JOINT RESEARCH CENTER FOR PANEL STUDIESSELECTED DISCUSSION PAPER SERIESSDP2012-005
March, 2013

Subjective Well-being of Involuntary Non-regular Employees:
Evidence from Japanese Household Panel Data*

Isamu Yamamoto*
Risa Hagiwara**【Abstract】
This paper investigates whether non-regular employees are worse off with theiremployment status, by examining the subjective well-being measured by the mental
health index based on the individual panel data drawn from the Keio Household Panel
Survey (2004 through 2012). We find that the majority of non-regular employees
intentionally choose their employment status, as involuntary non-regular employment
accounted for 3.5% of the entire sample, and 16.0% of non-regular employment. We alsofind that once controlling for the individual fixed effects, the subjective well-being does
not differ among employment status except for the involuntary non-regular employment.
Specifically, non-regular employees are not necessarily worse off as far as they
intentionally choose their employment status. If they involuntarily choose thenon-regular employment, however, they tend to experience poor mental health and thus
their subjective well-being would be lowered. These results imply the importance of theseparation of non-regular employment into the voluntary and involuntary one.

* Keio University** Keio University

Joint Research Center for Panel Studies
Keio University

Subjective Well-being of Involuntary Non-regular Employees:
Evidence from Japanese Household Panel Data*†‡

Isamu Yamamoto and Risa Hagiwara

Abstract
This paper investigates whether non-regular employees are worse off with
their employment status, by examining the subjective well-being measured by
the mental health index based on the individual panel data drawn from the KeioHousehold Panel Survey (2004 through 2012). We find that the majority of
non-regular employees intentionally choose their employment status, as
involuntary non-regular employment accounted for 3.5% of the entire sample,
and 16.0% of non-regular employment. We also find that once controlling forthe individual fixed effects, the subjective well-being does not differ among
employment status except for the involuntary non-regular employment.
Specifically, non-regular employees are not necessarily worse off as far as they
intentionally choose their employment status. If they involuntarily choose thenon-regular employment, however, they tend to experience poor mental healthand thus their subjective well-being would be lowered. These results imply theimportance of the separation of non-regular employment into the voluntary andinvoluntary one.

†‡*

Keio University (E-mail: yamamoto@fbc.keio.ac.jp)
Keio University (E-mail: hagi09291985@z2.keio.jp)

The data analysed in this study consists of individual data drawn from the Keio Household
Panel Survey (2004 through 2012). We thank Kotaro Tsuru, Fumio Ohtake, and Sachiko
Kuroda for their valuable comments. Any errors remained in this paper are entirely theauthors’.

1. Introduction
Non-regular employees now account for 30% of all employees in Japanese labor market.
Non-regular employees can be defined as those who are hired by his/her employer with
fixed term, part-time, and/or indirect contract. Those include part-time and temporary
workers, contract employees, and dispatched workers from temporary agencies.
Although non-regular employees play an important role in the firm as a buffer stock
against the business cycle, their weak labor conditions such as lower wage, larger job
instability, and lower coverage of social security are often regarded as a problem. Since
the financial crisis in 2008 when many non-regular employees lost their job, authorities
have vigorously debated whether non-regular employment should continue under thecurrent regulations, or whether regulations should be tightened to protect their job.
By focusing on the subjective well-being of various types of employees, thispaper investigates whether non-regular employees are worse off with their employment
status. Even if non-regular employees face weaker labor conditions, their utility may not
be lowered as far as they intentionally choose the non-regular employment status.
However, if they are forced to choose that status under the demand or other constraints,
their utility could be lowered, in which case the labor market policies such as tightening
the regulations for the use of non-regular workers would be justified. For this reason,
this paper distinguishes involuntary non-regular employment from voluntary one. Some
workers engage in non-regular employment by choice but others do because they could
not find regular employment or they face certain constraint that makes it difficult for
him/her to choose the regular employment.
Although social and academic attention has been focused on unemployment sofar, only limited interest has been focused on involuntary non-regular employment.
Considering the increase in non-regular employees under growing economic uncertainty,
however, the focus on the separation of voluntary and involuntary of non-regular
employment might provide new insights. For example, if workers’ well-being decreases
with non-regular employment status because they are involuntary, then labor market
policy should target not only on the unemployment but also on the involuntary
non-regular employment. On the other hand, if non-regular employment would notdecrease worker’s well-being, then we may regard that non-regular employment could
contribute to preventing the unemployment rate from rising and helping support
workers’ livelihoods in the time of recession.
Using the individual data drawn from the Keio Household Panel Survey (KHPS),1

we investigate five employment statuses: regular employment, involuntary non-regular
employment, voluntary non-regular employment, unemployment, and not in labor force.
The analysis primarily contains two parts. The first part addresses the nature of
involuntary non-regular employment based on the descriptive statistics and simple
estimations. Previous studies that examined involuntary non-regular employment
include Nagase [1997], Barrett and Doiron [2001], Wakisaka [2003] and Noda andYamamoto [2009]. For example, Nagase [1997] points out that the wage disparity thatexists between regular and part-time workers cannot be explained by the Rosen’s [1974]
compensating wage differentials. In addition, wage disparities might possibly occur
because: (1) involuntary part-timers account for approximately 15% of all parttime-workers, and (2) middle-aged or older workers, workers who possess low levels of
education, and workers who work long hours, tend to become involuntary part-timers.
Using Canadian data, Barrett and Doiron [2001] reveal that involuntary part-timeworkers receive lower wages. Furthermore, Noda and Yamamoto [2009] demonstratethat labor supply behaviours differ between voluntary and involuntary non-regular
employment and the discouraged worker effect is markedly evident for voluntary
non-regular employment,1 rather than for involuntary one. In contrast to these studies,
this paper comprehensively reveals the nature of involuntary non-regular employment
based on individual panel data, by comparing with various types of employment status(i.e., regular employment, voluntary non-regular employment, unemployment, and notin labor force).
The second analysis in this paper explores the degrees to which the individuals’
levels of utility differ for each employment status. In particular, it explores whether
utility declines when individuals engage in involuntary non-regular employment. The
relationship between workers’ utility and employment status has been examined inseveral studies such as Clark and Oswald [1994], Winkelmann and Winkelmann [1998],
Ohtake [2004], and Sano and Ohtake [2007]. However, most of these studies havefocused on the unemployment and noted that unemployment tend to lower workers’
subjective well-being. However, none of the previous studies have examined how
voluntary or involuntary non-regular employment would affect worker’s subjective
well-being. It should be noted that in the field of epidemiological studies, a number ofstudies such as Virtanen et al. [2003] and Baba and Kondo [2005] have verified therelationship between the degree of subjective health and employment status. For
1

In addition, Wakisaka [2003] indicates that many male, older workers, unmarried workers,
and household breadwinners are groups among the involuntary non-regularly employed.

2instance, using Finnish workers’ data, Virtanen et al. [2003] demonstrate that subjective
health indicators do not differ whether employment contract are fixed or not. In thesestudies, however, the observed individual attributes (e.g., income and assets, educational
background, family structure) or unobserved individual effects are not sufficiently
controlled, while much attention is paid on the risk factors related to worker’s healthsuch as smoking and obesity. Therefore, the analysis conducted in this paper attempts todifferentiate itself from these previous studies.
The results of this paper can be summarized as follows. First, involuntary
non-regular employment accounted for 3.5% of the entire sample, and 16.0% of
non-regular employment. This indicates that the majority of employees engaged in
non-regular employment are those who have voluntarily chosen that employment status.
However, the number of people engaged in involuntary non-regular employment isequal to approximately 1.5 times the number of unemployed individuals. This figure
cannot be ignored. Typical features of involuntary non-regular employment are single,
either in their twenties, forties, of fifties, contract or temporary workers, and blue-collar
workersintransportation,communications,manufacturing,

construction,

andmaintenance industries. With respect to business cycles, the number of involuntary
non-regular employees tends to increase during recessions. It is also shown thatinvoluntary non-regular employment does not have a similarity in labor supply
behaviour with voluntary non-regular employment but do with those who are
unemployed.
Next, comparing subjective well-being measured by the mental health index, it
becomes clear that regular employees exhibit lower mental health index than other
employment statuses, especially the involuntary non-regular employment. This featureis also confirmed by the Tobit and least square estimations of mental health index, sothat the mental health index of regular employees is higher than other employment
statuses. However, the fixed effect estimation shows that only involuntary non-regular
employment has the significant differences in mental health index from the regular
employment. These results imply that once controlling for the individual fixed effect,
subjective well-being as measured by the mental health index does not differ among
employment status except for the involuntary non-regular employment. The important
finding of this paper is that non-regular employees are not necessarily worse off as far
as they intentionally choose their employment status as is the case for the majority of
non-regular employees. However, if they involuntarily choose the non-regular
employment, they tend to experience poor mental health and thus their subjective
3

well-being would drop.
The structure of this paper is as follows. In Section 2, we explain the data
employed in this paper and provide several basic facts for voluntary and involuntary
non-regular employment. In Section 3, we explore whether workers’ subjective
well-being measured by the mental health index differs depending on their employment
status. Then, in the final section, we summarise the results and provide a conclusion.

2. Basic Facts for Non-regular Employment
2.1. The distribution of involuntary non-regular employment(1) Data
We use the individual data drawn from the Keio Household Panel Survey (hereafterreferred to as “KHPS”). The KHPS, sponsored by the Japanese government, is alongitudinal survey of individuals that was initiated in 2004 and is conducted every
January by Keio University. Among all such surveys in Japan, KHPS has the broadest
coverage. From among the entire Japanese resident population (male and female) aged
between 20 and 69, 4,000 individuals were randomly selected by using two-stage
sampling.2 Because the respondent’s spouses were also surveyed, approximately 7,000
individuals were included in the 2004 KHPS data set. Consecutive surveys of the same
individuals have been conducted since 2005, with a response rate of 82.7 percent frompreviously surveyed individuals in 2005, 86.4 percent in 2006, 91.3 percent in 2007,
90.9 percent in 2008, 92.6 percent in 2009, and 93.5 percent in 2010. The sample size
was increased for the 2007 survey, when another 1,400 individuals were selected afresh.
The survey questions cover a wide range of topics, including occupation, income andexpenditures, and assets and liabilities. According to Kimura [2005], who conducted adetailed analysis of the KHPS sample characteristics, no significant differences arefound between the distribution of major variables compiled in the KHPS survey
questions and Japan’s other official statistics, including the Population Census and theLabor Force Survey, both of which are conducted by the Ministry of Internal Affairs
and Communications.
In the KHPS, survey respondents are asked to state their types of employment
2

The total size of this population was 85.75 million people, which is 67.2 percent of the total
Japanese population (according to population estimates made in February 2004).

4and their job titles at work (employment status), by which we can classify all
individuals into four employment status: regular employment, non-regular employment,
unemployment, and not in labor force. Furthermore, non-regular employees are asked tochoose from the following statements to describe why they work under particular formsof employment status: (1) “Although I wished to work full-time, I was unable to findemployment in companies that could employ me full-time;” (2) “Because thewage/working condition/service was good;”(3) “I cannot work full-time because of
personal reasons;” and (4) “Other.” Thus, in this paper, non-regular employees whochose (1) “Although I wished to work full-time, I was unable to find employment incompanies that could employ me full-time;” were classified as involuntary non-regular
employment. Likewise, people who chose statements (2) through (4) were classified asvoluntary non-regular employment. Therefore, each individual is classified into five
employment status. In the analysis, we use the subsample of individuals under the agesof 54 years excluding those who are employed by public sector.
(2) Employment status
First, the distribution of the employment status shown in Figure 1 reveals that the 2004
through 2012 averages equal 52.0% regular employment, 3.5% involuntary non-regular
employment, 21.6% voluntary non-regular employment, 2.4% unemployment, and20.6% not in labor force. The non-regular employment comprises 25.1% of all
employees. Involuntary non-regular employment comprises only 3.5% of the entire
sample, or 13.8% of non-regular employees. In other words, the majority of non-regular
employees are engaged in self-chosen voluntary non-regular employment, and the
involuntary non-regular employment can be regarded as the minority.
However, some attention should also be focused on the fact that the number of
involuntary non-regular employment equals approximately 1.5 times the number ofindividuals who are unemployed. Moreover, Figure 1 shows that the involuntary
non-regular employment increases during recession while it tends to decrease during
times of economic recovery. Specifically, the percentage of involuntary non-regular
employment is lowest (3.0%) during the boom period of 2007. The percentage rises to
3.9% in 2010 after the financial shock.
Figure 2 shows that the percentage of involuntary non-regular employment ishigher for women, rather than for men. However, this may related with the fact that the
proportion of non-regular employment is much higher for women. In fact, looking at thepercentage of involuntary non-regular employment among non-regular employees, we
5find that men, rather than women, had a higher percentage of involuntary non-regular
employment. With respect to men, involuntary non-regular employment is high among
those in their 20’s, as well as for the singles. With respect to women, involuntary
non-regular employment is high for those in their 20’s, for the singles, and for those
aged between 40 and 50 years. We could understand that single individuals have no
choice other than to accept non-regular employment if only that status is available. As
for women, we could infer that even if they prefer to engage in regular employment
between the ages of 40 and 50 when they re-enter the labor market after giving birth toand raising children, it is less likely that they find regular employment job opportunity
due to shorter labor market experience and smaller human capital.
Furthermore, Figure 3 lists the percentages of involuntary non-regular
employment among non-regular employees across individual attributes such as detailed
employment status, educational background, occupation, and industry. The figure showsthat the percentage of involuntary non-regular employment is low among part-timers
but high for contract employees as more than 30% are involuntary. It also shows that
educational backgrounds have only a weak relationship with the percentage of
involuntary non-regular employees. Regarding occupations, there are many involuntary
non-regular employees in security jobs or blue-collar occupations (e.g., transportation,communications, manufacturing, construction, maintenance, freight, and so on).
Likewise, it is shown that the percentage of involuntary non-regular employment islarger in the manufacturing, transportation, information, and communications industries.
(3) Reduced-form multinominal logit model for employment status
To understand what kind of individual attributes have an effect on the choice of
employment status, we here estimate the reduced-form multinomial logit model withfive employment status: regular employment, involuntary non-regular employment,
voluntary non-regular employment, unemployment, and out of labor force. The
individual attributes include age, educational background, family background (marital
status, the number of preschool aged children, the number of people living together),and the jobs-to-applicants ratio for each residential prefecture. The descriptive statistics
of each variable are listed in Table 1.
Table 2 summarises the estimation results of the multinomial logit model withthe marginal effects of the selection probability for each employment status. Looking at
Table 2, we find that the significant factors that have positive effects on the choice of
involuntary non-regular employment include female gender, aged 40 and over,6high-school graduate, single, no preschool aged children, fewer number of people
resided with respondent, and a low jobs-to-applicants ratio. Many of these factors werealso confirmed in the figures discussed.
It is interesting to note that the jobs-to-applicants ratio has different effect on
involuntary and voluntary non-regular employment. That is, involuntary non-regular
employment increases but voluntary one does not change as the economy worsens andthe regional labor market becomes less tight. Counter-cyclical feature of the involuntary
non-regular employment is similar to the unemployment, and thus we may point out
that involuntary non-regular employment is closer in nature to unemployment.
2.2. Similarities between involuntary non-regular employment and unemployment
The fact that involuntary non-regular employment is closer to unemployment, rather
than to voluntary non-regular employment, can also be confirmed in tests that use themultinomial logit model illustrated in Table 2. If involuntary non-regular employment isvery similar to voluntary one in labor supply behaviour, the coefficients in themultinominal logit model would become closer each other. To check this possibility, we
conduct the Wald test, which determines whether the coefficient of each explanatory
variable is significantly different among the employment status. Table 3 summarizes thechi-squared statistics for the null hypothesis that the coefficients are identical for eachcombination of employment status. The table indicates that the null hypothesis isrejected at 1% level for all combinations, and thus we can regard all five employment
status as different categories. Furthermore, we find that the chi-squared statistics for the
combination of involuntary non-regular employment and unemployment is the smallest,
which implies that involuntary non-regular employment can be considered more similar
to unemployment, than to voluntary non-regular employment.
The similarity between involuntary non-regular employment and unemployment
can also be confirmed by observing the transition between employment statuses. Figure
4 shows the probability of engagement in regular employment for each year based oneach employment status of the previous year. Although over 90% of the regular
employment continued to be regular employment, the transition rate to regular
employment from other employment statuses is extremely low (below 20%). However,
the transition rates to regular employment from involuntary non-regular employment
and unemployment are relatively high (12% and 18%, respectively).
Thesimilarities

betweeninvoluntary

non-regular

employment

andunemployment described above can be interpreted in a number of ways. First, if the7

employment opportunity derived by non-regular employment did not exist, then it islikely that those who engaged in involuntary non-regular employment would have been
unemployed. Considering this point, we can state that even if some are involuntary, theincrease in non-regular employment contributed to reducing unemployment under the
prolonged recession in Japan. Second, if the involuntary non-employees are worse off
than other employment statuses such as regular and voluntary employment, we shouldtake care of the involuntary non-employees as we do the unemployed person. Thus, wewill examine whether the subjective well-being of involuntary non-regular employees
are indeed lower than other employment statuses in next section.

3. The Subjective Well-being of Involuntary Non-regular Employment
3.1. Measure for the subjective well-being
Based on KHPS individual data, we examine whether individuals’ subjective well-being
differs depending on their employment status. As a proxy variable for subjective
well-being, we employ the mental health index that is calculated from the individuals’
awareness of psychosomatic symptoms (stress) surveyed in the KHPS.
In behavioural economics, the levels of happiness and/or satisfaction are often
used as subjective well-being indicators. However, as noted by Kahneman and Krueger[2006], these indicators are likely to be influenced by factors such as survey methods(e.g., question order) and environment (e.g., the weather). Thus, the use of these
indicators is often criticized for having measurement errors. In contrast, psychosomatic
symptom indicators or mental health index rely on detailed questions that describe
symptoms related to the mind and body in straightforward ways. Therefore, thoseindexes can be considered as robust against survey methods or environments. In fact, in
studies conducted by Kahneman and Krueger [2006] and Smith et al. [2005], strong
correlations among health indicators are provided as evidence that supports the validityof subjective well-being indicators such as happiness and levels of satisfaction.
However, it should be noted that because psychosomatic symptom indicators merely
measure individuals’ condition of mental health, these indexes solely capture a portionof each person’s utility (in particular, some negative portions).
In the KHPS, respondents are asked to rate their psychosomatic symptoms bychoosing qualifiers for statements such as “I often tend to feel irritated” from thefollowing selections:”happens frequently,” “happens sometimes,” “hardly happens at
8

all,” “never happens.” We rate respondents based on the 11 psychosomatic symptoms3
on a scale from 0 (never) to 3 (happens a lot). Then, the totals of all symptoms aredefined as the mental health index, whose scores ranged between 0 and 33. This mental
health index indicates that the higher the score is, the greater the level of psychosomatic
symptoms and stress or the lower the well-being is. Because questions related to
psychosomatic symptoms were not included in the KHPS surveys in 2004 and 2007, weonly use seven waves: 2005, 2006, 2008, 2009, 2010, 2011, and 2012.4
3.2. Distribution of Mental Health Index
Figure 5 shows histograms of the mental health index by employment status, and eachgraph contains the histogram for regular employees for purposes of comparison.
Looking at Figure 5, we first find a spike occurs at zero in each of the distributions
shown in this figure, which implies that certain portion of employees reports nopsychosomatic symptoms. We also find that each employment status exhibits a
distribution that is positioned further to the right side than that of regular employment.
Importantly, this feature is particularly noticeable for involuntary non-regular
employment. Thus, it is possible to state that many involuntary employees have higher
mental health index than other employment statuses, which implies that involuntary
non-regular employees are worse off in terms of mental health index. In fact, comparing
the mean values of the mental health index for each employment status in Figure 6, we
see that the regular employment is 14.3 while involuntary non-regular employment is
16.5, voluntary non-regular employment 15.7, the unemployment 15.8, and not in labor
force 15.7. These figures indicate that stress is highest for those individuals engaged in
involuntary non-regular employment.
3.3. Analytical Framework
If subjective well-being index or mental health index differs across employment status,
3

The 11 specific psychosomatic symptoms are: “There are times when I get headaches or feel
dizzy;” “There are times when I get palpitations or shortness of breath;” “There are times
when my stomach and bowels are irregular;” “There are times when my back, hips, orshoulders are sore;” “I tend to tire more easily;” “I tend to catch cold more easily;” “I tend tofeel irritated more often;” “I have more difficulty falling sleep;” “I don’t feel like seeing
people now;” “I am dissatisfied with my life at the moment;” and “I feel worried about the
future.”4
In addition, samples who responded that their normal state of health was “Not good” andsamples who responded that they had been hospitalized within the past two years were also
eliminated from the analysis. However, even if these samples had been included in theanalysis, no major difference would have occurred in this paper’s analytic results.

9what types of theoretical backgrounds can be related? If individuals rationally selecttheir employment status without any constraints, it is natural to expect that subjective
well-being index does not differ across employment status. However, if there exist some
types of constraints in choosing individuals’ employment status, then individuals’
subjective well-being index would decline because of those constraints.
One possible constraint might arise for regular employees. As noted by Stewart
and Swaffield [1997], Ham and Reilly [2002], and Bryan [2004], workers are often
unable to change their working hours. Even though they wish to reduce their working
hours, it is difficult to do so especially in Japan since long working hours are regarded
as the norm. Many regular employees would prefer to cut their working hours (Kuroda
and Yamamoto [2011]) but they could not in reality. This kind of work hour constraint
for regular employees may reduce their subjective well-being or mental health index.
The other constraint could arise for involuntary non-regular employment andunemployment, as a result of demand shortages or mismatches in demand and supply.
Thus, it is easy to imagine that subjective well-being would decline due to involuntary
non-regular employment or unemployment.
To illustrate these possibilities, the following is a simple model that incorporates
the employment status and certain constraints in choosing employment status. Denoting
individual utility as U, its optimal level as U*, and the potential mental health index or
psychosomatic symptoms as Y*, the observed index Y can be expressed as follows:Y

Y * if Y * c
0 if Y * cwhere Y *f (U * U )g ( L, X ),where the deviation of individuals’ utility level from the optimal one (U*– U) is
determined by the employment status L and the individual attribute X. The model
assumes that the potential psychosomatic symptom (stress) Y* increases depending on
the deviation of individuals’ utility, but the actual mental health index is not observed
until the stress exceeds the threshold c. Using the Tobit model as well as therandom-effect and fixed effect model, we estimate how the mental health index isdetermined depending on the employment status and other individual attributes. As aseries of variables for X, we include gender dummy, household income, married dummy,
pre-school age children dummy, number of people in household, savings, and debt
values.

10

3.4. Estimation Results
The estimation results are shown in Table 4. Looking at the columns 1 to 4 in which
individual attributes are not controlled, we find that involuntary non-regular
employment has significantly positive effects on the mental health index regardless of
estimation methods.
Comparing the results from columns 1 and 2, we can confirm that the estimatedcoefficients are very similar each other. Thus, we assume that a bias coming from thecensoring in mental health index is negligible. Then, looking at columns 3 and 4 fromrandom-effect and fixed-effect model, we see different coefficients and significancefrom those in columns 1 and 2. These differences indicate the importance to account for
the unobservable individual effects. In particular, we find clear differences between the
fixed-effect model and others in the coefficients of voluntary non-regular employment,
unemployment, and not in labor force. These coefficients are significantly positive incolumns 1 to 3, but once controlling for the unobserved individual effects in column 4
by the fixed-effect model, they become insignificant. On the other hand, column 4shows that the involuntary non-regular employment is still significant even if we control
for the unobserved fixed effects.
Next, looking at the columns 5 to 8 in which individual attributes are controlled,
we see similar results. That is, the employment status has a positive correlation withmental health index, and the involuntary non-regular employment has the biggest
coefficient. But once controlling for unobserved individual fixed effects, the significant
differences in mental health index among employment status will disappear except for
the involuntary non-regular employment. The effect of involuntary non-regular
employment on the mental health index is significantly positive, even if we control forobserved and unobserved individual effects.
From these estimation results, we can state that most of the employment status
does not affect worker’s subjective well-being measured by mental health index, but the
involuntary non-regular employment does have an effect to reduce his/her subjective
well-being. In other words, workers other than the involuntary non-regular employees
do not face large constraints in choosing their employment status so that their subjective
well-being does not differ. However, the constraint for the involuntary non-regular
employees is large enough so that they are worse off by involuntarily working as
non-regular employees.
It is also important to note that the mental health index for the involuntary
non-regular employment is larger than that for unemployment. As noted above, many11

previous studies, such as Clark and Oswald [1994], Winkelmann and Winkelmann[1998], Ohtake [2004], and Sano and Ohtake [2007] state that unemployment lowers
subjective well-being. But, this paper demonstrates that the impact of involuntary
non-regular employment on the subjective well-being is larger than that ofunemployment, which implies the importance of a focus on the involuntary non-regular
employment.5

4. Conclusion
This paper investigated whether non-regular employees are worse off with theiremployment status, by examining the subjective well-being measured by the mental
health index based on the individual panel data drawn from the Keio Household Panel
Survey (2004 through 2012). We focused on five employment status: regular
employment, involuntary non-regular employment, voluntary non-regular employment,
unemployment, and not in labor force, and derived the following findings.
First, involuntary non-regular employment accounts for 3.5% of the entire
sample, and 16.0% of non-regular employment. This indicates that the majority of
employees engaged in non-regular employment are those who have voluntarily chosenthat employment status. However, the number of people engaged in involuntary
non-regular employment is equal to approximately 1.5 times the number of unemployedindividuals. This figure cannot be ignored. Typical features of involuntary non-regular
employment are single, either in their twenties, forties, of fifties, contract or temporary
workers, and blue-collar workers in transportation, communications, manufacturing,
construction, and maintenance industries. With respect to business cycles, the number of
involuntary non-regular employees tends to increase during recessions. It is also shownthat involuntary non-regular employment does not have a similarity in labor supply
behaviour with voluntary non-regular employment but do with those who are
unemployed.
Next, comparing subjective well-being measured by the mental health index, it
becomes clear that regular employees exhibit lower mental health index than other
employment statuses, especially the involuntary non-regular employment. This featureis also confirmed by the Tobit and least square estimations of mental health index, so
5This fact has also been confirmed in recent studies conducted by Tsuru et al. [2011] who findthat happiness index is lower for workers engaged in involuntary non-regular employment.

12

that the mental health index of regular employees is higher than other employment
statuses. However, the fixed effect estimation shows that only involuntary non-regular
employment has the significant differences in mental health index from the regular
employment. These results imply that once controlling for the individual fixed effect,
subjective well-being as measured by the mental health index does not differ among
employment status except for the involuntary non-regular employment. The important
finding of this paper is that non-regular employees are not necessarily worse off as far
as they intentionally choose their employment status as is the case for the majority of
non-regular employees. However, if they involuntarily choose the non-regular
employment, they tend to experience poor mental health and thus their subjective
well-being would be lowered.
These results imply the importance of the separation of non-regular employment
into involuntary and voluntary one when discussing policy implication. It may be thecase that non-regular employees face greater instability and lower wages in their jobs.
However, it is worth noting that the majority of the non-regular employees does
intentionally choose their employment status, and their subjective well-being or mental
health index is not lower than other employment statuses. Thus, considering a buffer
stock role of non-regular employment in the firm, we could point out that the labormarket policies tightening the regulations for the use of non-regular employees should
be discussed carefully. Among non-regular employees, much attention and policy
treatment should be focussed on those who involuntarily choose their employment
status. Such a policy to promote their transition to the regular employment should be
explored.

13

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15

Figure 1. Employment Status across Years

16Figure 2.Employment Status Based on Gender, Age, and Marital Status.
(1) Men(2) Women

17Figure 3. Percentage of Involuntary Non-regular Employmees(1) Type of non-regular employment and educational background(2) Occupation.

18

(3) Industry.

19

Figure 4. Transition Rates to Regular Employment.

Note: The probability of transitions to regular employment during the following year has
been calculated for each type of employment for the previous year.

20Figure 5. Distribution of Mental Health Indexes(1) Involuntary non-regular employment(2) Voluntary non-regular employment

21

(3) Unemployment.

(4) Not in labor force

22Figure 6. Differences in Mental Health Indexes

23

Table 1. Descriptive Statistics

Regular employment

Men dummy

Involuntary nonregular employment

Voluntary nonregular employment

Unemployment

Not in labor force

0.77

0.34

0.08

0.41

0.03

(0.42)(0.47)(0.28)(0.49)(0.16)

Age dummy (Base = 20's)
30's

40's

50's

0.31

0.23

0.30

0.35

0.42

(0.46)(0.42)(0.46)(0.48)(0.49)

0.39

0.36

0.41

0.34

0.32

(0.49)(0.48)(0.49)(0.47)(0.47)

0.18

0.26

0.19

0.18

0.16

(0.39)(0.44)(0.39)(0.38)(0.37)

Educational background dummy values (Base = Junior college/technical college graduate)College graduate

Senior-high school graduate

Junior-high school graduate

0.33

0.18

0.12

0.18

0.15

(0.47)(0.38)(0.33)(0.38)(0.36)

0.41

0.51

0.48

0.46

0.41

(0.49)(0.50)(0.50)(0.50)(0.49)

0.07

0.08

0.09

0.13

0.09

(0.26)(0.28)(0.29)(0.33)(0.29)

Family structure
Married dummy

Pre-school age children dummy

No. of people in household
Jobs-to-applicants ratio on prefectural basis
Sample size

0.83

0.61

0.84

0.62

0.95

(0.38)(0.49)(0.37)(0.48)(0.22)

0.22

0.08

0.13

0.14

0.40

(0.41)(0.26)(0.34)(0.34)(0.49)

3.83

3.50

3.94

3.55

3.95

(1.41)
0.85(0.33)
12,594(1.41)
0.81(0.32)
838(1.36)
0.85(0.33)
5,223(1.40)
0.82(0.31)
570(1.23)
0.84(0.32)
4,981

Note: Figures in brackets represent standard deviation.

24

Table 2. Reduced Form Multinomial Logit Model for Employment Status

Regular employment

Involuntary nonVoluntary nonregular employment regular employment

Unemployment

Not in labor force

0.682***-0.0116***-0.321***-0.00180-0.348***(0.00491)(0.00215)(0.00517)(0.00193)(0.00464)-0.0683***-0.000436

0.0234*0.0131**0.0323***(0.0171)(0.00496)(0.0125)(0.00509)(0.00730)

40's-0.0554***0.0101*0.01360.00783

0.0238***(0.0177)(0.00539)(0.0127)(0.00511)(0.00767)

50's-0.0930***

0.0314***0.005380.01020.0460***(0.0203)(0.00785)(0.0138)(0.00632)(0.0101)

Men dummy
Age dummy (Base = 20's)
30's

Educational background dummy values (Base = Junior college/technical college graduate)College graduate

Senior-high school graduate

Junior-high school graduate

0.0339***-0.00458-0.0326***-0.00813**

0.0114*(0.0127)(0.00442)(0.00928)(0.00361)(0.00593)-0.0471***

0.0114***0.0364***0.00303-0.00378(0.0104)(0.00383)(0.00746)(0.00345)(0.00413)-0.0291*

0.000704

0.01980.0138**-0.00520(0.0173)(0.00579)(0.0125)(0.00617)(0.00645)

Family structure-0.0185-0.0667***

0.0470***-0.0470***0.0852***(0.0147)(0.00771)(0.00924)(0.00703)(0.00367)-0.0710***-0.0181***-0.0675***-0.00437

0.161***(0.0124)(0.00433)(0.00785)(0.00414)(0.00903)

No. of people in household

0.00481-0.00246**

0.00678***-0.00149-0.00763***

Jobs-to-applicants ratio on prefectural basis(0.00338)
0.00975(0.0125)(0.00114)-0.0132***(0.00450)(0.00240)0.0144(0.00934)(0.00101)-0.00907**(0.00409)(0.00145)-0.00190(0.00543)

Married dummy

Pre-school age children dummy

Notes: 1. Marginal effects of the selection probability for each type of employment. Figuresin brackets represent standard errors (white robust standard errors).
2. Sample size equals 24,206. The pseudo-coefficient of determination equals 0.2775.
The log likelihood is -21002.
3. ***, **, and * indicate that these are at statistically significant levels of 1, 5, and10%, respectively.

25

Table 3. Similarities between Employment Statuses
Regularemployment

Involuntary nonVoluntary nonregular employment regular employment

Unemployment

Not in labor force

Regular employment

-

960.7

4,615.7613.8

2,813.4

Involuntary non-regular employment

4,615.7

-

551.5

53.2

1,038.3

Voluntary non-regular employment

960.7

551.5

-

473.5

1,121.0

Unemployment

4,615.7

53.2

473.5

-

844.0

Not in labor force

2,813.4

1,038.3

1,121.0844.0-

Note: Chi-squared test statistics for the null hypothesis that the coefficients in the multinomial
logit model in Table 2 are identical for each combination of employment status isreported. The null hypothesis is rejected at the 1% level significance for all
combinations.

26

Table 4. Estimation Results for Mental Health Index(1)
Tobit model

Without Individual Attributes
(2)
(3)

(4)

(5)

With Individual Attributes
(6)
(7)

(8)

Least-squares
estimation

Random-effectestimation

Fixed-effectestimation

Tobit model

Least-squares
estimation

Random-effectestimation

Fixed-effectestimation

2.343***

2.284***

1.257***

0.582*

1.382***

1.340***

0.713***

0.569*(0.279)(0.272)(0.256)(0.324)(0.286)(0.279)(0.266)(0.324)1.444***

1.407***

0.847***

0.0300

0.133

0.125

0.116

0.0181(0.124)(0.119)(0.154)(0.254)(0.163)(0.157)(0.183)(0.255)1.552***

1.554***

0.700**-0.1010.853**

0.867**

0.233-0.0981(0.377)(0.361)(0.308)(0.368)(0.379)(0.362)(0.315)(0.369)1.471***

1.441***

0.860***-0.241

0.196

0.196

0.0962-0.208(0.135)(0.130)(0.177)(0.301)(0.181)(0.175)(0.213)(0.303)-0.0656***-0.0651***-0.0250-0.00456(0.0190)-0.610***(0.179)-0.00635(0.150)0.0333(0.0412)-0.0218***(0.00716)
0.00678(0.00422)-1.780***(0.0182)-0.623***(0.172)-0.0111(0.144)0.0371(0.0392)-0.0217***(0.00706)
0.00613(0.00412)-1.740***(0.0166)-0.408*(0.236)-0.0528(0.140)0.0400(0.0530)-0.00776(0.00712)
0.00395(0.00370)-1.679***(0.0200)-0.114(0.435)-0.229(0.160)0.0612(0.0828)0.00408(0.0102)0.00417(0.00416)(0.147)(0.142)(0.201)1.413***

1.388***

0.816***(0.208)(0.199)(0.210)2.021***

1.972***

1.385***(0.217)(0.207)(0.230)2.282***

2.210***

1.721***(0.233)(0.224)(0.247)

Type of employment (Base = Regular employment)
Involuntary non-regular employment

Voluntary non-regular employment

Unemployment

Not in labor force

Household income (million yen)
Married dummy
Pre-school age children dummy
No. of people in household
Savings (million yen)
Debt (million yen)

Men dummy

Age dummy (Base = 20's)
30's

40's

50's

Constants
Log likelihood

14.15***

14.25***

14.48***

14.99***

14.43***

14.54***

14.71***

14.86***(0.0714)(0.0682)(0.106)(0.122)(0.259)(0.247)(0.322)(0.480)-55192-55809-44133-55038-55650Within R-squared
Between R-squared
Overall R-squared
Sample size

17,07017,0700.00010.01370.014
17,0700.00070.001
0.000
17,07017,06917,069-441270.003
0.03120.03
17,0690.00110.000
0.0004
17,069Notes: 1. Figures in brackets represent standard errors (white robust standard errors).
2. ***, **, and * indicate that these are at statistically significant levels of 1, 5, and10%, respectively.

27