal effects from Probit and OLS regressions

male

(1)

(2)

(3)

getting health checkups

number of health checkups

getting cancer screening-0.0486-0.2414**(0.0205)age

0.0039(0.0429)***(0.0007)
income0.00120.0113*0.0037-0.2102***

0.0985-0.0549-0.1755**-0.1272

population size
BMI
hospital
health anxiety
hyperbolicity dummy

0.0505***smoking × hyperbolicity dummy

sport × hyperbolicity dummy

Observations

0.0533*(0.0364)***0.1297***

0.0530(0.0526)*

0.0191(0.0175)

0.00330.0025-0.0030(0.0025)(0.0052)(0.0031)0.1985(0.0217)**-0.0298***

0.0576(0.0647)-0.00170.00880.0142(0.0073)(0.0158)(0.0095)***-0.08860.2566(0.0400)***(0.0770)***-0.2253

0.0623(0.0475)***-0.0482(0.0182)(0.0400)(0.0244)0.00110.03370.07240.0403(0.1045)**0.12630.0682(0.0368)(0.0220)-0.0239-0.0680-0.0531(0.0883)***-0.13160.1411(0.0537)***(0.0369)***-0.2875

0.0319(0.0224)***-0.0535(0.0430)(0.0871)(0.0531)

2,696

2,696

2,135

R-squared

0.158

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

12**(0.0644)***(0.0171)

0.0664***(0.0106)(0.0316)0.1233**(0.0569)(0.0080)****0.0086-0.0395

0.0847***(0.0317)-0.0043(0.0166)
drinking × hyperbolicity dummy

0.0703***0.0406(0.0364)(0.0442)drinking-0.2983

0.0293(0.0183)(0.0477)
sport-0.34140.0024(0.0369)***(0.0827)***(0.0390)
smoking***(0.0833)(0.0345)
college graduate-0.1063***(0.0070)(0.0624)***(0.0322)unemployed

0.22650.0113(0.0007)***(0.0520)(0.0268)
housewife***(0.0569)***(0.0234)
part-time-0.4648***(0.0008)(0.0114)(0.0227)full-time

0.0421-0.2038(0.0251)***(0.0012)**(0.0053)
self-employed0.0194(0.0014)(0.0007)
household

***

***

4.2 Results for the number of health checkups
To check the robustness of our Probit results, we conducted an OLS regression on thenumber of health checkups taken. As shown in Table 4, such demographic variables as gender,
age, income, household size, occupational status, level of education, and hospitalization within
the past year are significantly estimated with the same signs as in the Probit regression.
Population size is significant and negative in the OLS regression, indicating that residents insmaller cities, towns or villages have more health checkups. In addition, the OLS estimates of
the hyperbolic dummy, health-related variables, and their interaction terms have the same signs
and significance results as the Probit estimates.

4.3 Results for cancer screening
The rightmost column of Table 4 shows study results related to cancer screenings. The first
result worthy of note here is that, for housewives, the likelihood of getting a cancer screening issignificantly less than the likelihood of having a normal (routine) health checkup. We also findthat in the case of cancer screening, the hyperbolic dummy and its interactions with thehealth-related variables are not significant—a result that differs from what was found for normal
checkups. This result implies that time preference does not affect one’s behavior with regard togetting a cancer screening. In addition, the magnitudes of the significant influences of gender,
age, income, and household size are larger in the regression on cancer screening than in the
regression on health checkups. Taken together, these differences support the proposition thatgetting a cancer screening involves a somewhat different set of influences than is the case forother types of health checkups. Policy decision makers would do well to take these differences
into account when designing policies intended to stimulate participation in cancer screening.

5. Conclusion
In this study, Japanese survey data was used to empirically investigate the determinants ofan individual’s participation in health checkups. In contrast to many previous studies, we
included in our regression models variables representing an individual’s time preference.
Consistent with results reported in previous studies, we find that certain personal attributes do
indeed influence health checkup behaviors. Females, individuals earning higher incomes, andthose having higher levels of education are more likely to get health checkups. Moreover, since
the higher the age, the higher the risk of illness, older individuals have a greater probability ofgetting health checkups. Additionally, individuals having more family members and who are
more connected to their community are more likely to have a health checkup, suggesting the13

importance of positive involvement with family and neighbors.
Importantly, the hyperbolicity of the time discounting function, which is introduced into
the regression models as another personal attribute, is significant and positive in its effect on an
individual’s participation in health checkups. Our result suggests that hyperbolic discounters are
more likely than non-hyperbolic discounters to have health checkups. There are several possible
explanations for this. Since hyperbolic discounters show a tendency to engage in unhealthy
behavior, they tend to be unhealthy and therefore may be more inclined to actively seek healthcheckups. Another possible explanation is that hyperbolic discounters may get health checkups
to assess their health status, which serves as the basis for controlling their behavior.
Based on the results associated with our health-related variables and their interactions withthe hyperbolic discounting dummy, we find that hyperbolic discounters and non-hyperbolic
discounters differ significantly with regard to the influence of the various health-related
variables on the probability of getting health checkups. This suggests the need to consider time
preference when formulating policy to encourage health checkups.
Knowing what factors influence participation in health checkups is critical to the creationof a system and an environment that makes it more likely that individuals will get healthcheckups. Establishing an efficient system that makes people aware of their own healthcondition through regular checkups is likely to lead to cost-effective measures such as healthmaintenance and disease prevention that will help alleviate many of the problems related toaging in Japan.

Acknowledgement
We wish to thank the Global COE program at Osaka University for providing the data weused in this study. Financial support for this study is provided by the Japanese Ministry of
Education, Culture, Sports, Science and Technology through Grant-in-Aid for Scientific Research(C) 15K03353. All views expressed in this paper and any errors are the sole responsibility of theauthors.

References
Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common
stock investment. Quarterly Journal of Economics, 261-292.
Croson, R., & Gneezy, U. (2009). Gender differences in preferences. Journal of Economic
Literature, 47(2), 448-474.
Cutler, D. M., Glaeser, E. L., & Shapiro, J. M. (2003). Why have Americans become more
obese?. The Journal of Economic Perspectives, 17(3), 93-118.
14

Estes, R., & Hosseini, J. (1988). The gender gap on Wall Street: an empirical analysis ofconfidence in investment decision making. The Journal of Psychology, 122(6), 577-590.
Funahashi, H., Nishida, T., Okamura, Y., and Sakakibara, H. (2013). Attributes of
non-participants aged 40-59 years in specific health check-ups. Japanese Journal of Public
Health, 60, 3, 119-127, in Japanese.
Frederick, S., Loewenstein, G., and O'donoghue, T. (2002). Time discounting and time
preference: A critical review. Journal of Economic Literature, 40(2), 351-401.
Ikeda, S., Kang, M. I., & Ohtake, F. (2010). Hyperbolic discounting, the sign effect, and the
body mass index. Journal of Health Economics, 29(2), 268-284.
Ikeda, S., & Kang, M. I. (2015). Hyperbolic Discounting, Borrowing Aversion and Debt
Holding. Japanese Economic Review, 66(4), 421-446.
Iwasaki, M., Otani, T., Yamamoto, S., Inoue, M., Hanaoka, T., Sobue, T., & Tsugane, S. (2003).
Background characteristics of basic health examination participants: the JPHC Study
Baseline Survey. Journal of Epidemiology, 13(4), 216-225.
Kang, M. I., & Ikeda, S. (2016). Time discounting, present biases, and health-related behaviors:
Evidence from Japan. Economics & Human Biology, 21, 122-136.
Mitsuhashi, Y., Kishi, R., Eguchi, T., Miyake, H., Sasatani, H., Maeda, N., and Horikawa,N.
(2006). Study of influence of social support and networks on participation in periodic healthexamination of the elderly at home comparison of three regions with different social
backgrounds. Japanese Journal of Public Health, 53, 2, 92-104, in Japanese.
Mitsuhashi, Y., Kishi, R., Eguchi, T., Miyake, H., & Maeda, N. (2003) Factors associated withparticipation in medical checkups of the elderly at home comparison of 3 regions with different
social backgrounds. Japanese Journal of Public Health, 50, 1, 49-61, in Japanese.

Ministry of Health, Labor and Welfare: Data Health Creation Handbook. (12/2014)
http://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000061273.html (accessed: 12/10/2016)
Ministry of Health, Labor and Welfare. Heisei 28th ed., Health, Labor and Welfare White Paper:
Consider a social model that overcomes population aging. (10/2016)
http://www.mhlw.go.jp/wp/hakusyo/kousei/16/ (accessed: 12/25/2016)
Ministry of Health, Labor and Welfare. Information on medical system reform: Regarding
specific medical examination and specific health guidance
http://www.mhlw.go.jp/bunya/shakaihosho/iryouseido01/info02a.html(accessed: 12/25/2016)
Ministry of Health, Labor and Welfare. The 22nd Study group on medical examination andhealth guidance etc. by insurer. (7/2016) 1-13.
http://www.mhlw.go.jp/stf/shingi2/0000130002.html (accessed: 11/25/2016)
Morimoto, S. (2009) Hyperbolic Discounting and Household Consumption: An Analysis Based15

on a Large-scale Survey in Japan. Journal of Behavioral Economics and Finance, 2, 49-59,
in Japanese.
Ohtake, F., Akesaka, M. & Saitou, M. (2014) Impact of the Great East Japan Earthquake on
Japanese economic preference. Journal of Behavioral Economics and Finance. 7, 92-95, inJapanese.
Takahashi, K., Kudo, K., Yamada, Y., Shao, L., Ishikawa, H., & Fukao, A. (2008) Relationshipbetween health behavior and social support for the prevention of lifestyle-related diseases.
Japanese Journal of Public Health, 55, 8, 491-502, in Japanese.
Thaler, R. H. (1981). Some empirical evidence on time inconsistency. Review of Economic
Studies, 23, 165-180.
Yamane, S., Takahashi, T., Kamesaka, A., Tsutsui, Y., & Ohtake, F. (2013). Socio-Emotional
Status, Education, and Time-Discounting in Japanese Non-Smoking Population: A
Multi-Generational Study. Psychology, 4(02), 124.
Yoshida, Y., Iwasa, H., Kwon, J., Furuna, T., Kim, H., Yoshida, H., & Suzuki, T. (2008).
Characteristics

of

non-participantsincomprehensive

healthexaminations(“Otasya-kenshin”) among an urban community dwelling elderly: Basic research forprevention of the geriatric syndrome and a bed-ridden state. Japanese Journal of Public
Health, 55, 4, 221-227, in Japanese.

Appendix

The following the questionnaire items were used to create the variables used in the study.

Male dummy: Your gender: 1. Male 2. Female

Age: When were you born? (Write In Number for Month and Year)

Income: Approximately how much was your salary for 2009 (including business income if you
are self-employed)?

Salary per month ¥_________

Household size: How many people are currently living in your household?

Occupations dummy:
a: What are your occupations (Including part-time work)?
1. Office work (administrative support, sales)
2. Sales and related occupations
16

3. Managerial occupations (section chief or superior positions at government or privatecompany)
4. Specialist/Technical Experts (Teacher, Medical Doctor, Engineer, Writer)
5. Service occupations (Hairdresser, Hall staff, Receptionist, Taxi driver, Security staff)
6. Industrial occupations (carpenter, service engineer, production worker)
7. Farming, fishing, and forestry
8. Housewife/Househusband
9. Student
10. Retired (except Housewife/Househusband)
11. Unemployed (except Housewife/Househusband)
12. Othersb: What is your type of employment?
1. Employee of private company or nonprofit
2. Government employee
3. Manager or private company or nonprofit
4. Self-employed
5. Employee of family businessc: What is your employment status?
1. Full-time employee
2. Part-time employee
3. Student part-time employee
4. Temporary work (sent to a company from a temporary job agency, internship, specificproject for a company, etc.)
5. Contract worker
6. OtherCollege graduate: Please indicate the highest level of education (or equivalent) completed byyou. If you are still in school, check the category you are in now.
1. Graduated from Elementary/ Junior High School
2. Some High School – no degree
3. Graduated from High School
4. Some College (including Technical College) – no degree
5. Graduated from College (including Technical College) - Associate’s Degree (2 year)
6. Some University (including old-education-system high school) – no degree
7. Graduated from University (including old-education-system high school) – Bachelor’s
17Degree (4 year)
8. Some post graduate studies – no degree
9. Master’s Degree –MS, MA, MBA, etc.
10. Some doctoral studies – no degree
11. Doctoral Degree – DVM, Ph.D., DDS, etc.

Health anxiety: Do the following statements hold true for you? If “it is particularly true for you”,
choose “1”, and if “it doesn’t hold true at all for you”, choose “5”. Of course, you may
choose any number in between.
“I have anxieties about my health”
1(It is particularly true for you) ~5(It doesn’t hold true at all for you)

Hospital: During the last 12 months, have you been in a hospital or clinic as an in-patient
overnight or longer? This includes child birth.

Smoking: Do you smoke?
1. Don’t smoke at all
2. Hardly smoke
3. Smoke sometimes
4. About 10 cigarettes a day
5. About a pack a day
6. More than 2 packs a day
7. Used to smoke but had quit

Sport: Do you exercise?
1. Almost everyday
2. A few times in a week
3. About once a week
4. About once a month
5. Don’t exercise at all

Drinking: Do you drink alcoholic beverages?
1. Don’t drink at all
2. Hardly drink
3. Drink sometimes
18

1. Yes

2. No

4. One can of beer (12 oz.) or its equivalent a day, everyday
5. Three cans of beer (12 oz. × 3) or its equivalent a day, everyday
6. Five cans of beer (12 oz. × 5) or its equivalent a day, everyday

19