Interactions between Regional Public and Private
Investment: Evidence from Japanese Prefectures

Tomomi Miyazaki

March 2016
Discussion Paper No.1608

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANInteractions between Regional Public and Private Investment:
Evidence from Japanese Prefectures
Tomomi MiyazakiGraduate School of Economics, Kobe University, 2-1, Rokkodai-cho, Nada-ku, Kobe, Hyogo
657-8501, Japan

This paper examines the effects of government investment on private sector capital
investment by investigating Japanese prefecture data. Empirical results show that
crowding-out effect is observed in some sectors especially in rural areas where thegovernment had invested heavily before the 1990s. This suggests that public
investment is not necessarily useful to stimulate the economic activities throughprivate capital formation there.

JEL classification: E62, H54, R42

Keywords: Regional public investment; Crowding-in/out effect; Sector investment

I would like to thank Takao Fujii, Keigo Kameda, Masumi Kawade, Haruo Kondoh, Hideki Konishi,
Joongho Kook, Shigeki Kunieda, Isidoro Mazza, Tomoya Mori, Hiroshi Morita, Sei-lu Mun, Hikaru Ogawa,
Etsuro Shioji, Masao Tsuri, Takashi Unayama, and Masato Yodo for many useful comments. Delegates at
the Summer Workshop on Economic Theory and seminar participants at Kyoto Sangyo University, Waseda
University, and Kyoto University are also acknowledged. This work has been financially supported by theJapan Society for the Promotion of Science (Grant-in-Aid for Scientific Research # 26380361) and
Financial Aid for Enhancement of International Research Collaboration by Kobe University. The usual
disclaimer applies.
E-mail: miyazaki@econ.kobe-u.ac.jp

11. Introduction
In the wake of the 2008 global financial crisis, governments in many developed countries
included region-specific infrastructure investment in economic stimulus packages. For
example, the American Recovery and Reinvestment Act of 2009 provided close to one billion
dollars to upgrade drinking water to rural areas and ensure adequate water supplies indrought-affected western US communities. Furthermore, Australia's third stimulus
measure included a provision for local community infrastructures.1 These facts tell us thatpolicy makers might earmark public investment for specific local districts as part ofstimulus packages to support income distribution in a region rather than macroeconomic
stabilization.
The purpose of this study is to examine the regional effects of public investment on
business investment by examining data from Japanese prefectures. In the 1990s, when thegovernment formulated numerous economic stimulus packages, public investment inJapan was more frequent in rural areas than in urban regions as argued in Section 3.1. Thismeans that the approaches presented above were pursued in practice even before the 2008
global financial crisis in Japan, thereby implying that policy makers were expecting public
investment to a positive effect on the demand side of the economy in rural areas. However,public investment has two asymmetric effects, especially in terms of private sector capital investment:

1

For details, see OECD (2009), Stoney and Krawchenko (2011), and the website of Recovery.gov(http://www.recovery.gov/).

2

crowding-in and crowding-out. If crowding-out effect is observed in rural areas, public investment

discourages private business investment there, which thereby contradicts the intent of the policy makers.
Although some recent studies have examined the regional interaction between public andprivate investment in Japan, it remains controversial. Kawasaki et al. (2013) examine theeffects of public capital on private business investment using prefecture-level data andobserved crowding-in effect. Moreover, Kameda (2015) examines the regional effects ofpublic investment on the index related to the value of shipments and employment by usinga panel VAR (vector autoregression) model and concluded that public investment has
positive effects on these indices. However, Brückner and Tuladhar (2013) reported thatpublic construction has a positive but insignificant effect on regional private investment.
Incidentally, these earlier studies neglect the distinction of various sectors investment inresponse to public investment, and regional distinctions such as urban or rural. To deal
with these, first, we examine the behavior of sector capital investment in response to public
investment by dividing the private investment into sectors such as finance and insurance,transportation and communication, and utilities. Second, we estimate the effects of public
investment by separating the prefectures into urban an area group and a rural one. By
doing this, our study can be helpful in ascertaining whether or not regional public
investment is useful in rejuvenating the economic activities in a specific region throughcapital formation in the private sectors.
3This paper is organized as follows. Section 2 summarizes the explanation of the economic
stimulus packages in Japan. Section 3 presents the data, the testable hypothesis, and theempirical framework underlying this research. Section 4 reports the estimation results. We
show that while the investments in the transportation and communication industries arestimulated by public investment, the crowding-out effect appears in some industries suchas the finance and insurance and services especially for 36 prefectures classified into rural
areas. Our results imply that policy makers should pay more attention to this whenplanning economic stimulus involving regional public investment in rural areas where thegovernment had invested heavily before the 1990s. Section 5 presents our conclusion.

2. Background: Economic stimulus packages in Japan and the movement of public
investment
Public investment (public work) has been frequently included in economic stimulus
packages in Japan over the past decades. Specifically, stimulus packages implemented inAugust 1992, April 1993, and September 1995 have comprised more than half of Japan’s
stimulus packages. Table 1 details these fiscal stimulus packages in Japan in the 1990s.
Before the 2008 global financial crisis, most developed countries mainly used monetary
policy for macroeconomic stabilization. However, as shown in previous research, such as
4

Ihori (2006), Doi and Ihori (2009), Miyazaki (2010), Asako (2012), and Miyazaki (2015), theJapanese government had actively used fiscal policy for economic stabilization even before
the crisis. Above all, the reason public investment has been used, as shown in Table 1, isthat it has been expected to serve as economic stimulus, redistribution of income among
regions, and infrastructure-improvement, as presented by Asako (2012).
Here, we examine whether or not the Japanese government also implemented public
investment as regional income redistribution by depicting the changes in per capita public
investment to prefectural GDP (hereafter PGDP). These changes are shown in Figures 1a
to 1f, which cover the periods 1980-2009, for six geographic groups: Hokkaido and Tohoku,
Kanto, Chubu and Koshinetsu, Kansai, Chugoku and Shikoku, and Kyushu and Okinawa.
All figures show that the per capita public investment to PGDP was large in rural areas
such as Aomori, Iwate, Kochi, and Kagoshima in the 1990s, when the government often
planed economic stimulus packages. However, the ratio of public investment to PGDP did
not change significantly in urban areas such as Tokyo, Kanagwa, Aichi, Osaka, andFukuoka during our sample period.
This tells us that the Japanese government has used public investment for regional
income redistribution rather than macroeconomic stabilization. Therefore, as we havealready claimed, the Japanese case is worthy of investigation to further examineinteractions between public and private regional investment.
53. Methodology: Empirical framework
3.1. Datasets
Data on public investment (public capital formation of general government), prefectural
GDP and population came from the database of the 2013 prefectural economic and fiscal
model of the Cabinet Office in Japan (accessed last August 1, 2015).2 Data on private
investment came from the prefectural private capital stock provided by the Cabinet Officein Japan (accessed last on August 1, 2015).3 There are nine comparable sectors; agriculture,
forestry, and fisheries, mining, manufacturing, utilities, wholesale and retail, finance andinsurance, real estate, transportation and communication, and services (for example,
personal services such as entertainment, education, hospitality, and welfare and hospitals,
and business services such as advertising and automobile and mechanical repairs). This
follows Fujii et al. (2013). The “aggregate investment” that we examine is the sum of theinvestment of these nine sectors. The construction industry is also included in the
categories from the Cabinet Office. However, the increase in the investment in theconstruction industry is difficult to identify. This increase is obscured by the expectation of
the industry’s productivity improvement, assumed as the crowding-in effect that we define
further later, and the purchase of the materials and/or machinery accompanying the work2

For more detail, please see http://www5.cao.go.jp/keizai3/pref_model.html. Notably, we cannot exclude
the housing investment from public capital formation in this database. Therefore, we had to conduct theempirical investigation including public sector housing investment.
3

http://www.esri.cao.go.jp/jp/sna/data/data_list/kenmin/files/contents/main_h21stock.html.

6orders from the government. Therefore, we exclude the construction industry.
We use data from the general government excluding other public enterprises. Since somepublic enterprises in Japan have been privatized, companies categorized into suchenterprises have changed during our sample periods. It could be difficult to capture theeffects of such public investment properly if we include these in our estimation, andtherefore, these are excluded.4
Our annual panel covers 47 prefectures in Japan for the periods 1980 to 2009. Since theinitial year of the prefectural economic and fiscal model is 1980, we set 1980 as the firstyear of our sample periods. Furthermore, we set the final year of per capita public
investment to per capita PGDP as 2009 because the data for sector private investment in2010 were not available when we last accessed the website.
All data are expressed in real terms using 2000 as the deflator. Both datasets (public andprivate investment) are calculated on the basis of the System of Integrated Environment
and Economic Accounting proposed by the United Nations in 1993 (SNA93 data).5
We assume that public investment is a public good in that a public capital is a

4 Similarly, there were two major privatizations of public enterprises during this time: Nippon Telegraphand Telephone Public Corporation (currently, Nippon Telegraph and Telephone Corporation, NTT) in
1985; and, Japan National Railways (currently, Japanese National Railways, JR) in 1987. Reflecting this,the data for transportation and communications increased drastically in the mid to late 1980s because
NTT and JR were included in the private sectors following their privatization. Although we limit the
sample periods after 1988 to deal with this for investment in transportation and communications, theestimate results are relatively unchanged compared with those shown in Tables 2a to 5b. For details,
please see Appendix 1.
5 Another option is to use SNA68 data instead of SNA93 data, following Artis and Okubo (2011) and
Brückner and Tuladhar (2013). However, it is desirable to use data based on the new method as much as
possible. Therefore, we use SNA93 data.

7

non-excludable good, which potentially affects the economic activities of all industries,
following Pereira and Andraz (2001). This means that in our estimation, aggregate public
investment has either a positive or negative effect on each industry’s business investment.
Thus, we did not divide the public investment into either central or local government, as in
the case of Kameda (2015), or into sectors such as roads, educational facilities, and sewers.6

3.2. Definitions of “crowding-in” and “crowding-out” and our hypothesis
Our hypothesis is to investigate whether or not crowding-in and crowding-out effects areobserved in prefectures in Japan. Here, we summarize the arguments on the effects ofpublic investment on business investment and define the “crowding-in” and “crowding-out”
effects examined here.
Public capital may have a complementary relationship with private capital. For example,
infrastructure, such as airports, roads, and sewage systems, creates favorable conditions
for private enterprises through a reduction in transportation costs or savings of time. Thus,
implementation of public investment is expected to raise the productivity of private capital,
thanks to public facilities or future infrastructure, and as a result, private enterprises may

6

The data of the Public Administrative Investment (Gyosei-toshi jisseki in Japanese) and the Annual
Report on Local Public Finance (Chiho Zaisei Tokei Nenpo in Japanese) provided by the Ministry ofInternal Affairs and Communications (MIAC) are also available. The data of Public Administrative
Investment can be separated into categories such as roads and metropolitan parks and we can gather thedata on local government investment from the Annual Report on Local Public Finance. Notably, while
public and private investment data from the Cabinet Office do not include land acquisition, public
investment data provided by the MIAC do include this. As it is desirable that the data excludes landpurchases for business investment and for public investment so as to examine the interactions between
public and private investment, we do not use the data provided by the MIAC.

8

increase current business investment.7 This argument is based on some earlier researchsuch as Aschauer (1989), Voss (2002), Atukeren (2005), and Afonso and St. Aubyn (2009).
The “crowding-in” effect is defined as the increase in current business investment byaccompanying the increase in the public investment from a complementarity between
public and private capital.8
The “crowding-out” effect of public investment is usually classified twofold; direct
crowding-out and indirect. Direct crowding-out argues that government investment,
whether financed by taxes or government debt, competes with the private sector for the useof physical and financial resources, and as a result, it discourages the investment activitiesof the private sector; indirect crowding-out argues that the increase in public investment
raises interest rates and then the increase in interest rates leads to a cutback in business
investment.
Notably, the crowding-in effect is not changed by the interest rate. This effect is basicallya direct relationship between public and private investment. To better compare thecrowding-out and crowding-in effects, it is reasonable to focus solely on the “direct”

7

Usually, it takes some years for investment to accumulate as capital stock and affect economic activity.
For example, Nose (1973) argues that it takes seven years on average for public capital to have a
productivity effect. Therefore, of note is the assumption that private enterprises increase their current
investments based on the “expectation” of an increase in productivity of private capital due to the new
public facilities or infrastructure.
8 Notably, the effects of public capital on business investment are assumed in both the crowding-in andout effects, as in Aschauer (1989), Annala et al. (2008), Mitsui et al. (1995), and Kawasaki et al. (2013).
However, as it takes more than a few years for public investment to become public capital and affect the
productivity of private capital, the specification that pubic capital directly affects private investment may
not be appropriate. In fact, the effects of public capital on private capital are examined separately from theinvestigation of the relationship between public and private investment in recent research such as Hatano(2010) and Hunt (2012). Following these, we do not assume the effects of public capital.

9

crowding-out effects, leaving indirect crowding-out for another research study.

3.3. Empirical model
Many empirical studies examine the interactions between public and private investment.
Most of them use econometric methods such as VAR or vector error correction models.
However, Furceri and Sousa (2011) examine the impact of the changes in the share of
public expenditure to GDP on the growth of real private expenditure. Of note is the factthat dependency on public sector investment may differ among prefectures, meaning some
prefectures may depend more heavily on public investment or public employment thanothers. Similarly, this regional difference of dependency on public works may also affect theprivate sector activity in a region. Furceri and Sousa’s (2011) framework has an advantage
in capturing these effects, rather than examining a simple relationship between public andprivate expenditure using certain econometric methods. Furthermore, Furceri and Sousa(2011) leave the effects of interest rates as of an avenue for future research. Following theirline of thinking, we judge that their specification is most suitable for our research.9
The basic specification is as follows: GI 
 GI 
I it i  t  1 it    2 it 1    it ,Iit
Y
 it 
 Yit 1 

(1)

9Although Furceri and Sousa (2011) do not specifically identify the direct and indirect effects in theircrowding-out effects argument, it is plausible to assume that they examine direct crowding-in or out
effects considering their specification and their arguments on interest rates.

10where i and t are prefecture and year indices, respectively;  i is the prefecture-specificeffect that captures the regional characteristics unchanged through time;  t is a set ofyear dummies, which captures the aggregate (country-level) business conditions; I it isprivate investment, GI it is public investment, and Yit is PGDP10;  it is an error term.
Here, the definition of the crowding-in effect is the interaction between “current” public
and private investment. Thus, at first glance the dynamic effects should not be consideredin our estimation. However, public investment implemented in the end of a fiscal year may
also affect business investment at the beginning of the next fiscal year. Accordingly, the GI 
one-year lagged value of  it  is also assumed in equation (1).11
 Yit  i is expected to be both positive and negative. If either  1 or  2 is estimated to bepositive, the contribution of public investment to PGDP increases the growth in thebusiness investment, and we interpret that crowding-in effect is observed. Conversely, ifeither 1 or  2 is estimated to be negative, we interpret that crowding-out effect isobserved.
Moreover, since the change in public investment differed between urban and rural areas
 GI 
as discussed, asymmetric effects in  it  may be observed. Following this, we also
 Yit 10

Furceri and Sousa (2011) express these variables in per capita terms. Since our research uses the data
within one country, we do not do so.
11 Furthermore, if we assume a longer lag, we cannot identify the effect of public investment and of public
capital because some public capital may affect the private sector’s activity in a few years. Thus, we avoid alonger lag in contrast to Furceri and Sousa (2011), who examine the effects of government consumptionthat do not accumulate as capital stock.

11

conduct an estimation using a coefficient dummy variable that captures a specificprefectural group. Specifically, we introduce the dummy variable UDUM , which takes 1 if a
prefecture is categorized as “urban prefectures” and 0 otherwise. We select 11 prefecturesas urban prefectures: Saitama, Chiba, Tokyo, Kanagawa, Shizuoka, Aichi, Osaka, Hyogo,
Nara, Hiroshima, and Fukuoka. This follows Hayashi (2003) and Miyazaki et al. (2015).
 GI 
 GI 
We multiply this dummy by  it  and  it 1  so as to specify the effects of the Yit 
 Yit 1 

public investment to PGDP in urban prefectures. In this case, the specification is asfollows:

 GI 
 GII it
  i  t   1 it    2 it 1 
I it
YY it 
 it 1 
 GI 
 GI
  3 * UDUM *  it    4 * UDUM *  it 1    it .
YY
 it 
 it 1 （2）Here, whereas both  3 and  4 capture the effects in urban prefectures,  1 and  2
identify the effects in 36 prefectures other than urban ones, in other words, the ruralprefectures.

12

4. Empirical results
4.1. Basic results
We estimate equation (1) by the least squares method with both time and country fixed
effects (LSDV). Tables 2a and 2b report the estimation results for equations (1) and (2),respectively.
 GI 
Table 2a reports that while current share public investment to PGDP (  it  ) has Yit positive and significant effects on investment in agriculture, forestry, and fisheries andtransportation and communication, it has negative and significant effects on investment in GI it 1 
 has Yit 1 

wholesale and retail industries and services. Table 2a also shows that 

positive and significant effects on investment in transportation and communication and themining industry. According to Table 2b, business investments in the urban prefectures
decrease in industries such as agriculture, forestry, and fisheries, mining, wholesale and GI 
retail, and transportation and communication. For rural prefectures,  it  has positive Yit and significant effects on the business investment in transportation and communication,

 GI



and  it 1  has positive and significant effects on the mining industry. However, Yit 1 
negative and significant crowding-out effects are observed in finance and insurance, realestate, and services.

134.2. Alternative frameworks
We re-estimate equations (1) and (2) using another estimation method and underalternative specifications.
First, we add some explanatory variables. Here, we added a jobs-to-applicants ratio( JOBit ) and the growth rate of “lending outstanding” (Lit).12 The jobs-to-applicants ratio
Litis from the Report on Employment Service estimated by the Ministry of Health, Labor andWelfare, and the data for “lending outstanding” are from the Bank of Japan's Financial andEconomic Statistics of Prefectures. The jobs-to-applicants ratio controls the businessconditions in each prefecture, and “lending outstanding” represents the loans fromfinancial organizations, which also affect the investment behavior of private companies.
Since we cannot obtain the 2007 end of the business year data on such lending, theestimations shown in Tables 2a and 2b become unbalanced panel data and the number ofobservations is reduced. Needless to say, “lending outstanding” is not added as anindependent variable in the finance and insurance industry. The results are shown in GI 
 GI 
Tables 3a and 3b. While most of the results of  it  and  it 1  are the same as in Yit 
 Yit 1 
Tables 1a and 1b, respectively, the effects on investment in finance and insurance and real

12 The average or effective tax rate of local corporate tax is also assumed as an additional regressor．

However, while corporate business income of each prefecture provided by the Cabinet Office, which
comprises the denominator of the average or effective tax rate as a tax base, covers only the income ofprivate enterprises until 1993, it expands the scope to all companies including public enterprises after
1997, as shown in Fukazawa (2009). Since it is difficult to calculate the average or effective tax rate of
local corporate tax, we do not use this variable.

14

estate are negative and significant in Table 3a. Further, the effect on investment in realestate is positive and significant in the urban prefectures, as shown in Table 3b.
Second, the public investment to PGDP decreased after 2002 owing to the cutback ofpublic investment and intergovernmental transfers needed to finance it. To deal with this,we reestimate the model by ending the sample periods in 2001. The results are reported inTables 4a and 4b. In this case, most results are unchanged from our benchmark case.
 GI 
However, in urban prefectures the coefficient of  it  is estimated to be positive and Yit 

 GI it 1  is also estimated to Yit 1 

significant for finance and insurance, and the coefficient of 

be significant for real estate, as shown in Table 4b. Moreover, we affirm a crowding-out
effect for the wholesale and retail industry for both urban and rural prefectures.
Finally, while the public investment affects the private investment, the contribution ratioof public expenditures to PGDP may increase when business conditions get worse. This
type of reverse causality may cause concern in our specification. Moreover, current
business investments may be also dependent on investments in previous periods. To deal
with these issues, we also estimate equation (1) using the system GMM (general methodsof moment) procedure (one-step estimation) developed by Arellano and Bover (1995) and
Blundell and Bond (1998). This method avoids the downward bias of the coefficient of thelagged dependent variable, even in finite N and T cases such as ours (N = 47 and sample
periods = 30), compared with the method developed by Arellano and Bond (1991). It also15

helps avoid the problem of weak instruments. To avoid the problem of too many
instruments (Okui, 2009; Roodman, 2009), we assume possible lagged values of
instrumental variables at most at one period.
These results are shown in Tables 5a and 5b. Basically, we assume one-period lag for the
dependent variable, but in some industries the null of the AR (2) test is rejected when we
assume one or two period lags forI it. To address this, we assume a three-year lagged
I itvalue for five industries, as shown in Tables 5a and 5b. Even if we estimate the model byusing another method, we obtain positive and significant results for transportation andcommunication, and negative and significant for services in Tables 5a and 5b. It is notable GI 
that  it  has positive and significant effects on the aggregate investment, as shown in Yit 

Table 5a, and this occurs in rural prefectures rather than urban prefectures, as shown in
Table 5b. It also has a positive and significant effect on investment in the mining industry
in both urban and rural prefectures, as shown in Table 5b, unlike our basic case.

4.3. Discussion of the results
On the basis of the estimation results shown in Tables 2a to 5b, we discuss the private
sector’s business investment response to public investment.
First, we review the difference in policy impact by sector. While negative impacts in theservice industries were observed, our results strongly confirm the crowding-in effects in the16mining and transportation and communication industries. This is in contrast to the resultsin Fujii et al. (2013), which indicate that crowding-out effects are observed in these threeindustries. Public infrastructure, such as roads, airports, and railways, which share almost
40 % of all public capital in Japan, as shown in Figure 2,13 creates a favorable condition fortransportation company or tourism industry through the reduction in transportation costs
or savings of time. Moreover, since the capital in the information and telecommunication(IT) industry may bear a strong complementary relationship with IT infrastructure interms of technology production, the construction of optical cables would raise theproductivity of the IT industry in the future, and as a result, induce IT companies’ current
investment. The mining industry relies heavily on materials transportation by trucks, andit may benefit significantly from improved infrastructure, as in the case of transportationcompanies or the tourism industry. In this regard, public investment may induce current
investment in these industries through the path as defined previously. On the other hand,since public investment discourages investment in the service industry, we can conclude
that it competes with this industry for the use of physical and financial resources.
Second, we review the effects in rural areas, where public investments implemented aspart of stimulus packages have been heavily allocated. The positive effects on thetransportation and communication industry’s investment summarized above are13

For details, please see http://www5.cao.go.jp/keizai2/jmcs/docs/pdf/jmcs2012_digest.pdf.

17

particularly notable. However, the estimated coefficients are less than unity. Although we
also confirm crowding-in effects in the mining industry for all cases, the share of thisindustry is very small. On the other hand, we observed crowding-out effects for the services
and the finance and insurance industries in all cases. Incidentally, finance and insurance,
especially financial institution, is essential to support the financial activity of the private
sectors in a region as argued in some works such as Abiko and Yoshioka (2003) and Horie(2009). Furthermore, the service industry is expected to play an important role even in
rejuvenating the rural areas because its productivity is one of major determinants ofeconomic growth.14 Since we can observe strong, crowding out effects among industries
that substantially revitalize both financial and industrial activities in rural areas, heavy
allocation of public stimulus investment packages in these areas does not appear to benecessarily adequate in terms of stimulating the capital formation of private sectors.

5. Conclusion
This study examines the interactions between regional public and private investmentusing Japanese prefecture data. We also investigate the behavior of sector capital
investment by classifying the categories of private investment into specific sectors. Our

14

For example, please see the website: http://www.rieti.go.jp/en/papers/contribution/morikawa/05.html.

18

empirical results show that while the investments in mining and transportation andcommunication are stimulated by public investment, crowding-out effects are observed in
the finance and insurance industry and services in rural areas.
Our results suggest that government investment activities do not always encourage
private enterprise investment in a region. This appears especially so in rural areas, thus
implying that although the Japanese government implemented economic stimulus
packages numerous times, including regional public investment for rural areas, suchpolicies were not necessarily adequate in terms of stimulating the economic activities
through private capital formation there. The policy implication drawn from our results isthat policy makers should be aware of this when they plan economic stimulus including
regional public investment.
Moreover, political factors such as pork-barrel politics have had a strong impact on public
investment policy in Japan. These factors may also affect private business investment
through public investment. It would be worthy of investigation to examine the extent theeffect of public investment has on business investment depending on political variables.
Additionally, although we use prefecture level data, it would be also necessary toinvestigate the model using municipal level data. Since the degree of population
concentration and firm concentration is different even within one prefecture, policy effects
may also be different within a prefecture. These issues remain for future research.
19

Appendix 1
Figure A.1 depicts the movement of investment in transportation and communications.
From this figure, you can understand that this had increased drastically in the late to
mid-1980s, when the investments of two major companies, JR and NTT, had been addedsince their establishment. To deal with this, we reestmate Equations (1) and (2). The
 GI 
results are shown in Table A.1 and A.2, respectively. The coefficients of  it  are Yit estimated to be positive and significant for all cases in Table A.1, and we strongly confirm
the crowding-in effects in rural areas, following Table A.2.

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Arellano, M., Bover. O., 1995. Another look at the instrumental variables estimation oferror component models. Journal of Econometrics 68 (1), pp. 29-51.
Aschauer, D. A., 1989. Does public capital crowd out private capital? Journal of Monetary
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Japan.
Artis, M., Okubo, T., 2011. The intranational business cycle in Japan. Oxford Economic
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Atukeren, E., 2005. Interactions between public and private investment: Evidence fromdeveloping countries. Kyklos 58 (3), pp. 307-330.
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data models. Journal of Econometrics 87, pp. 115-143.
Brückner, M., Tuladhar, A., 2010. Public investment as a fiscal stimulus: Evidence fromJapan’s regional spending during the 1990s. IMF Working papers, pp. 1-34.
Brückner, M., Tuladhar, A., 2013. Local government spending multipliers and financial
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Doi, T., Ihori, T., 2009. The public sector in Japan. Edward Elgar Publishing, Chelthenham,
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Ihori, T., 2006. Fiscal policy and fiscal reconstruction in Japan. International Tax and
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24

Figure 1a. The change in the contribution of per capita public investment to per capita
PGDP (Hokkaido and Tohoku)

Hokkaido

Aomori

Iwate

Miyagi

Akita

Yamagata

Fukushima

18%
16%
14%
12%
10%
8%
6%
4%
2%
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009

0%

Figure 1b. The change in the contribution of per capita public investment to per capita
PGDP (Kanto)
IbarakiTochigi

Gunma

Saitama

Chiba

Tokyo

Kanagawa

8%
7%
6%
5%
4%
3%
2%
1%
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009

0%

25

Figure 1c. The change in the contribution of per capita public investment to per capita
PGDP (Chubu and Koshinetsu)
Niigata

Yamanashi

NaganoToyamaIshikawa

Fukui

Shizuoka

Gifu

Aichi

Mie

25%
20%
15%
10%
5%

1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009

0%

Figure 1d. The change in the contribution of per capita public investment to per capita
PGDP (Kansai)
Shiga

Kyoto

Osaka

Hyogo

Nara

Wakayama

16%
14%
12%
10%
8%
6%
4%
2%

1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009

0%

26

Figure 1e. The change in the contribution of per capita public investment to per capita
PGDP (Chugoku and Shikoku)
Tottori

Shimane

Okayama

Hiroshima

Tokushima

Kagawa

Ehime

Kochi

Yamaguchi

30%
25%
20%
15%
10%
5%

1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009

0%

Figure 1f. The change in the contribution of per capita public investment to per capita
PGDP (Kyushu and Okinawa)
Fukuoka

Saga

Nagasaki

Kumamoto

Oita

MiyazakiKagoshima

Okinawa

16%
14%
12%
10%
8%
6%
4%
2%
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009

0%

27

Figure 2. Breakdown of public capital (gross capital stock) in Japan (FY=2009)
others1.0%
Agriculture,
Forestry and
Fisheries
12.7%
Sea coast, soil, andwater conservation
10.8%

Road
32.3%

Educational
facitilies
11.4%

Water facility
5.7%
City park
1.3%

Sewer
10.4%

Public rental house
6.0%

Waste disposal
1.9%

AirportRailways 0.5%
2.1%

Source: Public Capital in Japan, published by the Cabinet Office in Japan(http://www5. cao.go.jp/keizai2/jmcs/docs/pdf/jmcs2012.pdf).

Note: Others include the postal service, national forests, and industrial water service.

28

[分類名]
[パーセンテージ]

Table 1. Fiscal Stimulus Packages in the 1990s (JPY trillion)
1992

1993

AugustApril

Tax cut

September

0.2

1994

1995

February

April

1998
September

5.9

1999

April

November

4.6

6

NovemberCashtransfers to

0.7

households
Total public
works

6.3

7.2

2

3.7

4.5

5.6

1.5

1.8

1.6

5

0

7.7

3.1

3.6

2.9

3.4

6.7

1.6

3.6

2.9

0.3

1

1.5

5.4

1.4

4.6

3.6

4

(Public
investment)
Public works
by central
government
Public works
by localgovernments
Other
government
investment
Other
government

4.5

5.8

4

5.7

1.5

5.2

4.4

9.1

11.3

10.8

13.2

6

15.3

6.9

14.3

16.7

23

18.2

measuresTotal size of
economic
stimulus
packages

Note: This Table follows Brückner and Tuladhar (2013). Other government investment includes theinvestment in fields such as science and technology, education and social welfare, alternative energy andenvironment, and natural disaster relief. All government investment in the economic stimulus packages in
April 1995 was for natural disaster relief because this package was planned as countermeasures for theGreat Hanshin-Awaji earthquake.

29

Table 2a. Estimation Results of Equation (1): Estimation Method = LSDV, NOB = 1316
Dependent
Variable GI it  Yit Aggregate
Investment

Manufacturing

Agriculture,etc.

MiningWholesaleand Retail

0.457

0.128

4.650 *

0.811

-0.472 *(0.455)(1.283)(2.581)(1.102)(0.268)

 GI 
 it 1  Yit 1 

-0.128-0.771-1.4992.004 ***

0.366

Const.

(0.517)0.011(0.014)(1.335)0.006(0.031)(3.032)0.090(0.067)(0.677)-0.190 ***(0.017)(0.287)0.042 ***
(0.006)

R2

0.471

0.293

0.480

0.855

Finance andInsurance

Real Estate

0.061
Transportation
andCommunication

Utilities

Services-0.382-0.0950.656 **

-0.318-0.235 **(0.256)(0.171)(0.247)(0.562)(0.103)-0.119-0.3440.415 *

0.157

0.071

(0.270)-0.043 ***(0.005)(0.225)0.126 ***
(0.004)(0.234)-0.043 ***(0.004)(0.535)-0.028 ***(0.010)(0.115)0.035 ***
(0.003)

0.872

0.981

0.954

0.681

0.968

Dependent
Variable GI it  Yit 

 GI 
 it 1  Yit 1 

Const.

R2

Note: Robust standard errors are in parentheses. Asterisks indicate significance levels: ＊=10%, ＊＊=5%,and ＊＊＊=1%. For brevity, dummy variables for years are not shown.

30

Table 2b. Estimation results of Equation (2): Estimation Method=LSDV, NOB=1316
Dependent
Variable

Aggregate
Investment

Manufacturing

Agriculture,etc.

MiningWholesaleand Retail

0.509

0.070

2.922

1.222

-0.298(0.471)(1.364)(2.659)(1.123)(0.263)-0.180-0.8570.954

1.511 **

0.227

UDUM

(0.531)-0.548(1.352)
1.398(2.778)14.054(0.596)-3.851 **(0.255)-2.019 ****  GI it (0.583)(1.702)(9.063)(1.794)(0.729)

UDUM

0.411

1.450

-23.618 ***

4.411

0.934

*  GI it 1 
 Y it 1 (1.075)(2.361)(6.630)(3.561)(0.952)

Const.

0.010(0.014)0.006(0.031)

0.097(0.065)-0.191 ***(0.017)0.041 ***
(0.006)

R2

0.471

0.294

0.069

0.483

0.857

Finance andInsurance

Real EstateTransportation andCommunication

Utilities

Services-0.457 *-0.0980.715 ***

-0.247-0.222 **(0.264)(0.175)(0.250)(0.572)(0.103)0.158-0.400 *

0.372

0.099

0.041

UDUM

(0.278)0.978(0.236)0.375(0.234)-0.713 *(0.547)-0.802(0.110)-0.046*  GI it (0.668)(0.305)(0.405)(0.751)(0.196)UDUM-0.122

0.778

0.259

0.413

0.329

*  GI it 1 (0.476)(0.509)(0.406)(0.671)(0.337)-0.043 ***(0.005)0.126 ***
(0.004)-0.043 ***(0.004)-0.013(0.013)0.035 ***
(0.003)

0.872

0.981

0.954

0.681

0.968

 GI it  Yit 

 GI 
 it 1  Yit 1 

 Y 
 it 

Dependent
Variable GI it  Yit 

 GI 
 it 1  Yit 1 

 Y 
 it  Y it 1 

Const.

R2

Note: Robust standard errors are in parentheses. Asterisks indicate significance levels: ＊=10%, ＊＊=5%,and ＊＊＊=1%. For brevity, dummy variables for years are not shown.

31

Table 3a. Estimation Results of Equation (1) (The case adding some variables): Estimation
Method = LSDV, NOB=1316 for finance and insurance, and 1222 for other sectors
Dependent
Variable GI it  Yit 

 GI 
 it 1  Yit 1 

JOBit
Lit
Lit

const

R2
Dependent
Variable GI it  Yit Aggregate
Investment

Manufacturing

Agriculture,etc.

MiningWholesaleand Retail

0.141-0.4794.360 *

0.711

-0.608 **(0.470)(1.274)(2.551)(1.065)(0.280)-0.300-0.849-1.527

1.784 **

0.345

(0.534)0.041 ***
(0.007)(1.379)0.051 ***
(0.021)(3.331)
0.013(0.059)(0.799)0.035 *
(0.023)(0.305)0.010 *
(0.007)0.007-0.134-0.160

0.028

0.023

(0.051)-0.016(0.016)0.491(0.119)-0.018(0.037)0.304(0.293)0.092(0.073)0.058(0.076)-0.213 ***(0.022)0.439(0.037)0.033 ***
(0.008)
0.848

Finance andInsurance

Real EstateTransportation andCommunication

Utilities

Services-0.497 *-0.1580.497 **

-0.378 -0.346 ***(0.265)(0.172)(0.245)(0.557)(0.111)

 GI 
 it 1  Yit 1 

-0.004-0.450 *

0.324

0.135

-0.037JOBit(0.279)0.027(0.009)(0.247)0.018 ***
(0.007)(0.266)0.020 **
(0.011)(0.563)-0.014(0.011)(0.126)0.021 ***
(0.004)0.105 ***

-0.0160.057

0.025 **

(0.033)0.107 ***
(0.007)0.980(0.043)-0.055 ***(0.008)0.956(0.040)-0.009(0.016)0.678(0.015)0.020 ***
(0.004)
0.966

Lit
Lit

Const.

R2-0.060 ***(0.008)0.873

Note: Robust standard errors are in parentheses. Asterisks indicate significance levels: ＊=10%, ＊＊=5%,and ＊＊＊=1%. For brevity, dummy variables for years are not shown.

32

Table 3b. Estimation Results of Equation (2) (The case adding some variables): Estimation
method = LSDV, NOB = 1316 for finance and insurance, and 1222 for other sectors
Dependent
Variable GI it  Yit 

 GI 
 it 1  Yit 1 Aggregate
Investment

Manufacturing

Agriculture,etc.

MiningWholesaleand Retail

0.197-0.526

2.997

1.165

-0.431(0.471)(1.339)(2.604)(1.055)(0.269)-0.344-0.9400.749

1.404 **

1.197

(0.536)(1.375)(0.292)(0.650)(0.267)UDUM-0.756

1.168

13.434-6.034 **-2.345 ****  GI it (0.764)(1.638)(10.084)(2.564)(0.649)

UDUM

0.424

1.529

-28.112 ***

3.792

1.474

*  GI it 1 
 Y it 1 (1.149)(2.569)(9.000)(5.001)(1.013)0.041 ***

0.053 ***

-0.0002

0.035 *

0.010 *

(0.007)(0.021)(0.059)(0.023)(0.007)0.007-0.133-0.161

0.028

0.023

(0.051)(0.119)(0.298)(0.076)(0.037)-0.016-0.0190.109-0.215 ***0.033 ***

(0.016)(0.037)(0.071)(0.022)(0.008)

0.491

0.304

0.067

0.442

0.850

FinanceandInsurance

Real EstateTransportation andCommunication

Utilities

Services-0.577**-0.1900.543 **

-0.308-0.337 ***(0.275)(0.172)(0.246)(0.563)(0.111)0.020-0.507 *

0.284

0.093

-0.072

 Y 
 it 

JOBit
Lit
Lit

const

R

2

Dependent
Variable GI it  Yit 

 GI 
 it 1  Yit 1 

(0.284)(0.256)(0.263)(0.570)(0.118)

UDUM

1.099

0.770 ***

-0.606-1.0110.009*  GI it  Y 
 it 

(0.677)(0.270)(0.452)(0.783)(0.207)

UDUM

0.048

0.963

0.406

0.343

0.492

*  GI it 1 
 Y it 1 (0.457)(0.582)(0.452)(0.677)(0.406)0.028 ***

0.019 ***

0.020 **

-0.0140.022 ***

(0.009)(0.006)(0.456)(0.011)(0.004)0.106 ***

-0.016

0.057 *

0.025 **

JOBit
Lit
Lit

Const.

R

2(0.033)(0.043)(0.039)(0.015)-0.0600.107 ***

-0.055 ***-0.0090.020 ***

(0.008)(0.007)(0.008)(0.016)(0.004)

0.873

0.981

0.956

0.678

0.966

Note: Robust standard errors are in parentheses. Asterisks indicate significance levels: ＊=10%, ＊＊=5%,and ＊＊＊=1%. For brevity, dummy variables for years are not shown.

33

Table 4a. Estimation Results of Equation (1) (The case limiting sample periods to
1980-2001): Estimation Method = LSDV, NOB=940.
Dependent
Variable GI it  Yit 

 GI 
 it 1  Yit 1 

const

R

2

Dependent
Variable GI it  Yit 

 GI 
 it 1  Yit 1 

const

R2

Aggregate
Investment

Manufacturing

Agriculture,etc.

MiningWholesaleand Retail

0.154

0.175

1.620

-0.062-0.811 **(0.566)(1.505)(3.131)(1.423)(0.343)-0.0810.148-4.0881.997 *

0.348

(0.597)(1.518)(3.319)(0.355)

0.010

0.006

0.082

0.041 ***

(0.014)(0.029)(0.068)(1.020)-0.192***(0.017)

0.479

0.288

0.026

0.429

0.821

Finance andInsurance

Real EstateTransportation andCommunication

Utilities

Services-0.5150.068

0.660 **

-0.602-0.415 ***(0.349)(0.213)(0.333)(0.690)(0.145)0.054-0.3950.490-0.071

0.045(0.344)-0.043 ***(0.005)(0.290)0.126 ***
(0.004)(0.298)-0.043 ***(0.005)(0.664)-0.014(0.013)(0.142)0.035 ***
(0.003)

0.870

0.978

0.954

0.551

0.953

(0.006)

Note: Robust standard errors are in parentheses. Asterisks indicate significance levels: ＊=10%, ＊＊=5%,and ＊＊＊=1%. For brevity, dummy variables for years are not shown.

34

Table 4b. Estimation Results of Equation (2) (The case limiting sample periods to
1980-2001): Estimation Method = LSDV, NOB=940
Dependent
Variable

Aggregate
Investment

Manufacturing

Agriculture,etc.

MiningWholesaleand Retail

0.271

0.158

0.839

0.517

-0.592 *(0.560)(1.553)(2.872)(1.474)(0.337)-0.163-0.052-2.364

1.548 *

0.181

UDUM

(0.596)-1.442(1.506)0.318(3.171)8.967(0.835)-7.107 **(0.304)-2.681 ****  GI it (0.972)(1.555)(14.565)(2.931)(0.740)

UDUM

0.834

2.307

-19.128 **

4.647

1.718

*  GI it 1 
 Y it 1 (1.158)(2.637)(8.931)(4.400)(1.060)

const

0.009
(0.014)0.006(0.029)0.088(0.066)-0.195 ***(0.017)0.041 ***
(0.006)

R2

0.479

0.288

0.003

0.434

0.824

Finance andInsurance

Real EstateTransportation andCommunication

Utilities

Services-0.642 *0.040

0.718 **

-0.513-0.421 ***(0.360)(0.220)(0.336)(0.701)(0.149)0.075-0.5110.450-0.151

0.001

UDUM

(0.356)
1.061 *

(0.306)0.421(0.290)-0.714(0.672)-1.084(0.133)0.099*  GI it (0.866)(0.325)(0.588)(0.949)(0.241)UDUM-0.1351.360 **

0.402

0.823

0.510

*  GI it 1 
 Y it 1 (0.720)(0.558)(0.488)(0.702)(0.512)-0.043 ***(0.005)0.126 ***
(0.004)-0.043 ***(0.005)-0.014(0.013)0.035 ***
(0.003)

0.871

0.978

0.954

0.552

0.953

 GI it  Yit 

 GI 
 it 1  Yit 1 

 Y 
 it 

Dependent
Variable GI it  Yit 

 GI 
 it 1  Yit 1 

 Y 
 it 

Const.

R2

Note: Robust standard errors are in parentheses. Asterisks indicate significance levels: ＊=10%, ＊＊=5%,and ＊＊＊=1%. For brevity, dummy variables for years are not shown.

35

Table 5a. Estimation Results of Equation (1). Estimation Method = System GMM.
Dependent
Variable

Aggregate
Investment

Manufacturing

Agriculture,etc.

MiningWholesaleand RetailI it 1

-0.564 ***-0.395 ***-0.451 ***0.088-0.045(0.051)(0.033)(0.087)(0.058)(0.032)-0.419 ***-0.245 ***-0.339 ***(0.082)(0.059)(0.063)-0.207-0.140-0.246 ***(0.166)(0.103)(0.072)0.887 *

0.645

4.945

2.060 *

-0.470 *(0.507)(1.286)(3.167)(1.127)(0.250)-0.092-0.896-2.8093.777 **

0.291

NOB

(0.525)-0.163 ***(0.011)1222(1.134)0.019(0.032)1222(4.086)
0.375 ***
(0.119)1222(1.330)-0.202 ***(0.017)1316(0.358)0.037 ***
(0.008)
1316

AR(1)
AR(2)
Dependent
Variable-4.179 ***1.417
Finance andInsurance-3.685 ***-0.126-3.987 ***0.092-2.934 ***0.854-4.650 ***0.501

Real EstateTransportation

Utilities

Services-0.132 **-0.410 ***-0.067

0.034

0.018

(0.065)(0.053)(0.057)(0.046)(0.065)-0.083 *-0.475 ***(0.046)(0.035)-0.339 ***-0.534 ***(0.132)(0.073)-0.671 *0.276

0.582 *

-0.189-0.243 **(0.384)(0.216)(0.314)(0.787)(0.099)-0.481-0.099

0.311

0.402

0.026

NOB

(0.414)0.138 ***
(0.009)1222(0.163)-0.048(0.038)1222(0.224)-0.039 ***(0.055)1316(0.667)-0.014(0.013)1316(0.133)0.034 ***
(0.086)
1316

AR(1)
AR(2)

-4.799 ***-1.177-4.816 ***1.052-4.071 ***0.147-4.820 ***0.603-4.536 ***1.468

Iit  2

Iit 3

I it 1

I it  2

I it 3

 GI it  Yit 

 GI 
 it 1  Yit 1 

const

I it 1

Iit  2

Iit 3

I it 1

I it  2

I it 3

 GI it  Yit 

 GI 
 it 1  Yit 1 

Const.

Note: Robust standard errors are in parentheses. Asterisks indicate significance levels: ＊=10%, ＊＊=5%,and ＊＊＊=1%. For brevity, dummy variables for years are not shown.

36

Table 5b. Estimation Results of Equation (2): Estimation Method = System GMM
Dependent
Variable

Manufacturing

Agriculture,etc.

MiningWholesaleand Retail-0.564 ***-0.397 ***-0.433 ***0.088-0.044(0.051)(0.033)(0.090)(0.057)(0.032)-0.417 ***-0.248 ***-0.329 ***(0.082)(0.060)(0.063)-0.198-0.143-0.246 ***(0.168)(0.103)(0.069)0.918 *

0.431

1.999

2.104 *

-0.287(0.509)(1.417)(2.980)(1.140)(0.248)-0.051-1.075-1.2582.873 **

0.125

UDUM

(0.517)-0.514(1.136)
3.123(3.824)27.593 *(1.227)0.507(0.312)-1.455*  GI it (0.935)(2.364)(14.695)(1.592)(0.955)UDUM-0.4952.532-7.2178.353 **

1.401

*  GI it 1 
 Y it 1 (1.014)(1.907)(6.262)(3.599)(1.407)NOB-0.164 ***(0.011)1222

0.328 ***
(0.048)1222

0.180 **
(0.076)1222-0.203 ***(0.019)1316

0.037 ***
(0.008)
1316

AR(1)
AR(2)

-4.186 ***1.456-3.675 ***-0.110-4.035 ***0.120-2.909 ***0.810-4.684 ***0.614

I it 1

Iit  2

Iit 3

I it 1

I it  2

I it 3

 GI it  Yit 

 GI 
 it 1  Yit 1 

 Y 
 it 

Const.

Aggregate
Investment

37

Table 5b. Estimation Results of Equation (2): Estimation Method = System GMM (cont.)
Dependent
Variable

Finance andInsurance

Real EstateTransportation

Utilities

Services-0.132 **-0.410 ***-0.068

0.036

0.016

(0.066)(0.052)(0.057)(0.045)(0.064)-0.081 *-0.477 ***(0.047)(0.036)-0.319 **-0.538 ***(0.133)(0.075)-0.798 **0.238

0.616 *

-0.073-0.259 ***(0.397)(0.221)(0.316)(0.834)(0.098)-0.393-0.156

0.228

0.296

-0.036UDUM(0.438)1.096(0.174)0.575(0.215)-0.224(0.686)-0.980(0.120)0.255*  GI it (1.101)(0.440)(0.529)(1.076)(0.244)UDUM-0.550

0.684

0.757

0.867

0.612

*  GI it 1 
 Y it 1 (0.639)(0.518)(0.514)(0.757)(0.481)NOB

0.007(0.006)1222

0.515 ***
(0.021)1222-0.039 ***(0.006)1316-0.014(0.013)1316

0.034 ***
(0.009)
1316

AR(1)
AR(2)

-4.792 ***-1.067-4.778 ***1.122-4.054 ***0.170-4.817 ***0.578-4.491 ***1.603

I it 1

Iit  2

Iit 3

I it 1

I it  2

I it 3

 GI it  Yit 

 GI 
 it 1  Yit 1 

 Y 
 it 

Const.

Note: Robust standard errors are in parentheses. Asterisks indicate significance levels: ＊=10%, ＊＊=5%,and ＊＊＊=1%. For brevity, dummy variables for years are not shown.

38

Figure A.1. The investment of transportation and communications
12,000,000
10,000,000
8,000,000
6,000,000
4,000,000
2,000,000

1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009

0

Source: Prefectural private capital stock provided by Cabinet Office in Japan.

39

Table A.1. Estimation results of Equation (1). (The case that sample periods are limited
after 1988)
Basic caseI it 1

The case adding

The case limitingsome variables

until 2001

System GMM-0.037

I it 1(0.052)
 GI it  Yit 

 GI 
 it 1  Yit 1 

0.972 ***

0.797 ***

0.117 ***

1.041 ***

(0.269)(0.269)(0.411)(0.362)

0.330

0.208

0.436

0.373

(0.221)(0.258)(0.321)(0.271)

0.021 **

JOBit

(0.011)
Lit
Lit-0.084(0.076)

const

NOB

0.126 ***

0.110 ***

0.127 ***

-0.039 ***(0.006)(0.011)(0.006)(0.003)

1034

940

658

987

AR(1)

-3.290 ***AR(2)-0.315

Note: Robust standard errors are in parentheses. Asterisks indicate significance levels: ＊=10%, ＊＊=5%,and ＊＊＊=1%. Dummy variables for year are not shown for the sake of brevity.

40

Table A.2. Estimation results of Equation (2). (The case that sample periods are limited
after 1988)
Basic caseI it 1

The case adding

The case limitingsome variables

until 2001

System GMM-0.036

I it 1(0.053)
 GI it  Yit 1.050 ***

0.858 ***

1.249 ***

1.100 ***

(0.274)(0.274)(0.413)-0.362

0.259

0.149

0.364

0.268

(0.214)(0.244)(0.295)(0.264)UDUM-0.785 *-0.694-0.805-0.305*  GI it (0.438)(0.491)(0.679)(0.627)

UDUM

0.507

0.710

0.840 *

0.835

*  GI it 1 (0.398)(0.479)(0.490)(0.548)

 GI 
 it 1  Yit 1 

 Y 
 it  Y it 1 

0.021 **

JOBit

(0.012)
Lit
Lit-0.086(0.076)

const

NOB

0.127 ***

0.109 ***

0.127 ***

-0.039 ***(0.006)(0.011)(0.006)(0.003)

1034

940

658

987

AR(1)

-3.274 ***AR(2)-0.371

Note: Robust standard errors are in parentheses. Asterisks indicate significance levels: ＊=10%, ＊＊=5%,and ＊＊＊=1%. Dummy variables for year are not shown for the sake of brevity.

41