Infrastructure Revenue Bondfor
Sustainable Growth
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,QYHVWRUV*RYHUQPHQWRegional Development Company issues bonds

Buyers of Infrastructure Bond
(long term institutional investors)
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Case Study: Southern Tagalog Arterial Road
(STAR) , Philippines
Micro-data The Southern Tagalog
Arterial Road (STAR)
project in Batangas
province, Philippines(south of Metro Manila) is
a modified Built-OperateTransfer (BOT) project.
 The 41.9 km STAR
tollway was built toimprove road linkage
between Metro Manila
and Batangas City,
provide easy access to
the Batangas
International Port, andthereby accelerateindustrial development in
Batangas and nearby
provinces.
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The Southern Tagalog Arterial Road
(STAR Highway), Philippines, Manila
Tax Revenues in three cities
Yoshino and Pontines (2015) ADBI Discussion paper 549

CompletionGDP growth rate

Railway

Uzbekistan Railway

Time
Divide regions affected and not affected by railway connection to “Treated group” and “Control group”
Naoyuki Yoshino - Umid Abidhadjaev. “Impact evaluation of infrastructure provision: case studies from Japan and Uzbekistan”.

December 14-15, 2015.

Islamabad, Pakistan

GDP
GDP

Term

Connectivity spillover
effectRegional spillover
effectLaunching
Effects

Short

2.83***[4.48]

0.70[0.45]

1.33[1.14]Mid

2.5***[6.88]

0.36[0.29]

1.27[1.46]

Long

2.06***[3.04]-0.42[-0.29]

2.29**[2.94]

Short

0.19[0.33]

0.85[1.75]-0.18[-0.20]

Mid

0.31[0.51]

0.64[1.30]-0.02[-0.03]

Long

0.07[0.13]-0.006[-0.01]

0.50[0.67]

Postponed Effects

1.76*[1.95]-1.49[-0.72]

2.58*[2.03]

AnticipatedShort-1.54[-1.66]

1.42[0.78]-1.32[-0.92]

Mid

0.32[0.44]

0.84[1.42]

0.13[0.13]

Long

0.11[0.15]

0.10[0.16]

0.87[1.19]-0.14[-0.20]-1.71[-1.35]

1.05[1.44]

2 years

1 yearAnticipated

Postponed Effects

Neighboring
spillover effectNote: t-values are in parenthesis. t-value measures how many standard errors the coefficient is away from zero.
legend: * p<.1; ** p<.05; *** p<.01
Naoyuki Yoshino - Umid Abidhadjaev. “Impact evaluation of infrastructure provision: case studies from Japan and Uzbekistan”.

December 14-15, 2015.

Islamabad, PakistanJapanese Bullet Train

Impact of Kyushu Shinkansen Rail on
CORPORATE TAX revenue during 1st PHASE OF OPERATION period{2004-2010} , mln. JPY (adjusted for CPI, base 1982)
ϭ
ϵ
ϴ
Ϯ

ϭ
ϵ
ϴ
ϯ

ϭ
ϵ
ϴ
ϰ

Variable
Treatment2
Number of tax
payers

ϭ
ϵ
ϴ
ϱ

ϭ
ϵ
ϴ
ϲ

ϭ
ϵ
ϴ
ϳ

ϭ
ϵ
ϴ
ϴ

ϭ
ϵ
ϴ
ϵ

Regression 1-4772.54[-0.2]
5.8952514*[1.95]

Treatment3

ϭ
ϵ
ϵ
Ϭ

ϭ
ϵ
ϵ
ϭ

ϭ
ϵ
ϵ
Ϯ

ϭ
ϵ
ϵ
ϯ

ϭ
ϵ
ϵ
ϰ

Regression 2

5.8957045*[1.95]-15947.8[-0.87]

Treatment5

ϭ
ϵ
ϵ
ϱ

ϭ
ϵ
ϵ
ϲ

ϭ
ϵ
ϵ
ϳ

ϭ
ϵ
ϵ
ϴ

Regression 35.896112*[1.95]

ϭ
ϵ
ϵ
ϵ

Ϯ
Ϭ
Ϭ
Ϭ

Ϯ
Ϭ
Ϭ
ϭ

Ϯ
Ϭ
Ϭ
Ϯ

Regression 45.8953585*[1.95]

Ϯ Ϯ
Ϭ Ϭ
Ϭ Ϭ
ϯ ϰ

Ϯ
Ϭ
Ϭ
ϱ

Ϯ
Ϭ
Ϭ
ϲ

Regression 55.8629645*[1.91]-13250.4[-1.06]

Treatment7-6883.09[-0.7]

TreatmentCon
Constant-665679[-1.35]-665418[-1.35]-665323[-1.35]-665358[-1.35]-28030.8[-0.65]-658553[-1.32]

N
R2
F

799
0.269215
1.934589

799
0.269281
2.106448

799
0.269291
2.074548

799
0.2692412.100607

799
0.269779
8.497174

Ϯ
Ϭ
Ϭ
ϳ

Ϯ
Ϭ
Ϭ
ϴ

Ϯ
Ϭ
ϭ
Ϭ

Ϯ
Ϭ
ϭ
ϭ

Ϯ
Ϭ
ϭ
Ϯ

Ϯ
Ϭ
ϭ
ϯ

COMPOSITION OF
GROUPSGroup2
Group5
Kagoshima
Kagoshima
Kumamoto
Kumamoto
FukuokaGroup3
Oita
Kagoshima
Miyazaki
Kumamoto
FukuokaGroup7
Kagoshima
Kumamoto
FukuokaOita
Miyazaki
Saga
Nagasaki

Note: Treatment2 = Time Dummy {1991-2003} x Group2. etc. t-values are in parenthesis. Legend: * p<.1; ** p<.05; *** p<.01.
Clustering standard errors are used, allowing for heteroscedasticity and arbitrary autocorrelation within a prefecture,
but treating the errors as uncorrelated across prefectures


Ϯ
Ϭ
Ϭ
ϵ

GroupCon
Kagoshima
Kumamoto
FukuokaOsaka
Hyogo
Okayama
Hiroshima
YamaguchiImpact of Kyushu Shinkansen Rail on
CORPORATE TAX revenue during 2nd PHASE OF OPERATION period{2011-2013} , mln. JPY (adjusted for CPI, base 1982)
ϭ
ϵ
ϴ
Ϯ

ϭ
ϵ
ϴ
ϯ

ϭ
ϵ
ϴ
ϰ

Variable
Treatment2
Number of tax
payers

ϭ
ϵ
ϴ
ϱ

ϭ
ϵ
ϴ
ϲ

ϭ
ϵ
ϴ
ϳ

ϭ
ϵ
ϴ
ϴ

ϭ
ϵ
ϴ
ϵ

Regression 1
72330.012**[2.2]
5.5277056***[3.13]

Treatment3

ϭ
ϵ
ϵ
Ϭ

ϭ
ϵ
ϵ
ϭ

ϭ
ϵ
ϵ
Ϯ

ϭ ϭϵ
ϵ ϵϰ
ϵ
ϯ

Regression 2

5.5585431***[3.14]
104664.34*[2]

Treatment5

ϭ
ϵ
ϵ
ϱ

ϭ
ϵ
ϵ
ϲ

ϭ
ϵ
ϵ
ϴ

ϭ
ϵ
ϵ
ϳ

Regression 3

5.558603***[3.14]

ϭ
ϵ
ϵ
ϵ

Ϯ
Ϭ
Ϭ
Ϭ

Ϯ
Ϭ
Ϭ
ϭ

Ϯ
Ϭ
Ϭ
Ϯ

Regression 45.5706545***[3.14]

ϮϬϬϲ

Regression 55.9640287***[3.07]

80998.365**[2.34]

TreatmentCon

N
R2
F

Ϯ
Ϭ
Ϭ
ϱ

82729.673**[2.1]

Treatment7

Constant

Ϯ Ϯ
Ϭ Ϭ
Ϭ Ϭ
ϯ ϰ

-568133.98**[-2.07]-573747.28**[-2.08]-574245.87**[-2.08]-576867.56**[-2.09]179632[1.58]-642138.87**[-2.1]

611
0.350653
5.062509

611
0.352058
5.486197

611
0.352144
5.351791

611
0.352874
5.431088611
0.364088
16.55518

Ϯ
Ϭ
Ϭ
ϳ

Ϯ
Ϭ
Ϭ
ϴ

Ϯ
Ϭ
ϭ
Ϭ

Ϯ
Ϭ
ϭ
ϭ

Ϯ
Ϭ
ϭ
Ϯ

Ϯ
Ϭ
ϭ
ϯ

COMPOSITION OF
GROUPSGroup2
Group5
Kagoshima
Kagoshima
Kumamoto
Kumamoto
FukuokaGroup3
Oita
Kagoshima
Miyazaki
Kumamoto
FukuokaGroup7
Kagoshima
Kumamoto
FukuokaOita
Miyazaki
Saga
Nagasaki

Note: Treatment2 = Time Dummy {1991-2003} x Group2. etc. t-values are in parenthesis. Legend: * p<.1; ** p<.05; *** p<.01.
Clustering standard errors are used, allowing for heteroscedasticity and arbitrary autocorrelation within a prefecture,
but treating the errors as uncorrelated across prefectures


Ϯ
Ϭ
Ϭ
ϵ

GroupCon
Kagoshima
Kumamoto
FukuokaOsaka
Hyogo
Okayama
Hiroshima
YamaguchidŽƚĂůƚĂǆƌĞǀĞŶƵĞ͕ŵůŶ͘:WzϲϬϬϬϬϬ
ϱϬϬϬϬϬ
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΀ϭϵϴϮͲϭϵϵϬ΁

ŽŶƐƚƌƵĐƚŝŽŶ
΀ϭϵϵϭͲϮϬϬϯ΁

KƉĞƌĂƚŝŽŶϭ΀ϮϬϬϰͲϮϬϭϬ΁

KƉĞƌĂƚŝŽŶϮ΀ϮϬϭϭͲϮϬϭϯ΁

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NOTE:
Context: 44 developing countries, 1991-2010
Methodology: Production function approach
Point of novelty and findings:
Study incorporated infrastructure variable into neoclassicalgrowth framework and demonstrated that controlling for share ofworking age population with university level of education
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