Poverty Incidence,
Infrastructure Development and
Human Capital: an Empirical Study
Of Provinces in the Philippines
Edgardo Manuel Miguel M. Jopson
De La Salle University – Manila
Email: manuel.jopson@gmail.com / edgardo_jopson@dlsu.ph
Cellular number: (+63) 916 464 4412
Abstract:
Current literature in economic development emphasizes the impact of investing in infrastructure and human
capital. In the Philippines with its provinces’ different economic situations, a generalized policy recommendation
can yield problematic results. Using data gathered from the National Statistics Office (NSO), the Department of
Public Works and Highways (DPWH), as well as the National Statistical Coordination Board (NSCB) from 2009,
and employing Ordinary Least Squares estimation procedure, the study aims to present the possible relationships
between poverty and specific factors of economic development, such as the valuation of buildings, roads,
educational attainment and population growth. This study may be of contribution by providing a clearer picture on
the rural economic situation of the Philippines and aid policymakers in decision making.
JEL Classification: O15, O18, O40
Keywords: Poverty, infrastructure, human capital, rural economic development, Philippines
1.
Introduction
poverty, the Philippines must address this problem in
the most efficient way that it can.
1.1 Background of the Study
The problem of poverty is far from being
eradicated in most developing countries. In the
Philippines alone in 2012, there is an estimated
22.9% of the population of the country is considered
poor, with the Autonomous Region of Muslim
Mindanao having the highest level of poverty
incidence at 46.9% (National Statistical Coordination
Board, 2013). To meet the Millennium Development
Goal of the United Nations of eradicating extreme
In a study made by the Asian Development Bank
in 2007 the critical constraints to poverty reduction
are access to economic opportunities (lack and slow
growth of productive employment and opportunities),
human
development
(access
to
primary
and
secondary education and health service), access to
basic social services and productive assets (basic
infrastructure, poor’s limited access to financing and
land) and the lack on the coverage of social safety
nets (ADB, 2007: 41-48). These problems are indeed
faced by mostly the Filipino lower class, and until
countries in terms of its Human Development Index
today still are so. By studying the economic situation
or HDI at 0.627 (UNDP, 2011). Functional literacy
of the Philippines in a broader perspective that
rate is still at 84.1%, with 81.9% for males and
encompasses not just physical and material outputs
86.3% for females in 2003. In the year 2006 life
but includes health, wellness, education, political
expectancy for females and males were 72.5% and
situations,
the
67.8%. GDP per capita for both current and 1985
and
prices are at 68,989 and 14,653 respectively. What do
investments - it would be possible to find appropriate
these numbers represent? Functional literacy rate, life
solutions to the problem of poverty.
expectancy, primary and secondary enrolment rate
among
infrastructure
others
in
development,
relation
to
employment,
and GDP per capita are the components for the
According to the Asian Development Bank, the
main causes of poverty in the country are the
following:
a.
b.
Low to moderate economic growth for
possible to infer that the Philippines is in need of
the past 40 years;
increasing its HDI.
Low
growth
elasticity
of
poverty
sector;
regard to the initial construction, but provides the
community an overall positive benefit from it;
e.
High inflation during crisis periods;
f.
High levels of population growth;
g.
High and persistent levels of inequality
(income and assets) which dampen the
impacts
of
economic
expansion; and
h.
indicators for growth in an economy. Building
infrastructure not only provides employment with
Failure to fully develop the agricultural
positive
Infrastructure development is an integral part of
economic development, as it is one of the key
Weakness in employment generated and
the quality of jobs generated;
d.
measuring the quality of living of a group of people,
and from merely inspecting these numbers, it is
reduction;
c.
computation of HDI, which is used as a yardstick for
Recurrent shocks and exposure to risks
as economic crisis, conflicts, natural
disasters, and “environmental poverty”
(ADB, 2009: 2)
creating roads and bridges make transportation of
goods and services easier, more efficient and less
costly. Buildings create space for commerce,
government and housing, water pipelines and
sewerage systems provide households, businesses
and government buildings clean water and hygienic
disposal of human waste and dirt, parks and other
recreational areas provide additional income for the
economy from both foreign and local tourism leading
to an increase of commercial establishments, an
The Philippines is currently a developing
increase of employment and an overall increase of
economy that is beginning to make its presence felt in
the economy’s GDP. Infrastructures give way to a
the international market, with a quarterly gross
multitude of human activities just waiting to be
domestic product growth for the year 2012 rate as
established.
follows: 6.3%, 6.0%, 7.2% and 6.8%. However, in
2011 the Philippines still ranks 112 out of 187
2
Capital deepening is a vital concept in capital
Although Catanduanes is an internationally known
theory. Given a steady state Economy with one kind
surfing spot, it still draws significantly less tourists
of capital good, capital deepening is defined as the
than Camarines Sur, due to it being a kept secret
case wherein the per worker capital good stock is a
among pro surfers (Puraran Surf Beach Resort,
decreasing function of its own rate of interest . In
2013). According to the Provincial Framework and
Neo-classical macroeconomics which focuses on
Physical Development Plan (PDPF) of Catanduanes,
capital accumulation and its links to saving decisions,
although the growth rate of the travellers to
′ (𝑘)
= 𝑝𝑟𝑖𝑐𝑒 and the rate of
Catanduanes has shot up to 198% from 2008 to 2009,
where r is the principal rate
Camarines Sur has still hauled in 38,385 foreign
of return and 𝛿 is the rate of depreciation, lead to a
tourists and 147,758 domestic tourists- significantly
per capital return that is higher than before (Hirota,
less than Catanduanes’ 8,984 foreign tourists and
1979), which can be done by providing more
36,722 domestic tourists; hence indicated in the
employment in the economy as well as increasing its
PDPF are policies to increase their revenues in
capital (McEachern, 2012). Take for example the
tourism by investing in eco-tourism. In comparison to
province of Catanduanes, barely featured in mass
Camarines Sur’s performance, Catanduanes has
media, literature and politics, it is the easternmost
shown improvement as a rural province which can be
province in the Bicol region. However according to
seen from its academic performance as well as its
the NSCB Catanduanes is the top Bicol province in
significant spike in tourism.
the marginal condition 𝑓
return ( 𝑟 + 𝛿 = 𝑓
′ (𝑘))
HDI, ranking 21st among the provinces of the country
(National Statistical Coordination Board, 2013), and
1.2 Statement of the Problem
when we consider its human capital we can find some
In the Philippines where the population of the
interesting data. In 2011’s Civil Engineering Board
poor and oppressed greatly outnumber the elite and
Exam, the top 1, 2 and 3 are from the Catanduanes
powerful, it has become more difficult to determine
State Colleges, and obtained a passing rate of
key indicators in terms of the quality of life of every
69.84%- well above the national mean of 34.28% and
individual, even more difficult to make sound
has
board
decisions when it comes to finding solutions to
examination top passers since (GSRubio/PR and
alleviate poverty by maximizing the limited resources
Information Services, 2013); for the Board Exam for
the
Nurses has had an 85% passing rate in 2009, and in
significance of capital deepening and infrastructure
2007 ranked 45 of all provinces in the Philippines;
development with respect to poverty incidence,
and in the Licensure Examination for Teachers in the
which pertains to policies that may be made in terms
elementary level in 2007 has ranked 40 of all
of allocation of resources to particular sectors of the
provinces with a passing rate of 42% (National
economy that will be at most opportunity cost-
Statistical Coordination Board, 2007). However the
minimizing and maximizing its effect to benefit
province’s
society.
consistently
had
income
civil
engineering
generating
activities
in
country
has.
This
study
determines
the
comparison to other provinces in the same region
such as Camarines Sur, such as tourism, is less.
1.3 Objectives
3
attributed to the United Nation’s Millennium
This research paper intends to:
Development
1.
Present an econometric model that would
allow the proponent to determine the
relationship of poverty via the poverty
Goals
in
developing
a
global
partnership for development and for eradicating
extreme poverty, which is for the improvement of the
quality of living of the people of the economy.
incidence with infrastructure development
and capital deepening and make relevant and
1.5 Scope and Limitations
statistically sound conclusions;
2.
policy via an increase in government
spending, with regards to creation of new
roads,
developing
providing
human
scholarships
(additional
school
capital
and
years),
e.g.
training
and
its
significance to the well-being of society that
will be determined by the significance of the
relationship of poverty with infrastructure
development
and
human
capital
Least Squares estimation method1 and is limited to a
cross-section analysis, which might not perfectly
capture reality, however does not mean that it should
be considered insignificant altogether. The data used
in this research will be drawn from the databases of
the National Statistical Coordination Board, NSO,
and
DPWH.
This
study
will
also
include
infrastructure development and capital Deepening
determinants such as the value of new constructions,
population and number of households with access to
development;
3.
The method used in this study uses the Ordinary
Describe the effect of an expansionary fiscal
Provide a supplementary aide to policy
makers and make sound recommendations
water, as well as the average years of schooling.
2. Review of Related Literature
from the regression analysis generated from
2.1 Poverty Incidence
the econometric method.
The World Bank uses three key factors to
1.4 Significance of the Study
measure poverty:
The study attempts to determine whether or not
there
is
a
significant
link
on
a.
infrastructure
One has to define the relevant welfare
measure.
development and capital deepening to the poverty
b.
incidence of the country. It can serve as a
One has to select a poverty line – that is a
contribution to the field of rural development in the
threshold below which a given household or
Philippines in the continuous efforts of the county to
individual will be classified as poor.
c.
attain its macroeconomic goals to sustainable
One has to select a poverty indicator– which
economic growth and development. It may also aide
is used for reporting for the population as a
policymakers in the rural areas in the country in
whole or for a population sub-group only.
creating sound economic decisions and policies as
well as future projects that may benefit their
respective communities and how it may affect the
well-being of every individual. The paper can also be
1
According to the Gauss-Markov Theorem, holding all
assumptions true, OLS is BLUE (Gujarati & Porter, 2009).
4
For welfare measure, the World Bank does not
solely depend on monetary measures on welfare,
than in monetary units, which is more abstract
(World Bank, 2011).
rather it considers the level of consumption in a
higher regard, since consumption is a better outcome
indicator, better measured, and better reflects a
household’s ability to meet its basic needs. Why is it
a better indicator? Actual consumption is more
closely related to a person’s well-being in the sense
of having enough to meet current basic needs.
Income is only one of the components which will
allow
consumption
of
goods
(others
include
In terms of the non-monetary part, certain facets
of a human being’s wellbeing is being analysed,
namely health and nutrition, education, composite
indices of wealth and other subjective perceptions. It
is based on the judgement in terms of each of the
component’s
“poverty
line”,
for
example,
in
education; the poverty line is at some level of
illiteracy (World Bank, 2011).
questions of access, availability, etc.). In terms of its
In terms of the problem of choosing a poverty
ability to be measured, in poor agrarian and urban
line, there are two main ways: by absolute poverty
economies with many informal settlers, income flows
lines, or relative poverty lines.
may change in an unpredictable way during the year.
For farmers, one added difficulty in estimating
1) Relative poverty lines: These are defined with
income includes excluding the inputs purchased for
respect to the overall distribution of income or
agricultural production from the farmer’s revenues.
consumption in a given country; an example
Finally, large shares of income are not monetized if
would be to set the poverty line at 50 percent of
households consume their own production or
the country’s mean income or consumption.
exchange it for some other goods, and it might be
2) Absolute poverty lines: These are set in some
difficult to price these. Estimating consumption may
absolute standard of what households should be
be difficult for the institutions that measure
able to have in order to meet their basic needs.
consumption of these individuals, but it may be more
For monetary measures, these absolute poverty
substantial if the consumption module in the
lines are often based on estimates of the cost of
household survey has been better designed. And
basic food needs, to which a provision is added
finally, why does it better reflect the household’s
for non-food needs. There are two methods:
ability
a)
to
meet
basic
needs?
Consumption
The food-energy intake method: defines
expenditures reflect not only the goods and services
the poverty line by looking for the
that a household can command based on its current
consumption expenditures or income level at
income, but also whether that household can access
which a person’s typical food energy intake
credit markets or household savings at times when
is enough to meet a predetermined food
current income is low or even negative, due perhaps
energy requirement. If applied to different
to seasonal variation or harvest failure. Basically
regions or provinces within the same
consumption for the people that these institutions will
country, the essential food consumption
conduct studies upon can grasp the idea of
pattern
consumption in a much more concrete way rather
consuming the needed nutrient amounts will
of
the
population
group
just
5
vary. This technique can result to variances
does not reach the defined threshold (e.g. percentage
in poverty lines in excess of the cost-of-
of the population with less than 3 years of education)
living differential facing the poor.
(World Bank, 2011).
b) The cost of basic needs method: values an
explicit bundle of foods typically consumed
by the poor at domestic prices. To this, a
specific allowance for non-food goods,
consistent with the expenditures of the poor,
is added. However defined, poverty lines
will always have a high arbitrary element;
an example would be the calorie threshold
underlying both methods might be assumed
to vary with age. (World Bank, 2011)
In choosing a poverty indicator, one must take
into account that the poverty measure itself is a
2.2 Human Development Index (HDI)
Conceptualized by the UNDP in 1990, the
Human Development Index (HDI) attempts to
quantify human development. As it recognizes the
complications of human development, the HDI may
not be that comprehensive to be able to capture all
the facets of the development of the human being.
However the UNDP points out that this simple
composite method can already draw attention to the
issues of human development quite effectively
(National Statistical Coodrination Board, 2013).
statistical function which interprets the comparison of
The computation for HDI is done in 7 steps. The
the indicator of well-being and the poverty line which
first step is to identify the indicators to be used for
is made for each household into one aggregate
HDI, namely Health, which is measured by life
number for the population as a whole or a population
expectancy;
sub-group. Many alternative measures exist but the
literacy rate as well as combined primary, secondary
following three measures are most commonly used:
and tertiary enrolment rate; and income, measured by
the incidence of poverty, which is also known as the
real income per capita. Next is to set the appropriate
headcount index, the depth of poverty, known as well
maximum and minimum value of each of the
as the poverty gap, and poverty severity, or the
indicators above. Then we compute for the index for
square of the poverty gap. However this research will
each indicator as follows:
education
measured
by
functional
only be using the headcount index.
The headcount index is the portion of the
𝐴𝑐𝑡𝑢𝑎𝑙 𝑉𝑎𝑙𝑢𝑒𝑋 − 𝑀𝑖𝑛 𝑉𝑎𝑙𝑢𝑒𝑋
𝑀𝑎𝑥 𝑉𝑎𝑙𝑢𝑒𝑋 − 𝑀𝑖𝑛 𝑉𝑎𝑙𝑢𝑒𝑋
population whose income or consumption is below
After which we can compute for the average
the poverty line, i.e. the share of the population that
functional literacy rate and enrolment indices to
cannot afford to buy a basic basket of goods. An
generate the education index by getting:
analyst using several poverty lines, which we can say
one for poverty and one for extreme poverty, can
estimate the incidence of both poverty and extreme
poverty, due to the nature of the measurement.
𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 𝑖𝑛𝑑𝑒𝑥 = 1/2(𝐹𝑢𝑛𝑐𝑡𝑖𝑜𝑛𝑎𝑙 𝑙𝑖𝑡𝑒𝑟𝑐𝑦 𝑟𝑎𝑡𝑒
+ 𝐸𝑛𝑟𝑜𝑙𝑚𝑒𝑛𝑡 𝐼𝑛𝑑𝑖𝑐𝑒𝑠)
Then we calculate for the income index:
Similarly for non-monetary indicators, poverty
incidence measures the share of the population which
𝑝𝑟𝑜𝑣𝑖𝑛𝑐𝑒 ′ 𝑠 𝑟𝑒𝑎𝑙 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎 𝑖𝑛𝑐𝑜𝑚𝑒 − min 𝑖𝑛𝑐𝑜𝑚𝑒 𝑙𝑒𝑣𝑒𝑙
max 𝑖𝑛𝑐𝑜𝑚𝑒 𝑙𝑒𝑣𝑒𝑙 − min 𝑖𝑛𝑐𝑜𝑚𝑒 𝑙𝑒𝑣𝑒𝑙
6
After which we obtain the second income index,
amount of money invested in constructing a building
income index II by converting a province’s price per
is a better and more meaningful determinant for
capita income into purchasing power parity then
determining the quality of infrastructure that is being
compute for income index as follows
constructed, rather than just counting the frequency
that a building is being made in the area. In a general
log 𝑦 − log 100
𝑖𝑛𝑐𝑜𝑚𝑒 𝑖𝑛𝑑𝑒𝑥 𝐼𝐼 =
log 40 000 − log 100
sense, the more one invests in a certain province
there would be a greater incentive for getting a return
And finally we assign the weights to the various
on investment. Since the infrastructure is created for
components to compute for HDI of the given
the benefit of the individuals interested in using it,
economy. (Human Development Network, 2008). For
the value of the building would be a better indicator.
the purpose of this research, the proponent has
As for the proponent’s reason for choosing number of
chosen to estimate the effect of an increase in HDI to
good condition roads as another measure for
poverty through average school years in order to
infrastructure
better pinpoint its effect since average school years is
hypothesizes that having better roads means that
a function of the index, and better captures the actual
there is a more efficient transportation in the area,
conditions of human development.
and when there is a more efficient way of moving
development,
the
proponent
from one place to another within the province, it
2.3 Infrastructure Development
would be easier to make transactions and will be
Infrastructure, by definition, is the system of
beneficial to the community with regards to
public works of a country, state, or region as well
providing general access to their communities. Hence
as the
good quality roads are considered by the proponent
resources
(as
personnel,
buildings,
or
equipment) required for an activity (Merriam-
as an ideal measure for infrastructure development.
Webster, 2013). Infrastructure development is the
economy’s investment in terms of its infrastructure,
2.4 Human Capital
may it be of the construction of roads, highways,
Human capital formation is truly an integral part
buildings, bridges and any relatively permanent and
of measuring the development of a certain economy.
fixed structure development that will benefit the
It is possible to have great infrastructure development
economy in terms of its efficiency to transport goods
but without the optimal capital depth, one cannot
and services, its ability to house the people, business
sustain its economic existence. Increase in the quality
and government offices, for an extended duration of
of labour, investment in capital, increase in current
time.
capital 𝐾𝑡 are but examples of capital deepening.
For this research the proponent has chosen value
Given a steady state Economy with one kind of
of buildings and number of good condition roads (in
capital good, capital deepening is defined as the case
kilometres)
infrastructure
wherein the per worker capital good stock is a
development. The proponent has chosen the value of
decreasing function of its own rate of interest . In
buildings
infrastructure
Neo-classical macroeconomics which focuses on
development because the proponent believes that the
capital accumulation and its links to saving decisions,
as
as
an
an
indicator
indicator
for
of
7
the marginal condition 𝑓 ′ (𝑘) = 𝑝𝑟𝑖𝑐𝑒 and the rate of
return ( 𝑟 + 𝛿 = 𝑓
′ (𝑘))
iv. The marginal propensity to consume +
the marginal propensity to save =1;
where r is the principal rate
of return and 𝛿 is the rate of depreciation, lead to a
v.
Law of motion of population 𝑃 =
𝐶𝑜 𝑒 𝑛𝑡 ;
per capital return that is higher than before (Hirota,
𝑑𝑘
vi. Law of motion of capital 𝐾̇ = =
1979),
𝑑𝑡
𝑠𝑌𝑡 − 𝛿𝐾𝑡 ;
The basis of capital deepening is rooted in the
vii. Technology is free;
Harrod-Neutral production function, which is in its
basic form 𝑌 = 𝐹(𝐴𝐿, 𝐾), where Y is income, A
viii. Continuous t in e;
defined as the technological shifter, L defined as
ix. All L are fully employed;
labour and K is defined as capital (McEachern,
2012).
x. minimal government role.
For the purpose of this research, the proponent is
limiting the components of capital deepening into
The Solow Growth model can be expressed as
follows;
three components: access to clean water, growth rate
𝑠𝑦 = (𝛿 + 𝑛 + 𝑔)𝑘
of population, and the human development index, in
which the proponent will use the mean years in
school as a proxy to the human development index
since literacy rate is an integral part of this index.
Where sy is the proportion of income saved, 𝛿 as
depreciation rate of capital, n as the growth rate of
population, and g as the growth rate of technology.
3. Theoretical Framework
This
research
used
The above equation is also known as the breakeven
a
neoclassical
macroeconomic growth theory, and will be creating a
model that fits the assumptions of a Solow-Swan
Growth model. In a macro economy, there are three
indicators of growth and development: increase in
infrastructure; technological development and capital
deepening.
According to the Solow-Swan Growth model,
holding its assumptions constant;
investment; the balanced growth path; the steady
state in the macro economy (McEachern, 2012).
Clearly in this model we can see that capital is an
integral part of 3 variables: Depreciation of capital,
e.g. infrastructure, population, and technology.
Therefore capital can improve by affecting one of
these variables.
Now, since income is inversely related with
poverty, whereas an increase in income per capita in
an economy decreases the number of people living
i.
Constant returns to scale;
below the poverty line, with of course assuming that
ii.
Inada Condition;
the increase in income is distributed among the
iii. Population grows at a constant rate 𝑛,
people of the economy.
Since income is not our
capital depreciates at a constant rate 𝛿,
immediate concern in this study and our dependent
and technology grows at a constant
variable that captures the effects of economic growth
rate 𝑔;
8
is a poverty incidence, then the Solow growth model
initial semi log model specification, shown by the
is an essential primary tool to capture development.
following:
As for our capturing variables within the Solow
𝑃𝐼𝑖 = 𝛽1 𝑖 + 𝛽2 ln 𝐵𝑙𝑑𝑔𝑖 + 𝛽3 𝑅𝑜𝑎𝑑𝑖 + 𝛽4 𝐻2 𝑂𝑖
growth, we use value of building constructions as
+ 𝛽5 𝑛𝑖 + 𝛽6 𝐸𝑑𝑖 + 𝜀𝑖
well as road development to capture infrastructure
development, and its contribution to the model is on
the depreciation rate of capital. As there is an
increase in infrastructure expenditure, then capital
4.2 A Priori Expectations
The following variables with their A Priori
expectations are presented in the following table:
infrastructure will depreciate less and less since
allocation of resources to infrastructure will decrease
𝑃𝐼
Poverty incidence- the
percentage of the population
wear-and-tear and will be more updated and efficient
that is under the poverty line
(Estache, 2003) (Calderón & Servén, 2003).
per
of
the
Philippines. Source: NSCB
As for human development, population growth
rate is affected by many factors, which include the
province
ln 𝐵𝑙𝑑𝑔
Value
of
building
health and wellbeing of the people. As we have
constructions for 2011- the
reviewed in the literature, a human development
amount (in Php) used for the
improvement will reduce poverty by increasing
development of buildings in
income per capita, as individuals who are more
the
efficient tend to work better and provide better
Philippines. The proponent
opportunities for the person to grow, which in turn
opted
to
set
of
in
the
natural
logarithmic form to observe
improves the economy. We use years of schooling as
percentage changes. Expected
a capturing variable of human development, as
to have a negative effect on
education is one of the most appalling reasons in the
poverty incidence. Source:
literature that promote growth and development.
4. Empirical Analysis
provinces
NSO Quick Stats
𝑅𝑜𝑎𝑑
Distance of good roads (in
km)- distance of road in
4.1 Model Specification
kilometres considered in good
condition by the Department
For this research the regression model to be
of
formed is based on economic theories, research
Public
Works
Highways
materials gathered as well as the proponent’s
and
(DPWH).
Expected to have a negative
intuition. Using the classical linear regression model
effect on poverty incidence.
through the ordinary least squares estimation, this
Source: DPWH
will establish the empirical portion of the theoretical
framework which will determine the empirical
validity of the research. This cross-section study
𝐻2 𝑂
Percentage
of
households
with access to clean water.
Expected to have a negative
across the 78 provinces of the Philippines with the
9
effect on Poverty Incidence.
5.2 Regression of the Original Model
Source: NSCB
𝑃𝐼𝑖 = 𝛽1 𝑖 + 𝛽2 ln 𝐵𝑙𝑑𝑔𝑖 + 𝛽3 𝑅𝑜𝑎𝑑𝑖 + 𝛽4 𝐻2 𝑂𝑖
𝑛
Population
growth
rate.
+ 𝛽5 𝑛𝑖 + 𝛽6 𝐸𝑑𝑖 + 𝜀𝑖
Expected to have a negative
effect on poverty incidence,
to be interpreted as additional
human capital. Source: NSO
Quick Stats
𝐸𝑑
Presented
above
is
the
original
model
constructed. It consists of poverty incidence as the
dependent variable to the value of buildings
constructed, distance of DPWH certified good roads,
Mean years of schooling (set
percentage of households with access to potable
as
3
water, population growth rate and the average years
HDI).
in school which serves as a proxy to the HDI which is
Expected to have a negative
a function of literacy index, life expectancy index and
a
proxy
components
for
of
the
effect on poverty incidence.
the income index (see review of related literature).
Source: NSCB
4.3 Data Gathered
The data that the proponent will use is collected
from the National Statistical Coordination Board
The proponent used an Ordinary Least Square
(OLS) estimation method having all the Classical
Regression
National Statistics Office, as well as the Department
of Public Works and Highways (DPWH) for the data
dates are not exactly the same, this study is more
met,
Zero Mean Assumption i.e. 𝐸(𝑢𝑖 ) = 𝜇 = 0;
b.
Homoscedasticity i.e. 𝑣𝑎𝑟(𝑢𝑖 ) = 𝜎 2 ;
c.
No perfect Multicollinearity among all
independent variables;
d.
Non- autocorrelation;
e.
Zero
covariance
between
independent
variables and the stochastic disturbance
interested in averages through time, and since time is
term;
not of the essence of this study, a simple cross section
is used.
assumptions
a.
regarding the distance of DPWH at par with the
current standard of the department. Although the
(CLM)
enumerated as follows:
(NSCB), the National Statistics Office (NSO), and
from the provinces Quick Stats, also taken from the
Model
f.
Number of observations should be greater
than number of parameters to be estimated;
5. Estimation and Inference
g.
Sufficient variation in the values of the
independent variables (Gujarati & Porter,
5.1 Summary of the Data
From the data gathered, 78 (with the exception
for the information gathered on DPWH which seems
to lack information on five provinces, namely
Basilan, Lanao del Sur, Sulu, Tawi-Tawi, and
Maguindanao) of the provinces have provided varied
2009).
With the CLM assumptions taken into account
and met, then it according to Gauss and Markov, the
OLS estimate is the best linear unbiased estimator
(Carter Hill, Griffiths, & Lim, 2011). However for
this research, the proponent will only test for the
statistics to the information needed in this research.
10
three critical assumptions, namely Multicollinearity,
else we accept the null hypothesis that it does not
Autocorrelation, and Homoscedasticity.
affect the dependent variable. In this case population
growth rate and Education prove to be well in the
Running the regression analysis2, the estimated
coefficient values of the model are presented in the
generated model:
𝑃𝐼𝑖
range of the acceptance of the alternative- which is to
say that these variables do have some correlation
regarding the poverty incidence of the Philippines.
Synthesizing these results, we can infer that three
= 107.3815 − 0.0697046 𝑙𝑛𝐵𝑙𝑑𝑔𝑖
out of the five variables that have been tested with
− .0361233 Road𝑖 −1.378105 𝐻2 𝑂𝑖 − 3.702187𝑛𝑖
respect to the poverty incidence of the Philippines
− 7.765788𝐸𝑑𝑖 + 𝜀𝑖
has some significant impact, namely population,
5.3 Significant Statistical Findings on the Original
distance of good roads and education. A 1 unit
Model
increase in the population growth rate of the province
corresponds to a 3.702187 % decrease in poverty
The interpretation of the results generated has
incidence – it means that as we increase population
provided some interesting and meaningful results. In
poverty incidence decreases. Probably because more
determining the validity of the model, one has to look
population corresponds to a larger work force that
at the R-squared and the probability values of the
increases the production in a certain province hence
independent variables. First of all, we have to
increasing the income per individual and eventually
consider the fact that all the a priori expectations for
reducing the number of the individuals succumb to
every explanatory variable in the model have been
poverty, however intuitively speaking this can only
met- which proves that intuitively speaking the model
be possible when this increase in population is
is correct.
utilized in the economy i.e. provision of primary and
Regarding the coefficient of the R-squared of the
model, we can see that it is at .4606; meaning to say
that 46.06% of the model explains the real world. We
can see that this coefficient is adequate- lower than
50%- however cannot be discounted as insignificant.
Considering that the data used is cross-section which
usually has a low R-squared, the Goodness-of-Fit of
the data indicated by the R-squared proves that it is a
relatively good model.
secondary education, jobs, etc. that reduces poverty.
Evident in the analysis, in the generated model there
is a significant finding where there is a 7.765788%
decrease in poverty for every 1% increase in the
average years in school (Education)- this may imply
that as the education does have a very significant
impact on the poverty incidence of the Philippines.
For Roads, there is a .0361233% decrease in poverty
incidence for every 1 kilometre increase in the
distance of roads deemed by DPWH of good
Now giving a thought on the validity of the
independent variables by looking at the probability
condition, which may signify that there is a potential
decrease in poverty when roads are constructed.
values, we set the critical region at p-value < 0.05
5.4 Corrective Measures and Corrected Model
2
Results in Appendix A
11
Since there is no problem in the model regarding
the two determinants of growth and development,
multicollinearity and heteroscedasticity, it is safe to
thus verifying the macroeconomic theories behind the
say that the model generated is indeed a good
model. In terms of the a priori expectations, it is safe
approximate of what occurs in the real world3.
to say that the model fits these expectations, since in
However it is in the best interest of the researcher to
the empirical test the relationship of poverty
find a better alternative model that has more
incidence to value of buildings, good roads, access to
significant
safe water, population growth, and education is
variables
to
better
explain
the
phenomenon of poverty.
negative.
Since the value of buildings and access to safe
We can observe that there is a . 697046 %
water are clearly insignificant due to the results
decrease in the poverty incidence level when the
generated, the best action to take is to find a way to
value of building construction increases by 1%. This
improve the model in such a way that more of these
corresponds to a significant change in poverty
intuitively sound components of poverty can result to
incidence and has indeed met with the a priori which
significant figures, statistically speaking. However
indicates that infrastructure development has a
since there is no reason to do a corrected model, then
significant impact on alleviating poverty. However
the proponent has no choice but to accept the model
due to the insignificance of the p-value, it must be
as it is.
considered insignificant in this study, however
further research may be conducted to prove
𝑃𝐼𝑖 = 𝛽1 𝑖 + 𝛽2 ln 𝐵𝑙𝑑𝑔𝑖 + 𝛽3 𝑅𝑜𝑎𝑑𝑖 + 𝛽4 𝐻2 𝑂𝑖
+ 𝛽5 𝑛𝑖 + 𝛽6 𝐸𝑑𝑖 + 𝜀𝑖
otherwise.4 With regards to access to safe water,
which also has a p-value greater than the 5% or even
10% confidence interval, this research considers the
impact of a percentage increase in the households
With the following estimates:
with access to safe water as an insignificant factor
with respect to poverty. Similar to the result from
𝑃𝐼𝑖
= 107.3815 − 0.0697046 𝑙𝑛𝐵𝑙𝑑𝑔𝑖
− .0361233 Road𝑖 −1.378105 𝐻2 𝑂𝑖 − 3.702187𝑛𝑖
value of building, further research may be conducted
to prove otherwise.
− 7.765788𝐸𝑑𝑖 + 𝜀𝑖
At the 90% confidence interval, since the p-value
is at 0.0595, there is enough ground to deem good
6. Conclusion
quality roads as a significant factor in reducing
I have presented a model that illustrates the
poverty, and due to the a priori expectations to the
possible effects of infrastructure development and
effect of infrastructure development on poverty the
capital deepening with respect to the poverty
proponent will use it as a gauge to measure the
incidence of the Philippines. In both the original and
strength of it as a determinant of growth and
corrected models, they have shown that there is
development. The generated model suggests that for
indeed a negative relationship between poverty and
3
See Appendix B.
4
In the first run of the regression that the proponent has
conducted, it has given a significant result for the increase
in the value of buildings (See Appendix C)
12
every 1 kilometre increase in the length of road
thus aides in the alleviation of poverty (McEachern,
considered by the DPWH at par with their standards,
2012).
then there will be a . 0361233% decrease in poverty
incidence, a slight but possibly present change as
additional good roads not only provide employment
in the construction of it, but also provide accessibility
in the province, gaining the confidence of investors
due to the accessibility, providing more employment
Regarding the coefficient of the R-squared of the
generated model, we can see that it is at .4606;
meaning to say that 46.06% of the model explains the
real world. We can see that this coefficient is
adequate- lower than 50%- however cannot be
discounted as insignificant. Considering that the data
and reducing poverty.
used is cross-section which usually has a low RAside from the value of building constructions,
squared, the Goodness-of-Fit of the data indicated by
the model suggests that there is also a 3.702187%
the R-squared proves that it is a relatively good
decrease in poverty incidence for every 1 unit
model.
increase
in
the
growth
rate
of
population.
Considering that this value is significant, there is a
corresponding decrease in poverty for an increase in
The model’s results, which its findings can be
summarized as follows:
population can be interpreted in many ways- that
a.
All a-priori expectations are met;
population should not be considered a problem given
b.
there may be a 3.70% decrease in
that this human resource is utilized by provision of
poverty incidence for every 1%
education and employment, or that population is not a
increase in the population (1 unit
problem at all and in fact we must promote an
increase
increase in population, or that this research is merely
population);
stating out the fact that the overpopulation issue does
c.
in
the
growth
of
there could be a 7.77% decrease in
not hold as much importance when it comes to
poverty incidence for every 1 unit
alleviating poverty- with this the research is limited
increase education;
to.
d.
poverty incidence for every 1
The impact of education to poverty is indeed
kilometre
econometrically significant. Having a probability
e.
the
macroeconomic
theory
in
education, that by Psacharopolous the more time
road
The model may explains 46.06% of
the real world;
increase in the average years of schooling. The
demonstrates
in
their standards;
0.000) it is indeed possible to infer that there could be
results generated by the econometric model clearly
increase
considered by DPWH at par with
value to a point that it is negligible (at rounded off
a 7.765788 % decrease in poverty for every unit
there could be a . 04% reduction in
f.
No problems with the critical
assumptions of the classical linear
regression model.
invested in education, the higher the rate of return,
13
The proponent has generated estimates of the
business perspective, the goods produced will be
more efficiently transported from the farm, to the
econometric model:
local and urban marketplace and eventually to the
𝑃𝐼𝑖 = 𝛽1 𝑖 + 𝛽2 ln 𝐵𝑙𝑑𝑔𝑖 + 𝛽3 𝑅𝑜𝑎𝑑𝑖 + 𝛽4 𝐻2 𝑂𝑖
+ 𝛽5 𝑛𝑖 + 𝛽6 𝐸𝑑𝑖 + 𝜀𝑖
consumers. An increase in the value of building
constructions, though deemed insignificant in this
study, also proves to yield a negative impact on
Presented as follows:
poverty, which gives enough evidence, though
𝑃𝐼𝑖
statistically insignificant for this research, to claim
= 107.3815 − 0.0697046 𝑙𝑛𝐵𝑙𝑑𝑔𝑖
that
− .0361233 Road𝑖 −1.378105 𝐻2 𝑂𝑖 − 3.702187𝑛𝑖
constructions may lessen the population of the
− 7.765788𝐸𝑑𝑖 + 𝜀𝑖
impoverished by providing employment in the
indeed
a
higher
spending
on
building
construction of the buildings plus additional space for
This study suggests that the best way to alleviate
poverty for the Philippine economy is for the
government to increase its allocation of budget on
educational programs, such as increasing the number
of public schools up to the secondary level, increase
the generation of scholarship programs in order to
further increase the number of graduates inclusive of
but not limited to academic scholarships, merit
scholarships and pay-it-forward programs that will
increase the literacy rate of the county. An increase in
businesses and local government units to provide
additional employment in the community. In order to
fully maximize these conditions, the government
must fully utilize its resources in order to maximize
the results of its projects to address the problem of
poverty in the country; that is to say that good
governance and clean auditing of government funds
must be integral in order for this model’s generated
result to be of any use, since in this study we assume
that good governance is a constant.
population will actually lessen poverty if and only if
the additional increase in population will correspond
The model generated could not immediately be
to an increase in education and job opportunities in
judged as a failure due to the insignificance of
order to fully utilize the additional human resource.
building construction and access to safe water,
Given these information, the proponent recommends
although it is in the best interest of the proponent to
that the government should focus in projects
generate a greater amount of significant results rather
regarding the deepening of the pool of capital
than the indicated beforehand. Throughout the
available in the country that includes investment in
research, the proponent has not drifted away from the
human capital in the form of education, as well as the
sound economic theories that this study is based
full utilization of the population.
upon, and the a priori expectations have always been
It is also suggested that the government should
also
increase
its
budget
allocation
on
road
met.
Nevertheless,
this
study can serve
as a
improvement to provide access to rural communities
supplementary study, an addition to the studies made
in order to efficiently transport goods and services
with regard to the topic of poverty. Poverty is one of
from one point to another i.e. in an agricultural
the most pressing problems that humanity faces as a
14
race. In the world that we live in today, with all the
technological
advancements
and
scientific
breakthroughs, it is integral to find ways in order to
reduce poverty in order to aide in the attainment of
United Nation’s Millennium Development Goals
where alleviation of poverty is one of them.
15
Appendix A.
Summary of the Data
From the data gathered, 78 (with the
exception for the information gathered on DPWH
which seems to lack information on five provinces,
namely Basilan, Lanao del Sur, Sulu, Tawi-Tawi, and
Maguindanao) of the provinces have provided varied
statistics to the information needed in this research.
Presented hence is a summary of the information
gathered:
Table 1. Data Summary
Variable
Observations
Mean
S.D.
Min
Max
Poverty incidence
78
33.46154
14.55056
0
61.6
Value of building constructions
78
1604619
2650193
4712
1.50E+07
Distance of good road
73
65.77438
57.60592
0
258.1
Households w/ access to safe water
78
0.7822965
0.368596
0.009443
2.560873
Population growth rate
78
1.620769
0.632611
0.08
4.12
Average years in school (up to secondary)
78
8.455128
1.383732
4.6
12.6
Presented below is the summary of the results
yielded using the data gathered from NSCB, NSO
and DPWH. The results will be then examined for
possible
problems
in
heteroscedasticity,
multicollinearity and autocorrelation. This table
displays the variables involved in the econometric
model, with their corresponding estimated
coefficients, probability values, standard deviations,
and the coefficient of determination represented by
the value of the R-squared.
Table 2. Dependent Variable: Poverty Incidence
(OLS Estimation: Across 78 Provinces of the Philippines)
Value of
Estimate
Significance5
Constant
***
(11.02191)
-0.0697046
(s.e.)
(.8792123)
Distance of good roads
*
(0.229012)
Access to safe water
-1.378105
(s.e.)
(3.605055)
**
(s.e.)
Average years in school
-3.702187
(2.09804)
***
(s.e.)
-7.765788
(1.414965)
Root MSE
11.028
R-squared
0.4606
Adjusted R-squared
F-Test
Ramsey RESET
6
-0.0361233
(s.e.)
Population growth rate
5
107.3815
(s.e.)
Value of building constructions (ln)
***
0.4204
11.44 (5,67)
0.06556
Legend: * -significant at the 10% level; ** -significant at the 5% level; *** -significant at the 1% level
Ho: no omitted variable bias; H1: omitted variable bias present. Accept Ho at 95% level of significance.
16
Appendix B.
Testing for the Critical Assumptions
Multicollinearity
According to Gujarati & Porter (2009),
multicollinearity is a fact of life; it cannot be
removed or isolated. However it is possible for us to
test whether or not the level of multicollinearity is
tolerable, dangerous or perfect.
In the instance wherein there is perfect
multicollinearity it is safe to assume that it would be
impossible for any researcher to find any estimates
for the X values, since their standard errors will be
infinite
(determinant
will
be
zero).
If
multicollinearity is less than perfect but at a
dangerous level, this may result to bloated standard
errors, insignificant p-values of the t statistics though
the R-squared is deemed a fitting model; this results
to a wholesale acceptance of the null hypothesis,
which increases the probability of committing a type
II error. This may cause the researcher to omit good
regressors for the model since these X estimates will
be deemed insignificant.
Presented below is the result of the
multicollinearity test via analysis of the VarianceInflating Factor, commonly known as the VIF:
Table 3. VIF Test
Variable
VIF
Value of building constructions (ln)
1.51
0.662259
Average years in school (up to secondary)
1.45
0.687475
1.1
0.909241
Households w/ access to safe water
1.08
0.924616
Distance of good roads (DPWH)
1.03
0.97049
Mean VIF
1.24
Population growth rate
To determine the severity of multicollinearity in
the model, we must look at the generated values for
the VIF whether or not they are greater than or equal
to 10, otherwise the level of multicollinearity is
tolerable. As we can see, all the VIF values generated
are less than 10. Moreover the individual VIF’s
tolerance levels, taken into account by 1/VIF are all
greater than 10%. Therefore it is safe to conclude that
the model has a tolerable level of multicollinearity.
Autocorrelation
The term autocorrelation may be defined as
“correlation between members of series of
observations ordered in time or space, simply put:
𝐸(𝑢𝑖 𝑢𝑗 ) = 0 This phenomenon causes an
overestimation of the R-squared, as well as incorrect
t-statistics as well as p-values. The root cause of this
is from the underestimation of the standard errors,
leading to wrong policy recommendations and
counterintuitive signs in the econometric model.
1/VIF
Since this research deals with a cross-section data,
then there is no need to test for problems in
autocorrelation since it only appears in time-series
data. (Gujarati & Porter, 2009)
Homoscedasticity
Homoscedasticity is the equal spread of
variances, symbolically speaking it is written as
𝐸(𝑢𝑖2 ) = 𝜎 2 , ∀𝑖 = 1,2, … , 𝑛. If plotted in a graph, the
points should not follow a pattern. The problem of
heteroscedasticity (or heteroskedasticity) is most
common
in
cross-section
data.
When
Heteroscedasticity is not properly treated, it will
cause the OLS to no longer be the Best Linear
Unbiased Estimate (BLUE), since it causes the values
of R-squared, t-stats, standard errors to be all wrong.
Using the Breusch-Pagan-Godfrey test for
Heteroscedasticity, we obtain the following results:
17
Table 4. Breusch-Pagan test for heteroscedasticity
Ho: Constant variance
Variables: fitted values of poverty incidence
Chi-squared (1)
0.11
Prob > Chi-squared
0.7379***
Decision: Accept Ho at 95% confidence interval
To interpret this result we must consider that
given that the null hypothesis indicates
homoscedasticity while the alternative indicates
heteroscedasticity, if the Prob > chi2 presented in the
test is greater than 0.05, the null hypothesis is
accepted; which implies that the model exhibits
homoscedasticity. Since the probability is at 0.7379-
significantly greater than the acceptance level at 0.05,
then it is safe to accept the null hypothesis and say
that there is homoscedasticity; which implies that
there is no problem of heteroscedasticity in the
model.
A similar result has come up with a generation of
the White’s Test:
Table 5. White's Test for heteroscedasticity
Ho: homoscedasticity
Ha: unrestricted heteroscedasticity
chi-squared (20)
Prob > chi-squared
14.1
0.8256***
Decision: Accept Ho at 95% confidence interval
18
Appendix C.
Previous Test Run with corresponding VIF, BreuschPagan-Godfrey Test and White’s Test
Table 3. Dependent Variable: Poverty Incidence (natural log)
(OLS Estimation: Across 78 Provinces of the Philippines)
Constant
Significance7
Value of
Estimate
***
4.97415
(s.e.)
Value of building constructions (ln)
(.44817)
**
-0.0816
(s.e.)
Distance of good roads
(.343216)
*
-0.0011684
(s.e.)
(0.0010007)
Access to safe water
-0.1459578
(s.e.)
(0.1546506)
Population growth rate
***
-0.2838653
**
-0.2526639
(s.e.)
HDI (ln)
(0.9415)
(s.e.)
(0.0972426)
Root MSE
.4765
R-squared
0.3657
Adjusted R-squared
0.3176
F-Test
***
7.61 (5,66)
1.108
VIF
Breusch Pagan test
Heteroscedasticity
***
0.0049
7
Legend: * -significant at the 10% level; ** -significant at the 5% level; *** -significant at the 1% level
Since VIF < 10, then tolerable level of multicollinearity
9 Presence of heteroscedasticity
8
19
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OPICS/EXTPOVERTY/EXTPA/0,,contentMDK:2024
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World Bank. (2011). Defining Welfare Measures. Retrieved
March 28, 2013, from World Bank Website:
http://web.worldbank.org/WBSITE/EXTERNAL/T
OPICS/EXTPOVERTY/EXTPA/0,,contentMDK:2024
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