Discussion Paper No.449

Effect of Subjective Sleeplessness Symptoms on JapaneseLabor Productivity: An Empirical Study
Fengming CHEN, Fusae OKANIWA

2021.3TOHOKU ECONOMICS RESEARCH GROUPGRADUATE SCHOOL OF ECONOMICS AND
MANAGEMENT TOHOKU UNIVERSITY
27-1 KAWAUCHI, AOBA-KU, SENDAI,
980-8576 JAPANTERG, Discussion Paper No.449

Effect of Subjective Sleeplessness Symptoms on JapaneseLabor Productivity: An Empirical Study
Fengming CHEN, Fusae OKANIWA

2021.3TOHOKU ECONOMICS RESEARCH GROUP

Abstract
Sleep is an essential activity for every individual. However, many people suffer fromsubjective sleeplessness symptoms and insomnia, which affect their daily lives and work.
In this study, we focus on the effects of subjective sleeplessness symptoms on laborproductivity. Using anonymous data from the 2013 National Survey of Living Conditionsin Japan, we find that the respondents' subjective sleeplessness symptoms decrease hourly
labor income by about 1% in comparison with those who report no such symptoms. Ourfindings also show that heterogeneity by gender exists in the estimation, with the effectof sleeplessness observed to be statistically significant only for females.

キーワード：Sleeplessness symptoms, Productivity, Anonymous data, IV

GRADUATE SCHOOL OF ECONOMICS AND
MANAGEMENT, TOHOKU UNIVERSITY
２７－１KAWAUCHI, AOBA-KU, SENDAI,
９８０－８５７６ JAPAN1

Effect of Subjective Sleeplessness Symptoms on Japanese Labor
Productivity: An Empirical Study
Fengming CHEN1†, Fusae OKANIWA2‡

1. Introduction
The purpose of this study is to statistically examine the effect of insomnia on laborproductivity in Japan. The average daily sleep duration of Japanese people is the shortest
among OECD countries, with the highest percentage of people sleeping between six andseven hours per day (Ministry of Health, Labor and Welfare, 2018). Meanwhile, Japan's
hourly labor productivity of $46.8 has been the lowest among the seven major OECD
industrialized countries since 1970 (OECD, 2018a,b). Hafner et al. (2016) found that (1)sleep deprivation reduces workplace productivity, and (2) the associated economic losses(loss as a percentage of the size of each country's economy) are the largest in Japan among
OECD countries.
Regarding sleep and productivity, previous medical research has shown that sleep
deprivation is associated with lower cognitive ability and performance (Cappuccio et al.,2010; Walker, 2017). In the field of economics, health is considered part of human capital,
and many empirical studies have been conducted on the effect of health on laborproductivity (Oishi, 2000; Hamaaki and Noguchi, 2009; Iwamoto, 2000; Yuda, 2010;
Sato, 2016). Conversely, existing studies in economics, on sleep and labor productivity
have treated it exogenously as a leisure activity under budget constraints. Therefore, little

1†

Graduate School of Economics and Management, Tohoku University, Assistant Professor✉：cfmdbdx@gmail.com
2‡

Graduate School of Economics and Management, Tohoku University, Assistant Professor✉：fusae.okaniwa.b4@tohoku.ac.jp
2

consideration has been given to elucidating the mechanisms of the relationship between
sleep duration and economic performance or the determinants of sleep choice that reflect
endogeneity (Giuntella et al., 2015). This points to a lack of evidence on this issue in thefield of economics (Gibson and Shrader, 2018).
This study statistically examines the impact of subjective sleeplessness symptoms
on worker productivity using anonymous data from the 2013 National Survey of Living
Conditions. The findings show that:
1) Subjective sleeplessness symptoms have a significant effect on lowering the laborproductivity of respondents.
2) There is a significant difference in the impact of sleeplessness between men andwomen.
Based on these results, it is necessary to consider the effect of sleeplessness on
worker productivity and account for any gender differences when examining the social
cost of sleeplessness.
The rest of this paper is organized as follows. Section 2 provides an overview ofprevious studies. The data used in this study are introduced in Section 3. Section 4
presents the variable definitions and descriptive statistics. Section 5 explains theestimation method and the results. Finally, Section 6 summarizes the study and discusses
it limitations.
2. Literature Review
2.1 Health and Productivity
Since 1972, when the economist Grossman first incorporated the concept of health into
his theoretical model and developed the health capital investment model, active researchhas been conducted in Japan and abroad on the relationship between health and labor3

productivity. Currie and Madrian (1999) and Kuroda (2018) showed that most existing
empirical studies support the conclusion that health status significantly affects laborproductivity. For example, Sato (2016) used the Keio Household Panel Survey (KHPS)
and conducted an analysis using ordinary least squares (OLS) and instrumental variable
models with fixed effects (IV-FE), taking endogenous and unobservable personal
attributes into account. The results show that among males, worsening health status has asignificant negative impact on wage rates.
Subjective health is the most frequently used health indicator to assess overall healthstatus (Oishi, 2000; Kamimura and Komamura, 2017; Sato, 2016; Noguchi, 2014; Yuda,
2010). According to Grossman (1972), as individuals age, their health capital stock
declines, and appropriate health investments are necessitated to maintain a certain stock.
However, many respondents ignore the decline in health stock due to aging, which may
prevent an accurate reflection of individuals' health status through their subjective healthlevel. For example, if they have specific subjective symptoms, they may attribute them toaging and exclude them from their personal health assessment. Therefore, some studies
assess respondents' health status by specifically inquiring about subjective symptoms or
by using the status of disabilities in daily life, rather than the subjective health level(Iwamoto, 2000).
In addition, while analyzing the relationship between health and labor productivity,
there is a need to consider the endogenous nature of health status. Existing studies haveused exercise habits, sleep duration, and the history of disease as instrumental variables(Kamimura and Komamura, 2017; Sato, 2016; Hamaaki and Noguchi, 2010; Yuda, 2010).
However, the validity of these variables due to the issue of weak correlation remains
controversial (Kuroda, 2018).
4

2.2 Sleep and Productivity
Previous studies have shown that there is a negative correlation between wages andsleep duration for men (Stafford and Duncan, 1980). The introduction of daylight-saving
time reduces sleep duration and leads to poor performance, including increased workrelated accidents (Barnes and Wagner, 2009); an increase of one hour in average sleep
time results in higher productivity than would accrue from a rise of one year of education(Psacharopoulos and Patrinos, 2004). However, these studies do not take the endogenous
nature of sleep time into account. One of the first studies to mention the causality between
sleep duration and productivity was Biddle and Hamermesh’s work (1990). They
conducted a panel data analysis on the effect of working hours on sleep and found that anincrease of one working hour was associated with approximately ten minutes of decreased
sleep time. However, due to potential reverse causality, the empirical model does notconsider the effect of sleep duration on productivity, even though the theoretical modelemphasizes this possibility.
In contrast, several economic studies in recent years consider the endogeneity ofsleep duration using instrumental variables. These studies typically utilize circadianrhythms as instrumental variables. Living organisms have the ability to change theirinternal environment (body temperature, hormone secretion, etc.) in concert with changesof day and night (movement of the sun, etc.), and humans are known to follow a circadianrhythm of about 24 hours. Based on physiological evidence that the circadian rhythm
induces sleep, the strong relationship between sleep and sunset time is used for estimation.
First, Gibson and Shrader (2018) modeled a sleep production function by extending
Becker's (1965) household production function and Gronau's (1977) time-use model to5examine the effect of sleep duration on productivity. Using data from the American Time
Use Survey, they conducted an empirical analysis using the two-stage least squares(2SLS) method with instrumental variables. The instrumental variables were (1) the
annual average sunset time in each region, (2) the declination of each region, and (3) theinteraction term between sunset time and declination. They use the difference in sunset
time between regions in the U.S., where the sun sets earlier in the east than in the west;
hence, the earlier the sunset, the greater the tendency to fall asleep sooner. On the other
hand, since work and school start-times are not strongly affected by earlier sunset times(Hamermesh et al., 2008), the estimates were based on the hypothesis that there is no
relationship between sunset time and wages. The analysis revealed that increasing the
average sleep time by one hour per night increases wages by 1.1% in the short run and by
about 5% in the long run. In addition, focusing on the non-linear relationship between
sleep duration and wages, the results of quadratic estimation indicate that the optimal
sleep duration to maximize wages is about nine hours. However, the analysis does notcontrol for time-invariant unobserved effects. In addition, the problem of a weak
instrumental variable exists in some estimations.
Kajitani (2019) also controlled for both time-invariant and time-varying endogenous
effects by accounting for the endogeneity of wages and hours of sleep through an
instrumental variable model with fixed effects. Data were taken from the Keio Household
Panel Survey (KHPS) from 2004 to 2017 to analyze the effect of sleep on the productivityof male workers in Japan. Outside temperature fluctuation was used as an instrumental
variable, one of the environmental changes that contribute to circadian rhythms. He useddata on the average annual temperature among cities and the average annual sunshine
duration as instruments. The results showed that an additional hour of sleep per week6

increases wages by 3–5%, which is generally consistent with the results of Gibson and
Shrader (2018) mentioned above. Note that the statistical validity of the instrumental
variables was confirmed by tests of endogeneity and weak instruments. The author also
stated that there is no significant long-run relationship between the instrumental variables
and the explained variable of productivity, on the basis of Graff et al. (2018). However,from the perspective of environmental economics, there are several studies showing that
productivity decreases because of severe temperatures in the agricultural and industrial
sectors (Zhang et al., 2018), but no detailed theoretical justification has been provided forthis.
2.3 Contributions
It is necessary to consider the endogeneity of sleep and to use instrumental variables
for estimation. If the instrumental variable on circadian rhythms adopted in previous
studies is used for estimation in the case of Japan, it will likely be difficult to correctly
identify appropriate latitudes due to the long vertical shape of the Japanese archipelago.
Additionally, as mentioned above, the temperature data used by Kajitani (2019) is
undeniably correlated with labor productivity, the explained variable. Therefore, in thisstudy, variables related to sleep duration and exercise habits used in Sato (2016) andIwamoto (2000 are used as the instrumental variables. Although subjective symptoms of
sleeplessness are thought to be correlated with a lack of sleep (short sleep), the symptoms
for both are not necessarily identical. For example, people may claim "I can't sleep" even
if they have enough sleep time, due to subjective factors such as difficulty falling asleep,
waking up many times during the night, and not feeling rested enough due to shallow
sleep. In addition, since sleep duration is not believed to affect the hourly wage rate
directly, it is a reasonable instrumental variable (Sato, 2016). Furthermore, exercise has
7a positive effect on sleep quality (Vanderlinden et al., 2020; Glavin et al., 2021).

3. Data
We use anonymous data (Questionnaire B) from the 2013 Comprehensive Survey of
Living Conditions (Ministry of Health, Labor and Welfare). This survey is conducted tosurvey basic matters relating to people's lives, such as health, medical care, welfare,pensions, income, and to obtain the basic data necessary for the planning and operationof the Health and Labor Administration. Large-scale surveys have been conducted every
three years since 1986, and the 2013 survey is the 10th large-scale survey. This largescale survey is known to be one of the most representative surveys in Japan, collecting
relevant information from respondents through five questionnaires: household
questionnaire, health questionnaire, nursing care questionnaire, income questionnaire,and savings questionnaire.
The anonymous data used in this study are those for which certain confidentiality
measures have been taken to protect specific individuals or corporations' privacy,
following Article 2, Paragraph 12 of the Statistics Law. The resampling of the anonymous
data is done in two stages, census tracts and households, and the weight of each record isadjusted for uniformity.
4. Definitions of Variables
4.1 Indicators of Labor Productivity
According to Kuroda (2018), productivity indicators can be categorized into theindividual, firm, and aggregate levels. In this study, we use the logarithm of hourly wages,
taking data availability into account. Specifically, based on Yuda (2010), we define thehourly wage rate as equal to employment income (annual income in the previous year)/52
8

weeks/working hours per week.
4.2 Subjective Sleeplessness Symptoms
Based on survey data, we identify the subjective symptoms of "unable to sleep (asleep disorder)" and create a subjective sleeplessness symptom dummy. We use a sample
of 1,356 respondents who responded to the question of whether they had subjective
sleeplessness symptoms; 6.5% of the respondents are found to have a sleep disorder. In
this study, we define the subjective sleeplessness symptoms dummy as 1 if the
respondents reported having subjective sleeplessness symptoms. Otherwise, this dummytakes the value of zero.
4.3 Control Variables
Based on previous studies (Kamimura and Komamura, 2017; Sato, 2016; Yuda,
2010), our control variables include a male dummy, high school dummy, junior/technicalcollege dummy, university/graduate school dummy, tenure, tenure squared, regular
employment dummy, occupation dummies, and firm size dummies. Table 1 shows thedescriptive statistics.
Table 1 Descriptive StatisticsNln(Hourly labor income)

1,356

0.363

0.416

Min
0.003

Sleep fewer than five hours

1,356

0.122

0.328

0

1

Sleeplessness (subjective symptom)

1,356

0.065

0.246

0

1

Male

1,356

0.485

0.500

0

1

High school

1,356

0.434

0.496

0

1

Junior/technical college

1,356

0.226

0.419

0

1

University/Graduate school

1,356

0.271

0.444

0

1

Tenure1,356

13.51512.661050Regular employment

1,356

0.580

0.494

0

1

Administrative and managerial workers

1,356

0.063

0.242

0

1

Specialist professionals

1,356

0.271

0.445

0

1

Clerical workers

1,356

0.157

0.364

0

1

9

Mean

S.D.

Max
8.750

Sales workers

1,356

0.088

0.283

0

1

Service workers

1,356

0.181

0.385

0

1

Security workers

1,356

0.008

0.090

0

1

Agriculture, forestry and fishery workers

1,356

0.003

0.054

0

1

Manufacturing process workers

1,356

0.098

0.298

0

1

Transport and machine operation workers

1,356

0.025

0.156

0

1

Construction and mining workers

1,356

0.023

0.150

0

1

Carrying, cleaning and related worker

1,356

0.046

0.209

0

1

Workers not classified by occupation

1,356

0.038

0.190

0

1

Company size (under 100 persons)

1,356

0.430

0.495

0

1

Company size (100~999 persons)

1,356

0.278

0.448

0

1

Company size (above 1,000 persons)

1,356

0.190

0.393

0

1

Company size (government and municipal office)

1,356

0.102

0.302

0

1

Source: Author’s calculations using data from the 2013 Comprehensive Survey of Living Conditions.

5. Results
5.1 Econometric Model
This study estimates an augmented wage function by adding an explanatory variable
for subjective sleeplessness symptoms to the Mincer-type wage function (Mincer, 1974).
As mentioned earlier, to take endogeneity into account, a dummy variable is used as the
instrumental variable, which takes the value of one for average sleep duration of fewer
than 5 hours. The estimation is conducted using a two-stage estimation method. In thefirst stage, subjective sleeplessness symptoms are the dependent variable. The
instrumental and control variables are fed into the regression equation to obtain thepredicted value of subjective sleeplessness symptoms. In the second stage, the logarithmof hourly labor income is used as the dependent variable. The predicted value of
subjective symptoms and control variables obtained in the first stage are regressed as
explanatory variables to estimate the parameters.

5.2 Overall Estimation Results10Table 2 shows the estimation results of subjective sleeplessness symptoms (dummy),
on hourly labor income (log). In Table 2, A1 is the estimation result using OLS without
considering the endogeneity problem of subjective symptoms. The partial regression
coefficient value for the subjective symptom dummy is -0.106, but it is not statisticallysignificant. In other words, the results show that sleeplessness symptoms do not affectlabor productivity. Conversely, A2 is the estimation result after correcting theendogenous bias and applying the instrumental variable method. The partial regression
coefficient value for the subjective symptom dummy is -0.962, which is statisticallysignificant at the 10% level, indicating a large difference in the significance level and themagnitude (absolute value) of the coefficient compared to the estimation results in A1.
This result is consistent with the results of previous studies.
A3 summarizes the results of the first stage of estimation. The partial regression
coefficient value for the average sleep time dummy (fewer than 5 hours) is estimated tobe significantly positive at the 1% level, indicating that sleep time lower than five hours
is highly correlated to the existence of subjective sleeplessness symptoms. In addition,
the F-value of the first stage is 37.46, which is larger than the standard value of 10,
suggesting that there is no problem of weak correlation.

Table 2 Estimates of the Impacts of Sleeplessness on Labor Productivity（OLS と IV）

ln(Hourly labor income)Sleeplessness (subjective symptom)

A1

A2

OLS
-0.106(0.087)IV-0.962*(0.509)Sleep fewer than five hours
Male
High school-0.294***(0.053)0.174*(0.092)

11-0.279***(0.055)0.188*(0.097)A3
F-Stage

0.124***(0.020)0.017(0.016)0.017(0.028)

Junior/technical college
University/Graduate school
TenureTenure squared
Regular employment
Administrative and managerial workers
Specialist professionals
Clerical workers
Sales workers
Service workers
Security workers
Agriculture, forestry and fishery workers
Manufacturing process workers
Transport and machine operation workers
Construction and mining workers
Carrying, cleaning and related worker
Company size (100~999 persons)Company size (above 1,000 persons)Company size (government and municipal
office)Constant
Adj-R2N
F-First Stage
Partial R2

0.308***(0.102)0.423***(0.103)0.013**(0.006)0.000(0.000)-0.135**(0.058)0.053(0.139)-0.186(0.130)-0.236*(0.134)-0.345**(0.151)-0.123(0.133)-0.425**(0.198)0.208(0.296)-0.229*(0.136)-0.344**(0.167)-0.198(0.148)-0.422**0.316***(0.107)0.406***(0.108)0.016**(0.007)-0.000(0.000)-0.135**(0.059)0.049(0.142)-0.186(0.133)-0.248*(0.137)-0.404***(0.155)-0.142(0.136)-0.509**(0.206)0.127(0.301)-0.240*(0.140)-0.264(0.184)-0.220(0.153)-0.446**(0.171)(0.176)-0.031(0.057)0.071(0.064)0.173**(0.069)-1.359***(0.142)
0.080
1,356

-0.051(0.061)0.084(0.065)0.193***(0.072)-1.310***(0.148)
0.018
1,356

0.005
(0.031)-0.018(0.031)
0.003(0.002)-0.000*(0.000)0.001(0.016)-0.000(0.045)0.001(0.037)-0.012(0.038)-0.062(0.041)-0.020(0.038)-0.104(0.082)-0.073(0.126)-0.004(0.041)0.074(0.055)-0.020(0.056)-0.023(0.046)-0.026(0.016)0.008(0.019)0.024(0.025)0.045(0.042)
0.034
1,356
37.46
0.027

Note: 1. *, **, and *** represent statistical significance at the 10%, 5% and 1% level, respectively. 2. Figures inparentheses are robust standard errors.

125.3 Estimation Results by Gender
Due to the heterogeneity between men and women, we estimate the impact ofsubjective sleeplessness on labor productivity by gender. The control variables are thesame as those used in section 5.2. Table 3 shows the estimation results. First, regarding
the male group results (B1), the partial regression coefficient for the subjective symptom(sleeplessness) dummy is -0.231, but this is not statistically significant. This indicates thatsleeplessness does not affect labor productivity. Reviewing the female group results (B3),the sleeplessness dummy is deemed to be significant, indicating that labor productivity issignificantly lower for such respondents compared to those without sleeplessness
symptoms. Finally, the first-stage F-values of B2 and B4 are both larger than the standard
value of 10, indicating that there is no problem of weak correlation.

Table 3 Estimates of the Impacts of Sleeplessness on Labor Productivity by Gender (IV)Male

Female

B1

B2

B3

B4

IV

F-StageIV

F-Stage-0.231
Sleeplessness (subjective symptom)（0.644）Sleep fewer than five hours
High school
Junior/technical college
University/Graduate school
TenureTenure squared
Regular employment
Administrative and managerial workers-1.606**(0.769)0.004(0.114)0.099(0.136)0.220*(0.125)0.016**（0.008）-0.000（0.000）0.147*（0.083）-0.14413

0.106***(0.031)-0.021(0.041)0.000(0.049)-0.046(0.044)0.001(0.003)0.407**(0.170)0.513***(0.173)0.582***(0.195)0.014(0.011)0.143***(0.027)0.049(0.039)0.021(0.041)
0.004(0.046)0.003(0.002)-0.000(0.000)-0.000(0.000)-0.000(0.000)0.032(0.027)-0.355***(0.090)-0.013(0.020)-0.0200.148-0.090(0.070)(0.244)(0.085)-0.023(0.065)-0.037(0.198)0.011(0.045)-0.042(0.070)-0.093(0.194)-0.000(0.045)-0.103(0.072)-0.306(0.216)-0.037(0.049)-0.048(0.068)-0.113(0.101)-0.099(0.162)-0.026(0.069)0.032(0.078)-0.046(0.079)-0.095(0.192)-0.012(0.044)0.964***(0.203)-0.187(0.216)-0.207(0.696)-0.225(0.195)-0.058(0.232)-0.001(0.051)
0.174(0.139)-0.065(0.166)-0.069(0.080)-0.223(0.250)0.007(0.056)0.119*-0.005-0.187*-0.038*（0.072）0.149**(0.027)(0.096)(0.021)0.028(0.029)0.006(0.118)-0.015(0.027)0.039(0.039)0.181(0.118)0.018(0.033)0.088(0.071)-1.415***(0.221)

0.021(0.052)

0.015
657
11.61
0.018

699

0.042
699
27.45
0.039

（0.158）-0.356**
Specialist professionals
Clerical workers
Sales workers
Service workers
Security workers
Agriculture, forestry and fishery workers
Manufacturing process workers
Transport and machine operation workers
Construction and mining workers
Carrying, cleaning and related worker

Company size (100~999 persons)Company size (above 1,000 persons)Company size (government and municipal
office)Constant（0.142）-0.468***（0.165）-0.525***（0.197）-0.229(0.166)-0.480**(0.218)-0.272(0.201)-0.368**(0.154)-0.542***(0.189)-0.400**（0.163）-0.689***(0.206)（0.071）0.230***（0.085）-1.619***（0.184）

Adj-R2N
F-First Stage
Partial R2

0.134
657

Note: 1. *, **, and *** represent statistical significance at the 10%, 5% and 1% level, respectively. 2. Figures inparentheses are robust standard errors.

6. Discussion
This study examines the effect of subjective symptoms of sleeplessness on the laborproductivity of respondents, using anonymous data from the Comprehensive Survey of
Living Conditions. Taking the endogeneity problem of self-reported sleep deprivation
14

into account, we adopt the instrumental variable method. Based on data availability, adummy variable for sleep duration was used as an instrumental variable, following Sato(2016). The estimation results show that 1) in the analysis using the full sample,
sleeplessness (subjective symptom) significantly reduces labor productivity; 2)heterogeneity exists in the estimation by gender, with the effect of sleeplessness observed
to be statistically significant only for females.
Two problems exist in the analysis of this study. First, the cross-sectional data do
not allow us to take into account the issues of missing variables and measurement bias.
Second, in previous studies, the residential area's latitudinal information was collected
and used as an instrumental variable. However, in this study, due to the nature of theanonymous data, we are unable to collect data on the respondents' residential areas.

Acknowledgments
In preparing this paper, we received valuable comments from Hiroshi Yoshida (Tohoku
University) and Midori Wakabayashi (Tohoku University). The authors would also liketo express their gratitude to the Ministry of Health, Labor and Welfare for providing uswith the anonymized data of the 2013 Comprehensive Survey of Living Conditions under
Article 36 of the Statistics Act. The contents of this paper are parts of the results of a
project conducted by the Graduate School of Economics and Management, Tohoku
University, entitled "An Empirical Study on the Economic Evaluation of Dementia Using
Anonymous Data from the 2013 Comprehensive Survey of Living Conditions" (PI:Professor Hiroshi Yoshida). The figures and tables in this paper were created based onanonymous data and differ from those published by the Ministry of Health, Labor andWelfare. All errors are the sole responsibility of the authors. This work was supported by
15

JST COI Grant Number JPMJCE1303.
Declaration of Interest: None
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