Fiscal Stimulus Effectiveness in Japan: Evidence
from Recent Policies

Tomomi Miyazaki
February 2015
Discussion Paper No.1508

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANFiscal Stimulus Effectiveness in Japan: Evidence from Recent
Policies
Tomomi MiyazakiGraduate School of Economics, Kobe University, 2-1, Rokkodai-cho, Nada-ku, Kobe,
Hyogo 657-8501, Japan
Abstract

This paper examines the effects of Japanese fiscal policy after the 2008 global
financial crisis using a mixed vector autoregression/event study approach. We focus
on the effects of stimulus packages with environmental benefits. The empiricalresults show that a tax break and subsidy program designed to promote the adoptionof eco-friendly cars helped stimulate automobile production, while a similar program
intended to promote the purchase of energy-efficient appliances had no effect onappliance production.

JEL classification: E23, E62, H30
Keywords: Fiscal policy effectiveness in Japan; Environmental stimulus package;
Eco Subsidy

 This is a revised version of Discussion Paper No. 1416.


Tel. & Fax: +81 78 803 6831.

E-mail address: miyazaki@econ.kobe-u.ac.jp

11. Introduction
In the wake of the 2008 global financial crisis, governments in many developed
countries enacted economic stimulus packages. In some countries, including the UnitedStates and Japan, these included environment-related policies. Specifically, theJapanese government subsidized consumer purchases of energy-conserving home
appliances from May 2009 to March 2011, offering refundable “eco points” onenergy-efficient appliance purchases. Additionally, a program of tax breaks andsubsidies for purchasing “eco-friendly” cars (henceforth, “the eco-car program”) was
implemented between April 2009 and February 2011.1 Under this program, if a
consumer bought a new car with emissions below a given threshold or with low fuelconsumption, such as a gas-electric hybrid, the automotive tax would be lowered andthe price subsidized.2
Unlike most other developed countries, the Japanese government had regularly
implemented fiscal stimulus packages including increases in public investment and tax
cuts even before the 2008 global financial crisis. As a result, a considerable amount of
empirical research has examined the effects of fiscal policy in Japan, such as Ihori et al.
1

The effective periods for the tax break and subsidy program was extended after February 2011 but
through a policy approach separate from the stimulus package. We thus do not include the extended
policy period in our empirical approach.
2 For information on the fiscal policy response after the global financial crisis in Japan, please see
Iwaisako (2010) and Asako (2012). For the details of the eco-car program, please see Alhulail and
Takeuchi (2014).

2

(2003), Miyazaki (2009), Miyazaki (2010), Kozuka et al. (2012), Rafiq (2012), Vu (2012),
Fujii et al. (2013), Kameda (2014), and Morita (2015). Most of these studies suggest thatthe “conventional” Japanese fiscal stimulus moves, such as increasing public
investment and cutting taxes, are ineffective. On the other hand, eco-friendly policies
may be more useful policy instruments for economic stabilization if their policy effects
are proved by empirical investigation to substantially stimulate the economy.
However, to our knowledge, there has been no research examining the size andpersistence of fiscal policy in Japan after the global financial crisis that focuses on theseenvironment-related stimulus packages. Rafiq (2012), Vu (2012), and Morita (2015)
examine the size and persistence of Japanese fiscal policy by considering the post-crisis
period through Vector Autoregression (VAR) models. However, these studies do not focus
on the environment-related policies. Alhulail and Takeuchi (2014) examine the effects ofthe eco-car program on sales of ten eco-friendly vehicles in Japan but do not examine
the size and persistence of the policy’s effects.
The purpose of this paper is thus to examine the effects of fiscal policy in Japan after
the 2008 global financial crisis by considering these two environment-related stimulus
measures: subsidies for purchasing energy-efficient home appliances (hereafter the“eco-points program”) and the eco-car program. To do so, we examine the policies’ effects

3

using time series analysis in conjunction with an event study. To capture policy changes,
we construct dummy variables based on the announcement or implementation of thepolicies. This approach allows us to consider not only the size and persistence of eachpolicy change’s impact but also to compare the effects of the different stimulus policies
implemented after the devastating financial crisis.
The remainder of the paper proceeds as follows. Section 2 explains the methodology
and data, including how dummy variables are constructed and used to identify fiscal
shocks. Section 3 then provides the empirical results, reporting that while the eco-car
program had positive and significant effects on the automobile industry, the eco-points
program’s effect on electrical appliance production was insignificant. Section 4
concludes the paper.

2. Methodology
2.1. Constructing the Dummy Variables
In our empirical approach, we use dummy variables to identify fiscal policy shocks
based on official documents. When selecting the dummy variables, we face anidentification problem. If we allow the dummy associated with each major policy change

4to have its own distributed lag effect, we cannot identify the effects of each individual
fiscal policy because the times at which they were announced and/or implemented are
too close together. Therefore, the value of a given policy dummy variable is changed
from 0 to 1 in the month in which the policy was implemented and remains 1
throughout the time period for which the policy was in effect, as in the case of
Blanchard and Perotti (2002) and Miyazaki (2010).
To construct dummy variables capturing the effects of environmental policies, we thus
clarify the start and end dates of the policy’s implementation periods. The Eco-point andEco-car variables thus indicate the implementation periods for the eco-points andeco-car programs, respectively. Eco -Point equals 1 from May 2009 to March 2011 andEco-Car equals 1 from April 2009 to February 2011.

2.2. Outline of VAR Estimation
We include these variables in our empirical models. As the examined policies are
assumed to be targeted at increasing production of specific industries, we focus on theindividual industries most directly impacted by each policy. We estimate two separate
VAR models that include industry-specific policy dummies and production indexes.3

3

We also estimate our VAR model using the index of industrial production, which denotes the
production of all mining and manufacturing industries, instead of y tEl or y tAuto . However, the impulse

5The first (“Case 1”) includes Eco point and uses log y tEl , where ytEl denotes electric
appliance production. The second model (“Case 2”) includes Eco Car and uses log y tAuto ,Autowhere yt

indicates automobile production.

Throughout the analysis, we follow the recent convention seen in the VAR literatureand use levels, rather than the first or second differences, for all series. As Hamilton(1994) argues, a levels specification yields consistent estimates regardless of whether or
not cointegration exists, whereas a difference specification is inconsistent if somevariables are cointegrated. The lag length is set as eight in Case 1 and six in Case 2
based on the likelihood ratio test of Sims (1980).
Now we can set up the structural VAR model AL X t   t , where X t  Di , log y tj  ,AL  A0  A1 L    Ap Lp is a pth-order lag polynomial of the two-by-two coefficientmatrix Ak k  0,1, , p  , and  t   D ,  y j  is vector of serially uncorrelated structuraltdisturbances with a mean zero and a covariance matrix   . Here Di is a dummy
variable specifying the policy change (“Eco Point” or “Eco Car”) and log y t is thejlogarithm of the index of productions ( log y t

ElStructural

disturbancesareassumedidentification procedure implies thator log y t

Auto

to

be

).

orthogonalized,therecursive

A0 in the structural form becomes a lower

response functions are found to be insignificant.

6

triangular matrix, and the ordering in the VAR determines the degree of exogeneity ofthe variables. We treat the policy dummy variable as being the most exogenous, as it
takes policymakers and the legislature over a month to learn of shocks to economic
activity, and it is very difficult to make discretionary adjustments to fiscal policy within
the space of a month. Therefore, there should be no feedback from the current economic
variables to fiscal expansion.4 This justifies our treatment approach.

2.3 Datasets
We estimate the model using monthly data, as in the case of Miyazaki (2009) andKozuka et al. (2012). This is motivated by the short duration of the post-crisis sample
period, making it difficult to set a certain level of lag length when using quarterly data.
El

There are two variables that capture production; y t , an indicator of electric
appliance production (minsei-yo denki kikai in Japanese), and y t

Auto, one for automobile

production (jou-you sha in Japanese).5 They are set equal to 100 in 200. y tEl includes
the production of air conditioners and refrigerators, which were targeted by the
eco-points program.6 These data are downloaded from the homepage of the Ministry of

4This follows the arguments in Blanchard and Perotti (2002) and Miyazaki (2009).
To capture the effects of each policy, we would ideally focus on the production of the items mostaffected by the policies, such as gas-electric hybrid cars, air conditioners, and refrigerators. However,the data provided by METI is not available at such a detailed level.
6 The eco-points program also targeted LCD televisions capable of receiving digital terrestrial
5

7

Economy, Trade and Industry (METI) (http://www.meti.go.jp/statistics/). Both y tEl andy tAuto are seasonally adjusted using the X12-ARIMA method. We take the logarithm fory tEl and y tAuto , and the sample period is from January 1980 to December 2012.7

3. Empirical Results
3.1. Estimation Results
Figures 1a and 1b report the estimation results for Case 1 and Case 2, respectively.
The solid line depicts the estimated response and the dotted lines represent the 95%
confidence intervals based on asymptotic distributions.8
Figure 1a indicates that the effect of the eco-points program is not statisticallysignificant. However, the eco-car program has a positive and significant effect on
automobile production, as shown in Figure 1b. This implies that this program wasbroadcast signals, which are included in the “electric machines for home use” data category (minsei-yo
denshi kikai in Japanese). LCD televisions are the only items in this category targeted by the policy,
whereas y tEl covers a broader set of target items. Further, the Japanese government encouraged
households to purchase this type of television leading up to July 2011, when all of Japan’s TV
programming was switched from analog broadcasting to digital terrestrial broadcasting. If we examine
the effects on electric machines for home use, it is clear that we cannot disentangle the policy effects ofthe eco-points program from the increase in the production of LCD televisions before this transition.
As such, we use y tEl .
7

Another option would be to end the sample period in February 2011 in order to eliminate theinfluence of the Great East Japan Earthquake. When this is done, however, the impulse response
functions are not estimated to be significant.
8 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.

8effective at stimulating production of the automobile industry. However, the magnitudeof the effect is very small, and it becomes insignificant four months after
implementation.

3.2. Alternative Frameworks
We re-run the VAR analysis under alternative specifications. First, we re-estimate themodel by changing the ordering such that X t  log ytj , Di . Second, we control for the realeffective exchange rate ( et ), as foreign events and economic changes can strongly affectproduction in these two industries. In this specification, the ordering in the VAR is

X t  Di , log ytj , log et and the lag lengths are set as five and nine in Cases 1 and 2,

respectively. Finally, we limit the sample period after January 1990 as, following
Christiano (1986) and Cecchetti and Karras (1994), we note a structural change in thedata after this month. The lag length is set as four in both cases.9
The results are reported in Figures 2a to Figures 4b, showing that the results are notchanged from the original specification: while the eco-points program has no positiveeffect on log y t , the eco-car program stimulates production in the automobile industry.
El

The effects on the real effective exchange rate are not estimated to be significant.

9

The lag lengths used for the additional tests in this subsection are also chosen using the likelihood
ratio test of Sims (1980).

94. Conclusion
This study examined the effects of Japanese fiscal policy following the 2008 global
financial crisis. We focused on two environment-related stimulus packages: theeco-points program for purchases of energy-efficient appliances and the eco-car program,including a tax break and subsidy for environmentally friendly vehicles. The impulse
response function estimation results report that while the eco-points program had no
significant effect, the eco-car program had a positive impact on automobile production.
However, this effect was small and soon became insignificant. When evaluating theeffects of Japanese discretionary fiscal policy following the global financial crisis, it
must thus be concluded that even a policy with a positive effect generated very limited
benefits for the economy.
Incidentally, when both packages were implemented, the announcements noted theirtermination dates. The increased automobile production might thus reflect consumers’
incentives to purchase before the end of the eco-car tax program period: such aphenomenon might not be applicable to the case of electrical appliances.
There are some limitations of our analysis. First, we did not examine effects on

10

consumption or investment. In addition, to evaluate the effects more precisely, we
should consider production of individual items, such as refrigerators or hybrid cars.
Furthermore, more recent econometric techniques, such as factor-augmented ortime-varying VAR models, could be employed within our approach. These extensions areleft for future research.

Acknowledgments
The author would like to thank Akihiro Kawase, Yuji Kawano, Masao Tsuri, andPrateek Goorha for their insightful comments and suggestions. The delegates of 12th
international conference of the Japan Economic Policy Association and the 51th annual
meeting of the Public Choice Society are also acknowledged. This work was financially
supported by a Grant-in-Aid for Scientific Research (Young Scientist) and financial aid
from the Zengin Foundation for Studies on Economics and Finance. The usual
disclaimer applies.

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Figure 1a. Impulse response function for the electrical appliance production index, y tEl

Eco point→
.02

.01

0

-.01

-.02
0

10

20

95% CI30

Impulse response function

Note: The solid line indicates the estimated response and the dotted lines represent the 95%

confidence intervals.

15

Figure 1b. Impulse response function for the automobile production index, y tAuto

Eco car→
.06

.04

.02

0

0

10

20

30

Impulse response function

95% CI

Note: The solid line indicates the estimated response and the dotted lines represent the 95%

confidence intervals.

16

Figure 2a. Impulse response function for the electrical appliance production index, y tEl(Variable order changed)

Eco point→
.02

.01

0

-.01

-.02
0

10

20

30

Impulse response function

95% CI

Note: The solid line indicates the estimated response and the dotted lines represent the 95%

confidence intervals.

17

Auto

Figure 2b. Impulse response function for the automobile production index, y t(Variable order changed)

Eco car→
.02

.01

0

-.01

-.02

0

10

20

30

Impulse response function

95% CI

Note: The solid line indicates the estimated response and the dotted lines represent the 95%

confidence intervals.

18

Figure 3a. Impulse response function for the electrical appliance production index ( y tEl )and real effective exchange rate ( e t ).

Eco point→

Eco point→

.01

0

-.01

-.02
0

10

20

30

0

10

20

30

Impulse response function

95% CI

Note: The solid line indicates the estimated response and the dotted lines represent the 95%

confidence intervals.

19

Auto

Figure 3b. Impulse response function for the automobile production index ( y t)

and real effective exchange rate ( e t ).

Eco car→Eco car→

.06

.04

.02

0

-.02
0

10

20

30

0

10

20

30

Impulse response function

95% CI

Note: The solid line indicates the estimated response and the dotted lines represent the 95%

confidence intervals.

20

Figure 4a. Impulse response function for the electrical appliance production index, y tEl(Sample periods limited to post-January 1990)

Eco point→
.02

.01

0

-.01

-.02
0

10

20

30

Impulse response function

95% CI

Note: The solid line indicates the estimated response and the dotted lines represent the 95%

confidence intervals.

21

Auto

Figure 4b. Impulse response function for the automobile production index, y t(Sample periods limited to post-January 1990)

Eco car→
.06

.04

.02

0

-.02
0

10

20

30

Impulse response function

95% CI

Note: The solid line indicates the estimated response and the dotted lines represent the 95%

confidence intervals.

22