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Faculty of Economics, Thammasat University
THAMMASAT REVIEW OF
ECONOMIC AND SOCIAL POLICY
Credit Guarantee Optimization of State-owned Enterprises
Chayanisa Chaisuekul
Investigating relationship between Government Spending
and Economic Growth: Public Spending and long-run GDP
level
Kritchasorn Jarupasin
Linkage of Technological Innovation toward ICT base and
Economic Output in CLMV Region
Theara Chhorn
Volume 4, Number 2, July - December 2018
ISSN 2465-390X (Print)
ISSN 2465-4167 (Online)
THAMMASAT REVIEW OF
ECONOMIC AND SOCIAL POLICY Volume 4, Number 2, July – December 2018
ISSN 2465-390X (Print)
ISSN 2465-4167 (Online)
Thammasat Review of Economic and Social Policy
Thammasat Review of Economic and Social Policy (TRESP) is a
double-blind peer reviewed biannual international journal published in
June and December. The journal is managed by the Research Committee
under the supervision of the Academic Affairs Division of the Faculty of
Economics, Thammasat University. Our editorial board and review panel
comprise of academicians and practitioners across various areas of
economic and social policies. The goal of the journal is to provide up-to-
date practical and policy-oriented analysis and assessment of economic and
social issues, with particular focus on Asia and the Pacific region.
However, research findings from other parts of the world that are relevant
to the theme of the journal may be considered.
Aims & Scopes
Our journal is dedicated to serve as a platform for debate and
critical discussion pertaining to the current issues of public policy. The
outcome of such research is expected to yield concrete policy implications.
Some of the targeted issues include urban and regional socio-economic
disparities, ageing society, healthcare, education and welfare policies,
environmental and natural resources, local communities, labor migration,
productivity, economic and political integration, political economy,
macroeconomic instability, trade and investment, fiscal imbalances,
decentralization, gender issues, behavioral economics and regulations; and
law and economics. The journal makes its best effort to cater a wide range
of audience, including policymakers, practitioners in the public and
business sectors, researchers as well as graduate students.
Articles should identify any particular issue concisely, address the
problems of the research explicitly and supply sufficient empirical data or
strong evidence and substantial argument to support the discussion of
policy initiatives asserted by the author(s). Theoretical and applied papers
are equally welcome provided their contributions are policy-relevant.
Authors are responsible for the published articles. The views and
opinions expressed in the articles do not necessarily reflect those of the
Editors and the Editorial Board.
Advisory Board
Dean, Faculty of Economics, Thammasat University, Thailand
Ian Coxhead, University of Wisconsin-Madison, United State
Tran Van Hoa, Centre for Strategic Economic Studies, Victoria University, Australia
Suthipun Jitpimolmard, Khon Kaen University and Thailand Research Fund, Thailand
Medhi Krongkaew, National Institute of Development Administration, Thailand
Duangmanee Laovakul, Thammasat University, Thailand
Arayah Preechametta, Thammasat University, Thailand
Sakon Varanyuwatana, King Prajadhipok's Institute, Thailand
Editor-in-Chief
Euamporn Phijaisanit, Thammasat University, Thailand
Associate Editor
Pornthep Benyaapikul, Thammasat University, Thailand
Editorial Board
Kirida Bhaophichitr, Thailand Development Research Institute, Thailand
Brahma Chellaney, Center for Policy Research, New Delhi, India
Aekapol Chongvilaivan, Asian Development Bank, Manila, Philippines
Emma Jackson, Bank of England, UK
Armin Kammel, Lauder Business School, Vienna, Austria
Somprawin Manprasert, Bank of Ayudhya, Thailand
Gareth D. Myles, School of Economics, University of Adelaide, Australia
Voraprapa Nakavachara, Chulalongkorn University, Thailand
Songtham Pinto, Bank of Thailand, Thailand
Pathomdanai Ponjan, Fiscal Policy Office, Ministry of Finance, Thailand
Watcharapong Ratisukpimol, Chulalongkorn University, Thailand
Sasatra Sudsawasd, National Institute of Development Administration, Thailand
Maria-Angeles Tobarra-Gomez, University of Castilla-La Mancha, Spain
Soraphol Tulayasathien, Fiscal Policy Office, Ministry of Finance, Thailand
Editorial Assistants
Darawan Raksuntikul
Sorravich Kingsuwankul
Panit Buranawijarn
Editorial and Managerial Contact
c/o Mrs. Darawan Raksuntikul
Thammasat Review of Economic and Social Policy (TRESP)
Faculty of Economics, Thammasat University
2 Prachan Road, Bangkok 10200, Thailand
Tel. +66 2 696 5979
Fax. +66 2 696 5987
E-mail: [email protected]
Url: http://www.tresp.econ.tu.ac.th
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July-December 2018
Editorial Introduction 1
ARTICLES
Credit Guarantee Optimization of State-owned Enterprises 6
Chayanisa Chaisuekul
Investigating relationship between Government Spending and
Economic Growth: Public Spending and long-run GDP level
Kritchasorn Jarupasin 28
Linkage of Technological Innovation toward ICT base and
Economic Output in CLMV Region 76
Theara Chhorn
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
1
Editorial Introduction
In this issue, Chayanisa Chaisuekul from Fiscal Policy
Research Institute, Thailand, conducted an investigative
research on government credit guarantee, which can be
considered one of the fiscal instruments for public support on
infrastructure and public goods investment. Her article,
“Credit Guarantee Optimization of State-owned Entreprises”,
studies the risk profile of government credit guarantees in
Thailand which are overseen by the Public Debt Management
Office (PDMO) of the Ministry of Finance. Government credit
guarantees are particularly effective in cases where the
government is the best stakeholder to anticipate, control, and
minimize risk. They are best viewed as endogeneous risks
since the risks can potentially be influenced by government
policy.
The state-owned enterprises (SOEs) examined in this
study are categorized into financial institutions and non-
financial institutions. The paper studies the relationship
between default events and credit rating of these two types of
SOEs over the period 2009-2014. In the case of financial
institutions, a separate definition for a ‘default event’ was
utilized (focusing instead on the rollover and restructuring of
debt) as no financial institution SOEs failed to pay scheduled
debt service in that time period. Intuitively, this may be due to
the fact that a financial institution defaulting on a payment has
the potential to be much more disruptive than a non-financial
institution from the government perspective. The probability of default for financial and non-financial SOEs was estimated
using the hybrid forward model.
Government credit guarantees can entail both benefits and risks for the government. In Thailand, the role of the
PDMO is to manage the fiscal risk stemming from government
guarantees. The optimal credit guarantee values were
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
2
calculated using linear programming models of two types:
maximizing net benefits, and minimizing net expected loss.
The paper found that the two methods yield similar results.
The probability of default for both types of SOEs closely
follows rank-order in terms of credit rating. However, in
contrast to the literature, it was found that in the time period
studied, the probability of default for financial institution
SOEs was higher than that of non-financial institutions, for a
given credit rating. This is perhaps due to the turbulent time
period which was studied where the government of Thailand
embarked on many new initiatives which were not all
successful. Further studies may consider longer time periods
to further clarify the picture.
The second article of this issue is “Investigating
relationship between Government Spending and Economic
Growth: Public Spending and long-run GDP level” by
Kritchasorn Jarupasin from Office of the National Economic
and Social Development Board (NESDB). This article
analyses the impacts of fiscal policy using a Solow-type model
with transitional dynamics, allowing for persisting effects of
fiscal policy à la Gemmell, Kneller and Sanz (2016). Previous
studies have looked at, through various methods and with
various dependent and fiscal variables, the effects of fiscal
policy changes on economic growth. While findings reveal
evidence for both significant and non-significant effects on
short-run and long-run growth, the literature indicates that
careful design is required to isolate effects and derive results.
The article displays the effects of fiscal policy changes
in 38 countries, with 17 developing countries and 21 high-
income OECD countries. The data indicates that, on average,
high-income countries spend more (as a percentage of Gross
Domestic Product, GDP) on healthcare and social welfare,
while developing countries spend more on general public
services. Unweighted averages suggested that, as a percentage
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
3
of GDP, developing and high-income countries spend similar
amounts on education. Pooled mean group (PMG) estimator
with an autoregressive distributed lag (ARDL(1,1)) structure
was employed to analyse short-run dynamics and long-run
equilibrium relationships between fiscal change and growth.
There is evidence that an increase in the share of a particular
type of spending could improve the level of per capita GDP in
the long run for developing countries in the sample, for
example, in healthcare and in general public services. For
high-income countries, the model suggests additional
spending on education could boost long-term GDP levels.
However, how this additional spending is financed also has
implications for growth; the data suggests that in the high-
income country group, excise taxes play a large role in
determining the effectiveness of spending increases.
Overall, one important key point from the study
implies that increasing revenue through distortionary taxes
should be avoided, since it reduces economic growth rate.
Moreover, the growth impacts of fiscal changes vary by
different implicit financing elements. In developing countries,
the focus should be on increasing the share of spending on
general public services and healthcare.
The third article, “Linkage of Technological
Innovation toward ICT base and Economic Output in CLMV
Region,” by Theara Chhorn, Chiang Mai University, considers
the relationship between information and communication
technology (ICT) development in Cambodia, Lao PDR,
Myanmar and Vietnam, and economic growth and
development, with a view towards providing policy
recommendations.
Technological innovation and progress through
improvements in ICT has driven economic growth and
reduced poverty across the world. In the vein of similar studies
in the literature on technological progress and growth, ICT is
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
4
assumed to augment labour and capital productivity.
Estimation methods used include the fixed-effects and
random-effects model, the maximum-likelihood random
effects model, and feasible generalised least squares
estimation. The different approaches are used to obtain more
efficient and consistent estimates, as well as minimize the
effects of heteroskedasticity and serial autocorrelation.
Estimation of the impact of technological progress is
proxied in two ways in the study: (i) number of internet server
connections, and; (ii) imports of computers and other ICT-
related goods. The literature suggests that estimation of both
factors should be positively correlated with economic growth.
The study finds that, for the period 1995-2016, internet server
connections have a positive and significant relationship with
per capita GDP growth. However, there is opposite
relationship between imports of computers and ICT products,
i.e. increased imports lead to lower per capita GDP growth.
The study supports the notion that greater access to
technology has the potential to improve economic growth and
development. Policymakers should thus harness this
relationship to bolster domestic development, as well as take
steps to attract increased investment in technology. Further
research should, however, be conducted to identify why
greater imports of computer and ICT products are negatively
related to economic development in the CLMV region.
Thammasat Review of Economic and Social Policy
(TRESP) is a young biannual double-blind peer reviewed
international journal published in June and December. Its first
publication was in December 2015. The Faculty of
Economics, Thammasat University and the Editorial Team of
TRESP seek to provide an effective platform for reflecting
practical and policy-oriented perspectives that links the
academic and policymaking community. Having devoted to
our ‘knowledge-for-all’ philosophy so as to drive our society
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
5
forward, the Faculty decided that TRESP published in an open
access model. Authors are responsible for the published
articles. The views and opinions expressed in the articles do
not necessarily reflect those of the Editors and the Editorial
Board. For further information and updates on this journal, or to submit an article, please visit our website at
www.tresp.econ.tu.ac.th.
Euamporn Phijaisanit
Editor-in-Chief
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
6
Credit Guarantee Optimization of State-owned
Enterprises
Chayanisa Chaisuekul
Senior Researcher
Fiscal Policy Research Institute
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
7
ABSTRACT
The objective of this paper is to estimate the credit risk of state-
owned enterprises (SOEs) in the form of probability of default
(PD) and then use it to analyze credit guarantee optimization.
Estimation of the probability of default by the Hybrid Model
found that the estimated PD for both financial and non-
financial SOEs are ranked by credit rating grade (the rank
ordering property), except for the 3rd rating grade PD of non-
financial SOEs. Analysis of the optimal credit guarantee for
each SOE by Linear Programming model found that the results
of maximizing the net benefit and the results of minimizing the
net expected loss from credit guarantee are similar. Moreover,
the value of expected loss implies that the magnitude of credit
risk must be mitigated and managed with appropriate tools by
the Ministry of Finance.
Keywords: Government credit guarantee, Credit risk, State-
owned enterprises
JEL Classification: C61, H63
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
8
Introduction
1.1. Government credit guarantees
Government credit guarantees are a fiscal instrument of
government financial support for infrastructure and public
goods investment in cases where the government is the best
organization to anticipate risk, control risk exposure, and
minimize the cost of risk (IMF, 2005).
In Thailand, the Public Debt Management Office
(PDMO) of the Ministry of Finance has guaranteed credit
(loan and bond) to some state-owned enterprises (SOEs) since
20051. Its goals are to give SOEs access to finance at a lower
financial cost (loan interest rate) and to get a greater amount
of credit from financial institutions with a favorable borrowing
term because they can benefit from the government's high
credit standing. In addition, the government’s support for
SOEs also support economic growth and social development,
such as infrastructure development projects2 and supporting
farmers.3
1.2. Fiscal risk from government credit guarantee
Government credit guarantees create a contingent liability
or government obligation for the Ministry of Finance. The
Ministry of Finance must repay the outstanding guaranteed
loans of state-owned enterprises if those state-owned
enterprises default on the loans. It is the uncertainty as to whether the government will have to pay, and if so the timing
1 Under the Thailand's Public Debt Management Act of 2005 (B.E. 2548) 2 E.g. High-speed train project by the State Railway of Thailand (SRT) 3 E.g. Rice pledging scheme and agricultural credit for rural development
project by Bank for Agriculture and Agricultural Cooperatives (BAAC)
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
9
and amount of spending that is complicated for estimation of
fiscal risk management (IMF, 2005).
The fiscal risk from government credit guarantees are
classified as endogenous risks that are generated from
government activities or where the probability of the event can
be influenced by government actions (IMF, 2016).
Table 1. Government Credit Guarantee Fee Rate
Credit
rating
grade
Guarantee Fee Rate
(Percentage of outstanding guaranteed loan)
5 years
but 10 years
of loan
period
Government
agency - 0.01 0.05 0.10 0.15
SOE
1 0.01 0.05 0.10 0.15
2 0.05 0.10 0.15 0.20
3 0.10 0.15 0.20 0.25
4 0.15 0.20 0.25 0.30
5 0.20 0.25 0.30 0.35
6 0.25 0.30 0.35 0.40
7 0.30 0.35 0.40 0.45
8 0.35 0.40 0.45 0.50
Source: The Guarantee Fee Rate and Condition Ministerial
Regulation of 2008 (B.E. 2551)
Currently, the Ministry of Finance of Thailand partially
manages this fiscal risk by charging a fee for credit guarantees
from state-owned enterprises. The fee pricing is around 0.01-
0.5 %, depending on the credit rating grade4 and the loan
4 The credit rating grade has 8 levels from grade 1 (the lowest risk) to
grade 8 (the highest risk) that is evaluated by the PDMO every fiscal year.
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
10
period, which reflects the level of credit risk of each state-
owned enterprise. Nevertheless, the Ministry of Finance can
use the SOEs credit rating grade to evaluate fiscal risk
exposure from credit guarantee each fiscal year to take
advantage of the value of such contingent liabilities and to
determine the optimal value of credit guarantee for each SOE
which the Ministry of Finance has the highest net benefit from
credit guarantees, and the least risk from credit guarantees for
the SOE in each fiscal year.
Credit Risk of State-owned Enterprises
2.1. Credit risk from government credit guarantee
A financial instrument that is similar to government credit
guarantees are bank loans. The credit risk of bank loans
consists of three components, Exposure at default (EAD), Loss
given default (LGD), and Probability of default (PD). Then,
the magnitude of credit risk from credit guarantees is a
multiplied result of EAD, LGD, and PD (Naksakul, 2006;
Public Debt Management Office, 2014).
In the case of government credit guarantees, EADs are the
risk of disbursement that can be estimated from the value of
guaranteed loans outstanding, while LGDs are risk in
collateral value that can be estimated from a proportion of loss
value after recovery from collateral that is equal to one since
no collateral is required in this case. So, the variable that must
be estimated is the probability of default or default risk.
2.2. Simulated default for state-owned enterprises
Default events are important data for estimating the
probability of default. Standard & Poor’s Global Rating (2017)
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
11
and the Bank of Thailand (2016) define "default event" in the
general case as the event in which "the debtor or the counter-
party cannot pay back the loan principal or interest on the due
date contained in the original terms of a debt issue". However,
in practice, when referring to sovereign debt, including
government, state-owned enterprises, and other government
agency debt, both in the form of loan and bond, default events
never occur when considering the definition of default in the
general case. Especially in the case of Thailand if the state-
owned enterprise fails to pay the loan principal or interest on
the due date, the Ministry of Finance will give assistance such
as finding a new source of funds to repay original debts,
according to financial market conditions and financial status
of state-owned enterprises at that time.
Because of the special characteristics of SOE debt, it is
necessary to define a new default event as applied to SOEs to
calculated their PD. Default events for state-owned enterprises
are defined in this article according to Public Debt
Management Office (2014) which defined SOEs default
events as “when their liabilities greater than their assets in a
year, when they have EBITDA5 negative for three consecutive
years, when they received a credit rating grade as 8 in a year,
or when they received a credit rating grade as 7 for three
consecutive years.” Those conditions of the Public Debt
Management Office (2014) apply only to the non-financial
state-owned enterprise 6 ; none of the financial state-owned
enterprise have performance that meet these conditions.
5 EBITDA is Earnings before Interest, Taxes, Depreciation and
Amortization that calculated in the following manner: EBITDA =
Operating Profit + Depreciation Expense + Amortization Expense. 6 The total number of state-owned enterprises that are given credit rating
by the Public Debt Management Office in each fiscal year is 20 SOEs,
including 16 non-financial institutions and 4 financial institutions.
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
12
Therefore, the Public Debt Management Office (2014) can
estimate default probabilities only for non-financial SOEs.
To resolve that limitation to estimate all types of SOE’s
default probabilities, consider the characteristics of sovereign
debt. The Standard & Poor’s Global Rating (2017) defines a
default for sovereign debt as “when they fail to pay scheduled
debt service on the due date or tenders an exchange offer of
new debt with less favorable terms than the original issue, or
when their notes or bonds are converted into a new currency
of less than the equivalent face value.” The default definition
of Standard & Poor’s Global Rating (2017) focuses on the
characteristic of the loan agreement, not the SOE’s
performance like the default definition of the Public Debt
Management Office (2014). Thus, focusing on the
characteristics of a loan agreement is more appropriate to
define SOE’s default event.
Therefore, to properly define default events for Thailand’s
SOEs, consider S&P Global Rating’s default condition with a
framework for public debt management of the Ministry of
Finance of Thailand, in the case of state-owned enterprises. A
framework for public debt management found that the type of
SOE’s debt management, which meets the S&P Global
Rating’s default condition, is debt management with rollover
method7. Thus, this article defined a new definition of default
for SOE (Simulated default) as "when the SOE either issues
new bonds to repay original bond with less-favorable terms
than the original bond or negotiates with the bank creditors a
rescheduling of principal or interest at less-favorable terms
than in the original loan."
However, with limited access to SOE insights data, i.e.,
we did not have more details about the payment terms of the
pair of loan, which rollover or issue new bonds to repay
7 The SOE issue new bonds to repay original bond
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
13
original bonds. So, this article will use default condition as
“when the SOE manages its debt with rollover” to be a proxy
for our default condition as defined above.
Debt data on SOEs during the fiscal years 2009-2014 were
used in this study; it was found that this period has data on 110
SOEs where their credit was rated by Thailand's public debt
management office. Among them, it was found that there were
4,087 contracts for loans and bonds that were outstanding, and
restructured debt as rollover totaled 355 contracts that
presented in Table 2.8
Table 2. Number of SOE’s loan and bond that restructure
debt as rollover during the years 2009-2014
Fiscal
year
The number of
state-owned
enterprises that
PDMO's credit
rated
The number of
loan and bond
outstanding
The number of loan
and bond that
restructure debt as
a rollover
- Enterprises contracts contracts
2009 19 658 47
2010 17 705 54
2011 16 672 5
2012 18 683 95
2013 20 690 85
2014 20 679 69
Total 110 4,087 355
8 In practice, the public debt management office did not collect data about
payment structure between pairs of loan or bond that restructure debt as
rollover and their original issue, so this study uses the loan and bond that
restructure debt as rollover every case as a simulated default event instead
of the new definition of default for SOE which mentioned above. As a
result, this estimation will give a conservative probability of default.
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
14
Note: This data, includes both non-financial institutions and
financial institutions, and their debt, including all currencies.
2.3. Probability of default for state-owned enterprises
The nature of SOE debt is low default portfolios, which
limits default events and thus general estimation models
cannot estimate PD in the case of no default events occurring
and may yield an underestimation of credit risk. So, the
estimation of probability of default for SOE in this study will
use the model that is appropriate for SOE debt characteristics,
which is the Hybrid model of Roengpitya (2012). The Hybrid
model was based on two existing estimation approaches,
including the most prudent estimation of Pluto and Tasche
(2006) and the maximum likelihood of Forrest (2005). In this
study, we use the Hybrid model with forward method to
estimate PD and assume that there is no asset correlation
between SOEs. The PD estimates are split between SOEs that
are non-financial institutions and SOEs that are financial institutions.
First of all, let the general likelihood function of N rating
grade be defined as ℒ(𝑝1,…,𝑝𝑁) = ∏ ℒ(𝑝𝑖)𝑁𝑖=1 , where 𝑝1,…,𝑝𝑁
are the PD estimates for each rating grade. Let 𝑖 =1 be the lowest risk grade and 𝑖 = 𝑁 be the highest risk grade. In the non-financial institutions case, 𝑖 =1, 2, 3, … , 7 9 , while financial institutions rank from, 𝑖 =3, 4, 5, 6.10 Then, using the concept of the most prudent to collapse the rating grade –
assuming that the N rating grade satisfy the rank order
9 Total of PDMO's credit rating grade is eight grades, but this estimation
ignores grade 8 because this grade never rated to any state-owned
enterprise. 10 During the fiscal year 2009-2014, state-owned financial institutions
received a PDMO's credit rating in grade 3-6 out of the total grade (8
grades).
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
15
requirement, then we must have 𝑝1 ≤ 𝑝2 ≤ 𝑝3 ≤ ⋯ ≤ 𝑝𝑁 . And to find the upper bound of 𝑝1, the most prudent implies that the condition is 𝑝1 = 𝑝2 = 𝑝3 = ⋯ = 𝑝𝑁 = 𝑝 –in the general likelihood function, and we get the new likelihood
function:
ℒ(�⃗�𝑖) = 𝑝∑ 𝑘𝑗
𝑁𝑗=𝑖 (1 − 𝑝)∑ (𝑛𝑗−𝑘𝑗)
𝑁𝑗=𝑖 (1)
Next, let ℒ̅ = 𝑒(−0.5⋅𝜒2(∝,𝑁))
∙ ℒ(𝑀𝐿𝐸) where
ℒ(𝑀𝐿𝐸) = ∏ ℒ(𝑝𝑀𝐿𝐸𝑖 )𝑁𝑖=1 is the maximum likelihood value
that is evaluated at the estimated PD from the maximum
likelihood method and set confident level is 0.95 and degree
of freedom is N.11 The hybrid forward method begins with
solving for the best grade PD first so 𝑝1 solves
(𝑝1)∑ 𝑘𝑗
𝑁𝑗=1 (1 − 𝑝1)
∑ (𝑛𝑗−𝑘𝑗)𝑁𝑗=1 = ℒ̅ (2)
The PD estimates for other rating grades will be solved
through the following iterative process. The estimated PD for
grade 𝑖 = 2, …, N is 𝑝𝑖 that solves
𝓛(�⃗�𝑖) = (𝑝𝑖)∑ 𝑘𝑗
𝑁𝑗=𝑖 (1 − 𝑝𝑖)
∑ (𝑛𝑗−𝑘𝑗)𝑁𝑗=𝑖 =
ℒ̅
∏ ℒ(𝑝𝑀𝐿𝐸𝑙 )𝑖−1𝑙=1
(3)
This model can estimate the probability of default for both
financial and non-financial institution presented in Table 3 and
Table 4. This study can resolve previous limitations in studies
that only estimated PD for non-financial institutions because
of the new definition of simulated default for state-owned
11 The degree of freedom in case of non-financial institution and financial
institution is 7 and 4 respectively, according to the results of the credit
rating grade of the Public Debt Management Office in the past fiscal year
2009-2014.
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
16
enterprise and new model that is more appropriate with low
default portfolio than the earlier model.
Table 3. The estimated PD for Non-financial state-owned
enterprises (Percentage)
Model Hybrid forward MLE The most
prudent
Rating grade (i) 𝒑𝒊𝑵𝑩 𝒑𝑴𝑳𝑬
𝒊 𝒑𝑴𝑷𝒊
1 1.8202 2.0548 8.3220
2 2.1529 1.2987 8.6504
3 2.0951 3.8462 9.0805
4 10.4135 7.0485 10.7972
5 14.6874 12.6984 11.9110
6 15.2261 10.6816 11.7999
7 22.8343 14.8876 14.8876
In addition, this study found that the estimated PD for both
financial and non-financial are ranked by credit rating grade
(the rank ordering property), except the 3rd rating grade PD of
non-financial. However, the Hybrid forward PD (𝑝𝑖𝑁𝐵) has
failed to rank order condition less than the maximum
likelihood PD (𝑝𝑀𝐿𝐸𝑖 ).
In case of financial institutions, estimation of PD found
that all the Hybrid forward PD are ranked by credit rating
grade and the Hybrid forward model can estimate PD in rating
grade 6. The maximum likelihood model and the most prudent
model cannot estimate this because there is no default event
occurring at this rating grade. This is to be expected as it is
characteristic of Low default portfolios, hence the Hybrid
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Volume 4, Number 2, July – December 2018
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forward model can solve this characteristic of Low default
portfolios.
Table 4. The estimated PD for Financial state-owned
enterprises (Percentage)
Model Hybrid
forward MLE
The most
prudent
Rating grade (i) 𝒑𝒊𝑩 𝒑𝑴𝑳𝑬
𝒊 𝒑𝑴𝑷𝒊
3 12.0480 5.3571 12.0301
4 12.5684 7.4510 12.5337
5 19.9955 15.3846 15.1951
6 54.5415 n.a. n.a.
Note: During the fiscal year 2009-2014, state-owned financial
institutions received a PDMO's credit rating in grade 3-6 out of the
total grade (8 grades).
It should be noted that when we compare the estimated
PD for financial and non-financial SOEs at the same credit
rating grade, it was found that the financial institutions were
more likely to default on debt than the non-financial
institutions (see Figure 1), in spite of the fact that financial
institutions should have a lower default. That result may be
due to the data used during 2009 and 2014 which experienced
many changes in government policies and interventions, such
as intervention in the Bank for Agriculture and Agricultural
Cooperatives and Government Housing Bank. Even these
policies have ended, the liabilities and obligations from
implementing these policies remain with these banks since the
majority of loans and bonds that rollover debts restructure are
from these banks and these debts was from government
projects.
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Volume 4, Number 2, July – December 2018
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Figure 1. The estimated PD of financial and non-financial
state-owned enterprise
Thus, these estimated PD reflect the magnitude of the
credit risk of each state-owned enterprise which are important
data to risk management process of the Ministry of Finance in
the next section.
Credit Guarantee Optimization
3.1. The objectives of government credit guarantees and risk
exposure
Government credit guarantees to state-owned enterprises
create both benefits and fiscal risk for the Ministry of Finance.
Government credit guarantees were used as a fiscal tool for
financially supporting SOE investment in the production of
public goods and services and infrastructure investment.
1.82 2.15 2.1010.41
14.69
15.23
22.83
12.05 12.5720.00
54.54
1 2 3 4 5 6 7
rating grade
non-bank bank
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Volume 4, Number 2, July – December 2018
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Meanwhile, this government transaction creates a fiscal risk
and obligation to the Ministry of Finance as the guarantor.
The Public Debt Management Office (PDMO), the main
player in public debt management, should manage the fiscal
risk from government guarantees. Risk management may be
within the design of the guarantee system, including the
optimal credit guarantees, which the PDMO stands to have a
maximum benefit from while fulfilling the goals of the
government credit guarantee, while also experiencing
minimum fiscal risk, under the relevant legal framework.
Based on portfolio selection theory, the expected utility of
the investor is a function that depends on the expected return
and risk of investing. However, the PDMO is a government
agency that is not intended for profit, but it has the primary
purpose of managing public debt of the country. So, the net
benefit from credit guarantees of PDMO depends on achieving
the goals of government credit guarantees to SOEs against the
fiscal risk of this operation, which can be characterized as
𝑛𝑒𝑡 𝑏𝑒𝑛𝑒𝑓𝑖𝑡 = 𝑎𝑐ℎ𝑖𝑒𝑣𝑖𝑛𝑔 𝑡ℎ𝑒 𝑜𝑏𝑗𝑒𝑐𝑡𝑖𝑣𝑒𝑠 − 𝑓𝑖𝑠𝑐𝑎𝑙 𝑟𝑖𝑠𝑘(4)
Consider the goals of government credit guarantees in
Thailand: one is to provide SOEs with access to finance at a
lower financial cost (loan interest rate) and another one is to
support those that have high credit risk to get access to finance
because they can benefit from the government’s high credit
standing. The fiscal risk from government credit guarantees is
the credit risk of SOEs that can be estimated in the form of the
value of the net expected loss from credit guarantees. Thus, the
net benefit from credit guarantees of PDMO is as the following
equation:
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Volume 4, Number 2, July – December 2018
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𝐵(𝑋𝑗) = {[∑ (𝑅𝑗𝑁𝐺 − 𝑅𝑗
𝐺)𝑋𝑗20𝑗=1 ] + ∑ 𝑋𝑗
20𝑗=1 } − {∑ 𝑃𝐷𝑗𝑋𝑗
20𝑗=1 −
∑ 𝑟𝑗20𝑗=1 𝑋𝑗}(5)
Where 𝑋𝑗 is guaranteed loan of state-owned enterprise 𝑗;
𝑗 =1, 2, 3,…, 20. Achieving the goals of government credit guarantees to SOEs is (i) to provide state-owned enterprises
with access to finance at a lower financial cost that is evaluated
by the spread of interest rate between non-government
guaranteed loan and government guaranteed loan (𝑅𝑗𝑁𝐺 − 𝑅𝑗
𝐺)
and (ii) to support those that have high credit risk to get access
to finance that is evaluated by the amount of guaranteed loan
of each state-owned enterprise (∑ 𝑋𝑗20𝑗=1 ). The net expected
loss from credit guarantees is the value of expected loss after
recovery by the total value of guarantee fee. The expected loss
from credit guarantees is the sum of multiple of the probability
of default (𝑃𝐷𝑗 )12 by guaranteed loan (𝑋𝑗 ) of state-owned
enterprise 𝑗 . Income from government guarantee fees is a multiple of the fee rate (𝑟𝑗)
13 by guaranteed loan (𝑋𝑗) of the
state-owned enterprise 𝑗.
3.2. Laws and regulations
Considering the optimal guaranteed loan of each state-
owned enterprise (𝑋𝑗∗), the value of guaranteed loan at which
the Public Debt Management Office receives a maximum the
net benefit from credit guarantees is subject to the relevant
laws and regulations. The relevant laws and regulations are as
follows:
12 Use the estimated PD from the previous section. 13 The fee rate is set by the Guarantee Fee Rate and Condition Ministerial
Regulation of 2008 (B.E. 2551), that fee rate varies by the credit rating
grade and loan period for each state-owned enterprise.
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Volume 4, Number 2, July – December 2018
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(i) A quasi-budget constraint: in each fiscal year the Ministry of Finance can guarantee no more than 20% of
the annual budget that is in force at that time14. This can
be expressed as ∑ 𝑋𝑗 ≤ 0.2𝐴20𝑗=1 where 𝐴 is the annual
budget that is in force at fiscal year 2014.
(ii) Fiscal Sustainability Framework: in each fiscal year the government can generate public debt outstanding no
more than 60% of GDP. This can be expressed as
∑ 𝑋𝑗 + 𝐵 ≤ 0.6 𝐺𝐷𝑃20𝑗=1 where 𝐵 public debt is
outstanding at the end of fiscal year 2013 and the
forecast of other public debt in fiscal year 2014.
(iii) Exchange rate risk requirement: in each fiscal year, the proportion of foreign debt to export income must not be
more than 9%15. This can be expressed as ∑ 𝑋𝑗𝑓 +20𝑗=1
𝐶 ≤ 0.09 𝐸𝑋𝑃 where 𝑋𝑗𝑓
is the value of guaranteed
loan in foreign currency of state-owned enterprise 𝑗 and 𝐶 is another foreign currency debt outstanding of the government at the end of the fiscal year 2013.
(iv) Leverage ratio: the Ministry of Finance can provide credit guarantees to each state-owned enterprise an
amount (𝑋𝑗) that, after summing with that SOE's other
debt (𝐷𝑗), does not exceed three times the capital of the
SOE (𝐸𝑗). In the case where the SOE is a Public Limited
Company (𝑗 = 9) the amount must not exceed six times the capital of the SOE, and similarly for SOEs which are
14 Section 28 of Public Debt Management Act of 2005 (B.E. 2548) 15 The Regulation of the Ministry of Finance on Public Debt Management
of 2006 (B.E. 2549)
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Volume 4, Number 2, July – December 2018
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financial institutions ( 𝑗 = 17, 18, 19, 20). This can be expressed as 𝐷𝑗 + 𝑋𝑗 ≤ 3𝐸𝑗 ; 𝑗 = 9 and 𝐷𝑗 + 𝑋𝑗 ≤ 6𝐸𝑗 ;
and 𝑗 = 17, 18, 19, 20 respectively.
Furthermore, the optimal guaranteed loan also considers
the demand for loan of each state-owned enterprise in a fiscal
year that can be expressed as 𝑋𝑗 ≤ 𝐿𝑑𝑗 where 𝐿𝑑𝑗 is the
demand for loan of state-owned enterprise 𝑗 in fiscal year 2014.
3.3. Optimization for government credit guarantees
The study of the optimization of credit guarantees of state-
owned enterprise using linear programming models is divided
into two groups: maximizing the net benefit from credit
guarantee, and minimizing the net expected loss from credit
guarantee. This optimization found that (i) subject to all the
relevant laws and regulations, and demand for fund of each
state-owned enterprise in that time, the results from
maximizing the net benefit and the results from minimizing
the net expected loss from credit guarantee are similar in terms
of optimal guaranteed loan distribution, the value of the net
benefit, and the value of the net expected loss from credit
guarantees (models 1.1 and 2.1). In addition, (ii) adding the total value of guaranteed loan, which happened in fiscal year
2014 as constraints of the optimization, it was found that high
risk enterprises have been reduced to a guaranteed amount, if
considered with a focus on the net benefit of PDMO, while
some enterprises are not guaranteed, if considered with a focus
on the net expected loss value (models 1.2 and 2.2).
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Volume 4, Number 2, July – December 2018
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Table 5. The optimal values from credit guarantee
optimization
Model Model 1.1 Model 1.2 Model 2.1 Model 2.2
Method Maximize Maximize Minimize Minimize
Objective
function
Net
Benefit
Net
Benefit
Net
Expected
loss
Net
Expected
loss
Constraints ≤ ≤ = =
1. Budget
constraints I √ √ √ √
2. Budget
constraints
II
√ √ √ √
3. Exchange rate
risk
constraints
√ √ √ √
4. Leverage
constraints √ √ √ √
5. Demand for
loan √ √ √ √
6. Total of credit
guarantee - √ - √
Optimal values (Million Baht)
Guaranteed
loan
SOE1 =
751.45
SOE1 =
751.45
SOE1 =
751.45
SOE1 =
751.45
SOE5 =
8,810.00
SOE5 =
8,810.00
SOE5 =
8,810.00
SOE5 =
8,810.00
SOE7 =
30,033.13
SOE7 =
15,790.15
SOE7 =
30,033.13
SOE7 =
30,033.13
SOE8 =
20,539.94
SOE8 =
20,539.94
SOE8 =
20,539.94
SOE8 =
20,539.94
SOE10 =
3,000.00
SOE10 =
3,000.00
SOE10 =
3,000.00
SOE10 =
3,000.00
SOE11 =
10,532.99
SOE11 =
10,532.99
SOE11 =
10,532.99
SOE11 =
290.00
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Volume 4, Number 2, July – December 2018
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Table 5. (Continued) Model Model 1.1 Model 1.2 Model 2.1 Model 2.2 Optimal values (Million Baht)
Guaranteed
loan
SOE13 =
3,000.00 SOE13 =
3,000.00 SOE13 =
3,000.00 -
SOE16 =
1,000.00 SOE16 =
1,000.00 SOE16 =
1,000.00 -
SOE17 =
285,360.90 SOE17 =285,360.90
SOE17 =285,360.90
SOE17 =285,360.90
Total of credit
guarantee 363,028.41 348,785.42 363,028.41 348,785.42
Net benefit 320,228.76 309,046.31 320,228.76 306,578.97 Net expected
loss 45,765.10 42,576.90 45,765.10 45,115.41
Conclusion and Implication
Estimation of default probability with the Hybrid Model
found that the estimated PD for both financial and non-
financial SOE is ranked by credit rating grade (the rank
ordering property), except the 3rd rating grade PD of non-
financial SOE. It should be noted that financial SOEs have a
higher estimated PD than non-financial SOEs at the same
rating grade despite the fact that financial institutions should
perhaps have a lower default risk than non-financial
institutions. However, data limitations must be acknowledged
as the estimation of PD in this study used data over a period of
six years, even if it is under the requirements specific for PD
Estimation of the Bank of Thailand (2012) that require use of
data of at least five years. In the future, a longer time series
data of SOE debts may be used, and thus the estimated PD may
be more consistent with financial theory, including satisfying
the rank order condition and the financial institution PD less
than the non-financial institution PD.
Moreover, estimated PD in this article represents the size
of the credit risk of each SOE's credit rating grade that
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
25
increases by each rating grade exponentially, while the
guarantee fee rate increases by each rating grade linearly. This
is so despite the fact that, in financial theory, the guarantor
should charge a guarantee fee equal to the default probability
of each borrower to mitigate their risk exposure. This implies
that the Public Debt Management Office just manages partial
risk from a guarantee. However, in the case of SOEs, the
Public Debt Management Office cannot charge a guarantee fee
more than as prescribed in the Guarantee Fee Rate and
Condition Ministerial Regulation of 2008 (B.E. 2551) and
increasing the fee contradicts the goal of credit guarantee to
SOE, which helps SOE to reach the financial source with a
lower cost.
Nevertheless, the Public Debt Management Office can use
this estimated default probability to recognize size of credit
risk from their credit guarantees to better manage risk, for
example counter-guarantees funds. Moreover, estimated
default probability can be used to enhance the efficiency of
guarantee allocations by using PD as a criterion in the decision
about the optimal value of guaranteed loans for each state-
owned enterprise.
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References
Bank of Thailand. (2012). Notification of the Bank of Thailand
Re: Rule of Credit Risk Internal Ratings-Based
Approach by Internal Ratings-Based Approach.
Bangkok: Bank of Thailand.
Bank of Thailand. (2016). Notification of the Bank of Thailand
Re: Rules for Credit Ratings and Reserves Requirement
of Financial Institutions. Bangkok: Bank of Thailand.
Forrest, A. (2005). Likelihood Approaches to Low Default
Portfolios. Discussion paper, Dunfermline Building
Society, September 2005.
International Monetary Fund. (2005). Government Guarantees
and Fiscal Risk. Prepared by the Fiscal Affairs
Department (In consultation with other departments).
Approved by Ter-Minassian, Teresa.
International Monetary Fund. (2016). Analyzing and
managing fiscal risks-Best Practices.
Naksakul, P. (2006). Credit Risk Models in Relation to Capital
Reserve Framework of Financial Institutions. Bangkok:
Bank of Thailand.
Pluto, K., & Tasche, D. (2006). Estimating Probabilities of
Default for Low Default Portfolios. In B. Engelmann &
R. Rauhmeier (Eds.), The Basel II Risk Parameters:
Estimation, Validation, and Stress Testing (pp. 79–103).
Berlin, Heidelberg: Springer Berlin Heidelberg.
https://doi.org/10.1007/3-540-33087-9_5
Public Debt Management Office. (2014). Improvement of Credit Rating model for State-owned Enterprises.
Bangkok. Ministry of Finance, Thailand.
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
27
Roengpitya, R. (2012). Proposal of New Hybrid PD
Estimation Models for The Low Default Portfolio
(LDPs), Empirical Comparisons and Policy
Implications. Discussion paper. Bangkok: Bank of
Thailand.
Standard & Poor's Global Rating. (2017). Default, Transition,
and Recovery: 2016 Annual Sovereign Default Study
and Rating Transitions
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
28
Investigating relationship between Government
Spending and Economic Growth: Public
Spending and long-run GDP level
Kritchasorn Jarupasin*
Plan and Policy Analyst
Office of the National Economic and Social Development
Board
Office of the Prime Minister
Thailand
* The contents of this article do not reflect the author’s affiliation. This
article is a part of the author’s doctoral thesis submitted to the University
of Exeter. The author thanks Professor Gareth D. Myles, Professor Ben
Zissimos, Professor Norman Gemmell and Dr. Xiaohui Zhang for their
kind advice and support during his doctoral study. Sincere gratitude must
also be extended to the Royal Thai Government for the research grant. All
remaining errors rest with the author.
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
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ABSTRACT
This study investigates the relationship between public
spending and long-run GDP per capita. While most fiscal-
growth studies put emphasis on the relationship in the public-
policy endogenous growth model, this analysis allows for
Solow-type transitional dynamics where the effects of fiscal
policy can be persistent. Moreover, the long-run and short-run
effects of fiscal changes are identified separately in this
analysis using the groups of countries comprising 38 countries
(17 developing countries and 21 high-income OECD
countries). Our results show that an increase in total spending
which is financed by non-distortionary taxes only enhances the
level of GDP per capita in high-income OECD countries. With
a given level of total spending, increases in the shares of
healthcare and general public services spending can improve
the levels of GDP per capita in developing countries. On the
other hand, increasing the share of education spending in a
high-income OECD country is conducive to increasing the
level of GDP per capita
Keywords: Fiscal policy, Economic growth, Public
expenditure, Government
JEL Classification: E62, H50, O23, O47
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
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1. Introduction
The fiscal-growth studies in a number of studies focus on
the public-policy endogenous growth model. In those studies,
permanent growth effects of fiscal changes are analysed
without transitional dynamics. The analysis of fiscal policy
impacts in this study allows for Solow-type transitional
dynamics, but the effects of fiscal policy may be persistent
according to the framework proposed by Gemmell, Kneller
and Sanz (2016).
Gemmell et al.’s (2016) study was motivated by the recent
fiscal stimulus enacted after 2009 in order to counteract the
global financial crisis. Governments’ spending choices in
these short-term packages are partially influenced by their
ambitions to comply with long-term growth objectives. With
this policy design, there are two different questions to be
addressed: how forceful is the evidence that long-run income
levels or growth rates react to changes in public spending, and
if they do, which expenditure types produce most considerable
impacts? In the following section, we attempt to respond to
these questions similar to Gemmell et al. (2016) by looking at
both developing countries and high-income OECD countries.
2. Literature review
In terms of the period of study, recent studies on fiscal
policy and long-run size of economy (either level of GDP or
rate of growth) include recent data, especially the Acosta-
Ormaechea and Morozumi’s (2013) study which uses data
from 1970 to 2010. Other studies (Afonso & Jalles, 2014;
Arnold et al., 2011; Gemmell, Kneller & Sanz, 2011; Gemmell
et al., 2016; Xing, 2012) also cover periods from the 1970s
until 2010. Ojede and Yamarik (2012) focus on an earlier
period; 1967 to 2008.
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In terms of the sets of fiscal variables used, these studies
either put emphasis on tax policy (Arnold et al., 2011; Ojede
& Yamarik, 2012; Xing, 2012), public spending (Gemmell et
al., 2016), or both types of variables at once (Afonso & Jalles,
2014; Gemmell et al., 2011). In addition, Afonso and Jalles
(2014) look at both functional and economic classes of fiscal
variables according to the Government Finance Statistics
(GFS) definitions provided by the International Monetary
Fund (IMF).
As well as being categorised by their focus (on revenue
and/or expenditure), the effects of fiscal changes can be
classified by their impact on the size of economy (short-run or
long-run impact). Some of these studies focus only on
permanent growth effects (Acosta-Ormaechea & Morozumi,
2013; Afonso & Jalles, 2014), while others distinguish
between the long-run and short-run impacts of changes in
fiscal variables (Arnold et al., 2011; Gemmell et al., 2011;
Gemmell et al., 2016; Ojede & Yamarik, 2012; Xing, 2012).
Our study pays specific attention to the latter set of studies.
While many studies that differentiate between the long-
run and short-run effects of fiscal change capture the size of an
economy by using the growth rate of GDP or the growth rate
of GDP per capita, Arnold et al. (2011), Xing (2012) and
Gemmell et al. (2016) use the level of per capita GDP.
Gemmell et al. (2016) claim that using this specification is
advantageous since it allows the degree of persistence in GDP
growth responses to be identified by the data, rather than by
using a functional form incorporating permanent effects. For
this reason, our study will focus on the impact on level of GDP
per capita.
The three studies (Arnold et al., 2011; Gemmell et al.,
2016; Xing, 2012) referred to above use cross-country data,
whereas Ojede and Yamarik (2012) evaluate the growth
effects of tax policy at state level. Instead of investigating the
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
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growth effects of fiscal policy, Lamartina and Zaghini (2011)
test the validity of Wagner’s law in high-income OECD
countries.
Although there are differences in the model specifications
for investigating change in fiscal composition and their effects
on either the level of GDP or economic growth, the findings
are, to a certain degree, harmoniously aligned. We go on to
discuss previous findings, econometric methods, variables
included in the model, the role played by budget constraint,
and other econometric issues in these studies. 2.1. Previous findings
This strand of literature, like the permanent growth effects
of fiscal change studies in endogenous growth model, mainly
considers high-income countries and, more specifically, high-
income OECD countries (Arnold et al., 2011; Gemmell et al.,
2011; Gemmell et al., 2016; Xing, 2012). Other studies, e.g.
Acosta-Ormaechea and Morozumi (2013) and Afonso and
Jalles (2014), consider a wider set of countries.
Some studies find the reallocation of fiscal composition to
be robustly related to long-run growth or GDP level, while
others do not. In order to understand this incongruity clearly,
we need to take several aspects of the preceding results into
consideration. Firstly, there are two different types of fiscal
variables being considered, namely public expenditure and
public revenue. Secondly, we need to consider the way in
which an increase in public expenditure is financed. We
previously refer to this as an implicit financing element. For
example, Gemmell et al. (2016) find that an increase in total
spending enhances GDP per capita level in the long run when
financed by non-distortionary taxes. Thirdly, fiscal variable
classifications can be interpreted differently when we analyse
the impacts of changes in these variables on GDP or growth of
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Volume 4, Number 2, July – December 2018
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GDP. This depends on the aspect of fiscal change we need to
evaluate in order to assess its impact. The following
paragraphs summarise the key findings of the papers
mentioned earlier.
Arnold et al. (2011) find that shifting taxes on income
towards consumption and immovable property enhances long-
run GDP per capita. In particular, increasing revenue by
raising current taxes on immovable property and consumption
is least harmful to growth. Arnold et al.’s (2011) findings are
supported by Xing (2012), suggesting that shifting tax revenue
away from corporate income, personal income, and
consumption taxes, and towards property taxes is associated
with a higher level of income per capita in the long run. When
investigating state-level data, Ojede and Yamarik (2012)
obtained different results from Arnold et al. (2011) and Xing
(2012). They found that increases in sales and property taxes
reduce long-run real income growth.
Gemmell et al. (2011) observe that the growth effects of
fiscal policy in the short run appear to persist. Although some
fiscal variables only have transitory effects, others might have
persistent growth effects. However, the positive growth effects
associated with productive spending are often counteracted by
the negative effects of tax changes.
Gemmell et al. (2016) raise awareness of the significance
of financing methods for increasing any type of public
expenditure when determining long-run GDP level. By using
pooled mean group estimators (PMG) with contemporaneous
correlation, they find robust long-run positive effects on GDP
per capita levels for reallocating total spending towards
transportation and communication, and education spending.
In contrast, Afonso and Jalles (2014) find that an increase
in government revenue has no significant impact on growth.
Moreover, the coefficients of government expenditures appear
to have highly significant negative signs.
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Acosta-Ormaechea and Morozumi (2013) find that an
increase in education spending offset by a fall in social
spending seems to be robustly related to higher growth rates.
These results also hold true at the general government level.
Their results also show that education spending promotes
growth as well as public capital does in the long run.
2.2. Improvement of econometric methods
Recent developments in data collection have improved
the availability of data, so it has become possible to investigate
the compositional change of public spending and its impact on
long-run GDP per capita level or growth.
From above reason, the updated data can be used under
the assumptions of short-run heterogeneity and long-run
homogeneity. This econometric method proposed by Pesaran
Shin, and Smith (1999) is pooled mean group estimators
(PMG). It is a compromise between the fixed effects model
and the mean group estimator (MG). While intercept, short-
run coefficients and error variances are allowed to differ across
groups, the long-run coefficients are equal. This method has
been analysed by Arnold et al. (2011), Gemmell et al. (2011),
Ojede and Yamarik (2012), Xing (2012), and Gemmell et al.
(2016).
2.3. Variables included in recent studies
Dependent variable
The choice of dependent variable is distinctly separable
between growth of GDP and level of GDP. While most studies
use the growth rate of either GDP or GDP per capita, Arnold
et al. (2011) use a change in log of GDP per capita and a
change in log of total factor productivity (TFP) of a given firm.
Similarly, Xing (2012) also uses a change in log of real GDP
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
35
per capita as a dependent variable. While Gemmell et al.
(2011) use a change in the growth rate of GDP in one of their
studies, the level of GDP per capita is employed by Gemmell
et al. (2016). The dependent variable in Ojede and Yamarik
(2012) is the change in growth rate of real income.
It is important to note that all of these studies (Arnold et
al., 2011; Gemmell et al., 2011; Gemmell et al., 2016; Ojede
& Yamarik, 2012; Xing, 2012) estimate the results with an
error correction model. When interpreting the results, we need
to refer back to the equations in terms of the autoregressive
distributed lag model: i.e. the long-run level of GDP per capita
impact from fiscal change is analysed in Xing (2012), rather
than the growth effect (change in log of real GDP per capita).
Afonso and Jalles (2014) use the real growth rate of GDP
per capita, and Acosta-Ormaechea and Morozumi (2013)
select the growth of output per capita.
Fiscal variables
Different classes of expenditure and revenue can be
considered. Two broad categories of each type of fiscal
variables are included, following the example of Kneller,
Bleaney and Gemmell (1999) and based on the framework
proposed by Barro (1990); namely productive expenditure,
unproductive expenditure, distortionary taxes and non-
distortionary taxes. We will now describe some of the fiscal
variables included in recent studies.
Arnold et al. (2011) focus on tax structures which can be
classified mainly into income taxes, consumption taxes and
property taxes. Ojede and Yamarik (2012) and Xing (2012)
also emphasise on the composition of tax revenues.
Gemmell et al. (2011) use broad categories of revenue and
expenditure: productive expenditure, non-productive
expenditure, distortionary taxes and non-distortionary taxes.
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36
Gemmell et al. (2016) utilise broad categories of revenue
similar to those used by Gemmell et al. (2011) and functional
classifications of public expenditure, namely transportation
and communication, education, health and housing etc.
Afonso and Jalles (2014) focus on both aggregate levels
and GFS (Government Finance Statistics) classifications of
fiscal variables. This includes functional and economic
classifications of both government expenditure and revenue.
Acosta-Ormaechea and Morozumi (2013) also look at both
economic and functional classifications of public expenditure.
Economic classifications include the compensation of
employees, other expenses and the net acquisition of non-
financial assets. Functional classifications include defence,
transportation and communication, health, education, and
social protection expenditures.
Non-fiscal control variables
A number of factors can be used as non-fiscal control
variables. The criteria used to decide which variable should be
chosen are highly dependent on the type of question or
particular model being investigated. Since using pooled mean
group estimators limits the number of control variables due to
a decrease in the degree of freedom, this strand of literature
often only includes a few non-fiscal control variables in
analyses.
Arnold et al. (2011) and Xing (2012) include investment
rate, human capital and population growth. Gemmell et al.
(2011, 2016) use investment rate and employment growth.
Like Gemmell et al. (2011, 2016), Ojede and Yamarik (2012)
include growth in private employment and private investment
share in their set of non-fiscal control variables.
While Afonso and Jalles (2014) use population growth,
investment, education and trade openness, Acosta-Ormaechea
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37
and Morozumi (2013) include initial GDP per capita and initial
human capital in their set of non-fiscal control variables.
2.4. The role of government budget constraint
Government budget constraint needs to be considered in
order to avoid the production of invalid results due to biases
occurring as a result of not including both revenue and
spending variables in the same equation. Recent studies take
government budget constraint into account while avoiding
perfect multicollinearity in accordance with the specification
in Kneller et al. (1999). As a result, the important role played
by the implicit financing element is highly relevant in this
analysis.
2.5. Other econometric issues
There are also other econometric issues which should be
addressed, such as endogeneity and a robustness check.
Firstly, Afonso and Jalles (2014) investigate the
robustness of their results by adding variables (labour force
participation and unemployment rates) into their baseline
regression. Similarly, Acosta-Ormaechea and Morozumi
(2013) add inflation, openness, population growth and terms
of trade growth into their original set of control variables.
Different specifications, including lagged fiscal variables and
the different developmental levels of countries in the sample,
might also be considered.
Secondly, using pooled mean group estimators to analyse
the error-correction model requires some tests as prerequisites.
Gemmell et al. (2016) tested the order of integration and
cointegration, autoregressive distributed (ARDL) lag
structure, and weak exogeneity. They found that their
variables are best treated as non-stationary. Imposing two-lags
of ARDL tends to strengthen the case for significant causal
Thammasat Review of Economic and Social Policy
Volume 4, Number 2, July – December 2018
38
effects from a number of public spending categories on the
level of long-run GDP per capita. Their estimated results offer
relatively strong support for the theory that expenditure share
variables can be considered to be weakly exogenous, allowing
interpretation of the estimated long-run expenditure
parameters as capturing causal effects on GDP. The issues
tested in Gemmell et al. (2016) will be further investigated in
Section 4 of our study. Public expenditure composition is
analysed in the next section.
3. Public expenditure composition of countries in our
sample
In this section, we analyse the data on public expenditure
composition for 38 selected countries in our sample according
to the availability of control variables, which is mainly
affected by labour growth. The set of countries in Table 1 is
divided into two main groups: 17 developing countries and 21
high-income OECD countries.
As shown in Figure 1, total public spending in the group
of countries in our sample has slightly increased in the past
four decades. This can be seen from the increase in unweighted
10-year average total spending to GDP from 23.42% in 1972-
1981 to 25.44% in 2002-2011 for developing countries. For
high-income OECD countries, the level of total public
spending to GDP increased from 32.55% in 1972-1981 to
35.19% in 2002-2011. Public spending in high-income OECD
countries increased significantly during the 1970s and 1980s
but subsided in later periods. On the other hand, the proportion
of government spending to GDP in developing countries
increased consistently during 1992-2001 and 2002-2011.
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Table 1. List of countries in our study by group
Developing countries High-income OECD countries
Bolivia Morocco Austria* Korea, Rep.
Brazil South Africa Canada* Luxembourg*
Cameroon Thailand Chile Netherlands*
Costa Rica Tunisia Denmark* New Zealand*
Dominican Republic Turkey Finland* Norway*
Egypt, Arab Rep. Nepal France* Portugal
India Hungary Spain*
Indonesia Iceland* Sweden*
Iran, Islamic Rep. Ireland UK*
Malaysia Israel United States*
Mauritius Italy
Note: Our 14 OECD countries included in Gemmell et al.’s (2016) group of 17
OECD countries
3.1. The composition of public spending
Table 2 presents the average amount of particular types of
public spending by groups of countries as percentages of GDP.
In percentage terms, government spending in our sample of
high-income OECD countries is obviously higher than in our
sample of developing countries. The same also applies to
many other types of spending, although not to spending on
general public services. The level of spending on education as
a share of GDP is relatively similar across different groups of
countries in the sample, with an average of around 3.38% of
GDP.
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40
Figure 1. Unweighted 10-year averages of total public
spending as percentages of GDP for groups of countries in
our sample (1972-2011)
Table 2. Unweighted averages of public spending by type as
percentages of GDP for groups of countries in our sample
(1972-2012)
Developing
countries
High-income
OECD countries
Total spending 23.96 35.74
General public services 3.80 2.73
Defence 2.07 2.41
Transportation and communication 1.42 1.64
Education 3.38 3.39
Healthcare 1.45 3.62
Social welfare 3.13 12.59
0
5
10
15
20
25
30
35
40
1972-1981 1982-1991 1992-2001 2002-2011
Developing OECD
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The composition of public spending in different groups of
countries varies depending on the policies and problems that
particular governments encounter. The unweighted averages
of public spending by type as percentages of total spending
from 1972 to 2012 for developing countries and high-income
OECD countries are shown in Table 3. In developing
countries, general public services (16.2%), education (14.1%)
and social welfare (11.9%) spending are crucial elements of
government budgets. In contrast, high-income OECD
countries spend a large proportion of public expenditure on
social welfare (34.7%), healthcare (10.1%) and education
(9.7%). Social welfare spending accounts for more than a third
of total public spending in high-income OECD countries.
Table 3. Unweighted averages of public spending by type as
percentages of total spending for groups of countries in our
sample (1972-2012)
Developing
countries
High-income
OECD countries
General public services 16.20 7.65
Defence 8.69 7.30
Transportation and communication 6.51 4.71
Education 14.10 9.67
Healthcare 6.23 10.05
Social welfare 11.93 34.72
The public spending composition of developing countries
is presented in Figure 2 using unweighted 10-year averages for
the period from 1972 to 2011. It is clear that social welfare
spending as a proportion of total public spending has increased
significantly over time. In contrast, spending on defence, and
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Volume 4, Number 2, July – December 2018
42
transportation and communication has decreased significantly
relative to other types of public spending.
The proportions of most spending types, including general
public services, education and social welfare spending, in
relation to total spending in high-income OECD countries
have not changed dramatically in the past forty years as can be
seen in Figure 3. Healthcare spending has increased more
noticeably over time than other types of expenditure, whereas
spending on defence has been decreasing.
Figure 2. Unweighted 10-year averages of spending by type
as percentages of total spending for developing countries in
our sample (1972-2011)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1972-19811982-19911992-20012002-2011
General public services
Defence
Transportation and
communication
Education
Healthcare
Social welfare
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43
Figure 3. Unweighted 10-year averages of spending by type
as percentages of total spending for high-income OECD
countries in our sample (1972-2011)
The following subsection explains the estimation method
used to analyse long-run relationship between fiscal variables
and GDP per capita.
4. Public spending and long-run GDP per capita
investigating heterogenous panel data: estimation method
In this section, we discuss the econometric methods used
to study the relationship between public spending and long-
run levels of GDP per capita in our sample’s groups of
countries. Later (in Section 5), we present the estimates
separately, according to the country groupings, i.e. developing
countries and high-income OECD countries. This section
includes the discussion of pooled mean group estimator
(PMG), and tests for cointegration and ARDL lag structure.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1972-19811982-19911992-20012002-2011
General public services
Defence
Transportation and
communication
Education
Healthcare
Social welfare
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4.1. Pooled mean group (PMG) estimator
The endogenous growth model in Devarajan, Swaroop
and Zou (1996) captures the permanent growth effects from
fiscal changes without transitional dynamics (Gemmell et al.,
2016). Allowing for Solow-type transitional dynamics while
the effects of fiscal change may be persistent requires a more
flexible functional form than that of Devarajan et al. (1996).
Using an autoregressive distributed lag (ARDL) model
parameterised in error correction form in Gemmell et al.
(2016) allows both the short-run dynamic and the long-run
equilibrium relationships between GDP and fiscal variables to
be identified separately. The ARDL(p,q) specification is:
𝑦𝑖,𝑡 = ∑ 𝛼𝑖,𝑗𝑦𝑖,𝑡−𝑗 + ∑ 𝛽𝑖,𝑗𝑋𝑖,𝑡−𝑗 + 𝜇𝑖 + 𝜀𝑖,𝑡𝑞𝑗=0
𝑝𝑗=1 (1)
where Xi,t-j includes all explanatory variables. Equation (1) can
be expressed in error correction form:
𝑔𝑖,𝑡 = ∆𝑦𝑖,𝑡 = ∅𝑖(𝑦𝑖,𝑡−1 − 𝛽𝑖𝑋𝑖,𝑡) + ∑ 𝛼𝑖,𝑗∗ ∆𝑦𝑖,𝑡−𝑗
𝑝−1𝑗=1
+ ∑ 𝛽𝑖,𝑗∗𝑞−1
𝑗=0 ∆𝑋𝑖,𝑡−𝑗 + 𝜇𝑖 + 𝜀𝑖,𝑡 (2)
where ∅i captures the error correcting speed of adjustment and βi captures the long-run equilibrium relationship between y
and X with short-run effects measured by β*i,j. The estimates
of long-run coefficient βi are not affected by the choice
between Xi,t and Xi,t-1 in determining the long-run relationship.
While Arnold et al. (2011), Ojede and Yamarik (2012), and
Xing (2012) use Xi,t, Gemmell et al. (2011, 2016) prefer Xi,t-1.
We use Xi,t in our study, since it provides better computational
convenience in our statistical package than using Xi,t-1.
Blackburne and Frank (2007) suggest several approaches
which can be taken in order to estimate Equation (2). Firstly,
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45
a fixed effect (FE) estimation approach could be used when
data from each group is pooled and only the intercepts are
allowed to differ across groups. Pesaran and Smith (1995)
show that these regressions are likely to be biased if the
assumption of homogeneity of the short-run parameter
estimates across countries is rejected. Secondly, the model
might be fitted separately for each group and the arithmetic
average of coefficients could be calculated by using mean
group estimators (MG). The MG estimators allow both short
and long-run parameter heterogeneity. Thirdly, Pesaran et al.
(1999) proposed a PMG estimation that combines both
methods of pooling (FE) and averaging (MG). The intercept,
short-run coefficients and error variances are allowed to differ
across groups, but the long-run coefficients are constrained to
be equal across groups. Furthermore, Pesaran et al. (1999)
have also demonstrated that allowing for short-run parameter
heterogeneity results in more reliable estimates of the long-run
responses.
We present the results of PMG estimates, as the Hausman
test prefers PMG to MG.1 The implication of the results from
the Hausman test is that the assumption of homogenous long-
run parameter estimates across countries is valid. The PMG
method selected is then comparable to Gemmell et al.’s (2016)
study.
Our study investigates the long-run relationship between
public spending and the GDP per capita level of the 38
countries (see Table 1) which are classified as developing
countries (17 countries) and high-income OECD countries (21
countries). These groups of countries were selected based on
the availability of control and fiscal variables. The groups of
developing countries and high-income OECD countries in our
1 We do not show the results of the Hausman test in this paper; however,
they could be provided by request.
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46
sample have been analysed separately, in a similar way to
Gemmell et al. (2016), by looking at the effects of total public
expenditure and public expenditure composition. The study
period is 1972-2012.
Our dependent variable gi,t is the change in log of GDP
per capita. Although the growth rate of per capita GDP is the
dependent variable, as shown in Equation (2), the regression
measures the impacts of fiscal and other variables on long-run
per capita GDP level. Equation (2) is only a re-
parameterisation of Equation (1). As discussed earlier,
Gemmell et al. (2016) argue that using level specification
allows the identification of the degree of persistence in GDP
growth responses.
The non-fiscal control variables included in this study are
labour force growth (LG) and investment ratio to GDP (K).
Labour force growth before 1990 is assumed to be constant
(from the average of available data) in a number of countries
in the sample where accurate data is not readily available.
When taking government budget constraint into account, our
fiscal control variables include the ratio of total expenditure to
GDP, distortionary taxes to GDP, non-distortionary taxes to
GDP and budget surplus to GDP. In the cases where we
consider public spending composition, the expenditure share
of a particular type of public spending in relation to total public
spending is added individually. The list of variables included
in this study is shown in Table 4.
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Table 4. List of variables for this study Variables Description of the variables
y
K
LG
tot_gdp
distax_gdp
tgs_gdp
SURBP
TOT
gps_tot
def_tot
trc_tot
edu_tot
hea_tot
soc_tot
Log of GDP per capita (2005 USD)
Gross capital formation (% of GDP)
Labour force growth (%)
Total public spending (% of GDP)
Distortionary taxation (% of GDP)
Non-distortionary taxation (% of GDP)
Budget balance (% of GDP)
Total public spending in local currency unit
Spending on general public services (% of
TOT)
Spending on defence (% of TOT)
Spending on transportation and
communication (% of TOT)
Spending on education (% of TOT)
Spending on health (% of TOT)
Spending on social welfare (% of TOT)
The first part of the analysis of each group of countries in
our sample looks at the total public expenditure effect with
four different implicit financing elements: budget deficit;
distortionary taxes; non-distortionary taxes; and a mix of both
distortionary and non-distortionary taxes. In the second part of
the analysis, we use budget deficit as an implicit financing
element, focussing on the impact of shifting expenditure
towards a particular type of public spending composition on
the long-run GDP per capita level. There are two groups of
countries consider