Graduate School of Economics, Kobe University
Working Paper Series No. 329

Effects of work hours and health-related information on undertaking health checkups:
An empirical study using Japanese micro data1
Riko Noguchi2
Graduate School of Economics, Kobe University

December 24, 2019

Abstract
With the advancement of the improvement of medical technologies, people are living longer andmore healthily. However, there is a gap between average life expectancy and a healthy life. People
can take control of their health care by viewing the results of health checkups and following theadvice of doctors. Using medical data from the Hyogo branch of the Japan Health Insurance
Association, this study investigates the determinants of employees in small and medium
enterprises in their decision to undertake health checkups. Specifically, we examine what kind ofworkers participate in health checkups and investigate what kind of health-related information
has an impact on the decision-making process for undertaking health checkups. The estimation
results suggest that work hours increase the probability of participating in health checkups. This
may suggest that employees with especially long work hours are received an active check by theirindustrial physician to undertake health checkups. The number of dentists visits and theappearance of hyperlipidemia also increase the probability of undertaking a health checkup. These
results indicate that employees who take care of their health are likely to confirm their healthcondition via checkups from the Japan Health Insurance Association.
Keywords: health checkup; work hours; health-related information; Japan Health Insurance
Association; small and medium enterprise workers
JEL classification: I12; J21

This study is supported by the joint study group of data health at Kobe University based on thecooperation with the Hyogo branch of the Japan Health Insurance Association.
2 Graduate School of Economics, Kobe University, 2-1 Rokkodai-cho Nada-ku, Kobe, 657-8501,
Japan. E-mail: riko.noguchi@stu.kobe-u.ac.jp
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1. Introduction
Along with the advancement of medical technologies, people in Japan are living longer but
there is a gap between average life expectancy and a healthy life. To reduce this gap, theprevention of lifestyle-related diseases has been emphasized. For example, according to
KENKOUNIPPON21 (i.e., Health Japan 21), a project initiated in 2013 by the Japanese Ministryof Health, Labor, and Welfare, goals related to health promotion and disease prevention have been
set based on the Health Promotion Act. Meanwhile, to prevent lifestyle-related diseases,
individuals are encouraged to be proactive. If people consider their own health by having healthcheckups, they can take control of their health care based on the results and doctors’ advice.
People expect a positive relationship between checkups and early discovery of disease andprevention of severe conditions, thus it is important to clarify the reasons for undertaking healthcheckups. Using combined medical data from the Japan Health Insurance Association, this study
investigates the determinants of employees in small and medium-sized enterprises deciding toundertake health checkups.
Many previous studies have reported the effects of socio-demographic factors on undergoinghealth checkups [1, 2, 3, 4]. Moreover, the availability of support from the those close to individuals
also affects taking health checkups [5,6].

As a previous study on factors affecting the rate of health checkups, Yamada [7] focused onhealth-related information, and investigated the influence of age, income, the number of medical
examinations, and past medical history on undertaking health checkups. Since the number of
medical examinations and the presence of a past medical history indicate that an individual has
regularly visited medical institutions, such individuals may have a lower probability of receivinga health checkup. The results of using three types of medical examination, days of hospitalization,
outpatient, and dentistry, indicated that the number of days of hospitalization had a negative effect,
and the number of days as an outpatient or for dentistry had a positive effect on participating in
health checkups. Regarding the positive effect of outpatient and dentistry, the subject may havereceived a health checkup to confirm the results of past treatment intervention. In addition, as aresult of including dummies for diabetes and heart disease in terms of health status, a negative
effect was only reported for the diabetes dummy [7].
With reference to health conditions, work style is important for discussions on healthbehavior. For regular workers, working hours are a major part of the day, and for non-regular
workers, working hours also may be an important factor in planning a lifestyle. There are
numerous studies on the relationship between work style and health [8], [9]. For example, Kouno
and Saito (2010) showed that salary gaps and retirement rates have a negative effect on employee
health [8]. Suzuki (2011) reported that long working hours lead to high levels of obesity [9]. On
the whole, work style and its outcomes have a significant impact on lifestyle diseases.
2Many previous studies have reported the relationship between undergoing health checkups
and the working environment. Kawaguchi et al. (2010) reported that many self-employed men do
not receive a specific medical checkup and that their awareness of such checkups remains low[10]. In addition, using data from the Japan Health Insurance Association, Adachi and Nukiba(2016) found that lifestyles and the rate of undertaking health checkups differ depending on thetype of business [11].
This study examines what kind of workers participate in health checkups and investigates
what kind of health-related information on health status has an impact on the decision-making
process for undertaking health checkups.

2. Analysis framework

This study uses data on employees working at enterprises that joined the Hyogo branch of
the Japan Health Insurance Association3. Although employees who work in small and mediumsized companies and their dependent families are members of the Association, we only use dataon the insured employees themselves and exclude that of their families. Therefore, the sample
only includes employees4. A total of 1,741 responded to the “Survey on Mental and Physical
Health and Work Environment” and we obtained their consent as the subjects of the analysis.
Regarding whether the subjects have undertaken health checkups, we used their healthcheckup questionnaire data. However, due to data limitations, we cannot strictly say that a subject
definitely does not undergo health checkups if such data does not exist or is not available. This isbecause the questionnaire data may not be submitted due to the status of cooperation with theJapan Health Insurance Association, or when a non-regular worker in a large organization is amember of the Japan Health Insurance Association, but their data has not been submitted5.
In addition, the target of the general health checkup for a lifestyle-related disease prevention
health checkup is an insured person who is 35 to 74 years old in the current year. Therefore, theanalysis is performed by excluding the respondents under 35 and over 75. For the health checkup
status of the subjects, we used the data received in 2017.
The “Survey on Mental and Physical Health and Work Environment” has questions related
to work styles such as working hours, business types, and employment forms. Furthermore, basedon the data, we use variables about the number of days they visited a medical institution visitedand the number of appearances of lifestyle-related diseases. In summary, the final data for analysis

3

The health checkups used in this study are different from those conducted in the workplace.

In this data, employees do not necessarily have a health checkup.
The issue of creating a variable representing whether a subject has undertaken a health check is
discussed in Section 4.
3
4

5

is a combination data collected from the “Survey on Mental and Physical Health and Work
Environment,” and health checkup questionnaire data.
In the empirical study, we examine not only the entire sample, but also two subsamples. In
the first subsample, we exclude employees working for companies in the public service industry;
and in the second subsample, in addition to public service industry, we also exclude the academic,
professional, and technical service industries. In addition, most of the members currently affiliatedwith the Japan Health Insurance Association are small and medium-sized enterprises. Therefore,
the industry ratio of the sample used here differs from that from the national statistics. In thisregard, Tables 1 and 2 show the number of employed workers by industry in this sample. The
tables also show the number of workers by the industry for each company size.
After grouping the entire sample according to the presence or absence of data on healthcheckups, the characteristics of industry and company scale for each group are described below.

3. Characteristics of Group A and B samples

We divided the data into group A and group B. Subjects were categorized into Group A if
checkup questionnaire data was available in a company where the subject works. If no checkup
data was available, the sample was categorized into Group B. Tables 1 and 2 present thecharacteristics of company size and industry in each group.
First, Table 1 shows the classification of industries by group size for Group A. Among thesamples, there are many subjects working in the medical and welfare field. Furthermore, thenumber of employees working in this field is the largest in for companies with 5 to 9 employees
to 100 to 299 employees.
In addition, for subjects working in a small company that belong to Group B, it is difficult to
conduct a health checkup in their companies, so even though there is a high possibility ofundertaking a health checkup at the Japan Health Insurance Association however they did not.
From Table 2, of the Group B subjects working in a small company (5–9 employees) 12.12%
were in industries such as the medical/welfare, 10.61% in academic, professional, and technicalservices, and 10.61% in public affairs. Next, for companies with 10–29 employees, medical andwelfare industry accounted for 25.35%, the construction industry for 12.68%, and the academic,
professional, and technical service industry for 11.27%. Moreover, for companies with 30–49
employees, medical and welfare industry accounted for 41.09%, the construction industry for10.85%, and the public service industry for 9.3%.

4

Table1 Frequency distribution of company size and industry: Group A

5

Table2 Frequency distribution of company size and industry: Group B

6

4. Estimation method

We use work hours and health information such as the number of days in hospital and thenumber of appearances of symptoms related to lifestyle-related diseases from received data. For
patients aged 35 to 74 years old, we examine the determinants of whether the subject undertakes
health checkups.
The estimation model is as follows:𝑃(𝑐ℎ𝑒𝑐𝑘𝑢𝑝𝑖 ＝１) = 𝛷(𝛽0 + 𝛽𝑊 𝑊𝑖 +𝛽𝐻 𝐻𝑖 + 𝛽𝑋 𝑋𝑖 )The estimation was performed using a logit model in which 𝑊𝑖 is working hours, 𝐻𝑖 is theinformation about health, and 𝑋𝑖 is a vector of personal attributes.
It should be noted that there may be two reasons for the lack of questionnaire data, whichshould be reported by the enterprises after their employees undertook the health checkups, fromthe Japan Health Insurance Association. First, the subject may not have undergone a healthcheckup, and second, the data was not reported to the Association. To distinguish these two
statuses clearly and create the variable of whether a subject has undertaken a health check moreaccurately, we used the data of Group A only to generate a binary variable representing undergoinga health checkup. The data of Group B was excluded because the subjects in this group are those
who worked in such companies that such data reported to the Japan Health Insurance Association,and if the companies did report, they generally reported that all employees had undertaken healthcheckups.
Table 3 shows descriptive statistics of variables used for estimation. The number of
employees taking health checkups represents the total sample for our estimation. A total of 88%
of employees took health checkups, in other words, data is available for 575 employees, while nodata exists for 78 employees. Work hours are generally distributed around eight hours. A total of
45% of the sample is female. About 70% of the sample have a spouse or partner and 50% have children.
Days of hospitalization, as an outpatient, or for dentistry represent the number of full days a person
spent in a medical institution this year. In addition, the number of cases of high blood pressure,
hyperlipidemia, and diabetes are also represented . On these health-related variables (the number ofvisit days and lifestyle-related diseases), there are individual differences widely. Regular employees
make up the majority and enterprises with the fewest employees (5–9 employees) account for 5% of
the sample.

7

Table 3 Descriptive statistics

obs.

mean

std. dev.

min

max

having health checkups

653

0.88060.32460

1

work hours

653

7.80381.00444

15

spouse or partner

653

0.73810.44

01

female653

0.45330.49820

1

days of hospitalization

653

0.70294.0489059visit days as outpatient

653

8.464

10.86

0

84

visit days for dentistry

653

2.928

5.1493025high blood pressure

653

1.35073.4806023

hyperlipidemia

653

1.412

3.8112024diabetes

653

1.04293.7192044income653

293.1394

166.511998

1390

children

653

0.513

0.87670

434-39 years old

653

0.15010.35740140-44 years old

653

0.13940.34660

145-49 years old

653

0.16390.37040

150-54 years old

653

0.13320.34010

155-59 years old

653

0.15010.35740160-64 years old

653

0.1761

0.38120

165-69 years old

653

0.06740.25090170-74 years old

653

0.01990.13980

1

regular employment653

0.53750.4990

0

1

non-regular employment653

0.39200.48860

1

company officers/self-employed653

0.04750.2128

0

1

other

653

0.0214

0.145

0

1

no. of employees (5-9)

653

0.05210.22230

1

8

5. Estimation results

Table 4 shows the logit estimation results for the entire sample. We estimate two models
using (i) a dummy variable with fewer than 10 employees in the company, and (ii) a dummy
variable with fewer than 50 employees in the company to control for company size.
First, according to the estimation result using (i), the variables of working hours, the numberof dental treatment days, the number of the appearance of hyperlipidemia, dummies for 40–44,
45–49, 50–54, and 60–64 years old (based on 35–39 years old) are supported by significant andpositive effects on the decision to undertake health checkups. The result of working hours implies
that the longer a subject’s working hours are, the more likely they are to undertake a healthcheckup. This may be because employees with especially long working hours always receive an
active check by their industrial physician and are prompted to take health checkups. In addition,
health information such as the number of visits to a dentist and the number of the appearance ofhyperlipidemia increases individuals’ probability of participating in a health checkup. Moreover,according to the estimation results using a dummy with fewer than 50 employees, the female
dummy is significantly positive, which suggests that female employees have a higher probabilityof undertaking a health checkup.
The estimation results for the entire sample include people who work for companies in the
public affairs, academic, professional, and technical service industries. In such large organizations,companies can conduct health checkups in their workplace, thus, it is convenient for theiremployees to undertake these. To avoid this effect, we additionally estimate the model by
excluding samples from the aforementioned industries.
Table 5 shows the estimation results using a dummy variable with fewer than 10 employees
in the company for two subsamples excluding the public affairs industry only, and excluding
public affairs, academic, professional, and technical service industries, respectively6. Comparedto the results of the entire sample, the spouse or partner dummy became positively significant in
the estimation of both subsamples. This result indicates that an individual who has a spouse orpartner has a higher probability of undertaking a health checkup.

The estimation results using a dummy variable with fewer than 50 employees in the company
are similar to the results in Table 5 and are not reported here to conserve space.
96

Table 4 Estimation results for all samples

(1)

(2)

Marginal

RobustMarginal

Robusteffects

std. err.

effects

std. err.

work hours

0.04730.01450.04630.0142

spouse or partner

0.04560.02980.04640.0301

female0.05810.02740.05790.0274days of hospitalization

0.00010.00290.00020.0029visit days as outpatient

0.00040.00130.00060.0014

visit days for dentistry0.00660.00300.00650.0030high blood pressure

0.00050.00410.00060.0041

hyperlipidemia

0.01080.00470.01060.0046diabetes-0.00240.0037-0.00220.0037

income0.00010.00010.00010.0001children-0.00690.0149-0.00850.014840-44 years old

0.07580.0232***0.07550.0229***45-49 years old

0.06220.0255**0.06210.0251**50-54 years old

0.05180.0274*0.05300.0266**55-59 years old

0.00340.03910.00370.038760-64 years old

0.08670.02430.08620.024065-69 years old

0.02770.04620.02590.046370-74 years old

0.01600.06870.00750.0742

non-regular employment-0.0157

0.0296-0.0106

0.0296-0.07420.0899-0.0874

0.0957other-0.0357

0.0974-0.03730.1001

no. of employees (5-9)

0.05050.0376
0.03390.0223

***

**

**

***

company officers/ self-employed

No. of employees(fewer than 50)

Observations
Log pseudolikelihood
Pseudo R2

653

653

-216.04919-215.67034

0.0956

0.0972*** p<0.01, ** p<0.05, * p<0.1
10

***

**

**

**

***

Table 5 Estimation results for samples excluding public affairs and academic, professional, andtechnical services(4) excluding public service(3) excluding public service

and academic, professional,
and technical service industries

Marginal

RobustMarginal

Robusteffects

std. err.

effects

std. err.

work hours

0.03800.0137 ***0.03500.0135 **spouse or partner

0.05280.0306 *0.05640.0312 *female0.03870.02740.04150.0278days of hospitalization

0.00220.00320.00180.0028

visit days as outpatient

0.00240.00180.00200.0018

visit days for dentistry

0.00720.0033 **0.00580.0032 *high blood pressure

0.00040.0049-0.00110.0048hyperlipidemia

0.00980.0058 *0.00940.0056 *diabetes

0.00020.00620.00110.0062income0.00004

0.00010.000020.0001children-0.0073

0.0138-0.00370.014140-44 years old

0.05390.0242 **0.04830.0251 *45-49 years old

0.04210.02580.05110.0237 **50-54 years old

0.05850.0232 **0.05320.0240 **55-59 years old

0.00010.03900.00640.038160-64 years old

0.07300.0228 ***0.07290.0226 ***65-69 years old

0.0191

0.05170.02830.046570-74 years old

0.00450.09050.00990.0804

non-regular employment-0.01170.0285-0.0301

0.0310company officers/self-employed-0.0695

0.0884-0.08810.1016other-0.0809

0.1118-0.1177

0.1282

no. of employees (5-9)

0.03350.03820.04680.0360Observations
Log pseudolikelihood
Pseudo R2

542

511

-166.2129-150.3579

0.10880.1055*** p<0.01, ** p<0.05, * p<0.1

11

6． Conclusion

This study uses data of employees who work at companies that joined the Hyogo branch of
the Japan Health Insurance Association and investigates which factors affect the participation in
the health checkups. As a characteristic of each group with and without health checkup data, weconfirmed that many employees work in the medical and welfare field by examining the company
size and industry of each group.
In addition, if small size companies do not have health checkup data, it may imply it isdifficult to conduct health checkups in their workplace, so there should be a high possibility ofundergoing a medical checkup at the Japan Health Insurance Association. However, sample’s data
in Group B do not exist.
Moreover, estimation results indicate that working hours, female dummy, and age increase
the probability of undertaking a health checkup in the full sample. The subjects’ healthinformation, such as the number of dental treatment days and the appearance of hyperlipidemias,
has a positive effect on their participation in health checkups. Furthermore, according to theestimation of the subsamples excluding the public affairs, academic, professional, and/or
technical services, we additionally find that whether an individual has a spouse or partner has asignificantly positive effect on their participation in health checkups.
Finally, the current study has two limitations. First, the data of Group B were excluded
because no health checkup questionnaire data was reported to the Japan Health Insurance
Association. We should determine a suitable way to distinguish if this occurred due to the factthat employees in these companies did not undertake a health checkup or that although several
employees undertook health checks, the companies did not report the data. Successfully
distinguishing this issue can enrich the data for further research. Second, since the analysis isbased on data for a single year, an individual’s behavioral changes regarding undertaking a healthcheckup and/or their health status changes are not considered. Panel data should be used toinvestigate these issues. Therefore, there is room for improvement in the future.

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