Unemployment Benefits and Liquidity Effects
Qifan Fu∗
May 29, 2024

Abstract
Utilizing variations in cash-on-hand, stemming from epidemic shocks and the accelerated provision of unemployment benefits, this paper estimates their impact on the
duration of unemployment. The analysis demonstrates that individuals facing liquidity
constraints exhibit a significant increase in the elasticity of the unemployment duration with respect to unemployment benefits. Furthermore, I finds that the additionalliquidity provided by the reduced waiting period for Unemployment Insurance (UI)
benefits extends the duration of unemployment among the voluntarily unemployed.
These findings suggest that the increase in unemployment duration due to unemployment benefits is not solely attributed to moral hazard but is also driven by liquidity
effects.
Keywords: Unemployment Benefits, Liquidity Effects, Unemployment Benefits Waiting Period, Voluntary Unemployment.
JEL Codes: J64, J65∗

Qifan Fu: Graduate School of Economics, Osaka University, Japan. Email: u670245b@ecs.osaka-u.ac.jp.
Acknowledgments: I would like to thank Social Science Japan Data Archive (SSJDA) for letting me accessthe JPSED data. Funding was provided by the Otsuka Toshimi Scholarship Foundation.

1

Introduction

Unemployment insurance is primarily designed to alleviate the uncertainty faced by workers
during periods of unemployment by the provision of transfer payments (Landais et al. [2021];
Chetty [2006]). Unemployment benefits serve to offer financial liquidity to the unemployed,enabling them to sustain basic consumption levels (Schmieder and Von Wachter [2016]).
However, empirical studies often reveal that the presence of unemployment benefits influences
the job search efforts of individuals1 , resulting in an extended duration of unemployment(Boone et al. [2021]; Farber and Valletta [2015]; Farber et al. [2015]). Prior research hascontended that this phenomenon stems from the moral hazard problem inherent in thesubsidized model of unemployment insurance (Krueger and Meyer [2002]; De Groot andVan der Klaauw [2019]). Nevertheless, recent studies increasingly emphasize the role of theliquidity effect as a significant driver of these outcomes.
The distinction between the two is relevant for assessing the welfare impact of unemployment benefits. This is because the liquidity effect implies that unemployment benefits are asocially beneficial response that can correct borrowing market failures when the unemployed
are unable to smooth consumption. According to Chetty [2008]’s model, under conditionsof incomplete credit, unemployment benefits can affect search efforts through liquidity effects. This paper therefore provides a new estimate of the liquidity effect in unemployment
insurance.
I use survey data from the Japanese Panel Study of Employment Dynamics to estimate
the effect of unemployment benefits on the duration of unemployment. In my empiricalstrategy, I utilize two indicators, the individual subjective evaluation of whether income orsavings satisfy consumption in the past year and the asset situation, to classify the liquidityconstrained and unconstrained groups. Estimating the effect of unemployment benefits on
the duration of unemployment in these two clusters separately, I find that unemployment
1

Recent studies such as Chodorow-Reich et al. [2019] argue that the effect of unemployment insurance
on labor supply is not as significant as previously thought.

1benefits have a greater impact on the unemployed with restricted liquidity compared to theunemployed with unrestricted liquidity. In the liquidity constrained group, a 10% increase in
unemployment payments is associated with a 3-4% increase in the duration of unemployment.
This finding implies that unemployment benefits have a stronger impact on the unemployed
who are unable to smooth their consumption.
To address the potential variance in preferences across different asset populations as
indicated by Card et al. [2007a], I use the COVID-19 epidemic shock as an exogenous variable
for the change in cash on hand. Common sense would dictate that the unemployed during the
COVID-19 epidemic were more likely to face liquidity problems (Baqaee and Farhi [2022]). It
also means that if the duration of unemployment during this period is particularly responsive
to cash on hand, it suggests that the estimated liquidity effect would be larger. The results
suggest that the effect of unemployment benefits on the liquidity-constrained unemployedis 2-7% larger in the group that has been exposed to an exogenous shock. We can knowthat the exogenous reduction in cash on hand further exacerbates the effect on the liquidity
constrained unemployed. This fact proves that the liquidity effect accounts for a large partof the link between unemployment insurance benefits and the duration of unemployment.
To further validate the robustness of these findings, I utilize a policy change in Japaneseunemployment benefits to estimate the effect of cash on hand on the duration of unemployment. According to the Department of Health and Labor Standards, employees who leave for
their own reasons after October 1, 2020, will have their benefit restriction period shortened
from the previous three months to two months, with a maximum of two uses within five
years. This implies that an unemployed individual with the same duration of unemployment
receives an additional month of benefits after the policy shock, equivalent to approximately
0.5 to 0.8 months of wages. The empirical results find that the increase in cash on hand dueto the policy shock prolongs the duration of unemployment for the voluntarily unemployed
and reduces their reemployment hazard by 19%. Overall, this paper verifies that cash on
hand has a significant effect on the duration of unemployment through all three of these2methods. These findings imply that the extension of unemployment duration attributed
to unemployment benefits is predominantly driven by the liquidity effect. This conclusion
diverges from prior studies that hinted at a negative impact of unemployment benefits, emphasizing that the welfare effect of such benefits may have been markedly underestimateddue to insufficient recognition of the crucial role played by liquidity considerations.
This paper is related to several pieces of literature. In the literature on how cash on handaffects the duration of unemployment, Chetty [2008] begins by presenting a model based
entirely on moral hazard and liquidity effects and estimates the elasticity of unemployment
duration with respect to cash on hand for U.S. workers. LaLumia [2013] explores this theme
further, using seasonal variations in Earned Income Tax Credit (EITC) rebates to assess
liquidity effects. Landais [2015] utilizes a kink in the U.S. unemployment insurance benefit
schedule to identify the effect of the level of unemployment insurance benefits on the potential
duration of unemployment. One of the contributions of this paper is a new estimate of theeffect of cash on hand on the duration of unemployment. And the conclusions rely on changes
in liquidity conditions due to exogenous epidemics and policy shocks, whereas such changes
in cash on hand do not depend on any unobserved endogenous variables.
Second, I examine the impact of unemployment benefits on the voluntarily unemployed,
an aspect that has not been previously explored in existing research. Due to legal restrictions
in many countries, quitters are often ineligible for unemployment benefits, which has led totheir frequent exclusion from previous studies of unemployment (Card et al. [2007b]). In
this paper, I exploit the opportunity presented by the policy shift towards the voluntary
unemployed in Japan to estimate the impact of unemployment benefits through the change incash on hand caused by the policy shock. Considering the low rate of unemployment benefits
received by the unemployed in Japan (Kitazawa [2015]; Sasaki et al. [2013]), the elasticity
estimates of the voluntary unemployed in this paper may be of interest to policy makers2 .
This study also provides an estimate of the effect of unemployment benefits on unemployment
2

Paying attention to voluntary unemployment is necessary because, especially during the recession, firms
have an incentive to get the unemployed to quit their jobs on their own in the form of severance payments.

3

exit hazard in Japan under general conditions. Consistent with previous research utilizing
Japanese data, my findings underscore that unemployment benefits significantly reduce theexit hazard for the unemployed (Ohkusa [2004]; Kohara et al. [2013]).
This paper also expands the literature on the role of unemployment insurance during
COVID-19. Debate over the expansion of unemployment benefit programs is inevitable during recessions (Rothstein [2011]; Hagedorn et al. [2013]; Ganong et al. [2020]; Romer andRomer [2022]). Holzer et al. [2021] argue that during COVID-19 more generous unemployment benefit programs in the United States contributed to higher unemployment, and theyshow that households that were not subject to liquidity constraints were less sensitive to termination of benefits. Conversely, other studies suggest that the U.S. unemployment benefit
program during this period provided essential liquidity to low-income households (Bachas
et al. [2020]) and played a significant role in stimulating spending (Casado et al. [2020]). In
contrast, this paper concludes that during the COVID-19 epidemic, unemployment was agreat inequality shock for those who were associated with facing borrowing constraints. In
conjunction with Chetty [2008] model, under conditions of intensified liquidity constraints,
the extension of the unemployment period due to unemployment benefits indicates an increase in the optimal level of benefits from unemployment insurance.
The rest of the paper progresses as follows. Next Section 2 describes the SIPP data that
I use, Section 3 discuss the heterogeneous effects o unemployment benefits on the durationof unemployment. Section 4 explores the effects arising from shortened UI benefits waiting
period, and the estimates are used to identify liquidity effects. Section 5 concludes.

2

Context and Data

The data used in this paper are sourced from the Japanese Panel Study of Employment
Dynamics (JPSED), generously provided by the Recruit Works Institute in Japan. The
dataset encompasses the employment status of Japanese workers across the nation spanning4

from 2015 to 2021. It comprises information on various aspects, including unemployment
insurance, duration of unemployment, demographic characteristics (such as gender, age, education, place of residence, reason for leaving, etc.), household income, and job characteristics(such as location, occupation, industry).
Given the absence of direct data on the duration and amount of unemployment insurance
benefits, this study estimated individual unemployment benefits using the formula provided
by the Ministry of Health, Labour and Welfare of Japan for calculating unemployment
benefits3 . The daily amount of unemployment benefits is given by the following formula,

U B = γ(age,wage) ∗ wage

(1)

where wage represents the daily wage before unemployment and γ represents the benefit rate
(50% − 80%). The value of γ is determined by age and the daily wage before unemployment.
The higher the value of wage, the lower the benefit rate. When wage ≥ 5030, the benefit
formula can be specifically transformed into U B = 0.9∗wage−0.3(wage − 5030)/7350∗wage.
And the data include data on whether or not they receive unemployment benefits, whichallows us to accurately identify the impact of unemployment benefits.
In this study, I measure the duration of unemployment using survey data, which are basedon individuals’ recollections of when they became unemployed and when they subsequently
returned to work.4 To focus my analysis, I exclude individuals who were unemployed for theentirety of the previous year, thereby removing those who may have permanently exited thelabor market. The dataset employed in this study offers several advantages for analyzing
unemployment duration.
Card et al. [2007b] argue that the perceived impact of unemployment benefits on the
duration of unemployment may vary depending on the definition of an ’unemployment spell’.
Specifically, defining the unemployment spell as the duration of benefit receipt tends to
3

The formula is based on the unemployed person’s age, six months’ income from the previous occupation,and the duration of being an unemployment insurance beneficiary.
4This refers to the continuous duration of unemployment over the past year.

5yield higher estimates of the impact of unemployment benefit expansion on unemployment
duration (Farber and Valletta [2015]). Conversely, if the unemployment spell is defined as thetime taken to find a new job, the estimated impact is significantly lower. This distinction is
crucial as it implies that unemployment benefits substantially extend the duration of benefit
receipt, but have minimal influence on the likelihood of re-employment. The concern is thatthe moral hazard associated with unemployment benefits might be exaggerated based on
how unemployment duration is defined.
In light of this, this study defines the unemployment spell as the period from job departure
to re-employment. This approach allows for a more precise estimation of the influence of
unemployment benefits on re-employment likelihood.
Additionally, this definition of the unemployment spell offers a more accurate measureof total unemployment duration, especially for those who are voluntarily unemployed. This
group is subject to a mandatory two-month waiting period for benefit receipt due to regulatory constraints, meaning that reliance on the length of unemployment benefit payments
could underestimate their actual unemployment duration. Descriptive statistics, which include data on the duration of unemployment and unemployment insurance benefits, arepresented in Table 1.
To examine the role of liquidity in the impact of benefits on duration, I categorize individuals into liquidity subgroups based on whether their income or savings can cover living
expenses. Individuals whose income or savings fall short of meeting basic living expenses
in the past year are classified as the liquidity-constrained group. Furthermore, I categorize individuals into liquidity subgroups based on both their ability to meet living expenses
and property ownership. Specifically, those with insufficient income to cover expenses andwithout property ownership are classified as part of the liquidity-constrained group. This
classification approach provides a more accurate assessment of individuals’ liquidity situation, considering variations in consumption habits and subjective judgments.
Table 1 reveals that there are no significant differences between the two subgroups in6

terms of individual characteristics such as age, education, spouse, work experience, andskill training. However, when examining unemployment duration trends, it becomes evident
that the liquidity-constrained group experiences longer periods of unemployment comparedto those with unrestricted liquidity. Additionally, individuals in the liquidity-constrainedgroup receive slightly lower unemployment benefits.

3

Effects of UI Benefits on Unemployment Durations

3.1

Hazard Model Estimates

Given the presence of censored data5 , this paper uses Hazard model estimates to identify
the effect of UI benefit on search durations. I estimate hazard models of the following form:h (t, i) = h0 (t) · exp [β1 log(ubi ) + β2 xit ](2)

here, h (t, i) denotes the unemployment exit hazard rate for individual i over an unemployment duration of t months, h0 is the baseline hazard rate for state t, ubi is the unemployment
benefit for individual i, and xi is a set of control variables. The β1 provides the elasticityof the hazard rate with respect to unemployment insurance benefits (ubi ). β2 reports thevaluation of the effect of personal characteristics, including gender, age, education, spouse,skill training, and reason for unemployment on the duration of unemployment.
To further investigate the role of liquidity effects in the UI-duration link, similar to
Chetty [2008], this study empirically classifies liquidity based on subjective recall judgmentsof whether the unemployed’s income in the past year met their daily consumption and theassets (property) in their personal names. The estimation of equation1 above is repeatedseparately for the liquidity-constrained and unconstrained groups, which allows us to focus
5This refers to instances where individuals are still unemployed in the last month of the survey, implying that their unemployment duration may extend beyond the survey’s conclusion, potentially leading toincomplete data.

7

more on directly comparing the differences between the two groups of unemployed.

3.2

Graphical Evidence and Non-Parametric Tests

To demonstrate the effect of unemployment benefits on the duration of unemployment, I
begin by providing graphical evidence based on the Kaplan-Meier curve fitted by The nonparametric maximum likelihood estimator. The Figure 1 shows that in the pooled sample,
unemployment benefits are associated with lower unemployment exit rates. As can be seen in
the figure, about 70% of the group that receive lower benefits after 3 months of unemployment
exited unemployment, compared to 60% in the higher welfare group.
To further illustrate the role of the liquidity effect, this study divides individuals into twogroups: liquidity-constrained and non-liquidity-constrained. This classification is based on
subjective judgments regarding whether their income, including savings, meets their daily
consumption needs. Here the use of subjective income-expenditure evaluations as compared
to pre-unemployment wage or income levels minimizes the endogenous effect on the durationof unemployment. Because there is a strong correlation between the pre-unemployment
wage level and the amount of the unemployment benefit, it leads to the possibility that thedifference in the duration of unemployment between subgroups with different liquidity issimply a difference in the elasticity of the unemployment benefit.
Descriptive statistics for these two groups are reported in Table 1. The data on age,education, and income from their previous occupation do not exhibit significant differences
between the two groups, indicating that the level of benefit amounts is roughly similar
between them. Consequently, by examining the survival curves of the liquidity-constrainedgroup, it becomes possible to determine the differential impact of unemployment benefits on
the unemployed individuals facing liquidity constraints.
Figure 2 shows the effect of unemployment benefits on the duration of unemployment for
the unemployed with different liquidity constraints. Figure 2b suggests that unemployment
benefits reduce the job finding rate of the liquidity constrained unemployed. And higher
8unemployment benefits exhibit lower hazard rates. For example, after the third monthof unemployment, about 40 percent of the group receiving higher unemployment benefits
remain unemployed, while 30 percent of the group with lower unemployment benefits remain
unemployed. The liquidity constrained unemployed are more sensitive to the amount of thebenefit. In contrast, the job finding rate of the group in Figure 2a, where liquidity is not
restricted, is not significantly sensitive to unemployment benefits. In this case, the fact thathigher benefit amounts do not lead to greater unemployment suggests that unemployment
benefits do not pose a significant moral hazard problem in the liquidity unconstrained group.

3.3

Estimation Results and Liquidity Effects

Taking into account censored data in the analysis, I estimated regression 2 to examine theeffect of unemployment benefits on the hazard rate of exiting unemployment across pooled
samples. The estimation results are presented in Table 2, where it is observed that a 10%
increase in unemployment benefits leads to a decrease of about 2.8% in the hazard rate
within the sample6 .
To assess the robustness of the results, this paper examines the effect of unemployment
benefits on the duration of unemployment under different Baseline fit regression models.
The results, presented in Columns 2 through 5 in Table 2, demonstrate consistent estimates.
The first column shows the semi parametric estimates of Eq.2, while the results of theparameter estimates are shown in columns 2 through 5. It shows that regardless of whether
the distribution is assumed to be consistent with a weibull or some other form of distribution,
the estimates are close to those of the semi parametric model, ranging from about 0.27 to
0.28.
Furthermore, Table 3 presents the elasticity estimate of the hazard rate with respect tounemployment benefits, adjusted for individual characteristics of the unemployed, at ap6

This finding aligns with the outcomes estimated by Sasaki et al. [2013] using administrative data, albeit
indicating a lower elasticity of the hazard rate concerning the benefit level compared to observations in othercountries, such as the United States (Schmieder and Von Wachter [2016]).

9

proximately 0.29, with a standard error of 0.02. The control variables reveal several points
of interest. Notably, individuals with a partner have a lower likelihood of exiting unemployment, approximately 20% less than their single counterparts. This finding could suggest thatsingle unemployed individuals are more responsive to unemployment benefits, as those withpartners might be less vulnerable to the financial pressures of unemployment. Additionally,
the data indicates a similar trend among unemployed homeowners. Those owning property
have a 4% lower exit hazard from unemployment compared to renters. While there is no
direct evidence linking property ownership to individual liquidity, referencing Chetty [2008]
we can infer that the potential liquidity constraints faced by unemployed renters—stemmingfrom additional financial burdens during unemployment—may elevate their urgency to exit
unemployment. This dynamic potentially contributes to a higher unemployment exit hazard
for renters.
To corroborate the insights illustrated in Figures 2, the entire dataset was partitioned
into subgroups according to liquidity constraints. This stratification seeks to mitigate theimpact of potential confounding variables, thereby enabling a more nuanced analysis of how
liquidity interacts with the relationship between unemployment benefits and the duration ofunemployment.
First I divided the liquidity subgroups based on whether income or savings met consumption in the past year. Column 1 of Table 4 shows the estimation of Equation 2 for theliquidity-constrained unemployed. The results show that a 10% increase in unemployment
benefits leads to a 4.7%7 reduction in the probability of exiting unemployment in the presence of liquidity constraints, a value that is much higher than the overall average shown in
Table 3. In contrast, in Column 2 of Table 4, unemployment benefits have a significantly
lower impact on the unemployed with unconstrained liquidity. This is consistent with myhypothesis that the effects of unemployment benefits are heterogeneous for the unemployedwith different liquidity statuses.
7

The 4% estimate of the liquidity effect here in this paper is highly consistent with the results obtainedby Landais [2015] using a kink regression.

10

I consider new liquidity subgroups based on property ownership status to further scrutinize the robustness of the results. Columns 3 and 4 of Table 4 present the impact of
unemployment benefits on the duration of unemployment for individuals who owned andrented property, respectively. The findings mirror those discussed earlier, as both Column 1
and Column 3 showcase considerably higher elasticity estimates compared to the group withunconstrained liquidity.

3.4

Estimation Results and COVID-19

Addressing the concern that liquidity-based grouping might introduce issues related to unobserved omitted variables is crucial. For instance, unemployed individuals whose incomes
are insufficient to cover their expenses might have a lower preference for leisure, leading to amore significant substitution effect. To circumvent this, I employ changes in liquidity statescaused by shifts in the exogenous environment to assess the effect of unemployment benefits
on the duration of unemployment.
In this approach, the year 2020 serves as a pivotal demarcation. Numerous studies havesubstantiated the severe liquidity constraints imposed by the COVID-19 pandemic (Bartik
et al. [2020]; Han et al. [2020]). By utilizing this external shock as a natural experiment, it’s
possible to observe variations in unemployment durations attributable to changes in liquidity,
thereby providing a more accurate assessment of the impact of unemployment benefits under
different liquidity conditions. This method helps in isolating the effects of unemployment
benefits from the confounding influences of individual liquidity preferences.
In the context of my hypothesis, households affected by the shock of the COVID-19
pandemic are anticipated to undergo more pronounced depletion of on-hand liquidity andencounter heightened borrowing constraints. Consequently, it is hypothesized that the hazard rate in relation to unemployment benefits will exhibit greater elasticity for households
during the COVID-19 epidemic. Employing an approach akin to equation 2, I proceed toestimate the influence of unemployment benefits on households facing liquidity constraints
11

over time:h (t, i, j) = h0 (t) · exp [β1 log(ubi ) + β2 Liquidity ∗ log(ubi ) + β3 xitj ](3)

Where β1 provides the elasticity of the hazard rate with respect to unemployment insurance
benefits (ubi ) during period j. Liquidity = 1 denotes liquidity constraints, and beta2 corresponds to an estimate of the elasticity of the hazard rate with respect to the ubi at period jof the liquidity-constrained distribution.
The main concern associated with this methodology is that, within the framework of
COVID-19, individuals might undergo extended periods of unemployment influenced byfactors unrelated to alterations in liquidity status, such as Medicare sequestration policies.
This assumption proves invalid for equation 3, as, regardless of their liquidity status, allhouseholds uniformly experience the impact of these policies over the same timeframe. In
essence, for j ≥ 2020, the β2 estimates gauge the elasticity of unemployment duration
concerning benefits for those individuals constrained by liquidity and simultaneously affectedby the shocks induced by COVID-19.
Specification 1 of Table 5 presents the estimate of Equation 3 for the full sample without
controlling for xij . The estimate of β2 is -0.25 for the interaction between liquidity status andunemployment benefits. This finding indicates that unemployment benefits in the same units
significantly decrease the probability of exiting unemployment in the liquidity-constrainedgroup compared to the liquidity-unconstrained individuals.
In Column 2, where I exclude the post-2020 sample, the estimates of the interaction termshow a decrease under the same set of components as in Column 1. Conversely, in Column 3,
which includes data after 2020, the coefficient estimate of the interaction term (β2 ) between
liquidity status and unemployment benefits is -0.29. This higher estimate indicates that the
impact of the same unit of unemployment benefit becomes larger for the liquidity-constrainedgroup among post-2020 individuals. Considering the fact that the post-2020 sample was12affected by the epidemic and faced greater liquidity constraints, the results in Column 3
imply that the influence of unemployment benefits on the duration of unemployment stems
more from the liquidity effect. Columns 4 and 5 report similar estimates of Equation 3,
controlling for individual characteristics.
Contrary to the straightforward classification of liquidity subgroups in the previous section, the application of changes in liquidity resulting from exogenous environmental shifts inthis part of the analysis can more effectively mitigate the impact of endogenous variables.
This approach strengthens the validity of the findings by isolating the effect of external
shocks on liquidity status, thereby providing a clearer view of the relationship between unemployment benefits and unemployment duration under varying liquidity conditions. Simultaneously, echoing the conclusions of most prior research (Catherine et al. [2020]), the resultspresented in Table 5 suggest that unemployment benefits assume a heightened significancein safeguarding the welfare of the unemployed during the COVID-19 crisis.

4

Effect of Shortened UI Benefits Waiting Period

4.1

Estimation Strategy

To facilitate labor mobility from mature industries to growing ones and respond to evolving
work styles, the Japan Senate passed a supplementary resolution during the 2017 ordinary
session of the Diet. This resolution called for discussions on shortening the waiting period
for individuals who voluntarily become unemployed. Initially set at one month, the waiting
period had been extended to three months in 1984 to discourage easy job quitting. Effectivefrom October 1, 2020, a new policy came into effect, reducing the waiting period for voluntarily unemployed individuals from three months to two months (Source: Ministry of Health,Labour and Welfare). While the primary goal of this policy is to promote labor migration,it also provides a quasi-natural experimental setting for this study to examine the liquidity
effect.
13

Unlike traditional policies that lengthen the potential length of benefits, shortening thewaiting period seems to be more effective in removing the interference of moral hazard. Because longer benefits imply that individuals need to extend their search duration accordingly
to obtain additional benefits, this may twist the individual’s marginal incentive to find a newjob. In contrast, a shorter waiting period more effectively addresses the liquidity challenges
faced by the unemployed during the initial period without income. This approach enables
individuals to receive unemployment benefits sooner without necessitating a change in theirjob search strategy.
On the other hand, a shorter waiting period for benefits is less likely to raise moral hazard concerns. Distinguishing the involuntarily unemployed from the voluntarily unemployed,
voluntary unemployment often implies unemployment caused by an individual’s dissatisfaction with his or her current job, and usually this type of unemployment implies that the
unemployed may strategically look for a replacement before voluntarily quitting their job
in order to resist the risk of being unemployed for too long. As can also be seen in Table
3, the dummy variable for voluntary unemployment significantly increases the risk of quitting unemployment. Therefore, shortening the benefit waiting period by just one month isunlikely to incentivize voluntary unemployed of the switching type to change their searchstrategy by burdening the unemployment cost for 2 months. That is, shortening the benefit
waiting period only affects individuals conducting job searches similar to the involuntarily
unemployed without creating additional moral hazard problems.
It is also important to note that although the Government has previously discussed shortening the waiting period for unemployment benefits, the exact timing of implementation has
not been communicated in advance. Consequently, individuals were unable to strategicallyplan their time out of work. So it implies that the voluntarily unemployed in 2021 canexogenously receive an extra month of benefits compared to the unemployed in 2020 under
the same conditions. Estimates of the liquidity effect can be obtained as accurately as possible by verifying the impact of policy-driven changes in cash on hand on the duration of

14

unemployment. When the estimate is negative, it suggests that an increase in cash on handsignificantly reduces the exit hazard of unemployment. A larger effect of cash on hand on
the duration of unemployment indicates a larger liquidity effect, implying a better welfare
effect of unemployment insurance for liquidity-constrained individuals.
To evaluate the impact of the policy, I analyze the differences in unemployment durations
between voluntary and involuntary unemployed individuals before and after the policy’s
implementation. Mirroring the approach in Equation 3, I estimate the Cox proportional
hazards model using the following form:log(hti ) = β1 pi + β2 voluntaryi ∗ pi + β3 xit(4)where h is the hazard rate. The coefficient β2 represents the average treatment effect of areduced waiting period (pi = 1) on the hazard rate of exiting unemployment for voluntarily
unemployed individuals. Here, a dummy variable for voluntary unemployment is constructedbased on individuals’ self-reported reasons for unemployment, as captured in the survey data.
To refine the analysis, this study specifically excludes individuals who are retired from beingclassified under involuntary unemployment. Additionally, those who have left their jobs due
to pregnancy and childbirth are omitted from the voluntary unemployment category. This
exclusion is based on the rationale that these two groups typically have access to alternative
welfare support systems. To simplify the analysis, the focus is narrowed down to only thosereasons for unemployment that make individuals eligible for unemployment benefits.

4.2

Results

Table 6 presents the estimates derived from Equation 4. In the first column, without adjusting for individual characteristics, the coefficient for voluntarily unemployed individuals is
0.16, aligning with the coefficient reported in Table 3. This reflects a higher exit probability
from unemployment for the voluntarily unemployed, potentially due to their dissatisfaction

15

with their current job or having sufficient time to secure a better alternative.
The coefficient estimate for the interaction term under this specification is -0.1925 witha p-value of 0.091, indicating a 19% reduction in the unemployment exit hazard for thevoluntarily unemployed affected by the policy. This suggests that the early receipt of unemployment benefits, by increasing the cash at hand for individuals, potentially prolongs
unemployment duration in this group. It’s important to note that this early receipt of benefits is unconditional, involving a one-time increase in payments equivalent to one month’s
benefit without altering the individual’s job search strategy. This differs from both the increase in unemployment benefits and the extension of benefit duration, which can distort
the marginal returns to unemployment by incentivizing longer job searches for additional
benefits. However, the early receipt of benefits does not require extending unemployment
duration for additional gains, thereby the estimate of the interaction term responds to thestochastic liquidity effect. When controlling for individual characteristics in Column 2, thecoefficient estimate on the interaction term is -0.22, with the results remaining robust.
The final three columns of Table 6 examine policy heterogeneity. In Column 3, the sample
is restricted to individuals with low education levels (less than 16 years of education, includes
two-year vocational schools). The coefficient estimates for the interaction term between the
dummy variable for shortening the benefit waiting period and voluntary unemployment aresubstantially higher compared to the estimates for the full sample, and are associated witha reduction in the reemployment hazard rate of about 26%. This indicates that shortening
the waiting period significantly impacts the welfare of voluntarily unemployed individuals
with lower education levels.
Column 4 provides estimates for the male sample. In contrast to women, male unemployed individuals appear to be more responsive to the policy, a result that may seemcounterintuitive. This could be attributed to the inherently lower hazard rates of females ex-16

iting unemployment8 and their relatively better liquidity status9 , making them less sensitive
to immediate cash availability. While the small sample size of male unemployed individuals may raise concerns about estimate accuracy, the results suggest that the policy has
different welfare effects for men and women. Machikita et al. [2013] analyzing data fromJapan, reached similar conclusions. He concluded that extending unemployment benefits forprime-aged males with longer tenures in their previous occupations would constitute a more
effective welfare approach.
In Column 5, the estimates of Equation 4 are presented for single, voluntary unemployedindividuals. In this specification, receiving unemployment benefits one month early has an
impact approximately 1.94 times greater than the estimate for the full sample, resulting in adecrease of -0.3 in the re-employment hazard rate. This straightforward result highlights thelarger liquidity challenge faced by single individuals compared to those with a partner, whose
household finances equalize the cost of unemployment. It emphasizes that unemployment
benefits prolong the job search for single individuals through liquidity effects.

4.3

Robustness Checks and Falsification Tests

The variation in the duration of unemployment between voluntarily and involuntarily unemployed may not be solely influenced by policy factors in 2021; instead, it could be determinedby other unobservable factors. To address this concern, I introduce a random disaggregationof the classifications of voluntarily unemployed individuals, without consideration for the
specific reasons for their unemployment. The policy of reducing the waiting period for benefits is effective only for those voluntarily unemployed. However, since the unseen time factor
impacts all individuals, randomly disrupting the unemployed categories provides a level ofconfidence in the results.
Following the incorporation of a dummy variable for artificial voluntary unemployment,
8

In Table 3, the reported estimates indicate a lower unemployment exit hazard for females compared tomales.
9
In my sample, women constitute as much as 66% of the liquidity unconstrained group, which could
suggest a tendency towards higher saving preferences among women.

17

Table 7 showcases the estimates derived from Equation 4. These results indicate that the previously significant interaction between the policy and the voluntary unemployment dummy
variable becomes insignificant. There is also a reversal in the estimate’s sign within thesame timeframe. This reversal highlights that the policy does not have a significant effecton the randomly categorized unemployed during this period. While this analysis does not
fully eliminate the concern regarding omitted variables, it does offer some support for thevalidity of the policy effect estimates outlined in Table 6.
To provide further evidence of the impact of the policy aimed at reducing the waiting
period for benefits on voluntarily unemployed individuals, I utilized data from other years
to conduct robustness tests. By accurately maintaining the categorization of voluntary
unemployment based on the reasons for unemployment, I employed data from 2019 and2020 to estimate the difference in the hazard of unemployment exit. The estimates derived
from this specification are reported in Column 2 of Table 7.
Firstly, the estimate for the time variable in Column 2 is -0.19, with a standard error of
0.07. This represents a significant 19% decrease in the hazard of exiting unemployment in2020 for those involuntarily unemployed (voluntaryi = 0), potentially reflecting the effects
of prolonged unemployment spells due to epidemic shocks. These results do not conflict withthe findings presented in Table 6. The samples in Table 6’s specification are limited to thepost-epidemic shock period, and the external constraints faced by the unemployed, such as
diminished job opportunities and heightened search costs, remain consistent both before andafter the policy’s implementation. More importantly, the estimates of the interaction terms
in specification 2 are found to be non-significant, indicating that voluntary and involuntary
unemployment do not tend to differ significantly over time outside of the policy period.
Overall, the difference in unemployment exit rates between voluntary and involuntary unemployed individuals disappears when introducing either a fabricated category of voluntary
unemployment or an artificial time variable. This result provides evidence that shortening
the benefit waiting period does increase the length of unemployment for the voluntarily

18

unemployed. Furthermore, in conjunction with the analysis in the previous section, thiseffect of cash on hand on the duration of unemployment appears to stem more from liquidity
effects.

5

Discussions

The elasticity of unemployment duration with respect to unemployment benefits for individuals facing liquidity challenges significantly rises, especially when their consumption exceeds
income, or during an recession triggered by the epidemic. Moreover, the voluntary unemployed after the benefit restriction period is shortened receive the equivalent of 0.8 months
of income earlier, which eases the liquidity of the voluntary unemployed in the three months
prior to unemployment. I find that the additional cash from the policy shock lengthens theduration of unemployment for the voluntary unemployed and reduces their reemployment
hazard by 19%. These findings suggest that providing more generous unemployment insurance benefits to the unemployed is likely to extend the duration of unemployment throughliquidity effects.
The results of this study can be used to evaluate the impact of unemployment benefits on
individual welfare, particularly as a crucial policy consideration during economic downturns.
The results suggest the necessity for society to absorb the costs associated with periodsof unemployment, given that the liquidity effect ensures unemployed individuals derive thefull welfare benefit from higher quality job matches (Nekoei and Weber [2017]; Shimer andWerning [2008]). However, this study does not address the optimal trade-off between unemployment insurance and moral hazard risks. According to Landais et al. [2018], this requires
not only a partial equilibrium analysis of labor supply responses to UI but also an observationof equilibrium employment responses (Landais et al. [2010]).

19

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21

Table 1: Descriptive Statistics for full JPSED sample

Mean

Std.Dev

Min

PooledMedianMax

N.Valid

65.00
2.00
2.00
2.00
1.00

40094.00
40094.00
40094.00
40094.00
40094.00

584.40
2.00
1.00
11.00
8328.77

12304.00
40094.00
26095.00
40094.00
26904.00

Pre-layoff
AgeFemaleSpouseChild
Educ. (graduate)

36.82
1.62
1.55
1.61
0.67

13.67
0.49
0.50
0.49
0.47

18.00
1.00
1.00
1.00
0.00

34.00
2.00
2.00
2.00
1.00

During Unemployment
Previous Work Experience (months) 60.46
Training
1.68
Voluntary unemp.
0.71
Unemp. duration
4.20
Indiv. unemp. benefits (daily)
4011.19102.71
0.47
0.45
3.03
1759.12

0.00
1.00
0.00
1.00
2061.00

Liquidity-Unconstraint
Mean
Std.Dev N.Valid

19.33
2.00
1.00
3.00
4268.37

Liquidity-Constraint
Mean Std.Dev N.Valid

Pre-layoff
AgeFemaleSpouseChild
Educ. (graduate)

37.31
1.66
1.49
1.57
0.66

13.10
0.47
0.50
0.50
0.47

27494.00
27494.00
27494.00
27494.00
27494.00

35.76
1.53
1.69
1.69
0.69

14.78
0.50
0.46
0.46
0.46

12600.00
12600.00
12600.00
12600.00
12600.00

0.47
109.030.47
3.22
1777.07

12600.00
3598.00
7523.00
12600.00
7730.00

During Unemployment
Training
1.68
Previous Work Experience (months) 60.31
Voluntary unemp.
0.73
Unemp. duration
3.91
Indiv. unemp. benefits (daily)
4071.29

0.47
99.98
0.44
2.89
1748.2927494.00
8706.00
18572.00
27494.00
19174.00

1.68
60.81
0.67
4.81
3862.10Notes: The sample (JPSED) encompasses all private sector job separations in Japan during the period
from 2016 to 2022. Individual characteristics, including age and educational background, were matchedusing pre-unemployment data. A dummy variable indicating voluntary unemployment was created basedon the reason for unemployment. Liquidity was defined according to the description provided in Section 2,
and subgroups with restricted and unrestricted liquidity were established. These two subgroups exhibit no
significant differences in terms of individual characteristics.

22

Table 2: UI Benefits and Hazard Rate – Pooled Sample

Dependent variable:
Hazard Rate: h(t,i)
(1)

(2)

(3)

(4)

(5)

log(UI Benefit)-0.2723∗∗∗(0.02511)-0.28340∗∗∗(0.02457)-0.2742∗∗∗(0.02404)-0.2883∗∗∗(0.02454)-0.2897∗∗∗(0.02603)

Baseline fit
ModelControls
Iterations
Observations

semi-parametriccox phN
29
12,639weibullcox phN
712,639loglogistic
cox phN
612,639gamma
cox phN
712,639weibullcox phY
512,427

Notes: Table 2 presents the estimated impact of UI Benefits on the unemployment exit hazard
rate in Japan for the period 2016-2022. Parametric models are applied to all columns except thefirst. In the first column, I used Semi-Parametric models and ran the analysis with 10 bootstrap
samples to estimate standard errors. The estimates of the coefficients for the control variables incolumn 5 are shown in Table 3. Standard errors are in parentheses. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01.

23

Table 3: UI Benefits and Hazard Rate – Control Variable

Dependent variable:
Hazard Rate: h(t,i)
(1)

(2)

(3)

(4)

log(UI Benefit)-0.2897∗∗∗(0.02603)-0.2778∗∗∗(0.02919)-0.2804∗∗∗(0.0255)-0.2943∗∗∗(0.02605)Age-0.004906∗∗∗(0.0009751)-0.004643∗∗∗(0.001031)-0.004808∗∗∗(0.0009548)-0.005033∗∗∗(0.0009756)

Male0.1228∗∗∗(0.02355)

0.1061∗∗∗(0.02064)

0.118∗∗∗(0.02302)

0.1276∗∗∗(0.02359)

Spouse-0.2012∗∗∗(0.02269)-0.1848∗∗∗(0.02358)-0.1887∗∗∗(0.02217)-0.2084∗∗∗(0.02272)

Years of Education-0.0003175(0.005804)

0.002345(0.006297)

0.0003606(0.005678)-0.0006864(0.00001)

Homeownership-0.04308∗(0.02222)-0.03479∗(0.03011)-0.0383∗(0.02174)-0.04644∗∗(0.02223)

Voluntary Unemp.

0.0753∗∗∗(0.0251)0.08057∗∗∗(0.02955)

0.07714∗∗∗(0.02453)

0.0761∗∗∗(0.02513)

Baseline fit
Model
Iterations
Observations

weibullcox ph512,427semi-parametriccox ph32
12,427loglogistic
cox ph512,427

gamma
cox ph512,427

Notes: Table 3 presents the estimated impact of UI Benefits on the unemployment exit
hazard rate in Japan for the period 2016-2022. Standard errors are in parentheses. ∗ p<0.1;∗∗ p<0.05; ∗∗∗ p<0.01.

24

Table 4: UI Benefits and Hazard Rate – Liquidity

Dependent variable:
Hazard Rate: h(t,i)
(1)

(2)

(3)

(4)

log(UI Benefit)-0.4712∗∗∗(0.04873)-0.2303∗∗∗(0.02869)-0.3953∗∗∗(0.07804)-0.2683∗∗∗(0.03780)

Baseline fit
Model
Liquidity-Constraint
Iterations
Observations

weibullcox phY
53,226

weibullcox phN
79,413

weibullcox phY
61,313

weibullcox phN
75,192

Notes: Table 4 presents the estimated impact of UI Benefits on the unemployment exit hazard rate in Japan for the period 2016-2022. Contrasting with
Table 2, where the sample is segmented based on liquidity constraints to explore the influence of liquidity on the elasticity of unemployment benefits, thissection presents a more detailed stratification. In this case, columns 1 and 3
represent the liquidity-constrained group. Columns 1 and 2 categorize individuals based on whether their income sufficed for consumption in the past
year. Furthermore, columns 3 and 4 provide additional insight by including
information on assets. Standard errors are in parentheses. ∗ p<0.1; ∗∗ p<0.05;∗∗∗ p<0.01.

25

Table 5: UI Benefits and Hazard Rate – Liquidity
Dependent variable:
Hazard Rate: h(t,i)
(1)

(2)

(3)

(4)

(5)

log(UI Benefit)∗∗∗-0.2294(0.02616)∗∗∗-0.2487(0.03061)∗∗∗-0.1786(0.05110)∗∗∗-0.2789(0.036200)-0.2298∗∗∗(0.05502)

Liquidity-Constraint

1.7660∗∗∗(0.28400)

1.5800∗∗∗(0.32110)1.9600∗∗(0.66410)

1.1360∗(0.610200)

1.1080(0.78220)log(UI Benefit) X Liquidity-0.2571∗∗∗(0.03445)-0.2299∗∗∗(0.03902)-0.29250∗∗∗(0.08013)-0.1799∗∗(0.073890)-0.1972∗∗(0.09426)

Baseline fit
Model
Sample / Year
Controls
Iterations
Observations

weibullcox ph[2015,2021]
N
6
12,639weibullcox ph[2015,2019]
N
7
9,057

weibullcox ph[2020,2021]
N
6
3,582

weibullcox ph[2015,2019]
Y
7
8,894

weibullcox ph[2020,2021]
Y
7
3,533

Notes: Table 5 presents the effect of unemployment insurance benefits on the hazard rate of unemployment
exit in Japan across different annual periods within the full sample. In contrast to Table 4, this analysis
employs an interaction term between the liquidity-constrained dummy variable and the unemployment benefits portfolio, instead of segmenting into liquidity subgroups. Standard errors are in parentheses. ∗ p<0.1;∗∗ p<0.05; ∗∗∗ p<0.01.

26

Table 6: UI Benefits and Hazard Rate - Limitation Period
Dependent variable:
Hazard Rate: h(t,i)
(1)

(2)

(3)

(4)

(5)

P

∗∗

0.1932(0.07878)∗∗∗

0.2238(0.08026)∗∗∗

0.3111(0.09433)∗

0.2353(0.1248)

0.3995∗∗∗(0.10180)

Voluntary Unemp.

0.09118(0.06145)

0.1005(0.06317)

0.113(0.07379)

0.2142∗∗(0.0776)0.1887∗∗(0.08103)

P X Voluntary Unemp.

-0.1925∗∗(0.09100)-0.2216∗∗(0.09255)-0.2693∗∗(0.10910)-0.2869∗∗(0.1461)-0.3749∗∗∗(0.12000)

Baseline fit
Model
Sample / Year
Controls
Educational Duration <16
Male sample only
Without a spouse
Iterations
Observations

weibullcox ph[2020,2021]
N
N
N
N
4
3,553

weibullcox ph[2020,2021]
Y
N
N
N
5
3,511

weibullcox ph[2020,2021]
N
Y
N
N
5
2,432

weibullcox ph[2020,2021]
N
N
Y
N
4
1,310

weibullcox ph[2020,2021]
N
N
N
Y
4
1,839

Notes: Table 6 presents the estimates of Equation 4. Columns 3 through 6 introduce specifications controlling for low education, male, and single individuals, respectively. These specifications are employed toassess the heterogeneous effects of policy shocks. Standard errors are in parentheses. ∗ p<0.1; ∗∗ p<0.05;∗∗∗ p<0.01.

27

Table 7: UI Benefits and Hazard Rate - Limitation Period (Robustness Checks)

Dependent variable:
Hazard Rate: h(t,i)
(1)

(2)

P

0.01085(0.05109)-0.1988∗∗∗(0.07113)

Voluntary Unemp.

-0.06192(0.05312)

0.01995(0.06205)

P X Voluntary Unemp.

0.04863(0.07748)

0.1264(0.08407)

Baseline fit
Model
Sample / Year
Randomized Voluntary Unemp.
Iterations
Observations

weibullcox ph[2020,2021]
Y
4
3,553

weibullcox ph[2019,2020]
N
4
3,772

Notes: Table 7 provides estimates of the robust test results forEquation 4. Standard errors are in parentheses. ∗ p<0.1; ∗∗ p<0.05;∗∗∗ p<0.01.

28

Figure 1: Stratified NPMLE for interval censored data

0.2

0.4

0.6

0.8

UB below mean 0
UB above mean 1

0.0

Estimated Survival

1.0

Effect of UI Benefits on Durations:
Pooled Sample

2

4

6

8

10

Months UnemployedNotes: The figure presents the results obtained from non-parametric estimation of thecore sample in the JPSED data. Figure 1 graphically examines the effect of unemployment
benefits on the duration of unemployment.

29

Figure 2: The Effect of Liquidity Effects in The UI-Duration Link

1.0

Effect of UI Benefits on Durations:
Liquidity Constrained

0.2

0.4

0.6

0.8

UB below mean 0
UB above mean 1

0.0

0.2

0.4

0.6

Estimated Survival

0.8

UB below mean 0
UB above mean 1

0.0

Estimated Survival

1.0

Effect of UI Benefits on Durations:
Liquidity Unconstrained

2

4

6

8

10

2

Months Unemployed

4

6

8

10

Months Unemployed(a) Liquidity-Unconstrained Sample(b) Liquidity-Constrained Sample

Notes: Figure 2 presents the effect of unemployment benefits on unemployed individuals
with different liquidity statuses. Figure 2b visually illustrates the impact of high and low
benefit levels on the duration of unemployment within the subgroup characterized by restricted liquidity. Likewise, Figure 2a presents the results for the subgroup with unrestrictedliquidity.

30