HIAS-E-66

The Public Investment Multipliers:
Evidence from Stock Returns of Narrowly Defined Industry inJapanNobuyuki Kanazawa
Hitotsubashi Institute for Advanced Study, Hitotsubashi University

March 2018

Hitotsubashi Institute for Advanced Study, Hitotsubashi University
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The Public Investment Multipliers:
Evidence from Stock Returns of Narrowly DefinedIndustry in Japan
Nobuyuki Kanazawa⇤
March 30, 2018
Abstract
This paper provides an evidence on the macroeconomic impacts of public investment. I extract public investment news shocks from the excess return ofnarrowly defined road pavement firms in Japan and use them as an instrument
for future government spending. I found that the extracted news shocks predict
the future government spending through changes in future public investment.
The result shows that the public investment multiplier is 1.64 a year after theshock and about 5 after four years, which is considerably larger than fiscal multipliers estimated in previous studies. The findings highlight the importance ofthe types of government spending, in addition to the amount of the spending,in determining the overall e↵ects of countercyclical fiscal policies.

Keywords: Fiscal multiplier; Stock returns; Public Investment; Infrastructure
Investment.
JEL Classification: E32;E62;H54.

⇤

Hitotsubashi Institute for Advanced Study (HIAS), Hitotsubashi University (email:n.kanazawa@r.hit-u.ac.jp).

11

Introduction

What are the macroeconomic impacts of public investment? Despite that public investment typically constitutes a large part of countercyclical fiscal packages, researchon its impacts is relatively limited. Much of the current literature, instead, focuseson the defence spending variation to estimate the e↵ects of government spending. In
this regard, there is a gap between the state of knowledge about the e↵ects of government spending, which is mostly learned from defence or total government spending,and the estimate needed to assess the fiscal policies today, which is the e↵ects of the
public investment that are frequently used for countercyclical stimulus packages. The
distinction between the two types of spending could be significant; while the government spending is usually treated as a waste in macroeconomic models, the public
investment is likely to improve productivity as reported by Fernald (1999). The goalof this paper is to fill this gap by estimating the public investment multipliers by
exploiting the variation in public investment news shocks, which is extracted fromthe excess return of road pavement firms in Japan.
The challenge in estimating the public investment multipliers stems from the two
common characteristics of this type of spending. First is the long implementationlag, which makes the spending highly predictable. As emphasized by Ramey (2011b)
and Leeper, Walker, and Yang (2013), when government spending is anticipatedlong before the actual outlays, failure to control for the anticipation e↵ects leads
to incorrect inference. Since infrastructure construction takes years to complete, theanticipation e↵ects associated with estimating its impacts is likely to be substantial.
On a related note, Ramey (2016) highlighted the difficulty of controlling for the
anticipation e↵ects in the US by pointing out that “most of the spending on the
U.S. highway system was anticipated once the highway bill was passed in 1956.” Theinfrastructure investment in Japan, on the other hand, has been driven by a sequenceof long-term plans, which are frequently revised and renewed, providing unexpectedvariations in how much infrastructure investment was to be conducted in the near
future.1 Together with this variation in the infrastructure investment, I overcome theproblem of anticipation e↵ects using the excess return of the road pavement firms
in Japan following Fisher and Peters (2010). As per the efficient market hypothesis,
1
Public investment = infrastructure investment + public inventory investment. Sine infrastructure investment accounts for the majority of the public investment, I will use public investment andinfrastructure investment interchangeably.

2

any movement in the stock prices reflects unexpected shocks to the beliefs about
future profits of the firms. If the profitability of the selected road pavement firms
depend heavily on public road investments, the shocks to their excess returns canbe interpreted as a surprise public road investment news shock. Moreover, since theroad investment constitutes the largest fraction of public investment, the public roadinvestment news shocks could generally be interpreted as public investment news
shocks. The use of excess return of road pavement firms makes it possible to extract
the public investment news shocks that are less likely to su↵er from the problem ofanticipation e↵ects.
The second challenge is related to the endogenous nature of the infrastructure
investment. Even though the public infrastructure spending is mainly motivated by
the long-term goal of developing a stock of infrastructure, the spending is often used
for countercyclical stimulus purposes. This makes simply estimating the multipliers
using the excess return of the road pavement firms inappropriate. For this reason,
I extract a purified measure of excess returns by regressing the excess return on
a number of contemporaneous and lagged economic and financial variables. The
residuals from the regression is my measure of excess return that should be orthogonal
to other shocks that a↵ects current and future output.2 I verify that the extracted
news measure is orthogonal to the current state of economy by conducting a series ofrobustness checks.
The extracted excess return shocks are noisy measures of news shocks about future infrastructure spending. To this end, I employ the local projection-IV method
using the extracted measure of the excess return shocks as an instrument variable
and estimate the macroeconomic impacts of the public investment. The use of the
IV alleviates the problem of measurement errors likely contained in the excess return news shocks because it uses only the variation in the excess return shocks thatare associated with the variation in the total government spending. As with the
IV identification strategy in the microeconomic literature, my identification strategy
relies on two crucial assumptions, which are that my instrument variable 1) captures news about the future public investment (relevance condition) and 2) a↵ects
output only through the government spending (exclusion restriction). The exclusion
restriction is dealt with by regressing the excess return on current and lagged macroeconomic/financial variables as described above. The relevance condition is directly
2This is same as placing the excess return at the last in the causal ordering of VAR behindmacro/financial variables.

3

testable. I show that the residual variation of excess returns predicts both the future
government spending as well as future public investment in a statistically significant
manner. Importantly. the residual variation does not predict the future government
consumption, suggesting that my measure of news shocks predicts total government
spending only though the public investment component of the spending.
Following Ramey and Zubairy (2016), I estimate the cumulative public investment
multipliers of output. I found the road investment multiplier of about 1.64 a year after
the news shock and about 5 after four years. The estimated multipliers are considerably larger than conventional estimates of fiscal multipliers. These results, however,are in line with other studies that estimated a large public investment multiplier at
the state-level in the US as reported in Leduc and Wilson (2013). The estimatedpublic investment multipliers are robust to di↵erent specifications. Furthermore, thestudy found that the public investment crowds in consumption and investment, andthe multipliers associated with them are both considerably larger than multipliers
in previous studies. Additionally, the public investment multipliers for tax rate andinterest rate are found to be positive and statistically significant, suggesting that thelarge public investment multiplier cannot be explained by passive tax or interest rate
policies. Finally, I show some evidence that is consistent with a declining multiplierof output over time. The evidence suggest that the public investment multipliers on
the current stock of infrastructure might be smaller than the multipliers 30 years ago.

1.1

Related Literature

There is an extensive literature that studied the e↵ects of government spending on
macroeconomy. As summarized by Ramey (2011a), two popular identification strategies are 1) military spending news shocks pioneered by Ramey and Shapiro (1998)
and 2) Blanchard and Perotti (2002)’s identification strategy (BP method hereafter)that imposes an assumption that the government spending does not react to the current economic conditions on a VAR estimation. Using the identification scheme suchas these, the literature typically estimates the government spending multipliers by
exploiting the variation in defence or total government spending. Surveying the large
body of work, Ramey (2011a) concluded that the reliable estimates of fiscal multipliers are between 0.8 - 1.5.3 In contrast to much of the existing studies, I provide
3
Ramey (2011a) also emphasized that any reasonable person can claim the multipliers between
0.6 - 2.0.

4

the estimate of fiscal multipliers using the variation in the public investment and findthat the public investment multipliers are considerably larger than the conventional
estimates of fiscal multipliers.
One of the few studies that explored the e↵ects of public investment is Leducand Wilson (2013). They estimated the e↵ects of road investment at the state-levelby identifying the highway spending news shocks at the local state. Their estimatessuggest infrastructure spending multipliers of 3 on impact and even larger multiplier 6-8 years out. Their estimates of local multipliers are considerably greater thanthe conventional estimates of fiscal multipliers, which suggests that the infrastructure spending might have significantly higher multipliers even at the national level.
Pereira (2000) investigated the e↵ects of highway spending using the BP method andthe US macroeconomic data from 1956 to 1997. He found a multiplier of about 2, but
his estimates are likely contaminated by the anticipation e↵ects because the study
does not control for the changes in expectation.4 Chandra and Thompson (2000)
used the US county data and compare the local earnings before and after the completion of a new highway at the local county. They found that earnings are higher
during the periods of highway construction (1-5 years before completion) compared towhen the highway is completed and that earnings after completion grows over many
years. Leigh and Neill (2011) exploited the institutional setting unique to Australia
and estimated local e↵ects of highway construction. Noticing that local government
spending in Australia is dispensed largely based on discretionary earmarks rather
than formulas, they used political power of localities as an instrument variable forfunds dispersed to communities. They found that local highway funds decreases local
unemployment rates. The current paper contributes to the literature by providing thedirect estimates of the macroeconomic impacts of public investment after controlling
for the anticipation e↵ects.
A number of researchers estimated the fiscal multipliers in Japan. Recently,
Miyamoto, Nguyen, and Sergeyev (2017) estimated the fiscal multipliers in Japan
under the normal periods as well as under the zero lower bound (zlb) periods using
the BP methods after controlling for the chages in expectation. They found the fiscal
multipliers of 0.6 under the normal periods and 1.5 under the zlb periods. Kuttner
and Posen (2002) used the BP method to find the government expenditure multipliers
well in excess of one. Bayoumi (2001) also employed the BP methods and reported
4

As emphasized by Ramey (2011b), the government spending shocks extracted from the BP
method is highly likely to be anticipated5the multiplier of 0.65. Using the same dataset as Miyamoto, Nguyen, and Sergeyev(2017), I find the multiplier of 1.64 a year after the public investment news shocks.
Methodologically, this paper follows Fisher and Peters (2010) who used the excess
return of top three military contractors in the US as a military spending news shock.
Applying the same framework, Morita (2017) used the excess return of constructionindustry and the sign-restriction VAR to estimate the fiscal multipliers in Japan. Recently, Fieldhouse, Mertens, and Ravn (2017) used the excess return of the Fannie
Mae and Freddie Mac to estimate the impacts of their activities on mortgage market
and aggregate economy. Both methodologically and conceptually, this paper is related
to Shioji (2017) and Shioji (2018) who identified the news shock about public investment in Japan by combining the narrative approach and the excess return approach.
He first identified dates in which significant news about future course of public investments are released on major newspapers such as the news about fiscal stimulus
packages, winning the right to host major international sports events, and disasters(e.g. large earthquakes). He then measures the surprise component of the public
investment news shocks by comparing the movements of stock prices between highly
government-dependent construction firms and less dependent construction firms on
those dates. Using this clean identification strategy, he found that public investmentnews shocks have a positive and significant impact on output. The current paper isdi↵erent from Shioji’s in that the current paper takes into account the possibility thatthe expectation grows gradually on non-significant news days and that changes in expectation occurs in a subtle manner. The identification scheme is also di↵erent in thatthe current paper uses the narrowly defined “road pavement” firms, which are notcategorized as highly government-dependent firms in Shioji’s due to the reportedly
low share of public orders received by these road pavement firms5
Finally, the paper is motivated by the findings reported by Fernald (1999) who
reported that when the road investment increases, productivity growth improves disproportionately in more vehicle-intensive industries in the US. Evidenced by disproportionate benefits that the vehicle-intensive industries received, he concluded that5
The reported share of public order for road pavement firms is on average about 30-40% between
1980-2014 according to the annual Financial Report. It is not entirely clear why the reported shareof public order is relatively low for these narrowly defined firms despite that “major contractors”
reported in the same Financial Report are mostly GSE-type agencies such as Japan Highway Public
Corporation and Nippon Expressway Companies. One possible explanation is that the reported
share only includes public orders from the formal government agencies and orders from the GSEtype agencies such as“special companies” including Japan Highway Public Corporation and Nippon
Expressway Companies are classified as private.

6the road investment induces productivity growth. The finding suggests that road investment is di↵erent from other types of government spendings in a sense that while
government spending is usually treated as a waste in macroeconomic models, roadinvestment increases productivity.
The rest of the paper is organized as follows. Section 2 reviews historical background of public investment policies in Japan. Section 3 describes the empiricalstrategy. Section 4 explains the data. Section 5 shows baseline results. Section 6shows estimated multipliers for various macroeconomic variables. Section 7 shows result from rolling estimation. Section 8 discusses the results in light of rational agent
models. Finally, Section 9 concludes.

2

Infrastructure Investment in Japan

This section provides narrative evidence of uncertainty surrounding the history of
infrastructure investment policies in Japan. After the devastation of the WWII, public infrastructure investment was viewed as one of the most important government
policies in Japan. The Japanese infrastructure investment policies have been a↵ectedby various factors that are often irrelevant to the current state of economy such as
the long-term goals of developing stock of infrastructure, an international agreement
between the US and Japan to expand the Japanese domestic demand to correct large
trade imbalances, and the concerns for fiscal sustainability. With each additional variation in these factors a↵ecting the infrastructure investment policy, a new unexpectedvariation is added to the series of public investment shocks.
Among the many factors listed above, the long-term plan for the provision of
infrastructure is the most influential set of factors that has shaped the Japanese infrastructure investment policies. The long-term plans that had an influence over theprovision of infrastructures can be classified into two: 1) high-level comprehensive
plans and 2) component-specific infrastructure plans. Yada (1999) highlighted that
there are four high-level comprehensive plans that reflect the overall stance of theJapanese government toward the country’s economic and social development: theEconomic Planning (EP), Comprehensive National Development Plans (CNDP), National Land Use Planning (NLUP), and Basic Plan for Public Investment (BPPI). Of
these four plans, the latter three plans have direct implications about the provision of
infrastructure, which are summarized in Table 1. The goal of the CNDP was to reduce7the imbalances among di↵erent prefectures. The NLUP supplements the CNDP inthat it aims for equal development of the lands throughout Japan. Finally, the BPPI
was issued after a long series of negotiations between the US and Japan, which finallysettled in 1990, with both sides agreeing on correcting the large and long-running
trade imbalances by expanding the Japanese domestic demand. Because their goals
span for many years (often more than ten years), these plans do not directly aim at
stimulating the short-run economic outcomes.
While the high-level comprehensive plans were useful for publicizing the overall
government’s stance on future course of public investment, they are often criticized
for lack of enforcement and for a low rate of achievement. Kuroda (1996) and Koyama(2011) attribute the cause of the low achievement to the absence of agencies who took
responsibility in implementing the policies. In addition to the three comprehensive
plans, there were 15 long-term plans that are designed specifically for each componentof public infrastructure investment, which are summarized in Table 2.6 Notable features of these plans are that they state the amount of planned investment explicitly
for the next 5 years and that the plans expire and are renewed after every 5 years.
These plans are designed, proposed, and executed independently by various government agencies.7 After initial submission of the proposals, the Ministry of Financereviews them and negotiate with each responsible government agency to determine
the final amount of the planned investment.
If the component specific plans are perfectly determined by the high-level comprehensive plans, then the announcements of new component-specific plans are perfectly
anticipated, resulting in few variations in beliefs about public investment policies
that only come from revisions of the comprehensive plans.8 However, the extent towhich the comprehensive plans have an influence over the component-specific plans
is unclear. The component-specific plans are sometimes directly a↵ected by the highlevel comprehensive plans. The BPPI had a particularly strong influence on thecomponent-specific plans because the BPPI was a bilateral international agreement.
Yet, while the comprehensive plans su↵er from low rate of achievement, the amount
6

Table 3 shows an example of the plans for the road investment.
Including Ministry of Transport, Ministry of Construction, National Land Agency, Forestry
Agency, and Ministry of Agriculture, Forestry and Fisheries. Ministry of Transport, Ministry ofConstruction, Ministry of Land, National Land Agency, and Hokkaido Development Agency were
integrated into the Ministry of Infrastructure, Transport and Tourism in 2001.
8
Though one could argue that the information about each new comprehensive plan is released
partially over time, giving some variations to the beliefs about public investment policies.
78

of planned investment announced in the component-specific plans was often fully realized by the target periods. The evidence is suggestive that the two types of plans
are not totally in accord with each other because if the component-specific plans aresolely based on the comprehensive plans, they should either both fully achieve thegoals or both fail to achieve the goals. Thus, there should be some elements of uncertainty about the amount of planned investment in the component-specific plans
before the announcement. In fact, in rare instances when major newspapers releaseprediction about the amount of planned investment for the component-specific plans,
they usually mispredict.9 Importantly, the 15 component-specific plans were renewed
in di↵erent years, and thus, there are variations in the timing of the announcement
for these 15 plans.
The comprehensive plans and the component-specific plans are both determined
after a series of public and non-public discussions. This institutional setting makes it
difficult to single out exact timing when people’s belief about future course of public
investment changed. Thus, taking into account the possibility that the information
about the future investment policy was revealed in a gradual and subtle manner could
be an important consideration.
After the collapse of the housing bubble in the early 1990s, the Japanese government launched a series of large fiscal stimulus packages, which most often include
plans for public investment and infrastructure construction. With the mounting public debt, however, the public became wary of the fiscal sustainability and, consequently, the long-term plans were successively terminated in the early 2000s. Since
then, most of the component-specific plans were integrated to a single “provision ofsocial capital” plan that does not state explicit amount of planned investment. The
share of public investment per GDP since 2000 declined substantially as shown in
Figure 1.
9
For example, on Feb 11, 1997, the Yomiuri Shimbun predicted the planned investment for the
12th road maintenance plan to be more than 100 trillion yen. The actual amount was 78 trillion yen.
On May 20th, 1982, the Yomiuri Shimbun reported a meeting of national road users association, in
which the participants demanded that the planned investment for the 9th road maintenance plans
to be 50 trillion yen. The actual amount was 38.2 trillion yen.

9

3

Empirical models

This section outlines the empirical strategy of the study. I first discuss the excess
returns of the road pavement firms. I then discuss the local projection-IV estimation.

3.1

Excess Returns of Road Pavement Firms

I follow Fisher and Peters (2010) and treat excess return of road pavement firms as
a news shock about future public investment. If the movement in the stock returnreflect unexpected change in belief about future road investment, the use of excess
return should alleviate the problem of anticipation e↵ects.
The choice of road pavement firms is motivated by the following two facts: 1) theroad construction constitutes the largest fraction of total infrastructure investment,
and 2) the road constructions are almost exclusively initiated by government-related
agencies. First, the road construction has been the single largest component of theinfrastructure investment in Japan, ranging from 20-35% of total public infrastructure investment between 1970-2013 as shown in Figure 2. Since it accounts for thelargest fraction of overall infrastructure investment, the changes in the road investment should be reflected in the overall changes in the public investment or even in the
total government spending. Thus, in an e↵ort to search for a group of firms whose
stock return movement can be translated as a credible indicator for the future public
investment or future government spending, the road pavement firms seems to be firstand appropriate choice. Second, the road investment in Japan is almost exclusively
initiated by government-related agencies. Figure 3 show the survey result from the 50
largest construction firms in Japan, illustrating that the share of road investment orders issued from the government related agencies has been almost always above 90 %since 1985. The dominance of the government-related agencies in the issuance of road
construction orders suggests that the firms that specialize in the road construction areheavily government-dependent. If that is the case, the changes in public investment
policies should be priced in to the road pavement firms’ market valuation. Below, I
conduct a preliminary test to assess the government-dependence of the selected roadpavement firms by checking the correlation between the sales of road pavement firms
and the government spending.
I define road pavement firms according to the 2007 Japan Standard Industry

10Classification. The road pavement firms are the firms whose main segment of activity
is classified as D-0631 (pavement construction). Pavement firms that fall into thiscategory are Nippo, Toa Road Corporation, Maeda Road, Nippon Road, Seiki-Tokyu
Kogyo, Mitsui-Sumiken Road, and Sato-Watanabe. The stock prices for the first
five firms are available from 1977, while stock prices for Mitsui-Sumiken Road andSato-Watanabe are available only from 1996 and from 2013, respectively. In order tomaintain consistency, I drop the latter two firms from the dataset.
Figure 4 plot the detrended (third polynomial) log of real government spending
and the detrended log of real sales for two groups of firms. On the left panel, the salesof top four construction firms (in blue dotted line) and total government spending(in red solid line) are depicted.10 The sales of top four construction firms are studied
here to compare and contrast the results with the road pavement firms. The sales of
top construction firms align closely with the the total government spending until the
housing bubble collapsed in beginning of the 1990s. Immediately after the bubble
burst, the sales of the top construction firms plummeted. The government spending,on the other hand, steadily increased even after the burst of the bubble and eventually
peaked in 1997. The dynamics of the sales of the top four construction firms and total
government spending since then diverged, yielding the contemporaneous correlation
between them of 0.079.
The right panel of Figure 4 shows detrended sales of the five road pavement firmsin blue dotted line and total government spending in red solid line. In contrast to the
top four construction firms, the sales of the road pavement firms line up well withthe total government spending throughout the periods, yielding the contemporaneous
correlation of 0.722. Most notably, the sales did not collapse immediately after the
bubble burst but continued to rise and peaked in 1997 when the total government
spending also peaked. The rise of the sales in the early 2010s also coincides withthe rise of the total government spending. The initial preliminary test suggests thatthe sales of the selected road pavement firms depend significantly on the government
spending and that the stock prices of these firms can be a credible indicator of thetotal government spendings. Additionally, Figure 5 show the relationships between
detrended sales of the firms and the public investment. The figure conveys the samemessage that the correlation between the public investment and the sales of the roadpavement firms is high while the correlation with the sales of top four construction
10
Top four construction firms are: Taisei corporation, Obayashi corporation, Shimizu corporation,
and Kashima.

11

firms is close to zero.
Motivated by these findings, I use the excess return of the selected road pavement
firms as indicators for public investment news shocks. I define excess return to be thedi↵erence between the stock price of the road pavement firms and the average stock
price for the market:
ERtRoad = log(Stock PriceRoad)tlog(Stock PriceMarket
)
t

(1)

where Stock PriceRoad
is the road pavement firms’ average stock price and Stock PriceMarkettt
Road
is the average Nikkei stock price index. Obviously, ERt
is endogenous becauseroad investment was frequently used for stimulus purposes and because it likely
reflects overall economic conditions. I thus regress ERtRoad on current and pastmacroeconomic and financial variables and use the orthogonalized residuals as instruments/news shocks about public investment policies. This identification strategy
is same as estimating the VAR with the excess return placed at the last in the causal
order.
Following Fieldhouse, Mertens, and Ravn (2017), the excess return shocks aredefined as the residual from the following regression:b t + b(L)Vt 1 + erRoad
ERtRoad = ↵b + ⇠Wt(2)where Wt includes log di↵erence in output, government spending, tax income, difference in unemployment rate, GDP deflator, log of fiscal stimulus package, andrecession/exit indicator. Vt 1 includes four lags of all the variables in Wt and thefour lags of ERtRoad , excess return of Nikkei construction industry, 1-year, 3-year and5-year Japanese Government bond rates, and log change in real exchange rates. The
baseline model does not include the current financial variables as the controls because
assuming that the ERtRoad does not a↵ect the current financial variables might be too
restrictive. In the robustness check, I show a version of result that includes current
financial variables as the controls. The residual from the regression, ertRoad , is themeasure of public investment news shocks that are orthogonal to the current state ofmacroeconomy.

12

3.2

Measuring multipliers

Using the extracted measure of public investment news shocks, I estimate the e↵ectsof public investment shocks using the local projection IV method (Jordà (2005) and
Ramey and Zubairy (2016)). The local projection method estimates impulse response
functions directly by regressing a variable of interest h-periods ahead on shocks andlagged controls variables. The local projection IV method is same as the local projection method except that it uses the shocks as instruments for a variable and use themto estimate the causal impacts of the variable of interest on the dependent variable.
One of the advantages of the local projection method is that it is a model-free estimation and does not require imposing linear restrictions on the estimated dynamic
responses.
I first estimate the impulse responses to a shock in ertRoad . I estimate the following
regression for di↵erent horizons h:
yt+h = ↵h +Road+ ✓h (L)Xt 1 + ⌫t+h
h ert

(3)

where yt+h = Yt+hYt Y1t 1 is the sum of output from t to t + h normalized by output
in period t 1. ertRoad is the measure of public investment news shocks extracted
from equation 2. The regression also includes controls, Xt 1 , which are four lagsof log output, government spending, tax income, as well as level of unemployment
rate, and ERRoad . My choice of including the unemployment rate as the control
variables is based on Barro (1981) and Barro and Redlick (2011), who argued thatthe unemployment rate contains extra information about the business cycle movementcompared to output data.
To take into account the dynamic aspect of responses in output and government
spending, I calculate the cumulative output multiplier following Ramey and Zubairy(2016):hh
X
X
Gt+j Gt 1
yt+j = h + Mh
+ h (L)Xt 1 + ut+h
(4)
Yt 1
j=0
j=0
P
G
G
where Gt is the government spending in periods t and hj=0 t+jYt 1 t 1 is the sum of
the government spending from t to t+h normalized by output in period t 1. Because
both dependent variable and government spending are normalized by output, Mh has
the direct interpretation as the cumulative output multiplier. Mh is estimated using
the instrument, ertRoad . The controls are the same as equation 3. I calculate 90%
13confidence bands using the heteroskedasticity and autocorrelation consistent (HAC)
standard errors. I choose automatic bandwidth selection for estimation.

4

Data

Most of the data are identical to Miyamoto, Nguyen, and Sergeyev (2017). All variables are expressed in per capita and deflated by the GDP deflator except for thefinancial variables. The stock prices for road pavement firms as well as Nikkei average are from the Nikkei FinancialQuest. I use the adjusted closing price on thelast day of each quarter for the stock price of the firms. As Miyamoto, Nguyen,
and Sergeyev (2017) notes, data for government spending are created as the sum ofadjusted government consumption and public investment so that the data are consistent with other related studies. Adjusted government consumption is calculated as
the government consumption less transfer of goods.11 Tax data, obtained from the
National Accounts starting in 1980Q1, are the total of direct and indirect taxes minus
subsidies. The dataset spans from 1980Q2 to 2014Q1.

5

Results

This section shows the estimated results. I first describe the extracted news shock
and discuss their relevance as an instrument variable for government spending. I thenestimate the public investment multiplier of output.

5.1

Extracted shocks

The extracted shocks, ertRoad , from equation 2 are shown in Figure 6. The extracted
shocks are very volatile and there is no noticeable sign of state-dependence. I test
the relevance condition of the IV by estimating the regression equation 3 with thedependent variable specified as the cumulative change in the government spending
normalized by output. Figure 7 show impulse response of government spending following a shock in ertRoad . The response of government spending is initially zero, but
11

Please refer to Appendix 7 of Miyamoto et al. (2017) for the discussion of the construction of
the adjusted government spending.

14

it gradually increases and peaks in the 5th quarters. The response is positive andstatistically di↵erent from zero except for the first few quarters. Figure 8 shows theassociated robust F-statistics. The F-statistic is initially low, but it increases shapely
and peaks in the 5th quarter. The F-statistics exceeds the threshold value of 23.1
between the 3rd quarter and the 7th quarter. The result is somewhat strinking, giventhat Ramey (2016) reported that the F-statistics of Fisher and Peters (2010)’s military spending news shocks does not exceed 5 for all horizons when its relationshipwith the total government spending was examined. In addition, the shape of the Fstatistics is di↵erent from the shapes of F-statistics using a typical BP shock, in which
the peak would appear in the very short horizons. The fact that the F-statistics only
peaks after 4 quarters indicates that the extracted shocks capture “news” element of
the government spending. In fact, the F-statistics from the military spending news
shock by Ramey and Shapiro (1998) exhibits a similar pattern, in which the peak
only comes a year after the shock.
Even though the F-statistics indicates that the extracted measure of news shocks
predicts the total government spending, it is not clear what component of the spending
was predicted by the news shocks. To credibly estimate the public investment multipliers, the news shocks should predict the total government spending only throughpublic investment component of the spending and not through public consumption.
To this end, I estimate the regression equation 3 using (a) public investment and (b)public consumption as a dependent variable.12 The left panel of Figure 9 show the
response of public investment. The response is adjusted so that the peak response
becomes 1. The pattern of response is similar to the total government spending; theinitial response is close to zero, but it increases as the number of horizon increases.
The response is statistically significant for almost all horizons. The right panel of
Figure 9 show the response of government consumption. The response is adjusted sothat the magnitude of the shock is the same as the shocks that increased the public
investment by 1 at its peak. In contrast to the response of public investment, theresponse of public consumption is weak and is not positive. Instead, the results indicate that the responses of public consumption are sometime weakly negative at longer
horizons. The evidence suggests that the extracted measure of news shocks predict
the increase in total government mostly through the increase in public investment
component of the government spending and not through the public consumption.
12

Both of the variables are expressed as a cumulative sum and normalized by the output.

15

5.2

Output multipliers: baseline model

I now present the responses of output to an unexpected public investment news shockthat increases government spending by 1 percent of output using equation 3. The
impulse response of output is shown in Figure 10. The response is initially zero, but
it gradually increases and peaks at 4.9 percent in the 12th quarter. The output, then,eventually returns to zero in the 24th quarter.
Next, I estimate the output multiplier that takes into consideration the dynamics
of the government spending response. Figure 11 plot the output multipliers and theirconfidence bands. I omit the initial 2 quarters from the figure for an illustration
purpose because the confidence bands are too wide due to the low F-statistics during
these horizons. The result shows that the output multiplier is 1.64 four quarters after
the shock. This estimate is larger than conventional estimates of output multipliers
for the US and other countries including Japan. The multiplier, however, is in linewith the on-impact fiscal multiplier during the zlb periods as reported in Miyamoto,Nguyen, and Sergeyev (2017) and is smaller than the road investment local fiscal
multiplier reported by Leduc and Wilson (2013). The multiplier steadily increases
and peaks in the 17th quarter at 4.95, which is considerably larger than the fiscal
multiplier even during the zlb periods and is in line with the peak multiplier from
Leduc and Wilson (2013). The multiplier then starts to slowly decline. The 90%
confidence bands are initially large and include zero at short horizons, but they shrink
as the number of horizon increases, making the multiplier statistically significant at
the middle and long horizons.

5.3

Robustness check

I perform several robustness checks of the baseline results. To make sure that theextracted excess return shocks are orthogonal to the current state of economy, I add
more control variables to equation 2. Specifically, I performed the following four robustness checks: 1) use public investment deflator instead of GDP deflator, 2) add
public works order, 3) add public construction order, and 4) control contemporaneous financial variables. The public investment deflator is used instead of the GDP
deflator because it might be a more suitable indicator for the cost of infrastructure
construction. Public works order and public construction orders are added so thatthe residual variation in the excess return do not respond to the increase in the public
16

investment orders that are not yet reflected in the public investment data. All results
are shown in Figure 12, and they remain largely the same as the baseline results.
The only meaningful di↵erence is found when contemporaneous financial variables
are included in equation 2. In this case, the multiplier at the short horizons becomes
more than twice larger than the baseline multiplier, which suggests that the assumption of no contemporaneous e↵ects from the excess return of road pavement firms
to other financial variables might be too restrictive. Despite this di↵erence at the
shorter horizons, the multiplier at middle and longer horizons remains close to thebaseline result.
I also examine how changing the baseline specification influence the estimates ofthe multipliers. I first include a quadratic time trend. Second, I normalize changes
in government spending and output in the equations 3 and 4 by a potential output
estimated by the HP-filter. With the exception of multipliers at the longer horizons
for the case of the quadratic trend, the results are similar to the baseline model. Whenthe trend is included, the standard errors become wider at the longer horizon, whichreflects low F-statistics during these horizons. However, the multipliers at shorterand middle horizons remain similar to the baseline results even when the trend isincluded. Normalization with the potential output does not make any meaningfuldi↵erence in the estimated results.

6

Multipliers of Other Variables

I found that the public investment multipliers are considerably larger than conventional estimates of government spending multipliers. I investigate if the public investment multipliers for other macroeconomic variables are also di↵erent from theprevious studies.

6.1

Consumption and investment multiplierAs with the case for output multiplier, I compute the cumulative multipliers for
consumption and investment. For example, I estimate the consumption multiplier

17

using the following regression:hX

ct+j =

j=0Ccch + Mh

h
X
Gt+j
j=0

Gt 1

Yt 1

+

c
h (L)Xt 1 + ut+h

(5)

C

where ct+j = t+jYt 1 t 1 . Mhc is the cumulative consumption multiplier and is estimatedusing the instrument, ertRoad . The controls are the same as equation 4 plus the fourlags of the dependent variable.
The left panel of Figure 13 show the multiplier of consumption. The multiplier isabove 1 starting from the second quarter and stays at around 1 for more than 20 quarters. The investment multiplier, on the other hand, is close to zero at short horizons
and gradually rises to 3 by the 17th quarters. The consumption multiplier is similar
to the estimated consumption multiplier during the zlb periods in Miyamoto, Nguyen,
and Sergeyev (2017). The investment multiplier is considerably larger than the estimated investment multipliers both during the normal and zlb periods in Miyamoto,Nguyen, and Sergeyev (2017), in which the multiplier even during the zlb peaks ataround 1.2. The result shows that the public investment multipliers for consumption
and investment are also very large.

6.2

Unemployment multiplier

To investigate if the public investment has a similarly large impact on labor market,
I estimate the multiplier of the unemployment rate. As with the the output andother multipliers, I estimate the multiplier of the unemployment rate as the cumulative percentage point change in the unemployment rate in response to a change ingovernment spending by 1 percent of output. Figure 14 show the multipliers of theunemployment rate. The multiplier is weakly positive during the first three quarters,
which then gradually decreases and reaches -0.7 in the 17th quarter. The positive
multiplier at short horizon is not statistically significant, while the negative unemployment rate multiplier at longer horizons is statistically significant at 90% level.
The unemployment rate multiplier is similar to the multipliers during the zlb periods
in Miyamoto, Nguyen, and Sergeyev (2017). Since my sample include both normal
and zlb periods, the result is indicative that the public investment multiplier for theunemployment rate is also greater in magnitude than the previous estimates of thegovernment spending multiplier.
18

6.3

Tax multiplier and interest rate response

What explain the larger public investmnet multipliers? Ramey (2011a) highlighted
that the magnitude of multipliers crucially depends on the tax and monetary policy
responses. She explained that the government spending multipliers are generally
larger when the spending is deficit financed and when monetary policy response is
weak. To explore the possibility that tax and interest rate responses explain thelarge public investment multipliers, I estimate the tax multiplier and the interest
rate response. The tax rate is defined as a ratio of tax revenues to output. As withthe case of other multipliers, the cumulative multipliers of the average tax rate are
estimated using equation 5. The left panel of Figure 15 show the tax multiplier. The
tax multiplier is initially close to zero, but it increases to 2 at its peak in the 17thquarter. Since the average tax rate seems to respond positively to the government
spending, the large public investment multipliers is unlikely to be explained by the
tax response.
The right panel of Figure 15 show the response of short-term nominal interest
rate. The response is calculated using equation 3 with the dependent variable being replaced with the interest rate h periods ahead. The figure indicates that the
short-term interest rate responds positively to the public investment news shocks.
The result suggests that the large public investment multiplier is also unlikely to be
explained by the weak monetary policy response. These results suggest that the estimated public investment multiplier is large not because of unresponsive tax or interest
rate policies, but is likely due to other reasons including di↵erent characteristics ofthis type of spending.

6.4

Inflation rate multiplier

Lastly, I estimate the cumulative multiplier of inflation. The multipliers are estimatedusing the impulse response of the nominal price index over the cumulative change ingovernment spending as a dependent variable in the equation 5. I use both the GDP
deflator and the CPI measures of inflation, and they both produce similar results.
Figure 16 show the multipliers of inflation using the GDP deflator on the left
panel and using the CPI measure on the right panel. The results from both measureof inflation indicate that the news shock is deflationary in the initial 7 quarters.
19

The inflation rate multipliers then rise to a positive territory after 12 quarters andbecome statistically significant. In summary, the public investment shocks are initiallydeflationary but increase the inflation rate at longer horizons.

7

Rolling estimation: declining output multiplier?

Previous sections show that the public investment multipliers on average is larger
than the conventional estimates of government spending multipliers between 1980
and 2014. Another important policy relevant question is: what is the public investment multipliers given the current stock of infrastructure? If the public investment isdi↵erent from other government spending for its productive capacity, one would thinkthat the e↵ects of public investment might depend on the marginal gain in the aggregate productivity due to the additional stock of public investment. To investigate
this question, I estimate a twenty-year rolling-window regression using the equation
4.
Figure 17 show the cumulative output multipliers in the 4th quarter (on the left)
and in the 6th quarter (on the right) using di↵erent twenty-year sample periods. The
result is indicative of the declining output multipliers over time. The fourth quarter
multiplier estimated using the 1981-2001 window is about 4 whereas the multiplierusing 1990-2010 window is about 0.2. Despite that the standard error generally
increases as the sample periods cover more recent periods, there is a general trendthat the estimated multiplier declines over time. The declining trend is less obvious forthe 6th quarter, but the trend is evident prior to 2001. The analysis does not provide
rigorous statistical evidence of the declining multipliers, but it is suggestive that the
public investment multipliers on the current stock of infrastructure are smaller thanthe multipliers 30 years ago.

8

Are the findings consistent with rational agent
model?

I found that the public investment multiplier of about 1.64 a year after the shock andabout 5 after four years, which is considerably larger than conventional estimates ofgovernment spending multipliers. I discuss briefly if the findings can be rationalized20

using the macroeconomic models.
Despite that the fiscal multiplier is one of the most debated topics, much of thetheoretical models treat the government spending as wastes. Few theoretical models
studied the role of productive government spending. One of these exceptions are Baxter and King (1993), who showed that public investment multipliers can be between
4 and 13 in the long-run. Another example is Leduc and Wilson (2013) who built
an open economy, monetary union model to study the e↵ects of public investment.
Their model produced on-impact multiplier of 0.3 and peak multiplier of 2. They
also reported that highly persistent spending shock can produce peak-multiplier of
7. Morita (2017) built a model with price and wage stickiness, rule-of-thumb households, debt financing, and wage union, and computed the multiplier of about unity.
Using a neoclassical growth model, Uhlig (2010) showed the the public investment
multiplier can exceed unity in the short-run, but it could also be negative in the longrun depending on tax policy. Finally, Leeper, Walker, and Yang (2010) showed thatmultipliers depend on the implementation lag and that substantial delay produces
negative multiplier.
Overall, the public investment multipliers of 2 to 5 do not necessarily contradict
with the multipliers implied by the existing macroeconomic models. Future research
agenda includes building a theoretical model to explore a mechanism behind theempirical findings of the current paper.

9

Conclusion

In this paper, I estimated the government spending multipliers using the public investment news shocks extracted from the excess return of road pavement firms in Japan.
I found that the residual variation in the excess return of road pavement firms predict
the future government spending and that it predict the government spending throughchanges in public investment.
Using the residual variation in the excess return, I found that the public investment multipliers are considerably larger than the government spending multiplier
reported in previous studies. The estimated public investment multipliers are about
1.64 a year after the shock and about 5 after four years. The public investment
are also found to crowd in consumption and investment. Moreover, the cumulative21

multipliers of consumption and investment are both larger than the estimated multipliers in previous studies. In addition, similarly large public investment multipliers
are reported for the unemployment rate.
I highlighted that the large public investment multipliers are unlikely to be explained by unresponsive interest rate and tax rate policies as both rates increase after
the increase in public investment. Interestingly, the inflation rate is found to go down
initially after the spending shock but it rises after 2-3 years.
Finally, I show an evidence that is consistent with the hypothesis of a declining
multiplier. Using rolling estimations, I found that the public investment multipliers
exhibit a general declining trend over time. The evidence is suggestive that the
public investment multiplier depends on the marginal productivity of the investment
and that the public investment multipliers on the current stock of infrastructure aresmaller than the multipliers 30 years ago.
This paper provides one of the first direct macroeconomic estimates on the impactsof public investment. The findings suggest that, in addition to the amount of the
spending, the type of government spending is also an important consideration when
conducting countercyclical fiscal policies.

22References
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25

Tables
Table 1: Comprehensive Public Investment Plans(A) Comprehensive National Development Plan (CNDP)
Approved on
Coverage periods
Comp. National Dev. Plan
2nd Comp. National Dev. Plan
3rd Comp. National Dev. Plan
4th Comp. National Dev. Plan
5th Comp. National Dev. Plan

1962.10.5
1969.5.30
1977.11.4
1987.6.30
1998.3.31(B) National Land Use Planning (NLUP)
Approved on
National Land Use Plan.
2nd National Land Use Plan.
3rd National Land Use Plan.
4th National Land Use Plan.

1976.5.18
1985.12.17
1996.2.23
2008.7.4(C) Basic Plan for Public Investment (BPPI)Approved on
Basic Plan for Pub. Invest.
New Basic Plan for Pub. Invest.

1990.6.28
1994.10.7

Planned investment

target year: 1970
1966-1985
1976-1989
1986-2000
target year: 2010-2015

N/A
130-170 trillion yen
370 trillion yen
1000 trillion yen
N/ACoverage periods

Planned road investment

1972-1985
1982-1994
1992-2005
2004-2017

210,000 ha
240,000 ha
200,000 ha
70,000 ha

Coverage periods

Planned investment

1991-2000
1995-2004(2007)

430 trillion yen
630 trillion yen

Notes: coverage periods in fiscal years. Planned investment for 2nd, 3rd, and 4th CNDP are expressedin yen in 1965, in 1975, and in 1980, respectively. Planned investment for NLUP include various goals
including roads, farm land, forest, plain field, rivers and waterways, and housing land. The table shows
the goal for road pavement only. Planned investment for BPPI are expressed in nominal values at thetime of plan approval. The coverage periods for New Basic Plan for Public Investment were initially
1995-2004 but were later expanded to 1995-2007.

26

Table 2: List of Long-term Infrastructure Plans

(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)

First plan began induration

discontinued in

1951
1954
1960
1992
1972
1963
1963
1970
1961
1967
1966
1966
1976
1965
1983

5 years
5 years
5 years
5 years
5 years
5 years
5 years
5 years
5 years
5 years
5 years
5 years
5 years
10 years
5 years

2001
2002
2003
2003
2002
2002
2002
2002
2002
2002
2002
2000
2001
continuing
2002

Fishing Harbors
Roads
Erosion and Flood Controls
ForestMunicipal Parks
Sewage System
Waste Disposal
Coastal Areas
Dock and Harbor
AirportTraffic Safety Facilities
Housing
Coastal Fishing Ground
Land Improvement
Steep Slope Failure Prevention

Notes: in fiscal year.

Table 3:Plans

1st plan
2nd plan
3rd plan
4th plan
5th plan
6th plan
7th plan
8th plan
9th plan
10th plan
11th plan
12th plan

5-year Road Investment/Maintenance
Begins inEnds inPlanned investment

1954
1958
1961
1964
1967
1970
1973
1978
1983
1988
1993
1998

1958
1962
1965
1968
1971
1974
1977
1982
1987
1992
1997
2002

0.26 trillion yen
1 trillion yen
2.1 trillion yen
4.1 trillion yen
6.6 trillion yen
10.35 trillion yen
19.5 trillion yen
28.5 trillion yen
38.2 trillion yen
53 trillion yen
76 trillion yen
78 trillion yen

Notes: in fiscal year.

27

Figures
Figure 1: Government spending and public investment as a share of GDP(a) Government Spending/GDP(b) Public Investment/GDP

Notes: Data from SNA.

28

Figure 2: Percent of Road Investment

Notes: Data from Comprehensive Statistics on Construction (Kensetsu-Sougou Toukei) conductedby Ministry of Land, Infrastructure, The series in the figure is calculated by dividing the road
construction orders by total infrastructure construction orders, both in 12-month moving-average.

Figure 3: Share of Road Construction Orders from Government-related Agencies

Notes: Data from Current Survey on Orders Received for Construction, big 50 constructors (Agroup survey) conducted by Ministry of Land, Infrastructure, Transport and Tourism. The series in
the figure is calculated by dividing the road construction orders by total infrastructure construction
orders, both in 12-month moving-average.

29

Figure 4: Sales and total government spending(a) Sales of top 4 construction firms(b) Sales of 5 road pavement firms

Notes: Top 4 construction firms are Taisei corporation, Obayashi corporation, Shimizu corporation,
and Kashima. Road pavement firms are Nippo, Toa Road Corporation, Maeda Road, Nippon Road,
Seiki-Tokyu Kogyo
Source: Annual financial report

Figure 5: Sales and public investment(a) Sales of top 4 construction firms(b) Sales of 5 road pavement firms

Corr(Salest , Pub.Invt ) = 0.028

Corr(Salest , Pub.Invt ) = 0.71730Figure 6: ertRoad

Figure 7: Government spending impulse response

Notes: Shaded are represents 90% confidence bands. The responses are adjusted so that the peake↵ect is 1.

31

Figure 8: First stage robust F-statistic

Notes: The threshold is 23.1 for one instrument for the 5% critical value for testing the null hypothesisthat the two-stage least squares bias exceeds 10% of the OLS bias, and 19.7 for 10% critical value.

Figure 9: Response of public investment and public consumption(a) Public investment(b) Public consumption

Notes: Shaded are represents 90% confidence bands. The responses are adjusted so that the peak
response of public investment is 1.

32

Figure 10: Response of output

Notes: Shaded area represents 90% confidence bands

Figure 11: Cumulative output multipliers

Notes: Shaded area represents 90% confidence bands

33

Figure 12: Robustness checks(1) Public investment deflator(2) Public works order(3) Public construction order(4) Contemporaneous financial var.

(5) With trend(6) Potential output

34

Figure 13: Consumption and investment multipliers

Consumption

Investment

Notes: Shaded areas represent 90% confidence bands

Figure 14: Unemployment rate multipliers

Unemployment rate
Notes: Shaded areas represent 90% confidence bands

35

Figure 15: Tax multiplier and interest rate response

Tax

Short-term nominal rate

Notes: Shaded areas represent 90% confidence bands

Figure 16: Inflation multipliers

Inflation (GDP deflator)

Inflation (core CPI)

Notes: Shaded areas represent 90% confidence bands

36

Figure 17: Rolling estimation(a) 4 quarters after the news(b) 6 quarters after the news

Notes: The year of a reported multiplier corresponds to the middle year of the 80-quarter window.
For example, a multiplier reported for 2000Q1 is estimated over the period 1990Q1-2010Q1.
Notes: Shaded areas show confidence bands with 1 standard error

37