GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No. 258

Procrastinators and hyperbolic discounters:
Probability of transition from temporary to full-time employment

SunYoun Lee and Fumio Ohtake

May 2012

GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

Procrastinators and hyperbolic discounters:
Probability of transition from temporary to full-time employment

SunYoun Leea*, Fumio OhtakebaOsaka School of International Public Policy, Osaka University, Osaka 560-0043, Japanb

Institute of Social and Economic Research, Osaka University, Osaka 567-0047, Japan

Keywords: time preferences, temporary employment, ATT estimation
JEL Classification: D03, D90, J01*

Correspondence: International Public Policy, Osaka University, 1-31 Machinakaneyama, Toyonaka,

Osaka, 560-0043 Japan Tel (Fax): 81-6-6879-8572. E-mail: s-lee@osipp.osaka-u.ac.jp1Abstract
Temporary agency work (TAW) is believed to facilitate the matching between
firms and on-the-job searchers. This leads to shortening of the unemployment and job
search duration. On the other hand, firms that hire temporary workers have lessincentive to train them, which makes it difficult for low-skilled temporary workers tofind a better job in future. Current literature has not established whether TAW
employment improves the welfare of either or both the employers and employees.
Therefore, this paper examines the effect of TAW employment in the Japanese labormarket on employment transitions focusing on individual time preferences. Investments
in one’s career involve a trade-off between immediate costs and later rewards, and thus,
individual heterogeneity in time preferences may explain the behavioral patterns oflabor force. We find that TAW employees have a tendency toward impatience andhyperbolic discounting. In addition, those who have held temporary job are less likely
to move into full-time job positions, but no significant wage differences are observed.
The strength of the negative effects on the transition probabilities declines over time but
the significant effects remain over the following years. Our results indicate that TAW
for employees in Japan is more likely to function as a “dead-end” rather than “steppingstone” toward stable full-time employment.

2

1. Introduction
In standard economics, temporary work has been thought to enhance labor market
efficiency (Autor, 2008). A temporary agency, which facilitates the matching between
firms and on-the-job searchers, is believed to play a role in shortening unemployment
spells and thus improving the welfare of both the employers and employees. Temporary
work has therefore been expected to function as a “stepping stone” toward stable
full-time employment (Guell and Petrongolo, 2007; Ichino et al., 2005; Malo andMunoz-Bullon, 2002). Temporary workers, however, receive less work-related training
than their counterparts in permanent employment (Booth et al., 2002), and thus it
becomes more difficult for them to find a better job in a stable position as time
progresses (Autor and Houseman, 2010). Furthermore, long duration temporary
contracts lead to a reduction in workers’ average job search intensity (Kahn, 2009),
which again lessens the chances of moving into a more stable employment status.
In this study, we majorly focus on the factor that determines the probability ofengaging in temporary agency work (TAW) and the effect of past work experience in
the TAW sector on the transition to a permanent position, everything else being equal.
When examining the effects of TAW employment on job transitions, we pay attention toindividual heterogeneity in time preferences that may affect career paths. Specifically,
we predict that behavioral patterns of employees concerning short-term and long-term
impatience explain the probability of entering the TAW sector, which further
significantly affects the probability of moving into full-time jobs and the wage
differentials between those who have work experience in the TAW sector and those who
do not.
The effects of time preferences on career consequences have been widely
examined as the investments in one’s career involves a trade-off between immediate
costs and later rewards. Notably, the exit rate from unemployment status is affected by
the degree of patience, which varies by the assumptions of exponential and hyperbolic
discounting (DellaVigna and Paserman, 2005; Paserman, 2008). Impatient agents withexponential time preferences tend to lower the reservation wage, and this may increase
the exit rate from unemployment. Hyperbolic agents, in contrast, are likely to defer job
search activities and may not lower the level of reservation wages when they finallysearch for a job, leading to a longer duration of unemployment. Job-to-job transitions
involving internal job promotions and external job mobility are also explained byindividual time preferences, although it is yet to be established how exponential and/or
hyperbolic discounting affects job mobility (Drago, 2006; van Huizen, 2010).
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Our paper empirically examines the long-term welfare of temporary workers inJapan, mainly focusing on the following: i) investigation of the relationship between
time preferences and the probability of engaging in the TAW sector; and ii) examinationof the probability of TAW workers moving into full-time regular employment and thedifferences in wages between workers who have held a temporary job and those whohave not. The remainder of this paper is organized as follows. Section 2 explains thetheoretical background of time preferences that affect individual behaviors anddescribes TAW employment in the Japanese labor market. Section 3 introduces the dataand how we constructed the main variables regarding time preferences. Section 4
presents the econometric framework and section 5 reports the estimates of the averagetreatment effect on the treated (ATT). This paper may clarify the behavioral
characteristics of TAW employees and their employment transition dynamics in theJapanese labor market.

2. Time discounting
2.1 Discount function
A decision maker is assumed to maximize utility over intertemporal choices using
a discounted utility model. The intertemporal utility function of preferences can be
expressed as the individual’s well-being at time t+k u

multiplied by his/her

discount function (δ ) (Paul, 1937). The discount function (δ ) is further expressed by
the discount rate (γ), the individual’s pure rate of time preference, and it is often
interpreted as the relative weight attached at time t to each individual’s well-being attime t+k. The value of each subsequent period decreases at a constant rate, which isreferred to as exponential discounting.
U u ,u∑T… , uT, where δ

δ

(1)

γ

A potential problem of the exponential discounting rate model is that a person may
not have well-formed plans about costs and benefits and display inconsistent time
preferences. In order to allow for time inconsistency, quasi-hyperbolic discounting
models have been proposed as an alternative to the standard exponential model (Laibson,1997).
U u ,u… , uTu∑Tδ, where 0

1

(2)

4

In this model, theparameter is newly introduced; ifequals one, it isequivalent to the exponential discounting function (1). The implied discount factor fromthe current to the subsequent period is, while the discount factor in the sufficiently
distant future is simply . In other words, the per-period discount rate between thecurrent and the subsequent period is
two distant future periods isand the per-period discount rate between any. The former is higher than the latter, and it implies thatagents prefer a larger reward in the sufficiently distant future over a smaller one at the
moment. These hyperbolic agents with inconsistent time preferences are likely to face ahigh degree of short-term impatience again, when the distant future comes near.
The exponential discounting parameters (δ) capture time-consistent, long-term
impatience and the hyperbolic discounting parameters (β) capture time-inconsistent,
short-term impatience. As presented in equations (1) and (2), when β 1, agents’ (β, δ)preferences are time consistent and capture the standard discounted utility model, andwhen β 1, agents’ (β, δ) preferences follow a quasi-hyperbolic time discounting
function. Hyperbolic discounting is the most-studied discount utility anomaly. Anotherfeature of inconsistent time preferences, which is contradictory to the standard model of
discounted utility wherein the discount rate should be the same despite the types ofintertemporal choices, is the “sign effect.” The discount rates for gains are known to behigher than those for losses of the same magnitude (Thaler, 1981); for example, people
are reluctant to borrow and favor a lower interest rate for borrowing than for saving.
2.2 On-the-job search and job-to-job transitions
The effects of time preferences have been examined on on-the-job searches by the
unemployed (DellaVigna and Paserman, 2005) and job-to-job transitions, includinginternal promotions and external job mobility (Drago, 2006; van Huizen, 2010). In
standard economic models of on-the-job searches, under which agents are assumed to
discount future costs and benefits exponentially, individuals with higher discount rates
tend to be impatient. Impatience has two inconsistent effects on job search effort andreservation wage decision. While impatient agents are more likely to accept the job
offer despite low wages and exit unemployment, they prefer to avoid the immediate
costs and defer job search activities (DellaVigna and Paserman, 2005). The effect of
impatience on job search varies by the assumption of exponential and hyperbolic time
preferences. If time preferences are constant over time, the reservation wage effectworks more strongly than the search effect, thereby leading to an increase in the5unemployment exit rate. If agents have hyperbolic time preferences, however, thesearch effect dominates the reservation wage effect, and thus, they stay unemployed
longer. In other words, hyperbolic agents, who are more sensitive to the immediate costsof an employment search, are likely to delay their job search activities to a later period,
such as writing the resume or contacting employers, and thus they are less likely to exitfrom unemployment.
As for the effects of time preferences on job-to-job transitions, Drago (2006) findsthat patience is negatively related to external job mobility and positively to work effort;
patient agents are more likely to stay in the same workplace and pursue the job
promotion or position change, while impatient agents are more likely to switch jobs forbetter wages. These empirical results, however, may have some limitations associatedwith the assumptions and methodologies: the study assumes that career effort isexogenous and job search is characterized by immediate benefits; and it does not
examine the impact of impatience on search behavior aside from its effect on job
mobility. Taking these limitations into consideration, van Huizen (2010) finds thecontrary result that patience has positive effects on both work and search effort, aspredicted by the hyperbolic discounting model. It is explained that hyperbolic agents arelikely to procrastinate both internal and external job mobility efforts because these bothare investment activities involving immediate costs.
2.3 TAW employment in Japan
In Japan, a dual labor market comprised of a sector of highly stable employment
and a considerably less stable sector of part-time, temporary contract workers has
emerged since the 1970s and has become an important policy agenda, especially after
the Lehman shock (Rebick, 2005; Okudaira et al., 2011). There has been some evidenceof rigid mobility within the dual labor market that starting one’s career in less stable
rather than regular employment reduces one’s probability of being in regular
employment in the future (Ariga and Okazawa, 2011). Our data also suggest rigidity on
job transitions between employment types (Appendix 2). On average, only 7.1% of
employees who were in the TAW sector moved into permanent jobs over the past four
years. Most part-time and dispatch workers remain in the same sector after one year andmore. In particular, the mobility of male labor forces between sectors is less fluid thanthat of females. The persistence of the dual labor market leads to the conclusion that a
temporary contract may act as a trap for long-term unstable employment.
In Japan, since the enactment of “The Worker Dispatch Law” in 1985, which allows
project-based employment and the dispatching of temporary workers to demanders, the6number of temporary workers has rapidly increased. After the Lehman shock in 1998,
however, many temporary workers were laid off, and this has drawn the public’s
attention to the issue of unstable employment status and the need for legal protection.
New rigid legislation was then enacted to impose a limitation on the contract term of
temporary workers who are registered with TAW agencies and dispatched to
manufacturing companies. The contract limitation excludes the TAW employees whoare classified under the 26 professional categories that require company-specificknowledge and experience. The new legislation on TAW employment in Japan wasformulated to assist temporary workers to become full-time stable employees after acertain period of work experience in the TAW sector. However, it may bring theunexpected outcome of temporary workers simply being laid off when their contract
expires and rarely moving into permanent jobs.
3. Data and methodology
3.1 Global COE Panel data
The empirical analyses in this paper are based on data from a questionnaire survey
titled “Preference and Life Satisfaction Survey,” conducted in Japan. This is a panel
survey that was started in February 2004 as part of the Osaka University 21st Century
Center of Excellence Program. It has been conducted annually since 2004 using arandom sample drawn from 6,000 individuals by a placement method. For the analysis
on individual behavioral patterns of time preferences on TAW employment and its
further effects on the probability of transition to permanent jobs, the survey data of
2009–2011 were used. However, since each year’s questions concern present time
preferences under the hyperbolic discounting function in a different way, we mainly
used 2010 data to estimate the exponential and hyperbolic discounting parameters, andwe used childhood time preferences to analyze the long-term effect on the probability oftransition and wages over a four-year period.
3.2 TAW employment
The sample includes all labor force participants aged 20 or over. Our dataset
enables us to identify the following employment types at the time of the survey:
permanent employees (PE), temporary employees (TE), dispatched employees (DE),self-employed (SE), unemployed and persons who carry out job search activities (UE),and those who are not employed and do not look for jobs (NE). Students, retired persons,and housewives are dropped from the sample. Since TE and DE are hired on short-term
7

contracts and their job turnovers are high, only those who are in the labor force at thetime of the survey are observed; in other words, those whose contracts just expired
around the time of the survey were not observed. The percentage of dispatched workers
among the employed in 2010 is on average 1.72% (9.6 million out of 547.8 million)(Statistics Bureau, 2012), and this low percentage is reflected in our dataset; only 1.07%of the labor force (42 out of 3,938) in the data is categorized into DE. With this small
number of observations, examining the effect of TAW on the probability of moving into
permanent jobs, when everything else is controlled, is not possible. Thus, to identify
individuals in the TAW sector, we asked respondents whether they have ever been
temporary employees over the past 2 years. TAW employees are defined as those whoare registered with temporary agencies for a temporary assignment duration ranging
from i) one day, ii) 2–10 days, iii) 10 days–3 months, to iv) more than 3 months.
The possible career progression of our sample can be presented with multiple
career paths (Appendix 1). Before respondents ever held temporary work, they were
either unemployed or employed; after their experience in the TAW sector, they became
permanent employees or other types of employees. Those who did not have any jobs
before the TAW employment could have taken a job offer from the TAW sector orremain jobless. Those who already had jobs, on the other hand, could have pursued
promotions or a position change, or quit their jobs and taken temporary work until they
could find other jobs offering higher wages. If the analysis is based on the premise thatsome of the unemployed take temporary work and the others remain jobless, thesesubjects may display similar behavioral patterns concerning the job search andreservation wage of the unemployed predicted by exponential and hyperbolic
discounting, as examined by DellaVigna and Paserman (2005). In addition, if we canassume that some of the employed continue working for the same employer, and theothers quit their jobs and take temporary jobs until they find better jobs, these subjects
may have hyperbolic discounting characteristics, as found by van Huizen (2010).
However, our data have limitations in examining these on-the-job search and job
mobility effects from a time preference perspective, as shown in the previous research,
because the employment type of subjects whose short temporary contracts are expired
are not clarified during the two-year period. Despite these limitations, because
individual decisions on career paths, including whether to take temporary jobs, involvea trade-off between immediate costs and delayed rewards, we need to control for
individual heterogeneity in intertemporal choices. Thus, this study predicts that
individual time preferences and differences in the degree of patience between the nearand distant future explain the probability of becoming TAW employees and moving into8

permanent jobs afterward.
3.3 Measure of time preferences
We use the responses to questions concerning time preferences to construct themain independent variables that affect the probabilities of becoming TAW employees
and subsequently have a negative effect on the transition to full-time employment. To
measure the discount rate and hyperbolic discounting “during childhood,” we used the
responses indicated on a five-point scale to the following questions: “When did you
work on your summer vacation homework?” and “When did you plan to do it?” The
scale ranged from 1 for the end of the vacation to 5 for the beginning of the vacation.
Agents who have a preference for delayed rewards over immediate costs to finish theassignment are likely to procrastinate their homework during childhood. Thus, they
allocate more effort to finish the assignment rather than pursue the short-term rewards.
In other words, the lower the long-term discount factor δ and short-term discountfactor β, the more subjects care about the immediate costs compared with the distantrewards.
We also constructed proxies for the “present” time discounting using the responses
to the hypothetical question stated below. The present time preferences may be
inversely correlated with the probability of having TAW experience because people
would display hyperbolic characteristics because of their experience in the TAW sector.
On the other hand, individual time preferences during childhood change because of
education and/or work experience, and in that case, the present discounting factor
reflects one’s actual time preferences that affect the individual decision on career paths.
Thus, we control individual time preferences using both childhood and present time
discounting factor. We asked the respondents to choose between two options
concerning intertemporal choices. A single choice between two intertemporal options
only reveals an upper or lower bound on the discount rate. To identify the discount rate
more precisely, we present subjects with a series of choices that vary the hypotheticalrewards over seven days.
[Hypothetical Question]“Let’s assume that you are required to spend time cleaning a park. You need to spend twohours today AND next week. It seems that the litter in the park will decrease more than
expected, so the number of hours you need to clean will be less. To account for this change,
you have the option to shorten the hours by one hour today or shorten some hours in 7 days.
Compare the hours and timing below in Option “A” with Option “B” and indicate for each9row which option you prefer.”
Option “A”

Option “B”(Shorten today)(Shorten in 7 days)

1

1 hour50 minutes

A

B

2

1 hour

1 hourA

B

3

1 hour

1 hour 5 minutes

A

B

4

1 hour

1 hour 10 minutes

A

B

5

1 hour

1 hour 15 minutes

A

B

6

1 hour

1 hour 20 minutes

A

B

7

1 hour

1 hour 30 minutes

A

B

8

1 hour2 hoursA

B

Circle Option “A” or “B”

If a respondent chooses option A, which shortens the time by 1 hour today but by
no time in 7 days rather than option B, which shortens the time by 50 minutes in 7 days
but by no time today, the respondent is supposed to clean for 1 hour today and 2 hours aweek later. The respondents would choose an option that they think would provide morefavorable conditions. We expected that many respondents would choose option A at the
beginning because the total shortening of time of option A is longer than that of option
B, but eventually switch to option “B” at a certain point when they feel indifferent
between options A and B over two different time periods. Those with a higher discountrate would not change to option B until a shortened time in 7 days is substantiallylonger than that of today.
From each question, we computed the discount rate of each row (γ :
where

γ

,

is the length of the shortened time of option A (B). Then the discount rateof an individual is computed by estimating a log-normal distribution for the discountrate’s log (1+γ) of options (Kimball et al., 2008). For example, if the respondent selects
option A for the first 3 rows and switches to option B in the 4th row, then log (1+γ) is
computed from a log-normal distribution of the discount rate of the 3rd and 4th row.
Then, we converted the discount rates (γ to the discounting factors (δ by taking the
exponential of the discounting rates and inversing the values δ
1/ 1 γ ). The
discount factor for the near future (βδ is constructed from the intertemporal choices
between today and 7 days, and the discount factor for distant futures (δ is constructedfrom the same choices with difference time sets―90 days versus 97 days. Next a proxy
for the hyperbolic discounting parameter (β) is calculated by the ratio of βδ to δ.
10

Someone who currently prefers immediate rewards over future benefits but who
suspects that they will have the same preferences in the distant future, might attempt tochoose avoiding the immediate rewards in the distant future choice sets. Thesehyperbolic agents doubt that their preferences will change over time and eliminate an
option that seems inferior now but might attract them later.

4. The econometric framework
4.1 Notation
The ATT attempts to reduce the bias in the estimation of treatment effects with
non-randomized observational data sets. To estimate the actual effect of a treatment on
the potential outcome of an individual, Rosenbaum and Rubin (1983) proposed thepropensity score matching method (PSM), which compares outcomes performed bytreated and control subjects who are as similar as possible. The average treatment effectof the program is then calculated as the mean difference in outcomes across the treatedgroup and the statistical counterfactual group that has similar observed characteristics to
those of the treated group.
To estimate how treated agents would have performed if they had not received thetreatment, the untreated agents that have similar characteristics are substituted for thecounterfactual group. The PSM approach is used to construct this unobserved orcounterfactual group. In case of a binary treatment, the treatment indicator T equals one
if individual i receives treatment and zero otherwise, and X is the multidimensional
vector of pre-treatment characteristics. The potential outcomes are then defined asfor each individual i, where i =1, …, n. The average treatment effect of anindividual i can be estimated as follows:
0 |
1
0 |
1
E
1, p X1 |0 |1, p X
1, p X
The potential outcome of the treated group who has not received the treatment
E

1

(E
0 |
1 ) is not observed. What is actually observed is
0 |0 but it
may not be a good alternative because the group without the treatment would have an
outcome different from that with treatment regardless of the treatment effect; this is
known as selection bias. A proper substitute for this counterfactual group is theuntreated agents who are similar to the treated agents and are found on the basis of theprobability of taking treatment given a vector of observed variables. The fact that only
inside the range of the observed data points is extrapolated is different from ordinary11regression.
4.2 PSM methods
To validate the PSM methods, the following two assumptions should be satisfied.
First, given a set of observable covariates X that are not affected by treatment, theoutcomes Y of the control group should be independent of treatment assignment T.
This assumption is called “conditional independence” or “unconfoundedness”:
Y| X . For a given propensity score, exposure to treatment―work experience as
TAW employees over the past 2 years―should be random and therefore treated andcontrol groups differ only in their error term, which is approximately independent of X.
The probability of transition to permanent employment of the control group should not
influence the choice of the individual to engage in the TAW sector over the past 2 years.
A second condition, called “common support,” ensures that there are sufficiently
large observations in the overlapped region of propensity score distribution:P
1|
1. The substantial region of propensity score distribution should beoverlapped between those who have two years of experience of TAW employment andthose who do not. The overlap condition for individuals with the same x value in X are
allowed to have a positive probability of being in treated and control groups.
When the two assumptions hold, the balancing hypothesis that observations withthe same propensity score must have the same distribution of (un)observablecharacteristics independently of treatment status is tested. The test is repeated until themeans of each characteristic are equal for treated and control units in all intervals of thepropensity score. If the means of one or more characteristics are unequal, the balancing
properties are unsatisfied and a better parsimonious specification of the covariates isrequired. If treatment and control groups with the same propensity score are identically
distributed, the ATT model is established using the PSM estimator.
1,
E |
1,
We use a stratified nearest-neighbor matching algorithm that matches treatment
and control cases on the closest propensity score. C i denotes the set of control units
matched to the treated unit i with an estimated value of the propensity score of . The
nearest neighbor matching set is C i. More explicitly, withcross-section data and within the common support, the treatment effect can be written as
follows (see Heckman et al., 1997; Smith and Todd, 2005):
τPSM

1
NT

ω YC

YT
T

C

12

=

NT

=

∑ T YTNT∑ T YT∑ T ∑ C ω YC
NT

∑ C ω YC

NT is the number of participants i and ω
outcomes for the matched nonparticipants j: ω

is the weight used to aggregate
if

C i

and ω

0

otherwise.

5. Results
5.1 Descriptive statistics
The number of people who have ever held temporary work in 2010 is 7.2% (Table1). The sample used for the analysis includes all persons classified as employed andunemployed. Approximately half of the samples have been hired as permanent full-time
employees at the time of survey collection, of which 3.4% answered that they haveexperience working as temporary workers over the past two years. Table 2 shows
average individual characteristics and time preferences variables, which were collectedfrom the same questionnaire for both the treated and control groups from the same
Japanese labor market. The observable characteristics of the treated and control groups,
which are balanced on the basis of the observed variables, are very similar and thedifferences between the treated and control groups are smaller than between the treated
and the entire control group.
Time preference variables indicate that individuals with TAW experience have atendency for procrastination or impatience, and they have a lower (higher) discountfactor (rate) and a lower hyperbolic discounting parameter. This suggests that TAW
employees are likely to be impatient and have inconsistent time preferences. The lastcolumns of Table 2 reveal that the treated group with the TAW experience shows
potentially lower earnings and more limited employment opportunities to move into
permanent jobs in the future. The important questions addressed in this study are, when
all observatory variables are held constant, whether those who have ever held temporary
work remain in the informal sector or move into permanent jobs and whether theearnings of individuals who have work experience in the TAW sector are statisticallydifferent from those who do not.

13

5.2 Effects of behavioral characteristics
The propensity score was predicted from a logistic regression of TAW employment
on individual characteristics such as income, age, assets, marital status, number ofchildren, and educational backgrounds (Table 3). Models (1) and (2) are controlled bychildhood time preferences imputed from summer vacation homework (see Table 1).
Models (3) and (4) use the present time preferences computed from the hypotheticalquestion about cleaning a park.
Models (1) and (3) estimate exponential discounting parameters when β equals 1,
and models (2) and (4) present hyperbolic discounting parameters given the same β.
Time preferences may play a significant role in identifying the behavior of subjects who
have been in the TAW sector compared with other types of employees, ceteris paribus: i)exponential discounting parameters (δ) are negatively correlated to TAW employment
but the childhood discounting parameter has no significant effect; ii) the degrees ofhyperbolic discounting parameters (β), with the same discount factors (δ), havesignificant negative effects in our model. Including the hyperbolic discounting
parameters (β) in the model, which capture time-inconsistent and short-term impatience,
improves the explanatory power of the model, and their effects on TAW employment
are significant given the long-term impatience parameters (δ). This implies that themodels support the hyperbolic discounting function, whereby discount rates are notconstant but declining.
Although these results should be interpreted with caution, they may suggest thatagents who prefer immediate pay despite potential long-term loss caused by unstable,
temporary types of employment have a high probability of working in the TAW sector.
This includes subjects that were either unemployed or employed before they had taken
the TAW work. The then unemployed would have found more stable, permanent jobs if
they had dedicated more time and energy in searching for jobs that adequately engage
their capabilities, while the then employed may have received internal promotions if
they had not quit and taken temporary work. Our findings that TAW employment isrelated to hyperbolic discounting parameters ( β ) may imply that agents haveinconsistent time preferences over intertemporal choices.
A significant role of behavioral characteristics is also found concerning thebalancing property when PSM are employed. When we predict the propensity property
without controls for time preferences, the balancing property is not satisfied and adifferent specification of the propensity score needs to be considered. In other words,
without time preference variables, the model fails to find the control groups that are
distributed identically throughout all blocks. This suggests that time preference
14

characteristics contribute to identifying the significant differences between those whohave work experience in the TAW sector and those who do not.
Besides the discount parameters, Table 3 presents parameters of other observed
characteristics. Agents who have less financial assets, are not married, and women are
more likely to enter the TAW sector. The sign effect does not appear to be significant
concerning whether the individual has held temporary work but it controls one featureof the discounted-utility anomaly and improves the explanatory power of the models.
With the controls of individual characteristics and sign effect, agents’ time preferences
appear to have significant effects on the probability of engaging in the TAW sector:
hyperbolic agents may have a higher probability of becoming temporary workers. For
the labor force in the TAW sector, it may gain immediate rewards from earnings but lose
the chance to obtain a more stable job if TAW employment has a negative effect on theprobability of transition to full-time employment. However, if TAW employment acts asa spring board, the gains and losses may work in opposite ways.
5.3 Impact of TAW employment experience: Transition probability & wages
By the PSM methods, the closest group that has similar observatoary
characteristics was found, and the treated and control groups are approximatelybalanced on the variables predicting the propensity score. The density distributions of
the propensity scores are found in Figure 1. The bottom-half of the graph shows thepropensity scores distribution for the non-treated, while the upper-half refers to thetreated individuals. This figure presents the existence of a substantially overlapped
propensity distribution between the treated and the untreated. The effects of TAW
employment on the probability of transition to permanent employment and wage
differences are reported in Table 4.
Row (i) shows a base model that includes no observatory characteristics when
predicting the propensity score and further estimating the ATT. The following rows (ii)and (iii) include the behavior patterns of impatience (Table 3: model (2) of PSM) andtime inconsistency (Table 3: model (4) of PSM), as well as the individual characteristics
explained in section 5.1. In all cases, we found that the experience in TAW employment
has a significant negative effect on the probability of moving into a stable job. Whenconfounding variables are properly controlled, the effects of the TAW employment on
the transition probability are still negatively significant. Those who have experience
working in the TAW sector have a significant and negative effect of 26 (or 31)
percentage points on the probability of becoming permanent employees in comparison
with those who have the same observatory characteristics but do not have work15

experience in the TAW sector.
Table 4 also reports the TAW employment effects on wages with the same structure
as the transition probability. With no controls for individual characteristics, it seems that
the TAW employment experience has a significant negative effect on the hourly wage.
When the individual behavioral characteristics are controlled, however, the statisticalsignificance disappears. This is different from the case of the transition probability,
which shows significant negative correlations with the TAW experience even when theconfounding variables are controlled. The TAW experience may defer the agents frommoving into a more stable job but it does not necessarily mean that their wages are
significantly lower than those for the other types of employees. It is possible thatproject-based workers or seasonal workers who are hired on a contingency basis do notearn less than other types of employees for the time being, but the wage gap may widen
in the long run.
5.4 Long-term impact of the TAW employment experience
To observe the long-term effects on the transition probability and wages, we ran aregression to estimate the effect of TAW employment at the time of survey (t) on theprobability of transition to permanent employment and hourly wages after one to three
years (t+1, t+2, t+3). The comparison of each subsequent period allows us to observe
the change in the strength of the effects over time. The effect of the TAW experience on
wages is not statistically significant in either the long term or the short term (Table 5).
There is a possibility that the negative effect on wages between TAW and permanent
employees may be shown after a much longer period of time, but at least within 3 years
such effects are not observed in our data.
In case of transition probability models, however, the effects decrease in thefollowing years but still remain statistically significant. Those who have ever had pastwork experience in the TAW sector had difficulty achieving stable employment in thefollowing years. The findings that experience working as TAW employees has anegative effect in Japan may be explained by the characteristics of the rigid Japaneselabor market: the transition to other employment types is difficult and any experience ofbeing hired as TAW workers in the past might provide negative signals to potential
employers in Japan.
5.5 Robustness test & unmeasured bias
Our research interest lies in those who are involuntarily in the TAW sector but haveattempted to move to permanent jobs. One may question the presence of individuals,
16

more likely women, who select the TAW employment type for better work-life balance
despite the unstable status and lower wages. Regardless of gender, older cohorts
approaching retirement age may have a lower probability or less incentive to move into
the primary sector. To investigate whether gender and age differences have a significant
effect on the transition probability, we examine the effect of TAW employment with thesample grouped by sex and by different age ranges: younger cohorts aged less than 40,
the median age (52), and retirement age (60) (Table 6).
The results indicate that being in the TAW sector decreases an individual’s
probability of being in permanent employment and its significant negative effects
remain even when the sample is grouped by gender or age. In particular, with thecontrols for the present time preference characteristics, the significant negative effects
appear to be constant over all ranges of age. The effect of TAW employment on hourly
wages, on the other hand, is not found to be significant in the whole sample. The effects
with a restricted sample by gender or age are also not observed, and in addition, the signof the effects are not consistent. The restricted samples who are males and older than theretirement age and have held temporary work have a probability of earning less but they
are not significant at the 10% significance level. The specification requires carefulinterpretation when the sample becomes too small: for example, the sample aged 60 andover or those aged 40 and less.
5.6 Sensitivity Analysis
Propensity-score matching estimators are not consistent estimators for treatment
effects if unobserved variables that affect the assignment process are also related to theoutcomes. In order to estimate the extent to which such “selection on unobservables”
may bias the probability of moving into a stable permanent job, we use Rosenbaum’s(2002) procedure for bounding the treatment effect estimates (see Becker and Caliendo,
2007). If there is a hidden bias, two individuals with the same observed covariates x
have different chances of receiving treatment. The odds that individuals receive
treatment are then given by
P/ 1
P/ 1

P
P

P
P

P 1
P 1

and
P
P

P
P

, and the odds ratio is as follows:
exp βxexp βxγuγu

exp γ uu

If both units have identical observed covariates―as implied by the matchingprocedure—the x vector cancels out. To have no hidden bias, u should be equal to u ,implying that there are no differences in unobserved variables, or γ should be zero,
implying no influence of unobserved variables on y. Rosenbaum (2002) shows that the17equation implies the following bounds on the odds ratio that either of the two matchedindividuals will receive treatment:

γ

P

P

P

P

eγ .

Since transition probabilities are binary outcomes, we use the Mantel–Haenszel(1959) test statistic proposed by Aakvik (2001). The results are presented in Table 7.
The Q+MH (Q−MH) statistic adjusts the MH statistic downward for positive (negative)
selection. If eγ Γ 1, it means both matched individuals have the same probabilityof engaging in the TAW sector. If both Q+MH and Q−MH statistics are significant, theestimators are robust against the unobserved factors. In other words, there appears to be
no hidden bias that influences the assignment into treatment.
As for the upper bounds under the assumption that we have overestimated thetreatment effect, the significance levels on all bounds are less than the 0.01 level. This
indicates that the study is insensitive to the upward bias. Given the negative estimatedtreatment effect, the bounds under the assumption of underestimation deserve more
attention. With the value of Γ = 2, the result remains significant at the 1% level andis on the decrease but still significant at the 10% level (summer vacation
homework) and 5% level (cleaning a park). This suggests that negative selection bias
does not occur. Since an unmeasured variable does not appear to cause the odds ofreceiving the treatment to differ between the treatment and comparison groups, theconfidence intervals for the effect of TAW employment on the transition to permanent
jobs seem reliable.
Regarding the effects of the TAW employment on hourly wages, which arecontinuous variables, we check the Rosenbaum bounds (Table 8). Without any
controlled observatory characteristics, TAW experience seems to have a significant
negative effect on hourly wage, but we could not find any significant short-term andlong-term effects when individual characteristics and time preferences were held
constant. Regarding the negative selection bias, when the value of Γ reaches 1.1 orhigher, the significance levels decline and become less than 0.01. This implies that a
downward bias in the estimated treatment effects does not happen, but the significancevalues under the assumption of overestimation indicate that the effects may beoverestimated. This indicates that individuals who are most likely to become TAW
employees tend to have higher hourly wages even without treatment, given that they
have the same observatory characteristics as the individuals in the comparison group.
6. Concluding Remarks
There are both positive and negative views about TAW employment. From a18

positive perspective, temporary contracts are believed to avoid labor inflexibility tosome extent. For firms, it provides a compelling incentive to utilize TAW employees asa buffer stock of employment or for leave replacement. As for employees, TAW could
act as stepping stones to permanent employment in good jobs. On the other hand, thelow levels of retention and motivation, the lack of opportunities for career advancement,
lower satisfaction in certain job components, and less work-related training are thought
to be very negative aspects of TAW, because they act as deterrents to workers moving
into full-time regular employment. These characteristics may lead to a longer durationof TAW employment once they are hired as temporary workers.
We examined whether TAW employment is explained by individual heterogeneity
in intertemporal choices and whether the TAW experience has a negative or positiveeffect on the probability of moving into a permanent job and wages in the long run. Dueto the several career paths available before, after, and during the experience in the TAW
sector for the past 2 years, we may not have accurately defined the effects of time
preferences on TAW employment from a time preference perspective. However,individual decisions on career paths including whether to take temporary jobs involve a
trade-off between immediate costs and delayed rewards, and thus, time preferences may
account for individual unobserved choice characteristics. The estimation results indicatethat agents with TAW employment appear to have time inconsistent preferences andthus, they present higher discount rates in the near future than in the distant future.
Another area of interest is the Average Treatment Effect on the Treated, or ATT, whichmeasures the impact of experiencing TAW employment on transition probabilities andwages. The treated and control groups have identical distributions over a substantial
range. The results estimated inside the range of the observed data points indicate thatthose who have ever held temporary work have a lower probability of moving into
permanent jobs and the significant negative effect lasts a long time, although the effectsizes decrease as time progresses. On the other hand, wage differentials are notsignificant between those who have work experience as TAW employees and those who
do not. The effects of TAW employment on wages remain insignificant over these
periods.
With the available data, we are unable to accurately compare the impact of TAW
employment before and after the enactment of the new legislation in 2010, because ourmost recent data collected in 2011 contain information on TAW experience during theperiod 2009–2010. We need further data containing respondents with TAW experience
only after 2010 to compare with those with TAW experience before 2010. Although wecannot assure whether the effect of TAW experience during the period 2009–2010 stems
19

from the enactment of the new legislation, the fact that TAW experience during the
given period has a significant, larger, and negative effect on the probability of transition
into permanent employment in 2011 implies that the negative effect might have been
reinforced by the strict legislation. Then, one may question how the legislation
correlates with the behavioral patterns of temporary workers.
A stricter legislation may play a role of making impatient, hyperbolic agentsrecognize their own self-control problems and driving then to pursue the long-run
rewards with payment for the immediate costs. To be more specific, the level ofsophistication versus naivety has tangible implications on public policy formulation andimplementation. If the current TAW workers are sophisticated enough to pay for theimmediate costs for the rewards accrued in the distant future, the strict legislation may
act as a stimulus device for them to attempt for the stable jobs more eagerly than before.
It could then lessen the number of people preferring short-run rewards, who otherwise
would have become full-time workers with a better pay. However, if they are too naive,the prohibition on the dispatch work may discourage them from finding any kind of jobs
from the first. In this case, the policies that educate and train people to find jobs thatmatches their abilities rather than stricken may contribute to the welfare of theemployees. The further research with additional data is required to examine how
hyperbolic agents who are more likely to engage in temporary work would behave after
the enactment of the legislation.
To assess the robustness of matching estimators, we also examined whether there
exist unobserved variables that affect assignment into treatment and the outcome
variable simultaneously. We used the bounding approach suggested by Rosenbaum andthe results indicate that we do not observe any effects caused by an unmeasured variable
regarding the transition probability, and the effects are neither downward nor upward
biased across all bounds. This shows that the inference in the regression results ontransition probabilities would not change because of different values on unobserved
factors despite being identical on the matched covariates. Because of the limited time
period of our data, we were not able to accurately examine the long-term effect or the
impact after the legislation enactment from time preferences’ perspective. Nevertheless,the combined evidence that this study finds suggests that TAW for employees in Japan
during the 2009-2011 time period is more likely to function as a “dead-end” rather than“stepping stone” toward stable full-time employment.

20

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22

Table 1 Definitions of variables
Variables

Definitions

obs

mean

s.d.

Summer vacation homework:
Proxy for exponential discounting()

Response to the question “When did you plan to do your homework assignments?” on a 5-point scale,from 1 (at the end of the vacation) to 5 (at the beginning of the vacation).

1664

3.977

0.958

Summer vacation homework:
Proxy for hyperbolic discounting()

Using the responses to the question “When did you do your homework assignments?” on a 5-point
scales, from 1 (at the end of the vacation) to 5 (at the beginning of the vacation) and the responses tothe question as regards to when the subjects planned to do the homework explained above, thehyperbolic discounting was constructed by dividing the second responses (action) by the first
responses (plan) or differencing from the first responses (plan) to the second responses (action).

1664

0.708

0.323

Cleaning a park:
Proxy for exponential discounting()

Responses to the question “Let’s assume that you were required to spend time cleaning a park, and you
have the option to shorten the hours by one hour in 90 days or shorten some hours in 97 days”
Discounting rates for every choice computed and converted to discounting factors for the distantfuture.

1446

0.903

0.359

Cleaning a park:
Proxy for hyperbolic discounting()

Comparing with discounting factors for the near future (today versus in 7 days), short-run impatience
is computed dividing discounting factor of the near future by discounting factor of the distant future.

1352

1.026

0.246

Sign effect

The difference in the discount rate imputed from receipt of $100 tomorrow to the discount rate
imputed from payment of the same amount tomorrow.

1827

0.002

0.077

Household income (excluding
oneself)

Per-capita household income (excluding oneself) in million yen.

1664

3.194

3.300

Ages of repondents
Per-capita household property including lands and housings in million yen
Per-capita household financial assets in million yen
Per-capita household mortgages in million yen
Per-capita household debt holding other than mortgages in million yen
Number of children at the time of survey
A binary indicator for marrital status which equals 1 if married and 0 otherwise
A continuous variable ranging from 1 (lower than middle school level)–5 (higher than graduate schoollevel) [Descriptive Stats: High school level]
A binary indicator for females which equals 1 for female repondents 0 otherwise
A binary indicator which equals 1 if the respondent has ever held temporary work for the past 2 years
and 0 otherwise
A binary indicator for employment status which equals 1 if respondents have permanent jobs and 0
otherwise.
A continuous variable computed by a ratio of hourly/monthly/yearly wage to labor hours (Maximum
value is five hundred yen).

1664
1664
1664
1664
1664
1664
1664

48.01118.747
11.231
6.181
0.782
1.800
0.808

10.95920.63615.882
8.901
1.971
1.061
0.394

1664

0.460

0.499

1664

0.436

0.496

1664

0.072

0.259

1630

0.556

0.497

1541

1713.990

2156.826

AgeHousehold property (Land /Household financial assets
Household Housing Loans
Household other debts
Number of children
Married (=1)
Educational Level
Female (=1)
dispatched experience (=1)
PE (Permanet Employment)
Hourly wage

23

Table 2 Descriptive statistics of treated and control groups
Variables
Summer Vacation
Homework

Mean

Treated Group
S.E
Min

Max
1

Mean
5

3.98

Matched Controls
S.E
Min
0.96

Max
1

Mean
5

3.98

Matched Controls
S.E
Min
0.96

Max

Proxy for exponential discounting

3.99

0.96

1

5

Proxy for hyperbolic discounting

0.65

0.34

0.20

2.00

0.71

0.32

0.20

2.00

0.71

0.32

0.20

2.00

Proxy for exponential discounting

0.86

0.37

0.38

1.57

0.91

0.36

0.38

1.57

0.91

0.36

0.38

1.57

Proxy for hyperbolic discounting

1.00

0.15

0.24

1.85

1.03

0.26

0.24

4.16

1.03

0.25

0.24

4.16

-0.003

0.07

-0.34

0.40

0.001

0.08

-0.41

0.40

0.001

0.08

-0.41

0.40

Cleaning a Park
Gain & Loss asymmetry(Discount utility anomaly)
Individual Characteristics

Dependent variables

Sign EffectHousehold income (excluding oneself)in million yen
AgeHousehold property (Land / Housing) inmillion yen
Household financial assets in million yen
Household Housing Loans in million yen
Household other debts in million yen
Number of children
Married (=1)
Educational Level 1 (Junior high schooland lower)
Educational Level 2 (High school)
Educational Level 3 (Junior college)
Educational Level 4 (University)
Educational Level 5 (Graduate schooland higher)
Female (=1)
TAW experience (=1)
PE (Permanent Employment)
Hourly wage (unit: yen)

3.05

2.95

0

13.5

3.11

3.21

0

17

3.21

3.33

0

19.5

47.15811.8552274

47.782

10.8852175

48.077

10.887

21

75

17.63

20.32

0

100

17.58

18.98

0

100

18.83

20.66

0

100

7.04
4.9
0.76
1.67
0.69

8.97
8.0
1.95
1.15
0.46

1.25
0
0
0
0

40
30
10
5
1

8.60
6.3
0.77
1.81
0.82

9.42
8.9
1.92
1.06
0.39

1.25
0
0
0
0

75
30
10
5
1

11.56
6.3
0.78
1.81
0.82

16.25
9.0
1.97
1.05
0.39

1.25
0
0
0
0

100
30
10
5
1

0.06

0.24

0

1

0.05

0.22

0

1

0.05

0.21

0

1

0.45
0.25
0.22

0.50
0.43
0.41

0
0
0

1
1
1

0.46
0.16
0.30

0.50
0.37
0.46

0
0
0

1
1
1

0.46
0.16
0.30

0.50
0.36
0.46

0
0
0

1
1
1

0.03

0.16

0

1

0.03

0.17

0

1

0.03

0.17

0

1

0.66
1.00
0.25
1325.73

0.48
0.00
0.44
843.3901
0529.631
1
15163.94

0.43
0.00
0.57
1702.81

0.50
0.00
0.49
2237.78

0
1
0
0
0
1
500.00 57377.05

0.42
0.00
0.58
1744.71

0.49
0.00
0.49
2225.2100
0500.0010157377.05

24

Table 3 Estimation of the propensity score
Summer Vacation Homework(1)

Cleaning a Park

(2)

(3)

(4)

Discounting parameters-0.673 **-1.118 *(0.327)Sign Effect(0.657)-0.062-0.108-0.546 *-0.612 **(0.104)(0.109)(0.293)(0.294)-0.581-0.581-1.209-1.260(1.268)(1.268)(1.312)(1.313)

Individual Characteristics
Household income(excluding him/herself)
AgeHousehold property (Land /Housing)
Household financial assets
Household Housing Loans
Household other debts
Number of children
Married (=1)
Educational Level

Female(=1)
Cons
Number of Observations
Log-Likelihood
LR statistic-0.049-0.049-0.041-0.042(0.037)(0.037)(0.038)(0.038)

0.006

0.009

0.007

0.006

(0.010)(0.010)(0.011)(0.011)

0.008

0.008

0.008

0.008

(0.005)(0.005)(0.005)(0.005)-0.040 ***-0.039 ***-0.037 ***-0.038 ***(0.013)(0.013)(0.013)(0.013)-0.017-0.017-0.034 **-0.033 **(0.013)(0.013)(0.015)(0.015)-0.003-0.005-0.006-0.007(0.052)(0.052)(0.057)(0.058)-0.032-0.029-0.146-0.150(0.109)(0.108)(0.116)(0.117)-0.520 **-0.519 *-0.417-0.413(0.264)(0.266)(0.275)(0.275)Controlled

Controlled

Controlled

Controlled

0.994 ***

1.026 ***

0.893 ***

0.904 ***

(0.233)(0.233)(0.237)(0.237)-2.097 ***-1.531 *-1.472 *-0.270(0.790)(0.840)(0.734)(1.009)

1973

1973

1493

1493

-470.332-467.467-358.651-356.794

54.44 ***

60.17 ***

57.99 ***

61.71 ***

Standard errors in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1
Note: A proxy for hyperbolic discounting parameter β used for the model (2) is imputed from theratio of when respondents did homework (action) to when they planned doing it (plan). When we used theproxy calculated from the difference between action and plan, the parameter is still significant at 5% level(coefficient: −0.198; standard error: 0.082). When the sample is grouped by gender, the hyperbolic
discounting parameters are still negatively significant among males (coefficient, −1.035; standard error,

25

0.573), but not among females (coefficient, −0.467; standard error, 0.413). It implies the effect ofindividual time preferences work more strongly on male employees’ probability of being in the TAW
sector. Moreover, when we controlled for childhood and present time preferences together (correlation:
hyperbolic discounting, −0.0058; exponential discounting, 0.0419), the effect of hyperbolic discountingof childhood remains significant (coefficient, −0.721; standard error, 0.359). This suggests that thechildhood time preferences have a significant effect on the probability of engaging in temporary workwith or without the control for the present time preferences.

26

Table 4 Effect of TAW employment on transition probability and wage differences

(i)
(ii)
(iii)

(i)
(ii)
(iii)

Transition Probability
TreatedControl
ATT
Group
Group
238
2846
-0.309120
108
Treated
Group
238
120
108

Std. Err.

T value

0.029-10.547-0.2630.063-4.16497-0.311
Hourly Wages
Control
ATTGroup
2367 -433.215
9636.15392
15.0580.068-4.591

Std. Err.

T value

64.86199.451128.411-6.679
0.364
0.117

111

27

Table 5 Long-term effects of TAW employment

2009
2010
2011

2009
2010
2011

Time
t
t+1
t+2
t
t+1
t
Time
t
t+1
t+2
t
t+1
t

Transition Probability
Treated Group Control Group
ATT
142
126
-0.29
142
123
-0.217142
117-0.198120
111-0.263120
99-0.138124
111-0.356
Hourly Wage
Treated Group Control Group
ATT
238
209 -112.982
238
182
146.576
238
160
44.738120
9636.153120
89-7.858124
107 -145.011

Std. Err.
0.06
0.062
0.063
0.063
0.067
0.061

T value-4.802-3.512-3.143-4.164-2.064-5.885

Std. Err.
104.004
143.535
99.31
99.451106.787
92.301

T value-1.086
1.021
0.45
0.364
-0.074-1.571

Note: To control for individual heterogeneity in time preferences, the degree of impatience duringchildhood is used since the questions for each year is concerning the present time preferences under
the hyperbolic discounting function, in a different way.

28

Table 6 Effects of TAW employment by gender and age
Transition Probability

Summer Vacation Homework
TreatedGroup
Whole Sample
High propensity score region
Male
FemaleOder than 60
Aged 60 and lessOlder than 52
Aged 52 and lessAged 40 and lessControlGroup

ATTStd. Err.

T value120

111-0.2630.063-4.164

37
41
79
17
103
36
84
36

26
35
70
10
94
33
71
28

-0.482-0.287-0.289-0.25-0.143-0.029-0.217
-0.41

0.125
0.112
0.075
0.161
0.07
0.103
0.082
0.13

-3.866-2.57-3.861-1.553-2.043-0.286-2.66-3.149Cleaning a Park
TreatedGroup
Whole Sample
High propensity score region
Male
FemaleOder than 60
Aged 60 and lessOlder than 52
Aged 52 and lessAged 40 and less,,Control
Group
108
43
40
68
17
91
33
75
35

ATT
97
36
37
55
15
79
30
65
28

Std. Err.
-0.311-0.321-0.315-0.13-0.333-0.244-0.245-0.236-0.306

0.068
0.11
0.11
0.085
0.126
0.077
0.114
0.087
0.135

T value-4.591-2.925-2.869-1.531-2.646-3.166-2.151-2.717-2.273

Hourly Wage

Summer Vacation Homework
TreatedGroup
Whole Sample
High propensity score region
Male
FemaleOder than 60
Aged 60 and lessOlder than 52
Aged 52 and lessAged 40 and lessControl
Group
120
37
41
79
17
103
36
84
36

TreatedGroup
Whole Sample
High propensity score region
Male
FemaleOder than 60
Aged 60 and lessOlder than 52
Aged 52 and lessAged 40 and lessControl
Group
108
38
40
68
17
91
33
75
35

ATT
96
23
35
63
11
90
29
64
26

Cleaning a Park,

36.153-8.2260.737
1.558-874.679-89.35-55.593-12.89727.085

Std. Err.
99.451
157.127
319.474
103.165495.296150.451400.827103.228
131.479

T value
0.364-0.0520.002
0.015-1.766-0.594-0.139-0.125
0.206

,
ATT
92
15.0582754.40233 -1913.822
47-58.111
14 -336.002
78
128.595
25 -374.791
62 -182.425
24-70.775

Std. Err.
128.411152.541
1750.449
104.706
327.884
123.263343.799206.274
135.524

T value
0.117
0.357-1.093-0.555-1.0251.043-1.09-0.884-0.52229

Table 7 Mantel–Haenszel test statistics
Probability of transition to permanent employment (2010)

Summer Vacation Homework
Gamma
1
1.05
1.1
1.15
1.2
1.25
1.3
1.35
1.4
1.45
1.5
1.55
1.6
1.65
1.7
1.75
1.8
1.85
1.9
1.95
2

Cleaning a Park
Gamma
1
1.05
1.1
1.15
1.2
1.25
1.3
1.35
1.4
1.45
1.5
1.55
1.6
1.65
1.7
1.75
1.8
1.85
1.9
1.95
2

,

Q_mh+ Q_mhp_mh+
p_mh3.87824
3.87824 0.000053 0.000053
4.06885
3.70478 0.000024 0.000106
4.24343.53214 0.000011 0.000206
4.410813.36769 5.10E-06 0.000379
4.57173.2107 2.40E-06 0.000662
4.72658
3.06053 1.10E-06 0.001105
4.87594
2.91659 5.40E-07 0.001769
5.020172.7784 2.60E-07 0.002731
5.15966
2.64551 1.20E-07 0.004078
5.29472
2.51753 6.00E-08 0.005909
5.425672.3941 2.90E-08 0.008331
5.55276
2.27492 1.40E-08 0.011455
5.67623
2.15969 6.90E-09 0.015398
5.796312.04816 3.40E-09 0.020272
5.91321.9401 1.70E-09 0.026184
6.02707
1.8353 8.30E-10 0.033231
6.13809
1.73356 4.20E-10 0.041498
6.246431.63471 2.10E-10 0.051055
6.352211.53858 1.10E-10 0.061954
6.45557
1.44502 5.40E-11 0.074226
6.55663
1.35391 2.80E-11 0.087883,

Q_mh+ Q_mhp_mh+
p_mh4.04673
4.04673 0.000026 0.000026
4.23106
3.88223 0.000012 0.000052
4.39833
3.71685 5.50E-06 0.000101
4.55877
3.55934 2.60E-06 0.000186
4.71297
3.40899 1.20E-06 0.000326
4.861433.26518 5.80E-07 0.000547
5.00459
3.12737 2.80E-07 0.000882
5.14286
2.99507 1.40E-07 0.001372
5.27657
2.86787 6.60E-08 0.002066
5.40606
2.74538 3.20E-08 0.003022
5.531592.62727 1.60E-08 0.004304
5.653432.51323 7.90E-09 0.005982
5.77182.40299 3.90E-09 0.008131
5.88692
2.29631 2.00E-09 0.010829
5.99898
2.19296 9.90E-10 0.014155
6.108142.09273 5.00E-10 0.018187
6.21458
1.99545 2.60E-10 0.022997
6.318431.90094 1.30E-10 0.028655
6.41983
1.80904 6.80E-11 0.035222
6.51891
1.71962 3.50E-11 0.042751
6.61578
1.63255 1.80E-11 0.051282

Note: Gamma is log odds of differential assignment due to unobserved factors; Q_mh+ (－): Mantel–Haenszel statistic[assumption: overestimation (underestimation) of treatment effect]; p_mh+(－): significance level [assumption:overestimation (underestimation) of treatment effect].

30

Table 8 Rosenbaum bounds
Wage differences (2010)
Gamma
1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2
Gamma
1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2

sig+
0.210814
0.357605
0.513926
0.656422
0.771505
0.855944
0.913292
0.94986
0.971993
0.984819
0.991983
sig+
0.085028
0.16958
0.281704
0.408655
0.535744
0.651143
0.748005
0.824255
0.881178
0.921831
0.949799

Summer Vacation Homework,
sigt-hat+t-hatCI+
0.210814
40.983640.9836-65.9836
0.106721
19.0164
68.5792-89.5025
0.049595-2.45892
91.6096-110.929
0.021497-22.0319
109.836-134.52
0.008803-40.0661
132.992-152.763
0.00344-59.7705
152.486-172.865
0.001293-75.4224
168.967-191.020.000471-88.7705
184.628-210.443
0.000167-102.254
203.989-225.9780.000058-115
219.313-240
0.00002-129.892
236.943-259.264
Cleaning a Park,
sigt-hat+t-hatCI+
0.085028
72.4772
72.4772-31.3934
0.03662
51.721396.7844-54.0827
0.014715
29.4486
117.122-76.8033
0.005595
12.2951138.907-96.1494
0.002035-6.50771
156.982-119.566
0.000714
-22.5
177.5
-137.523
0.000243-34.5753
198.907-153.962
0.000081-47.7049
217.437-173
0.000026-62.5228
236.202-189.566
8.40E-06-75.9563
252.969-206.992
2.60E-06-87.3179273.5-221.721

CI158.197
186.066214.918
242.259265.509291.275
321.949342.623365.984
393.063
414
CI192.213
224.918
255.373289.982322.5
349.69381.5
406.795
434.542462.204
491.514

Note: Gamma: log odds of differential assignment due to unobserved factors, sig+ (－): upper(lower) bound significance level, t-hat+ (－): upper (lower) bound Hodges-Lehmann point estimate,
CI+ (－): upper (lower) bound confidence interval (a = .95)

31

Summer vacation homework (β, δ)Cleaning a park (β, δ)Figure 1 Histograms for propensity scores by treatment status

32

Appendix 1 Data structure regarding career paths
A: 2 years ago

B: For the past 2 years

C: The current status①Permanent employment (PE)Experience in the TAW sector②not PE
Unemployed (UE or NE)③Permanent employment (PE)Never in the TAW sector④not PE⑤Permanent employment (PE)Experience in the TAW sector⑥not PE

Employed (PE, TE or DE)⑦Permanent employment (PE)Never in the TAW sector⑧not PE

33

Appendix 2 Transition matrix of employment turnover
2008-2011(Whole)Permanent

Permanent

90.6%

4.2%

0.2%

Selfemployed
Familybusinesses
1.7%

1.5%

Not
employed(no job
search)1.8%

Part-time
Dispatch
Self-employed
Family business
UnemployedNot employed(no job search activities)

6.9%
7.1%

78.9%
30.4%

1.3%
48.6%

3.7%
0.0%

4.6%
9.3%

4.6%
4.6%

669
33.75

4.8%

5.5%

0.0%

83.3%

1.6%

4.8%

525.25

4.4%

19.6%

1.5%

3.0%

42.1%

29.4%

246

2.0%

3.4%

0.1%

2.0%

6.2%

86.2%

912

Part-time

Dispatch

Unemployed

Average Obs.
1373.5

2008-2011(M ale)Permanent

Permanent

91.9%

3.5%

0.2%

Selfemployed
Familybusinesses
1.4%

1.6%

Not
employed(no job
search)1.5%

Part-time
Dispatch
Self-employed
Family business
UnemployedNot employed(no job search activities)

13.2%
10.1%

68.5%
44.6%

3.4%
30.0%

3.3%
0.0%

4.5%
4.2%

7.1%
11.1%

148.75
11.5

6.4%

2.6%

0.0%

86.3%

0.9%

3.8%

284.5

7.2%

12.6%

2.6%

3.5%

43.2%

30.8%

74.75

3.0%

2.1%

0.2%

2.2%

4.4%

88.1%

253.75

Dispatch

Selfemployed
Familybusinesses

UnemployedNotemployed(no job
search)Average Obs.

Part-time

Dispatch

Unemployed

Average Obs.
1011

2008-2011(Female)Permanent

Permanent

87.1%

6.2%

0.4%

2.5%

1.4%

2.4%

362.5

Part-time

5.0%

81.9%

0.8%

3.9%

4.6%

3.8%

520.25

Dispatch
Self-employed
Family business
UnemployedNot employed(no job search activities)

5.6%

22.2%

58.7%

0.0%

12.4%

1.1%

22.25

2.8%

9.0%

0.0%

79.8%

2.4%

6.0%

240.75

3.1%

22.7%

1.1%

2.7%

41.6%

28.7%

171.25

1.5%

4.0%

0.1%

1.9%

7.0%

85.5%

658.25Part-time34