Stock Market Response to Public Investment under the Zero Lower Bound:
Cross-industry Evidence from Japan ¨Tomomi Miyazaki¨Kazuki Hiraga§ Masafumi Kozukaª¨

The corresponding author would like to thank UC Irvine and the University of Essex for the opportunity
to do research there. The authors also would like to thank Shio Ando, Kohei Aono, Jiawei Chen, Yongkok
Choi, Linda Cohen, Bernd Hayo, Amihai Glazer, Hiroshi Gunji, Shigeyuki Hamori, Yasuo Hirose, Hirokuni
Iiboshi, Masami Imai, Keigo Kameda, Akiomi Kitagawa, Junko Koeda, Yasuyuki Komaki, Tatsuhito Kono,
Takashi Kuramoto, Kenneth Kuttner, Hiroshi Morita, Stefan Nieman, Masao Ogaki, Michinao Okachi,
Tatsuyoshi Okimoto, Elisabeth Schulte, Masahiko Shibamoto, Ryusuke Shinohara, Takayuki Tsuruga,
Kozo Ueda, and Tuan Khai Vu for insightful comments and suggestions. We would also like to acknowledgedelegates at the Annual Conference of Japan Society of Monetary Economics and the Conference on
Economic Design, as well as seminar participants at Daito Bunka University, Doshisha University, Hosei
University, Philipps Universität Marburg, Tohoku Gakuin University, UC Irvine, and the University ofTokyo. We also appreciate Prof. Nobuyuki Kanazawa, who kindly provides us with the data on policy shocks
used in Kanazawa (2018). This work has been financially supported by the Japan Society for the Promotionof Science (Grant-in-Aid for Scientific Research #16K03688 and #16K17135, and Fund for the Promotion of
Joint International Research (Fostering Joint International Research) #16KK0057) and Financial Aid fromthe Zengin Foundation for Studies on Economics and Finance. Takuya Matsuyama, Ayu Tomita, and Koichi
Watanabe provided excellent research assistance. We extend our gratitude to Kai Lance Yoshioka for
proofreading the text. The usual disclaimer applies.
¨
Corresponding Author: Associate Professor, Graduate School of Economics, Kobe University. 2-1,
Rokkodai-cho, Nada-ku, Kobe, Hyogo 657-8501, JAPAN. E-mail: miyazaki@econ.kobe-u.ac.jp§
Associate Professor, School of Political Science and Economics, Tokai University.
E-mail: khiraga581470@gmail.comª
Professor, Faculty of Economics, Setsunan University.
E-mail: koduka-po.osu@hotmail.co.jp

1This research examines the effects of public investment on stock returns using Japanesecross-industry data. The empirical results show that public investment shocks have strong
and stimulating effects on stock returns when the nominal interest rate is at the zero-lower
bound (ZLB) while negative responses dominate outside of the ZLB period. Furthermore, theimpulse responses for the non-manufacturing industries are larger than those of the
manufacturing industries. Our results imply that the government should increase public
investment under the zero-interest bound to prop up the stock market but cut back once theeconomy is no longer in a liquidity trap.

JEL classification: E44, G12, H54
Keywords: Public investment; Zero lower bound; Local projection method;

2

The solution is straightforward. It is to fix the problem of deficient demand not byattempting to further loosen monetary conditions, but by boosting public spending…
Productive public investment would also enhance the returns on private investment,
encouraging firms to undertake additional projects. (Eichengreen 2016)1

1. Introduction

Since the global financial crisis (GFC), several developed countries have been plagued by aprotracted recession that keeps the short-term nominal interest rate close to the zero-lower

bound (ZLB). In the wake of this recession, many economists, including Eichengreen (2016),

underscore the effectiveness of public investment to spur private sector investment, arguingthat crowding out effects are not likely to occur given extremely low interest rates. However,

the reality is that public investment has a positive impact on the stock market first.

For more details, please see
https://www.socialeurope.eu/why-the-world-economy-needs-fiscal-policy-to-overcome-stagnation.
We define public investment as investment in public infrastructure such as livelihood social capital, soil
and water conservation, agricultural use, as well as production use. We exclude investments in scienceand technology, education and social welfare, alternative energy, the environment, and natural disaster
relief. Therefore, our definition for public investment is slightly different from that of Eichengreen (2016),
who also includes investment for research and education.
1

3

Afterward, it improves firm balance sheets, enabling firms to embark on further

investments. That is why the stock market, whose participants are forward looking,immediately responds to the announcement or implementation of public investment projects,

even though the productivity effect of public capital appears in the future. Therefore, therelationship between public investment and stock market response is worth investigating,not just the direct effect of public investment on private investment. To the best of ourknowledge, however, the relationship between public investment and the stock market

during the ZLB period remains underexplored.2

The purpose of this study is to examine the effect of public investment on the stock market

between the non-ZLB and ZLB periods using Japanese sectoral panel data. To do this, we

calculate impulse response functions (IRFs) using the local projection method developed by

Jordà (2005) and Stock and Watson (2007). Studies in Japan have two advantages over those

2 As stated later, Belo and Yu (2013) demonstrate this point using both theoretical and empirical models,
without dividing the situation between the ZLB and non-ZLB periods. Appendix 1 of this paper
summarizes their theoretical foundation. When it comes to the research using Dynamic Stochastic
General Equilibrium (DSGE) model, for instance, Mumtaz and Theodoridis (2017) investigate the effectsof fiscal policies on stock prices in the US; however, while Mumtaz and Theodoridis (2017) address thechanges in the policy stance from passive to active (and vice versa), they do not compare the policy effects
between the ZLB periods and others.

4

in other countries. First, there is ample data on the ZLB period in Japan because thenominal interest rate has been near the ZLB since the late 1990s. Second, the Japanese

government has used public investment in a bid to bolster the sluggish stock market even

before the short-term interest rate became close to zero.

We use a quarterly panel of returns on common stocks (hereafter, stock returns) on 27

industries. 3 We adopt the former because stock return is usually considered stationary

whereas stock price/index is not. We use sectoral panel data for two reasons. First, policy

effects may be different among sectors. For example, the manufacturing industry benefits

substantially from the production of infrastructures such as roads, water and sewer systems,

etc. However, the services and banking sectors do not seem to benefit much. Second, we can

avoid the problem of reverse causality running from the stock price index to public

investment policies, which can occur in an analysis that uses aggregate data. In general, thegovernment decides public investment policies based on a representative stock price index

such as the Nikkei 225 or the S&P 500. We mitigate concerns for reverse causality using

3

This is calculated by the equation "!"#$%& ()*"! +,)-*.)/ ()*"!
0.$#1 23*#&!"## × 100 (%). Note that our stock return data

covers common stocks (or stocks other than preferred stocks).

5

sectoral panel data.

Our main findings are as follows. First, public investment (public capital) shocks have astimulating effect on stock returns in the ZLB period, but not outside. Second, the estimatedimpulse response for the manufacturing industry is a little bit larger than that of non-manufacturing industries during the ZLB period. We check the robustness of our findings

under alternative model specifications; changing the lag length of public investment shocks,

using a second indicator to calculate public investment shocks, and adding the dummy

variable that takes the value of 1 during the asset bubble periods yield similar findings. By

comparing policy effects between the manufacturing and non-manufacturing industries, wewere able to confirm that the manufacturing industry responded positively to public

investment shocks regardless of how the model was specified under the ZLB environment.

However, the effects on non-manufacturing industries are not robust. The principal

implication of our results is that, whereas public investment should be included in economic

stimulus packages in the ZLB period to achieve stock market resurgence, it would befavorable for the government to curtail it once the economy has escaped the ZLB.

6

This paper is related to two bodies of literature. The first deals with fiscal policy

effectiveness under ZLB. There have been a multitude of theoretical and empirical works on

the fiscal policy effectiveness under the ZLB: Christiano et al. (2011), Eggertsson (2011),

Woodford (2011), Ramey (2011), Auerbach and Gorodnichenko (2013), Dupor and Li (2015),

Bouakez et al. (2017), Bilbiie et al. (2018), Miyamoto et al. (2018), Ramey and Zubairy (2018),

and Bouakez et al. (2019), etc. Above all, a recent paper by Bouakez et al. (2017) examines

the effects of public investment on inflation, interest rate, GDP, and its components in a ZLB

environment. However, none of these papers take into account the effect on the stock market,

which public investment affects first as we mentioned earlier. In this regard, ourcontribution to this field is to fill the gap between the extant studies and the realistictransmission path of public investment that leads the way to economic recovery.

Second, our paper is also related to the literature regarding the effects of public

investment on firm activities such as: Aschauer (1989), Pereira and Roca-Sagales (2001),

Boscá et al. (2002), Pereira and Andraz (2003), Annala et al. (2008), Afonso and Aubyn(2009), Hatano (2010), Hunt (2012), Belo and Yu (2013), Fujii et al. (2013), Miyazaki (2018),

7

and Wu et al. (2019). Much work examines how the positive externality of public capital

stimulates the private enterprise capital formation. But surprisingly little work explores

how it can energize the stock market; a paper by Belo and Yu (2013) is the only exception,

which examines the effects of public investment on stock returns in different sectors.

However, they did not compare the effects between the ZLB period and others. Hence, we

can differentiate ourselves by utilizing the long ZLB experience in Japan.

The rest of this paper is organized as follows. Section 2 describes public investment andstock price target policies in Japan. Section 3 presents the study’s empirical framework.

Section 4 reports the estimation results. Section 5 concludes.

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2. Background: Public infrastructure investment and stock target policy inJapan
2.1. Public investment and macro stabilization policy in JapanAs demonstrated by the word doken-kokka (“construction state”) 4 , the Japanese

government has injected a huge amount of money into public infrastructure. Figure 1 shows

the movement of public investment (public capital formation) per GDP for six developed

countries. This figure shows that Japan’s public investment to GDP ratio was the highest

among said six countries until the mid-2000s. Although the government has been curtailing

public investment since 2001, Japan is still among the top countries.5

In Japan, public investment is often used as a tool for macroeconomic stabilization. Unlike

in most developed countries, fiscal policy has often been employed as a tool for

macroeconomic stabilization in Japan even before the GFC. The government deployed fiscal

stimulus packages as a supplementary budget (extra budget) almost every year in the 1990s:

4 For details on the “construction state,” see the Economist (2008,https://www.economist.com/asia/2008/07/23/this-is-japan).
5 Junichiro Koizumi’s cabinet (2001–2006) initiated public investment cutbacks as part of the so-called“structural reform of the government,” a fiscal consolidation achieved by decreasing government
expenditures and privatizing public corporations. Further, the Democratic party-led coalition government(2009–2012) reduced public investment while attempting to increase welfare expenditures.

9

five times during the so-called “first Heisei recession” (1992-1995) following the burst of theasset price bubble and three times after Japan’s financial crisis (1997-1999).6 As shown in

Figures 2a and 2b, public works (which is equivalent to public investment as defined in thisstudy) comprised as much as 46 % of total stimulus packages implemented in the former

half of the 1990s. Though the share fell to less than 20% after Japan’s financial crisis, public

investments were still included in the stimulus packages in the 1990s.

Public investment has been used as a stabilization policy tool for two reasons. First, as

mentioned by Ishi (2000), most of Japan’s macroeconomics specialists and bureaucrats incharge of economic policy are so-called traditional Keynesians. Second, the Japanese

government follows the “golden rule for public finance,” which allows the government toissue construction bonds. Article IV of the Public Finance Act approves bond issuance in the

General Account (the Japanese central government’s budget) so as to finance public

investments, but prohibits the government from issuing bonds to finance a deficiency of thebudget. 7 By issuing construction bonds, the government used public infrastructure

6 We exclude the supplementary budget of April 1995 because its purpose was not to stimulate theeconomy but to aid relief efforts following the Great Hanshin-Awaji earthquake.
7 As a matter of fact, the Japanese government has issued bonds to fill the gap between revenues and10

investment to stimulate the economy.

2.2. Public stimulus investment and stock price targeting

The Japanese government has made use of economic stimulus packages not only to pumpup the macroeconomy but also to reinvigorate the stock market since the 1990s. For example,

Fukuda and Yamada (2011) argue that the stock price was used as an indicator for

macroeconomic policy decisions. The stock price is indeed an important indicator forJapanese economic policy in terms of the effects on company’s financial statements, theactivities of both financial and non-financial sectors, and shareholders. First, changes in thestock price affect corporate performance: they directly influence a company’s balance sheet

and profit-loss statement because Japan uses current-value accounting. Second, Japanesebanks often hold the stocks of various companies regardless of industry type. Needless tosay, the changes in stock price affect banks’ lending behaviors, which also leads to impact

on business activities of non-financial corporations and hence propagates business cycles.

expenditures as well. This is called “special deficit-financing bonds,” which must be approved by the
National Diet along with a law effective only for a year whenever the government seeks to issue.

11Finally, as mentioned in Fukuda and Yamada (2011), Japanese politicians are pressured bytheir constituents, some of whom are also shareholders, to implement expansionary policies

so as to halt the fall in asset prices.

These facts tell us that economic stimulus packages in Japan have been used for the

purpose of stock market resurgence. What is more, public investment shared a part of thesestimulus packages. In light of these factors, it is worthwhile investigating the relationshipbetween public investment and stock market response in Japan.

3. Empirical framework
3.1. Outline and the procedure to extract policy shocks

To examine the impact of public investment, first we calculate public investment shocks

using factor-augmented VAR (FAVAR) estimation. Then, we calculate IRFs using the local

projection method. Local projection has several advantages for IRF estimation. First, thisapproach is relatively robust to misspecifications of the data generating process. Second, we

12can economize on the number of estimated parameters compared to the panel vector

autoregression model.

We use the public capital data rather than investment data to extract policy shocks.8 If

public investment has a positive impact on stock returns, it may be explained by the path of

public capital rather than that of public investment: public capital stock increases themarginal productivity of private enterprises through positive production externality,

thereby raising stock returns. This would best be captured using public capital data. On theother hand, now that it is similar to public investments because we take first differences,

we can also call this shock “public investment shock.”We calculate public investment shocks using FAVAR estimation developed by Bernanke etal (2005). Appendix 1 offers the details on our FAVAR estimation and how to extract public

investment shocks. FAVAR has the advantage of being able to identify the public investment

shocks because it enables us to extract the policy shocks by applying principal component

8 Whereas we basically use aggregate public capital data, we also examine the effects of infrastructure
capital to check the robustness of the results in Section 4.3. This is why infrastructure such as roads,
airports, and port facilities have direct effect on firms’ economic activities. However, while the sectoral
data is available within the Japanese public capital data, public investment (government capitalformation) data of the Systems of National Account, which has been frequently used, does not offer each
item of the government investment. In this sense, it is also favorable to use the public capital data.

13

analysis to the VAR model while considering various potential factors as either slow-moving

or fast-moving. We calculate the standard error of the estimated coefficients used to

calculate our IRFs by bootstrap replications to address the problem of generated regressor.

To extract the shocks, we estimate using quarterly macroeconomic time series data for

public capital, tax revenues, total factor productivity (TFP), and aggregate (sector average)

and sectoral stock returns. TFP is added to control for the supply-side factors of the stock

market, and we account for intertemporal government budget constraints by including tax

revenues following Owyang and Zubairy (2013). We also include the stock return of 27

industries that we would like to focus on our paper. We do this in order to extract common

factors across industries.9 The industries considered here are shown in Table 1.

Stock returns are in levels, while we take the logarithm difference of TFP, tax revenues,

and public capital because these three variables follow an I (1) process. Slow-moving

variables in FAVAR estimation are those predetermined in the current period, such as

9 We could also utilize other macroeconomic variables such as GDP, exchange rate, interest rate, and thefactors related with labor market as in Hiraga et al. (2018). However, these macroeconomic variables
should be added to the equation that calculates IRFs as independent variables in our estimation.
Therefore, we do not use the procedure employed in Hiraga et al. (2018).

14

output and employment. Following this, the slow-moving variables are TFP, tax revenues,

and public capital. Although we also use these variables as the observable economic

variables for our FAVAR estimation, they are employed as part of the slow-moving variablesin FAVAR estimation like Bernanke et al. (2005), Shibamoto (2007), and Fujii et al. (2013).10

Fast-moving variables are those sensitive to contemporaneous economic news or shocks,

such as asset prices. In this regard, aggregate and sectoral stock returns are used as our

fast-moving variables.11 As mentioned in Morita (2014), Shioji and Morita (2015), Shioji(2017), and Kanazawa (2018), since stock prices in the construction industry tend to reflectcurrent news on public investment expansion, this industry suffers from the endogeneity

bias caused by the reverse causality. Therefore, we exclude the construction industry inisolating common factor across industries and calculating IRFs.

Regarding policy shocks, we could also use the method based on Fischer and Peters (2010)or Miyamoto et al. (2018). Since we use sectoral data unlike the two studies, it is favorable

As discussed later, we also check the robustness of the results by adding the sectoral firm investment
per unit of capital stock and sectoral deflators to the group of slow-moving variables. The results areshown in Appendix 4 of this paper. The main results and implications remain to be the same, although
the IRFs become a little bit smaller than that of our basic specification.
11 Note that TFP is aggregate-level data because sectoral TFP data is not available on a quarterly basis.
10

15

for us to utilize the information of as many industries as possible. The method developed by

Fischer and Peters (2010) uses the stock returns of related industries; Shioji and Morita(2014), Shioji (2017), and Kanazawa (2018) apply this method to examine the effectivenessof Japanese fiscal policy using information on construction companies. However, unlike themethod developed by Fischer and Peters (2010), which focuses on one or two industries

directly affected by a public expenditure, our strategy enables us to capitalize information

on a broad range of industries.12 In this respect, our method is better suited to extract policy

shocks in sectoral panel estimation.

We can also employ variables such as sectoral investment or the deflators among industries

as common factors. However, the Japan Securities Research Institute, which provided thestock returns data, only offers data on sectoral stock returns. Although there are otherinstitutions that have data on sectoral investments and deflators, their classification ofindustries is different from that used by the Japan Securities Research Institute. This

12 We also estimate the model by using the shock calculated by Kanazawa (2018), which utilizes news
shocks from the excess return of road pavement firms; whereas the implications drawing from the results
are unchanged, the estimated IRFs are not as stable as our basic specification. The detailed results areshown in Appendix 5 of this paper.

16

makes it harder for us to use these data with the stock return data with full confidence.13

3.2. Estimation equation of impulse response functions

Using the public investment shocks, we first estimate Equation (1) for each future period

k following Miyamoto et al. (2018) and Ramey and Zubairy (2018):,%%
𝑅!,#$% = 𝐼#&'( × (𝛼),!
+ 𝛽),% 𝐺𝑠ℎ𝑜𝑐𝑘# + / 𝛾),*𝑅!,#+* + 𝜑) (𝐿)𝑦#+. )
*-.
,
%
%
%
+(1 − 𝐼#&'( ) × (𝛼(,!
+ 𝛽(,% 𝐺𝑠ℎ𝑜𝑐𝑘# + / 𝛾(,*𝑅!,#+* + 𝜑( (𝐿)𝑦#+. ) + 𝜀!,#
,

(1)

*-.

where 𝑅!,# represents stock returns for industry i in period t (quarterly). 14 𝐼#&'( is thedummy variable which takes 1 if the economy is in the ZLB at period t and 0 otherwise. We

define the ZLB period to be after 1995 Q4, following Miyamoto et al. (2018).

As a robustness check, we also estimate the model that has these variables included in the common
factors as mentioned before.
14 Equation (1) implicitly assumes stock markets are not efficient, suggesting that the efficient market
hypothesis (EMH) is not valid and people can obtain extra profit from the market. Nagayasu (2003)
showed that the EMH is not confirmed in the Japanese equity market. He also pointed out that, although
the sample period (from January 1, 1990 to August 8, 2002) includes financial deregulation in Japan,
there are inefficiencies in the Japanese equity market due to corporate cross-shareholding, price keeping
operation policy by the Japanese government, and the restrictions on short-selling.
13

17

%
%
An industry fixed effect in each forecasting horizon k is expressed as 𝛼)!
or 𝛼(!
, 𝐺𝑠ℎ𝑜𝑐𝑘#is the public investment shock, and 𝑦#+. is a vector of controls with a lag operator 𝜑) (𝐿) or𝜑( (𝐿). 𝐺𝑠ℎ𝑜𝑐𝑘# is the structural public investment shock, which is calculated using theCholesky decomposition as shown in Appendix 2. In our specification, public investment(public capital) shock is plugged into the estimation equation as a “common shock” acrossthe sectors as in the case of Belo and Yu (2013).15 Meanwhile, as said, since a problem ofgenerated regressor may be worried, we calculate the standard error by 2000 bootstrap

replications. Further, we add three control variables: TFP shock, the first difference of GDP( 𝑌# ), the first difference of real effective exchange rate ( 𝑒# ), the first difference of
unemployment rate ( 𝐸 ), and the level of long-term interest rate ( 𝑟# ). Since 𝑅!,#$% isstationary and both public investment and TFP shocks are derived by taking the firstdifferences of public capital and TFP to make these two variables stationary. Also, 𝑟# is
stationary in a level specification. To be consistent with these variables, we also take thefirst difference for 𝑌# , 𝑒, and 𝐸# , which are non-stationary in a level specification. The TFPThis prevents us from adding time dummy variables. Furthermore, since 𝑅*,.+1 is stationary, we avoid
adding time trend to our estimation equation.
15

18

shock encompasses the productivity shocks that are common across industries. GDP, realeffective exchange rate, and unemployment rate are used to capture macroeconomic

fluctuations. Since government bonds are assumed to be an alternative to stock assets, thelong-term interest rate controls for the effects of alternative assets. Lag lengths for the

lagged dependent variable and five control variables are set to four, following many previous

studies using local projection method such as Miyamoto et al. (2018), Ramey and Zubairy(2018), etc. We also add seasonal dummy variables to control for seasonality.

Meanwhile, it would be an option to add some variables that capture sectoral determinantsof stock returns. However, as mentioned in Section 3.1, regarding the data collected by otherorganizations, the classification of industries is different from the stock return data,

preventing us from using these variables as independent variables in Equation (1). Instead,

we add these variables as factors in our FAVAR estimation as shown in Appendix 4.
The key parameters in Equation (1) are 𝛽),% and 𝛽(,% , representing the response of kperiod-ahead stock returns with respect to a current public investment shock. Note that all

coefficients in Equation (1) are separately estimated for each horizon k. We directly estimate

19

Equation (1), and as in the case of Tenreyro and Thwaites (2016), the cumulative IRFs arecomputed using the estimated 𝛽),% and 𝛽(,% for k=0, …. ,8, with confidence bands
computed using the standard errors of estimated coefficients 𝛽),% and 𝛽(,% .
In a standard local projection estimation, Equation (1) is estimated using least squares

dummy variable (LSDV) with White (1980)’s robust standard errors. The presence of alagged dependent variable and industry fixed effects may lead to a severe bias when theserial correlation of the dependent variables is high and the time-series dimension of thedata is short (Nickell, 1981). However, following the strategies used in many previous

studies, since the length of the time dimension (T=104) mitigates this concern, we proceedwith the LSDV estimator.

204. Empirical results and discussion
4.1. Dataset
Data on stock returns (R ) are drawn from the data of stock price earnings ratio provided

by the Japan Securities Research Institute. The original data on the stock price earnings

ratio is in the monthly rate. The number of industries is 27. All data are put into real terms

using the producer price index (PPI, base year=2005) provided by the Bank of Japan (BOJ).

The procedures are as follows. First, we calculate the annual rate of change of the PPI, whichis converted into quarterly data (the period average). We define this as our inflation rate forstock returns. Second, we calculate a quarterly average for the stock price earnings ratio

and annualize them. Finally, we deflate the annualized price earnings ratio using the PPI

inflation rate that we calculated before. We calculate potential GDP and TFP following

Kamada and Masuda’s (2001) procedure.16

Public capital data, which is used to calculate public investment shocks, and the data on

GDP came from the Cabinet Office of Japan. Official Cabinet Office data are annual and are

16

Further details are shown in Appendix 3 of this paper.

21

expressed in 2005 terms. Therefore, we converted the original annual capital stock data intoquarterly (initial value) data following the procedure employed by Kitasaka (1999) using theweight for each quarter calculated by the real general government gross capital formation

data.17 There are four ways to account for depreciation in public capital stock: the straight-

line method, declining-balance method, and the two types of depreciation shown in theOECD’s (2009) method. We report the results using the data calculated by the straight-line

method.

Data on the real effective exchange rate, tax revenues (=tax and stamp revenues of thecentral government), and long-term interest rate (the yield of 10-year government bonds)

come from the BOJ’s website. Unemployment rate refers to the total unemployment ratio (=(𝑁𝑢𝑛𝑏𝑒𝑟 𝑜𝑓 𝑢𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑒𝑑)/(𝑇𝑜𝑡𝑎𝑙 𝑙𝑎𝑏𝑜𝑟 𝑓𝑜𝑟𝑐𝑒), U3), which comes from the Labor Force Survey

by the Statistics Bureau, Ministry of Internal Affairs and Communication.

The sample period is from 1983 Q1 to 2008 Q4 in quarterly data. One might consider adding

the data from 2009 to 2014 like Miyamoto et al. (2018) to take advantage of the fact that17

For more details, please see Kitasaka (1999).

22

Japan experienced the ZLB for a longer time than the US or the euro area. However, wecannot do this for two reasons. First, to be consistent with the base year of the PPI, which

is used for putting the data of stock returns into real terms, it would be favorable to use data

expressed in 2005 terms. The public capital data expressed in 2005 terms goes until 2009.

Second, the data on the stock price earnings ratio is only available up to 2013. Therefore,

our sample period is different from that of Miyamoto et al. (2018).

4.2. Estimation results

Figures 3a and 3b plot the estimated IRFs of stock returns with respect to a public

investment shock from periods 0 to 8 based on Equation (1). The IRFs are the cumulative

sum of stock returns, where the horizontal axis measures quarter. We also report the 95 %confidence intervals based on a parametric bootstrap procedure with 2,000 repetitions.

Figure 3a shows that for non-ZLB periods, stock returns react negatively for 6 quarters.

Note that confidence intervals contain 0 after period 2. Figure 3b reports that public

investment shocks have a positive effect on stock returns in the ZLB period.

23

Figure 3b also shows stock return initially rises by 1.82%, with a peak response of about

5% 4 quarters after the shock. The possible explanation of this hump-shaped response is asfollows; the future profit via the positive production externality of future public capital is

expected to be sufficiently larger than current profit, which brings the incremental increasein stock returns. However, the returns peter out after a year as the future profit becomes

smaller.

When it comes to the non-ZLB period, we initially observe negative responses, and IRFs

gradually increase though statistically insignificant. The initial negative response can be

explained by the findings of Ardagna (2009), who reports that fiscal expansion driven by therise in government debt aggravates stock prices in OECD countries. As explained in Section

2, the Japanese government often issues construction bonds to procure funds for public

investments. Generally speaking, stock market participants ask for a higher premium on

government bonds in accordance with the rise in government debt, which raises the interest

rate paid on government bonds. The rise in the interest rate by deficit-financed public

investment crowds out private demand and consumption, which has a negative impact on24the stock market. This is especially plausible outside of the ZLB period because central

banks do not purposely suppress interest rates. This path explains the reason why weinitially observe negative responses in Figure 3a. However, since stock returns increase

because of the externality of future public capital, negative effects are gradually offset asshown in this figure.

We also compare effects between two industry groups as shown in Table 1: mining andmanufacturing industries (number of cross sections=16) and non-manufacturing industries(number of cross sections =10).

The results are shown in Figures 4a to 5b. Regarding the policy effects during the ZLB

period, positive responses of stock returns are observed for both industry groups. Further,

the IRF of the manufacturing group does not include zero for 6 quarters and is slightly larger

than that of the non-manufacturing group. In contrast, negative responses are still

dominant for both industries outside of the ZLB period, and confidence intervals do notinclude zero in the short run (< 2 periods ahead) for the manufacturing industry group.

25

4.3. Robustness issues

To check the robustness of our results, we calculate IRFs under alternative model

specifications. First, we use the data on infrastructure capital instead of aggregate public

capital to extract the public capital (investment) shock. Second, we add the first four lags of

𝐺𝑠ℎ𝑜𝑐𝑘# in Equation (1), following Teulings and Zubanov (2014). Finally, we re-estimate
Equation (1) by adding the dummy variable indicating the so-called asset price bubble period,

𝐷#/ ; this dummy variable takes the value of 1 from 1986 Q1 to 1991 Q1 and 0 otherwise.18
We do this in order to address the business cycle fluctuations during the asset bubble period.

𝐷#/ is added as an intercept dummy variable. We also multiply this by the industry fixed%%
effects for both regimes so that we have the variables 𝐷#/ ∗ 𝛼),!
and 𝐷#/ ∗ 𝛼(,!
.

The results are reported in Tables 2 to 4 for each case. 19 Overall, in spite of thesealternative specifications, we confirm positive responses in the ZLB period. In particular,when we add four-periods lag of 𝐺𝑠ℎ𝑜𝑐𝑘# to Equation (1), the size of the IRFs is larger than18 The Cabinet Office of Japan defines the asset price bubble period from December 1986 to January
1991. However, since stock prices in Japan began to surge in the early 1986 following the Plaza Accord in
the late 1985, we set 1986 Q1 as the initial period.
19 Since our estimation reports massive number of figures of impulse response functions, we omit thesefigures for the sake of brevity. Instead, we present the results in tables. Graphs of the impulse response
functions can be obtained from the author upon request.

26

our basic specification during the ZLB periods on the manufacturing industry group.

When it comes to the manufacturing industry group, regardless of the alternative

specifications, the IRFs are estimated to be positive and significant for at least four quarters.

On the other hand, regarding the non-manufacturing industry group, the IRFs in the ZLB

periods are found to be statistically insignificant on impact (horizon 0) when we add four-

periods lag of 𝐺𝑠ℎ𝑜𝑐𝑘# as shown in Table 4. More than that, the IRFs include zero 4 quarters
after the shock except the case that we add the dummy variable specifying the asset pricebubble period. Based upon these results, we cannot strongly support the positive responses

with respect to the non-manufacturing industry group even when the nominal interest rateis stuck at zero.

4.4. Discussion of the results

Our results suggest that public investment has a positive and statistically significant

impact on stock returns when the economy is in a liquidity trap. Thanks to the extremely

low interest rate environment, the crowding out effects mentioned in Section 4.2 are not

27

likely to happen in the ZLB periods.20 We observe the positive responses for about a year in

most cases, which is consistent with the results on output by Miyamoto et al. (2018). In thisrespect, we confirm the results of Miyamoto et al. (2018) regarding stock market responses

in Japan.

In terms of quantitative evaluation, our IRFs under the ZLB environment are well over 1.

Some notable studies mentioned in Section 1 such as Christiano et al. (2011), Eggertsson(2011), and Woodford (2011) report that fiscal multiplier at the ZLB is more than 1 using

standard New Keynesian models; so do Miyamoto et al. (2018) in Japan. Our empiricalresults support the findings of these studies on stock returns.

It is also notable that positive responses in the ZLB period are robust for the manufacturingindustry.

There are two explanations for this. First, as stated in Section 1, themanufacturing industry benefit more from public capital than non-manufacturingindustries do. Second, the artificially suppressed interest rate policy depreciates the valueof the yen, from which the manufacturing industry substantially benefit. This will also boost

For example, please also see the website for this point: http://larrysummers.com/2016/02/17/the-age-ofsecular-stagnation/.
20

28

the future productivity of the manufacturing industry under the ZLB environment. Fromthese two reasons, once public sector increases its investment, the enhancement of future

productivity is expected to be larger on firms classified into the manufacturing industry,

which leads to an increase in stock returns of manufacturing enterprises during the ZLB

periods.

5. Conclusion
This study examines the effects of public investment on stock returns using Japanese

sectoral panel data. Japan’s nominal interest rate has been in the ZLB for a long time andits government has used public investment with the aim to reinvigorate the stock market.

These facts make Japan a suitable case for an examination of the relationship between

public investment and the stock market in the ZLB economy. Our results show that, while

public investment (public capital) shocks have positive and persistent effects on stock

returns in the ZLB period, this is not the case outside of the ZLB. This is especially true of29the industry group consisting of machine, electrical equipment, transportation machine, etc.

Our findings imply that, whereas public investment contributes to the resurgence of thestock market when the nominal interest rate is stuck at zero, policy makers should cut backonce the economy has escaped the liquidity trap. This perspective is also supported by Glazer(2013), who states that the government should delay fiscal consolidation in a recession but

pursue it in a boom from the viewpoint of economic welfare. However, as a matter of fact,

the share of public investment within economic stimulus packages was smaller in the ZLB

period than out of the ZLB in Japan. Furthermore, the Japanese government has curtailed

public investment since 2001 as a part of fiscal adjustments. In these regards, fiscal

expansion before the ZLB period in Japan might “reduce the ‘fiscal space’ for responding to

the next crisis” (Krugman 2018). We warn policy makers not to make the same mistake as theJapanese government in trying to invigorate the stock market through public investment.

This study’s analysis could be fruitfully extended in three ways. First, whereas we focus on

empirical analyses, it would be needed to construct theoretical model to support our results.

Second, we could examine the effects of using firm-level data so as to compare the effects

30

among more disaggregated industries like Nekarda and Ramey (2011). Finally, a recent
paper by Ozdagli and Weber (2017) examine the stock market reaction with respect tomonetary policy considering network effects or spillover effects. It is also possible to apply

their framework to public investment as a fiscal stimulus.

Appendix 1. Theoretical explanation of supply-side effect of public capital on
stock return
This appendix introduces public sector physical capital into the neoclassical q-theory modelof investment following Belo and Yu (2013).

A.1. The setups

Firm technology is shown as follows:

𝑌# = 𝑒 0! (𝐺𝐾# )1 𝐾# ,

(A.1)

31

where Y is output, GK is public sector physical capital, K is private capital xt is profitability

shock (or productivity shock). Seminal parameter a is the productivity (profitability) of

public sector capital.

The accumulation processes of private and effective public capital are shown as Eq. (A.2)

and (A.3):

𝐾#$. = (1 − 𝛿)𝐾# + 𝐼# ,(A.2)

𝐺𝐾#$. = (1 − 𝛿)𝐺𝐾# + 𝐺𝐼𝐾# ,(A.3)

O # is the public sector investment rate, 𝐺𝐼# is the total investment in
where 𝐺𝐼𝐾# ≡ 𝐺𝐼# ⁄𝐺𝐾
O # is the total stock of public sector capital and 𝛿 23 is the
public sector capital, 𝐺𝐾
depreciation rate. The specification of Eq. (A.3) guarantees that the stock of effective public

sector capital is stationary21, the adjustment cost of private capital

21

Assumption of stationary is necessary condition to derive the empirical predictions as effective stock of32

4g(𝐼# , 𝐾# ) = 5 (𝐼𝐾# )5 𝐾# ,(A.4)

where IKt º I t K t is the private sector investment rate.

A.2. The firm’s maximization problem
Suppose that the firm is all-equity financed. Dividends Dt distributed by the firm to theshareholders are given by:

4

𝐷# = 𝑒 0! (𝐺𝐾# )1 𝐾# − 𝐼# − (𝐼𝐾# )5 𝐾# .

(A.5)

5

The cumulative dividend market value 𝑉 678 (𝑠# )is shown as follows:

𝑉 !"# (𝑠$ ) = max &𝐸$ (∑*)+, 𝑀$,$() 𝐷$() ,-,(A.6)%567 ,'567

public sector capital is equal to detrended stock of total public capital.

33

Subject to Eq. (A.2) and (A.3) for all dates t. 𝑠# = (𝐾# , 𝐺𝐾# , 𝐺𝐼𝐾# , 𝑥# ) is the vector of statevariables and 𝑀#,#$* is a market –determined stochastic discount factor at period t, which
is used to value the cash flows arriving in period t+j.

A.3. First-order conditions

We solve the maximization problem of Eq. (A.6). First order conditions with respect to 𝑙#
and 𝐾#$. are as follows:(A.7)

𝑞# = 1 + 𝑐 ∙ 𝐼𝐾# ,4

𝑞# = 𝐸# X𝑀#,#$. Y𝑒 0!"# (𝐺𝐾#$. )1 + 5 (𝐼𝐾# )5 + (1 − 𝛿)(1 + 𝑐 ∙ 𝐼𝐾# )Z[.

CombiningEq.

(A.2),(A.3),(A.7),

(A.8)

and

the

(A.8)

standard

asset

pricing,,
equation𝐸# \𝑀#,#$. 𝑅#$.
] = 1, in which 𝑅#$.
is the private sector investment return, we obtain

34

,
𝑅#$.
as follows:

'

,
𝑅#$.
=

9 $!"# :;.+< %& =23! $2>3! ? $4 ⁄5(>3!"# )( $(.+<)(.$4∙>3!"# ).

.$4∙>3!

(A.9)

Equation (A.9) explains the supply-side effect of public capital shown in the first term of thenumerator. If a is positive, stock return increases by a channel of positive production

externality of public capital.

Appendix 2. Explanation of the FAVAR model
Here we explain the econometric framework of the FAVAR model.22 Let 𝑌# be an 𝑀 ×1
vector of observable economic variables. Although the lags of 𝑌# are used as explanatory
variables in a standard VAR, they alone may not provide sufficient economic information.
We therefore assume that a 𝐾 ×1 vector of unobserved factors, where K is small, provides

22This section follows Bernanke et al. (2005).

35

the remaining information. We set 𝐾 =3 as the number of factors drawn by the dynamic

factor model of Bernanke et al (2005) and Fujii et al. (2013). The joint dynamics of (𝐹# , 𝑌# )are given by𝐹𝐹! ! $ = Φ(𝐿) ! !"# $ + 𝑢! ,𝑌!
𝑌!"#

(A.10)

where Φ(L) is a matrix of polynomials of finite order d and the error term ut has mean 0

with covariance matrix Σ. The equation is in reduced form, and contains a recursive

restriction that the unobservable factors (𝐹# ) do not respond to the public investment (public
capital) shock contemporaneously. This reflects the fact that the fiscal authority does not

immediately respond to the state of the industry sectors as shown in Section 1. Thus, we use

a six-variable system, where the ordering of the variables is 𝐹# (which contains threefactors), public capital, tax revenue, then TFP.23 We take first differences for public capital,

tax revenue, and TFP as mentioned in Section 3.1.

23 We check the robustness of the results by changing the order between tax revenue and TFP while
keeping the order between 𝐹. and public capital. The results are not substantially changed from ourbasic specification.

36

We use the two-step approach of Bernanke et al. (2005): after identifying 𝐹# using
principal component analysis, we estimate Equation (A.10) and compute the structural

shocks using Cholesky decomposition as done in Bernanke et al (2005), Shibamoto (2007),and Fujii et al. (2013).24

Equation (A.10) cannot be estimated because the factors are unobservable. We must

therefore assume that the factors affect a large number of variables to estimate Equation(A.10). This assumption allows us to infer the unobservable factors from observableeconomic time series variables. Let Xt be an 𝑁 ×1 vector of informational time series, where

N is large such that 𝐾 + 𝑀 ≪ 𝑁.25 We also assume that the informational time series 𝑋#
are related to the unobservable factors 𝐹# and the observable variables 𝑌# as follows:

𝑋# = ΛD 𝐹# + ΛE 𝑌# + 𝑒# ,(A.11)

24 Although Bernanke et al. (2005) estimate the FAVAR using the two-step approach and a Bayesian
method based on Gibbs sampling, they suggest that the two-step approach tends to produce moreplausible responses.
25

As Bernanke et al. (2005) point out, it is acceptable for N to be greater than T.

37where ΛD is an 𝑁 × 𝐾 matrix of factor loadings, ΛE is 𝑁 × 𝑀, and 𝑒# is an 𝑁 × 1 vector of
the error terms, which is weakly correlated with mean 0.

We perform the following procedure as the first step. Initially, the common components, Ct,

are estimated using the first 𝐾 + 𝑀 principal components of Xt. After that, following

Bernanke et al. (2005), the variables are classified as slow-moving or fast-moving. The slow-

moving variables are TFP, tax revenues, and public capital. As the fast-moving variables,

we use the stock returns of the 27 industries shown in Table 1 and aggregate stock returns(average of the 27 industries). Finally, after a principal component analysis is applied to theslow-moving variables to derive a vector of slow-moving factors, 𝐹#F . Finally, the following
regression is estimated:

𝐶c# = 𝑏G) 𝐹d#F + 𝑏H 𝑌# + 𝑒# ,(A.12)

where the estimated factors, 𝐹d# , are obtained from 𝐶c# − 𝑏H 𝑌# .
As the second step, we estimate Equation (1) by replacing Ft with 𝐹d#F , which is a vector of
estimated slow-moving factors. We can also calculate the structural shock 𝑢# = 𝛹 +. 𝜀# ,38

where 𝛹 +. is the inverse of the coefficient matrix in the related structural model. We

assume that 𝛹 = 𝐼 − 𝛹I (𝐼 is a 6 × 6 identity matrix) so that 𝑢# = 𝛹 +. 𝜀# can be written as𝑢# = 𝛹I 𝑢# + 𝜀# . The recursive identification procedure implies that 𝛹I becomes lower
triangular, and the ordering in the VAR determines the degree of exogeneity of the variables.

Here public capital is placed after Ft as mentioned earlier. Innovations in public capital are

public investment (public capital) shocks, which is equivalent to 𝐺𝑠ℎ𝑜𝑐𝑘# of Equation (1).

Appendix 3. Calculation of potential GDP
In order to obtain the total factor productivity (TFP) in Japan, we have to calculate theSolow residual without measurement error. In this study, we employ the production function

approach. The Economic Planning Agency (2000), Miyao (2001) and Kamada and Masuda(2001) utilize the method of calculation based on the Solow residual derived from Cobb–

Douglas-type production functions. Here, we follow the methods proposed in Kamada andMasuda (2001). Table A.1. presents the source of the data.

39

First, we denote real GDP as

Yt , capital stock as K t , total labor hours (working

population × working hours) as

Lt , Solow residual as At , operating ratio of capital as l ,and the coefficient labor input as a . The parameter a is defined as (Compensation of

employees)/ (total income) assuming perfect competition. The production function is thenln Yt = ln At + (1 - a ) ln lK t + a ln Lt .

(A.13)The real GDP and capital stock are 93 SNA in the Annual Report on National Accounts.
The industry consists of manufacturing and non-manufacturing sectors, and ln lK t can be
written asln lK t = ln(lm K mt + lnm K nmt )(A.14)

Here, m indicates the manufacturing industry and nm the non- manufacturing industry.

The data of operating ratio of capital in manufacturing industries can be utilized, and we40

standardize the highest value as 100%. However, we must consider the lnm because wecannot obtain this kind of data in Japan. Although some previous studies assumed that theoperating ratio in non-manufacturing industries is always 100%, this is indefensible. In

order to calculate the operating ratio of non-manufacturing industries, Miyao (2001) uses

the Business Survey Index (BSI) as a simplified method.26 We set the peak of BSI as 100%of the operation ratio.
Then, the Solow residual ( ln At ) is calculated as follows:

ln At = ln Yt - (1 - a ) ln lK t - a ln Lt(A.15)

Here, we regard the Solow residual as the TFP.

Appendix 4. Alternative framework using policy shocks with sectoral investment

In Kamada and Masuda (2001), the consumption of electric power is also considered in calculating therate of operation.
26

41

or deflators included in factors
We extract the policy shock by adding sectoral I/K or sectoral deflator as a factor to our

FAVAR model.

When it comes to I/K, we construct flow investment data (𝐼# ) following the equation:

𝐼# = 𝐾#J − 𝐾#( + 𝐷𝑒𝑝# ,(A.16)

where 𝐾#J is the tangible fixed asset at the end of period t, and 𝐾#( is initial tangible fixed
asset (or the one at the end of period t-1). Land is excluded and construction-in-progress

accounts are included. 𝐷𝑒𝑝# is depreciation and 𝐼# is the flow of investment in period t.
We obtain the data from the Seasonal Company Statistics Report by the Ministry of

Finance, Japan; however, since this data is revised at the end of every fiscal year to address

bankruptcy and opening of firms, some companies are dropped or added between the 1st

and the 2nd quarters.27 We modify the flow investment variable to deal with this following

27

Following the UK, the Japanese fiscal year is from April to the following March.

42

Ogawa (2003). First, we calculate the change in the number of firms from the 1st quarter tothe 2nd quarter for every year, and assume that the calculated change is equal to the change

in the number of firms in each of the quarters from the previous fiscal year. Then, we

multiply the calculated change with per capita flow, and add it to the total flow in eachquarter.

Second, we re-calculate the data series of capital stock ( 𝐾# ) following the perpetualinventory method. Using the flow data, we calculate capital stock as follows:

𝐾# = 𝐾#( + 𝐼# − 𝐷𝑒𝑝#

(A.17)

Using Equation (A.16) and (A.17), we calculate sectoral I/K.

As for sectoral deflators, we use the monthly sectoral consumer price index in Japan (2015base), which is available from the Ministry of Internal Affairs and Communication in Japan.

Estimation results are reported in Tables A2 and A3. Overall, these results reaffirm ourmain conclusions. The IRFs are estimated to be positive and statistically significant in the

ZLB periods for all industries. Further, we show negative and statistically significant results

43

outside the ZLB periods for up to a year in some cases. Although the size of the IRFs reported

in Table A3 is smaller than that of our basic specification during the ZLB periods, we havepositive and significant response for all industries and the manufacturing industry groupwhen the short-term nominal interest rate is close to the zero-lower bound.

Appendix 5. Estimation using the policy shock calculated by Kanazawa (2018)
Kanazawa (2018) extracts policy shocks using the excess return of road pavement firms as

a news shock about future public investment following Fisher and Peters (2010). We also

estimate Equation (1) by replacing our original shock with the shock of Kanazawa (2018).

We reported the results in Figures A1 and A2. We confirm the positive response for one

year in the ZLB periods, though the confidence intervals include zero in the 2nd period. On

the other hand, the IRF is estimated to be negative and significant at first. In this respect,

we can confirm our implication as well as basic results even when we use another shock.

Meanwhile, the estimated IRFs are larger. Further, they are not as stable as our44specification. These may be ascribed to the fact that the shock calculated using theinformation of quite a few industries may lead to some biases in panel data estimation. In

this respect, it is favorable for us to make use of policy shocks extracted based upon the dataof various industries.

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The Economist (2008). “This is Japan.” http://www.economist.com/node/11779435

Tenreyro, S. and G. Thwaites (2016). “Pushing on a String: US Monetary Policy is Less

Powerful in Recessions.” American Economic Journal: Macroeconomics 8:4, 43-74.

Teulings, C.N. and N. Zubanov (2014). “Is Economic Recovery a Myth? Robust Estimation of

Impulse Responses.” Journal of Applied Econometrics 29:3, 497-514.

White, T (1980). “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a

Direct Test for Heteroskedasticity.” Econometrica 48:4, 817-838.

Woodford, M (2011). “Simple Analytics of the Government Expenditure Multiplier,”

American Economic Journal: Macroeconomics 3, 1-35.

Wu, G. L. Q. Feng, and Z. Wang (2019). “Estimating Productivity of Public Infrastructure

Investment,” Working Paper, Nanyang Technological University.

53

Figure 1. Government gross capital formation per GDP among some developed countries

54

Source: OECD Economic Outlook

55

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

FranceJapan
United States

1999

1998

1997

1996

1995

1993

1992

1991

10
9
8
7
6
5
4
3
2
1
0

1994

CanadaGermany
United Kingdom%

Figure 2a. The content of fiscal stimulus packages in the former half of the 1990s
Tax cut
10%
Othergovernment
measures42%

Other
government
investment
3%

Public work
(Public
investment)
45%

56

Figure 2b. The content of fiscal stimulus packages in the latter half of the 1990s
Tax cut18%
Other
government
measures43%

Cashtransfers tohousholds
1%

Public work
(Public
investment)
17%
Other
government
investment
21%
Note: The source of numbers for both figures come from Brückner and Tuladhar (2014). The “Other
government investment” category includes investment in fields such as science and technology, educationand social welfare, alternative energy and the environment, and natural disaster relief. Further, we exclude
the supplementary budget in April 1995 to be compatible with the arguments in Section 2.1.

57

Figure 3a: IRF of stock returns with respect to public investment (public capital) shockoutside of the ZLB period (All industries, unit=%)

9
7
5
3
1
-1

0

1

2

3

4

5

6

7

8

-3
-5
Note: The solid line indicates the cumulative impulse response, and the dotted lines represent the 95 %confidence intervals based on a parametric bootstrap procedure with 2,000 repetitions.

58

Figure 3b: IRF of stock returns with respect to public investment (public capital) shock in

the ZLB period (All industries, unit=%)
13
11
9
7
5
3
1
-1

0

1

2

3

4

5

6

7

8

-3
-5
Note: The solid line indicates the cumulative impulse response, and the dotted lines represent the 95 %confidence intervals based on a parametric bootstrap procedure with 2,000 repetitions.

59

Figure 4a: IRF of stock returns with respect to public investment (public capital) shockoutside of the ZLB period (Manufacturing industry group, unit=%)

9
7
5
3
1
-1

0

1

2

3

4

5

6

7

8

-3
-5
Note: The solid line indicates the cumulative impulse response, and the dotted lines represent the 95 %confidence intervals based on a parametric bootstrap procedure with 2,000 repetitions.

60

Figure 4b: IRF of stock returns with respect to public investment (public capital) shock in

the ZLB period (Manufacturing industry group, unit=%)
13
11
9
7
5
3
1
-1

0

1

2

3

4

5

6

7

8

-3
-5
Note: The solid line indicates the cumulative impulse response, and the dotted lines represent the 95 %confidence intervals based on a parametric bootstrap procedure with 2,000 repetitions.

61

Figure 5a: IRF of stock returns with respect to public investment (public capital) shockoutside of the ZLB period (Non-manufacturing industry group, unit=%)

9
7
5
3
1
-1

0

1

2

3

4

5

6

7

8

-3
-5
Note: The solid line indicates the cumulative impulse response, and the dotted lines represent the 95 %confidence intervals based on a parametric bootstrap procedure with 2,000 repetitions.

62

Figure 5b: IRF of stock returns with respect to public investment (public capital) shock in

the ZLB period (Non-manufacturing industry group, unit=%)

10

5

0
0

1

2

3

4

5

6

7

8

-5

-10
Note: The solid line indicates the cumulative impulse response, and the dotted lines represent the 95 %confidence intervals based on a parametric bootstrap procedure with 2,000 repetitions.

63

Figure A1: IRF of stock returns with respect to public investment (public capital) shockoutside of the ZLB period (All industries, calculated using the shock of Kanazawa (2008))
50
40
30
20
10
0
0

1

2

3

4

5

6

7

8

-10
-20
-30
Note: The solid line indicates the cumulative impulse response, and the dotted lines represent the 95 %confidence intervals based on a parametric bootstrap procedure with 2,000 repetitions.

64

Figure A2: IRF of stock returns with respect to public investment (public capital) shock in

the ZLB period (All industries, calculated using the shock of Kanazawa (2008))
40
30
20
10
0
0

1

2

3

4

5

6

7

8

-10
-20
-30
-40
Note: The solid line indicates the cumulative impulse response, and the dotted lines represent the 95 %confidence intervals based on a parametric bootstrap procedure with 2,000 repetitions.

65

Table 1. Details about the industries

Broad category

Small classification

Fishery andagriculture
MiningManufacturing(15 industries)

Non-manufacturing(10 industries)

Food, fiber, pulp and paper (paper), chemical, petro-coal (petro), rubber,glass,

steel,

non-steel,metal,

machine,electricalequipment,

transportation machine, precision mechanical, and other machines
Land transport, shipping, air transport, warehousing, information andcommunication, wholesale and retail, finance, real estate, electrical andgas, and services

Note: The number of industries is in the parentheses.

66

Table 2. IRF of the robustness check (All industries, unit=%)

On impact(Horizon 0)

Horizon 4

Horizon 8

G=infrastructure
Non-ZLB-1.359*-1.721

1.587

ZLB

1.860*

4.654*

0.154

Non-ZLB-0.476-0.837

0.993

ZLB

1.159*

5.917*

3.406

Non-ZLB-1.199*-1.622

1.698

ZLB

1.711*

4.864*

0.961

lag=4 for X

Adding bubble dummy

Note: 𝐴∗ indicates that 0 is outside the region between the 95 % confidence intervals based on a parametric bootstrap
procedure with 2,000 repetitions. “ZLB” refers to the period during which the short-term nominal interest rate isclose to the zero-lower bound, and “non-ZLB” indicates the outside of the ZLB period. “G=infrastructure” reports thecase when we use infrastructure instead of all public capital data. “Lag 4 for 𝐺𝑠ℎ𝑜𝑐𝑘" ” reports the cumulative IRF when
we also add four-periods lags of 𝐺𝑠ℎ𝑜𝑐𝑘" in Equation (1). “Adding bubble dummy” is the case where we add the dummy
variables that takes 1 during the asset price bubble periods.

67

Table 3. IRF of the robustness check (Manufacturing industry group, unit=%)

On impact

Horizon 4(Horizon 0)

Horizon 8

G=infrastructure
Non-ZLB-1.382*-1.746

1.368

ZLB

1.76*

4.516*

0.402

Non-ZLB-0.731*-1.9550.355

ZLB

1.074*

6.696*

4.256

Non-ZLB-1.208*-1.771

1.321

ZLB

1.617*

4.720*

1.312

lag=4 for X

Adding bubble dummy

Note: 𝐴∗ indicates that 0 is outside the region between the 95% confidence intervals based on a parametric bootstrap
procedure with 2,000 repetitions. “ZLB” refers to the period during which the short-term nominal interest rate isclose to the zero-lower bound, and “non-ZLB” indicates the outside of the ZLB period. “G=infrastructure” reports thecase when we use infrastructure instead of all public capital data. “Lag 4 for 𝐺𝑠ℎ𝑜𝑐𝑘" ” reports the cumulative IRF when
we also add four-periods lags of 𝐺𝑠ℎ𝑜𝑐𝑘" in Equation (1). “Adding bubble dummy” is the case where we add the dummy
variables that takes 1 during the asset price bubble periods.

68

Table 4. IRF of the robustness check (Non-manufacturing industry group, unit=%)

On impact

Horizon 4(Horizon 0)

Horizon 8

G=infrastructure
Non-ZLB-1.297*-1.359

2.194

ZLB

2.03*

4.833

-0.330Non-ZLB-0.141

0.503

1.641

ZLB

1.280

5.123

2.370

Non-ZLB-1.156*-1.415

2.174

ZLB

1.878*

5.105*

0.434

lag=4 for X

Adding bubble dummy

Note: 𝐴∗ indicates that 0 is outside the region between the 90% confidence intervals based on a parametric bootstrap
procedure with 2,000 repetitions. “ZLB” refers to the period during which the short-term nominal interest rate isclose to the zero-lower bound, and “non-ZLB” indicates the outside of the ZLB period. “G=infrastructure” reports thecase when we use infrastructure instead of all public capital data. “Lag 4 for 𝐺𝑠ℎ𝑜𝑐𝑘" ” reports the cumulative IRF when
we also add four-periods lags of 𝐺𝑠ℎ𝑜𝑐𝑘" in Equation (1). “Adding bubble dummy” is the case where we add the dummy
variables that takes 1 during the asset price bubble periods.

69

Table A1. Source of the data on TFP
D ata

R eal G D P (chained), C om pensationof E m ployees, and T otal Incom e

C apital S tock

W orking P opulation

S ource
N ational A ccount (93S N A , seasonally adjusted andreference year 2000) in the A nnual R eport on N ational
A ccount, C abinet O ffice
C apital S tock on P rivate S ector (93S N A ) w ith
adjustm ent for privatization, C abinet O ffice
L abor F orce S urvey (for all industries), M inistry ofInternal A ffairs and C om m unication
M onthly L abor S urvey (A ll Industries, com panies w ith

W orking H oursm ore than 30 em ployees), M inistry of H ealth, L aborand W elfare

B usiness S urvey Index

B usiness S urvey Index for C apital Investm ent (L arge
C om pany, N on-m anufacturing), M inistry of F inanceIndices of O perating R atio of M anufacturing

O perating R atio of C apital(1995= 100), M inistry of E conom ics, T rade andIndustryW e separate the population into tw o categories:(1)from 15 to 64 years old and (2) m ore than 65 years

M axim um W orking P opulation

old. T hen, w e adjust the linear trend of the w orking
population to the peak of original data for eachcategory, and w e total these tw o adjusted linear
trends.
(In designed hours) W e separate three sam ples:(1)1978 Q 1-1987 Q 4 (2)1988 Q 1-1993 Q 4 (3)1994
Q 1-1997 Q 3. A nd for each sam ple, w e calculate theM axim um W orking H ours

linear trend and adjust it to the peak of the originaldata.
(O ut of designed hours) W e adjust the linear trend of
w orking hours to the peak of the original data.
T hen w e total these tw o adjusted linear trend.

70

Table A2. IRF when private sectoral investments per capital stock (I/K) are included asfactors in FAVAR estimation (Unit=%).

On impact(Horizon 0)

Horizon 4

Horizon 8

All Industries
Non-ZLB-1.470*-2.649*

0.131

ZLB

1.328*

4.888*

2.636

Non-ZLB-1.573*-2.989*-0.458ZLB

1.438*

4.808*

3.297

Non-ZLB-1.290*-2.1300.950

ZLB

1.171*

4.907*

1.653

Manufacturing

Industrygroup

Non-Manufacturing
Industry groupNote:

An asterisk (*) indicates that 0 is outside of the 95 % confidence interval based on a parametric bootstrap

procedure with 2,000 repetitions. “ZLB” refers to the period during which the short-term nominal interest rate isclose to the zero-lower bound, and “non-ZLB” indicates the outside of the ZLB period.

71

Table A3. IRF when sectoral deflators are included as factors in FAVAR estimation(Unit=%).

On impact

Horizon 4(Horizon 0)

Horizon 8

All Industries
Non-ZLB-1.152*-2.516*

0.305

ZLB

1.174*

3.952*

0.997

Non-ZLB-1.245*-2.686*-0.221ZLB

1.161*

3.697*

1.399

Non-ZLB-0.982*-2.287

1.137

ZLB

1.152*

4.247

0.222

Manufacturing

Industrygroup

Non-Manufacturing
Industry group

Note: 𝐴∗ indicates that 0 is outside the region between the 95 % confidence intervals based on a parametric bootstrap
procedure with 2,000 repetitions. “ZLB” refers to the period during which the short-term nominal interest rate isclose to the zero-lower bound, and “non-ZLB” indicates the outside of the ZLB period.

72