Effects of International Remittances on the Philippine Economy: A Cointegration Analysis

Abstract

This paper examines the effect of international remittances on the Philippines economy, both in the short run and in the long run, using a standard cointegration method. Results of the analysis show that remittances have a positive significant effect on the Philippines economy in the long run. This translates to a 0.018% increase in the economy's gross domestic product when the remittances sent by overseas workers to the Philippines increases by 1%. However in the short run, remittances negatively affect the economy's output, which implies that an increase in remittances sent to the country is associated with a decline in the economy's output

Full text

Eects of Internaonal Remiances on
the Philippine Economy: A Cointegraon Analysis
Moises Neil V. Seriño
Department of Economics, Visayas State University,
Visca, Baybay City, Leyte
moisesneil@yahoo.com / moisesneil@gmail.com
This paper examines the effect of international remittances on the Philippine economy, both in the
short run and in the long run, using a standard cointegration method. Results of the analysis show
that remittances have a positive signicant effect on the Philippine economy in the long run. This
translates to a 0.018% increase in the economy’s gross domestic product when the remittances sent
by overseas workers to the Philippines increases by 1%. However in the short run, remittances
negatively affect the economy’s output, which implies that an increase in remittances sent to the
country is associated with a decline in the economy’s output.
Keywords: remittances, Philippine GDP, time series, cointegration, short run dynamics
DLSU Business & Economics Review 21.2 (2012), pp. 47-62
Copyright © 2012 De La Salle University, Philippines
INTRODUCTION
International migration has grown rapidly
due to the globalization of economic activity and
its ensuing effects on labor migration (United
Nations Population Fund, 2006). This global
migration has been receiving mounting interest
from government, and from academics and the
media due to the fact that this phenomenon depicts
a continuous growth and an increasing trend. This
international movement, mainly from developing
countries into developed countries, has generated
a signicant improvement in the lives of migrants
and their families. These international migrants
receive higher wages and their families who are
left in their country of origin beneted through
the remittances (Migration Information Source,
2008).
Over the past two decades, developing
countries have experienced a signicant increase
in the bulk of remittances sent by international
migrant workers. The World Bank’s ofcial record
of remittance ow for year 2010 show that $325
billion was transferred to developing countries,
which signaled a recovery after the global
nancial crisis. This accounts for almost 75%
of the world’s total remittances which amounted
to $440 billion in 2010. Remittance ows to
developing countries are expected to grow at
lower but more sustainable rates of 7-8 percent
annually from 2011 to 2013, to reach $404 billion
by 2013 (Mohapatra, S. et. al, 2011).
The Philippines occupies a prominent position
among remittance-receiving and labor-exporting
countries. According to the Philippine Overseas
Employment Administration (POEA), in 2010

48 VOL. 21 NO. 2DLSU BUSINESS & ECONOMICS REVIEW
the total number of deployed overseas Filipino
workers, both land-based and sea-based, was
close to 1.5 million Filipinos; and the estimated
global stock of overseas Filipinos as of 2009
was over 8.5 million. This large number of
Filipinos abroad has positioned the country to
be one of the highest recipients of international
remittances. According to the World Bank, the
Philippines is reputed to be the world’s fourth
highest remittance recipient country, next only
to India, China and Mexico. Available data
from the Bangko Sentral ng Pilipinas (BSP)
indicates that in 2010, remittances of overseas
Filipino workers coursed through banks reached
a record high level of $18.8 billion (up by 8.16
percent from 2009 gures). The total amount
contributed close to 10% of the country’s gross
domestic product (GDP). BSP pointed out that
major driving factors that help accelerate growth
in remittances are: sustained demand abroad
for Filipino workers diversity of destinations;
and the skills of overseas Filipino workers. This
surge in remittances has continued to fuel a strong
domestic demand for consumption goods, boosted
the peso, alleviated the international debt burden,
tamed inflation, increased foreign exchange
reserves and contributed in general to a better
picture of the economy.
However, there are still contradicting views
as to what effect remittance flows have on
the migrants’ origin country. Some argue that
remittances create negative effects on the origin
country, as remittances have been used mostly
for consumption, rather than to increase the
productive capacity. Also, remittances present a
moral hazard or dependency syndrome that will
likely impede economic growth, as recipients tend
to reduce participation in productive activities
(Chami, et. al, 2003). On the other hand, many
studies suggest that the utilization of remittances
on consumption has its multiplier effect in terms
of increasing the demand for goods and services,
and of indirect investment, especially when
the money is used for purposes of health and
education, and for real estate, all of which have
positive effects on human development (Ramirez
and Sharma, 2006; Giuliano, et.al, 2006; and
Jongwanich, 2007).
Given the abovementioned views, this paper
aims to apply a standard cointegration method
to evaluate the effects of remittances on the
performance of the Philippine economy. This
study utilized a simplied macroeconomic model
to investigate the short-run and long-run effects
of remittances on the Philippine economy. A lot
of conicting issues can be found in the literature
which argues that remittances either support
economic growth or retard it. This study will
attempt to provide information that will help to
probe the conicting effects of remittances on the
Philippine economy. It will evaluate the effects
of remittances sent only through formal channels
(e.g. banks) from 1977 to 2006. However, it
should be noted that the effects of remittances
coursed through informal channels are beyond
the scope of this study. In addition, this study will
ll the gap in the literature on remittances and
output because few studies which are limited in
scope have employed the cointegration method
to analyze the effects of remittances on the
Philippine economy.
The rest of the paper is organized as follows:
Section II presents the review of related literature;
Section III outlines the methods explored in this
paper; Section IV presents the ndings of the study;
and the last section presents concluding remarks.
REVIEW OF RELATED LITERATURE
This section presents some of the highlights as
to how remittances affect the recipient country’s
economy. There are contrasting results given in the
literature with regard to the effect of remittances
on economic growth. Some studies suggest that
remittances support economic growth, while
others argue that they retard economic growth.
Given these, it can be observed that the effects of
remittances may depend on the recipient country’s
capacity to manage remittances and to maximize
the benets out of it, or to minimize the associated
negative effects of remittances on the economy.

SERIÑO, M.N. 49EFFECTS OF INTERNATIONAL REMITTANCES ON THE PHILIPPINE ECONOMY
Remittances Support Economic Growth
According to the UNDP (2005), remittances
are important for developing countries as the
amount can provide access to additional nancial
resources and ultimately, to the creation and
sustainability of livelihoods. Ratha and Maimbo
(2005) examine the importance of workers’
remittances as a stable source of external funding
in developing countries. The economic effect of
remittances increases the recipient’s household
income and the foreign exchange reserves of
the recipient’s country. Remittances contribute
to output growth if invested, and generate a
positive multiplier effect if they are consumed.
Ramirez and Sharma (2006) conducted a study
in Latin American countries using a panel unit
root and cointegration analysis. The results
of the study suggested that remittances have
a positive and signicant effect on economic
growth. Moreover, the impact of remittances on
growth is more pronounced in the presence of the
nancial development variable. The availability
of a strong and viable nancial institution is the
key point in maximizing the benet from the
remittances. Mundaca (2005) stressed that the
level of nancial development in Central America,
Mexico and Dominican Republic tends to
increase the responsiveness of economic growth
to remittances. This means that the effect of
remittances on growth in the long run is inuenced
by making financial services more generally
available. Another study on the link between
remittances and growth, which used a newly-
constructed cross country series for remittances
covering about 100 developing countries, found
that remittances boost growth in countries with
less developed nancial systems, by providing
an alternative way to nance investment and
help overcome liquidity constraints (Giuliano,
et.al, 2006). Jongwanich (2007), in his study on
the impact of workers’ remittances on growth in
17 developing Asia-Pacic countries, which used
panel data over the period 1993-2003, found that
a one percent increase in remittances would tend
to increase economic growth by 0.43. However,
the impact is only marginal, operating as it does
through domestic investment and human capital
development.
In the Philippines, several studies have
evaluated the effect of remittances on the economy.
Ang (2007), in his study on workers’ remittances
and economic growth in the Philippines, found
that on the national level, remittances do
influence economic growth positively and
signicantly. According to, Economic Planning
Secretary Cayetano Padrenga Jr. as quoted by
Riza Olchondra of the Philippine Daily Inquirer,
remittances will lead to continuing consumption
demand, which will also lead to continuing growth
in sectors that have been growing in the past,
thereby fueling economic growth. In addition,
Alcuaz (2007) of Bloomberg found positive
correlations between remittances and economic
growth.
On the household level, Tabuga (2007)
investigated the general relationship between
remittances and household expenditures in the
Philippines by doing a cross-sectional analysis of
the 2003 Family Income and Expenditure Survey
(FIES). Tabuga showed that there is evidence that
households receiving remittances tend to consume
consumer items more, but they also invest more
on education, housing, medical care and durable
goods. He reported that this has a benecial effect
on the economy because it potentially creates an
impact on local development.
Remittances hamper economic growth
A panel data analysis, which utilizes remittances
data for 28 years from 113 countries, indicates
that remittances do have a negative effect on
economic growth, indicating that the moral
hazard problem brought by remittances is severe
(Chami, R., Fullenkamp, C. and Jahjah, S.,
2003). Recipients of remittances tend to decrease
labor participation, reduce labor effort, limit job
searches and invest in riskier projects. Chami,
et al (2008) also mentioned that households are
reluctant to pressure the government in enacting
policy reform facilitating economic growth,

50 VOL. 21 NO. 2DLSU BUSINESS & ECONOMICS REVIEW
since remittances protect them against adverse
economic shock. Cáceres and Saca (2006) found
that in El Salvador, remittances lead to a decrease
in economic activity, international reserves, and
money supply; and an increase in the interest rate,
imports, and consumer prices. Ang (2007) found
that on the regional level, remittances do not affect
economic growth in the Philippines. This further
indicates that benets from remittances can hardly
be translated into development and growth. On
the household level, Ang, et. al (2009) examined
the role of remittances in increasing household
consumption and investment and their potential
for rebalancing economic growth. Results of
the study showed that remittances negatively
inuence the share of food consumption in the
total expenditure, implying that remittances do not
contribute toward rebalancing growth by creating
domestic demand.
In remittance-receiving countries, the Dutch
disease effect is manifested by strong empirical
evidence to the effect that remittances are positively
correlated to real exchange rate appreciation
(Chami, R. et al., 2008). Tuaño-Amador, et al.
(2007) found that there is evidence to suggest that
remittances have led to some symptoms of the
Dutch disease phenomenon in the Philippines. In
particular, the strong remittance trend may have
contributed to the recent appreciation of the peso
in real terms; but they do not nd a sharp decline
in economic growth when compared to countries
that suffer from the disease.
Burgess and Haksar (2005) studied migration
and remittances in the Philippines. Their ndings
revealed that the empirical evidence does
not clearly support the purported short-term
stabilizing effect on consumption of remittance
ows. Furthermore, as in other countries, the
longer term economic effect of remittance is
ambiguous. This nding is consistent with what
Ratha and Mohapatra (2007) presented in the
G8 outreach event on remittances, which was
that the evidence on the effect of remittances on
long-term growth is inconclusive. Remittances
may increase consumption and per capita income
levels, and reduce poverty and inequality; but
they do not directly impact growth. On the other
hand, a large outflow of workers can reduce
growth in the countries of origin. However when
remittances are used to nance education and
health, and to increase investment, then they could
have a positive effect on economic growth; which
makes ambiguous the effect of remittances on the
economy.
Motivation to remit and cyclicality
of remittances
To further understand the behavior of remittances
and their effect on the economy, it is logical to delve
into the motivation of workers to remit. Bouhga-
Hagbe (2006) looked for potential evidence of
altruistic motives behind the decision to remit by
workers in selected countries in the Middle East and
Central Asia. The results of the study suggest that
in the long run, remittances tend to be negatively
correlated to agricultural GDP. This supports the
view that altruism could play an important role
in the workers’ decision to remit. By altruism,
Bouhga-Hagbe (2006) means the willingness of
someone, in this case a worker living outside his or
her home country, to provide nancial assistance to
another who is in a situation of “hardship”.
So if remittances are altruistically motivated,
then one would expect the counter-cyclicality
nature of remittances. Tuaño-Amador, et al.
(2007) showed that in the Philippines, remittances
are quite the opposite; they are procyclical in
nature. Procyclicality suggests that portfolio
and investment considerations are as important
as altruistic considerations in inuencing trends
in remittances. Their methodology revealed
that the output differential impacts positively
on remittances after one quarter. This supports
the finding that remittances are procyclical.
However, this nding is in contrast to what Chami,
Fullenkamp and Jahjah (2003) found, which is
that remittances behave counter-cyclically in
developing countries. On the other hand, Ratha
(2003) argues that remittances are more stable
than private capital ows in the form of either debt
or equity, which often move procyclically, tending

SERIÑO, M.N. 51EFFECTS OF INTERNATIONAL REMITTANCES ON THE PHILIPPINE ECONOMY
to boost income during good times and to lower
income during bad times. Ratha and Mohapatra
(2007) noted that remittances used for investment
purposes behave procyclically, just as other
investment ows do; while remittances are more
likely to be countercyclical in poor countries.
METHODOLOGY
Empirical Model
To shed some light on the contrasting effect
of remittances on the Philippine economy, this
study employs a macroeconomic model, based on
what Glystos (2002) used to evaluate the impact
of remittances on consumption, investment,
imports and output in Mediterranean countries. It
is worth mentioning that the factor of income used
by Glystos (2002) is a kind of national income
consisting of GDP plus migrant remittances.
However in this study, the model was extended to
include initial GDP and to incorporate additional
sources of external funding, such as foreign direct
investment (FDI) and development aid (ODA).
The inclusion of FDI and ODA is proposed
in this study so as to control other sources of
external funding, since remittance is a form of
external funding too. In his study Glystos (2002)
introduced the inclusion of remittances into a
macroeconomic model; for which reason it is
also considered valid to include FDI and ODA
to serve as control variables to analyze the effect
of remittances on the Philippine economy. The
dynamism of the model is captured by introducing
a year lag of the economy’s output. Intuitively, the
previous performance of the economy affects the
current performance.
The model is thus postulated as follows:
Yt = c0 + c1Yt-1 + c2Const + c3Invt + c4Govt
+ c5Next + c6Remitt + c7FDIt + c8Aidt +
ε
t (1)
where Yt is the economy’s output measured in
terms of real GDP at constant prices at time t;
Yt-1 denotes the initial level of real GDP; Const
refers to the consumption; Invt to investment;
Govt to government expenditure; Next refers
to the net exports (Exports – Imports); Remitt
to the remittances sent my migrant workers;
FDIt and Aidt are the other external sources of
funding, foreign direct investment, and ofcial
development assistance; and εt is the error term.
Since all variables were estimated in logarithmic
form, estimates yield the elasticity of variables.
The main concern in this study is to see
how remittances affect the performance of the
Philippine economy. If remittances support
growth in the economy, then it is expected that
c6 is positive, meaning the surging increase of
remittances is positively associated with growth
in the economy, which is similar to what Ramirez
and Sharma (2006), Giuliano, et al. (2006),
Jongwanich (2007) and Alcuaz (2007) found out.
On the other hand if c6 is negative, it implies that
an increase in remittances sent to the Philippines
is associated with slowing down the growth of
the economy (Chami, et al., 2003; Burgess and
Haksar, 2005; and Ang, et al., 2009). This study
hopes to nd information that would help clear
the argument as to what effect remittances have on
the economy by evaluating their effects both in the
short run and in the long run. This study performs
a cointegration test to understand the long-term
relationship between output and remittances. The
short-term dynamics of the postulated model will
be estimated, using error correction models if the
variables involved are cointegrated.
Data Used
The main source of data in this study is the
World Development Indicator (WDI). Moreover,
OECD.stat1 was also used to retrieve data on
ofcial development assistance. The period of
coverage of this study is from 1977 to 2006 (29
observations) 2. Based on the national income
accounting, constructed were regression equations
which represent the household sector, the private
sector, the government sector and the external
sector. Table 1 presents the description of data
used in this study.

52 VOL. 21 NO. 2DLSU BUSINESS & ECONOMICS REVIEW
Unit Root Test Using Augmented Dickey Fuller
Test
Prior to estimating the regression model
(1), each variable was tested for the presence
of unit root to ensure stationarity of the series.
Stationarity of the data should be justied so
that a regression analysis can be conducted
meaningfully. To verify the hypothesis that
the time series variables are non-stationary the
Augmented Dickey Fuller (ADF) test was carried
out, using the Akaike and Schwarz info criterion
to determine the maximum lag length. The test
was rst conducted at levels and if unit root was
detected, testing was conducted at rst difference.
Two auxiliary regressions were considered in
ADF test3; an intercept with time trend (2) and
with intercept only (3).
∑
=
−− +∆+++=∆
m
i
ttitt YtY
1
1121
εαδββ
(2)
∑
=
−− +∆++=∆
m
i
ttitt YY
1
111
εαδβ
(3)
In the two equations, the parameter of interest
is δ. The null hypothesis (HO) and the alternative
(HA) were formulated as follows: HO: δ = 0
and HA: δ < 0. The null hypothesis indicates that
variable has unit root, whereas the alternative
hypothesis shows no unit root. The estimated
t-statistic is then compared with the appropriate
critical value in the Dickey Fuller table to
determine if the null hypothesis is valid.
Cointegration test
Doing a cointegration test requires that
variables involved have unit roots. It is suspected
that variables have unit roots at levels but become
stationary after rst differencing. These would
indicate that variables are integrated to the order
of 1 or I(1). The idea of a cointegration analysis
is that although two or more variables are non-
stationary, their linear combination might be
stationary. If variables are cointegrated, this
suggests that there exists a long-term equilibrium
or long run relationship between dependent and
independent variables.
Table 1.
Data Description
Variables Description
Y Real Gross Domestic Product
Yt–1 Initial Real Gross Domestic Product
Cons Consumption of durable goods, nondurable goods and services (Personal Consumption
Expenditure)
Inv Net additions to the (physical) capital stock in an accounting period, or, to the value
of the increase of the capital stock (Gross Capital Formation)
Gov Government Consumption/Expenditure
Nex Net Exports (Exports – Imports)
Remit Overseas remittance coursed thru banks
FDI Foreign Direct Investment
Aid Ofcial Development Assistant Received
Note: units used are standardized in 2000 constant prices US$

SERIÑO, M.N. 53EFFECTS OF INTERNATIONAL REMITTANCES ON THE PHILIPPINE ECONOMY
Two cointegration tests were explored in this study. The rst test used the usual ADF test applied
on the residuals of equation (1). This was veried by testing the residuals of the postulated model to
determine whether residuals are stationary or not. In testing whether the variables are cointegrated, a
new variable will be dened as et,
 ε
t = Yt – (c0 + c1Yt-1 + c2const + c3Invt + c4Govt + c5Next + c6Remitt + c7FDIt + c8Aidt ) (4)
The ADF test was administered to the residuals of the cointegrating equation. If null hypothesis of unit
root in (et) is rejected in favor of the I(0) alternative, then this implies that the variables are cointegrated.
On the other hand, the Johansen cointegration test uses the maximum likelihood procedure to
determine presence of cointgerating vectors. The Johansen test assumes that the dependent variable
is I(1). All variables at I(1) are grouped together and tested for cointegration, using the Johansen
cointegration test. In this study, the cointegration test considers linear deterministic trend in the data;
and a test was conducted both to include intercept and trend, and to use intercept only. The Johansen
cointegrating test is based on the trace statistics and maximum eigenvalue. The null hypothesis indicates
that there are no cointegrating relationships among the variables. If null is rejected in favor of the
alternative, then there is sufcient evidence to indicate that cointgeration is present among variables.
Error Correction Model
When variables are cointegrated, the results suggest the use of the error correction model (ECM). The
error correction model will allow us to understand the short-run dynamics of the relationship between
independent variables and dependent variable. The error correction model is postulated as follows:
∑ ∑ ∑ ∑∑
= = = =
−
=
−−−−
∆+∆+∆+∆+∆+=∆
na
h
nb
h
nc
h
ne
h
ht
nd
h
hththttt
NexcGovcInvcConscYccY
0 0 0 0
5
0
432110
∑ ∑ ∑
= = =
−−−− ++∆+∆+∆+
nf
h
ng
h
nh
h
tthththt ECTcAidcFDIcremitc
0 0 0
19876
ε
(5)
where na, nb, nc, nd, ne, nf, ng and nh are the lengths of lags included for each specied variable, and
ECT is the error correction term. ECT is computed based on the cointgerating vectors. If the variables
in (1) are not cointegrated, then the error correction term from (5) is eliminated; and the variables will
be analyzed in rst difference using the OLS method.
RESULTS AND DISCUSSION
It is imperative in any time series data to do visual inspection of the series before proceeding
to empirical analysis. Appendix 1 shows the linear graph of each variable plotted against time.
It can be observed that GDP, consumption and government expenditures seem to be trending
upward, although government expenditures did not display a smooth trend. The level of investment,
and the bulk of remittances and aid show an unsmooth and rough trend; but it still seems to be
moving upward. The level of aid extended to the Philippines shows an increasing trend until the
mid-1990s; and then it gradually decreases. Net exports and foreign direct investment displays
no clear trend. However, it might be suspected that a trend is present in foreign direct investment.

54 VOL. 21 NO. 2DLSU BUSINESS & ECONOMICS REVIEW
Since it is apparent that the presence of a trend is
observable among the majority of the variables
involved, this suggests that the data set is not
stationary, hence the unit root test is deemed
necessary.
Unit Root Test
Table 2 and 3 presents the results of the unit
root test conducted at levels and rst difference,
respectively. The results show that net export
has no unit root both in the Akaike and Schwarz
info criterion. This implies stationarity of the net
exports data at levels. Foreign direct investment
too was detected to have no unit root; but using
both criteria with trend and intercept included but
with only intercept included, unit root is present
in the data set. However, other variables have
contradicting results in terms of the presence
of unit root between Akaike and Schwarz info
criterion. GDP, consumption and investment
have no unit root ,using Akaike with trend
and intercept included; but Schwarz indicates
presence of unit root. Remittances and ODA were
consistently detected to have unit roots, implying
non-stationarity.
Table 2.
Unit root test for Stationarity at Levels
Variables Akaike Info Criterion Schwarz Info Criterion
Intercept Trend & Intercept Intercept Trend & Intercept
Real GDP 2.15 -3.51*2.15 -2.83
Consumption 3.34 -3.60*1.95 -2.49
Investment -1.12 -4.42** -1.91 -1.12
Government -0.40 -2.45 -0.40 -2.45
Net Exports -4.65*** -4.92*** -4.65*** -4.92***
Remittances -0.16 -2.85 -0.16 -2.85
FDI -2.49 -3.88** -2.49 -3.88**
ODA -1.71 -1.49 -1.71 -1.49
*, **, *** Signicant at 10%,5% and 1%
Table 3.
Unit root test for Stationarity at First Difference
Variables Akaike Info Criterion Schwarz Info Criterion
Intercept Trend & Intercept Intercept Trend & Intercept
Real GDP -0.98 -4.63*** -2.90*-4.63***
Consumption -1.28 -2.18 -1.28 -2.18
Investment -3.78** -2.72 -4.02** 3.95**
Government -3.84*** -3.80** -.384*** -3.80**
Net Exports - - - -
Remittances -8.17*** -8.22*** -8.17*** -8.22***
FDI -7.91*** -2.55 -7.91*** -7.77***
ODA -6.33*** -7.13*** -6.33*** -7.13***
*, **, *** Signicant at 10%,5% and 1%

SERIÑO, M.N. 55EFFECTS OF INTERNATIONAL REMITTANCES ON THE PHILIPPINE ECONOMY
After rst differencing, most variables reject
the null hypothesis of unit root (Table 3).
However, consumption variable still has unit
root. Result of consumption is somehow strange,
because at levels, it indicates that at 10% level of
signicance it has no unit root, but rst difference
result suggests presence of unit root. Another
unexpected result of rst difference unit root test
is FDI. At levels, it indicates no unit root at 5%;
but surprisingly it indicates presence of unit root at
rst difference, with trend and intercept included.
After second differencing4, all variables show no
evidence of unit root. Thus, stationarity of data
set is achieved.
Results of the unit root test indicate that
net export variable is stationary at levels, and
consumption variable attains stationarity at second
difference. The rest of the variables are integrated
to order of 1 or I(1), that is, stationary at rst
difference.
Cointegration Test
Since the unit root test indicated that variables
involved have unit roots except for net exports,
cointegration tests were carried out to examine
whether variables have long-run relationship.
Using the ADF test for testing the presence of
unit root at levels in the residuals of equation
(1), it is shown that ADF test statistic is highly
significant in both Akaike and Schwarz info
criterion, including intercept only. However,
if trend and intercept were included, ADF test
statistic is signicant at 5% and 10% in Akaike
and Schwarz info criterion, respectively. This
implies that variables are cointegrated. This
reafrms the claim that although two or more
variables are I(1), their linear combination might
be stationary.
Similarly, the result of a contegration test
using the method proposed by Johansen shows
that variables are cointegrated. In the context
of the Johansen cointegration test, net export
variable was not included in the test since it
attains stationarity at levels. Nevertheless, a
cointegration test including next export, shows
that variables are cointegrated; but results were
not reported. Table 5 presents the cointegration
test using Johansen’s test. Two test statistics
were considered, such as the Trace and the Max-
eigen statistics. These statistics were compared
to their corresponding critical value set at 5%
signicance level. Results show that the Trace
test indicates that there are ve (5) cointegrating
vectors at 5% level of signicance. As shown in
Table 5, the Trace statistic is lower than 5% in
hypothesized cointegrating vector at most four
(r≤4). Likewise, the Max-eigen value test rejects
the null hypothesis of at most 1 cointegrating
vector. This indicates two (2) cointegrating
equations at 5% level of signicance.
Based on the two cointegration tests, there
is sufcient evidence to indicate that variables
involved in this study are cointegrated. This
strongly implies that that there exists a long-run
relationship between GDP and the other variables.
The postulated model can well explain the long-
term movements of GDP in terms of national
income plus migrants’ remittances, while holding
for other external sources of funding.
Table 4.
ADF Test for Presence of Unit Root of Residuals at Levels
Variables
Akaike Info Criterion Schwarz Info Criterion
Intercept Trend & Intercept Intercept Trend & Intercept
ADF Test Statistic -4.12*** -3.28*-4.12*** -4.11**
*, **, *** Signicant at 10%,5% and 1%

56 VOL. 21 NO. 2DLSU BUSINESS & ECONOMICS REVIEW
Long-Term Dynamics of the Model
Table 6 shows the regression result by modied
OLS. The main concern of this study is to evaluate
the effect of the surging ow of remittances sent
by migrant workers to recipient families in the
Philippines. Results suggest that in the long run,
a 1% increase in remittances would increase
output by 0.018%, holding other factors constant.
The estimate is highly signicant (Table 6). This
is indeed plausible since remittances are private
transfers directly affecting the household level;
and these transfers are stable in the sense that
migrant workers are motivated to send remittances
back home to sustain their family. Remittances
sent by migrant workers are invested in education,
land, and household enterprises that are likely to
improve their lives in the long run (Yang, 2004).
The investment of remittances in safe, protable
ventures and in human capital could boost and
enhance economic growth in the long run. This
finding validates the claim that remittances
support economic growth in the Philippines.
Looking at the estimates of the three sources of
external funding, remittances, and foreign direct
investment, one can posit a positive relationship
to output, but aid shows a negative relationship. In
comparison with FDI, remittances have a higher
impact on the economy. This result is consistent
with the trend of the level of remittances now
exceeding foreign direct investments (World
Bank, 2007). Thus, its effect is greater than other
sources of external capital or funding.
With regard to other variables included, the
regression model implies that in the long run,
initial GDP, consumption and level of investment
have a positive impact on output and is highly
signicant. These results are consistent with those
suggested in the literature. However, government
expenditure, net exports and level of aid were
Table 5.
Johansen Cointegration Test
Statistic HO:r = 0 r ≤ 1 r ≤ 2 r ≤ 3 r ≤ 4 r ≤ 5
HA:r ≥ 0 r ≥ 2 r ≥ 3 r ≥ 4 r ≥ 5 r ≥ 6
Trace Statistic 125.62*95.75*69.82*47.86*29.79*15.49
(p-value) 0.0000 0.0000 0.0009 0.0058 0.0187 0.0626
Max-Eigen Statistic 63.57*47.00*31.21 23.42 18.51 11.19
(p-value) 0.0003 0.0071 0.1008 0.1561 0.1121 0.1451
Notes: r denotes the number of cointegrating vectors. The asterisk (*) indicates the rejection of the null
hypothesis of no cointegration at 5% signicance level
Table 6.
Estimate of the model by OLS for the whole period
Variable C lnYt-1 lnConstlnInvtlnGovtlnNextlnFDItlnODAtlnRemitt
Estimates 5.7251 0.420 0.5166 0.143 -0.3355 -0.0007 0.008 -0.023 0.0181
Std Error 0.9209 0.1075 0.1265 0.0304 0.097 0.0028 0.0036 0.0087 0.0052
p-value 0.00 0.0009 0.0006 0.0001 0.0025 0.8197 0.0362 0.0137 0.0022

SERIÑO, M.N. 57EFFECTS OF INTERNATIONAL REMITTANCES ON THE PHILIPPINE ECONOMY
reported to have a negative relationship with the
GDP. This should be interpreted with caution; it
will require further investigation which is beyond
the scope of this study.
Error Correction Model
Even though the variables involved are
cointegrated, (i.e. there is equilibrium relationship
among them in the long run), disequilibrium in
the short-run is plausible. Thus to understand the
short-run dynamics of the model, this necessitates
the estimation of error correction model. The
error correction terms are computed based on the
cointegrating vectors reported in the Trace test
(Table 5).
Appendix 2 shows the result of the estimation
of the error correction model with two lags6.
Results show that the error correction term is
negative and signicant at 5%. This strongly
implies that there exists short run equilibrium
among the variables involved. The error correction
model can explain the behavior of the GDP very
well given R2=0.9991. The error correction model
can be simplied by eliminating the insignicant
variables in the equation.
Short Run Dynamics of the Model
Table 7 presents the estimates of the error
correction model. Estimates show the short run
effect of the variables to economy’s output. It is
interesting to note that the level of consumption
in the rst and second period lag has a negative
impact on the level of GDP. Government
expenditure negatively affects GDP in the current
period; but in the rst lag period it turns out to be
positively affecting GDP. This may imply that in
the short run, it takes a year before government
expenditure translates its benet to the economy.
Also, the net export exhibits the same behavior
with government expenditure; but net export’s
Table 7.
Estimate of the error correction model
Variable Coefcient Std. Error t-Statistic Prob.
C 7.125*** 0.984 7.243 0.0000
∆lnGDP(-1) 0.683*** 0.107 6.373 0.0001
∆lnCONS 0.973*** 0.154 6.322 0.0001
∆lnCONS(-1) -2.114*** 0.317 -6.664 0.0000
∆lnCONS(-2) -0.389** 0.169 -2.307 0.0415
∆lnINV 0.139*** 0.017 7.918 0.0000
∆lnGOV -0.278*** 0.052 -5.326 0.0002
∆lnGOV(-1) 0.3457*** 0.067 5.155 0.0003
∆lnNEX -0.008*** 0.001 -6.242 0.0001
∆lnNEX(-1) 0.0135*** 0.002 6.343 0.0001
∆lnNEX(-2) 0.006** 0.002 2.632 0.0233
∆lnREMIT(-1) -0.010*** 0.002 -4.429 0.0010
∆lnFDI 0.015*** 0.002 8.737 0.0000
∆lnFDI(-1) -0.012*** 0.002 -4.936 0.0004
∆lnODA(-1) 0.045*** 0.010 4.154 0.0016
ECT(-1) -0.738*** 0.101 -7.243 0.0000
*, **, *** Signicant at 10%,5% and 1%

58 VOL. 21 NO. 2DLSU BUSINESS & ECONOMICS REVIEW
second period lag still has a signicant positive
impact on GDP in the short run. However, foreign
direct investment shows a different impact
compared with government expenditure and net
exports. In the short run, current FDI positively
affects economy’s output, but rst lag period of
FDI negatively inuence GDP. On the other hand,
rst lag period of aid positively affects GDP.
The variable of interest in this study, which is
remittances, shows that in the short run, current
level of remittances does not have an impact on
economy’s output; but the rst period lag exhibits a
negative relationship with the output. This result is
quite plausible since remittances are mainly used by
recipient families to boost household consumption.
Remittances are said to increase family income;
but it may likewise reduce family members’ work
effort (income effect)--a moral hazard on labor
supply (Business Mirror, 2008). For this reason it
is probable that in the short run, GDP is negatively
affected by level of remittances from the previous
year because the recipient family tends to reduce
participation in productive activities, given that they
receive a relatively higher amount of remittances.
This result is consistent with what Chami, et al.
(2003) and Chami, et al. (2008) argued. Holding
other factors constant, a 1% increase of remittances
in the rst period lag would tend to reduce the level
of GDP by 0.01% in the short run. Relatively, the
decline in the short run is less than the gain in the
long run, with respect to the effect of remittances
in the Philippine economy. Thus managing
remittances and harnessing its benets are good
for the economy. The negative effect in the short
run will be offset by the larger positive effect in the
long run. But it is still worthwhile to be cautioned
about the short-run retarding effect of remittances
on economic growth.
It is interesting to note that among the three
sources of external funding considered in this
study, it is the level of official development
assistance which shows a positive impact on the
economy’s output in the rst year lag. In the short
run, both the previous year’s level of FDI and
remittances negatively affects GDP; while ofcial
aid helps push the economy.
CONCLUSION
The main objective of this research study
is to determine the effect of remittances sent
by migrant workers to the performance of the
Philippine economy from 1977 to 2006, by
employing the method of cointegration analysis.
The cointegration test showed that the
variables involved in this study are cointegrated.
This shows evidence that there is a long-run
relationship between the level of GDP and the
independent variables considered. The result
of OLS estimation showed that in the long
run, remittances have a positive and signicant
effect on the output. Thus, a 1% increase in the
bulk of remittances sent by migrant workers
would increase the economy’s output by
0.018%, holding other factors constant.
With regard to the short-run dynamics of the
model, the error correction model shows that
the rst period lag of remittances negatively
affects the level of GDP; but the current level
of remittances has a positive but insignicant
inuence on the GDP. Thus in the short run,
holding other factors constant, a 1% increase in
the previous year level of remittance would tend
to decrease the economy’s output by 0.01%.
Remittances exhibit contradicting results with
regard to their effect on the performance of the
Philippine economy. In the short run, remittances
negatively affect the level of GDP; but in the
long run, remittances positively affect GDP. The
short-run effect of remittances possibly captures
the possible moral hazard the migrants’ family
would be exposed to, by reducing participation in
productive activities, since remittances increase
their current level of income. But in the long
run, when remittances are invested in education,
land, household enterprises and other safe and
productive ventures, they are more likely to
improve the lives of the migrants’ families; and
would translate to a positive effect on the economy
as a whole.

SERIÑO, M.N. 59EFFECTS OF INTERNATIONAL REMITTANCES ON THE PHILIPPINE ECONOMY
NOTES
1 OECD.stat is an online database for ofcial development
assistance
2 Data on remittances for the Philippines is available online
from year 1977 onwards.
3 `In the ADF test, three auxiliary regressions can be
tested. The third regression is without intercept
t
m
i
titt YY
εαδ
+∆+=∆ ∑
=
−−
1
11 ; but for the purpose of this
study, only two auxiliary regressions were carried out.
4 Second differencing of the variable was also conducted,
although results are not reported here.
5 This stipulates that government is expected to provide
a framework of political stability, rule of law, sound
macroeconomic policy to promote economic growth,
and physical and human infrastructures within which
an enterprise can ourish.
6 The maximum lag used was determined through manual
iteration of the model. The model was rst run using
rst lag, and then run using second lag. When the third
lag was included, Eviews reports error of insufcient
observation. Thus, maximum lag included is two.
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SERIÑO, M.N. 61EFFECTS OF INTERNATIONAL REMITTANCES ON THE PHILIPPINE ECONOMY
APPENDICES
Appendix 1.
Graph of the series
24.4
24.6
24.8
25.0
25.2
25.4
1980 1985 1990 1995 2000 2005
LGDP
24.0
24.2
24.4
24.6
24.8
25.0
25.2
1980 1985 1990 1995 2000 2005
LCONS
22.4
22.6
22.8
23.0
23.2
23.4
23.6
1980 1985 1990 1995 2000 2005
LINV
22.4
22.5
22.6
22.7
22.8
22.9
23.0
23.1
1980 1985 1990 1995 2000 2005
LGOV
17
18
19
20
21
22
23
1980 1985 1990 1995 2000 2005
LNEX
-7
-6
-5
-4
-3
-2
1980 1985 1990 1995 2000 2005
LREMIT
-9
-8
-7
-6
-5
-4
-3
1980 1985 1990 1995 2000 2005
LFDI
-19.5
-19.0
-18.5
-18.0
-17.5
-17.0
1980 1985 1990 1995 2000 2005
LODA

62 VOL. 21 NO. 2DLSU BUSINESS & ECONOMICS REVIEW
Appendix 2.
Error Correction Model with 2 Lags
Variable Coefcient Std. Error t-Statistic Prob.
C 13.23804 2.545168 5.201242 0.0350
DLGDP(-1) 0.502922 0.149417 3.365891 0.0781
DLGDP(-2) 0.274315 0.198291 1.383397 0.3007
DLCONS 1.095888 0.128703 8.514879 0.0135
DLCONS(-1) -2.257729 0.277351 -8.140317 0.0148
DLCONS(-2) -2.139597 0.724758 -2.952153 0.0981
DLINV 0.161686 0.020751 7.791724 0.0161
DLINV(-1) 0.044938 0.029339 1.531697 0.2653
DLINV(-2) -0.019214 0.024186 -0.794430 0.5102
DLGOV -0.492461 0.084274 -5.843568 0.0281
DLGOV(-1) 0.567485 0.101889 5.569624 0.0308
DLGOV(-2) 0.277240 0.116301 2.383813 0.1400
DLNEX -0.008312 0.001202 -6.912894 0.0203
DLNEX(-1) 0.021041 0.003854 5.459805 0.0319
DLNEX(-2) 0.012164 0.003695 3.292008 0.0812
DLREMIT 0.000935 0.002544 0.367721 0.7483
DLREMIT(-1) -0.019537 0.004298 -4.546154 0.0451
DLREMIT(-2) -0.014540 0.005181 -2.806188 0.1070
DLFDI 0.018394 0.001977 9.302216 0.0114
DLFDI(-1) -0.025376 0.005900 -4.300677 0.0500
DLFDI(-2) -0.001775 0.002088 -0.850157 0.4848
DLODA -0.004569 0.008169 -0.559321 0.6322
DLODA(-1) 0.094214 0.023082 4.081657 0.0551
DLODA(-2) 0.017726 0.010294 1.722071 0.2272
ECT(-1) -1.369931 0.263388 -5.201184 0.0350
R-squared 0.999078 Mean dependent var 0.029220
Adjusted R-squared 0.988014 S.D. dependent var 0.035935
S.E. of regression 0.003934 Akaike info criterion -8.989080
Sum squared resid 3.10E-05 Schwarz criterion -7.789231
Log likelihood 146.3526 Hannan-Quinn criter. -8.632302
F-statistic 90.29911 Durbin-Watson stat 2.035014
Prob(F-statistic) 0.011008