Expansionary Eﬀect of an Anticipated Fiscal Policy
on Consumption in Japan∗
Hiroshi Morita†
2 November 2011

Abstract
This paper investigates the eﬀect of an anticipated ﬁscal policy on consumption
in Japan. I identify an anticipated increment in public investment by using the excess stock returns on the construction industry and by applying the sign restriction
VAR. The result shows that GDP and consumption respond to a public investment
shock positively. Further, I demonstrate that the empirical facts are consistent
with the New Keynesian model that has a high elasticity of labor supply and alarge share of Non-Ricardians.

Keywords: Fiscal policy, ﬁscal foresight, sign restriction VAR
JEL codes: E62, H30∗

I would like to thank my supervisor Etsuro Shioji for the helpful comments and discussions. I also

acknowledge Naohito Abe, Jun-Hyung Ko, Tuan Khai Vu, Shiba Suzuki, and the participants at theMacroeconomic Lunch Seminar, Hitotsubashi University, for their valuable comments. Finally, I would
like to express gratitude to Hitotsubashi University’s GCOE Program for the grant that made it possible
to complete this study. Of course, all errors are my own.
†
Graduate School of Economics, Hitotsubashi University. E-mail: ed111004@g.hit-u.ac.jp1

1

Introduction

Does a ﬁscal policy stimulate an economic activity? This question is one of the biggest
concerns in Japan that suﬀers from a long depression after the collapse of the “bubble”
economy. The rich literature (e.g., Bayoumi (2001), Kuttner and Posen (2002), Kato
(2003), Ihori and Nakamoto (2005), and Watanabe et al. (2010)) has investigated theeﬀect of the ﬁscal policy in Japan by using VAR analysis.1 Most authors mention thatthe expansionary eﬀect of the ﬁscal policy on consumption and GDP has been reducing
in the recent years. Furthermore, there is a consensus on this result.
However, the previous work by scholars misses the fact that changes in ﬁscal policy
are anticipated. As noted in Blanchard and Perotti (2002) and Ramey (2011), the ﬁscal
policy entails two lags: the decision lag and implementation lag. The former indicates aperiod between the time when a regulation is submitted and the time when it is enacted,
while the latter refers to the period from the enactment of the regulation to the actual
enforcement. Owing to the existence of the implementation lag, although the actualadjustment on the ﬁscal policy has not been done yet, there is a possibility that agentsknow about the change in the ﬁscal policy and react to it immediately. Therefore, theempirical literature in which the increase in government spending is identiﬁed as a surpriseshock may fail to lead to correct results. In the context of this point of view, in this paper,I examine the eﬀect of the ﬁscal policy to take into account the possibility that the ﬁscal
policy is anticipated, which is called ﬁscal foresight. After Ramey (2011) pointed outthat the standard VAR analysis without ﬁscal foresight fails to capture the true eﬀectof the ﬁscal policy, several papers has attempted to estimate the eﬀect of an anticipatedﬁscal policy in the U.S. (e.g., Fisher and Peters (2010), Mertens and Ravn (2010), andTenhofen and Wolﬀ (2011)). As per my knowledge, this is the ﬁrst paper that estimates
1

Fiscal policy includes some types of policies. For example, it refers to the tax cut, or the increment

in government spending. In this paper, I deﬁne ﬁscal policy as an increase in public investment.

2the eﬀect of an anticipated ﬁscal policy in Japan.
Precisely, this paper adopts the approach of Fisher and Peters (2010). Their idea is
as follows. If the ﬁnancial market is eﬀective and agents are forward looking, the asset
prices reﬂect the information that is available at present. Hence, the news about ﬁscal
policy ﬂuctuates the stock price of the company related to the ﬁscal policy. On the basisof this idea, they identify government (military) spending shock as innovations on theexcess stock returns of the large U.S. military contractors. In this paper, I apply thisidentiﬁcation strategy to a relationship between public investment and the constructionindustry in Japan.
However, there is a problem with this method. As Fisher and Peters (2010) also
states, all variations in stock returns are not owing to the news about the ﬁscal policy.
Hence, all innovations in stock returns as an anticipated ﬁscal policy shock lead to wrong
results. In order to overcome this problem, I employ the sign restriction VAR developedby Uhlig (2005). By using this method, it is possible to identify the stock returns shockthat induces ﬂuctuations in public investment.
The main ﬁndings in this paper are as follows. First, as a result of Granger causality
test, the excess stock returns Granger-cause public investment. This implies that it isplausible to regard the variations in the excess stock returns as the leading indicator of
public investment. Second, in the full-sample estimation, it is found that an anticipatedpublic investment shock rises consumption and GDP. In return, labor, the real wage, andinvestment do not indicate a signiﬁcant response to the public investment shock. Third,
in a subsample analysis, the results show that the expansionary eﬀect of the ﬁscal policy
on GDP and consumption has been reducing recently, as reported in the previous studies.
However, the eﬀect is still positive. Therefore, the previous studies might underestimate
the eﬀect of the ﬁscal policy. Finally, it turns out that the results of the empirical analysis
can be replicated in the New Keynesian model with a high labor supply elasticity and a3

large share of Non-Ricardians.
The remainder of this paper is organized as follows. In the subsequent section, I
present the fact that excess stock returns of a construction industry can be regarded
as the leading indicator of public investment. In Section 3, the theoretical model isbuilt to ﬁnd out the feasible sign restrictions imposing VAR model. Section 4 explains
the estimation method of sign restriction VAR. In Section 5, I describe the data andspeciﬁcations of VAR model. Thereafter, the empirical analysis is carried out in Section
6. In Section 7, the feasible parameter values to match the empirical results are consideredin the discussion. The ﬁnal section presents conclusion.

2

Preliminary Analysis

This section presents several facts to support that the excess stock returns on the construction industry is a good leading indicator of public investment. I estimate two variables VAR including excess stock returns and public investment and perform Granger
causality test.2 The VAR model in this section is simply identiﬁed by using Cholesky
decomposition, where the excess stock returns is assumed to be order ﬁrst. Additionally,
the lag length is set to be four.
Figure 1 displays the impulse response functions (IRFs) of both variables to the excess
stock returns shock. The IRFs are normalized so that the response of public investment
at period 4 becomes one. On the basis of the results, it is observed that public investment
rises gradually, although it once falls at period 2, after the excess stock returns shock. In
particular, it increases rapidly from period 2 to period 4. Therefore, it can be consideredthat the excess stock returns is a variable that changes before the change in public
investment.

2

The data is explained in Section 5 and Appendix A.

4

Moreover, Table 1 shows the result of Granger causality test based on the same
VAR model. I conducted the test for diﬀerent lag lengths and speciﬁcations. The null
hypothesis that the excess stock returns does not Granger-cause public investment isrejected in all cases.
Based on the results in this section, I regard the excess stock returns as the leading
indicator of public investment. Thus, by applying this approach, it is possible to analyze
the eﬀect of an anticipated ﬁscal policy.

3

Theoretical ModelIn this section, I build a Gali et al. (2007) type New Keynesian (NK) model to ﬁnd
out sign restrictions. In order to replicate a positive consumption response to a surprisegovernment spending shock as seen in VAR analysis, Gali et al. type (2007) model has thefour prominent characteristics: price stickiness, a rule of thumb consumer, debt ﬁnancing,
and wage union. In addition to these features, this paper incorporates wage stickiness,∆I type investment adjustment cost and news process of government spending shock into
the model.3 4 The following are the details about the model.

3.1

Households

Households are divided into two types: optimizing or Ricardian households that are
denoted by R and have access to capital markets, and the rule of thumb or Non-Ricardian
households that are denoted by N and do not own any assets and just consume theircurrent disposal income in each period. A fraction µ ∈ [0, 1] of the population is NonRicardians, and the remaining population, 1 − µ, is Ricardians.
3

Since several studies that estimate structural parameters on the basis of DSGE model adopt ∆Itype investment adjustment cost, I employ it in order to operate calibration easily.
4
Colciago (2011) has already introduced wage stickiness into the Gali et al. (2007) type model.

5

R
Let cRt (i) and nt (i) represent the real consumption of and hours worked by Ricardians

who belong to type i labor union. Following Colciago (2011), each household provides adiﬀerentiated labor input nt (i) and obtains a nominal wage Wt (i). Ricardians maximize
a lifetime utility
]
1−γ
1+λ
(cR
− 1 (nRt (i))
t (i))
U = E0
β
−
,
1
−
γ
1
+
λ
t=0
∞
∑

[

t

(1)

subject to the budget constraint
R
R
R
k R
R
R
R
Pt cR
t (i) + Pt it (i) + Bt (i) = Wt (i)nt (i) + Pt rt kt−1 (i) + Rt−1 Bt−1 (i) + Dt (i) − Pt τt (i),(2)
and the capital accumulation equationRktR (i) = (1 − δ)kt−1
(i) +

{
( R
)}
it (i)
1−S R
iR
t (i),
it−1 (i)(3)

where capital letters denote a nominal variable. Pt is the aggregate price level, Bt is a riskless one-period bond, and Rt is the gross nominal return on bond. Since the intermediate
goods ﬁrms face a monopolistic competition and make excess proﬁts, Ricardians receiveR
dividends DtR (i). τtR (i) denotes lump-sum taxes paid by Ricardians. iRt (i) and kt (i)respectively denote the real investment and real capital stock. rtk is the real rental rateon the physical capital. Contrary to Gali et al. (2007), I assume that the adjustment
costs are proportional to the rate of change in investment, where S(1) = S 0 (1) = 0, andS 00 (1) > 0.5 This type of adjustment cost is called ∆I adjustment cost in Monacelli andPerotti (2008).6
Conversely, Non-Ricadians simply consume all of their current disposable income. By
denoting the consumption of and hours worked by type i Non-Ricardians as cNt (i) and5
6

In this paper, I deﬁne a parameter of investment adjustment cost as κ ≡ 1/S 00 (1).
Gali et al. (2007) adopts convex adjustment cost in which the costs of adjusting capital are propor-

tional to the investment-capital ratio.

6nN
t (i), they face the following budget constraint in each period:
N
N
Pt cN
t (i) = Wt (i)nt (i) − Pt τt (i),(4)

where τtN (i) denotes lump-sum taxes paid by Non-Ricardians.

3.2

Wage setting

As discussed above, each household provides a diﬀerentiated labor input indexed in i ∈[0, 1] and belongs to the labor union i. A perfectly competitive labor bundling ﬁrm
bundles a diﬀerentiated labor input nt (i) into the eﬀective labor nt by using the following
technology:
[∫ 1
nt =

nt (i)εw −1εw] ε εw−1
w

di

,

(5)

0

where εw is the elasticity of substitution across the diﬀerent types of labor input. As aresult of the labor bundler problem, the demand function for each diﬀerentiated labor
input is expressed as(
nt (i) =Wt (i)
Wt

)−εw
nt ,

for all i,and the aggregate nominal wage, Wt , is equal to] 1−ε1[∫ 1w
1−εw
Wt (i)
di
Wt =
.

(6)

(7)

0

As in Gali et al. (2007) and Colciago (2011), I assume that households are distributed
uniformly across unions. Hence, in each union i, a fraction µ is Non-Ricardians, and 1−µ
is Ricardians.
In each period, a labor union i sets its nominal wage Wt (i) under the Calvo (1983)
type staggered wage setting. A 1 − ρw of a labor union resets the optimal nominal wageWt∗ (i). Thus, the problem for a labor union is
[
]
1−γ
1−γ
∞
∑
−1
cR
− 1 nt+s (i)1+λcNt+s (i)t+s (i)smax Et(βρw ) (1 − µ)
+µ
−
,
1−γ
1−γ
1+λ
s=0
7

(8)

subject to (2), (4), and (6).
In the symmetric equilibrium, the ﬁrst order conditions can be written as
Wt∗ =

εw
εw − 1∑Et ∞(βρw )s n1+λ
t+s
[ s=0
],
∑∞
nt+s
nt+s
s
Et s=0 (βρw ) (1 − µ) Pt+s cR γ + µ Pt+s cN γ
t+s

(9)

t+s

and combining this and (7), the aggregate nominal wage at period t is given by[] 1
1−εw 1−εw
Wt = (1 − ρw )Wt∗ 1−εw + ρw Wt−1.

(10)Then, log-linearization of (9) and (10) around the steady state yields the dynamic equation of the real wage asŵt = Γŵt−1 + ΓβEt ŵt+1 + ΓβEt π̂t+1 − π̂t + κw Γγĉt + κw Γλn̂t ,(11)where a hat denotes the deviation from the steady state, and Γ = ρw /(1 + βρ2w ), andκw = (1 − βρw )(1 − ρw )/ρw .

3.3

Firms

The production sector consists of two types of ﬁrms: the monopolistically competitive
intermediate goods ﬁrms that produce diﬀerentiated intermediate goods and perfectlycompetitive ﬁnal goods ﬁrms that produce single ﬁnal goods by using intermediate goods
as the input. Each intermediate goods ﬁrm j ∈ [0, 1] produces an intermediate goodyt (j), and the production function is given byyt (j) = kt−1 (j)α nt (j)1−α ,(12)where kt−1 (j) and nt (j) respectively denote the capital stock and labor input used by theﬁrm j.
The ﬁnal goods ﬁrm has the following CES technology to produce ﬁnal goods yt :
[∫ 1
yt =

yt (j)
0

8

εp −1
εp

p
] ε ε−1
p

dj

.

(13)

By solving a proﬁt maximization problem of the ﬁnal goods ﬁrm, the demand function
for the intermediate goods is represented as(yt (j) =Pt (j)
Pt

)−εp
yt ,

(14)

and the ﬁnal goods pricing rule is written as
[∫ 1
1−εp

Pt =

Pt (j)

1
] 1−ε

dj

.

(15)

0

3.4

Price setting

Similar to wage unions, the intermediate goods ﬁrms set prices according to the Calvo(1983) mechanism. An intermediate goods ﬁrm j can change its price with the probability
1 − ρp . The optimal price Pt∗ (j) is determined by solving the problem:
[
]
∞
∑
s
∗
max Et(βρp ) Pt (j)yt+s (j) − Pt+s yt+s (j)mct+s ,

(16)

s=0

subject to the demand function (14), and where mct+s denotes the real marginal cost in
the period t + s.
The optimal price Pt∗ and aggregate price law of motion are written in a similar way
as in the case of wage:
Pt∗ =

εp Et
εp − 1∑∞(βρw )s Pt+s yt+s (j)mct+s
s=0∑
,
s
Et ∞
s=0 (βρw ) yt+s (j)

(17)

and
1
[
1−ε ]
Pt = (1 − ρp )Pt∗ 1−εp + ρp Pt−1 p 1−εp .

(18)

The New Keynesian Phillips Curve is obtained by log-linearization of (17) and (18) as
follows:π̂t = βEt π̂t+1 + κp m̂ct ,where κp = (1 − βρp )(1 − ρp )/ρp .
9

(19)

3.5

Fiscal policy and Monetary policy

The government budget constraint is
Pt τt + Bt = Pt gt + Rt−1 Bt−1 ,(20)

where gt denotes the real government spending. I assume a ﬁscal rule of the formτ̂t = φb b̂t−1 + φg ĝt ,

(21)

Bt
−B

where τ̂t ≡ τty−τ , b̂t ≡ Pt y P , and ĝt ≡ gty−g .
In return, the monetary authority is assumed to set the nominal interest rate rt
according to a simple Taylor ruler̂t = φπ πt ,(22)

where a hat denotes the log deviation from the steady state value.

3.6

Aggregation and Market clearing

Aggregate consumption, lump-sum taxes, capital, investment, bond, and dividends are
given byN
ct = (1 − µ)cRt + µct ;kt = (1 − µ)ktR ;bt = (1 − µ)bRt ;τt = (1 − µ)τtR + µτtN ;it = (1 − µ)iRt ;dt = (1 − µ)dRt .

The clearing conditions of factor and goods market are expressed as
∫ 1
nt =

∫ 1
nt (j)dj;

kt =0

kt (j)dj;0

yt = ct + it + gt .

10

3.7

News process

I assume that the dynamics of government spending is expressed asgĝt = ρg ĝt−1 + ²gt + ξt−p
,

(23)

g
where ²gt denotes a surprise government spending shock at the period t, and ξt−pdenotesan anticipated ﬁscal shock that realizes at period t but is announced in the period t − p.
In benchmark calibration, the foresight period p is set to be 3. This is on the basis of theresults in Section 2.

3.8

Calibration results and Sign restrictions

A period is assumed to be a quarter. I set the baseline parameters based on previous
works. The degree of risk aversion γ and an inverse of labor supply substitution λ are
estimated from 1.249 (Sugo and Ueda (2008)) to 1.912 (Iiboshi et al. (2008)) and from
2.077 (Iiboshi et al. (2008)) to 2.149 (Sugo and Ueda (2008)), respectively. Thus, theseparameters are set to be 1.5 and 2, respectively. In the baseline setting, the share of NonRicadrian households µ equals to 0.3 as suggested by the estimates in Hatano (2004) and
Iwata (2008). I take ρp = 0.70 and ρw = 0.55, which are average values of the estimatedresults in the literature. The baseline policy parameters are based on Gali et al. (2007).
The remaining parameters are displayed in Table 2.
In this paper, the impulse response functions (IRFs) are calculated under two parameterizations: a baseline NK model and a frictionless RBC model.7 As noted in Perotti(2008), there is no consensus on the theoretical eﬀect of the ﬁscal policy. Therefore, on
the basis of two polar models, I attempt to ﬁnd out sign restrictions that can be accepted
7

In a frictionless case, I set ρp = 0.01, ρw = 0.01, µ = 0.01, and κ = 100. Under these parameters,

price and wage are almost ﬂexible, and the large part of households are Ricardians. Moreover, aninvestment adjustment cost is ignorable.

11

widely.
Figure 2 shows IRFs of some key variables to an anticipated government spendingg. As noted above, I assume a three-period foresight (p = 3); that is, at period
shock ξt−p

1, everyone knows that the government spending increases at period 4. The solid lines
refer to the IRFs of RBC model, and the dash lines refer to that of NK model.
The IRFs under the RBC model are easy to understand. Even in the period when
the government spending does not change, an anticipated government spending shock
generates the negative wealth eﬀect. Hence, on the impact of news shock, the optimizing
households decrease their consumption and increase their labor supply. As a result, theoutput rises, and the real wage lowers. Investment soar up in the foresight period because
the households attempt to accumulate a capital stock before increasing tax on the basisof the consumption-smoothing motivation.
Conversely, IRFs under the NK model are diﬃcult to interpret. Similar to the RBC
setting, the response of consumption is negative on the impact because of the negative
wealth eﬀect. This eﬀect also increases the labor supply. However, under the staggered
pricing model, a negative response of consumption lowers the aggregate demand and shifts
the labor demand curve to leftward. Therefore, labor and the real wage decline on theimpact. When the government spending shock realizes, an increasing aggregate demandrises the labor demand. Therefore, labor and the real wage show positive responses atperiod 4. The path of output is the same as the one for labor. Since there exists anadjustment cost on investment, a positive response of investment seen in the RBC model
is not observed in the baseline case.
Based on the calibration results and discussions in Section 2, I impose sign restrictions
to the path of excess stock returns and public investment and GDP. Table 3 summarizes
sign restrictions that are adopted in VAR analysis. The period of news shock denotes12t = 1, while the period of the real shock denotes t = 3, 4, 5.8 The sign restrictionsin Table 3 indicate that an anticipated government spending shock aﬀects excess stock
returns positively, but it hardly aﬀects public investment on an impact. Additionally,following the results in Section 2, the restriction that the public investment reaches apeak at the timing of the real shock is also imposed. In addition to the excess stock
returns and public investment, a sign restriction is also imposed on GDP. Because GDP
responds positively in both models when the shock is realized, I adopt this restriction to
identify an anticipated government spending shock.

4

Estimation Methodology

Based on Uhlig (2005), I employ the sign restriction VAR to identify an anticipatedgovernment spending shock. The estimation processes are as follows.
First, I estimate the reduced form VAR model by OLS.
Yt = C(L)Yt−1 + ut ,(24)

where Yt is a vector of endogenous variables, C(L) = C ≡ [C0 , C1 , · · · , Cp ] is a vector
polynomial in the lag operator, and ut is a vector of reduced form residuals with thevariance-covariance matrix denoted by Σ. I deﬁne vt as a vector of structural shocks thatare mutually independent and normalized to be of variance 1, that is, E(vt vt0 ) = I. In
order to identify the shock, I have to specify a matrix A such as ut = Avt . A must be
satisﬁedΣ = E[ut u0t ] = AE[vt vt ]A0 = AA0 .
8(25)

In the theoretical model, I assume that the government spending shock is realized after three quarters

when the news was announced. In reality, the implementation lags vary with each ﬁscal package. Thus,
the period of the real shock is assumed to be t = 3, 4, 5.

13

Second, by using the estimated Ĉ and Σ̂, I take random draws C and Σ from a
Normal-Wishart family. According to Uhlig (2005), Σ−1 follows a Wishart distribution
W (Σ̂−1 /T, T ), with T being the sample size. Further, conditioned on Σ, the coeﬃcient matrix in its column-wise vectorized form, vec(C), follows a Normal distribution,
N (vec(C), Σ ⊗ (X 0 X)−1 ), where X is the data matrix.
Third, in the context of each draw (C, Σ) obtained in the second step, I randomly
generate matrix A such as A = A0 Q, where A0 is a lower triangular matrix that is givenby the Cholesky decomposition of Σ, and Q is an orthogonal matrix obtained by Q-R
decomposing a randomly generated matrix B ∼ N (0, 1).
Finally, the impulse response functions (IRFs) are calculated on the basis of each draw
(C, Σ, A). If IRFs satisfy sign restrictions in Table 3, they are candidates for valid IRFs,
and are reserved; otherwise they are discarded. By repeating the above processes, therange of IRFs that is consistent with the sign restrictions imposed in Table 3 is obtained.
In this paper, the numbers of random draws are 500 for generating C and Σ, and 700 for
generating A.

5

Data and Speciﬁcation

I use quarterly data of the real GDP, private consumption, non-residential investment,
public investment, tax revenue, the real wage, hours worked, and the excess stock returns
on the construction industry for the period 1968Q1-2009Q4. The series, except for excess
stock returns, are seasonally adjusted in per capita, and logarithmized. The ﬁrst ﬁvevariables are downloaded from the SNA database. Further, the series of the real wage
and hours worked are obtained from the Monthly Labor Survey and the Labor Force
Survey, respectively. The data on stock returns is used from Kabushiki Toushi Syuueki
Ritsu 2009 (Rate of Stock Returns 2009). By following Fisher and Peters (2010), I

14

construct excess stock returns by subtracting the returns of the construction industry
from the overall market returns. Moreover, similar to Fisher and Peters (2010), in order
to discern noise and low frequency movements, the accumulated excess stock returns isused in the analysis below. The details of data descriptions are in Appendix A.
The estimated system is ﬁve-variables VAR that includes the excess stock returns,
public investment, tax revenue, and GDP, and an additional variable. Further, it contains
a constant term and a oil shock dummy (1973Q4). The numbers of lags is chosen to befour as suggested by the Akaike information criterion. The benchmark estimation iscarried out in levels. Additionally, the system is estimated in the ﬁrst diﬀerences forrobustness checks.
In addition to the full-sample analysis, I also estimate the VAR model in two subsample. This is because several previous researches (e.g., Watanabe et al. (2010), and Ihori
and Nakamoto (2005)) point out that the eﬀect of the ﬁscal policy in Japan has changed
recently. By following Watanabe et al. (2010), the sample period is divided into pre- andpost-bubble period in this paper. The ﬁrst subsample is 1968Q1-1986Q4, and the second
subsample is 1987Q1-2009Q4.

6

Empirical Results

Figure 3 displays the response of variables to an anticipated public investment shock in
the full sample. The IRFs are normalized so that the responses of public investment are
1% at the peak. The solid lines and shaded areas indicate IRFs and one-standard error
bands, respectively. One can see that the evolutions of the excess stock returns, public
investment, and GDP are as sign restrictions. Here, I focus on the variables and theperiods in which any restriction is not imposed.
As it can be seen in the third row, an anticipated public investment shock has a15persistent positive eﬀect on consumption signiﬁcantly. Furthermore, the peak of consumption occurs at the same time as the peak of public investment. The response of
GDP on impact is also positive in spite of imposing no restrictions. With respect to thelabor variables, the real wage, and hours worked do not respond signiﬁcantly in the shortrun, and only the real wage shows a positive signiﬁcant response in the long run. The
large response of investment as seen in the RBC model is not observed, although it is not
signiﬁcant.
Overall, the estimated results in the full sample indicate that an anticipated public
investment shock increases consumption and GDP signiﬁcantly, while the responses ofthe other variables are almost insigniﬁcant.
Figure 4 shows IRFs in the ﬁrst subsample. As seen in the results of the full sample,
GDP exhibits a positive response during all horizons. Moreover, this positive response of
GDP leads to an increase in the tax revenue in the long run, and it is slightly signiﬁcant.
Consumption also responds positively and persistently although it is not signiﬁcant at
the moment of news shock. In the context of the labor market variables, it seems thatthe path of the real wage is similar to the full-sample estimation. In contrast, the IRFs ofhours worked change dramatically. The hours worked rises signiﬁcantly after the public
investment shock realizes, and it shows a hump-shaped response that is observed in thetheoretical analysis. Unfortunately, the response of investment is also insigniﬁcant in theﬁrst subsample. However, its point estimator displays a positive response to the news
shock, which is diﬀerent from the full-sample result.
Subsequently, I discuss about the second subsample reported in Figure 5. In many
empirical studies, it is said that the eﬀect of the ﬁscal policy in Japan is diminishing during
the Lost Decade or the Lost Two Decades. This fact can be also seen in my analysis. The
response of GDP is no longer signiﬁcant in the whole horizon. The conﬁdence intervals
of GDP contain zero in the ﬁrst two periods and after the eleventh period. Further, the16path of consumption returns to zero more rapidly as compared to the one in the ﬁrst
subsample. However, it observed that the public investment shock still has a positiveeﬀect on consumption even in the second subsample. The evolutions of the real wage
and hours worked are also diﬀerent from the ones in the ﬁrst subsample. The real wage
indicates positive responses to the news shock although it is insigniﬁcant, and it reaches
a peak when the news shock realizes. With regard to hours worked, we cannot say
anything because of the wide conﬁdence intervals in the short run, while in the long run,the response is slightly negative. In return, the investment shows the same dynamics as
seen in the full sample.
These results can be conﬁrmed in robustness checks. Figure 6–8 display IRFs underthe ﬁrst diﬀerences estimations. In this speciﬁcation, an anticipated ﬁscal policy increases
GDP and consumption. The path of investment is also similar to the levels estimation
except being signiﬁcant in the second subsample. However, signiﬁcant responses of thelabor variable are no longer observed.
The above results imply the following facts. As noted in several studies, the eﬀect of
the ﬁscal policy becomes weaker after the collapse of the bubble economy even if the ﬁscal
foresight is taken into account. However, in the post-bubble period, the expansion of the
public investment still stimulates consumption and GDP. Therefore, it can be concluded
that the results in the literature without ﬁscal foresight may underestimate the eﬀect of
the ﬁscal policy.

7

Discussion

As reported in the preceding sections, the empirical results show that an anticipatedpublic investment shock leads to an expansion in consumption. However, the theoreticalmodel in Section 3 does not produce a positive response on consumption. In addition to17

consumption, the evolutions of the other variables are also diﬀerent between theory andempirics. The aim of this section is to ﬁnd out the feasible parameters that are consistent
with the empirical results.
In order to perform the above-mentioned task, I focus on two parameters: the inverseof labor supply elasticity λ and the share of Non-Ricardians µ. The former determines
the slope of the labor supply curve (refer to (11) in Section 3); it becomes looser as
the value of λ becomes smaller. Thus, in the case of λ taking a small value, it can bepredicted that the decline of the real wage becomes small when the negative wealth eﬀect
shifts the labor supply curve rightward. In return, the share of Non-Ricardians aﬀects the
dynamics of the aggregate consumption through the behavior of Non-Ricardians. Since
they only consume their current labor income, the ﬂuctuations in disposable income arereﬂected in the path of their consumption.
Figure 9 depicts IRFs under three parameterizations: a benchmark (solid lines), λ =0.5 (dashed lines), and λ = 0.5, µ = 0.5 (chain lines). The value of λ is calculated by
combining the elasticity of wage with respect to an output of 0.7 that is estimated in
Nishizaki and Nakagawa (2000) with an elasticity of output with respect to hours of 0.3.
The large value of µ is referred to in Campbell and Mankiw (1989).
Consider the case that only λ is low. In this situation, agents change their labor supply
ﬂexibly since the labor elasticity is high. As explained above, the low value of λ reduces
the negative response of the real wage on the impact. At the same time, it is observed thatlabor increases more than the benchmark case when the shock realizes. This is because
a loose inclination of the labor supply curve ampliﬁes the expansionary eﬀect that isbrought by the rightward shift on the labor demand curve at the moment of realizing
the shock. GDP also responds greatly compared to the benchmark case. The expansion
of GDP dampens the negative eﬀect on consumption, although the path of aggregate isstill negative. This implies that in the case where most households are Ricardians, the18negative wealth eﬀect for Ricardians dominates a dynamic path of aggregate consumption.
Consequently, it is found that only a low value of λ does not replicate empirical results
adequately.
Furthermore, I consider the other case where a fraction of Non-Ricardians is also high.
At a glance, the IRFs seem to be matching the empirical results, at least qualitatively.
The aggregate consumption and real wage respond positively as observed in the empiricalanalysis. Furthermore, labor shows a hump-shape response as seen in the ﬁrst subsample
estimation. The intuition of these responses is as follows. At the moment of news
shock, the mechanism working on the variables is similar to the case λ = 0.5. Thus, on
the impact, the variables respond in the same manner as the previous case. However,consumption and the real wage show a large positive response when the shock realizes.
The existence of Non-Ricardian plays key roles in this result. In addition, in this case, theincrement in public investment rises the aggregate demand and subsequently, it induces
the rightward shift on the labor demand curve. This increases the consumption of NonRicardians through a rise in the real wage and labor. Moreover, in order that thisincrement in consumption rises the aggregate demand and subsequently rises the labordemand, consumption and the real wage need to grow further. In contrast to the ﬁrst
case, this eﬀect dominates the path of the aggregate consumption and leads to a positive
response on consumption since the share of Non-Ricardians is high.
On the basis of the exercise in this section, it is found that the combination of a highelasticity of labor supply and a large fraction of Non-Ricardians produces IRFs that is
consistent with the empirical results.

19

8Concluding Remarks

This paper has analyzed the eﬀect of an anticipated ﬁscal policy to regard the excess
stock returns on the construction industry as the leading indicator of public investment.
Additionally, in the estimations, I apply sign restriction VARs, in which signs are drawn
from the Gali et al. (2007) type New Keynesian model.
The main ﬁndings are as follows. First, in the full-sample estimation, an anticipatedﬁscal policy shock that is captured as an increment in the excess stock returns increases
GDP and consumption persistently. Conversely, labor, the real wage, and investment
hardly show signiﬁcant responses. Second, it is observed that the expansionary eﬀectof the ﬁscal policy on GDP and consumption has reduced recently, as pointed out inmany literatures. However, the results in this paper still show a positive eﬀect on thesevariables. This implies that the previous works may underestimate the eﬀect of the ﬁscal
policy owing to the ignorance of ﬁscal foresight. Finally, IRFs obtained in the empiricalanalysis can replicate in the NK model with a high elasticity of labor supply and alarge share of Non-Ricardians. In other words, in the economy where most agents faceborrowing constraints and change their labor supply ﬂexibly, the ﬁscal policy stimulates
GDP and consumption greatly. This consideration might be a clue to solve the question
why the eﬀect of the ﬁscal policy continues to decline in recent years. I shall address thisconcern in further research.

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growth in the 1990s.” Journal of International Economics, 53(2), 241-259.
[2] Blanchard, Oliver, and Roberto Perotti (2002), “ An Empirical Characterization of20

the Dynamics Eﬀects of Changes in Government Spending and Taxes on Output.”
Quarterly Journal of Economics, 117(4), 1329-1368.
[3] Calvo, Guillermo A. (1983), “Staggered prices in a utility-maximizing framework.”
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[4] Campbell, John Y., and Gregory N. Mankiw (1989), “Consumption, Income, andInterest Rates: Reinterpreting the Time Series Evidence.” In NBER Macroeconomics
Annual, edited by O.J. Blanchard and S. Fisher. MIT Press, 185-216.
[5] Colciago, Andrea (2011), “Rule-of-Thumb Consumers Meet Sticky Wages.” Journalof Monetary, Credit and Banking, 43(2)-(3), 325-353.
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[10] Iiboshi, Hirokuni, Shin-Ichi Nishiyama and Toshiaki Watanabe (2008), “An Estimated Dynamic Stochastic General Equilibrium Model of the Japanese Economy: A
Bayesian Analysis.” Mimeo.

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[11] Iwata, Yasuharu (2009), “Fiscal policy in an Estimated DSGE Model of Japanese
Economy: Do Non-Ricardian Households Explain All?” ESRI Discussion Paper
Series No.216.
[12] Kato, Ryo (2008), “Zaisei Seisaku Jyousuu no Nichibei Hikaku : Kouzo VAR to
Seidoteki Jyouhou wo Heiyoushita Approach.” International Department Working
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[14] Monacelli, Tommaso and Roberto Perotti (2008), “ Fiscal Policy, Wealth Eﬀects,
and Markup.” NBER Working Paper Series, No. 14584.
[15] Mertens, Karel and Morten O. Ravn (2010), “Measuring The Impact of Fiscal Policyin The Face of Anticipation: A Structural VAR Approach.” Economic Journal,120(544), 393-413.
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[17] Perotti, Roberto (2008), “In Search of the Transmission Mechanism of Fiscal Policy.”
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[18] Ramey, Valerie A. (2011), “Identifying Government Spending Shocks: It’s All in The
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[19] Sugo, Tomohiro and Kozo Ueda (2008), “Estimating a Dynamic Stochastic General Equilibrium Model for Japan.” Journal of the Japanese and International Economics, 22(4), 476-506.
22

[20] Tenfohen, Jörn and Guntram B. Wolﬀ (2010), “Does anticipation of Government
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[21] Uhlig, Harald (2005), “What are the Eﬀects of Monetary Policy on Output? Resultsfrom an Agnostic Identiﬁcation Procedure.” Journal of Monetary Economics, 52(2),381-419.
[22] Watanabe, Tsutomu, Tomoyoshi Yabu, and Arata Ito (2010), “Estimation of the
Fiscal Multiplier based on Institutional Information. Ihori, T. Fiscal Policy andSocial Insurance. Economic and Social research institute, Cabinet oﬃce, Governmentof Japan (in Japanese).

Appendix A

Data Source

Appendix A.1

GDP, private consumption, non-residential investment, and public investment

With respect to these data, the seasonal adjustment and real value series are downloaded
from the SNA database. 68SNA is used as the data for 1968–1979, while 93SNA is used
after 1980. I extend the data of 93SNA using the growth rate of 68SNA and construct
the data of the whole period. Moreover, by dividing them by total population obtained
from Population Census (Ministry of Internal Aﬀair and Communications), I obtain the
per capita data.

Appendix A.2

Tax revenue

The data source of tax revenue is also the SNA database. With regard to the data for
1980–2009, I sum up the data of “Taxes on production and imports, receivable” and23

“Current taxes on income, wealth ,etc., receivable” that are downloaded from the Income
and Outlay Accounts classiﬁed by the Institutional Sectors on General Government in
93SNA. In the context of the data for 1968–1979, the tax revenue is deﬁned as the sumof “Indirect taxes” and “Direct taxes” in 68SNA. Both of tax data are nominal and nonseasonal adjustment. Thus, I realize them using the GDP deﬂator that is constructed by
the same way as the above variables. Thereafter, I combine the tax data in 68SNA and
93SNA using the growth rate of 68SNA. Finally, the seasonal adjustment is performedby X-12-ARIMA. The tax revenue is also obtained as the per capita data.

Appendix A.3

Real wage

The series of the real wage is obtained from the Monthly Labor Survey (Ministry of
Health, Labor, and Welfare). Since the dataset that has already been seasonally adjusted
is only from 1970, I download the non-seasonal adjusted series and perform a seasonal
adjustment.

Appendix A.4

Hours worked

The data of hours worked is constructed as follows.
Hours worked = Aggregate weekly hours of work (Non-agricultural industries)
÷ Employed person in non-agricultural industries
These data are downloaded from the Labor Force Survey (Ministry of Internal Aﬀairs
and Communications).

24

Appendix A.5

Excess stock returns

Based on Fisher and Peters (2010), I deﬁne the excess stock returns of constructionindustry as
Excess stock returns = Stock returns on construction industry− Stock returns on whole market.
The data is used from Kabushiki Tōshisyueki Ritsu 2009. As noted, I employ the accumulated excess stock returns, which is the sum of the excess stock returns, in theanalysis.

25Appendix B

Table and Figure
Table 1: Granger Causality
lag length468levels

0.000

0.000

0.000

ﬁrst deferences

0.011

0.006

0.012

Notes: H0 : excess stock returns does not Granger cause public investment. The values denote p-value.

Table 2: Calibration Parameters
Parameter

Value

Description

β

0.99

Subjective discount rate

δ

0.06

Depreciation rateα

0.3

Share of capitalγ

1.5

Risk aversion

λ2

Inverse labor supply elasticityµ

0.3

Share of Non-Ricardians

κ

0.15

Investment adjustment cost

ρp

0.70

Calvo parameters on prices

ρw

0.55

Calvo parameters on wages

εp6

Elasticity of substitution in productionεw

6

Elasticity of substitution in labor input

sg

0.2

Steady-state share of government spending

φg

0.1

Elasticity of tax to government spending

φb

0.33

Elasticity of tax to debt

φπ

1.5

Monetary policy response of π

ρg

0.9

persistency of government spending shock

Notes: Calibration parameters are based on Iiboshi et al. (2008), Sugo and Ueda (2008), and Iwata(2009).

26

Table 3: Sign Restrictions
Excess Stock Returns
periodnews shock
AnticipatedFiscal Policy Shock+real shock

Public Investmentnews shock

real shock+−0.001 ∼ 0.001
(max)

GDP
news shock

real shock+

Notes: Sign restrictions imposing VAR analysis. Blank spaces mean that any sign restrictions are notimposed. The period of news shock denotes t = 1 and that of real shock denotes t = 3, 4, 5.

27

Excess Stock Returns
5
4
3
2
1
0
-1
5

10

15

20

15

20

Public Investment
2
1.5
1
0.5
0
-0.5
5

10

Figure 1: Impulse Response Function
Notes: Impulse response functions (IRFs) to an anticipated public investment shock. Solid lines andshaded areas indicate the estimated IRFs and one-standard error band bootstrapped conﬁdence intervals,
respectively.

28

Output

Aggregate Consumption

1

Investment

0

3

-0.2

2

0.5

1
-0.4
0

0
-0.6
-0.5

-1

-0.8
5

10

15

20

-2
5

Labor

10

15

20

Real Wage

5

10

15

20

Government Spending

1

1
0

0.5

-0.2
0

0.5
-0.4

-0.5

-0.6

-1

-0.8
5

10

15

20

0
5

10

15

20

5

10

15

20

Figure 2: Impulse Response Function
Notes: Impulse response functions to an anticipated government spending shock in the theoretical model.
Solid lines are IRFs under the frictionless parameterization; dash lines under the baseline parameterization.

29

Excess Stock Returns

Public Investment
2

10
1
5
0

0

-1
5

10

15

20

5

Tax Revenue

10

15

20

15

20

15

20

GDP

2

1
0.8

0
0.6
0.4
-2
0.2
-4

0
5

10

15

20

5

Consumption

10

Wage

1

1

0.8

0.5

0.6

0

0.4

-0.5

0.2

-1

0

-1.5
5

10

15

20

5

Labor

10

Investment
1.5

0.4

1
0.5

0.2
0
-0.5

0

-1
-0.2

-1.5
5

10

15

20

5

10

15

20

Figure 3: Impulse Response Functions (Levels) : 1968Q1-2009Q4
Notes: Solid lines are IRFs that are 50th quantiles of a valid draw and shaded areas are one-standard
error band that are 16th and 84th quantiles.

30

Excess Stock Returns

Public Investment

8

2

6
4

1

2
0

0

-2
-4

-1
5

10

15

20

5

Tax Revenue

10

15

20

15

20

15

20

GDP

2

1

1

0.8

0

0.6

-1

0.4

-2

0.2

-3

0
5

10

15

20

5

Consumption

10

Wage

0.8
0.5
0.6
0.4

0

0.2
-0.5

0
-0.2
5

10

15

20

5

Labor

10

Investment
2

0.2
1
0.1
0

0

-0.1

-1
5

10

15

20

5

10

15

20

Figure 4: Impulse Response Functions (Levels) : 1968Q1-1986Q4
Notes: Solid lines are IRFs that are 50th quantiles of a valid draw and shaded areas are one-standard
error band that are 16th and 84th quantiles.

31

Excess Stock Returns

Public Investment

15

2

10

1

5

0

0

-1

-5

-2
5

10

15

20

5

Tax Revenue

10

15

20

15

20

15

20

GDP

2
1
1
0

0.5

-1
0
-2
5

10

15

20

5

Consumption

10

Wage

1

0.6

0.8

0.4

0.6

0.2

0.4

0

0.2

-0.2

0

-0.4
5

10

15

20

5

Labor

10

Investment

0.6

1.5

0.4

1
0.5

0.2
0
0
-0.5
-0.2

-1

-0.4

-1.5
5

10

15

20

5

10

15

20

Figure 5: Impulse Response Functions (Levels) : 1987Q1-2009Q4
Notes: Solid lines are IRFs that are 50th quantiles of a valid draw and shaded areas are one-standard
error band that are 16th and 84th quantiles.

32

Excess Stock Returns

Public Investment

8

2

6

1.5

4

1

2

0.5

0

0

-2

-0.5
5

10

15

20

5

Tax Revenue

10

15

20

15

20

15

20

GDP

3
2
1

1
0

0.5

-1
-2
-3

0
5

10

15

20

5

Consumption

10

Wage
2

1

1

0.5

0

0

-1
5

10

15

20

5

Labor

10

Investment

0.6

4

0.4
2
0.2
0
0
-0.2

-2
5

10

15

20

5

10

15

20

Figure 6: Impulse Response Functions (First diﬀerences) : 1968Q1-2009Q4
Notes: Solid lines are IRFs that are 50th quantiles of a valid draw and shaded areas are one-standard
error band that are 16th and 84th quantiles.

33

Excess Stock Returns

Public Investment

6

2
1.5

4

1
2
0.5
0

0

-2

-0.5
5

10

15

20

5

Tax Revenue

10

15

20

15

20

15

20

GDP

2

0.8

1

0.6

0

0.4

-1

0.2

-2

0

-3

-0.2
5

10

15

20

5

Consumption

10

Wage

0.8

1.5

0.6

1

0.4

0.5

0.2

0

0

-0.5

-0.2

-1
5

10

15

20

5

Labor

10

Investment
3

0.2
2
0
1
-0.2

0

-0.4

-1
5

10

15

20

5

10

15

20

Figure 7: Impulse Response Functions (First diﬀerences) : 1968Q1-1986Q4
Notes: Solid lines are IRFs that are 50th quantiles of a valid draw and shaded areas are one-standard
error band that are 16th and 84th quantiles.

34

Excess Stock Returns

Public Investment

6

2
1.5

4
1
0.5
2
0
0

-0.5
5

10

15

20

5

Tax Revenue

10

15

20

15

20

15

20

GDP

1.5

1

1

0.8

0.5
0.6
0
0.4
-0.5
0.2

-1
-1.5

0
5

10

15

20

5

Consumption

10

Wage
1

0.8
0.6

0.5
0.4
0

0.2
0
5

10

15

20

5

Labor

10

Investment
3

0.2

2

0

1

-0.2

0
5

10

15

20

5

10

15

20

Figure 8: Impulse Response Functions (First diﬀerences) : 1987Q1-2009Q4
Notes: Solid lines are IRFs that are 50th quantiles of a valid draw and shaded areas are one-standard
error band that are 16th and 84th quantiles.

35

Output

Aggregate Consumption

1.5

1

1

0.5

0.5

0

0

-0.5

Investment

0

-0.5

-0.5

-1
5

10

15

20

-1
5

Labor

10

15

Real Wage

2

20

5

10

15

20

Government Spending
1

0.5

0.8
1

0.6

0

0.4

0
-0.5

0.2

-1
5

10

15

20
benchmark

5

10 15
λ=0.5

0
20
5
10
λ=0.5,µ=0.5

Figure 9: Impulse Response Functions

36

15

20