Causality Between Philippine Economic Growth and Debt

De La Salle University – School of Economics Causality Between Philippine Economic Growth and Debt

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Causality Between Philippine Economic Growth and Debt

Jayvee Roca

Undergraduate – DLSU-SOE

[email protected]

Abstract: This study examines the causal nexus that exists between Philippine economic growth and debt across

the years 1972-2012 using Johansen’s Maximum Likelihood Estimates Cointegration Test and the Vector Error

Correction Model (VECM). The Johansen’s Cointegration Test confirms that indeed there exists a cointegrating

relationship between the two variables which equates to the existence of a long run equilibrium relationship

between economic growth and debt in the Philippines. The results based on the VECM confirm that indeed

there exists long run causality between economic growth and debt; however, results show that the causality

runs from growth to debt rather than the commonly perceived alternative.

Keywords: VECM; Johansen’s MLE; Economic Growth; Debt; Causality

I. Introduction

The ASEAN region is considered to be one of

the rising stars in the world of international

relations as it poses to enter its integration stage

with its member countries to promote inclusive

growth and intra-regional free trade across all

nations; however, recent studies indicate that there

is a looming risk arising for companies in the

nations belonging to the ASEAN that are exposed

to the risk of fast growing financial leverage and

economic deterioration or the slowing down of

economic growth which is mainly due to the fact

that these firms engaged mainly in debt financing

to continue their operations and expansions

(Channel NewsAsia, 2014).

Philippines—one of the founders of the ASEAN

bloc and an equally active player in the integration

of member countries faces similar problems of

sustainable debt planning and financing. According

to Jubilee Australia (2008) the country faces huge

problem when it talks about financing its debt

specifically pointing out that more than 1 million

pesos is being poured out by the national

government just to pay its debt both locally and

internationally; furthermore, one of the most

interesting and rather controversial valuation

made by the article stating that the Philippines has

an exceeding debt amounting to 80 million USD

which equates to at least 7,000 Php per person in

order to pay its debt. One of the most crucial points

of external debt accumulation would be between

the years 1974-1978 wherein values are computed

to be three times as large as the years prior to this

time interval which is attributed to massive oil

price shocks and a major drop in global commodity

prices (Dohner & Intal, 1987).

ASEAN Integration will soon reach the peak

of its implementation and in turn will increase free

trade and competition within all the regions of

participating in the said group. Ever since the

occurrence of the Asian debt crisis in 1997 the issue

of external debt and its relationship with and

posing possible problems of achieving sustainable

long term inclusive economic growth has been the

focus of economists and researchers worldwide.

Thus, this research aims to provide empirical

evidences on the causality between the external

debts of the Philippines and long term economic

growth.

In recent years, economist and researchers

alike have been looking into the 10 bloc members of

the ASEAN and their growing debt risk that might

pose a problem during the initial integration phase

where there would be expected fluctuation initially

in these economies. This study seeks to answer the

research question “Do large external debt affect

long run economic growth in the Philippines?”

Further, the paper seeks to answer the question “Is

there an existing causal nexus between GDP per

capita & Debt?” With the questions in mind, the

paper seeks to test and analyze Philippine external

debt and economic growth using time series data to

test for co-integration and vector error correction

using available econometric models and techniques

in order to provide relevant information to aid

future studies.

II. Review of Related Literature

Sovereign debt or External debt can be

categorized in to three different degrees of

definition mainly external debt defined as debt in

the form of overseas currency debt, external debt as

debt from foreign individuals and finally external

debt as based on the place of issuance and

intermediaries of the debt contract (Panizza,

Sturzenegger, & Zettelmeyer, 2010). The study

shall be utilizing the definition “Total external debt


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is debt owed to nonresidents repayable in foreign

currency, goods, or services. Total external debt is

the sum of public, publicly guaranteed, and private

nonguaranteed long-term debt, use of IMF credit,

and short-term debt (Index Mundi, 2014).” One of

the most important indicators of economic growth

is Gross Domestic Product (GDP) more specifically

GDP per capita; GDP per capita is a measure of

living standards per person and this changes across

different countries (Mankiw, 2012).

Estimating the relationship between economic

growth and external debt is one of the most

controversial and widely debated issues among

economists and econometricians worldwide where

there are two dominating schools of thought mainly

those supporting that there exists negative

relationship between the two variables and that no

relationship exists between the two. The research

paper by Lof & Malinen (2013) uses data from 20

developed countries to test the relationship

between economic growth and sovereign debt using

Panel autoregressive model and found that even

high levels of sovereign debt do not hamper

economic growth. Results from Ceccheti, Mohanty

& Zampol (2011) show that at tolerable levels debt

actually increases economic growth they argue

however, that beyond a certain threshold debt

would deter growth. The study finds that for

governments the tolerable threshold shall be below

85% of GDP while for corporate entities and

households the thresholds are below 90 and 85%

respectively; the authors note that the figures

presented are merely rough estimates.

The study made by Herndon, Ash, & Pollin,

(2013) opposes the previous research by Reinhart

and Rogoff which states that a country having 90%

external debt actually deter economic growth and

the authors actually disprove this result using data

from 20 advanced economies spanning 65 years the

author argues that the initial study made by

Reinhart and Rogoff contain methodological errors

that ultimately led to inaccurate conclusions.

However studies like that of Akram (2011) which

uses data from Pakistan spanning 39 years and

find that there exists a debt overhang effect

wherein public sovereign debt actually deters

economic growth by deterring investments and

GDP per capita which is in contradiction with

previous literature used in this study; Further,

Kumar & Woo (2010) in their paper uses 40 years’

worth of data for advanced and emerging

economies for a panel regression that showed that

there is in fact a negative relationship between

external debt and long run economic growth.

Although the literature is vast in terms of this

field however no unanimous decision has been set

in stone regarding the true effect of external debt

and economic growth. Majority of the literature

regarding debt and economic growth mostly revolve

around developed economies and OECD countries;

however there exist minimal or almost inexistent

literature regarding the relationship of external

debt and economic growth in the majority of

countries in the Asian continent especially in the

Philippines.

III. Theoretical Framework

Theory of Intertemporal Trade

The Theory of Intertemporal Trade is an

important concept on international borrowing and

lending, the model is simplified by assuming that

borrowing and lending is nothing but another form

of trade; the core assumption of this theory is that

economic agents trade in the present in order to

gain something from the future; Further, countries

with low income tend to have very small savings

rate thus are not able to maximize advantageous

option autonomously hence leading to borrowing

from other nations (Krugman, Obstfeld, & Melitz,

2012). This model also provides us insight with a

model of uncertain expectations where increases of

future debt leads to uncertainty of being able to pay

the existing accumulated debt (Arnone, Bandiera,

& Presbitero, 2007). Implications that arises from

this model is crucial in understanding the causal

nexus that exists between debt and economic

growth since sovereign governments and firms

alike consciously borrow from different entities to

gain liquidity or monetary capital in the present

with knowing the fact that they have to give up a

portion of their expected return in the future.

Debt Overhang Effect

Debt Overhang is a concept in finance used

often by economists and financial analyst alike to

assess the debt burden of a market player in this

case, sovereign countries wherein the entity can no

longer pursue supplementary debt financing to

sustain their current state of growth. Investopedia

(2014) defines countries in this state as having a

declining standard of living due to grossly reduced

spending allocations in vital aspects of the

economy; further, it is the state where the debt

stock of a country exceeds its capability to pay.

Constrained Liquidity

Arnone, Bandiera & Presbitero (2007)

discusses the possibility of default which leads to

economic fluctuations which in turn constraints the

growth of capital markets and influx of foreign

investments. One method available to governments


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is to adjust market conditions through monetary

policy or the use of interest rates; however, there is

a limit to adjusting the market condition known as

Liquidity Trap where the tools used by policy

makers reaches its lower bound and can no longer

adjust and account for additional effects of liquidity

(Mankiw, 2012). The concept of constrained

liquidity arises from the intuitive fact that debt

payment is proportional to the cost of cancelling the

debt and which equates to a crowding out effect of

investments that is equal to the “Intertemporal

elasticity of substitution” (Arnone, Bandiera, &

Presbitero, 2007).

IV. Data & Methodology

Data used in this study exclusively covers data

in the Philippines ranging 1972-2012 from The

World Bank & IMF. Dataset covers 41 time series

observations per variable which are lngdp (natural log of GDP measured in Million US Dollar) and

lndebt (natural log of debt measured in Million US Dollar) producing 82 total observations in the study

which is considered to be sufficient in providing

long run or short run time series analysis and

inference. Statistical software’s STATA is used in

this study. Using the syntax summarize in the

statistical software STATA 12 we yield the

important summary statistics such as number of

observations, mean, standard deviation,

minimum/maximum value per variable which is

presented below. [See Appendix A for Data Table.]

. summarize

Variable | Obs Mean Std. Dev. Min Max

-------------+--------------------------------------------------------

year | 41 1992 11.97915 1972 2012

lndebt | 41 20.4263 .8739428 18.2346 21.5041

lngdp | 41 24.67198 .8406067 22.80488 26.2454

A-Priori Expectations

GDP & Debt

The relationship of debt and GDP is a rather

complex concept to understand especially analysis

of its effects on the macroeconomic level of analysis.

Macroeconomics is simply the study of aggregate

phenomenon in the entire economy (Mankiw, 2012);

this is includes the study of economic growth and

one way to fund the continual expansion of not only

entities but the economy is through debt financing.

Debt, in effect acts as an effective stimulant in the

natural flow of the economy in its initial entry to

the economy such that larger pool of funds are

available to the borrower in the case of

macroeconomics it would be sovereign governments

to spend on projects to stimulate growth in the

economy thereby increasing gdp per capita;

however, debt also has detrimental effects in the

long run. Given the concept of intertemporal trade,

an entity – in this case the Philippine government

– is willing to gain something – in this case funds

from foreign lenders – in the present and give up a

part of the anticipated profit in the future.

Integrating real life phenomenon, a sovereign state

does not always experience positive rates of return

hence explaining the detrimental effects of debt in

the long run.

To summarize, debt initially is expected to have

a positive (+) relationship with GDP per capita in

the short run while it is expected to have a negative

(-) or at best ambiguous effect in the long run.

Vector Error Correction (VEC) Model

The Vector Error Correction (VEC) Model is a

variant of the Vector Auto Regressive (VAR) that

accounts for co-integrated variables provided that

the non-stationary variables converge to common

long run equilibrium (Hill, Griffiths, & Lim, 2011).

According to Parlow (2010) “If for instance two time

series are co-integrated by a common factor (co-

integrating vector) it is not possible to use a

standard VAR-approach. We have to account for

this relationship and use an error-correction model

to get correct results”. The theorized VEC model to

be used in this study is based on the model from

Hill, Griffiths & Hill (2011) which assumes non-

stationarity of time-series variables at level

presented as:

ΔlnGDPt= β11 ΔlnGDPt-1 + β12 ΔlnDebtt-1 + 𝜈𝑡𝛥𝑙𝑛𝑔𝑑𝑝

[Eq. 1.1]

ΔlnDEBTt= β21 ΔlnGDPt-1 + β22 ΔXt-1 + 𝜈𝑡𝛥𝑙𝑛𝑑𝑒𝑏𝑡

[Eq. 1.2]

Where,

lnGDPt : Natural Log Rate of GDP per

capita across Time t

lnDEBTt:: Natural Log Rate of Debt across

Time t ΔlnGDP, ΔlnDEBT & ê are stationary

Augmented Dickey-Fuller Test

The Augmented Dickey-Fuller Test is a

commonly used tool to test for unit root in time

series data. The Augmented DF examines data

using the ԏ (tau) statistic with the notion that if

ρ=1 which is translated to be a random walk or

similarly translated as non-stationarity; however, if


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ρ<1 the null hypothesis is rejected and the

conclusion of stationarity is employed. Values that

have p > 1 translated as an explosive variable is

ignored since it is improbable to have such cases in

this field of study (Danao, 2002). Since both

variables in this case exhibit trend we therefore

add a constant and trend in the analysis which are

as follows:

ΔlnGDPt=β1+β2t+ γlnGDPt-1 + δ1ΔlnGDPt-

1+δ2ΔlnGDPt-2+…+δpΔlnGDPt-p+εt [Eq. 2.1]

ΔlnDEBTt=β1+β2t+ γlnDEBTt-1 + δ1ΔlnDEBTt-

1+δ2ΔlnDEBTt-2+…+δpΔlnDEBTt-p+εt [Eq. 2.2]

The first differenced equation shall assume a

non-constant and non-zero mean hence we assume

an equation that exhibits a constant but with no

trend:

ΔlnGDPt = β1 + γ lnGDPt-1 + εt* [Eq 2.3]

ΔlnDEBTt = β1 + γ lnDEBTt-1 +εt* [Eq. 2.4]

Engel-Granger Method

The Engel Granger Method is a two-steps

method in identifying whether or not co-integration

exists among variables; this method is basically a

DF test that utilizes the residuals taken after a

regression analysis. (Danao, 2002) It is noteworthy

however that the critical values presented in the

DF test is interpreted differently when residuals

are used instead of the individual variables given

in (Hill, Griffiths, & Lim, 2011). The equation used

in this study is as follows:

Δеt = γ еt-1 + vt [Eq. 3]

Where, vt is a white noise

Johansen’s Co-integration

Johansen’s Co-integration tests is a more

powerful test compared to the Engel & Granger test

since this test makes use of the maximum

likelihood estimates (MLE) and statistics such as

the Eigen value statistic and a trace statistic as

well; further, unlike the Engel & Granger this test

shows all co-integrating vectors that are available

in the model itself. This test is considered to be a

Vector Autoregressive (VAR) Approach which

assumes endogeneity of all variables in the model.

The crucial result is that there should be a rank > 0

to find existence of co-integration (Parlow, 2010).

Null hypothesis H0 of said test is that (rank = 0)

meaning there is no co-integration alternatively the

Ha (rank>1) is said to be the existence of co-

integration

Granger Causality

Granger Causality seeks to show causal

relationship among variables (Danao, 2002); in this

study’s case, it seeks to know whether lngdp cause

lndebt to change or it is the other way around. This

test is centered on the notion that a certain event

t+1 cannot cause an event t-1 similarly the null

hypothesis H0 is therefore that lnDEBT does not

Granger Cause lnGDP. The equations applicable to

this study would be as follows:

lnGDPt = ∑ 𝛼𝑡 𝑚𝑖=1 𝑙𝑛𝐺𝐷𝑃𝑡−𝑖 + ∑ 𝛽𝑡

𝑚𝑗=1 𝑙𝑛𝐷𝐸𝐵𝑇𝑡−𝑗∗

[Eq. 4.1]

lnDEBTt = ∑ 𝛾𝑡 𝑚𝑖=1 𝑙𝑛𝐷𝐸𝐵𝑇𝑡−𝑖 + ∑ 𝛿𝑡

𝑚𝑗=1 𝑙𝑛𝐺𝐷𝑃𝑡−𝑗∗

[Eq. 4.2]

V. Results & Discussions

Debt financing is a natural occurrence at both

micro and macro level since it is almost an

impossibility for a firm to continue operations and

expansion without the need for external sources of

capital. The Philippine Government through its

Bureau of Treasury recently released a report

showing that for the first 6 months of 2014 the

government has serviced a national debt equaling

277,935 in million pesos (Bureau of the Treasury,

2014); this report is considered to be high especially

given the fact that the national government

especially at its high seats is currently being trialed

in its own courts for alleged corruption & money

laundering.

The analysis of this study is set on the

Philippines setting where both debt and growth

rate experience high rates of growth recently

although reading historical observations and

evidences there are some breakpoints in the

relationship associated with debt and economic

growth. One best example of disturbances in the

relationship of both variables would be the

duration of the martial law regime, the beginning

of martial law mainly 1972 onwards presented a

favorable status in the economy growth is at its

highest levels which is mainly attributed by high

levels of foreign direct investments & equally heavy

borrowings both domestically and internationally;

however, due to escalating civil unrest especially

attributed by the assassination of Senator Benigno

“Ninoy” Aquino in 1983 led to large withdrawal of

foreign investments and shut downs of domestic

business which lead to higher need for borrowings

and which lead to what is known as the Philippine

Debt Crisis (Dohner & Intal, 1987). Another

important historical point that may affect the

relationship of the two variables would in 1997

which is mainly attributed to the sudden spike of


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foreign debts in the major economies of Asia which

stems from the high borrowings of private entities

mainly in the real estate market for expansionary

purposes then certain events led to the devaluation

of Asian currencies with a looming expansionary

bubble which then lead to the Asian Debt or

Financial Crisis (Economics Help, 2014).

This paper empirically analyzes the relationship

of the variables lnGDP and lnDEBT across 41 years

in the context of the Philippines using various

empirical techniques in the field of Econometrics

with the aid of the powerful statistical software

STATA12. Figure 1 provides us a graphical

summary of the representation of the time series

data used in this study. Upon careful analysis it

can be deduced that for both cases the leveled

variables are far from what is considered to be

stationarity from what is considered to be

stationary level as evidenced by the single

increasing line for the graphs of both variables

which is represented by the first column of Figure 1

.

Figure 1: Summary of Graphs Source: World Bank Data

; however, upon careful analysis of the first column

graphs it can be seen that at some level both

variables have the same line trend which is a

possible sign of co-integration among the two

variables. Moving forward with our analysis of

Figure 1 it can be seen that at the second column

STATA provides us with the graph of the variables

at the first differenced level which appear to be

somewhat stationary using the graphical approach

to analysis; however, more powerful analytical and

statistical tools are available to us courtesy of the

software STATA.

Lag Selection Using the pre estimation code –varsoc– in

STATA we are presented with a table summarizing

the optimal lag length to be used throughout the

study especially in the VECM. –varsoc- is a time

series syntax that utilizes the Final Prediction

Error (FPE), Akaike’s Information Criterion (AIC),

Schwarz’s Bayesian Information Criterion (SBIC)

and the Hann and Quinn Information Criterion

(HQIC) which are optimal tools to be used in

determining the proper lag order to be used in the

study (STATA13, 2014) which is important since

over selection of lags tend to add to the errors in

the model while too few may impair inferences that

may be deduced from the model (Hill, Griffiths, &

Lim, 2011). The test shows us that all criterions:

FPE (0.001229), AIC (-1.02914), SBIC (-0.875651)

agree that the optimal lag to be used in this study

shall be in the lag order 2 despite the result of

HQIC (-.663405) since the Hann Quinn information

criterion is optimally used in quarterly data with

over 120 sample (Reyna, 2007). [See Appendix B for test results]

Table 1. Summary of Lag Selection

Criterion Lag Selected

Final Prediction Error 2

Akaike’s Information

Criterion 2

Schwarz’s Bayesian

Information Criterion 2

Hann Quinn

Information Criterion 1

2324

2526

lngd

p

1970 1980 1990 2000 2010year

-.10

.1.2

.3

D.ln

gdp

1970 1980 1990 2000 2010year

2122

2324

25

lnde

bt

1970 1980 1990 2000 2010year

-.10

.1.2

.3.4

D.ln

debt

1970 1980 1990 2000 2010year


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Stationarity Test Since the lag selection model has determined the

optimal lag to be used in this study we can now

proceed with the diagnostics to be used in this

study. The first test shall be to verify the graph

presented in Figure 1 that the variables used in

this study would be non-stationary at level and

stationary at first differenced. Since variables both

provide a trend at level as evidenced by Figure 1 we

use the syntax –dfuller— followed by trend option

to determine the stationarity of the data provided.

As a result the, variables lngdp (-2.796) and lndebt

(-2.370) which are both greater than the 5% critical

value computed by ADF test which is equal to -

3.540 which imply that there exists unit root at

level of the variables or they are non-stationary at

level. Table 1 shows that in fact both variables

have unit root at level which indicates non-

stationarity; however literature provides us the

option to check first or second difference of the

variables to search for stationarity.

The graph shown in Figure 1 on first difference

of both variables indicates a situation where the

condition of a trend and zero mean is absent; hence,

we accompany the syntax –dfuller— with the

option notrend but with a constant. Table 1

summarizes the result of the Augmented Dickey

Fuller test wherein at first difference with notrend

the variables lngdp (-4.200) and lndebt (-4.936)

which are both less than the 5% critical value

computed by the ADF test equal to -2.961 which

implies that both are stationary at 5% critical value

and even at all levels of critical value which is a

favorable result since we have identified that both

variables have the form I(1) which indicates

stationarity of both variables at first difference. I(1)

result also indicates the possibility of co-integration

among variable and further tests such as VEC

model which requires stationarity at first difference

and non-stationarity at level may be implemented

in this study. [See Appendix C for full test results]

Table 2. Summary of Augmented Dickey-Fuller

Variable Constant,

Trend

First

differenced,

Constant,

Notrend

lnGDP Non Stationary

at all levels

Stationary at

all levels

lnDEBT Non Stationary

at all levels

Stationary at

all levels

Co-integration Test Engel & Granger Method A variable is considered to be integrated if it

has to be differenced n times before it reaches

stationarity (Danao, 2002). Since the ADF test

shows us that all variables utilized in this study is

stationary at I(1) then there is a possibility of co-

integration among the variables used in this study.

The Engel Granger Test is a two-step Dickey Fuller

Test applied to the residuals; the first step is to

regress and predict the residuals then proceeding

to a dickey fuller test with a modified critical

values. Using the STATA syntax –dfuller—to the

residuals the results yielded a z(t) or tau value

equal to -0.322 which is above the critical value for

the Engel Granger Method which is -3.37 at 95%

confidence level therefore we conclude that the

variables are not co-integrated in this case which is

an interesting result since it shows that under the

Engel Granger Method Debt & Economic growth

(represented by gdp per capita) are not co-

integrated or have no long run relationship in

between them. [See Appendix D for full test]

Johansen’s Cointegration Test

This study also provides another means of

testing co-integration mainly through the

Johansen’s Maximum Likelihood Estimation

Method. Using STATA syntax –vecrank- which

yields the Johansen Eigen & Trace statistic we are

able to deduce that since the trace statistic (0.2013)

at maximum rank = 1 is less than the prescribed

statistic at 5% CI (3.76) then this result imply that

there exists 1 error term for the equation meaning

that the variables are indeed co-integrated. Further

this result also implies that there exists long run

association between the two variables or in the long

run both variable move together. With this result

we shall proceed to the Error Correction Model

since the variables are deemed to be co-integrated.

[See Appendix E for full test]

Table 3. Summary of Co-integration Tests

Method Results

Engel & Granger

Method

No Co-integration

Johansen’s MLE Co-integration at Rank

1


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The researcher here faces a decision whether to

use the Engel & Granger or the Johansen’s MLE

test result; the researcher recognizes the different

computation used in both tests which explains the

different results in the test statistics. Upon careful

examination of available literature and considering

the relative size of the data used in this study, it is

optimal to choose Johansen’s maximum likelihood

estimates test for cointegration since this test

features all of the co-integrating relationship

available to our chosen variables and that

Johansen’s MLE also has less small sample bias

compared to the Engle & Granger Method.

Vector Error Correction Model (VECM) The VECM requires the condition that both

variables are co-integrated and that they are I(1)

meaning they are stationary at their first

difference. The researcher notes that if the

variables are not co-integrated then the more

appropriate model to be used shall be the

Unrestricted VAR rather than the VECM. STATA,

using the syntax –vec—which solves for VECM

model given our variables; STATA automatically

computes for the first difference of the variables in

executing the specified model which we are sure to

be stationary as shown by previous tests for

stationarity.

Table 3 is a comprehensive summary of the

result for the test conducted under the different

models of VECM. [See Appendix F for full tests] The first VECM model is where lngdp is

intentionally chosen first to be the error correction

model (Dependent Variable) followed by lndebt to

be able to have an interpretation focusing on the

first variable chosen in this case lngdp. The

interesting part about the VECM regression is the

presence of L._ce1 which is basically interpreted as

the co-integrating equation or error correction term

which among all other variables is considered to be

an indicator of long run causality. According to our

first result and initial analysis of the first VEC

Model it can be seen that no variable (other than

_cons) is considered to be significant in the VECM

this leads to the question of whether or not there

exist causal relationship of the variables in this

case. Upon careful probing by the researcher of the

test result, the insignificant (P>0.05) positive

coefficient (.0014093) of L_ce1 can be interpreted as

the fact that no long run causality running from

lndebt to lngdp exists in this case; further, using

the post estimation testing for short run causality

of the variables it can be deduced that the lag of

lndebt (P>0.05) does not cause lngdp or no short

run causality is running from lndebt to lngdp. [See Appendix G for test results].

Table 4. VEC Models

(1) (2)

D_lngdp

L._ce1 0.00 -0.02

(0.00) (0.02)

LD.lngdp 0.34 0.34

(0.19) (0.19)

LD.lndebt 0.03 0.03

(0.03) (0.03)

_cons 0.05* 0.05*

(0.02) (0.02)

D_lndebt

L._ce1 0.03** -0.34**

(0.01) (0.11)

LD.lngdp -0.68 -0.68

(0.92) (0.92)

LD.lndebt 0.27 0.27

(0.15) (0.15)

_cons -0.00 -0.00

(0.10) (0.10)

N 39 39

adj. R2

Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

The second column and lower half of Table 3

represents the summary for the second VEC model

utilized in this study wherein the variable lndebt was intentionally assigned to be first and then

followed by lngdp. In this case, L._ce1 is

interpreted the same way as before being the error

correction term and an indicator for long run

causality in the model itself. Coefficients and p-

values from the short run coefficients once again

show no significant relationship once again testing

for short run causality across variables it can be

seen that no short run causality exists from the lag

of lngdp to lndebt. [See Appendix G for test results] The interesting thing about the resulting 2nd VEC

model is that the negative coefficient (-.3383093)

for L._ce1 is strongly significant (p= 0.001) which

means that there exists a long run causality

running from lngdp to lndebt which is an

interesting result especially considering the fact

that the said term also measures the speed of

adjustment towards long run equilibrium.

Table 5. Summary of Results VECM

Flow Long Run

Causality

Short Run

Causality

lnDEBTtolnGDP No No

lnGDPtolnDEBT

YES

No


8

Serial Correlation Tests & Diagnostic Tests Serial Correlation is a problem endemic to time

series studies since it leads to ineffiency in the

estimates which in return almost often leads to

unreliable t statistics and incorrect conclusions

(Gujarati & Porter, 2009). Using the code –veclmar– in STATA we yield a Lagrange Multiplier

Test specifically for VEC Models. The results of the

VEC-LM test shows that for both VEC Models

(Model 1 & 2) Both show absence of serial

correlation at second order lag which was

predetermined by previous tests made in this

study; which is a favorable result since we can rely

on the estimates provided in this test. [See Appendix H for test results].

The researcher normalized the variables that

are used in this study i.e., Log of GDP per capita

(lngdp) and Log of Debt (lndebt) and thus improve

normality in the residuals. Further, since this

study uses annual data there is no need to test for

seasonality.

VI. Conclusions

The study seeks to show causal relationship

between Philippine Economic Growth represented

by gdp per capita and Philippine Debt through the

use of Johansen’s Maximum Likelihood Approach

Cointegration Test and the Vector Error Correction

Model. The analysis covered the years 1972 to

2012. It was discovered through the ADF test that

both variables were stationary at I(1) and through

the Johansen’s cointegration test that there exists

co-integration across the variables or there exists

long run relationship between the variables which

is specifically equal to rank 1. The Vector Error

Correction Models used in the study was able to

show that no long run or short run causality

running from debt to economic growth exits; rather,

the result strongly shows that there exists a long

run causality running from economic growth to

debt.

The study poses an interesting result since the

results poses a challenge to the long standing norm

believed by many that the causality runs from debt

to economic growth but in reality according to

empirical evidence presented in this study or at

least in the context of the Philippines it is the exact

opposite meaning that in the long run a past

change in the variable lngdp or economic growth

actually leads to a corresponding impact on current

debt further implying that an improvement in

economic activities actually leads to more

borrowings.

VII. Acknowledgements

The researcher would like to extend his

sincerest gratitude to Cesar Rufino, DBA for

imparting his knowledge and insights for the

econometric models and techniques applied in this

study; Mr. Alexander Roca for his assistance in

proof reading this work. Also, the developers of

STATA without whom this research would have

been impossible. Above all, the researcher

expresses his utmost appreciation to Almighty God

for the success of this study.

VIII. References Akram, N. (2011). Impact of Public Debt on the

economic growth of Pakistan. Pakistan

Development Review, 24.

Arnone, M., Bandiera, L., & Presbitero, A. (2007).

External Debt Sustainability: Theory and

Empirical Evidence. IMF-WORLD BANK

PROGRAM, 47.

Bureau of the Treasury. (2014). National

Governement Debt Service CY 2014. Manila:

Bureau of the Treasury.

Channel NewsAsia. (2014, September 10). S&P warns

of risk from rising debt among ASEAN firms.

Singapore, Singapore.

Danao, R. (2002). Introduction to Statistics and

Econometrics. Quezon: The University of the

Philippines Press.

Dohner, R., & Intal, P. (1987). Debt Crisis and

Adjustment in the Philippines. Developing

Country Debt and the World Economy (pp.

169-192). Chicago: University of Chicago

Press.

Economics Help. (2014). Financial Crisis Asia 1997 .

Retrieved October 31, 2014, from

economicshelp.org:

http://www.economicshelp.org/blog/glossary/fi

nancial-crisis-asia-1997/

Gujarati, D., & Porter, D. (2009). Basic Econometrics.

McGraw-Hill.

Herndon, T., Ash, M., & Pollin, R. (2013). Does high

public debt consistently stifle economic

growth? A critique of Reinhart and Rogoff.

Cambridge Journal of Economics, 23.


9

Hill, C., Griffiths, W., & Lim, G. (2011). Principles of

Econometrics 4th edition. Massacheuttes:

John Wiley & Sons Inc.

Index Mundi. (2014). External debt stocks, total (DOD,

current US$). Retrieved November 15, 2014,

from Economic Policy & Debt:

http://www.indexmundi.com/facts/indicators/

DT.DOD.DECT.CD

Investopedia. (2014). Debt Overhang. Retrieved

November 16, 2014, from Investopedia.com:

http://www.investopedia.com/terms/d/debtove

rhang.asp

Jubillee Australia. (2008, March 26). Latest News: The

Philippines’ debt problem and the continued

accumulation of illegitmate debt has once

again been put in the spotlight. Retrieved

December 8, 2014, from Jubillee Australia:

http://www.jubileeaustralia.org/latest-

news/the-philippines-debt-problem-and-the-

continued-accumulation-of-illegitmate-debt-

has-once-again-been-

Krugman, P., Obstfeld, M., & Melitz, M. (2012).

International Economics: Theory and Policy

9th Ed. Boston: Pearson.

Kumar, M., & Woo, J. (2010, July). Public Debt and

Growth. Retrieved November 16, 2014, from

International Monetary Fund:

http://www.imf.org/external/pubs/ft/wp/2010/

wp10174.pdf

Lof, M., & Malinen, T. (2013). Does sovereign debt

weaken economic growth? A Panel VAR

Analysis. Helsinki Center of Economic

Research, 12.

Mankiw, G. (2012). Principle of Economics, 6th

Edition. In G. Mankiw, Principle of

Economics 6th Edition (p. 856). Singapore:

Cengage Learning.

Panizza, U., Sturzenegger, F., & Zettelmeyer, J.

(2010). International Government Debt.

United Nations Conference on Trade and

Development (p. 28). Geneva 10: United

Nations.

Parlow, A. (2010). VEC models in Stata. Wisconsin:

University of Wisconsin.

STATA13. (2014). STATA13 help for varsoc. Retrieved

from STATA13:

http://www.stata.com/help.cgi?varsoc

The World Bank. (n.d.). Data: External debt stocks,

total (DOD, current US$) . Retrieved October

31, 2014, from The World Bank:

http://data.worldbank.org/indicator/DT.DOD.

DECT.CD?page=6


10

Appendix A: Data Table

Year lndebt lngdp

1972 18.45424 22.80488

1973 18.33716 23.03411

1974 18.23468 23.34657

1975 19.07712 23.42422

1976 19.81731 23.56221

1977 20.08371 23.70124

1978 20.30413 23.84591

1979 20.51605 24.03754

1980 20.76661 24.20298

1981 20.84845 24.29692

1982 20.72255 24.33797

1983 20.81373 24.22618

1984 20.60152 24.17034

1985 20.87894 24.14864

1986 20.95913 24.12007

1987 20.95427 24.22569

1988 20.81261 24.35783

1989 20.88582 24.47454

1990 20.63086 24.51451

1991 20.80551 24.53916

1992 20.81873 24.69311

1993 20.91368 24.71905

1994 20.78492 24.88347

1995 20.40527 25.02895

1996 19.82043 25.14028

1997 20.56682 25.13418

1998 21.17338 25.0028

1999 21.40755 25.14205

2000 21.50417 25.11804

2001 21.46444 25.05744

2002 21.3355 25.12212

2003 21.03852 25.15299

2004 20.6585 25.2382

2005 20.13537 25.35863

2006 18.98263 25.52901

2007 19.0318 25.72963

2008 19.00618 25.88004

2009 20.99609 25.84922

2010 20.97829 26.01953

2011 20.97519 26.13534

2012 20.97627 26.24545

Source: The World Bank

Appendix B: Lag Selection

. varsoc lngdp lndebt

Selection-order criteria

Sample: 1976 - 2012 Number of obs = 37

+---------------------------------------------------------------------------+

|lag | LL LR df p FPE AIC HQIC SBIC |

|----+----------------------------------------------------------------------|

| 0 | -73.5144 .203138 4.08186 4.11256 4.16894 |

| 1 | 23.1057 193.24 4 0.000 .001361 -.924635 -.832539 -.663405* |

| 2 | 29.0392 11.867* 4 0.018 .001229* -1.02914* -.875651* -.593761 |

| 3 | 32.6901 7.3018 4 0.121 .00126 -1.01027 -.795383 -.400736 |

| 4 | 32.9994 .61862 4 0.961 .001554 -.810776 -.534489 -.027087 |

+---------------------------------------------------------------------------+

Endogenous: lngdp lndebt

Exogenous: _cons

Appendix C: Augmented Dickey Fuller Test . dfuller lngdp,trend

Dickey-Fuller test for unit root Number of obs = 40

---------- Interpolated Dickey-Fuller ---------

Test 1% Critical 5% Critical 10% Critical

Statistic Value Value Value

------------------------------------------------------------------------------

Z(t) -2.796 -4.242 -3.540 -3.204

------------------------------------------------------------------------------

MacKinnon approximate p-value for Z(t) = 0.1985

. dfuller D.lngdp,notrend





11


------------------------------------------------------------------------------

Z(t) -4.200 -3.655 -2.961 -2.613

------------------------------------------------------------------------------


. dfuller lndebt,trend





------------------------------------------------------------------------------

Z(t) -2.370 -4.242 -3.540 -3.204

------------------------------------------------------------------------------


. dfuller D.lndebt,notrend





------------------------------------------------------------------------------

Z(t) -4.936 -3.655 -2.961 -2.613

------------------------------------------------------------------------------


Appendix D: Engel Granger Method

. dfuller resid,lags(2)noconstant

Augmented Dickey-Fuller test for unit root Number of obs = 38




------------------------------------------------------------------------------

Z(t) -0.322 -3.662 -2.964 -2.614

------------------------------------------------------------------------------


Appendix E: Johansen’s Co-integration Test

Johansen tests for cointegration

Trend: constant Number of obs = 39

Sample: 1974 - 2012 Lags = 2

-------------------------------------------------------------------------------

5%

maximum trace critical

rank parms LL eigenvalue statistic value

0 6 20.354415 . 17.9035 15.41

1 9 29.205509 0.36486 0.2013* 3.76

2 10 29.306156 0.00515

-------------------------------------------------------------------------------

5%

maximum max critical

rank parms LL eigenvalue statistic value

0 6 20.354415 . 17.7022 14.07

1 9 29.205509 0.36486 0.2013 3.76

2 10 29.306156 0.00515

-------------------------------------------------------------------------------

Appendix F: VEC Models


12

Vector error-correction model 1

Sample: 1974 - 2012 No. of obs = 39

AIC = -1.03618

Log likelihood = 29.20551 HQIC = -.8984404

Det(Sigma_ml) = .0007667 SBIC = -.6522811

Equation Parms RMSE R-sq chi2 P>chi2

----------------------------------------------------------------

D_lngdp 4 .088107 0.5465 42.17619 0.0000

D_lndebt 4 .425692 0.3025 15.17918 0.0043

----------------------------------------------------------------

------------------------------------------------------------------------------

| Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

D_lngdp |

_ce1 |

L1. | .0014093 .0017931 0.79 0.432 -.0021051 .0049238

|

lngdp |

LD. | .3362867 .191041 1.76 0.078 -.0381468 .7107203

|

lndebt |

LD. | .0265867 .0308738 0.86 0.389 -.0339249 .0870984

|

_cons | .0462585 .0199847 2.31 0.021 .0070893 .0854277

-------------+----------------------------------------------------------------

Vector error-correction model 2

Sample: 1974 - 2012 No. of obs = 39

AIC = -1.03618

Log likelihood = 29.20551 HQIC = -.8984404

Det(Sigma_ml) = .0007667 SBIC = -.6522811

Equation Parms RMSE R-sq chi2 P>chi2

----------------------------------------------------------------

D_lndebt 4 .425692 0.3025 15.17918 0.0043

D_lngdp 4 .088107 0.5465 42.17619 0.0000

----------------------------------------------------------------

------------------------------------------------------------------------------

| Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

D_lndebt |

_ce1 |

L1. | -.3383093 .1062365 -3.18 0.001 -.5465291 -.1300896

|

lndebt |

LD. | .2725765 .1491687 1.83 0.068 -.0197889 .5649418

|

lngdp |

LD. | -.6808652 .9230257 -0.74 0.461 -2.489962 1.128232

|

_cons | -.002363 .096557 -0.02 0.980 -.1916113 .1868853

-------------+----------------------------------------------------------------

Appendix G: Wald Test for Short Run Causality

( 1) [D_lngdp]LD.lndebt = 0

chi2( 1) = 0.74

Prob > chi2 = 0.3892

( 1) [D_lndebt]LD.lngdp = 0

chi2( 1) = 0.54

Prob > chi2 = 0.4607


13

Appendix H: Serial Correlation Test

Model 1

Lagrange-multiplier test

+--------------------------------------+

| lag | chi2 df Prob > chi2 |

|------+-------------------------------|

| 1 | 8.3582 4 0.07931 |

| 2 | 3.6075 4 0.46172 |

+--------------------------------------+

H0: no autocorrelation at lag order

Model 2

Lagrange-multiplier test

+--------------------------------------+

| lag | chi2 df Prob > chi2 |

|------+-------------------------------|

| 1 | 8.3582 4 0.07931 |

| 2 | 3.6075 4 0.46172 |

+--------------------------------------+

H0: no autocorrelation at lag order

Causality Between Philippine Economic Growth and Debt

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