The Growth and Distributive Impacts of Public Infrastructure Investments in the Philippines

Abstract

The government of the Philippines continues to implement reforms that aim to promote economic development and lift the country’s standard of living. This is critical as it has been lagging behind neighbouring East Asian countries with respect to economic size and per capita income. The bottlenecks the country faces include poor physical infrastructure (transport and utility infrastructures), low quality of education, volatile economic growth, high poverty rates and large income disparities.

Full text

The Growth and Distributive Impacts
of Public Infrastructure Investments
in the Philippines
Erwin Corong, Lawrence Dacuycuy, Rachel Reyes, and Angelo Taningco
Introduction
The government of the Philippines continues to implement reforms that aim to
promote economic development and lift the country’s standard of living. This is
critical as it has been lagging behind neighbouring East Asian countries with
respect to economic size and per capita income.
1
The bottlenecks the country
faces include poor physical infrastructure (transport and utility infrastructures),
low quality of education, volatile economic growth, high poverty rates and large
income disparities.
Various business surveys have pointed to the relatively poor quality of trans-
portation infrastructure in the country, such as airports, maritime ports, roads and
railroads. Energy and water infrastructures have also not been fully developed, and
concerns over a possible crisis in power and water have recently mounted. Public
E. Corong (*)
Center of Policy Studies, Monash University, Melbourne, Australia
e-mail: erwin.corong@monash.edu
L. Dacuycuy • R. Reyes • A. Taningco
School of Economics, De La Salle University, Manila, Philippines
e-mail: lawrence.dacuycuy@dlsu.edu.ph;reyesrc@dlsu.edu.ph;angelo.taningco@dlsu.edu.ph
1
Based on the World Bank’s World Development Indicators database 2009 Philippine GDP, at
constant 2000 prices and adjusted for purchasing power parity (PPP), stood at US$295.8 billion.
This is lower than in most other East Asian countries, including the People’s Republic of China or
PRC (US$8.2 trillion), Indonesia (US$877 billion), Japan (US$3.8 trillion), Republic of Korea (US
$1.2 trillion), Malaysia (US$348.2 billion) and Thailand (US$491.8 billion). This contrasts with
the situation, in 1980, when Philippine PPP-adjusted real GDP was US$126 billion, much higher
than Malaysia (US$67.3 billion) and Thailand (US$105.4 billion). Moreover, the PPP-adjusted
GDP per capita at 2005 prices for the Philippines was US$3,216, lower than in the PRC (US
$6,200), Indonesia (US$3,813), Japan (US$29,688), Korea (US$25,493), Malaysia (US$12,678),
Singapore (US$45,978) and Thailand (US$7,258).
J. Cockburn et al. (eds.), Infrastructure and Economic Growth in Asia,
Economic Studies in Inequality, Social Exclusion and Well-Being,
DOI 10.1007/978-3-319-03137-8_3, ©The Author(s) 2013
47

spending on education has also been criticized as being low compared to
neighbouring countries in the region, resulting in a weak public education system.
Against this backdrop, the government of the Philippines has engaged in policy
measures to improve the quality of public infrastructure (especially in relation to
transport and utilities) and public education in order to ensure and sustain robust
growth and to alleviate poverty. To speed up public infrastructure development in
the presence of fiscal constraints, the government has revived the promotion of
partnerships with the private sector (in Build-operate-transfer schemes), with the
private sector providing financial and technical expertise for selected infrastructure
projects.
This paper contributes to policy analysis in the Philippines by providing a
quantitative assessment of the growth and distributive impacts of increasing spend-
ing on public infrastructure, such as in transportation, utilities and education. Since
these issues are interlinked, a computable general equilibrium (CGE) model is
employed together with a micro-simulation model in order to trace the channels
whereby public infrastructure investments filter through the Philippine economy.
We use Philippine data in a dynamic CGE model developed by Dissou and Didic
(2011) which explicitly models public capital as an input in firms’ production
process. The results of the CGE simulations are then used as inputs into a micro-
simulation module following Cockburn, Duclos and Tiberti (2011) in order to
assess the distributive impacts of an increase in public infrastructure investments.
To provide input to policy makers, we conduct two experiments to assess the
potential immediate, short-run and long-run effects of increased public investment
expenditures, when financed by either higher taxes or foreign borrowing. The
policy focus of this paper leads us to stay within the confines of attainable
government policies by simulating a 25 % permanent increase in the public
infrastructure expenditures-to-GDP (PIE-GDP) ratio over time. This increase is
sufficient to achieve the government’s minimum target of a 5 % PIE-to-GDP ratio.
The next sections are as follows. Section “Public Infrastructure” provides a brief
survey of the public investment literature and the section “Public Infrastucture
Challenges” discusses issues relating to public infrastructure in the Philippines.
Section “Philippine Poverty Profile” presents a poverty profile of the Philippines.
Section “Methodology” describes the CGE model and the micro-simulation mod-
ule, then sections “Policy Experiments” and “Simulation Results” respectively
explain the simulation scenarios and the simulation results. Finally, the section
“Summary and Insights” provides insights and conclusions.
Public Infrastructure
Empirical research on the economic impact of public infrastructure is now wide-
spread. One strand in the literature makes use of econometric modeling techniques.
In a seminal paper, Aschauer (1989) uses an OLS approach to show that the capital
stock of public infrastructure is a determinant of total factor productivity in the
48 E. Corong et al.

United States. Isaksson (2009) adopts a panel data regression model—using ordi-
nary least squares (OLS), both fixed and random effects, and instrumental
variables—to analyze a group of 57 advanced and developing countries over
1970–2000. His research finds that public capital has a relatively strong impact
on industrial development and that public capital growth has the strongest impact
on rapidly growing economies and high-income economies.
Calderon and Chong (2004) use a generalized method of moments (GMM)
dynamic panel estimation model to capture the role of the volume and quantity of
infrastructure—particularly in energy, public works, railways, roads and
telecommunications—on income distributions in a set of 101 countries over
1960–1995. Their study reveals a negative relationship between the level of infra-
structural development and income inequality. Arslanalp, Bornhorst and Gupta
(2011) use a production function with estimated public capital in 48 advanced
and developing economies over 1960–2001. They find that increases in the stock of
public capital are associated with economic growth, with advanced economies
registering stronger short-run effects and developing economies having greater
long-run effects. Gupta et al. (2011) adopt a production function approach with a
GMM estimation. They use efficiency-adjusted public capital stock data for
52 developing countries, and find that this type of public capital has a significant
effect on output.
Other related studies have opted for general equilibrium techniques. Zhai (2010)
uses a global CGE model, and finds that regional infrastructure investment in
developing Asia would raise global income by US$1.8 trillion by the year 2020,
with 90 % of the gains accruing to the region. Moreover, such investment would
help boost global and regional trade. Dissou and Didic (2011) use a CGE model
with heterogeneous agents and public capital in a multi-sectoral and intertemporal
environment calibrated to the economy of Benin. They show, among other things,
that: increasing public investment has short-run Dutch disease effects, expected to
be offset by increased productive capacity in the long run; higher public infrastruc-
ture spending benefits non-constrained agents more than constrained agents; and
that the short-run private sector investment response depends on how the public
infrastructure is financed.
Unfortunately, empirical research on the role of infrastructure spending on
economic growth and poverty in the Philippines—a developing economy in South-
east Asia—is limited. Teruel and Kuroda (2005) use a translog cost function and
find that improvements in public infrastructure in the Philippines—particularly
road infrastructure—are instrumental in enhancing agricultural productivity in the
country. Savard (2010), using a top-down bottom-up computable general equilib-
rium (CGE) micro-simulation model, demonstrates the macro, sectoral and poverty
impacts of increasing public investment in the Philippines. The findings indicate
that: public investment positively impacts GDP and employment; the macro effects
do not differ substantially across the three public investment financing mechanisms
considered (income tax, value-added tax (VAT) and foreign aid); public investment
lowers poverty—the magnitude being strongest under VAT; and foreign aid is the
most equitable funding mechanism.
The Growth and Distributive Impacts of Public Infrastructure Investments in... 49

A contentious empirical issue is the estimation of the elasticity of output to
public capital, which has been criticized in several studies as being too high, as a
result of some methodological limitations or weaknesses. Isaksson (2009) points
out that this concern arose because Aschauer’s (1989) estimate of the effect of
public investment is impossibly large, ranging from 0.38 to 0.56, implying an
annual rate of return of no less than 100 %. Potential sources of this problem
vary and those cited in the literature include endogeneity, reverse causality (from
output growth to public capital), spurious correlation (due to non-stationarity of the
data), omitted state-dependent variables and lack of agreement regarding the
appropriate rate of return from public investment.
Furthermore, it has been conjectured that the large estimates on the elasticity of
output to public capital could emanate from: high public investment (as a propor-
tion of GDP), a situation which is prevalent in highly corrupt countries, as corrup-
tion tends to inflate public investments; from unproductive uses in public capital;
and from the composition of public capital. Several papers have attempted to
correct for these econometric and conceptual problems by accounting for the
elasticity of output to public capital, including Arslanalp, Bornhorst and Gupta
(2011), Gupta et al. (2011) and Isaksson (2009).
Public Infrastucture Challenges
It has been widely perceived that Philippine transport infrastructure—air transport,
ports, railroads, roads—is of poor quality and has not improved much in recent
years. The latest World Economic Forum’s (WEF 2010) Executive Opinion Sur-
vey, published in its Global Competitiveness Report (GCR) 2010–2011, ranked the
Philippines 113th out of 139 countries in the overall quality of its infrastructure,
giving the country a score of 3.2 (the possible score ranges from 1 [worst] to
7 [best]). More specifically, the Philippines ranked 97th in railroad infrastructure,
112th in air transport infrastructure, 114th in road infrastructure and 131st in port
infrastructure. This suggests that, by international standards, the overall quality of
Philippine infrastructure is relatively poor. Indeed, Fig. 1confirms that, between
2004 and 2010, infrastructure indicator scores deteriorated slightly in relation to air
transport, ports and railroads, while the score on road infrastructure remained
unchanged.
Infrastructure Trends
The road network in the Philippines expanded during the 1990s, then began to
deteriorate, falling to 200,037 km in 2003 (the most recent data available) from
202,123 km a year earlier. The proportion of paved roads in the national road
50 E. Corong et al.

network climbed during the mid-1990s, rising to 19.8 % in 1998, but then fell to
9.9 % in 2002. The length of rail lines stagnated between 1990 and 2008: the
country had 479 km of rail in the early 1990s, a number that increased to 491 km by
2004 and eventually fell back to 479 km by 2008.
The Philippines also ranked relatively low (101st of 139 countries) in the
2010–2011 WEF Executive Opinion Survey in terms of the quality of electricity
supply, garnering a score of 3.4 (the possible score ranges from 1 [insufficient] to
7 [sufficient and reliable]). Concerns over a looming power shortage or crisis in the
country were evident in 2010 amid intermittent power outages, particularly in the
southern part of the archipelago (Mindanao), as widespread droughts caused by El
Nino—which resulted in receding water reservoirs in hydroelectric dams—coupled
with poor maintenance work, have led to inadequate power supply. At that same
time, the disruptive weather had resulted in surging peak demand (DOE 2010).
Moreover, structural reforms in the power sector have faced bottlenecks, and not
enough new power capacity has come online in the country. Obstacles to power
sector reforms include delays in the privatization of the government’s power
generation assets—such as power plants, particularly those from the state-owned
National Power Corporation—hampering the rehabilitation of these assets and
limiting the participation of the private sector in the electricity supply industry.
Moreover, power supply in the Philippines is geographically concentrated in a
few areas, further contributing to the problem of inadequate power capacity. In a
recent assessment of the Philippines’ power situation, the Department of Energy
(DOE) of the Philippine government reported that: (i) In the country’s Luzon
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
2004-05 2005-06 2006-07 2007-08 2008-09 2009-10
Air transport
Ports
Railroa ds
Road s
Fig. 1 World Economic Forum’s Executive Opinion Survey scores on transport infrastructure
indicators in the Philippines, 2004–2010 (Source: World Economic Forum, Global Competitive-
ness Report, various issues)
The Growth and Distributive Impacts of Public Infrastructure Investments in... 51

region, the power generation capacity has been concentrated in the Northern and
Southern areas, with relatively large power loads in Metro Manila and neighbouring
provinces; (ii) Power generation capacity in the Visayas region has been concen-
trated in the Leyte-Samar grid; and (iii) In Mindanao, most of the power generation
capacity is located in the Northern areas but the bulk of electricity demand comes
from the Southern areas.
As electricity demand continues to increase (see Appendix 1, Fig. 14), there is an
urgent need to create more energy-related infrastructure in order to increase the
country’s power generation capacity. Over 2010–2013, the DOE together with
power firms plan to build four coal-fired plants across the archipelago. Furthermore,
the DOE has projected that the Luzon, Visayas and Mindanao power grids would
respectively need an additional capacity of 11,900 megawatts (MW), 2,150 MW
and 2,500 MW of capacity by 2030.
2
Access to water seems to have marginally improved over the years in the
Philippines (see Appendix 1, Fig. 15). The proportion of the overall population in
the country with access to an improved water source has climbed gradually, from
84 % in 1990 to 87 % in 1995, 88 % in 2000, 90 % in 2005 and 91 % in 2008. Urban
dwellers generally have better access to an improved water source than those in
rural areas. The share of the urban population with access to an improved water
source remained unchanged at 93 %, while the situation improved consistently in
rural areas from 76 % in 1990 to 87 % in 2008.
Despite improved water access, there is still a need for the Philippine govern-
ment to further expand water distribution and improve water infrastructure. The
government has admitted that there are certain challenges in the water sector such
as: water depletion in major cities, including Metro Manila and Metro Cebu;
rampant water pollution; increasing demand for water; low willingness to pay for
water; low cost recovery of investments; and institutional problems.
Government Policy on Infrastructure
The Philippine Infrastructure Public-Private Partnership (PPP) program is the
flagship policy agenda of the government in promoting infrastructure development
in the country. The PPP recognizes the private sector’s role as a catalyst of growth
and as an important source of infrastructure financing. Infrastructure projects
covered by the PPP program include those that aim to develop the agri-business,
educational, energy, environment, health, industry, information and communica-
tions technology, logistics, property, transportation, telecommunications and water
supply sectors.
2
Ibazeta (2010).
52 E. Corong et al.

The Medium Term Philippine Development Plan (MTPDP) 2004–2010 reported
that the Philippine government will prioritize transportation infrastructure-related
projects that boost the country’s trade and investments. These projects include
construction of roads and railroads that will decongest the country’s capital
(Metro Manila), major highways, roads and airports connecting tourism hubs, and
roll-on roll-off (RORO) ports. The government aims to boost infrastructure spend-
ing in the country through the Comprehensive and Integrated Infrastructure Pro-
gram (CIIP). The CIIP anticipates that the private sector would bring PHP400.9
billion in infrastructure financing, with PHP214.4 billion in the transport sector,
PHP112.3 billion in water supply, PHP70.7 billion in social infrastructure and
PHP3.5 billion in telecommunications.
3
Table 1shows the annual sectoral breakdown of planned infrastructure invest-
ment in the Philippines starting in 2009 and through to 2013 and beyond. Total
planned infrastructure spending in 2011 is 32.2 % lower than in the previous year, at
PHP564.9 billion (5.8 % of GDP); the power sector was expected to have the largest
allocation at PHP246.9 billion (43.7 % of total), followed by the transportation
sector at PHP133.2 billion (23.6 % of total). Infrastructure investments are planned
to be 18.7 % higher in 2012 on a year-on-year basis, at PHP670.7 billion (6.9 % of
GDP), and the largest chunk of investments (36.9 %) in 2012 was to be targeted to
government support for agrarian reform communities (ARCs). In 2013, the gov-
ernment plans lower infrastructure investments of PHP307.6 billion (3.2 % of
3
Paderanga (2010).
Table 1 Breakdown of Philippine infrastructure investment (by sector, 2009–beyond 2013,
billions of pesos)
Sector 2009 2010 2011 2012 2013 Beyond 2013
Transportation 123.8 247.6 133.2 102.2 63.6 171.9
Power 85.5 196.2 246.9 150.9 94.7 230.3
Water 36.5 68.8 68.2 112.2 49.6 179.2
Telecommunications 7.9 9.8 7.3 15.5 15.0 0.5
Social infrastructure 43.8 279.1 40.8 31.2 24.7 26.0
Support to ARC’s 23.5 22.0 58.4 247.3 55.7 3.3
Re-lending programs 5.0 9.0 10.2 11.3 4.3 13.4
Total 326.0 832.6 564.9 670.7 307.6 624.7
% of GDP 4.1 9.2 5.8 6.9 3.2 6.4
Source: National Economic and Development Authority (NEDA) and authors’ computation
Note: 2011 nominal GDP data is used to get the share of infrastructure investment for 2012, 2013,
and beyond 2013
ARCs agrarian reform communities, GDP gross domestic product
The Growth and Distributive Impacts of Public Infrastructure Investments in... 53

GDP), with the power sector receiving the greatest share of the total, at PHP94.7
billion (30.8 % of total). Beyond 2013, it is estimated that about PHP625 billion
(6.4 % of GDP) will be spent on infrastructure, with power, water and transporta-
tion being the largest recipients.
In 2010, the Philippine government’s expenditures (excluding interest payments
and spending on financial services) totalled PHP1,379.3 billion, of which 36.3 %
were on goods and services from production sectors, 33.8 % on social services,
24.5 % on general public services and 5.3 % on national defence.
4
The largest
single focus of public spending was education, at 17.4 % of public spending
(PHP240.6 billion), followed by transport and telecommunications infrastructure
(12.6 %, PHP174.3 billion). However, public spending on health-related infrastruc-
ture and on electricity/energy-related infrastructure were both relatively small,
respectively at 3.7 % (PHP50.9 billion) and 1.3 % (PHP17.8 billion).
Philippine Poverty Profile
Based on official accounts disseminated by the National Statistics Coordination
Board (NSCB) of the Philippine government, the poverty incidence (estimated
using per capita income data) among the Philippine households in 2009 was
estimated at 26.5 %, which is higher than the previously estimated poverty inci-
dences of 26.4 % in 2006 and 24.9 % in 2003. Philippine economic growth
fluctuated during this period, with real GDP growth of 1.1 % in 2009, 5.2 % in
2006 and 5.0 % in 2003. More recently, in April 2013, the NSCB reported that the
poverty incidence among the whole population for the first semester of 2012 stood
at 27.9 %, somewhat lower than the 2009 and 2006 first semester figures of 28.6 %
and 28.8 %, respectively. Moreover, income inequality in the country declined
somewhat during this period, with the Gini coefficient falling from 0.465 in 2003 to
0.458 in 2006 and slipping further to 0.448 in 2009.
Snapshot of Philippine Poverty
We now provide a description or characterization of poverty based on explicit
subgroup characteristics in order to highlight the regional variation and urbanity
4
Inclusive of interest payments (PHP276, 212.0 million) and payments for financial services
(PHP6, 994.7 million), public expenditures of the Philippines in 2010 totalled PHP1, 662.5 billion.
54 E. Corong et al.

differences of poverty estimates by using survey estimation techniques.
5
In constructing profiles, we consider the following attributes: (1) headship; (2) eco-
nomic activities of the household head which include occupation and class of work;
(3) marital status of household head; and (4) the type of household.
We estimated the poverty incidence for each of the household attributes based on
data from the 2006 Family Income and Expenditure Survey (FIES) (the full results
are shown in the Appendix 2, Table 9). Figure 2presents the poverty incidence by
household type, and sex and marital status of the household head. It shows that
single households or nuclear families have a higher poverty incidence (27.7 %) than
extended households (24.9 %); this may be due to the fact that extended households
have more access to resources, giving rise to relatively more reliable safety nets.
This is consistent with the findings of Albert and Collado (2004) which were based
on the 2000 FIES. We also find that roughly 29 % of male-headed households are
poor, whereas about 20 % of female-headed households are poor. By marital status
of the household head, the lowest poverty incidence is found among single-parent
households at 17.1 %, followed by households whose head is divorced (19.9 %),
0
0.1
0.2
0.3
0.4
0.5
Single household
Extended household
Male headed
Female headed
Single
Married
Widowed
Divorced/separated
Uknown
Type of
household
Headship Marital status of household head
Fig. 2 Poverty incidence based on type of household, sex and marital status of household head
(Source: Authors’ computation based on Philippine FIES 2006 (overall))
5
We computed estimates by using the survey’s total estimation module which allowed us to
compute for the total number of poor and non-poor households. The sampling weights that we use
pertain to probability weights assigned to respective households. The stratifying variable that we
use combined information on the province and urban/rural residence.
The Growth and Distributive Impacts of Public Infrastructure Investments in... 55

whereas households whose heads are married have a higher poverty incidence
(28.3 %).
Estimates of poverty incidence by class of worker (household head) and number
of household members employed are likewise presented in Table 9in the appendix.
The literature generally finds a strong relationship between poverty status and
involvement in economic activities. Our results show that households are more
likely to be poor when the head is self-employed and are less so if the head works
for the government. Our calculations also show that households with heads working
in the public sector have a lower poverty incidence compared with households
whose heads are working in the private sector. This can be easily explained by the
fact that, on average, civil servants earn more, and more stable, income than those
working in the private sector.
6
The incidence of poverty among self-employed
household heads is higher than among those employed in the private sector. In fact,
households whose heads are self-employed have the highest poverty incidence, at
34.7 %; this is somewhat expected since a significant portion of the workforce is
employed in the informal sector, which is dominated by unincorporated businesses.
Finally, households with eight employed members have a relatively lower poverty
incidence than those with less than eight employed members.
Methodology
A combination of computable general equilibrium (CGE) and micro-simulation
methodologies is employed to understand how public infrastructure investments
impact on the Philippine economy. We now briefly present the models and
underlying data.
The CGE Model
We employ a dynamic general equilibrium model developed by Dissou and Didic
(2011) to trace the channels via which public infrastructure investments filter
through the Philippine economy. To avoid repetitiveness, we only summarize the
salient features of the model and refer the interested reader to Dissou and Didic
(2011) for a more complete model specification.
7
In general, the model assumes a
6
However, we do not have evidence that private sector workers with comparable attributes relative
to government workers have better compensation.
7
For more details of the model, please see Dissou and Didic (2011).
56 E. Corong et al.

small open economy—consisting of households, firms and the government—that
produces and consumes tradable and non-tradable goods and has access to the
international capital market.
An important feature of this model is that it explicitly treats public capital as an
input into the firms’ production process, and thus allows us to quantify the growth
and distributive effects of public infrastructure investments on the Philippine
economy over time.
Public capital is assumed to be a pure public good
8
and enters firms’ production
functions as an externality that enhances output. This is because the accumulated
flows of public infrastructure investment generate positive externalities in the
production of goods and services by firms. Although data limitations restrict the
analysis to the effects of the public capital stock as a whole, productivity effects of
public infrastructure are allowed to vary across industries. Firms in all industries
make use of intermediate inputs, labour, physical capital and public capital to
produce a composite output that can be sold in both domestic and international
markets. However, public capital is a fixed input—as it is a decision variable at the
discretion of the government rather than of the firm—while other inputs are
controlled by the private sector.
The economic intuition behind the impact of public infrastructure on economic
growth in the model is as follows. In a scenario with fixed public capital and
increased supply of other inputs—such as labour, physical capital and intermediate
inputs—the productivity of labour and physical capital would deteriorate, thereby
hurting economic growth. For example, physical capital accumulation alongside
labour supply growth can result in negative externalities such as traffic congestion
and deteriorating infrastructure quality if not accompanied by higher investments in
public infrastructures. In order to mitigate these negative effects on the productivity
of private inputs and to spearhead economic growth, the stock of public capital must
increase through investments in public infrastructures.
As shown in Fig. 3, gross output is determined via a three-stage process. The
lowest stage involves the optimal determination of labour and private capital
through a constant elasticity of substitution (CES) function. The CES labour-
private capital aggregate is then combined with public capital through another
CES function to form a composite value added. In spite of the CES aggregator
formulation, the stock of public capital is a fixed factor with endogenous rates of
return reflecting its marginal product. Note that public capital is not a decision
variable for the firm since public capital stocks are accumulated through public
sector infrastructure investments. Finally, gross output is determined by combining
the composites of value added and intermediate inputs (a Leontief function of
individual intermediate inputs) through another CES function.
8
As a pure public good, services derived from public capital are not subject to congestion.
The Growth and Distributive Impacts of Public Infrastructure Investments in... 57

Another salient feature of the model is that it accounts for firm and household
heterogeneity. Households are divided into two types according to their access to
credit markets: (a) constrained (myopic) households do not have access to credit
markets. These households consume out of their current income, and at the same
time save a constant and strictly positive fraction of their disposable income
(Keynesian savings behaviour); and (b) non-constrained (forward-looking) house-
holds have access to credit in the capital market, where they can borrow and lend at
a fixed world interest rate. These households are thus able to smooth consumption
over time. Regardless of the household type, we assume that household labour
supply is perfectly inelastic, implying that households do not consider leisure as
part of their labour supply decision. Household income sources are: wages, capital
income (returns from both private and public capital) and transfers from the
government and from the rest of the world. Finally, all households consume on
the basis of a constant elasticity of substitution (CES) function.
Firms are also classified into two types according to their access to the credit
market. Non-constrained firms have access to capital markets where they can
borrow and lend at a fixed world interest rate and are owned by non-constrained
households. These firms determine their optimal levels of inputs and outputs
through intertemporal optimization. Constrained firms do not have access to capital
markets and are exclusively financed by constrained households who use their
savings to purchase the capital stock of these firms. In contrast to non-constrained
firms, constrained firms only maximize current profits. The government collects
income taxes directly on the labour income of both non-constrained and constrained
households and from the dividends of non-constrained households.
Real government spending on commodities is exogenous but grows overtime as
a function of population growth and technological progress. The current public
infrastructure-to-GDP ratio is exogenous. We treat this ratio as a policy variable
that can be modified to perform simulations in relation to increased public infra-
structure. Government savings is held fixed to ensure that the public sector cannot
increase its debt over time. Higher public investment in infrastructure is either
Fig. 3 Production structure
58 E. Corong et al.

financed by a uniform increase in production tax rates imposed on all firms or
through an increase in foreign financing, with payments to the latter being part of
foreign debt service payments in each period. The labour market behaves in a
neo-classical manner and wages adjust to ensure equilibrium in labour markets.
Similarly, commodity prices adjust to maintain equilibrium in the goods markets.
Total investment is financed by total savings: investment in constrained firms is
financed from the savings of constrained households; while dividends paid by
non-constrained firms to non-constrained households are net of investment expen-
ditures. In addition, the transversality condition imposed on asset holdings ensures
that the country cannot continuously increase its foreign debt, i.e., any increase in
debt today must be paid for by future increases in the current account balance.
Finally note that the fixed government savings provide the macro closure.
CGE Data and Parameters
The model uses an aggregated version of the latest available unofficial social
accounting matrix (SAM) for the Philippines (Cororaton and Corong 2009) as its
principal database. There are 12 sectors in the model: (1) crops and livestock;
(2) other agricultural products; (3) food, alcoholic beverages, and tobacco; (4) min-
ing; (5) paper and wood; (6) petrochemicals; (7) textiles and garments; (8) heavy
manufacturing; (9) light manufacturing; (10) other manufacturing; (11) public
services; and (12) other services. Three sectors are assumed to be comprised of
constrained firms: other agriculture, other manufacturing and other services; the
rest are comprised of non-constrained firms.
Table 2presents the basic structure of the Philippine economy in the base
scenario, following the country’s SAM. Of the 12 sectors, the light manufacturing
sector is observed to contribute the largest share to the country’s value added and to
total investment, exports and imports. The other services sector accounts for the
largest share of final consumption.
Table 3summarizes the CES elasticities for the production structure illustrated
in Fig. 6. Due to an absence of econometric estimates, we assume conservative
elasticities taken from estimates in the literature on developing countries. Note that,
although the assumed production elasticity of substitution found in the first three
columns of Table 3are the same for all sectors, their relative shares are different.
Relative shares are of greater importance than elasticity values as the simulation
results are driven more by the structure of the economy than by the differences in
the choice of elasticity parameters.
Similarly, the last two columns of Table 3show the elasticities for the
CES-Armington function (substitution between imports and domestic sales) and
the CET function which reflects substitution between exports and domestic sales.
These values were taken from the GTAP database.
The Growth and Distributive Impacts of Public Infrastructure Investments in... 59

Microsimulation Module
A top-down CGE microsimulation procedure is employed by using the results of the
CGE simulations as inputs into a microsimulation module in order to assess the
distributive impacts of higher public infrastructure investments. The microsimulation
module, which is based on Cockburn, Duclos and Tiberti (2011), uses the 2006
Family Income and Expenditure Survey (FIES) of the Philippines.
For brevity, we only summarize the microsimulation procedure (for details see
Cockburn et al. 2011). Per capita consumption in real terms for the base year and
Table 3 Parameters for CGE model (based on 2000 Philippine SAM)
Gross
output
Value
added
Labour-private
capital
CES
Armington CET
Crops and livestock 0.5 0.4 0.4 2.3 2.3
Other agriculture 0.5 0.4 0.4 2.8 2.8
Food, beverage and tobacco
processing
0.5 0.4 0.4 2.3 2.3
Mining 0.5 0.4 0.4 2.8 2.8
Paper and wood 0.5 0.4 0.4 2.1 2.1
Petrochemical 0.5 0.4 0.4 1.9 1.9
Textile and garment 0.5 0.4 0.4 2.3 2.3
Heavy manufacturing 0.5 0.4 0.4 2.8 2.8
Light manufacturing 0.5 0.4 0.4 3.0 3.0
Other manufacturing 0.5 0.4 0.4 2.8 2.8
Public services 0.5 0.4 0.4 1.9 1.9
Other services 0.5 0.4 0.4 2.6 2.6
Source: Authors’ computations
SAM social accounting matrix
Table 2 Characteristics of Philippine economy (based on 2000 Philippine SAM)
Value
added Consumption Investment Government Exports Imports
Crops and livestock 4 3.5 4.5 0 1.2 1.9
Other agriculture 0 3.2 0.1 0 0.8 0
Food, beverage and
tobacco processing
2 19.9 0.4 0 3.6 4.1
Mining 0.2 0.1 0 0 0.4 9
Paper and wood 1.7 0.7 0.3 0 2.1 1.8
Petrochemical 1.1 3.7 0.2 0 2.6 7.4
Textiles and garments 1.1 3.2 0.2 0 9.5 5.2
Heavy manufacturing 1.4 0.1 0.6 0 2.7 4.7
Light manufacturing 85.3 3 48.6 0 59.5 47.9
Other manufacturing 3.2 1 2.7 0 3 2
Public services 0 0.1 0 100 0.1 0
Other services 0 61.6 42.4 0 14.6 16
Source: Authors’ computations
SAM social accounting matrix
60 E. Corong et al.

the simulation periods is the variable of interest for estimating poverty and inequal-
ity changes across the different scenarios. According to the methodology followed
in this study, this variable is affected by the change in consumer prices as well as in
household revenues, here corresponding to incomes from wage and self-
employment activities. Consistently with the CGE model, we also took into account
the different marginal propensity to consumption for constrained and
non-constrained households.
Initially, the FIES is processed to classify constrained and non-constrained
households. A logit model specifies the probability of being a non-constrained
household (Y
i
¼1; Y
i
¼0 if constrained), which is defined as: has access to formal
credit institutions, has saved or has a savings account. The logit model shown in
Eq. 1estimates the probability that a given household his non-constrained ( p
h,nc
).
By implication, the complement of p
h,nc
gives the probability that a given house-
hold his constrained ( p
h,c
).
Logit πh
ðÞ¼αþβvXhþεhwith πh¼EY
hXh
j
ðÞ ð1Þ
where vector X
h
includes the Vcommunity and household socio-economic charac-
teristics of household h: household’s region and urban/rural residence, whether the
household head receives a fixed payment from work activities, the occupational
category the household head belongs to, the natural logarithms of real per capita
household consumption, household size, household head’s gender and age, as well
as the educational level of the household head and the household head’s age
squared.
Passing to labour activities, we considered one single category of worker (which
is perfectly mobile across all sectors) and we made the hypothesis of full employ-
ment. This is in accordance with CGE model’s hypotheses. As for revenues,
revenues for wage workers that reported missing incomes have been estimated by
a standard Heckman selection approach. Then, the change in the wage rate as
predicted by the CGE model has been used to simulate the variation in the wage
component across the different scenarios. Changes in revenues from self-
employment activities (included the component for own-consumption) were
derived from the variations in the sectoral (value of the) value-added as simulated
by the macro model. It is noteworthy here that the CGE results (concerning the
quantities variables) are provided in terms of productive worker, then taking into
account the change in population, labour force and technology over time.
To observe changes in household consumption levels following variations in the
prices of goods and household income, the nominal consumption for each good is
converted into real terms. Using a Cobb-Douglas utility function, which lays on the
hypothesis of fixed budget share, real or equivalent per capita consumption is:
eh,d,t¼yh,d,t
Γh,d,t
with Γh,d,t¼Y
K
k¼1
pk,d,t
pk,D,0

wh,d,k
ð2Þ
The Growth and Distributive Impacts of Public Infrastructure Investments in... 61

where y
h,d.t
is the total nominal per capita expenditures of each household hliving
in district dat time t;Γ
h,d,t
is the household-specific consumer price deflator which
takes into account both spatial (by comparing district dto the reference cluster D—
here, the capital region NCR) and temporal (by comparing time t to the reference
time 0) price differences; p
k,D,0
is the reference unit price, which corresponds to the
price of good kat time 0 estimated in the reference district D;p
k,d,t
is the unit price
at time tfor good kin cluster d;w
h,d,k
is the budget share for good kby household
hin district d. As for the economic sectors, we mapped the categories of consump-
tion commodities in the underlying micro and macro data and then aggregated by
nature of goods in order to have the same type of aggregates in the two models.
To be consistent with the household classifications in the CGE model, the micro-
simulation procedure takes into account the differences in savings and consumption
of all households, particularly non-constrained households which can change their
savings rate over time (in contrast to constrained households whose savings rate
remains fixed). Nominal per capita consumption for a household y
h,d,t
at time tis
calculated as:
yh,d,t¼yh,d,t¼0þX
j
k¼1
ΔRk
h,d,tph,nc 1snc,t
ðÞþΔRk
h,d,tph,c1sc
ðÞ

ð3Þ
where y
h,d,t
is defined as the sum of per capita consumption of household hin the
base year (y
h,d,t¼0
) and the per capita changes in the krevenue components (R),
namely wage and non-wage incomes. As already stated, changes in these sources
are taken from the CGE simulation results and plugged into the micro module.
As defined by Eq. 3, changes in the revenue sources are weighted by the probability
of household hbeing non-constrained p
h,nc
(and the complementary situation of
being constrained). Only the shares devoted to consumption are retained for
consumption: (1s
nc,t
) for non-constrained households and (1s
c
) for constrained
households, where s
nc,t
and s
c
are the saving rates for the two types of households.
Poverty effects are measured using the Foster-Greer-Thorbecke (FGT) Pαclass
of additively decomposable measures (Foster et al. 1984). Let z
D,0
be the real
poverty line, that is, a line measured in terms of the reference prices p
D,0
. The
FGT family index is then defined as:
PαzðÞ¼1
NX
H
h¼1
ρh,dnh,d
zD,0eh,d,tpk,D,0;pk,d,t;yh,d,t

zD,0

α
þ
ð4Þ
where f
+
¼max(0, f), Nis the number of households in the survey (and corre-
sponds to the sum of the sampling weights), n
h,d
is the size of the household h,ρ
h,d
is
the sampling weight of h, and αis a parameter that captures the “aversion to
poverty” or the distribution sensitivity of the poverty index.
The FGT poverty measure depends on the values that the parameter αtakes. We
calculate the poverty headcount for α¼0. The poverty headcount is the proportion
of the population that falls below the poverty line. When α¼1, the poverty gap
62 E. Corong et al.

indicates how far the poor are from the poverty line on average. Finally, when
α¼2, the severity of poverty is measured as the squared average distance of
income of the poor from the poverty line. The severity index is more sensitive to
the distribution among the poor because the poorest of the poor in the population are
weighted more heavily.
Inequality is calculated using the Gini coefficient, which is the most commonly
used measure of inequality. It computes the average distance between cumulative
population shares and cumulative income shares (Duclos and Araar 2006). The Gini
coefficient is calculated as:
Gini I2ðÞ¼
ð1
0
pLpðÞðÞκp;2ðÞdp ð5Þ
where L(p) is the cumulative percentage of total income held by the cumulative
proportion pof the population (ranked by increasing income) and krepresents the
percentile-dependent weights.
Policy Experiments
Using the CGE model described in the section “The CGE Model,” we conduct two
policy experiments to assess the potential effects of higher public investment in
infrastructure financed by: (1) international lending with a concessional interest rate
of 6 %; and (2) higher production taxes. In order to stay within reasonable limits of
attainable government policies we simulate a 25 % permanent increase in the public
infrastructure expenditure-to-GDP (PIE-to-GDP) ratio relative to the baseline. This
increase is sufficient to achieve the government’s minimum PIE-to-GDP ratio
target of 5 %.
As mentioned in the section “Public Infrastructure”, a contentious empirical
issue is the estimation of the elasticity of output to public capital. Given the absence
of econometric estimates for the Philippines, we assume a conservative exogenous
elasticity of output to public capital of 0.15 %—a lower-end estimate that is
consistent with most empirical studies. This conservative value was chosen to
account for concerns that large estimates of the output elasticity of public capital
could emanate from high public investment (as a proportion of GDP)—as corrup-
tion tends to inflate public investments, from unproductive uses in public capital
and from the composition of public capital. However, we undertake sensitivity
analysis to determine the robustness of the estimated economic and poverty impacts
to changes in the assumed elasticity of output to public capital.
Other variables that are exogenously determined in the model include the annual
population growth rate (1.8 %), the foreign concessional lending rate (6 %), and the
depreciation rate of the public and private capital stocks (15 %, respectively). Using
base year values from the SAM, in conjunction with exogenously given parameter
The Growth and Distributive Impacts of Public Infrastructure Investments in... 63

values and the transvertality condition, we calibrate and solve the dynamic CGE
model to reproduce the baseline path of the economy over a 50-year time horizon.
The Business as Usual (BaU) scenario is then used to make comparisons with the
counterfactual simulation results. Note that, in the BaU, all real variables are
expressed in efficiency units and all prices are held constant.
Simulation Results
We analyze the economy-wide effects of higher public investment in infrastructure
at the aggregate and the sectoral level encompassing three time frames: the imme-
diate period (first year), the short-run (fifth year) and the long-run (twentieth year).
Since investments made in the current year only become fully operational in the
following year, we first discuss the demand-side effects of an increase in the PIE-to-
GDP ratio in the immediate period. We then discuss the demand-side and the
supply-side effects arising in the short-run and the long-run. Note that all results
are presented as percentage deviations from the economy’s baseline trajectory.
Presenting results this way allows us to isolate the economy-wide effects arising
from higher public investment.
Scenario 1: 25 Percent Increase in the PIE-to-GDP Ratio
(International Financing)
Macroeconomic effects: The macroeconomic results of scenario 1—a 25 %
increase in the PIE-to-GDP ratio financed by international lending at concessional
interest rates—are shown in the first three columns of Table 4. An increase in public
infrastructure investment financed by international borrowing immediately leads to
real exchange rate appreciation (1.6 %), and thereby improves the purchasing
power of the Philippine economy.
As a result, in the first year, imports rise by 2.6 %, as the appreciation of the real
exchange rate immediately induces substitution away from domestically produced
consumer and capital goods to the relatively cheaper imported consumer and capital
goods. The appreciation of the real exchange rate further leads to a significant
reduction in exports (2.8 %) in the first period, as they become relatively more
expensive in the international market.
At the same time, total investment increases by 6.4 % which is 1.4 percentage
points more than in the scenario where an increase in the production tax finances
higher public infrastructure expenditures. Higher total investment in the current
scenario in the immediate period is primarily due to an expansion in private
investment. In fact, in the current scenario, private investment rises by 0.8 % in
the first year following increased public investment in infrastructure, while it falls
by 0.6 % in the scenario where production taxes finance higher public investment in
64 E. Corong et al.

infrastructure. Hence, in the absence of an increase in production taxes, domestic
firms are able to increase their profitability through higher capital goods production
and higher accumulation of the private capital stock.
Furthermore, in the first period, the price of investment goods rises by 1 %—the
highest increase of all periods considered in this scenario—because the
productivity-enhancing effects of public infrastructure investments do not start to
materialize until after the first year. Recognizing that increasing productivity
arising from public infrastructure investment will lead to higher returns on invest-
ment in the future, non-constrained firms, in the first year, increase their level of
investment by less than constrained firms (0.5 % vs. 1.4 %).
As well, total household consumption increases by 2.2 % in the first period,
which is 2 percentage points more than in the scenario where production taxes
Table 4 Macro-economic results (percent deviations from baseline)
International financing Production tax financing
First
Short
run
Long
run First
Short
run
Long
run
Real GDP 0.1 1.5 2.9 0.2 0.9 2.0
Wage rate 1.0 3.6 6.5 1.0 1.5 4.1
Price of investment good 1.0 0.6 0.2 0.4 0.2 0.0
Total investment 6.4 7.7 8.2 5.2 6.6 7.1
Public investment 25.6 27.1 28.7 25.2 26.5 27.8
Private investment 0.8 2.0 2.3 0.6 0.9 1.2
Constrained 1.4 1.7 1.9 0.5 0.2 0.1
Non-constrained 0.5 2.3 2.5 0.6 1.5 1.8
Total household consumption 2.2 2.5 2.7 0.2 0.4 0.6
Constrained 2.4 2.3 2.0 0.1 0.0 0.1
Non-constrained 1.9 2.7 3.3 0.6 0.8 1.1
Total exports 2.8 0.7 2.0 1.2 1.0 3.5
Total imports 2.6 3.0 3.5 1.0 1.9 2.5
Real exchange rate
a
1.6 0.9 0.5 0.6 0.5 0.4
Foreign saving 0.9 0.4 0.3 0.8 0.4 0.2
Total capital stock
a
0.0 3.8 8.2 0.0 3.3 7.2
Public capital stock
a
0.0 13.5 27.5 0.0 13.3 26.6
Private capital stock
a
0.0 0.7 2.1 0.0 0.1 1.0
Constrained
a
0.0 0.8 1.8 0.0 0.2 0.1
Non-constrained
a
0.0 0.7 2.3 0.0 0.3 1.6
Disposable income of constrained
households
2.4 2.3 2.0 0.1 0.0 0.1
Labour income 1.0 3.6 6.5 1.0 1.5 4.1
Capital income 2.7 4.3 5.3 0.1 2.0 3.5
Government revenue 8.4 9.6 10.9 6.9 8.3 9.6
Increase in production tax rate (%) – – – 27.0 24.9 22.4
Additional international borrowing (% of
GDP)
1.1 1.1 0.9 – – –
Source: Authors’ computation based on simulation results
a
A positive sign indicates a depreciation of the real exchange rate
The Growth and Distributive Impacts of Public Infrastructure Investments in... 65

finance increased public infrastructure investment. This is because consumption of
both constrained and non-constrained households rises by 2.4 % and 1.9 %, respec-
tively. Two factors drive this result. First, the appreciation of the real exchange rate
makes imported goods relatively cheaper, thereby inducing higher consumption.
Second, higher household income arising from increasing returns to labour and
capital provides an additional boost to household consumption.
However, in the first period, real GDP falls by 0.1 % as the negative demand-side
effects, at least in the immediate period, outweigh the positive demand-side effects of
increased public infrastructure investment. Namely, the increases in private invest-
ment and household consumption experienced in the first year following higher
public infrastructure investment are not sufficiently high enough to offset the stronger
demand for imported goods and the considerable decline in exports. However, as a
result of increased private investment and household consumption, the magnitude of
the fall in the real GDP in the first period is lower than in the scenario where public
infrastructure investment is financed by increased production taxes.
The positive, demand-side effects of higher public infrastructure investment
strengthen in the short-run and the long-run as a result of ongoing private capital
accumulation and improving productivity. In fact, when public infrastructure
investment is financed by international borrowing, the economy is able to accumu-
late more private capital stock than in the case where this investment is financed by
production taxes. Specifically, the total stock of private capital expands by 0.7 %
and 2.1 %, respectively in the short-run and the long-run in the current scenario,
compared with 0.1 % and 1 % in the production tax scenario.
The disposable income of constrained households rises further in the short and
the long-run, respectively by 2.3 and 2 %. This is largely due to the increase in these
households’ labour income (which rises by 3.6 % in the short-run and by 6.5 % in
the long-run) and to an increase in their capital income (which rises by 4.3 % in the
short-run and 5.3 % in the long-run). Higher incomes in turn lead to higher total
household consumption, which grows by 2.5 % and 2.7 % over the short-run and the
long-run.
Investments by constrained firms rise by 2 % in the short-run and by 2.3 % in the
long-run, while investments by non-constrained firms increase by 3.8 % and 8.2 %
over these time frames. Similarly, total investment in the short and the long-run
grows by more in this scenario (7.7 % and 8.2 %) than in the production tax
scenario, as higher public investment is complemented by a rise in private
investment.
Over time, the stronger real exchange rate appreciation resulting from the
continuous inflow of international financing results in slower export growth and
accelerated import growth (See Fig. 4). Although exports eventually recover due to
the productivity-enhancing effects of additional public infrastructure, long-run
potential export growth is somewhat lower than observed in the baseline. Height-
ened import demand and weakened exports demand are the primary reasons behind
a deteriorating trade balance over time, which is exactly opposite to the situation
observed in the production tax financing scenario. Nevertheless, public
66 E. Corong et al.

infrastructure investment when financed by international lending still exhibits
stronger positive economic effects over time, as reflected by the increase in real
GDP in the short-run (1.5 %) and the long-run (2.9 %) than when it is financed by
increased production taxes (Fig. 4).
Sectoral effects: We now analyze the sectoral effects of a 25 % rise in the
PIE-to-GDP ratio financed by international lending (Table 5). Exports fall in every
sector in the immediate period (by at least 1.1 % in the petro-chemicals sector and
by at most 6.4 % in the other manufacturing sector) as the appreciated real exchange
rate leads to a loss in all sectors’ competitiveness in the international market.
Similarly, in the first period, imports increase substantially in every sector (by at
least 0.3 % in the mining sector and by at most 5.1 % in the other manufacturing
sector), as domestic consumers substitute domestic goods for cheaper imported
products. The real exchange rate appreciation, together with stronger demand for
imported capital goods, boosts imports in the light manufacturing, heavy
manufacturing and construction services sectors since these sectors provide inputs
for (increased) public investment.
In the absence of a distortionary production tax, output expands in the crops/
livestock, other agriculture, food processing, petrochemical and other services
sectors (0.7 %, 0.3 %, 0.6 %, 0.1 % and 0.5 % respectively) in the immediate
period following increased public infrastructure investment. Unfortunately, this is
not the case for the textiles, light manufacturing, heavy manufacturing and other
manufacturing sectors. In these sectors, output contracts in the first period due to the
appreciation of the real exchange rate which makes imported capital goods (light
and heavy manufacturing) relatively cheaper, causing domestic producers of these
products to lose their competitiveness in the first year. Compared with the case
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930
Real GDP
Consumpon
Investment
Exports
Imports
Fig. 4 GDP: demand side effects (international financing) (Source: Authors’ computation based
on simulation results)
The Growth and Distributive Impacts of Public Infrastructure Investments in... 67

Table 5 Sectoral effects (Scenario 1: International financing, percent deviations from baseline)
Crops,
livestock
Other
agriculture
a
Food
processing Mining
Paper,
wood
Petro-
chemical
Textiles,
garments
Heavy
manufacturing
Light
manufact.
Other
manufact.
a
Public
services
Other
services
a
Gross output
First 0.7 0.3 0.6 0.9 0.4 0.1 1.0 0.9 1.5 3.2 0.0 0.5
Short-run 1.9 1.6 1.8 0.9 1.1 1.5 0.0 1.1 0.1 0.4 0.0 1.9
Long-run 2.4 2.7 2.4 3.5 2.6 2.8 1.4 3.7 2.3 2.6 0.0 3.2
Employment
First 1.0 0.7 0.8 1.1 0.5 0.1 1.2 1.1 1.7 3.1 0.1 0.8
Short-run 0.8 0.5 0.6 0.4 0.2 0.2 1.2 0.3 1.4 1.6 0.6 0.8
Long-run 0.2 0.2 0.0 0.8 0.1 0.2 1.0 1.0 0.2 0.1 1.2 0.6
Investment (sector of destination)
b
First 3.4 0 3.0 0.7 0.4 1.3 2.0 0.5 2.7 0 0.4 0
Short-run 2.9 0 2.7 2.6 2.3 2.6 0.9 2.8 1.4 0 1.1 0
Long-run 2.7 0 2.6 3.6 2.7 2.8 1.6 3.7 2.6 0 1.3 0
Exports
First 3.0 5.3 3.0 1.8 2.1 1.1 2.1 1.9 2.4 6.4 2.2 4.2
Short-run 0.4 1.0 0.0 0.4 0.1 0.9 0.9 1.2 0.9 2.3 4.4 0.6
Long-run 0.3 2.8 1.5 4.0 2.3 2.8 0.8 5.3 1.9 2.0 6.7 2.8
Domestic sales
First 0.9 0.7 0.8 0.8 0.1 0.2 0.3 0.8 0.3 1.3 0.0 0.8
Short-run 1.9 1.8 1.9 1.0 1.3 1.5 0.6 1.1 1.0 0.7 0.0 2.1
Long-run 2.5 2.7 2.4 3.4 2.6 2.8 1.8 3.4 2.9 3.0 0.0 3.2
Imports
First 4.9 7.0 4.8 0.3 2.0 1.6 1.5 0.3 1.9 5.1 2.2 6.2
Short-run 4.4 4.8 3.7 1.6 2.5 2.2 2.2 1.0 2.8 4.6 4.6 4.8
Long-run 4.7 2.6 3.4 2.8 2.9 2.8 2.9 1.6 3.8 4.0 7.2 3.6
Domestic demand
First 1.1 0.7 1.1 0.1 0.3 0.6 0.2 0.5 0.9 1.5 0.0 1.3
Short-run 2.1 1.8 2.0 1.5 1.5 1.7 1.1 1.1 2.0 2.5 0.0 2.3
68 E. Corong et al.

Long-run 2.6 2.7 2.5 2.9 2.7 2.8 2.2 2.9 3.4 3.4 0.0 3.2
Consumption
First 1.3 0.9 1.4 2.6 2.0 2.2 2.2 2.4 2.4 1.6 1.7 1.2
Short-run 1.9 1.9 2.1 2.7 2.3 2.5 2.3 2.7 2.5 2.1 0.8 2.0
Long-run 2.0 2.8 2.4 2.8 2.6 2.7 2.5 3.1 2.6 2.6 0.2 2.6
Source: Authors’ computation based on simulation results
a
Constrained industries: investment from constrained firms, by sector
b
These are constrained industries. Their Investments by sector of destination follow the baseline path
The Growth and Distributive Impacts of Public Infrastructure Investments in... 69

where higher production taxes finance increased public infrastructure investment,
total demand for goods and services from all sectors rises substantially. This is
because of the stronger demand for cheaper imports and the effect of producers
shifting towards the domestic market following exchange rate appreciation. This
effect persists in the short-run and the long-run.
The long-run supply-side effects of higher public investment resulting from
capital accumulation and improved productivity are felt by producers across the
entire economy (See Fig. 5). Moreover, the increase in output across sectors is more
or less similar given public infrastructure investment financed by international
borrowing compared with that of production tax financing.
Over time, the positive spillover effects of higher public infrastructure invest-
ment enhance the competitiveness of domestic producers in the international
market, supporting their export recovery (See Fig. 6). This contrasts with the results
observed in the case of tax-financed public infrastructure investment. In that case,
exports in the food processing sector and the petrochemical sector do not recover,
even in the long-run. Instead, imports continue to outpace exports in the long-run
due to the persistently higher real exchange rate (See Fig. 7).
As in the case where increased production taxes finance additional public
infrastructure investment, all sectors experience an increase in investment in the
long-run. Being an important producer of capital goods, heavy manufacturing
registers an important expansion of investment in the short run (1.4 %) as well as
in the long-run (2.6 %), resulting in substantial output and export growth over these
time frames. Overall, public investment financed by international financing benefits
-4
-3
-2
-1
0
1
2
3
4
5
0 5 10 15 20 25 30
CropsLvstck
OthAgr
FoodAlcTbc
Mining
PaperWood
PetChem
TexGrmnt
HeavyMfg
LightMfg
OthManuf
OthServices
Fig. 5 Effects on output, by sector (international financing) (Source: Authors’ computation based
on simulation results)
70 E. Corong et al.

all sectors almost equally in terms of output expansion in the long-run. The
reallocation of factors from the agricultural sector towards the heavy and light
manufacturing sectors that is observed in the tax-financing case is absent in the
current scenario.
-6
-4
-2
0
2
4
6
8
0 5 10 15 20 25 30
CropsLvstck
OthAgr
FoodAlcTbc
Mining
PaperWood
PetChem
TexGrmnt
HeavyMfg
LightMfg
Fig. 6 Effect on exports, by sector (international financing) (Source: Authors’ computation based
on simulation results)
0
1
2
3
4
5
6
7
8
0 5 10 15 20 25 30
CropsLvstck
OthAgr
FoodAlcTbc
Mining
PaperWood
PetChem
TexGrmnt
HeavyMfg
LightMfg
OthManuf
Public
OthServices
Fig. 7 Effect on imports, by sector (international financing) (Source: Authors’ computation based
on simulation results)
The Growth and Distributive Impacts of Public Infrastructure Investments in... 71

Scenario 2: 25 Percent Increase in PIE-to-GDP Ratio
(Production Tax Financing)
Macroeconomic effects: The macro-economic results of scenario 2—a 25 %
increase in the PIE-to-GDP ratio financed by higher production taxes—are shown
in the last three columns of Table 4. Total investment in the first year increases by
5.2 %, bolstered by the 25 % increase in public infrastructure investment. A slight
reduction in total private investment of 0.6 % is indicative of the presence of a
crowding-out effect. This immediate, negative crowding-out effect of tax-financed
public infrastructure investment arises as a result of higher prices of investment
goods (+0.4 %) and the higher production tax rate imposed on all firms. Total
private investment thus falls: non-constrained firms decrease their level of invest-
ment marginally more (0.6 %) than constrained firms (0.5 %).
Furthermore, in the first period, imports rise by 1.0 % as higher public invest-
ment boosts demand for imported capital goods which become relatively less
expensive than domestically produced goods. At the same time, exports fall by
1.2 % because domestic firms are less competitive on international markets due to
the higher cost structures associated with higher production taxes. The combination
of lower exports and higher imports results in real exchange rate appreciation of
0.6 % and a deterioration in the trade balance.
Surging demand for imported goods combined with falling exports and decreased
private investment lead to a 0.2 % fall in real GDP in the first period following an
increase in tax-financed public infrastructure investment. In fact, the rate of taxation
on production rises by 27 %
9
—relative to the baseline—to finance the 25 % increase
in the PIE-to-GDP ratio. The higher taxes impose an additional burden on firms in
the economy, reducing their capacity to pay wages and to generate capital returns
(1.0 and 0.1 % respectively) to factor owners. Indeed, lower factor returns cause
disposable income and consumption to fall marginally (0.1 %) among constrained
households; while the consumption of non-constrained households rises by 0.6 % in
anticipation of increased future income.
The public capital stock increases substantially relative to the baseline scenario
in both the short-run (13.3 % in 5 years) and the long-run (26.6 % in 20 years). The
accumulation of public capital enhances the marginal productivity of private factor
inputs—labour and private capital—over time, leading to increased real wages and
higher capital income. As a result, disposable income of constrained households
starts to rise over time to reach a slightly higher level than that recorded in the
baseline scenario. It is worth noting that higher wages allow the government to
collect more income taxes from households. Hence, the initially considerable rise in
the production tax rate needed to finance the 25 % increase in the PIE-to-GDP ratio
is dampened in the short and the long-run as higher income taxes help finance the
9
Note: this figure represents the uniform percentage change in the effective production tax rates,
and are not necessarily identical across industries. It is also worth mentioning that this increase is
not as large as it may seem given that initial production tax ranges from 0.7 % in paper and wood to
9 % in petrochemical sector. The largest new production tax rate is, for example, 11.4 %.
72 E. Corong et al.

increase in public expenditures. Likewise, higher marginal productivity of private
inputs mitigates the increase in the price of investment goods in the long-run,
effectively incentivizing private sector investment.
Higher public investment bolsters the total stock of capital in the economy, and
provides an impetus to private investment in both the short-run (6.6 %) and the
long-run (7.1 %). This phenomenon of rising public and private investment over
time appears to suggest that public infrastructure investments complement private
sector investments, i.e., that a crowding-in effect takes place in both the short-run
and the long-run. Since profitability is higher under improved productivity, both
constrained and non-constrained firms undertake more private investment in the
long-run. Non-constrained firms increase their level of investment by more than
constrained firms because they anticipate future changes in capital productivity,
whereas constrained firms increase their investment to a lesser extent due to the
constrained expectations of their owners (constrained households).
Higher productivity helps reduce the burden of higher production taxes and
supports improved competitiveness of domestic firms in the international market.
This stimulates exports growth, which eventually outpaces import growth in the
long-run. The real exchange rate appreciation observed in the first year tapers off in
the short and the long-run. Moreover, the higher export growth helps improve the
balance of trade. Total short- and long-run consumption respectively grow by 0.4
and 0.6 %, as consumption of both constrained and non-constrained households
rises in line with increased income.
The net effect of these changes is a relative increase in real GDP of 0.9 % in the
short run and 2 % in the long-run. This confirms that additional public infrastructure
investments positively affect the economy of the Philippines through productivity
and capital accumulation effects that begin to take hold in the short-run (Fig. 8).
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Growth rate
(% deviaon relave to baseline)
Time
Real GDP
Consumpon
Investment
Exports
Imports
Fig. 8 GDP: demand-side effects (production tax financing) (Source: Authors’ computation based
on simulation results)
The Growth and Distributive Impacts of Public Infrastructure Investments in... 73

Sectoral effects: In contrast to scenario 1, the direct consequence of using a
higher production tax to finance public investments in capital infrastructure is a
higher cost structure among firms, causing an immediate contraction in output in
every producing sector of the economy (See Table 6). In the first period, exports fall
significantly in each sector of the economy except in the public services sector as
domestic firms lose their competitiveness in the international market. Moreover, the
higher domestic cost structure together with increased demand for investment
goods leads to a fairly substantial increase in imports, particularly in the light
manufacturing (1.4 %), heavy manufacturing (1.7 %) and other services (1.9 %)
sectors. Likewise, the food/beverage/tobacco sector registers a 1.8 % rise in imports
as the domestic economy substitutes domestically produced goods for cheaper
imported products.
In the first year, total domestic demand falls in most sectors, with the exceptions
of the light manufacturing, heavy manufacturing and other services sectors, which
are more heavily used in public investment. Domestic demand improves in all
sectors in the short and the long-run, and this is particularly the case in the light
manufacturing, heavy manufacturing and other services sectors (See Fig. 9). In the
long run, the positive supply-side effects of higher public investment (capital
accumulation and improved productivity) benefit all producers in the economy.
This is particularly true for the light and heavy manufacturing, textiles and other
services sectors, which register significant output growth in the long-run. Although
relatively modest, many other sectors experience the same output expansion effect
(crops and livestock, other agriculture, food/alcohol/tobacco). However, output in
the food processing and petrochemical sectors remains below its baseline value in
the long-run due to increased imports.
-3
-2
-1
0
1
2
3
4
5
6
0 5 10 15 20 25 30
CropsLvstck
OthAgr
FoodAlcTbc
Mining
PaperWood
PetChem
TexGrmnt
HeavyMfg
LightMfg
OthManuf
Public
OthServices
Fig. 9 Effects on sectoral output (production tax financing) (Source: Authors’ computation based
on simulation results)
74 E. Corong et al.

Table 6 Sectoral effects (Scenario 2: production tax financing, percent deviations from baseline)
Crops,
livestock
Other
agriculture
a
Food
processing Mining
Paper,
wood
Petro-
chemical
Textiles,
garments
Heavy
manufact.
Light
manufact.
Other
manufact.
a
Public
services
Other
services
a
Gross output
First 0.4 0.4 0.9 1.4 0.4 2.6 0.4 0.8 0.4 2.1 0.0 0.0
Short-run 0.0 0.2 0.5 0.8 0.6 1.9 0.8 1.2 2.1 0.7 0.0 0.8
Long-run 0.4 0.8 0.0 1.1 1.8 0.7 2.0 3.5 4.7 1.3 0.0 1.6
Employment
First 0.2 0.0 0.4 1.4 0.0 2.1 0.0 0.4 0.1 1.6 0.3 0.5
Short-run 0.8 0.6 1.0 1.7 0.0 2.4 0.2 0.5 1.5 1.2 0.2 0.2
Long-run 1.4 1.1 1.6 0.9 0.1 2.3 0.3 1.6 3.0 0.4 0.8 0.2
Investment (sector of destination)
First 1.4 0.0 1.9 4.0 0.1 5.6 0.2 0.3 2.3 0.0 0.1 0.0
Short-run 0.0 0.0 0.2 0.3 1.3 1.4 1.7 2.8 4.3 0.0 0.7 0.0
Long-run 0.3 0.0 0.1 1.0 1.7 0.6 2.0 3.5 4.9 0.0 0.8 0.0
Exports
First 0.6 1.3 3.3 0.7 0.3 6.6 0.3 1.0 0.9 3.0 0.8 1.6
Short-run 0.4 0.2 2.5 0.3 1.2 6.1 1.2 1.8 2.0 1.6 1.9 0.3
Long-run 0.1 1.8 1.5 2.2 2.8 4.3 2.7 5.5 5.0 0.7 4.4 1.3
Domestic sales
First 0.4 0.3 0.8 1.5 0.4 2.2 0.4 0.8 0.3 1.5 0.0 0.1
Short-run 0.0 0.2 0.4 0.8 0.5 1.5 0.5 1.1 2.3 0.1 0.0 0.9
Long-run 0.4 0.7 0.1 1.0 1.5 0.4 1.6 3.2 4.4 1.6 0.0 1.6
Imports
First 0.3 0.6 1.8 2.4 0.5 2.5 0.6 0.5 1.4 1.7 0.7 1.9
Short-run 0.3 0.2 1.8 1.4 0.1 3.4 0.1 0.4 2.7 2.3 1.9 2.0
Long-run 0.9 0.3 1.7 0.2 0.3 3.8 0.5 1.0 3.7 2.6 4.6 1.9
Domestic demand
First 0.4 0.3 0.6 2.3 0.4 1.0 0.5 0.7 0.9 0.6 0.0 0.3
Short-run 0.0 0.2 0.2 1.3 0.4 0.2 0.4 0.9 2.5 1.3 0.0 1.0
(continued)
The Growth and Distributive Impacts of Public Infrastructure Investments in... 75

Table 6 (continued)
Crops,
livestock
Other
agriculture
a
Food
processing Mining
Paper,
wood
Petro-
chemical
Textiles,
garments
Heavy
manufact.
Light
manufact.
Other
manufact.
a
Public
services
Other
services
a
Long-run 0.4 0.7 0.2 0.0 1.3 0.7 1.3 2.6 4.0 2.0 0.0 1.6
Consumption
First 0.4 0.2 0.4 0.5 0.5 1.0 0.5 0.4 0.3 0.1 0.8 0.1
Short-run 0.4 0.6 0.1 0.6 0.8 0.9 0.7 0.7 0.5 0.3 0.3 0.2
Long-run 0.5 1.0 0.1 0.7 1.0 0.6 0.9 1.1 0.7 0.5 1.2 0.6
Source: Authors’ computation based on simulation results
a
Constrained industries: investment from constrained firms, by sector
76 E. Corong et al.

The positive spillover effects of a higher public capital stock improve the compet-
itiveness of domestic producers in the international market. Indeed, exports recover in
both the short and the long-run in nearly every sector (See Fig. 10), eventually
outpacing relative growth in sectoral imports (See Fig. 11). Food processing and
petrochemicals sectors are exceptions in this regard because the Philippines is a net
importer of food and oil. Export growth is concentrated in manufacturing sectors
(particularly textiles, light manufacturing and heavy manufacturing) which were
already export-oriented. Import growth outpacesexport growth in the crops/livestock
and food/beverage/tobacco sectors throughout the scenario, reflecting the Philippine
economy’s general dependence on imports in these sectors.
All sectors experience an increase in investment over time (Table 6). As a major
producer of capital goods, heavy manufacturing registers the greatest expansion in
investment (2.3 % in the first period and 4.9 % in the long-run). This strong investment
growth also explains the significant short- and long-run output and export growth in
the heavy manufacturing sector, since it directlybenefits from the positive supply-side
effects of higher public investment. The shadow price of capital immediately rises,
and continues to do so in the short run because the increase in public investment
crowds out private investment. This price eventually falls in the long-run due to the
productivity-enhancing effects of increased public spending on infrastructure.
In summary, the sectoral effects suggest that the productivity-enhancing effects
of higher public investment strengthen over time, with the manufacturing and
services sectors benefiting relatively more than the agricultural sector in terms of
greater output and exports (See Figs. 9and 10). Compared to scenario 1, the net
impact of the tax financing scenario is a reallocation of factors, particularly of
labour, from the agricultural sector towards the light manufacturing and heavy
manufacturing sectors.
-8
-6
-4
-2
0
2
4
6
8
0 5 10 15 20 25 30
CropsLvstck
OthAgr
FoodAlcTbc
Mining
PaperWood
PetChem
TexGrmnt
HeavyMfg
LightMfg
OthManuf
OthServices
Fig. 10 Effects on exports, by sector (production tax financing) (Source: Authors’ computation
based on simulation results)
The Growth and Distributive Impacts of Public Infrastructure Investments in... 77

Poverty and Inequality Effects
We now analyze the poverty and distributional effects of higher public investments
in the Philippines. As shown in Table 7, the direction of changes in the poverty
headcount and Gini inequality coefficient are identical in both financing scenarios,
although the magnitude of the impact is greater under the international financing
scenario. The poverty headcounts under the foreign and tax financing scenarios
respectively rise by 0.74 and 0.62 percentage points in the first year, but fall in the
short and long run (respectively 0.63 and 1.64 under foreign financing and
0.21 and 1.07 under tax financing).
These changes in poverty and inequality result from changes in household
income and consumer prices. Indeed, a decomposition of the factors behind
changes in the poverty headcount into income and price components (Table 7)
reveals that, during the first year and under the tax financing scenario, higher
consumer prices and lower income (wages and self-employment) both lead to a
higher incidence of poverty. In the foreign financing scenario, however, higher
wages limit poverty increase in the first year, but not by enough to offset the impact
of higher consumer prices, resulting in a higher incidence of poverty.
The poverty headcount falls in both the short and long run. This occurs because
the positive supply-side effects of increased public investment accrue over time,
leading to higher wages and returns to capital (Fig. 12). Higher factor returns in the
short and long run enhance the poverty-reducing effect of income, offsetting the
poverty-increasing effect of higher consumer prices. Regardless of the scenario, it
is the combined contribution of wage and self-employment income that allows the
-3
-2
-1
0
1
2
3
4
5
6
0 5 10 15 20 25 30
CropsLvstck
OthAgr
FoodAlcTbc
Mining
PaperWood
PetChem
TexGrmnt
HeavyMfg
LightMfg
OthManuf
Public
OthServices
Fig. 11 Effect on imports, by sector (production tax financing) (Source: Authors’ computation
based on simulation results)
78 E. Corong et al.

poverty headcount to fall in the medium and long run, although rising wage income
is the dominant factor in this regard.
Table 7also shows the changes in the poverty headcount by location (urban and
rural) and household type (constrained and non-constrained). Households in rural
areas are more sensitive to the productivity-enhancing effects of public investment,
as reflected by higher short- and long-term reductions in poverty headcounts than
their urban counterparts. Similarly, higher returns to factor income drive stronger
declines in the poverty headcount among non-constrained households than among
constrained households, especially in the long run.
Table 7 Poverty and inequality effects (percentage points from baseline)
International financing Tax financing
First
period
Short
run
Long
run
First
period
Short
run
Long
run
Poverty headcount
Base (national) 29.0
Simulation 0.74
a
0.63
a
1.64
a
0.62
a
0.21
a
1.07
a
Components of changes in poverty headcount
b
Growth 0.65 0.63 1.73 0.63 0.24 1.08
Redistribution 0.09 0.00 0.08 0.01 0.03 0.02
Change (in % points) in poverty headcount due to change in:
Wage 0.18 0.72 1.22 0.20 0.25 0.83
Self-employment 0.05 0.39 0.64 0.16 0.17 0.46
Own-consumption 0.00 0.00 0.00 0.00 0.00 0.00
Consumer prices 0.90 0.50 0.23 0.30 0.24 0.14
Residual 0.03 0.02 0.01 0.04 0.03 0.08
Poverty headcount (by location)
Urban 0.38 0.61 1.43 0.36 0.23 0.95
Rural 1.09 0.65 1.86 0.87 0.20 1.17
Poverty headcount (by household type)
Constrained 0.77 0.55 1.42 0.55 0.24 0.83
Non-constrained 0.73 0.64 1.68 0.63 0.21 1.10
Gini coefficient
Base (national) 0.42
Simulation (change in %
points)
0.036 0.013 0.004 0.016 0.003 0.006
Source: Authors’ calculation based on simulation results
Note: Base poverty headcounts are 14.2 (urban), 43.4 (rural), 45.4 (constrained) and 26.7
(non-constrained)
a
The difference (relative to the base year) is statistically different at the 1 % level
b
Decomposition based on Shapley value (see Araar and Duclos 2009 for details on using dfgtgr
command in DASP)
The Growth and Distributive Impacts of Public Infrastructure Investments in... 79

Finally, a decomposition of the factors behind changes in poverty headcounts,
into growth and redistribution components (Fig. 13), reveals that, in the long run,
the growth component reduces poverty in both the international financing and the
tax financing scenarios.
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
First Short run Long run First Short run Long run
Foreign financing Tax financing
Poverty Headcount
Wage
Self-employment
Own-consumpon
Consumer prices
Fig. 12 Contribution to changes in poverty headcount (scenarios 1 and 2, percentage points from
baseline) (Source: Authors’ calculation based on simulation results)
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
First Short run Long run First Short run Long run
Foreign financing Tax financing
Redistribuon Component
Growth Component
Fig. 13 Growth and redistribution components (changes in poverty headcount, percentage points)
(Source: Authors’ calculation based on simulation results)
80 E. Corong et al.

Sensitivity Analyses
In addition to the two policy scenarios analyzed above, we also used the model to
simulate alternative assumptions regarding the value of the elasticity of output to
public capital. We test the sensitivity of the results to our assumed value for the
elasticity of output to public capital (0.15) by recalculating our findings with
elasticities of 0.1 and 0.2.
We present these alternative results in Table 8for selected macroeconomic
indicators, the poverty headcount and the Gini coefficient. For the sake of compar-
ison, we present these selected statistics under the original assumption that the
elasticity of output to public capital is 0.15. The general trend observed here is that
the magnitude of the results increases with the elasticity of output to public capital.
We find that real GDP is at least 0.01 percentage points higher in the first year and is
no less than 1 percentage point higher in the long run; the long term impact rises
with the elasticity of output to public capital.
The results follow the same general trend in terms of changes in poverty and
inequality, both in the first year and in the long run. Indeed, the change in the
poverty incidence increases as the elasticity of output to public capital increases
from 0.1 to 0.2. Note that the long-run impact on inequality is slightly lower when
testing values of 0.15 and 0.2 for the elasticity of output to public capital. The
sensitivity analyses does, however, confirm that the effects of higher public
investment on the economy and on poverty in the Philippines are quantitatively
robust to differing assumptions in relation to the elasticity of output to public
capital.
Summary and Insights
In the Philippines, public expenditures on physical infrastructure (particularly
transportation and utility infrastructures) and the level of public educational spend-
ing are both comparatively low. The current government has embarked on policies
that aim to further promote robust economic growth and eradicate poverty, in line
with commitments to meet its MDGs. One of the policies being pushed primarily
concerns infrastructure. This paper contributes to the policy debate on the role of
public infrastructure in economic growth and poverty reduction in the Philippines.
Our preliminary results reveal that the positive supply-side effects of higher public
investment expenditure manifest over time through higher capital accumulation and
related improvements in productivity.
In conclusion, the simulation results suggest that a higher public infrastructure
investment-to-GDP ratio not only brings about positive real GDP effects, but also
reduces poverty and inequality in the short and the long-run. The simulation results
follow a generally similar pattern, although the magnitude of the results is greater
under the international financing scenario; this is due to the absence of higher
The Growth and Distributive Impacts of Public Infrastructure Investments in... 81

Table 8 Sensitivity of results to changes in elasticity of output to public capital
Foreign financing Tax financing
Elasticity of output to public capital
0.1 0.15 0.2 0.1 0.15 0.2
1st year LR 1st year LR 1st year LR 1st year LR 1st year LR 1st year LR
Aggregate results
Real GDP 0.0 2.0 0.1 2.9 0.1 3.9 0.1 1.0 0.2 2.0 0.2 3.0
Wage rate 0.7 4.3 1.0 6.5 1.4 8.9 1.1 2.1 1.0 4.1 0.8 6.4
Total investment 6.1 7.4 6.4 8.2 6.8 9.1 5.0 6.3 5.2 7.1 5.5 8.1
Total consumption 1.5 1.9 2.2 2.7 2.8 3.5 1.5 2.3 1.5 2.3 1.5 2.3
Total exports 2.2 0.9 2.8 2.0 3.3 3.3 0.7 2.5 1.2 3.5 1.8 4.6
Total imports 2.1 2.7 2.6 3.5 3.0 4.3 0.7 1.9 1.0 2.5 1.4 3.3
Real exchange rate 1.2 0.4 1.6 0.5 1.9 0.5 0.3 0.4 0.6 0.4 0.9 0.4
Government revenue 8.1 9.7 8.4 10.9 8.8 12.2 6.6 8.4 6.9 9.6 7.2 10.8
Additional production tax rate (%) – – – – – – 25.8 22.9 27.0 22.4 28.2 21.8
Additional foreign grant (% of GDP) 1.1 1.0 1.1 0.9 1.2 0.9 – – – – – –
Poverty and inequality
Poverty headcount 0.2 1.2 0.7 1.6 0.9 2.6 0.5 0.4 0.6 1.1 0.7 1.8
Gini coefficient 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0
Source: Authors’ calculation based on simulation results
LR long run
82 E. Corong et al.

production taxes that slightly hinder the competitiveness of domestic producers. For
instance, public infrastructure spending financed by international borrowing at
concessional rates of 6 % caused output to expand in all sectors in the long-run,
whereas output does not expand in all sectors in the production tax financing
scenario. Moreover, the decline in poverty is greater in both the short and long
run when increased public infrastructure spending is financed through international
borrowing rather than by production taxes. In other words, the selection of a
financing scheme for public infrastructure investment matters. The narrow tax
base in the country is an important factor that allows our simulation results to
confirm that international borrowing is a better alternative to tax financing—i.e., in
line with the goal of improving the economy’s physical infrastructure to create job
opportunities, improve productivity and complement its social protection measures.
Against this backdrop, the Philippine government needs to become more proac-
tive in finding ways to finance increased public investment expenditures. One
important policy response is to fast track public-private partnerships (PPPs), to
provide financial and technical assistance for infrastructure projects and to increase
public education spending. Another is for the government to source additional
international financing at concessional rates, or to devise measures to broaden the
tax base to finance public investments.
Providing financing for PPP projects in the Philippines is indeed an important
issue. The legal and regulatory environment as well as the institutional framework
for PPPs has already been established in the country since the 1990s, with PPPs
offering nine contractual arrangements—including build-operate-transfer (BOT),
build-own-operate (BOO) and build-lease-transfer (BLT) projects, among others.
As of August 2012, there were 22 PPPs in the Philippines, including a school
infrastructure project (the PSIP); this project aims to build 9,300 public school
classrooms for the Philippine government’s Department of Education through a
BLT at a total cost of US$239 million. The Philippines has been attracting greater
foreign and domestic investments amid improving investor confidence and a liquid
financial system. In fact, certain financial institutions—particularly banks and
insurance companies—have signalled keen interest in providing financial support
for the country’s PPP programs, including the PSIP.
To encourage greater private sector participation, government guarantees are
being provided to cover the risks inherent to PPP projects. However, if not properly
priced and managed, these guarantees create contingent liabilities that could poten-
tially worsen the government’s fiscal risks. The Philippine government thus needs
to adopt a better framework for granting guarantees: it should include a more
accurate pricing mechanism—such as a guarantee fee that fully takes into account
the different risks of the project and market conditions—in order to ensure a more
efficient allocation of government resources (Llanto 2007). A potential area for
future research is to simulate the macroeconomic, sectoral, poverty and income
distribution impacts of public infrastructure spending in each key infrastructure
sector in the Philippines: education, power, telecommunications, transportation and
water. Such an initiative would help policymakers in the country as well as donor
The Growth and Distributive Impacts of Public Infrastructure Investments in... 83

agencies better allocate their resources to fund the development of each infrastruc-
ture sector, thereby promoting inclusive growth and alleviating poverty and income
inequality.
Acknowledgements PEP is financed by the Department for International Development (DFID)
of the United Kingdom (or UK Aid) and the Government of Canada through the International
Development Research Center (IDRC). This particular program of research received separate
funding from the Australian Agency for International Development (AusAID). We thank partic-
ipants in several PEP general meetings, the 2013 GTAP annual conference in Beijing and the 2013
GDN annual conference in Manila for helpful comments. We also salute the support and advice
provided by governmental and non-governmental counterparts. In particular, the authors are
grateful to John Cockburn, Yazid Dissou, Jean-Yves Duclos and Luca Tiberti for technical support
and guidance, as well as Tomas Africa and Randy Spence for valuable comments and suggestions.
Open Access This chapter is distributed under the terms of the Creative Commons Attribution
Noncommercial License, which permits any noncommercial use, distribution, and reproduction in
any medium, provided the original author(s) and source are credited.
Appendix 1: Figures
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Fig. 14 Power generation/
consumption in the
Philippines, 1991–2010
(gigawatt-hours) (Source of
basic data: Department of
Energy, Republic of the
Philippines)
84 E. Corong et al.

Appendix 2: Tables
Table 9 Poverty incidence for selected subpopulation characteristics based on 2006
Philippine FIES
Variable Estimate
Standard
Error Variable Estimate
Standard
Error
Type of household Class of worker (household head)
Single
household
0.277 0.003 Worked for private
Establishment
0.257 0.004
Extended
household
0.249 0.004 Work for the government 0.194 0.008
Self-employed without
any employee
0.347 0.004
Employer in own family-
operated
0.237 0.008
Headship Worked with pay in own
family-op
0.173 0.051
Male headed 0.286 0.002 Worked without pay in
own family
0.165 0.026
Female headed 0.202 0.005
Marital status of household head Number of members Employed
Single 0.171 0.010 1 0.284 0.004
Married 0.283 0.003 2 0.267 0.004
Widowed 0.233 0.006 3 0.259 0.007
Divorced/
separated
0.199 0.014 4 0.273 0.011
Unknown 0.463 0.250 5 0.297 0.020
Job status of the household head 6 0.306 0.041
with
job/business
0.182 0.005 7 0.273 0.77
no job/business 0.287 0.002 8 0.161 0.107
Source: Authors’ computations using FIES
0
10
20
30
40
50
60
70
80
90
100
1990 1995 2000 2005 2008
%
National
Urban
Rural
Fig. 15 Proportion of
population with access to
improved water source in
the Philippines: 1990, 1995,
2000, 2005, 2008 (Source of
basic data: The World
Bank’s World Development
Indicators Database)
The Growth and Distributive Impacts of Public Infrastructure Investments in... 85

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