The Society for Economic Studies
The University of Kitakyushu
Working Paper Series No.2012-8(accepted in February 21, 2013)

Long working hours and loss of productivity in the healthcare industry: Evidence from Japan

Kazuyuki Inagaki
Department of Economics, Management and Information Science
Onomichi City University
1600-2 Hisayamada, Onomichi, Hiroshima 722-8506, Japan

Kenji Azetsu
Faculty of Economics and Business Administration
The University of Kitakyushu
4-2-1, Kitagata, Kokuraminami-ku, Kitakyushu, 802-8577, Japan

Corresponding author: Kazuyuki Inagaki
Department of Economics, Management & Information Science
Onomichi City University
1600-2 Hisayamada, Onomichi, Hiroshima 722-8506, Japan
E-mail address: inagaki@onomichi-u.ac.jp
Telephone: +81 848 22 8311
Fax: +81 848 22 5460

Long working hours and loss of productivity in the healthcare industry: Evidence from Japan

Abstract
This paper proposes a simple approach to examine the impact of long working
hours on the loss of productivity. This approach does not require data on productivity in
examining the loss of productivity. Therefore, this approach is easily applicable to thehealth care industry, in which output is usually unpredictable. A measure of the impact
is theoretically derived from the dynamic labor demand model, and its significance canbe checked by the estimation of the Euler equation. We use data for Japan where theoverwork of health care workers is a serious problem. There is evidence in favor of theassumption that long working hours lead to a serious loss of productivity in the case of
the health care industry. However, this assumption is not supported in other industries
such as manufacturing. Therefore, our results suggest that the performance of healthcare workers is particularly affected by long working hours.

Keywords: productivity; working hours; overwork; health care industry
JEL classification: I10; J23; C51

11. Introduction
According to the World Population Prospects, the aging population is expected toincrease rapidly in many countries. Since the elderly require more health care services,
both in terms of quality and quantity, it is likely that the demand for labor in the healthcare industry increases remarkably in these countries. The labor force is usually definedas Lt  H t N t , where H t denotes the number of working hours per worker, and N tdenotes the number of workers. Therefore, a rapidly aging population increases thedemand for the number of health care workers and/or the demand for the number ofworking hours per health care worker. However, it seems to be difficult to immediately
increase the number of health care workers such as physicians and nurses, since they arehighly skilled professionals. When the number of the elderly increases much faster than
the number of health care workers, the demand for the number of working hours per
health care worker must increase remarkably in order to increase the production ofhealth care services. Hence, the overwork of health care workers is likely to occur incountries with a rapidly aging population.1
The aging population is a common problem for many countries. This implies thatthe overwork of health care workers may also become a common problem for thesecountries in the near future. For these reasons, it is important to examine the impact of
the overwork of health care workers.

1

This is an explanation for a possible situation that leads to long working hours ofhealth care workers. In the health care and other industries, long working hours may becaused by different reasons. For example, Sousa-Poza and Ziegler (2003) point out thatfirms use long working hours as a mechanism to sort productive workers.

2It seems reasonable to suppose that the overwork of health care workers means
extremely long working hours in the health care industry (e.g., Spurgeon et al., 1997).
This notion is helpful in examining the problems that arise from the overwork of healthcare workers, since we can confine our attention to the impact of long working hours.
In general, long working hours lead to an increase in the fatigue of workers and,
consequently, a deterioration in their health, since an increase in the number of working
hours leads to a decrease in their leisure time and makes it difficult for them to pursue
health-promoting activities such as exercise (e.g., Ruhm, 2000). Hence, it is widely
accepted that long working hours have a negative impact on labor productivity (e.g.,
Barzel, 1973). For example, a disruption in production occurs when workers spend time
to improve their own health. Workers with fatigue have difficulty concentrating, which
negatively affect their performance in the workplace (e.g., Cahuc and Zylberberg, 2004).
They are more likely to make mistakes at work, and this has particularly important
implications in the health care industry, which plays an important role in improving andmaintaining population health (e.g., Gaba and Howard, 2002).
The purpose of this paper is to examine the impact of long working hours on theloss of productivity in the health care industry. Long working hours are directly related
to factors such as fatigue and absenteeism, which lead to the loss of productivity. This
concept is applicable to health care workers (e.g., Spurgeon et al., 1997). Therefore, ourinvestigation is helpful to interpret the impact of the overwork of health care workers.
For example, if the loss of productivity in the health care industry is high, a decrease in
the number of working hours significantly improves productivity, and this improvement
may offset a large part of the loss of output that arises from a decrease in the number ofworking hours (e.g., Leveson, 1967). In other words, our investigation can provide

3useful interpretations of the effects of labor market policies that aim to reduce theoverwork of health care workers. Information obtained from our investigation seems to
be a major concern for countries with a rapidly aging population.
An important issue is how to analyze the impact of long working hours on theloss of productivity in the health care industry. For example, several previous studies
examined the elasticity of output with respect to the number of working hours based on
the estimation of the production function (e.g., Anxo and Bigsten, 1989; Leslie andWise, 1980). This approach is useful in examining the impact of long working hours,
but seems to be not easily applicable to the health care industry since it is difficult for
empirical researchers to determine the appropriate measure of output in the health careindustry (e.g., Bothwell and Cooley, 1982).2
In this paper, we propose a simple approach for the investigation of the impact oflong working hours on the loss of productivity. Our approach requires only two types ofdata (the number of workers and the number of working hours), and therefore is little
affected by data constraints. In other words, this approach is easily applicable to thehealth care industry since we can examine the impact of long working hours on the loss

2

Many previous studies in this literature used data for the manufacturing industry (e.g.,

Anxo and Bigsten, 1989; Leslie, 1984; Leslie and Wise, 1980). However, in the case of
the health care industry, output is usually unpredictable and imperfectly understood byproducers. Therefore, it is more difficult to determine the appropriate measure of output
in the health care industry than in the manufacturing industry. For example, Bothwell
and Cooley (1982) used ambulatory encounters and hospital discharges as measures of
output in the health care industry.

4

of productivity without using data on output.
Our approach is based on the theory of the demand for labor. As mentioned above,
if a rapidly aging population leads to a remarkable increase in the demand for thenumber of working hours per health care worker, an empirical investigation based on
the theory of the demand for labor can provide useful insights into the overwork ofhealth care workers.3 The impact of long working hours on the loss of productivity isparameterized in our model. This model has a theoretical foundation since it is derived
as the solution to the optimization problem. Furthermore, this model is expressed as alinear regression model. Therefore, in the framework of our model, the estimation of the
impact of long working hours on the loss of productivity in the health care industry is
relatively easy to implement.
In this paper, we pay careful attention to the estimation of our model. Specifically,
we examine the unit root properties of the variables included in our model (e.g., Dickey
and Fuller, 1979). In addition, we use the generalized method of moments (GMM)
technique to correct for both endogeneity and serial correlation in our model (Hansen,
1982). To assess the appropriateness of the GMM estimation results of our model, we
use the test for weak instruments (Stock and Yogo, 2002). Hence, this paper seems toprovide reliable estimation results for the impact of long working hours on the loss of
productivity in the health care industry.
This paper is organized as follows: Section 2 explains our model. Section 3
describes the data. Section 4 reports our findings. Section 5 offers a discussion. Section

3

Several previous studies examined the supply of health care workers. For example,

see Phillips (1995) and Rizzo and Blumenthal (1994).

5

6 presents the conclusions.

2. ModelIn this paper, we use the dynamic labor demand model.4 To describe the impactof long working hours on the loss of productivity, the specification of the production
function is important. Therefore, we discuss this point first.
Let Lt  H t N t be the effective labor force, where H t denotes the number ofworking hours per worker and N t denotes the number of workers. The aggregate
production function may usually be given by F ( Lt ,  t ) , where F Lt  0 and  tdenotes a productivity shock observed at the beginning of period t . In this paper, todescribe the impact of long working hours on the loss of productivity, this production
function is modified as follows:Yt  F ( Lt ,  t )  G( H t )(1)

where G H t  0 and Yt denotes output.
In our model, the number of working hours is allowed to have both positive andnegative impact on output. The gross positive impact of the number of working hours
4

Inagaki (2012) also uses the dynamic labor demand model in examining the labormarket structure in the health care industry. We distinguish our work from this study byassuming the production function in which long working hours lead to a serious loss of
productivity. Since our focus is on the impact of long working hours on the loss of
productivity, this assumption serves our purpose.

6

on output is described by the first term F ( Lt ,  t ) on the right-hand side of equation (1),and this is usually assumed in the theory of production. On the other hand, the grossnegative impact of the number of working hours on output (i.e., the loss of productivity)
is described by the second term G ( H t ) , which represents the impact of factors such as
fatigue. Since G ( H t ) is an increasing function of H t , long working hours lead to afairly large loss of productivity. Therefore, the inclusion of G ( H t ) in the production
function provides useful information about the negative impact of long working hours
discussed in Section 1.
In this paper, we assume that G( H t ) has the following form:

G( H t ) cL(H t )22(2)

where cL is the positive parameter that determines the size of the loss of productivity
and, therefore, is the main focus of this paper.5
Information on the impact of long working hours on the loss of productivity is
5This functional form does not describe the positive impact on productivity such as theon-the-job training, since labor productivity does not increase as the number of working
hours increases. However, this positive impact is usually assumed in the case of shortworking hours (e.g., Barzel, 1973). In other words, our model implicitly assumes thatlong working hours are prevalent. As mentioned in Section 1, the overwork of healthcare workers is likely to become a common problem for many countries. Hence, thisassumption seems to be useful in examining the health care industry.

7

reflected in cL . If cL is not significant, the modified production function is reduced to
the former one. However, if cL is positive and significant, the inclusion of G ( H t ) in
the production function is empirically supported and, consequently, long working hours
lead to a fairly large loss of productivity. Therefore, the adequacy of the modification
can be examined by checking the significance of cL .6
Our model allows for the existence of labor adjustment costs, since it is widely
accepted that employers must incur costs when adjusting labor inputs (Hamermesh andPfann, 1996). Examples of labor adjustment costs include search and training costs for
new employees and extra pay for overtime. Let CH (H t ) and CN (Nt ) be the costsof adjusting the number of working hours and the number of workers, respectively,
where H t  H t  H t 1 and Nt  Nt  Nt 1 . In this paper, we assume that CH (H t )and CN (Nt ) have the following quadratic forms:

C H (H t ) 

6cH(H t ) 2
2

(3)

The inclusion of G ( H t ) is also useful for examining heterogeneity between thenumber of workers and the number of working hours in terms of productivity (e.g.,
Feldstein, 1967). For example, if cL is positive and significant, the impact of thenumber of working hours on output is smaller than that of the number of workers.
However, this point is open to debate in the case of the manufacturing industry (e.g.,
Anxo and Bigsten, 1989), and little is known about the heterogeneity in the health careindustry. Therefore, it is important to examine the sign and significance of cL .

8

C N (N t ) cN(N t ) 2
2

(4)

where cH and c N are the positive parameters that determine the size of the laboradjustment costs.7
A risk-neutral decision maker maximizes the present discounted value of profits( V ). The optimization problem is defined as

V ( H t 1, Nt 1 )  max F ( Lt ,  t )  G( H t )  wt Lt
 CH (H t )  CN (Nt )   t [V ( H t , Nt )](5)

where t denotes the expectations at the end of period t ;  , the discount factor; andwt , the real wage per working hour. The first-order conditions with respect to H t and
N t yield the following Euler equations:7

M t Nt  cL H t  cH H t   t [cH H t 1 ]  0(6)

M t H t  cN Nt   t [cN Nt 1 ]  0(7)This paper assumes symmetric labor adjustment costs since this assumption enables

us to more easily estimate the impact of long working hours on the loss of productivity,
which is the main focus of this paper. Although our model can be extended to the casewhere labor adjustment costs are asymmetric, we leave this issue for future research.

9where M t  F ( Lt ,  t )  wt .
To estimate this model, the actual values of H t 1 and N t 1 are substituted
into equations (6) and (7). Then, these equations are rewritten as

M t Nt  cL H t  cH (H t   H t 1 )  u1t(8)

M t H t  cN (Nt   Nt 1 )  u2t(9)where u1t and u2t are the error terms. Combining equations (8) and (9) yields thefollowing equation:

H t   H t 1
N t   N t 1H  t  ut
Nt
Ht
Nt

(10)

where   cN cH ,   cL cH , and ut  (u2t H t  u1t Nt ) cH .
The most important point to note is that the significance of  in equation (10) is
directly related to the significance of cL in equation (2). Therefore, the adequacy of theassumption that long working hours lead to a fairly large loss of productivity can bechecked by the estimation of  .
To estimate  , we rewrite equation (10) as

ht   nt   xt  ut

(11)

where ht  (H t   H t 1 ) N t , nt  (N t   N t 1 ) H t , and xt  H t N t . When a10specific value is substituted into the discount factor  , equation (11) is regarded as alinear regression model that includes three variables ht , nt , and xt .8 Hence, using
time series data on these variables, we can estimate  and  .
This estimation procedure is relatively easy to implement, since it is based on alinear regression model and requires only two types of data, H t and N t , which areused to calculate the data for ht , nt , and xt . Our model has a theoretical foundation
since it is derived as the solution to the optimization problem. Furthermore, using thismodel, we can examine the impact of long working hours on the loss of productivity
without specifying F ( Lt ,  t ) , which is a usual positive relationship between the labor
force and output. This seems to provide an advantage for empirical researchers who areparticularly interested in the possible negative relationship between the number ofworking hours and productivity G ( H t ) .

3. Data
3.1. Explanations for data
8

As Azetsu and Fukushige (2009) point out, it is likely that the composite error term

ut has non-zero mean even if u1t and u2t have zero mean, since u1t and u2t are
divided by N t and H t , respectively. In this case, the composite error term may be
rewritten as ut    et , where  is a constant term and et is a random variable withzero mean. Hence, the inclusion of a constant term in equation (11) seems to be usefulfor maintaining the econometric assumption that the error term in the regression modelhas zero mean. For this reason, we include a constant term in the regression model.

11

We use seasonally adjusted monthly data for the Japanese health care industry(medical and other health services; Major Group 83 of the Japan Standard Industrial
Classification). In this paper, we focus on Japan for the reasons provided in Section 3.2.
The sample period is from January 2000 to December 2010 because of data availability
issues. The data comprise of the regular employment index (2005=100) and the hours
worked index (2005=100). The data source is the website of the Japan Institute forLabor Policy and Training.
The Japanese health care industry includes establishments engaged in medical
treatment (e.g., hospitals, clinics of medical practitioners, dental clinics, and maternity
clinics and nursing). According to the Population Census 2005, nurses accounted for thehighest proportion of workers who were engaged in medical treatment in the health careindustry (45.4%), and physicians accounted for the second highest proportion of thoseworkers (11.3%).9
As mentioned in Section 2, our estimation procedure does not require output datain examining the loss of productivity. To estimate equation (11), only two types of data
(i.e., the number of workers and the number of working hours) are required. Since data

9

More than half of physicians in Japan are hospital physicians. For example, according

to the Survey of Physicians, Dentists and Pharmacists in 2008, physicians who were
engaged in hospital practice accounted for 60.8% to total physicians, and 3.1% of thosephysicians (1.9% of total physicians) opened hospitals (including representatives ofcorporations). On the other hand, physicians who were engaged in clinical practiceaccounted for 34.1% to total physicians, and 73.7% of those physicians (25.1% of total
physicians) opened clinics (including representatives of corporations).

12

units are different, we use index data.
The regular employment index is used as a measure of the number of workers.
The base year is 2005, and the base value is 100. This index is constructed from the dataon the number of regular employees, and is published by the Japan Institute for Labor
Policy and Training.
The hours worked index is used as a measure of the number of working hours.
Similar to the regular employment index, the base year is 2005 and the base value is 100.
This index is constructed from the data on the number of total hours worked (total ofscheduled and non-scheduled hours worked) per regular employee, and is published by
the Japan Institute for Labor Policy and Training.

3.2. Overwork of health care workers in Japan
In this paper, we focus on Japan to examine the impact of long working hours on
the loss of productivity in the health care industry, mainly because the overwork ofhealth care workers in Japan is a serious problem.
For example, the Ministry of Health, Labor and Welfare (MHLW) reported thatthe average number of working hours per week for physicians in Japan was 63.3 in2006. Physicians under 29 years of age worked about 80 hours per week on average,
and physicians between 30–59 years of age worked 60 or more hours per week on
average. Since the legal working hours in Japan are set at 40 hours per week, it is clearthat most physicians in Japan work extremely long hours. Furthermore, these figures arehigher than those in Europe and the U.S.10

10

Simoens and Hurst (2006) and Staiger et al. (2010) reported that in nine European

13

Similarly, in 2007, the Japan Federation of Medical Worker’s Unions indicatedthe results of a survey that was conducted on 1,355 physicians in Japan. The average
number of overtime hours per month was 62.9, and 30.9% of the physicians worked
more than 80 hours of overtime. The MHLW points out that overtime exceeding 80
hours per month has a strong relevance to pathogenesis of the brain and cardiac diseases.
In fact, the survey results indicated that 43.5% of the physicians were worried about
their health. Furthermore, the Japan Federation of Medical Worker’s Unions reported
that similar observations were also found for nurses in Japan.
It is clear that the prevalence of long working hours among health care workers inJapan influences their health status. Hence, in the case of Japan, the loss of productivity
in the health care industry is likely to be high.
Figure 1 around here
Furthermore, as mentioned in Section 1, the aging population can be one of theimportant factors that lead to the overwork of health care workers. The aging rate inJapan was 23.0 in 2010, and this figure was higher than those of most other countries
such as France (16.8), Germany (20.4), the U.K. (16.6), and the U.S. (13.1). Therefore,
more health care services are required in Japan. However, Japan had 2.2 practicing
physicians per 1,000 population, which was the lowest among the G7 countries. Hence,as mentioned in Section 1, the number of working hours per health care worker must
increase remarkably in order to increase the production of health care services. In fact,
as indicated in Figure 1, elderly (population over 65 years of age) per physician in Japanwas considerably high. Similar observations were also found for nurses. These results

countries and the U.S., physicians worked about 40 to 50 hours per week on average.

14

imply that, in the case of Japan, a large amount of work in the health care industry isaccomplished by a small number of health care workers. For this reason, long working
hours are prevalent in the Japanese health care industry, and our model presented in
Section 2 seems to be useful in examining the impact of this problem.

4. Empirical results
In this section, we report our empirical results. Before estimating equation (11), it
is necessary to examine the unit root properties of the variables included in equation(11), since we use time series data. In this paper, we use the unit root test developed byPhillips and Perron (1988).11
Table 1 around here
The results are reported in Table 1. To check the robustness of our results, thediscount factor  is set to 0.999, 0.950, and 0.900. The null hypothesis of a unit root
is rejected for all the variables. Therefore, we are not required to use cointegration
techniques when we estimate equation (11).12
Since it is found that all the variables do not have a unit root, the next step is to11

If we use the augmented Dickey-Fuller (ADF) test developed by Dickey and Fuller(1979), the results remain unchanged. The ADF test results are not reported in this paper
but are available from the authors upon request.
12

The deterministic trend for xt is found to be significant. However, if we use thedetrended data on xt , the following results remain essentially unchanged. Therefore, tomaintain consistency with the structure of the theoretical model, the deterministic trend
for xt is not adjusted in this paper.

15

estimate equation (11). For this purpose, we use the GMM technique, which corrects forboth endogeneity and serial correlation in equation (11). The set of instruments includes

nt and xt with 1 and 2 lags and a constant term. Similar to the above, the discountfactor  is set to 0.999, 0.950, and 0.900 to check the robustness of our results.
Table 2 around here
The estimation results of equation (11) are reported in Table 2. We find that and  are positive and significant. This result is consistent with the theoretical model
presented in Section 2. Furthermore, the validity of overidentifying restrictions is not
rejected. These results are not affected by the assumption of the discount factor  .
Table 3 around here
To assess the appropriateness of the estimation results of equation (11), it is also
important to test weak instruments.13 We follow Stock and Yogo (2002) and use thestatistic developed by Cragg and Donald (1993). The results are reported in Table 3. The
null hypothesis of weak instruments is rejected. Hence, it seems reasonable to supposethat equation (11) is successfully estimated.
Our main findings for the health care industry can be summarized as follows.
First, we find that  is positive and significant. Therefore, in the case of Japan, thehealth care industry has the aggregate production function in which extremely long
working hours lead to a serious loss of productivity. For example, the overwork ofhealth care workers in Japan discussed in Section 3 may decrease their production
13

The problem of weak instruments arises when instruments are weakly correlated withendogenous variables. For example, Stock et al. (2002) point out that standard GMM
inference is potentially unreliable if instruments are weak.

16

performance.
Second, we find that the gross negative impact of the number of working hours on
output, G( H t ) , is relatively large in the health care industry. For example, this impact
relative to the costs of adjusting the number of working hours, CH (H t ) , is calculated
from equations (2) and (3) as H G( H t )c (H )2Rt  L t 2    t 
CH (H t ) cH (H t ) H t 

2(12)

For simplicity, let us assume that the number of working hours per week is 55 for t  0
and 60 for t  1 .14 Using the estimate of  in the case where the discount factor is set to 0.999, we obtain R1  2.0 . Hence, in this case, the gross negative impact of thenumber of working hours on output is 2 times larger than the costs of adjusting thenumber of working hours.15
Third, we find that the costs of adjusting the number of working hours are lower
than the costs of adjusting the number of workers. This finding is confirmed from the14

For example, the average number of working hours per week for physicians in Japanwas 63.3 in 2006. For more details, see Section 3.
15

It is important to note that G( H t ) depends on the level of H t , whereas CH (H t )depends on the first difference of H t . Therefore, although we find that the estimates of  cL cH are small, this result does not necessarily imply that G( H t ) is smaller than
CH (H t ) .

17

estimation of  . This parameter is defined as   cN cH . From equations (3) and (4),cH and c N are the parameters that determine the sizes of the costs of adjusting thenumber of working hours and the number of workers, respectively. Since it is found that

 is significantly larger than 1, our result imply that cH  cN .16 This result provides
useful insights into the source of the overwork of health care workers. For example,
employers in the Japanese health care industry may prefer to increase the number ofworking hours rather than the number of workers when they attempt to increase theproduction of health care services, since the adjustment of the number of working hours
is less expensive. In other words, the overwork of health care workers in Japan is likely
to occur because of the cost structure in this industry.

5. Discussions
To interpret the relationship between long working hours and productivity in thehealth care industry in greater detail, a comparison with other industries is useful. For
this purpose, seasonally adjusted monthly data for the manufacturing (fabricated metal
products), wholesale trade (textile and apparel), and transport (transport and postal
activities) industries are used. The definition and source of the data are the same as
those reported in Section 3. However, to avoid the effects of the U.S. financial crisis in
2008, we use the data for the sample period from January 2000 to July 2008.17
To estimate equation (11), we use the GMM technique. For all the industries, the16

The t-value is 2.5, and the p-value is less than 0.01.

17

The results for the health care industry reported in Section 4 remain unchanged if we

use the data for the sample period from January 2000 to July 2008.

18

set of instruments includes nt and x t with 1 and 2 lags and a constant term. This set
is the same as that used in the case of the health care industry. The null hypothesis of aunit root is rejected for all the variables, and the null hypothesis of weak instruments isrejected for all the industries.18
Table 4 around here
The estimation results of equation (11) for the manufacturing, wholesale trade,and transport industries are reported in Table 4. For all the industries,  is positiveand significant, but  is not significant. Hence, our results suggest that the impact oflong working hours on the loss of productivity is larger in the health care industry thanin other industries such as manufacturing and wholesale trade.
We find that the relationship between long working hours and productivity in thehealth care industry is different from those in other industries such as manufacturing.
One possible explanation for this difference is that the ratio of workers who are nothealthy is higher in the health care industry than in other industries. As mentioned in
Section 2, the significance of  (or cL ) reflects the impact of factors such as fatigue.
For example, the Japan Federation of Medical Worker’s Unions found that the ratio of
female nurses who are not healthy to total female nurses was 38.2% in 2010. On theother hand, the MHLW found that for all industries, the ratio of female workers who are
not healthy to total female workers was 16.0% in 2007. These results suggest that thehealth status is lower in the health care industry than in other industries. Furthermore,
the Japan Federation of Medical Worker’s Unions also found that 81.4% of the female
18

These results are not reported in this paper but are available from the authors uponrequest.

19

nurses could not provide sufficient nursing care because of their overwork. 19 Thisimplies that the overwork of female nurses in Japan decreases their productivity. Fromthese results, it seems reasonable to suppose that the overwork of health care workers
and the resulting deterioration in their health increase the significance of  in thehealth care industry.
Another possible explanation for the difference is that the work load is higher inthe health care industry than in other industries. Since health care workers are required
to provide high quality services for improving and maintaining the health of patients, it
is likely that their physical and mental fatigue increases remarkably as the number ofworking hours increases. This implies that the labor performance in the health careindustry is strongly affected by long working hours. Hence,  may tend to be moresignificant in the health care industry than in other industries.

6. Conclusion
This paper examines the impact of long working hours on the loss of productivity
in the health care industry. The novelty of this paper is the application of the dynamic
labor demand model to the empirical analysis of this impact. In this paper, we assume
the aggregate production function in which long working hours lead to a fairly large lossof productivity, and the adequacy of this assumption is examined by the estimation of
the Euler equation. Although it is difficult to determine the appropriate measure of
output in the health care industry, our approach is little affected by such data constraints

19

Justina et al. (2009) suggest that low job satisfaction is significantly related to a

deterioration in the health of workers.

20

since this approach requires only two types of data (i.e., the number of workers and thenumber of working hours). In this framework, the impact of long working hours on theloss of productivity can be examined without using data on productivity. Therefore, it isrelatively easy for empirical researchers to apply this approach to the analysis of thehealth care industry.
In this paper, we use data for Japan where the overwork of health care workers is
a serious problem. We find that long working hours lead to a serious loss of productivity
in the health care industry. However, this impact is not significant in the manufacturing,
wholesale trade, and transport industries. Since work load is higher in the health careindustry than in other industries, the labor performance in the health care industry may
be more strongly affected by long working hours. On these grounds, we conclude that,at least in the case of Japan, the health care industry is characterized by the aggregate
production function in which extremely long working hours lead to a serious loss of
productivity.
Finally, we discuss some policy implications. Our results suggest that labormarket policies that aim to reduce the overwork of health care workers are effective in
improving productivity in the health care industry. Although the loss of output occurs
when decreasing the number of working hours, it is expected that a large part of the loss
is offset by the improvement of productivity and, consequently, the same amount of
output is produced by a slight increase in the number of health care workers. However,
employers in the health care industry may have less incentive to increase the number ofhealth care workers, since our results also indicate that the costs of adjusting the numberof health care workers are high in Japan. Given the rapidly aging population in Japan, it
is important to minimize the loss of output in the health care industry. Therefore, labor21

market policies that reduce the costs of increasing the number of health care workers arealso an important consideration for policy makers attempting to reduce the overwork ofhealth care workers in Japan.

Appendix. Data source(A) Regular employment index (2005=100)
Source: Japan Institute for Labor Policy and Training
Industrial classification: Medical and other health services
Frequency: Seasonally adjusted monthly data
Sample period: January 2000 to December 2010(B) Hours worked index (2005=100)
Source: Japan Institute for Labor Policy and Training
Industrial classification: Medical and other health services
Frequency: Seasonally adjusted monthly data
Sample period: January 2000 to December 201022

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Figure 1
Elderly per health care worker
4.0

110

11.6

24
22

100

3.6

11.2

20

90
3.2

18
80

10.8
16

2.8
70

14

10.4
2.4

60

2.0

12

50
Japan

US

10.0

Germany

10Japan

Phisicians per 1,000 population (left scale)
Elderly per physician (right scale)US

Germany

Nurses per 1,000 population (left scale)
Elderly per nurse (right scale)26

Table 1
Unit root test: Health care industry.

xt
ht
nt

–9.2011**  0.999–––86.7634**–125.4605**

  0.950–––86.7810**–85.4801**  0.900–––86.6567**–49.1328**

The test includes a constant term and a linear trend. The spectral estimation method
used is the Bartlett kernel, and the bandwidth parameter is selected by the Newey andWest (1994) procedure. ** indicates significance at the 1% level.

27

Table 2
Estimation results of equation (11): Health care industry.

  0.999
2.8359**[0.7330]  0.950
3.3413**[0.8479]  0.900
3.3082**[0.9300]

0.0138**[0.0046]

0.0123*[0.0055]

0.0116*[0.0058]

J-statistic

2.5533(0.2790)3.0488(0.2177)

3.3604(0.1863)



Numbers within square brackets are heteroskedasticity and autocorrelation consistent(HAC) standard errors. The spectral estimation method used is the pre-whitened Bartlett
kernel with a vector autoregression (VAR) model. The bandwidth parameter is selectedby the Newey and West (1994) procedure. The J-statistic is the test for the validity ofoveridentifying restrictions. Numbers within parentheses are p-values. ** and * indicate
significance at the 1% and 5% levels, respectively.

28

Table 3
Test for weak instruments: Health care industry.

Cragg-Donald statistic  0.999
24.1218

  0.950
23.9786  0.900
23.4705

A constant term and variables nt and x t with 1 and 2 lags are used as instruments.
The critical values are tabulated in Stock and Yogo (2002).

29

Table 4
Estimation results of equation (11): Other industries.
Manufacturing2.4150**[0.8224]

Wholesale trade2.5030**[0.6878]

Transport
1.4273**[0.5395]



0.0518[0.0346]

0.0441[0.0312]0.0282[0.0183]

J-statistic

1.9583(0.3756)

2.0777(0.3538)

2.2889(0.3184)

The discount factor is set to 0.999. Numbers within square brackets are HAC standard
errors, and numbers within parentheses are p-values. See the notes to Table 2 for theestimation procedures. ** indicates significance at the 1% level.

30