Effects of Monetary and Fiscal Policy Interactions on Regional
Employment: Evidence from Japan
Tomomi Miyazaki
Haruo Kondoh
March 2022
Discussion Paper No. 2206

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANEffects of Monetary and Fiscal Policy Interactions on Regional
Employment: Evidence from Japan§
Tomomi Miyazaki§
Haruo KondohªThis study examines the effects of the interaction between unconventional monetary
policy and fiscal stimulus on regional employment in Japan. A mixed vector
autoregressions (VARs)/event study approach is used. Our empirical findings firstshow that whereas employment recovery was salient in western Japan, it was not thecase in Tokyo metropolitan areas, the country’s main economic hub. Second, we
confirm employment recovery on female employees in all regions. However, we do
not observe this on male employees, implying the policy interaction did notnecessarily increase the number of regular workers, which might suppress a wage
hike in the entire country.
JEL classification: E24, E62, E63, R32,

Keywords: monetary-fiscal policy interaction under secular stagnation; regional employment; A
mixed vector autoregression/event study approach§This is a revised version of “Interactions between monetary and fiscal stimulus: Evidence from Japan.” Miyazaki

thanks the University of California, Irvine and the University of Essex for giving him the opportunity to be a visiting
researcher. We would like to acknowledge the following individuals for their insightful comments and suggestions on
the earlier version: Jacob Burgdorf, Nathan Cesneros, Melvyn Coles, Takero Doi, Ran Gu, Masayoshi Hayashi, Bernd
Hayo, Toshihiro Ihori, Ryuta Ray Kato, Stefan Niemann, Hikaru Ogawa, Eiji Okano, Koki Oikawa, Santiago
Oliveros, Michel Serafinelli, Kimiko Sugimoto, and Giannina Vaccaro. We would also like to acknowledge thedelegates of the workshop at the University of Essex and the UC Irvine, the Asia Pacific Economic Association
Conference, the Public Choice Society Conference, and the Singapore Economic Review Conference. This work has
been financially supported by the Japan Society for the Promotion of Science (Grant-in-Aid for Scientific Research#16K0057, #17K03764, and #20K01706), Nomura Foundation, and the Zengin Foundation for Studies on Economics
and Finance. The usual disclaimer applies.
§

Corresponding Author. Graduate School of Economics, Kobe University, 2-1, Rokkodai-cho, Nada-ku, Kobe,
Hyogo 657-8501, Japan E-mail: miyazaki@econ.kobe-u.ac.jpªDepartment of Economics, Seinan Gakuin University.

1The ratio of active job openings to applicants for positions exceeds one in all
47 prefectures and the warm winds of economic recovery are now reaching
Japan’s local areas as well.1

1. Introduction
Since the global financial crisis (GFC), many developed countries have employed both
unconventional monetary policies and economic stimulus packages in order to pull the
economy out of long-term economic downturn so called “secular stagnation.”2 As iswell known, one important objective of monetary policy is to support the goal of
maximum employment. Also, fiscal policies can create jobs or reduce unemployment
through various channels3. Therefore, the interaction between monetary and fiscal
stimuli is assumed as strong ammunition to stimulate employment or to reduce theunemployment rate.4 Meanwhile, each region may react asymmetrically depending on

1

Please see the website: https://japan.kantei.go.jp/98_abe/statement/201801/_00001.html.
Overall, conventional monetary policy is defined as adjustment of short-term policy interest rate. In
contrast, as summarized in Miyao and Okimoto (2020), roughly speaking, unconventional monetary
policy can be defined as the central banks’ various measures that employ unconventional policy
instruments and pursue further monetary accommodation once the policy interest rate has plunged toapproximately zero percent. Our study uses the term conventional or unconventional monetary policy
following these definitions.
3
From theoretical perspective, the New Keynesian model with staggered price and wage setting has beendeveloped like Gali (2010) to examine the relationship between unemployment and the design ofmonetary policy. Indeed, a recent paper by Nakamura et al. (2021) shows that the unemployment falls in
response to monetary policy shocks. With respect to fiscal policy, some theoretical papers like Monacelli
et al. (2010), Campolmi et al. (2011), and Faia et al. (2013) show that government expenditure or hiring
subsidies can decrease unemployment.
4
We define employment recovery as decrease in unemployment rate or increase in employment and both22the different sensitivities of regional economic activities with respect to common
macroeconomic shocks, as indicated by Clark (1998) and Campolieti et al. (2014).
Thus, it is worthwhile to investigate whether the interaction between unconventionalmonetary policy and fiscal stimuli, a common macroeconomic policy shock, hasdifferent effects at the regional level. However, to the best of our knowledge, the effectsof the aforementioned policy interaction on regional employment under long stagnation
periods have yet to be explored.
The objective of our research is to examine the effects of the interaction between
unconventional monetary policy and fiscal stimuli on regional employment under
stagnation in Japan. A study in Japan has favorable characteristics. First, Japan is aharbinger of secular stagnation, as Ito (2014) argues. As a matter of fact, the short-term
interest rate has been at the zero lower bound (ZLB) since the late 1990s. Second,
following this, the Japanese government implemented a large size monetary and fiscal
expansion as a part of the policy package called “Abenomics” in the 2010s.5 Someeconomists attribute the employment recovery to the interaction between
unconventional monetary policy and fiscal stimuli.6 On top of that, as noted earlier,of them.
For more details, please see Section 2.
6
For example, please see a dialogue named “The Japanese Economy in 2017 (1): Is Abenomics
Working?” The link is as follows: https://www.tkfd.or.jp/en/research/detail.php?id=399.
53

former Prime Minister Shinzo Abe, who initiated Abenomics, reiterated that theemployment recovery propagated to local areas. However, there is some criticism on theregional effects of the Abenomics policy. For example, Endo (2016) pointed out that
although Abenomics led to employment recovery in the Tokyo metropolitan areas, it
had not increased employment in rural areas up until 2016, when the government
prioritized the policy interaction as mentioned in Section 2. From these points of view,
the Japanese case is the most suitable for examining the effectiveness of theaforementioned monetary-fiscal policy interaction in increasing regional employment
under secular stagnation.
We focus on both the employment gap and the cyclical unemployment rate because
short-term policy measures affect cyclical components rather than the total employment
or unemployment rates consisting of both cyclical and structural factors. To capture
employment figures, we use the number of employees as an indicator of employment.
We also divide workers into men and women. Overall, the proportion of part-time
female employees is larger than that of their male counterparts; indeed, part-timeworkers make up more than half of the female employees, even in recent years in Japan.
Dividing employees into females and males is also useful for distinguishing thedifferent responses with respect to policy shocks.

4

We estimate a separate VAR for each region, following Owyang and Zubairy (2013)and Hayo and Uhl (2015). By doing this, we address the asymmetric regional-level
responses to macroeconomic policy shocks. When it comes to the data on the number of
employees and unemployment rate, we use the data from the Labor Force Survey
published by the Statistics Bureau, Ministry of Internal Affairs and Communication(MIAC). Although prefectural-level data are also available in the Labor Force Survey,
MIAC warns of the use of the prefectural data because they are less accurate than data
for the entire country or regional level survey.7 As mentioned earlier, the effects of thepolicy interaction as a part of Abenomics seem to differ between the Tokyo
metropolitan area and other rural areas in Japan. The use of regional data makes thecomparison between the Tokyo metropolitan area and other areas more distinct than theuse of prefectural-level data. Following these points, we use the quarterly data for nine
regions, as shown in Section 4.
We examine the policy effects using VAR analysis in conjunction with an event study,
as in the case of Blanchard and Perotti (2001), Miyazaki (2010), and Miyazaki (2016).
Using this approach, we construct dummy variables based on when the policies are

7

Roughly speaking, “prefecture” in Japan is equivalent to “state” in the US. We use the word “region” toindicate a group of prefectures. For details, please see Figure 5.

5announced or implemented. Using these dummies as variables in the VAR estimation,
we can examine the size and persistence of specific stimulus packages.
Our results can be summarized as follows: First, we confirmed the improvement inboth female and male workers in all regions of western Japan. However, this is not thecase in Southern Kanto region (or Tokyo metropolitan areas), a core region of economic
activities in Japan. This implies that when it comes to employment, rural regions reaped
more benefit from the policy interaction than the country’s major economic hub.
Second, we confirm an improvement in the female employment gap and a decrease in
the cyclically-adjusted female unemployment rate in almost all regions thanks to thepolicy interaction. However, this policy interaction is less effective in increasing male
employment than economic stimuli in the past. This implies that employment recovery
in many regions can be attributed to an increase in female employees, most of whom are
part-time workers. On the flipside, this alludes the policy interaction did not necessarily
boost the number of regular workers, which might not lead to wage hikes in all regions.
This is consistent with the phenomenon observed in the entire country. Finally, thequantitative effects of the policy interaction are not necessarily larger than those of pastfiscal stimuli for all regions. This means that the interaction of the aforementioned two
policies is not necessarily a silver bullet for stimulating employment in the short run,6

despite the combination of bulky buying of the Japanese government bonds (JGBs) by
the Bank of Japan (BOJ) and the increase in public investment. We also confirmed theseresults by three robustness checks: adding the yen-dollar exchange rate to the basic
VAR model, adding the female or male labor force participation ratio, and limiting thesample period to being after the first quarter of 1991.
This study is related to three bodies of literature. The first deals with the regional
effects of macroeconomic stabilization policies. These include Garrison and Chang(1979), Mathur and Stein (1980), Carlino and Defina (1998) (1999), Di Giacinto (2003),
Chodorow-Reich et al. (2012), Wilson (2012), Conley and Dupor (2013), Nakamura
and Steinson (2014), Dupor and McCrory (2018), Beraja et al. (2019), Chodorow-Reich(2019), and Furceri et al. (2019). For example, Wilson (2012) and Dupor and McCrory(2018) examine the effects of the American Recovery and Reinvestment Act of 2009 on
local employment and the local labor market. However, while recent studies focus on
the effectiveness of either monetary or fiscal policy, to the best of our knowledge,
surprisingly few recent studies examine the effects of the policy mix between
unconventional monetary policy and fiscal stimuli on regional employment undersecular stagnation. We do this.

7

Second, our study contributes to the research on the fiscal policy effectiveness inJapan. A significant body of research has already been conducted on this topic: Ihori etal. (2003), Miyazaki (2009) (2010), Rafiq (2012), Kato and Miyamoto (2013) (2014),
Brückner and Tuladhar (2014), Kameda (2014), Morita (2015), Miyazaki (2016),
Auerbach and Gorordnichenko (2017), Yoshino and Miyamoto (2017), Miyamoto et al.
(2018), Miyazaki et al. (2018), Funashima and Ohtsuka (2019), Kameda et al. (2021),
Bessho (2021), and Kanazawa (2021), etc. Above all, Miyazaki (2010), Kato andMiyamoto (2013) (2014), and Miyamoto et al. (2018) examine the effects of fiscal
stimuli on employment and unemployment as well. However, to our knowledge, ourstudy is the first to examine the regional effects of “Abenomics” on employment.
Finally, our research is also related to studies examining the effects of fiscal policy on
employment. Recently, there have been a multitude of studies that address this topic:
Pappa (2009), Mayer et al. (2010), Monacelli et al. (2010), Campolmi et al. (2011),
Brückner and Pappa (2012), Faia and Merkl (2013), Owyang and Zubairy (2013), Hayoand Uhl (2015), Dupor and Saif Mehkari (2016), and Atems (2019), etc. In particular,
Pappa (2009), Owyang and Zubairy (2013), Hayo and Uhl (2015), and Atems (2019)
utilize state-level data from the U.S. While most of them address the effects of fiscal
policy on employment without dividing the period into expansion and recession, we

8

address the regional effects of the fiscal stimulus accompanied by expansionary
monetary policy under long-term stagnation. In this regard, we can differentiate
ourselves from the previous studies.
The remainder of this paper proceeds as follows. Section 2 presents a brief explanationof the monetary and fiscal policy interactions under the Abe Cabinet. Section 3
discusses the empirical framework of the study. Section 4 presents the empirical results.
Section 5 concludes the paper.

2. A brief description of “Abenomics”
2.1. An overview of Abenomics
Shortly after his second inauguration on December 26, 2012, then Prime Minister
Shinzo Abe unveiled a policy package called “Abenomics.” This consists of “threepolicy arrows.”8 The first arrow is an aggressive monetary policy, which uses theunconventional measures of monetary policy, and the second is a flexible fiscal policy,
which encompasses both short-term stimulus measures and fiscal consolidation in the8

For some details on the “three policy arrows,” please see the website:
https://www.japan.go.jp/abenomics/_userdata/abenomics/pdf/170508_abenomics.pdf.

9long run.9 The third arrow represents growth-enhancing strategies, including structuralreforms.
As noted earlier, Japan has been in a protracted recession, where conventionalmonetary policy has proven less effective owing to the zero lower bound of short-term
interest rates since the late 1990s. While structural reforms that increase competitionand spur activities in the labor market are precise to accelerate economic growth in thelong run, the reforms under the ZLB environment may have contractionary effects in the
short run without the appropriate macroeconomic stabilization policy.10 The “three
arrows” of Abenomics is consistent with this notion; policy makers in Japan prioritized
the policy interaction over the structural reforms to stimulate the demand side ofmacroeconomy along the lines of Abenomics.11 By way of the policy packages on the
demand side (or the first and second arrows of Abenomics), the Abe Cabinet tried toachieve a 2% inflation target. On top of that, as indicated by Ito (2021), wage hikes
were also expected in parallel with the achievement of the inflation target.

9

We define “flexible fiscal policy” based upon some arguments by relevant ministers and policy makers.
Indeed, Doi (2018) reaffirms that flexible fiscal policy means fiscal consolidation as well as economic
stimulus.
10
For instance, Eggertsson et al. (2014) show structural reforms have short-term contractionary effects inthe short run under the ZLB environment using the open economy dynamic stochastic general equilibrium
model.
11
For this point, please see also Hayakawa (2016). As a matter of fact, the package of these three policies
is often called as the “first phase” of Abenomics in comparison to the packages announced in late 2015,
which laid more emphasis on the structural reforms. Please see also the link:
https://www.rieti.go.jp/en/events/17080101/pdf/k-1_jorgenson.pdf.

10

Indeed, both the government and the central bank initially pursued the measures tospur short-term economic recovery. First, Figure 1 presents the share of JGB holdings
by the BOJ. We highlight this because a large purchases of JGBs by the BOJ is one ofthe principal tools of unconventional monetary policy. As shown in Figure 1, the BOJ’s
holdings of the JGB have skyrocketed since 2013. Second, we show the movement ofpublic investment, which has mainly been used as a measure of fiscal stimulus packages
in Japan.12 Figure 2 depicts the growth rate of capital formation by the general
government (compared to the same quarter of the previous year). The government
increased its investments in a couple of years after the start of the Abe Cabinet, incontrast to the period from FY 2001 to 2012.13

2.2. Abenomics and employment recovery
As argued in Section 1, the interaction between unconventional monetary policy andfiscal stimuli may be useful in attaining employment recovery. Furthermore, as also
indicated in Section 1, Mr. Abe underscored that employment recovery was observed in
all the regions.

12

For more details, please see Miyazaki (2009), Miyazaki (2010), and Miyazaki et al. (2018).
Junichiro Koizumi’s cabinet (2001-2006) initiated public investment cutbacks as part of the “structuralreform of the government,” a fiscal consolidation achieved by decreasing government expenditures andprivatizing public corporations. Further, the Democratic party-led coalition government (2009-2012)
reduced public investment while attempting to increase welfare expenditures.

13

11

However, the reality was that job recovery differed at the regional level. For example,
Figures 3a and 3b show that employment (number of employees) increased between
2012 and 2017, and unemployment rate fell to 2.8 % in the fourth quarter of 2016 from
4.3% in the first quarter of 2013 in Southern Kanto area, which is equivalent to theTokyo metropolitan area. Meanwhile, when it comes to Shikoku, a typical rural regionin Japan, employment did not increase, whereas the unemployment rate decreased.
These are confirmed in Figures 4a and 4b, respectively. These figures indicate that all
regions did not necessarily benefit from Abenomics with respect to employment.
Here we have to consider two things to dig deeper into the effects of the interaction
between unconventional monetary policy and fiscal stimulus on regional employment or
unemployment. First, we should divide figures on employment into two components:
trends and cyclical components. This is why stabilization policy particularly affects
cyclical components. In this regard, when we discuss the effects of the interaction
between unconventional monetary policy and fiscal stimulus, we have to purify thecyclical factor of employment or the unemployment rate. Second, we have to examine
the differing effects on men and women. As stated in Section 1, the proportion of parttime female employees is larger than that of their male counterparts, which may lead todifferent reactions to policy shocks. With this in mind, we proceed to examine the12effects of the interaction between unconventional monetary policy and fiscal stimulus
on regional employment in Japan, not only by focusing on cyclical factors, but also on
the differing impacts on female and male workers.

3. Empirical framework
3.1. Empirical hypothesis
Our hypothesis is to check whether or not the interaction between unconventionalmonetary policy and fiscal stimuli increases employment and decreases unemployment
in the short run. The frame work to confirm Okun (1962)’s law is useful for addressing
the effects on employment figures in the short-run.
Usually, Okun’s (1962) law is examined by the estimating the following equation.14

𝑒! = 𝑎 + 𝑏𝑦! + 𝜀! ,

14

(1)

We follow the notation of Bova et al. (2015).

13

where 𝑒! is the employment gap (the deviation of the logarithm of current employment
from its trend) or the cyclical unemployment rate, 𝑦! is the output gap, and 𝜀! is thedisturbance.

3.2. Constructing dummy variables
In our empirical approach, we use dummy variables to identify fiscal policy shocks
based on official documents. While selecting the dummy variables, we faced an
identification problem. If we allow the dummy associated with each major policychange to have its own distributed lag effect, we cannot identify the effects of eachindividual fiscal policy because the time at which they were announced and/or
implemented are too close. To deal with this, we constructed dummy variables that takethe value of one throughout the time period for which the policy was in effect, as in thecase of Blanchard and Perotti (2002), Miyazaki (2010), and Miyazaki (2016).
To construct dummy variables capturing the effects of the interaction between
unconventional monetary policy and fiscal stimuli, we clarify the start and end dates ofthe policy’s implementation periods as follows. The dummy variable specifying thepolicies take the value of one from 2013 Q1 to 2016 Q4, which can be written as “𝐷"# ”

14

within our sample period (from 1983 Q1 to 2016 Q4).15 The initial period is set as 2013
Q1 because Abenomics was announced on the heels of Prime Minister Abe’s
inauguration, as shown in Section 2.1. By doing so, we captured the duration of themonetary and fiscal policy interaction implemented by the Abe Cabinet.
What is more, we construct other two policy dummy variables to compare the policy
effects with the past policies. One is “𝐷$% ,” which is equal to one from 1992 Q3 to 1996
Q3 and from 1998 Q2 to 2000 Q4. This captures the announcement and implementation
periods of the fiscal stimulus packages in the 1990s. The other dummy is “𝐷%& ”, whichtakes one from 2008 Q4 to 2010 Q2. This captures the stimulus packages implemented
by the coalition government of the Liberal Democratic Party and Komeito after the2008 GFC. The durations for these two dummy variables follow Miyazaki (2010) andMiyazaki (2016), respectively.

3.3. Outline of VAR estimation
We included the aforementioned dummy variables into our empirical models with
employment gap, cyclical unemployment rate, and output gap. We ran VARs for aseparate region, as in the case of Owyang and Zubairy (2013) and Hayo and Uhl (2015).

15

We discuss the reason why we did not include more recent periods in Section 4.

15

The estimating equations of our VAR models for 𝐸'! (the employment gap or cyclicalunemployment of region i in quarter t) are as follows:

*

*

*

𝐸'! = 𝑎% + 𝑎"' + 𝐸'!() + 𝑎,' + + 𝐸'!() + 𝑎#' + 𝑌!())+"*)+" ('*)+"*+𝑎-' + 𝐷$% !() + 𝑎.' + 𝐷%& !() +𝑎/' + 𝐷"# !() + 𝜀'!0 ,
)+"

)+"

(2)

)+"

where 𝐸'! is the employment gap (the deviation of the logarithm of current
employment from its trend) or the cyclical unemployment rate for each region (i) inquarter t, 𝑌! is the output gap of the entire country in period t, and 𝜀'!0 is thedisturbance. 𝑌! is the difference between the logarithm of the current output and its
trend, which is used to address the business cycle of the entire country.16
For 𝐸'! , we also added the indicator of other regions, ∑(' 𝐸'!() . We do so to address
the effects employment in other regions. This follows Owyang and Zubairy (2013).
Meanwhile, as stated by Owyang and Zubairy (2013), this specification suppresses
spatial error dependence. However, they also argue that spatial error dependence would

16

The Cabinet office’s Report on Prefectural Account does not provide the data regarding the regional
GDP and each item of private sector’s demand (private consumption and capital formation, etc.) on aquarterly basis. Although the Ministry of Economy, Trade, and Industry offers quarterly level data on
Industrial Production Index, the regional classification is different from ours. Therefore, we cannot add
regional level output, consumption, and investment.

16

only influence the estimation of contemporaneous effects, such as one region’s output
shocks on the output of other regions. Since we focus on the impulse responses withrespect to nationwide monetary and fiscal policy interactions, suppressing spatial error
dependence does not seriously affect our results. Therefore, we do not use techniquessuch as spatial VAR or global VAR following Owyang and Zubairy (2013) and Hayoand Uhl (2017).17
The impulse response function of each economic variable is calculated using reduced1

1

1

form residuals 𝜀! !" , 𝜀! "# , or 𝜀! $% (or policy shocks). Meanwhile, there should be no
feedback from the current endogenous variables on fiscal expansions, as indicated by
Blanchard and Perotti (2002) and Miyazaki (2009). Thus, when we incorporated thecontemporaneous dummy variables as regressors in Equation (2), an overidentification
restriction test must be done for the contemporaneous dummy variables and eacheconomic variable.18 However, since the p-value of the likelihood ratio (LR) test foroveridentification is 0.00 for all cases, the null hypothesis is rejected at the 1%
significance level, and the model is rejected. Therefore, certain identification strategies
such as structural VAR or Cholesky decomposition are not used.

17

As mentioned in Owyang and Zubairy (2013), this specification assumes that the spatial weights
determining the magnitudes of the coefficients are equal for all regions.
18
For this point, please see also Miyazaki (2010) and (2016). We follow the strategies of the two papers.

17

4. Empirical results
4.1. Data
The sample period is from 1983 Q1 to 2016 Q4. As stated earlier, Prime Minister Abe
had put more emphasis on the third arrow (or growth-enhancing strategies) in the latter
half of its tenure. On top of that, while the BOJ continued its unconventional monetary
policy, the government gradually attempted to shift toward fiscal consolidation after
2016.19 In this regard, it is favorable for us to not include the recent data to examine theeffects of the demand-side policies of Abenomics.
To capture the effects on employment, we use total unemployment ratio (= (𝑁𝑢𝑛𝑏𝑒𝑟𝑜𝑓 𝑢𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑒𝑑)/ (𝑇𝑜𝑡𝑎𝑙 𝑙𝑎𝑏𝑜𝑟 𝑓𝑜𝑟𝑐𝑒), U3) and the employment (= the number ofworkers).20 These data come from the Labor Force Survey by Statistics Bureau, MIAC.
The data on GDP comes from the Cabinet Office, Government of Japan. The regional
data of the Labor Force Survey consists of 10 regions as shown in Figure 5: Hokkaido,
Tohoku, Southern Kanto, Northern Kanto and Koshin, Hokuriku, Tokai, Kansai,

19

For example, please see the link below:
https://www.jsda.or.jp/en/activities/international-events/jss2016/01MrIchikawa_presentation.pdf.
20
Since U3 is available at a regional level on the Labor Force Survey, we have to use the U3 data tocapture unemployment.

18

Chugoku, Shikoku, and Kyushu and Okinawa. Meanwhile, owing to the Great East
Japan Earthquake, MIAC admits there are concerns about preciseness with regard to thedata on Tohoku. Therefore, we make ∑(' 𝐸'!() except Tohoku and run VARs for nine
regions.
We use Hamilton’s (2018) filter to extract the cyclical components of theunemployment rate, potential employment, and underlying GDP. There are several
advantages over the Hodrick and Prescott (1997) filter; for example, Hamilton’s (2018)
filter is robust to seasonal adjustment. Since we have to take several lags while filtering,the initial quarter in our VAR estimation is set as 1986 Q1.

4.2. Estimation results
Throughout the analysis, we follow the recent convention in the VAR literature anduse the levels for all series. As Hamilton (1994) argues, a level specification yields
consistent estimates, regardless of whether or not cointegration exists, whereas adifference specification is inconsistent if some variables are cointegrated. The upper
limit of the lag length is set to four in all cases to maintain sufficient degrees of
freedom, and the lag length is set as one for all cases according to the LR test in Sims
(1980).

19

Figures 6a–14f report the estimation results of impulse response functions with respect
to dummy variables that capture each fiscal stimulus package (𝐷$% , 𝐷%& , and 𝐷"# ) for
the nine regions. The solid line depicts the estimated response, and the dotted lines
represent the one standard error band following Perotti (2007) and Ramey (2011). The
one standard error band is based on asymptotic distributions for all cases.21 Tables 1a–
1d summarize the estimated responses shown in Figures 6a–14f.
The subscripts a, b, and c are used to represent the response functions on the female
employment gap and the female cyclically-adjusted unemployment rate with respect to
𝐷$% , 𝐷%& , or 𝐷"# . Likewise, the subscripts d, e, and f are used for the response
functions on the male employment gap and the male cyclically-adjusted unemployment
rate with respect to 𝐷$% , 𝐷%& , or 𝐷"# .
The results of policy interaction after 2013 can be summarized as follows. First,
improvements in both female and male workers were salient in western Japan. Second,most figures demonstrate that the monetary and fiscal policy interaction after 2013 (𝐷"#in each Figure) improved the figures for female workers. Finally, the quantitative effects

21

Other methods, such as Monte Carlo integration and bootstrap replications, could also be considered to
calculate the confidence intervals of the impulse response functions. However, since there is noestablished method for calculation, we report the results based on asymptotic distributions. For
confirmation, we also calculate the confidence intervals with 500 bootstrap replications, but the results
and implications are not fundamentally changed.

20

of Abenomics are not necessarily larger than those of the fiscal stimuli in the 1990s andafter the GFC. We provided a detailed interpretation as follows.

4.2.1. Comparison among regionsFirst, by comparing the policy effects across regions, non-Tokyo metropolitan areas
reaped larger benefits from the policy interaction with respect to both female and maleworkers. This is especially true for regions in western Japan, such as Kansai, Chugoku,
Shikoku, and Kyushu and Okinawa (as shown in Figures 11a–14f). The Kansai region
might have benefited from the increase in the number of tourists traveling to Kyoto,
which led to the recovery of employment in that region.22 The economic activities
within the Setouchi Industrial Area (Chugoku and Shikoku) may have been revitalized
because there are many factories and subsidiaries of famous auto manufactures andshipbuilding makers. The economic recovery of these regions could be attributed to thedepreciation of the yen, which the unconventional monetary policy mainly
contributed.23 Furthermore, since Kansai, Chugoku, Shikoku, and Kyushu are22

For this point, please see also Nguyen (2020).
This is because the effects of the unconventional monetary policy, which depreciates the yen, dominate
the appreciation of the yen caused by fiscal expansion. Indeed, Shirai (2018) underscores the monetary
policy under Abenomics contributed to the depreciation of the yen. In fact, we also confirmed the policy
interaction depreciated the yen in our robustness check. Although we omit these results for the sake ofbrevity in Section 4.3, please see the online appendix of this paper, which is downloadable from thewebsite: https://sites.google.com/view/tomomisite2/home/research/dpwp.

23

21

geographically close, as can be seen from Figure 5, the recovery of one area may
propagate to other areas. In this respect, the policy interaction depreciated the yen, andrejuvenated not only economic activities but also the employment of Kansai, Chugoku,
and Shikoku, which benefited each other.

4.2.2 Comparing policy effects between female and male employees
Second, we compared the differential effects of Abenomics on female and male
employees. Regarding female workers, we observe a positive response in theemployment gap and a decrease in the cyclically-adjusted unemployment rate in almost
all the regions. We show positive and statistically significant responses to the female
employment gap in Southern Kanto (Figure 7a), Hokuriku (Figure 9a), Tokai (Figure10a), Kansai (Figure 11a), Chugoku (Figure 12a), Shikoku (Figure 13a), and Kyushu
and Okinawa (Figure 14a). Furthermore, negative and statistically significant responses
were observed for the cyclically-adjusted female unemployment rate in Hokkaido(Figure 6a), Hokuriku (Figure 9a), Tokai (Figure 10a), Shikoku (Figure 13a), andKyushu and Okinawa (Figure 14a).
Although we confirmed the employment recovery of male workers in some regions,the effects of the policy interaction are less effective than in the case of female workers

22

in certain regions such as Kansai (Figure 11d), and Kyushu and Okinawa (Figure 14d).
For example, it takes about a year for the male employment gap to become positive in
Kansai. Although negative and significant results for the cyclically-adjusted male
unemployment rate are shown in Hokkaido (Figure 6d), Southern Kanto (Figure 7d),
and Kyushu and Okinawa (Figure 14d), the confidence intervals soon include zero, asshown in Figures 7d and 14d.
It is often said that there has been a steady increase in the number of part-time
employees under Abenomics.24 This can be attributed to the change in the job market
structure in Japan, which has gradually increased the number of part-time workers,
while reducing regular employees. Generally speaking, the proportion of female parttime workers is higher than that of male part-time employees. In this regard, even if thepolicy interaction between the unconventional monetary policy and fiscal stimulus after
2013 contributed to job recovery, our results suggest that this improvement might be
attributed to the increase in female part-time employees in most regions. On the flipside,
this implies that the policy interaction did not substantially boost the number of regular
workers, which might not lead to wage hikes for all regions. This is consistent with theobservation in the entire country, as pointed out by many economists.25

24
25

For example, please see Hoshi and Kashyap (2020) and Lincoln (2020).
For instance, Kawaguchi et al. (2021) point out that the real wage declined by 1.8 % from 2012 to23

In the meantime, unlike the stimulus after 2013, both female and male labor gaps
increased in response to the stimulus in the 1990s and the one after the GFC in somecases such as Hokkaido (Figure 6b, 6c, 6e, and 6f), Northern Kanto, and Tokai (Figures
8b and 8e, 10b and 10e). Furthermore, regarding male cyclically-adjusted
unemployment rates, negative and statistically significant responses were observed insome cases like Hokkaido (Figure 6f), Southern Kanto (Figure 7e), Hokuriku (Figure9e), Shikoku (Figure 13e), and Kyushu and Okinawa (Figure 14e). Especially withrespect to the 1990s, the proportion of male regular employees was larger in the 1990s
and the 2000s than in the 2010s. Therefore, it is quite natural that we observe morecases of employment recovery for male workers in the 1990s.26

4.2.3. Quantitative evaluation of Abenomics and the past economic stimuli
Finally, in comparison to the fiscal stimuli in the 1990s and after the GFC, thequantitative effects of Abenomics are not necessarily large. For example, in any case,the improvement in the employment gap is approximately 2 percentage points at thelargest (for example, Figure 7a, 9a, 10a, 11a, 12a, 12d, 13a, 13d, and 14a on 𝐷"# , and2017, of which 1.4 % is explained by changes in the increase in female workers.
In Appendix 1, we also discuss the reason why cyclically-adjusted unemployment rate is estimated to
be positive and statistically significant to the fiscal stimulus after the GFC (𝐷&' ).

26

24

Figure 6c, 8b, and 12c on 𝐷$% , or 𝐷%& ). For male employees, the size is less than one
percentage point, except for Figures 12d and 13d, regardless of 𝐷$% , 𝐷%& , or 𝐷"# .
From the viewpoint of quantitative evaluation of employment, the interaction between
unconventional monetary policy (which includes large purchases of the JGB by theBOJ) and fiscal stimuli after 2013 did not necessarily have a larger impact than the past
simple economic stimuli without intervention by the monetary authority.

4.3. Robustness check
To check the robustness of the results, we performed the VAR analysis underalternative specifications. First, we included the yen-dollar exchange rate to our VAR
system because the policy interaction between unconventional monetary policy andfiscal stimulus might have led to the depreciation of the yen and, hence, had positive
impacts on the economy. Second, we added the female or male labor force participation
ratio to control for the supply side effects of the labor market. Finally, we limited thesample period to being after the first quarter of 1991, following Christiano (1986) andCecchetti and Karras (1994). We note a structural change in the data after this quarter.
The results are summarized in Tables 2a–2d for comparison with the results of thebasic framework. As our robustness check yielded so many figures, we summarized the25results in these tables by concentrating on the results of the fiscal stimulus after 2013
(𝐷"# ) for brevity.27 Overall, these robustness checks confirm the principal results
reported in Section 4.2.
First, Table 1 shows that we confirm the positive and statistically significant results on
the female labor gap in all regions when we added the yen-dollar exchange rate andlimited the sample period to after the first quarter of 1991. In addition, once wecontrolled for the yen-dollar exchange rate, the responses of female cyclically-adjusted
unemployment rates became statistically significant in all regions. Second, theresponses to the male labor gap were robust in western Japan, regardless of alternative
frameworks. Third, when it comes to the male cyclically-adjusted unemployment rate,
although we confirm negative and statistically significant responses after adding the
yen-dollar exchange rate for all regions, we do not always observe statisticallysignificant results for the other two alternative frameworks for most regions.

27

For the figures of the impulse response functions, please check the online appendix of this paper.

26

5. Conclusion
This study examines policy effectiveness with regard to the interaction between
unconventional monetary policy and fiscal stimuli in Japan. Especially, we focus onregional employment with respect to female and male workers.
We first confirm that whereas the policy interaction contributed to job recovery on
both female and male employment in western Japan including some rural areas like
Chugoku and Shikoku, this was not the case in Southern Kanto region. Although there
is some criticism that the benefit generated from a large size monetary and economic
stimulus does not spread to rural areas, our results impugn this argument.
Second, while the policy interaction contributed to the increase in female employment
and decrease in female unemployment in all regions, this is not the case for maleworkers. As stated in Section 4.2.2, this is different from the results of the policy
responses in the 1990s. Following the fact that the proportion of non-regular employees
is larger in the case of female employees than male employees, the results imply that thejob recovery by the policy interaction might be mainly attributed to the increase infemale non-regular employees. This is also consistent with an evaluation of Abenomics
by some economists such as Lincoln (2020). This might not lead to a pay rise and,hence, curb price hikes in the entire country, both of which are the policy goals of the27Abe Cabinet, as mentioned in Section 2.1. Our results suggest that a change in the
structure of the labor force in Japan might prevent the government from attaining its
policy goals.
Finally, the effects of the policy interaction are not quantitatively larger than the pasteconomic stimuli, which lack intervention by the monetary authority. This means thatthe policy interaction was not necessarily a powerful ammunition to stimulate
employment in the short run, despite its scale and government propaganda.
Our results can also be generalized to other countries. In addition to several developed
countries that have been suffering secular stagnation since the GFC, it is also likely that
Asian countries will face such long-term economic downturns in the near future, as
pointed out by Cohen-Setton et al. eds (2018). Our results have some implications forboth developed and emerging countries. For example, we reveal that the transmission
paths of the policy interaction are related to the industrial structure of each region,
following the results of western Japan. This suggests that even when the government
plans a macro stabilization policy, the characteristics of each region should beconsidered. On top of that, since the quantitative effects may not be large according toour results, policymakers should not have high expectations regarding the effects of thepolicy interaction. Our results reaffirm that monetary and fiscal policies are justifiable28

as short-term measures; however, these policies are not sufficient to assure employment
recovery. This establishes that structural policies would also be indispensable to labormarket resilience.28
This study’s analysis can be fruitfully extended in three ways. First, due to theinaccuracy of the data, we avoided using prefectural-level data from the Labor Force
Survey. However, if more accurate data are available in the future, we will be able toexamine the effects of the policy interaction at the prefectural level. Second, although
we focused on empirical analyses, it is necessary to construct a theoretical model tosupport our results. Finally, more recent econometric techniques, such as factoraugmented or time-varying VAR models, could also be employed in our approach.

Appendix 1. The interpretation on cyclically-adjusted unemployment rate after theGFC
The responses of the cyclically-adjusted unemployment rate with respect to thestimulus packages after the GFC are estimated to be positive and statistically significant
in most regions such as Hokkaido, Southern and Northern Kanto, Hokuriku, Tokai,

28

For this point, please see also Hijzen et al. (2017).

29

Kansai, Chugoku, Shikoku, Kyushu and Okinawa to the stimulus (Figures 7c, 7f, 8c, 8f,
9f, 10c, 10f, 11c, 11f, 13c, 13f, 14c, and 14f). In most regions, unemployment increased
after the fiscal stimulus packages following the GFC. We can interpret the positiveeffects of fiscal stimulus on unemployment based upon Brückner and Pappa (2012):
once the fiscal stimulus is announced or implemented, people expect the economic
recovery. If that is the case, some people who dropped out of the job market would
likely resume their job search. In other words, “discouraged” job seekers return to themarket. As a result, total unemployment increases. This explains the effects of theeconomic stimulus on unemployment after the GFC.

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Table 1a. Summary of the estimation results on response functions(Female employment gap)
Stimulus

1990'safter 2013

stimulus

Hokkaido
Southern Kanto＋

Nouthern Kanto

Stimulus
after the
GFC

＋

+

ー

＋

＋

＋

Hokuriku

＋

＋

Tokai

＋

Kansai＋＋

Chugoku＋＋

Shikoku＋＋ーKyushu＋ー＋＋Note: “+” means that the impulse response function of female employment gap with respect to the dummy
variable capturing the fiscal stimulus after 2013 (𝐷() ), the one in the 1990s (𝐷*& ), and the stimulus after
the global financial crisis (GFC) (𝐷&' ) is estimated to be positive and statistically significant. “−” meansthat the impulse response function is estimated to be negative and statistically significant.

40

Table 1b. Summary of the estimation results on response functions(Cyclically-adjusted female unemployment rate)

Hokkaido

Stimulus

1990'safter 2013

stimulusーStimulus
after theGFC
ーSouthern Kanto

ー

Nouthern Kantoー

Hokuriku

ー

Tokai

ー

＋

＋
＋

Kansai＋

Chugoku
ShikokuーKyushuー＋ー＋

Note: “+” means that the impulse response function of cyclically-adjusted female unemployment rate withrespect to the dummy variable capturing the fiscal stimulus after 2013 (𝐷() ), the one in the 1990s (𝐷*& ),and the stimulus after the global financial crisis (GFC) (𝐷&' ) is estimated to be positive and statisticallysignificant. “−” means that the impulse response function is estimated to be negative and statisticallysignificant.

41

Table 1c. Summary of the estimation results on response functions(Male employment gap)
Stimulus

1990'safter 2013

stimulus

Hokkaido＋

Stimulus
after theGFC＋

Southern Kanto
Nouthern Kanto
Hokuriku

ー

＋

Tokai

ー

Kansai＋

Chugoku＋＋

Shikoku＋＋

Kyushu

＋

＋

＋
＋

ー
ー
＋
ー

Note: “+” means that the impulse response function of male employment gap with respect to the dummy
variable capturing the fiscal stimulus after 2013 (𝐷() ), the one in the 1990s (𝐷*& ), and the stimulus after
the global financial crisis (GFC) (𝐷&' ) is estimated to be positive and statistically significant. “−” meansthat the impulse response function is estimated to be negative and statistically significant.

42

Table 1d. Summary of the estimation results on response functions(Cyclically-adjusted male unemployment rate)

Hokkaido

Stimulus

1990'safter 2013

stimulusーSouthern Kanto

Stimulus
after theGFC
ーー

Nouthern Kanto
Hokuriku

＋

Tokai

＋

Kansai＋＋

Chugokuー＋

Shikoku

ー

＋

ー

＋

KyushuーNote: “+” means that the impulse response function of cyclically-adjusted male unemployment rate withrespect to the dummy variable capturing the fiscal stimulus after 2013 (𝐷() ), the one in the 1990s (𝐷*& ),and the stimulus after the global financial crisis (GFC) (𝐷&' ) is estimated to be positive and statisticallysignificant. “−” means that the impulse response function is estimated to be negative and statisticallysignificant.

43

Table 2a. Summary of the estimation results on the alternative frameworks incomparison to the basic framework (The response functions on female employment
gap with respect to the fiscal stimulus after 2013 (𝐷"# ))With
Basic caseexchangerate

Hokkaido
Southern Kanto

With labor
force

+
＋

Nouthern Kanto

After 1991 Q1
＋

＋

＋

＋

＋

＋

＋

Hokuriku

＋

＋

＋

＋

Tokai

＋

＋

＋

＋

Kansai＋＋＋＋

Chugoku

＋

＋

＋

＋

Shikoku

＋

＋

＋

＋

Kyushu＋＋＋＋Note: “+” means that the impulse response function of female employment gap with respect to the dummy
variable capturing fiscal stimulus after 2013 (𝐷() ) is estimated to be positive and statistically significant.
“Basic case” is the estimation results of our basic framework, “with exchange rate,” which is the caseadding yen-dollar exchange rate to the basic specification, “with labor force” indicates the specification
adding female labor force participation ratio, and “after 1991 Q1” stands for the case that the sample
period is limited after the first quarter of 1991.

44

Table 2b. Summary of the estimation results on the alternative frameworks incomparison to the basic framework (The response functions on cyclically-adjusted
female unemployment rate with respect to the fiscal stimulus after 2013 (𝐷"# ))With
Basic caseexchangerate

HokkaidoーWith labor
force

ー

ー

Southern Kanto

ーー

Nouthern Kantoー

Hokuriku

ー

ー

ー

Tokai

ー

ー

ー

Kansaiー

ChugokuーーAfter 1991 Q1
ーShikoku

ー

ー

ー

ー

Kyushuーーーー

Note: “−” means that the impulse response function of female cyclically-adjusted unemployment rate withrespect to the dummy variable capturing fiscal stimulus after 2013 (𝐷() ) is estimated to be negative andstatistically significant. “Basic case” is the estimation results of our basic framework, “with exchange
rate,” which is the case adding yen-dollar exchange rate to the basic specification, “with labor force”
indicates the specification adding female labor force participation ratio, and “after 1991 Q1” stands forthe case that the sample period is limited after the first quarter of 1991.

45

Table 2c. Summary of the estimation results on the alternative frameworks incomparison to the basic framework (The response functions on male employment gap
with respect to the fiscal stimulus after 2013 (𝐷"# ))With
Basic caseexchangerate

Hokkaido＋

Southern Kanto＋

Nouthern Kantoー

Hokuriku

With labor
force＋

After 1991 Q1
＋
＋

ー
＋

Tokai

ー

ー

ー

Kansai＋＋＋＋

Chugoku

＋

＋

＋

＋

Shikoku

＋

＋

＋

＋

Kyushu＋＋＋＋Note: “+” means that the impulse response function of male employment gap with respect to the dummy
variable capturing fiscal stimulus after 2013 (𝐷() ) is estimated to be positive and statistically significant,and “−” means the impulse response function is estimated to be negative and statistically significant.
“Basic case” is the estimation results of our basic framework, “with exchange rate,” which is the caseadding yen-dollar exchange rate to the basic specification, “with labor force” indicates the specification
adding female labor force participation ratio, and “after 1991 Q1” stands for the case that the sample
period is limited after the first quarter of 1991.

46

Table 2d. Summary of the estimation results on the alternative frameworks incomparison to the basic framework (The response functions on cyclically-adjusted
male unemployment rate with respect to the fiscal stimulus after 2013 (𝐷"# ))With
Basic caseexchangerate

HokkaidoーーSouthern Kanto

ーー

Nouthern Kantoー

Hokuriku

ー

Tokai

ー

Kansaiー

Chugokuー

ShikokuーKyushuーWith laborforceーAfter 1991 Q1
ー

ー

ー

ー

ー

Note: “+” means that the impulse response function of cyclically-adjusted male unemployment withrespect to the dummy variable capturing fiscal stimulus after 2013 (𝐷() ) is estimated to be positive andstatistically significant, and “−” means the impulse response function is estimated to be negative andstatistically significant. “Basic case” is the estimation results of our basic framework, “with exchange
rate,” which is the case adding yen-dollar exchange rate to the basic specification, “with labor force”
indicates the specification adding female labor force participation ratio, and “after 1991 Q1” stands forthe case that the sample period is limited after the first quarter of 1991.

47

-5

2000/ 1- 3.
7- 9.
2001/ 1- 3.
7- 9.
2002/ 1- 3.
7- 9.
2003/ 1- 3.
7- 9.
2004/ 1- 3.
7- 9.
2005/ 1- 3.
7- 9.
2006/ 1- 3.
7- 9.
2007/ 1- 3.
7- 9.
2008/ 1- 3.
7- 9.
2009/ 1- 3.
7- 9.
2010/ 1- 3.
7- 9.
2011/ 1- 3.
7- 9.
2012/ 1- 3.
7- 9.
2013/ 1- 3.
7- 9.
2014/ 1- 3.
7- 9.
2015/ 1- 3.
7- 9.
2000/3/1
2000/12/1
2001/9/1
2002/6/1
2003/3/1
2003/12/1
2004/9/1
2005/6/1
2006/3/1
2006/12/1
2007/9/1
2008/6/1
2009/3/1
2009/12/1
2010/9/1
2011/6/1
2012/3/1
2012/12/1
2013/9/1
2014/6/1
2015/3/1
2015/12/1
2016/9/1
2017/6/1
2018/3/1
2018/12/1
2019/9/1
2020/6/1

Fig 1. The ratio of the Japanese Government Bonds holding by the Bank of Japan
60%

50%

40%

30%

20%

10%

0%

Source: Flow of Funds Accounts, the Bank of JapanFig 2. The growth rate of capital formation by the government in Japan (compared to thesame quarter of the previou year)
15

%

10

5

0

-10

-15

Source: The Cabinet Office, Government of Japan

48

1983/ 1- 3.
10-12.
7- 9.
4- 6.
1986/ 1- 3.
10-12.
7- 9.
4- 6.
1989/ 1- 3.
10-12.
7- 9.
4- 6.
1992/ 1- 3.
10-12.
7- 9.
4- 6.
1995/ 1- 3.
10-12.
7- 9.
4- 6.
1998/ 1- 3.
10-12.
7- 9.
4- 6.
2001/ 1- 3.
10-12.
7- 9.
4- 6.
2004/ 1- 3.
10-12.
7- 9.
4- 6.
2007/ 1- 3.
10-12.
7- 9.
4- 6.
2010/ 1- 3.
10-12.
7- 9.
4- 6.
2013/ 1- 3.
10-12.
7- 9.
4- 6.
2016/ 1- 3.
10-12.
1983/ 1- 3.
10-12.
7- 9.
4- 6.
1986/ 1- 3.
10-12.
7- 9.
4- 6.
1989/ 1- 3.
10-12.
7- 9.
4- 6.
1992/ 1- 3.
10-12.
7- 9.
4- 6.
1995/ 1- 3.
10-12.
7- 9.
4- 6.
1998/ 1- 3.
10-12.
7- 9.
4- 6.
2001/ 1- 3.
10-12.
7- 9.
4- 6.
2004/ 1- 3.
10-12.
7- 9.
4- 6.
2007/ 1- 3.
10-12.
7- 9.
4- 6.
2010/ 1- 3.
10-12.
7- 9.
4- 6.
2013/ 1- 3.
10-12.
7- 9.
4- 6.
2016/ 1- 3.
10-12.

Fig 3a. The logarithm of employment (number of employees) in Southern
Kanto

16.9

16.8

16.7

16.6

16.5

16.4

16.3

16.2

Fig 3b. Unemployment rate in Southern Kanto

%
6

5

4

3

2

1

0

49

1983/ 1- 3.
10-12.
7- 9.
4- 6.
1986/ 1- 3.
10-12.
7- 9.
4- 6.
1989/ 1- 3.
10-12.
7- 9.
4- 6.
1992/ 1- 3.
10-12.
7- 9.
4- 6.
1995/ 1- 3.
10-12.
7- 9.
4- 6.
1998/ 1- 3.
10-12.
7- 9.
4- 6.
2001/ 1- 3.
10-12.
7- 9.
4- 6.
2004/ 1- 3.
10-12.
7- 9.
4- 6.
2007/ 1- 3.
10-12.
7- 9.
4- 6.
2010/ 1- 3.
10-12.
7- 9.
4- 6.
2013/ 1- 3.
10-12.
7- 9.
4- 6.
2016/ 1- 3.
10-12.
1983/ 1- 3.
10-12.
7- 9.
4- 6.
1986/ 1- 3.
10-12.
7- 9.
4- 6.
1989/ 1- 3.
10-12.
7- 9.
4- 6.
1992/ 1- 3.
10-12.
7- 9.
4- 6.
1995/ 1- 3.
10-12.
7- 9.
4- 6.
1998/ 1- 3.
10-12.
7- 9.
4- 6.
2001/ 1- 3.
10-12.
7- 9.
4- 6.
2004/ 1- 3.
10-12.
7- 9.
4- 6.
2007/ 1- 3.
10-12.
7- 9.
4- 6.
2010/ 1- 3.
10-12.
7- 9.
4- 6.
2013/ 1- 3.
10-12.
7- 9.
4- 6.
2016/ 1- 3.
10-12.

Fig 4a. The logarithm of employment (number of employees) in Shikoku
14.6

14.55

14.5

14.45

14.4

14.35

14.3

Fig 4b. Unemployment rate in Shikoku

%

7

6

5

4

3

2

1

0

50

Fig 5. The map of the 10 regions of the Labor Force Survey

Note: The black area is Hokkaido, the white areas are Tohoku, the area of the four prefectures marked in blue isequivalent to Southern Kanto, and the grey areas are Northern Kanto and Koshin (we shorten this to Northern Kanto).
Likewise, the light-green areas are Hokuriku, the prefectures group in yellow is Tokai, the group of prefectures
marked in green is Kinki (we describe as Kansai), the light-blue areas are Chugoku, the purple areas are Shikoku, andthe group of the islands in red is equivalent to Kyushu and Okinawa.

51

Fig 6a. Results of response functions on Hokkaido with respect to the stimulus after
2013 (𝐷"# ) (Female employment gap and cyclically-adjusted unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.03
.02
.01
0
-.01
0

5

10

15

GDP gap
.03
.02
.01
0
-.01
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 6b. Results of response functions on Hokkaido with respect to the stimulus in the
1990s (𝐷$% ) (Female employment gap and cyclically-adjusted unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.02

.01

0

-.01
0

5

10

15

GDP gap
.02

.01

0

-.01
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

52

Fig 6c. Results of response functions on Hokkaido with respect to the stimulus after the
global financial crisis (𝐷%& ) (Female employment gap and cyclically-adjusted
unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.04
.02
0
-.02
-.04
0

5

10

15

GDP gap
.04
.02
0
-.02
-.04
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 6d. Results of response functions on Hokkaido with respect to the stimulus after
2013 (𝐷"# ) (Male employment gap and cyclically-adjusted unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.03
.02
.01
0
-.01
0

5

10

15

Male employment gap
.03
.02
.01
0
-.01
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

53

Fig 6e. Results of response functions on Hokkaido with respect to the stimulus in the
1990s (𝐷$% ) (Male employment gap and cyclically-adjusted unemployment rate,)
GDP gap

Cyclically adjusted male unemployment rate

.015
.01
.005
0
-.005
0

5

10

15

Male employment gap
.015
.01
.005
0
-.005
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 6f. Results of response functions on Hokkaido with respect to the stimulus after the
global financial crisis (𝐷%& ) (Male employment gap and cyclically-adjusted
unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.02

0

-.02

-.04
0

5

10

15

Male employment gap
.02

0

-.02

-.04
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

54

Fig 7a. Results of response functions on Southern Kanto with respect to the stimulus
after 2013 (𝐷"# ) (Female employment gap and cyclically-adjusted unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.04

.02

0

-.02
0

5

10

15

GDP gap
.04

.02

0

-.02
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 7b. Results of response functions on Southern Kanto with respect to the stimulus in
the 1990s (𝐷$% ) (Female employment gap and cyclically-adjusted unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.01
0
-.01
-.02
-.03
0

5

10

15

GDP gap
.01
0
-.01
-.02
-.03
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

55

Fig 7c. Results of response functions on Southern Kanto with respect to the stimulus
after the global financial crisis (𝐷%& ) (Female employment gap and cyclically-adjusted
unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.02

0

-.02

-.04
0

5

10

15

GDP gap
.02

0

-.02

-.04
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 7d. Results of response functions on Southern Kanto with respect to the stimulus
after 2013 (𝐷"# ) (Male employment gap and cyclically-adjusted unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.02

.01

0

-.01
0

5

10

15

Male employment gap
.02

.01

0

-.01
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

56

Fig 7e. Results of response functions on Southern Kanto with respect to the stimulus in
the 1990s (𝐷$% ) (Male employment gap and cyclically-adjusted unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.015
.01
.005
0
-.005
0

5

10

15

Male employment gap
.015
.01
.005
0
-.005
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 7f. Results of response functions on Southern Kanto with respect to the stimulus
after the global financial crisis (𝐷%& ) (Male employment gap and cyclically-adjusted
unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.01

0

-.01

-.02
0

5

10

15

Male employment gap
.01

0

-.01

-.02
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

57

Fig 8a. Results of response functions on Northern Kanto with respect to the stimulus
after 2013 (𝐷"# ) (Female employment gap and cyclically-adjusted unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.03
.02
.01
0
-.01
0

5

10

15

GDP gap
.03
.02
.01
0
-.01
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 8b. Results of response functions on Northern Kanto with respect to the stimulus in
the 1990s (𝐷$% ) (Female employment gap and cyclically-adjusted unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.03
.02
.01
0
-.01
0

5

10

15

GDP gap
.03
.02
.01
0
-.01
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

58

Fig 8c. Results of response functions on Northern Kanto with respect to the stimulus
after the global financial crisis (𝐷%& ) (Female employment gap and cyclically-adjusted
unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.02

0

-.02

-.04
0

5

10

15

GDP gap
.02

0

-.02

-.04
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 8d. Results of response functions on Northern Kanto with respect to the stimulus
after 2013 (𝐷"# ) (Male employment gap and cyclically-adjusted unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.03
.02
.01
0
-.01
0

5

10

15

Male employment gap
.03
.02
.01
0
-.01
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

59

Fig 8e. Results of response functions on Northern Kanto with respect to the stimulus in
the 1990s (𝐷$% ) (Male employment gap and cyclically-adjusted unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.015
.01
.005
0
-.005
0

5

10

15

Male employment gap
.015
.01
.005
0
-.005
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 8f. Results of response functions on Northern Kanto with respect to the stimulus
after the global financial crisis (𝐷%& ) (Male employment gap and cyclically-adjusted
unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.01
0
-.01
-.02
-.03
0

5

10

15

Male employment gap
.01
0
-.01
-.02
-.03
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

60

Fig 9a. Results of response functions on Hokuriku with respect to the stimulus after
2013 (𝐷"# ) (Female employment gap and cyclically-adjusted unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.03
.02
.01
0
-.01
0

5

10

15

GDP gap
.03
.02
.01
0
-.01
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 9b. Results of response functions on Hokuriku with respect to the stimulus in the
1990s (𝐷$% ) (Female employment gap and cyclically-adjusted unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.01

.005

0

-.005
0

5

10

15

GDP gap
.01

.005

0

-.005
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

61

Fig 9c. Results of response functions on Hokuriku with respect to the stimulus after the
global financial crisis (𝐷%& ) (Female employment gap and cyclically-adjusted
unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.02

0

-.02

-.04
0

5

10

15

GDP gap
.02

0

-.02

-.04
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 9d. Results of response functions on Hokuriku with respect to the stimulus after
2013 (𝐷"# ) (Male employment gap and cyclically-adjusted unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.03
.02
.01
0
-.01
0

5

10

15

Male employment gap
.03
.02
.01
0
-.01
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

62

Fig 9e. Results of response functions on Hokuriku with respect to the stimulus in the
1990s (𝐷$% ) (Male employment gap and cyclically-adjusted unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.015
.01
.005
0
-.005
0

5

10

15

Male employment gap
.015
.01
.005
0
-.005
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 9f. Results of response functions on Hokuriku with respect to the stimulus after the
global financial crisis (𝐷%& ) (Male employment gap and cyclically-adjusted
unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.01
0
-.01
-.02
-.03
0

5

10

15

Male employment gap
.01
0
-.01
-.02
-.03
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

63

Fig 10a. Results of response functions on Tokai with respect to the stimulus after 2013
(𝐷"# ) (Female employment gap and cyclically-adjusted unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.03
.02
.01
0
-.01
0

5

10

15

GDP gap
.03
.02
.01
0
-.01
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 10b. Results of response functions on Tokai with respect to the stimulus in the
1990s (𝐷$% ) (Female employment gap and cyclically-adjusted unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.01

.005

0

-.005
0

5

10

15

GDP gap
.01

.005

0

-.005
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

64

Fig 10c. Results of response functions on Tokai with respect to the stimulus after the
global financial crisis (𝐷%& ) (Female employment gap and cyclically-adjusted
unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.01
0
-.01
-.02
-.03
0

5

10

15

GDP gap
.01
0
-.01
-.02
-.03
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 10d. Results of response functions on Tokai with respect to the stimulus after 2013
(𝐷"# ) (Male employment gap and cyclically-adjusted unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.03
.02
.01
0
-.01
0

5

10

15

Male employment gap
.03
.02
.01
0
-.01
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

65

Fig 10e. Results of response functions on Tokai with respect to the stimulus in the
1990s (𝐷$% ) (Male employment gap and cyclically-adjusted unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.015
.01
.005
0
-.005
0

5

10

15

Male employment gap
.015
.01
.005
0
-.005
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 10f. Results of response functions on Tokai with respect to the stimulus after the
global financial crisis (𝐷%& ) (Male employment gap and cyclically-adjusted
unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.01
0
-.01
-.02
-.03
0

5

10

15

Male employment gap
.01
0
-.01
-.02
-.03
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

66

Fig 11a. Results of response functions on Kansai with respect to the stimulus after 2013
(𝐷"# ) (Female employment gap and cyclically-adjusted unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.04

.02

0

-.02
0

5

10

15

GDP gap
.04

.02

0

-.02
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 11b. Results of response functions on Kansai with respect to the stimulus in the
1990s (𝐷$% ) (Female employment gap and cyclically-adjusted unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.01
.005
0
-.005
-.01
0

5

10

15

GDP gap
.01
.005
0
-.005
-.01
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

67

Fig 11c. Results of response functions on Kansai with respect to the stimulus after the
global financial crisis (𝐷%& ) (Female employment gap and cyclically-adjusted
unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.02

0

-.02

-.04
0

5

10

15

GDP gap
.02

0

-.02

-.04
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 11d. Results of response functions on Kansai with respect to the stimulus after 2013
(𝐷"# ) (Male employment gap and cyclically-adjusted unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.03
.02
.01
0
-.01
0

5

10

15

Male employment gap
.03
.02
.01
0
-.01
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

68

Fig 11e. Results of response functions on Kansai with respect to the stimulus in the
1990s (𝐷$% ) (Male employment gap and cyclically-adjusted unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.015
.01
.005
0
-.005
0

5

10

15

Male employment gap
.015
.01
.005
0
-.005
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 11f. Results of response functions on Kansai with respect to the stimulus after the
global financial crisis (𝐷%& ) (Male employment gap and cyclically-adjusted
unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.01
0
-.01
-.02
-.03
0

5

10

15

Male employment gap
.01
0
-.01
-.02
-.03
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

69

Fig 12a. Results of response functions on Chugoku with respect to the stimulus after
2013 (𝐷"# ) (Female employment gap and cyclically-adjusted unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.03
.02
.01
0
-.01
0

5

10

15

GDP gap
.03
.02
.01
0
-.01
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 12b. Results of response functions on Chugoku with respect to the stimulus in the
1990s (𝐷$% ) (Female employment gap and cyclically-adjusted unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.015
.01
.005
0
-.005
0

5

10

15

GDP gap
.015
.01
.005
0
-.005
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

70

Fig 12c. Results of response functions on Chugoku with respect to the stimulus after the
global financial crisis (𝐷%& ) (Female employment gap and cyclically-adjusted
unemployment rate,)
Cyclically adjusted female unemployment rate

Female employment gap

.04

.02

0

-.02
0

5

10

15

GDP gap
.04

.02

0

-.02
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 12d. Results of response functions on Chugoku with respect to the stimulus after
2013 (𝐷"# ) (Male employment gap and cyclically-adjusted unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.03
.02
.01
0
-.01
0

5

10

15

Male employment gap
.03
.02
.01
0
-.01
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

71

Fig 12e. Results of response functions on Chugoku with respect to the stimulus in the
1990s (𝐷$% ) (Male employment gap and cyclically-adjusted unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.03
.02
.01
0
-.01
0

5

10

15

Male employment gap
.03
.02
.01
0
-.01
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 12f. Results of response functions on Chugoku with respect to the stimulus after the
global financial crisis (𝐷%& ) (Male employment gap and cyclically-adjusted
unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.02

0

-.02
0

5

10

15

Male employment gap
.02

0

-.02
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

72

Fig 13a. Results of response functions on Shikoku with respect to the stimulus after
2013 (𝐷"# ) (Female employment gap and cyclically-adjusted unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.04

.02

0

-.02
0

5

10

15

GDP gap
.04

.02

0

-.02
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 13b. Results of response functions on Shikoku with respect to the stimulus in the
1990s (𝐷$% ) (Female employment gap and cyclically-adjusted unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.02

.01

0

-.01
0

5

10

15

GDP gap
.02

.01

0

-.01
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

73

Fig 13c. Results of response functions on Shikoku with respect to the stimulus after the
global financial crisis (𝐷%& ) (Female employment gap and cyclically-adjusted
unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.01
0
-.01
-.02
-.03
0

5

10

15

GDP gap
.01
0
-.01
-.02
-.03
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 13d. Results of response functions on Shikoku with respect to the stimulus after
2013 (𝐷"# ) (Male employment gap and cyclically-adjusted unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.03
.02
.01
0
-.01
0

5

10

15

Male employment gap
.03
.02
.01
0
-.01
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

74

Fig 13e. Results of response functions on Shikoku with respect to the stimulus in the
1990s (𝐷$% ) (Male employment gap and cyclically-adjusted unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.02

.01

0

-.01
0

5

10

15

Male employment gap
.02

.01

0

-.01
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 13f. Results of response functions on Shikoku with respect to the stimulus after the
global financial crisis (𝐷%& ) (Male employment gap and cyclically-adjusted
unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.01
0
-.01
-.02
-.03
0

5

10

15

Male employment gap
.01
0
-.01
-.02
-.03
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

75

Fig 14a. Results of response functions on Kyushu and Okinawa with respect to thestimulus after 2013 (𝐷"# ) (Female employment gap and cyclically-adjusted
unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.04

.02

0

-.02
0

5

10

15

GDP gap
.04

.02

0

-.02
0

5

10

15

68% CI

impulse-response function (irf)

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 14b. Results of response functions on Kyushu and Okinawa with respect to thestimulus in the 1990s (𝐷$% ) (Female employment gap and cyclically-adjusted
unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.02

.01

0

-.01
0

5

10

15

GDP gap
.02

.01

0

-.01
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

76

Fig 14c. Results of response functions on Kyushu and Okinawa with respect to thestimulus after the global financial crisis (𝐷%& ) (Female employment gap and cyclicallyadjusted unemployment rate)
Cyclically adjusted female unemployment rate

Female employment gap

.02

0

-.02

-.04
0

5

10

15

GDP gap
.02

0

-.02

-.04
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 14d. Results of response functions on Kyushu and Okinawa with respect to thestimulus after 2013 (𝐷"# ) (Male employment gap and cyclically-adjusted
unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.03
.02
.01
0
-.01
0

5

10

15

Male employment gap
.03
.02
.01
0
-.01
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

77

Fig 14e. Results of response functions on Kyushu and Okinawa with respect to thestimulus in the 1990s (𝐷$% ) (Male employment gap and cyclically-adjusted
unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.02

.01

0

-.01
0

5

10

15

Male employment gap
.02

.01

0

-.01
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard errorband.

Fig 14f. Results of response functions on Kyushu and Okinawa with respect to thestimulus after the global financial crisis (𝐷%$ ) (Male employment gap and cyclicallyadjusted unemployment rate)
GDP gap

Cyclically adjusted male unemployment rate

.01
0
-.01
-.02
-.03
0

5

10

15

Male employment gap
.01
0
-.01
-.02
-.03
0

5

10

15

68% CI

IRF

Note: The solid line indicates the estimated response and the dotted lines represent the one standard error
band.

78