東京大学 公共政策大学院

ワーキング・ペーパーシリーズ
GraSPP Working Paper Series
The University of Tokyo

GraSPP-DP-E-07-002 and ITPU-DP-E-07-002

Inter-Regional Air Transport Accessibility
andMacro Economic Performance In Japan
Katsuhiro Yamaguchi
April 2007

GraSPP Discussion Paper E-07-002

ITPU Discussion Paper E-07-002

GRADUATE SCHOOL OF PUBLIC POLICY
THE UNIVERSITY OF TOKYOHONGO, BUNKYO - KU, JAPANGraSPP-DP-E-07-002

Inter-regional Air Transport Accessibility andMacro-economic Performance in Japan
Katsuhiro Yamaguchi
April 2007

Professor, Graduate School of Public Policy
The University of Tokyo, Japan
7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
Phone: +81-3-5841-1710
Fax: +81-3-5841-1313
yamaguchi@pp.u-tokyo.ac.jp

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Discussion Papers are a series of manuscripts in their draft form. They are not intended
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the author.

Summary
During the past two decades, numerous studies have been undergone on
macro-economic effects of public investment including transport infrastructure. Not
much attention, however, has been paid to effects of actual accessibility realized bytransport industry and infrastructure on macro economic performance. Incorporatinginter-regional accessibility index as an economic environment determinant in
Cobb-Douglas production function and Barro regression framework, cross-sectional
regression was undertaken using panel data of 47 prefectures in Japan. Analysis
reveals that there has been significant productivity gain from improvement in airtransport accessibility between 1995 and 2000 particularly for agglomerated areas suchas the Tokyo metropolitan region1 .

Introduction
During the past two decades numerous attempts have been undertaken to
elucidate reasons behind stagnating economic performance. Aschauer (1989) followedby Munnell (1990a, 1990b) triggered discussions on importance of public infrastructure
on productivity. In their works, poor economic performance was attributed to thedeteriorating public infrastructure. Subsequent studies that employed the same
technology, however, yielded mixed result. Holtz-Eakin (1994), Mas (1996) and
Strum (1998) contends that empirical evidence of public capital’s impact on output is atbest fragile. Cobb-Douglas specification was criticized in a number of grounds.
Another vein of analysis on effects of public investment on economic
performance was the neoclassical growth model developed by Solow (1956) and Swan
(1956), empirical work of which was pioneered by Barro (1991) and Mankiw et al.
(1992). Using the cross-section regression, Easterly and Rebelo (1993) found thatpublic investment in transport and communication is robustly correlated with growth.
In terms of transport and growth, Ades and Glaeser (1999) revealed that openness
represented by rail development and distance to port has significant positive effect togrowth in less urbanized area using US data in the 19th century. Also, Bougheas et al.
(1999) showed that, ceteris paribus, level of infrastructure, which reduces cost oftransport, has positive effect on volume of trade.
Another growing interest with respect to effect of transport cost on economic
performance is the new economic geography. Fujita, Krugman and Venables (2001)
1

This paper was first presented at Air Transport Research Society (ATRS) World Conference 2005,
Rio de Janeiro, Brazil, July, 2005.

suggest that geography of economic growth is self-organizing in nature withagglomeration forces and transport cost serving as engines. There has not been much
empirical study conducted in this field, but from public policy’s point of view, it isimportant to understand whether improvement in transport would lead to more
agglomeration or facilitate catching up by peripheral areas.
Japan had not been the exception of debates on effectiveness of public
investment. The discussion, however, was from a somewhat different perspective.
During the economic dismay during the decade of 1990’s public investment on social
overhead (Ig) was doubled reaching 6% of GNP. Concerns were on whether theinvestment is worth it. Mandatory application of cost benefit analysis in major public
investments was introduced to check their efficiency in advance, and ex post facto,
methodology used by Aschauer and Munnel’s was applied to analyze productivity gains
from public investments. The results generally showed that marginal productivity
gains from public investment have decreased during the 80’s and 90’s. Effect oftransport infrastructure on growth has not been analyzed until recently. Nakazato(2001) applied Barro’s approach on road investment in Japanese prefectures through1960 to 1988 and reported that peripheral areas have positive effect through increased
accessibility, although the incremental effect of road investment declined in the 1980’s.
In terms of transport infrastructure, not much analysis has been undertaken
other than on road development. One reason is that contribution of transport
infrastructure, such as airport and railway, depends on how it is utilized. Also even if
unit service level of transport could be identified, its significance is subject to economic
geography. Transport cost has different meaning depending on whether you areaccessing to a core area or to a peripheral region. One such analysis by Yamaguchi
and Maku (2003) specified aggregate inter-prefecture and intra-prefecture accessibility
index to assess the impact on production function of prefectures in Japan. It revealed
that inter-prefecture accessibility improvement had positive and significant effect on
output, while intra-prefecture congestion had constrained economic performance inmajor metropolitan areas. Effect of accessibility on growth, however, is not analyzed.
In this paper two methodologies, Cobb-Douglas production function estimate
and Barro regression, are used to access the impact of air transport service on macro
economic performance of regions in Japan.
Macro Economic Performance in Japan

19
80
19
82
19
84
19
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00

%

Japanese economy experienced steady growth even after the two oil crises andenjoyed average annual growth of
Figure 1
Annual Growth in GDP (1990 price)
3.2% during the first half of 1980’s.
7
Then after G7 meeting at Plaza in 1985,
6
5
Yen appreciation coupled with excess
4
liquidity in the financial market led the32
economy into a boom, which
10eventuallyturned
into
an
-1
-2
unprecedented recession; burst of the-3
bubble (see Figure 1). Although theGDPgrowthtemporally
Figure 2
recovered in 1995 and 96,
Annual growth of per capita GDP (1995-2000)
average growth rate from 1992 to2000
wasmerely1%.
Government poured in public
Iwate
Okayama
Naganoinvestment in an endeavor toboost up the economy.
Igagainst GDP reached 6% during
47 Prefectures in Japan(ln2000-ln1995)÷５
the mid 1990’s, the so-called
Kagawa0.0216～0 .0361 (8)
Oita
Shiga
“lost decade.”
Performance in
0.0176～0 .0216 (10)0.0159～0 .0176 (8)0.0115～0 .0159 (10)regional economy was seemingly
0.001～0 .0115 (11)random. As depicted in Figure
2, discounted for public investment, there were rural prefectures such as Nagano, Oita
and Iwate that increased per capital GDP by 3% annually, while Shiga, Kagawa andOkayama that did not reach 1%.
Domestic Air Transport Market in Japan
Today, domestic aviation in Japan has established its role as a major mode ininter-city transportation. The market, however, has gone through history of growthand maturity over half aTrend in Domestic Air Transport Market in Japan
century to become what
Passenger km
itcurrently
looks.
80
20(billion passenger
Back in the 1960’s three
19
kilometers)
70
18
major airlines operated
1760
orderly
in
their
16
50
15
respective markets; i.e.
4F0igure 3
30
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002

20

14
13
12
11
10

Average air fare
(US cents perpassenger km)<PPP exchange
rate 146.2 \ /US$> (World Bank)

JAL on international and domestic trunk routes, All Nippon Airways (ANA) on
domestic trunk and local routes, Japan Air Systems (JAS, ex-Toa Domestic Airlines(TDA)) on domestic local routes. Marked by regulatory reform in 1985, window ofcompetition was opened. Initially route and frequency regulations were deregulated toincrease competition followed by airfare deregulation to allow normal and discount faresetting with higher flexibility. Consequently, the ratio of available seats in routes withmultiple airlines versus total available seats increased from 53% in 1985 to 81% in 2000.
Following these deregulatory steps, two new airline operations were launched in 1998;
Skymark Airlines on Tokyo-Fukuoka route, and Hokkaido International Airlines (AIRDO) on Tokyo-Sapporo route. Apart from subsidiaries of the major three companies, it
was a new entry in 35 years. Skymark Airlines offered normal fare at half the price andHokkaido International Airlines at 36% below incumbents’ fares.
This wasaccomplished by streamlining onboard service and administrative cost-cuts throughoutsourcing aircraft maintenance and airport service. As a result, “everyday low fare”
strategy won the popularity and the load factor rose as high as 80%. On the contrary,incumbent carriers suffered sudden drop in passengers where new airlines entered.
These routes were lucrative trunk routes with high business travel ratio. Faced withsuch a consequence, the incumbent carriers started to offer discount fares on specificflights just before and after the flights of new entrants. This strategy was quite
effective and by March 1999, the incumbent carriers regained their load factor to thesame level as that of a year ago. Such competitive force resulted in an annual increaseof 16.3% in passengers between Tokyo and Fukuoka, and 9.4% between Tokyo andSapporo. Route and frequency regulation and prior approval of airfare were finallyabolished in 2000.
Airport capacity was increased hand in hand with deregulation. Shortage of
take-off and landing slots at congested airports such as Haneda Airport in Tokyo, has
not been resolved. Based on Civil Aeronautics Law, the aeronautical authority set out
a new policy to review slot allocation in congested airports every five years by indicesevaluating consumer benefit and efficiency.
The model and empirical findings
1) Cobb-Douglas Production Function
Let Yit and Y jt be GDP adjusted for public investment in prefecture i andj in year t , Lit be labor input in prefecture i in year t , K it be private sector stockin prefecture i in year t , and ACC it be air transport accessibility index between

prefecture i and prefecture j (i ≠ j ) in year t . To impose constant return to scale
in private investment and labor input, put yit ≡ Yit / Lit , kit ≡ K it / Lit , then, Cobb-Douglas
specification of the macro production function including accessibility looks as follows.
As discussed in Meade (1952), air transport accessibility should be considered asatmosphere rater than un-paid factor. Thus, constant returns to scale is not imposed on
ACC it .

y it = {exp( β 0 + βACC it )}k itβ 4(1)

Taking natural log of formula (1), we getln y it = β 0 + β ACC it + β 4 ln k it + ε it

( ε it : random variable)(2)

Accessibility index is defined as, ACC ijt = S ijt * ln y it − ln y jt / ln GVijt .
GVijt , generalized cost of air transport between origin and destination prefectures, iscomposed of out of pocket cost (airfare) and value of time. As for value of time, notonly the average travel time of travel but also frequency of air transport is converted
into monetary terms and subtracted to reflect the differences in air service intensity.
ln y it − ln y jt

is incorporated to reflect the relative economic level of the origin anddestination prefectures since accessibility has different impact on the origin prefecture
depending on the counterpart’s economic level. Also, share of air transport ( S ijt ) is toreflect the magnitude of generalized cost of air transport between origin and destinationprefectures.
S ijt ：Share of air transport between prefecture i and prefecture j in year t
GVijt ：Generalized cost of air transport between prefecture i and prefecture j in year t
defined as, GVijt = Pijt + Wt (Tijt − Ω ln Fijt ) , where,

Pijt : Average air fare from prefecture i to prefecture j in year t

Wt : Value of time calculated from average wage in year t
Tijt : Average time of travel by air from prefecture i to prefecture j in year tisΩ : Frequency conversion ratio derived from air transport demand forecast survey

Fijt : Frequency of air transport between prefecture i and prefecture j in year ty it y jt : Per capita GDP in prefecture i or j ( Yit / Lit , Y jt / L jt )In order to account for simultaneity, accessibility index model based on partial
equilibrium was derived as follows. These functions were set in a simultaneous
equation system with the macro production function. Such a simultaneous equation
approach of production and public investment, had been taken by Duffy-Deno andEberts (1991).
The accessibility index model was specified based on Ohashi et al. (2003) andYamaguchi (2005) as follows.
Let number of airlines be n .
Aggregate passenger km would
be Q = nq where q is demand for a single airline. Let airfare be p . Then revenue fora single airline would be R = pq .

⎛ ∂p (Q, GDP) ∂Q ⎞⎟q + p(Q, GDP) .
Assuming Cournot competition, MR = ⎜⎜
∂Q
∂q ⎟⎠
⎝

Since

first-order condition for profit maximization in oligopoly is MR = MC , nMR = nMC .
Therefore,⎛ ∂p(Q, GDP) ∂Q⎞n⎜⎜q + p (Q, GDP) ⎟⎟ = nMC (q; shift parameters) ……(3)
∂Q
∂q
⎝
⎠

Assume demand function as follows.
p (Q, GDP ) = α 0 Q α1 GDP α 2Taking natural log yields ln( P) = α 0 + α 1 ln(Q) + α 2 ln(GDP) ……(4)Assume marginal cost function as follows.
MC ( q; dis tan ce, airshare ) = β 0 q β1 exp[β 2 (ln(dis tan ce)) + β 3 ( airshare )]Following reduced form is derived from (3).
ln(Q) =+β2

ln( β 0 n1− β1 ) − ln{α 0 (α 1 + n)}α2−ln(GDP)
α1 − β1α1 − β1α1 − β1ln(dis tan ce) +β3α1 − β1……(5)(airshare)

Equations (2), (4) and (5) were solved simultaneously by 3SLS.

According to Hayashi (2003), instrument variable in a simultaneous equation
system should be at least two more than the number of parameters including theexogenous variables in the equation. Following Easterly and Robelo (1993),
population and share of agricultural output was included as instruments.
Instrument variables for Cobb-Douglas regression were set as follows:
1) Natural log of private stock in origin prefecture2) Natural log of labor in origin prefecture3) Natural log of GDP(origin prefecture)*GDP(destination prefecture)4) Natural log of distance between origin prefecture and destination prefecture5) Share of air transport between origin prefecture and destination prefecture6) Population of origin prefecture7) Share of agricultural output in origin prefecture
Panel data were made available for 1995 and 2000, since transport survey data
were provided for these years only. Data for pairs of prefectures without air service
were eliminated, so that the panel data set was composed of 1410 for each year. All
the figures were converted to real value based on 1995. Data source of the variables
are described in Table 1.
Table 1
Yit

Data source of variables
sourceNational Accounts (ESRI,Cabinet Office)

K it

Private sector stock: Doi (2002)

Variable

Lit

ACC itPopulation census 1995 & 2000 (Ministry of Internal Affairs
and Communications)
Calculated from Net trunk route passenger travel survey, Airtransport demand forecast survey (Ministry of Land,
Infrastructure and Transport), National Accounts (ESRI,
Cabinet Office) JTB time table, Monthly labor survey(Ministry of Health, Labor and Welfare)

The result of 3SLS is listed in Table2. As expected, ACC it has significantly
positive effect on productivity. Also, it should be noted that coefficient for ACC it has
increased from 1995 to 2000 suggesting that Japanese prefectures have become moresensitive to air transport accessibility.
Table 2

Cobb-Douglas Simultaneous Equation Estimates (n=1410)

Equation (2)C

ACC

ln( K / L )Parameters for 1995
t statistics-.493053-9.22692**

1.57895
3.47719* *.858261
40.1941**

R 2 : .554344

Parameters for 2000
t statistics-.661276-10.8640**

1.97635
4.53812**.86809339.3313**R 2 : .549413

C

ln(Q )

ln(GDP )Equation (4)Parameters for 1995
t statistics

4.40660
21.8528**-.261739-37.7187**.120226
15.1012**R 2 : .499931

Parameters for 2000
t statistics

3.9330817.3001**-.278136-39.7614**.145030
16.0286**

R 2 : .523761

Equation (5)C

ln(GDP )ln(Dis tan ce)

AirshareParameters for -20.2971
1995-27.5989**
t statistics.8309421.5381738.2947** 29.1027**

1.68247
15.2469**

R2 :.624953

Parameters for -24.2923
2000-31.9708**
t statistics.910927
1.78420
41.4665** 33.7457**

1.38539
13.8818**

R2 :.673173（** significant at 1% level）
2) Barro Regression
Barro regression is specified in the following two forms. First is a basicmodel with initial per-capita output level and accessibility index as regressors.
1(ln y it +T − ln y it ) = α 0 + β 0 ln y it + γ 0 ACC 0 + ε it ……(6)T

where,

ACC 0 : airshare * ln yit − ln y jt /(GVijt +T / GVijt ) + ε it
According to Fujita, Krugman and Venables (1999), formation of hierarchicalurban system could be accelerated when transport cost is reduced thus leading toagglomerated regions outperforming economic performance of peripheral areas. To
test the hypothesis, in the second form, accessibility index is disaggregated into two.
One is to reflect a situation in which the origin prefecture has a higher per-capita output
than the destination prefecture’s, and the other to reflect the opposite. This is to
identify whether accessibility improvement with prefectures with lower per-capita

output is different to that with higher per-capita output.
1(ln y it +T − ln y it ) = α 1 + β1 ln y it + γ 1 ACC 1 + γ 2 ACC 2 + ε it ……(7)T

where,
ACC 1 : airshare * (ln y it − ln y jt ) * DUM 1 /(GVijt +T / GVijt )DUM 1 : 1 when ln yit > ln y jt , 0 when ln yit < ln y jt
ACC 2 : airshare * (ln y jt − ln y it ) * DUM 2 /(GVijt +T / GVijt )DUM 2 :1 when ln yit < ln y jt , 0 when ln yit > ln y jt
Instrument variables for Barro regression were set as follows:
1) Natural log of GDP in origin prefecture2) Natural log of GDP in destination prefecture3) Air transport accessibility between origin and destination prefectures in 1995
4) Air transport accessibility between origin and destination prefectures in 2000
5) Population of origin prefecture6) Share of agricultural output in origin prefecture
Table 3 Barro Regression Estimates (n=1410)
ln(Yit / Lit )
C
ACC 0
ACC 1
ACC 2
Parameters for .104403
Equation 66.60019**
t statistics-.584891E-02-5.66236**.031778
7.33908
**

-

-

R2 :
.034019

Parameters for .197074
Equation 75.71636**t statistics-.011908-5.28455**-.053532
6.31782**.035516
7.07183**

R2 :.030263（** significant at 1%
level）The result ofBarro regression islisted in Table 3. As
expected, initial level ofper capita output is

Per capita GDP from air transport accessibility change

Figure 447 Prefectures in Japanannual growth in 1995-2000-0.0002～0.0144 (9)-0.0018～-0.0002 (10)-0.0037～-0.0018 (7)-0.0051～-0.0037 (9)
Tokyo
-0.01
22～-0.0051 (12)negative. Accessibility has significantly positive effect on growth. In equation 7,
parameter for ACC 1 is higher than for ACC 2 . This suggests that accessibility change
is more sensitive to growth in a situation where the origin prefecture has a higher
per-capita output than the destination prefecture.
Since highly agglomerated
metropolitan area, such as Tokyo, has higher per-capita output, improvement in airtransport accessibility should have higher impact for these regions rather than rural
areas. To identify aggregate effect of accessibility change per prefecture, estimatedcoefficient for equation 6 was used to calculate the per-capita output growth if airtransport generalized cost in the accessibility index had not changed. The difference of
per-capita output growth compared to that of actual accessibility is displayed in Figure 4.
We could see that Tokyo metropolitan area and Fukuoka, center of Kyushu district, hadconsiderable benefit from air transport accessibility improvement. It is striking, on theother hand, that other prefectures had negative impact on growth from air transport
accessibility change between 1995 and 2000.
Conclusion
There was a missing link between air transport accessibility improvement andmacro economic performance. This paper tries to fill the gap. Incorporatinginter-regional accessibility index as an economic environment determinant in
Cobb-Douglas production function and Barro regression framework, cross-sectional
regression was undertaken using panel data of 47 prefectures in Japan. It was revealed
that there has been significant productivity gain from improvement in air transport
accessibility between 1995 and 2000 particularly in agglomerated areas such as theTokyo metropolitan region. Further research is necessary, however, to understandmore deeply the causal relationship between air transport accessibility, agglomeration
and regional growth.
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