Economic Impact of Water
Infrastructure:
Proposal of Evaluation
Naoyuki Yoshino
Dean, Asian Development Bank Institute(ADBI)
Professor Emeritus of Keio University
nyoshino@adbi.org

Direct Effect and Spill-over Effects

Production Function

Y=F( Kp, L, Kg )OutputDirect Effect
Y= Output, Kp= private capital, L = labor
Kg = public capital (infrastructure)2

Highway
Non-affected region (User charges)(low rate of return)
Spillover effectNon-affected
Employmentregion

Private investment
SME development
Spillover effect
Increase of property tax revenue
3Return the spillover effects to Investors

45Spillover effects  Return to investors
1956-60 1961-65 1966-70 1971-75 1976-80 1981-85
Direct Effect (Kg)

0.696

0.737

0.638

0.508

0.359

0.275

Indirect Effect (Kp)

0.453

0.553

0.488

0.418

0.304

0.226

Indirect Effect (L)

1.071
0.30480.907 0.740 0.580 0.407 0.317
0.292 0.2456 0.1996 0.1422 0.1086

20%Returned%Increment

43.8

39.6

38.5

39.3

39.6

1986-90 1991-95 1996-00 2001-05 2006-10
0.215
0.195
0.193
0.0776

0.181
0.162
0.155
0.0634

0.135
0.122
0.105
0.04540.1140.1
0.09
0.038

0.108
0.1
0.085
0.037

36.1

35.0

33.6

33.3

34.3

6

39.5

Case Study: Southern Tagalog Arterial Road(STAR) , Philippines (Micro-data Analysis)• 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 inBatangas and nearby
provinces.
7

Difference-in-Difference (DiD) Analysis
where:

D = 1 (Treatment group)
D = 0 (Control group)

T = Treatment period= Treatment Effect
Assumption:
Equal trends
between Treatmentand Control groups

Pre-Post8

The Southern Tagalog Arterial Road
(STAR Highway), Philippines, Manila
Tax Revenues in three cities
Yoshino and Pontines (2015) ADBI Discussion paper 549

Completion9Injection of Increased Tax revenues
actual rate of return
Increase of tax revenues by
spillover effects
user charges

time10GDP growth rate

Railway

Uzbekistan Railway: Connectivity is importantTimeDivide 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”.

11

December 14-15, 2015.

Islamabad, Pakistan

GDPGDPTerm

Connectivity 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]

1.76*[1.95]-1.49[-0.72]

2.58*[2.03]

Short-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]

1 yearAnticipated

Postponed Effects

2 years

Anticipated

Postponed Effects

Regional spillover effect

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

Additional tax revenue, Regional GDP growth and Railway
Company Net Income, LCU (bln.)

T(20)*∆Y∆Y Affected
Coefficients(Tax(Direct + Spilloverrevenue)
effects)

PeriodCompany netincome(Revenue Costs)

Short term(2009-2010)

2.83***[4.48]

16.0

79.9

315.5

Mid-term(2009-2011)

2.48***[6.88]

16.3

81.5411.7Long-term(2009-2012)

2.06***[3.04]

14.7

73.5

509.0

Source: Authors’ calculations

13Japanese Bullet Train

14

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

1983

1984

Variable
Treatment2
Number of taxpayers

1985

1
986

1987

1988

1989

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

Treatment3

1990

1991

1992

19931994Regression 25.8957045*[1.95]-15947.8[-0.87]

Treatment51995199619971998Regression 35.896112*[1.95]

1999200020012002Regression 45.8953585*[1.95]

2 20 00 0
3 420052006Regression 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]

NR2F

799
0.269215
1.934589

799
0.269281
2.106448

799
0.269291
2.074548

799
0.2692412.100607

799
0.269779
8.497174200
720082009201120122013

COMPOSITION OF
GROUPSGroup2Group5
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
1520
10GroupCon
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)
1982

1983

1984

Variable
Treatment2
Number of taxpayers

19851986198719881989Regression 172330.012**[2.2]
5.5277056***[3.13]

Treatment3

199019911992

1 199 94
93Regression 25.5585431***[3.14]
104664.34*[2]

Treatment5

1995199619971998Regression 35.558603***[3.14]

1999

2000

200
1

200
2Regression 45.5706545***[3.14]

2006Regression 55.9640287***[3.07]

80998.365**[2.34]

TreatmentCon

NR2F200582729.673**[2.1]

Treatment7

Constant

2 20 0
0 0
3 4-568133.98**[-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

2007

2008

2010201
120122013COMPOSITION OFGROUPS
Group2Group5Kagoshima
Kagoshima
Kumamoto
KumamotoFukuoka
Group3
Oita
Kagoshima
Miyazaki
Kumamoto
FukuokaGroup7
Kagoshima
Kumamoto
FukuokaOita
Miyazaki
SagaNagasaki

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
162009GroupCon
Kagoshima
Kumamoto
Fukuoka
OsakaHyogo
Okayama
Hiroshima
YamaguchiTotal tax revenue, mln. JPY600000
500000300000200000mln. JPY

400000Group 7Group 5Group 3100000

Group 20

Previous period Construction[1982-1990][1991-2003]

Operation 1[2004-2010]17

Operation 2Group 8[2011-2013]

Injection of Increased Tax revenues
actual rate of return

Increase of tax revenues byspillover effects
user charges

time
18

Estimation of water related Risk
1. Negative Effects of Flood, Typhoon etc.
How to measure negative impacts?
(1) <Direct effects>
Changes in Production of Agricultural products
Changes in Income tax revenues
Changes in Corporate tax revenues
Changes in Sales  Consumption tax(2)<Spillover effects>
Decline in supply of food and other goods
 Increase in prices of goods and services
19

Estimation of Indirect Effectsof Disaster to Macro economy(3) Impact of rising price of commodities Households’ consumption declines
 Increase of general price level

20Economic Effects of Dam Construction
1, Stable supply of clean water
2, Increase of property value (Ex. Manila water)changes in property prices
3, Industries come to the region
corporate tax revenues, increase in Sales
4, Clean water improves health condition
number of patients
5, Increase in Tax revenues will tell the impact
Income tax revenues
21

Case Study of Natural Disaster
1, Japanese Dam construction
2, Thailand flood case
3, Philippines typhoon case4, Difference in difference method
and Macro economic data
5, Estimation of the negative impact
6, Compare with construction costs
22

Possible Solutions
by use of community funds
Hometown Investment----------------------------------A Stable Way to Supply Risk
Capital
Yoshino, Naoyuki; Kaji Sahoko(Eds.), 2013,23

Revitalization of Tsukubane Hydro Power (Nara state)
250 investors, total 525 thousand US dollars, Japan
OriginalDam wasconstructed
more than
100 years
agoPrivate Financial Scheme of Wind Power
Collected by Individuals (started in 2001-9)
Investors
2000people
Each person

1000 US$Final
User
Hokkaido
GreenFundConstruction

Costs =2million
US $Sell toPowerCompanyPE
1+0.055%surcharge

Scheme of Financing Power Panels
Local Government
Subsidies (2/3)

Private Individuals
Hometown Trust Funds
1000 US$ -– 5000 US $377
Solar powerplants

PowerCompany

References
Yoshino N. Kaji, S. (2013) Hometown Investment Trust Funds,Springer, March 2013
Yoshino, N., Taghizadeh Hesary, F. (2014), ‘AnalyticalFramework on Credit Risks for Financing SMEs in Asia’. AsiaPacific Development Journal. United Nations Economic andSocial Commission for Asia and the Pacific (UN-ESCAP)
Yoshino, N. and Pontines, Victor (2015) “The Highway-Effecton Public Finance: Case of the STAR Highway in thePhillippines”, GIE | AAA Special Kick-off Edition, GIE Network
publishing.

27

Nakahigashi, M and Yoshino, N. (2016) “Changes in Economic Effect ofInfrastructure and Financing Method”, Public Policy Review, Vol.12, No.1.
Yoshino, Naoyuki （2010） “Financing Transport Infrastructure Investment”,OECD （ed.）, Southeast Asian Economic Outlook 2010, OECD Publishing.
Yoshino, Naoyuki （2012） “Global Imbalances and the Development ofCapital Flows among Asian Countries”, OECD Journal: Financial MarketTrends, Vol. 2012/1
Yoshino, Naoyuki and Masaki Nakahigashi （2004） “The Role ofInfrastructure in Economic Development”, ICFAI Journal of Managerial
Economics, 2, pp. 7-24
Yoshino, Naoyuki and Victor Pontines （2015） “The Highway Effect onPublic Finance: Case of the STAR Highway in the Philippines”, AsianDevelopment Bank Institute （ADBI） Working Paper No.549.
Yoshino, Naoyuki, Victor Pontines and Umid Abidhadjaev （2015） “Impact
Evaluation of Infrastructure Provision on Public Finance and Economic
Performance: Empirical Evidence from Philippines and Uzbekistan”, AsianDevelopment Bank Institute （ADBI）, Working Paper, No.548.
28

Infrastructure Finance
Infrastructure
investment
Increase
Rate of return
By injecting
Incremental
Tax revinues
Obtained bySpillover effects

Government
BudgetGovernment

Tax

Postal Saving
Post Insurance

Viability Gap Fund
Domestic Private Investors(Pension Funds, Insurance)
Overseas’ Pension investorsADB29

Viability Gap Fund and Government Burden

Investors only benefit
fixed rate of return

viability gap fundactual rate of return30