Do foreign inflows benefit Pakistani poor?
Ali, Muhammad ; Nishat, Muhammad
Foreign Inflow plays an important role in country's
development. The importance of foreign inflows is not limited to
developing countries; developed and emerging economies also attract
foreign inflows to supplement their resources to sustain growth. The
importance of foreign inflow in Pakistan is very well acknowledged and
documented, however the affect of these inflows on poor people of
Pakistan remains unanswered. This paper is an attempt to fill this gap
by studying the impact of foreign inflows on poverty reduction in
Pakistan through the channel of health, education and other indicators
related to human development. Our foreign inflow variable consists of
Foreign Direct Investment, Remittances and Foreign Assistance. Using
ARDL approach to co-integration on time series data for the period 1972
to 2008, we found that foreign inflows as a whole have increased poverty
levels in Pakistan. At disaggregated levels, we found that foreign
assistance is the major component of inflows which is responsible for
the positive relationship between inflows and poverty. We also found
positive relationship between poverty and infant mortality and foreign
inflows and female enrolment. The relationship suggests that increase in
foreign inflows would not only increase poverty but also would increase
infant mortality through indirect channel. The impact of inflow on
female enrolment was however found to be positive.
JEL classification: E00, F20, F21, F34, F35, I30
Keywords: Poverty, Economic Growth, Pakistan, Foreign Capital,
Foreign Debt, Aid, Remittances, Foreign Direct Investment
I. INTRODUCTION
Foreign Inflows plays an important role in development of a
country. Although significance of such inflows is much larger in
developing countries but it is not limited to them. Emerging economies,
even developed countries, also need foreign inflows to manage their
economy. However, size and the composition of such inflows are
determined on the basis of country specific requirements. The need of
foreign capital generally arises with the lack of capital in host
country and low saving and investment ratios. Low household income
reduces the government's earning from taxes and hence it reduces
government expenditures and consequently growth of the country slows
down. With the passage of time, less developed countries have become
more and more dependent on foreign inflows due to which their growth is
completely reliant on funds from other countries. The dependence usually
results in a shock on host country when these inflows are completely or
partially dried-up. Moreover, misallocation of funds is also a very
critical issue. If inflows are not well directed and not supported with
sufficient research on host country, they may adversely affect growth of
a country because of increasing poverty and unemployment rate with low
investment on human capital.
Foreign inflows are of critical importance to Pakistan. In addition
to the low saving and investment ratios and lack of physical and human
capital, Pakistan is faced with political and macroeconomic instability
due to which large and continuous flow of foreign inflows is required to
supplement its growth. As far as the composition is concerned, it has
changed over the years for Pakistan. Share of remittances in total
inflows decreased from 16.35 percent in 1980 to 12.48 percent in 2008 on
the contrary share of FDI increased from 0.26 percent to 9.96 percent in
the same period; depicting a huge shift in inflow concentration. Share
of foreign debt on the other hand, followed increasing trend from 1985
to 2000 but in 2008 it fell to 76.5 percent as compared to 93.91 percent
in 2000.
The impact of foreign inflows on poverty and economic development
is found to be controversial in the literature. In some studies positive
impact of foreign inflows was proved on poverty and economic
development, while other studies highlighted its negative effects
[Mohey-ud-din (2006)]. In case of Pakistan there are only few studies on
the relationship between inflows and poverty, for example Siddiqui, et
al. (2006), Zaman, et al. (2008) and Mohey-ud-din (2006). After thorough
literature review and analysis, these authors have explained the
relationship between foreign inflows and poverty but none of them have
computed the extent of the impact between the two variables. This paper
is therefore an attempt to fill this gap by numerically expressing the
relationship between inflows and poverty. First, we would attempt to
study the direct impact of foreign inflows on poverty reduction in
Pakistan. Secondly, relationship between poverty and infant mortality in
Pakistan would be derived to indirectly determine the relationship
between inflows and infant mortality. Third, impact of inflows on total
school enrolment in general and female enrolment in particular, would be
examined to determine the impact on education sector. Fourth, impact of
inflows on public expenditure on education and health would be examined.
II. STRUCTURE OF FOREIGN INFLOWS IN PAKISTAN
The composition of inflows in Pakistan for the year 2008 is
illustrated in Figure 1. Highest share in the inflows is of remittances
(42.5 percent) followed by FDI (33.9 percent), foreign debt (20.1
percent) and Grants (3.5 percent). In terms of percentage of GDP,
remittances have the highest ratio (4.1 percent) with FDI on second
place with 3.3 percent as percentage to GDP.
Table 1 compares the shares of each inflow in the total inflow
variable and also their percentage to GDP. From the table we can see
that the composition has changed over the years for Pakistan. Share of
remittances in total inflows followed mixed trend over the years. From
1975 to 1985 it increased from 17.9 percent to 62.9 percent then fell to
37.3 percent in 1995 and followed similar trend till 2008 when the share
of remittances in total foreign inflows was 42.5 percent. Similar uneven
trend was observed in terms of percentage share of remittances to GDP.
The share increased from 2.1 percent in 1975 to 3.4 percent in 1995 and
following the mixed trend it reached 4.1 percent in 2008. On the
contrary, share of FDI in total foreign inflows showed overall positive
trend. It increased from 1.2 percent in 1975 to 8.8 percent in 1995 and
to 33.9 percent in 2008. Similar increasing trend was observed in FDI as
a percent to GDP where it increased from 0.1 percent in 1975 to 0.8
percent in 1995 and further increased to 3.3 percent in 2008. The share
of grants in total foreign inflows depicted U-shaped curve, from 5.2
percent in 1975, it increased to 11.6 percent in 1990 then it started
falling and reached 3.5 percent in 2008. Grants as a percentage to GDP
increased from 0.6 percent in 1975 to 1.5 percent in 1990 after that it
started declining and reached 0.3 percent in 2008. As far as foreign
debt is concerned, it followed mixed trend over the years. Its share in
total foreign inflows decreased from 75.7 percent in 1975 to 47.8
percent in 1995 and to 20.1 percent in 2008. Similar trend was observed
in foreign debt as percentage to GDP where it decreased from 8.7 percent
in 1975 to 4.4 percent in 1995 and to 1.9 percent in 2008.
Figure 2 compares the trend of foreign inflows with real GDP. From
the figure we can see that both series are increasing with time and real
GDP is showing similar trend as of foreign inflows. The only
irregularity in the inflow variable is in the year 2001 in which all the
inflows experienced positive shocks following the attacks on World Trade
Towers in USA.
[FIGURE 2 OMITTED]
III. THEORETICAL FRAMEWORK AND ECONOMETRIC TECHNIQUES
Foreign Inflows
The linkage between Foreign Inflows and poverty seems to be quite
general but studies have shown that there are country specific outcomes
of the foreign inflows on poverty [Zaman, et al. (2008)]. Foreign
Inflows can affect poverty directly or indirectly. The direct impact
comes from the increase in household income while indirect affect comes
from the spillovers of different income generating activities directly
affected by foreign inflows [Carvalho, et al. (1996)]. Siddiqui, et al.
(2006) found that foreign inflows significantly affect poverty in
presence of trade liberalisation.
Foreign Assistance
Foreign assistance generally comprises of non-returnable grants
(Aid) and returnable foreign loans (Debt) with interest. In this study,
we have combined both Foreign Debt and Grant to for form a foreign
assistance variable and analysed its impact on different variables. It
is argued that foreign assistance, particularly aid, has negative or
insignificant impact on growth and poverty because it is not properly
utilised. Masud, et al. (2005) portrayed three main arguments coming out
of most of the aid effectiveness studies. (1) aid is often misallocated
(given to wrong recipients), (2) aid is not properly used/utilised by
the recipients and (3) GDP is not the correct measure for aid
effectiveness Boone (1996). They further explained that the argument
about the misallocation of foreign assistance is inappropriate most of
the time because objectives of the donors are not always to assist the
recipient countries in their development and poverty reduction but there
is underlying agenda coupled with each assistance agreement which is
more tilted in favour of donor's strategic interests. Keeping this
situation in mind, one cannot expect the foreign assistance to help in
poverty alleviation strategies and economic development. Gwin (2002)
found that foreign assistance have decreased poverty in the host
countries and increased their social development.
Kraay, et al. (2005) argued that the aid ineffectiveness is
directly linked to the improper utilisation. A modest increase in aid
can bring prominent results while huge amounts can end up giving zero
net output from the agreement.
Figure 3 represents the channel through which countries fall in the
poverty trap proposed by Kraay, et al. (2005). The authors proposed
that, in order for the country to bring itself out of the poverty trap,
it should direct the aid flows towards strategies that can increase the
saving rate in the country, this will not only increase the investment
rate but also with improve the capital accumulation in the country,
resulting in better rate of growth and country would be able to come out
of the poverty trap.
[FIGURE 3 OMITTED]
Infant Mortality
Boone (1996) attempted to find the relationship between aid and
infant mortality but found no significant impact on lower levels of
infant mortality. In countries with weak economic management, there is
no relationship between aid and change in infant mortality. While in
countries with good economic management there is evidence that aid
reduces the infant mortality in the host country Burnside, et al.
(1998).
Pro-public Government Expenditures
Pro-public government expenditures are recognised in different
categories in the literature. Verschoor (2002) identified the strongest
candidates to be classified as pro-poor expenditures as the social
sector expenditures (health, education and sanitation) while McGillivray
(2004) included the expenditure on rural roads, micro-credit and
agricultural extension and technology in the list of pro-public
expenditures as they may also be beneficial to the poor.
Literature gives us evidence that incidence of pro-public
expenditures is progressive i.e. marginal pro-public spending is
progressive. Thus, it can be said that expenditures, particularly on
health and education, increases human welfare [Gomanee, et al. (2003)].
In addition to the impact on the welfare of the individuals, it is also
necessary to make sure that distribution of such impacts is desirable.
There is a possibility that rich quintile of the population gets the
maximum out of public expenditures. Castro-Leal, et al. (1999) proved
the same by showing that there is a least possibility that poor will
benefit from education and health expenditures.
In another research, (1) it is shown that there is a weak link
between expenditures on health and education and poverty i.e.,
government social spending does not necessarily benefits the poor; hence
such expenditures may not reduce poverty. On the other hand, this does
not mean to reduce such expenditures as they may not benefit all the
poor but the public as a whole do get the benefit [Gomanee, et al.
(2005)]. More specifically, higher government spending on primary and
secondary education has greater impact on measure of education
attainment, higher spending on health results in reduction of infant
mortality rates [Gupta, et al. (2002)].
FDI, Growth and Poverty
Economic literature is rich with studies related to FDI as its
importance has been recognised by the economists since 1990's. FDI
is less volatile as compared to other sources of capital flows and does
not depict a pro-cyclical behaviour. Hence it is the favourite source of
capital inflows for developing countries [Ozturk and Kalyoncu (2007)].
FDI provides capital, productive facilities, technology and latest
managerial knowledge to the recipient countries [Hassan (2003)]. In
addition to this, FDI also brings foreign exchange, competition and
enhances the access to foreign markets [Mottaleb (2007); World Bank
(1999); Romer (1993); UNCTAD (1991)]. FDI also complements domestic
private investment which increases the employment; enhances the
spillover and human capital, the enhancement boosts overall economic
growth of recipient countries [Chowdhury and Mavrotas (2006)].
There are numerous studies on FDI and poverty separately but only
few of them analysed the direct impact of FDI on poverty like White
(1992), Carvalho and White (1996), and Siddiqui (1997). Other related
studies have used the impact of FDI on GDP as a proxy to depict the
impact of the same on poverty [Zaman, et al. (2008)]. For instance,
Borensztein, et al. (1998) studied the impact of FDI on economic growth
in framework of cross-country regression. They found FDI to be an
important vehicle for technology transfer, and FDI contributes
relatively more than domestic investment to growth. However, there is a
complementary relationship between FDI and domestic investment as former
causes the later to increase. De Mello (1999) used time series and panel
data (1970-1990) for a sample of OECD and non-OECD countries, the
results supported the findings of Borensztein, et al. (1998).
Bengoa, et al. (2003) used panel data for the period of 1970-1999
of 18 Latin American countries. Their findings suggest that there is a
positive correlation between FDI and economic growth in the host
countries. They noted that in order to benefit from long-term capital
flows, the host country requires, adequate human capital, liberalised
markets and economic stability. A panel data analysis of Li and Liu
(2005) for the sample of 84 countries for the period 1970-1999 showed
that through channel of human capital, FDI exerted a strong positive
effect on economic growth.
Durham (2004) analysed data for 80 countries from 1979 to 1998 and
found that foreign direct investment does not have direct positive
effects on growth; effects are contingent on the 'absorptive
capacity' of host countries. Herzer, et al. (2006) studied 28
developing countries and found that in majority of countries FDI has no
statistically significant long-run effect on growth. In very few cases,
both long run and short run relationship was found between FDI and
growth. But for some countries, there is also evidence of
growth-limiting effects of FDI in the short or long term.
Ozturk, et al. (2007) investigated the impact of FDI on economic
growth of Turkey and Pakistan for the period of 1975-2004. The findings
suggests that these two variables are co-integrated for both countries
studied and GDP causes FDI in the case of Pakistan, while there is
strong evidence of a bi-directional causality between the two variables
for Turkey.
The overall inflows of FDI in Pakistan are increasing but their
contribution to the growth is questionable. In Pakistan, FDI generally
comes to
the following sectors; energy, chemicals, foods and beverages,
machinery, construction and textiles. From comparative point of view,
despite of having increasing flows of FDI in the country, Pakistan is
lacking far behind it potential to attract FDI in various sectors. The
major reason behind the inability is perceptions of the investors and
the law and order situation in the country which has significantly
increased the risk associated with investment and hence increased the
cost of doing business in the country [Zaman, et al. (2008)].
Remittances, Growth and Poverty
Research has shown that a very high proportion of remittances are
spent on consumption instead of productive investments. Theoretically,
however, the relationship between remittances and growth can be positive
or negative. Remittances may generate positive spillovers through
efficient financial markets, easing the credit constraints of business
as well as common men or on the contrary, it may increase consumption
more than investment and negative chain of events can be triggered
through low labour participation, low investments and so on [Goldberg,
et al. (2008)].
One important feature of remittances is that it can indirectly
affect labour supply. This could reduce economic growth through reduced
labour supply. Moreover, large and consistent remittance inflows could
make the exports less profitable through appreciated real exchange rate.
However, remittances can reduce poverty through increase in income of
the recipient households which finances their consumption and hence
improves their standard of living [Jongwanish (2007)].
The positive impacts of remittances can emerge through a number of
channels. Figure 4 shows that channel through which remittances affects
poverty and economic growth. Remittances ease the credit constraints
often faced by citizens of developing countries by increasing their
household income. This does not only increase their consumption level
but also increases their savings which ultimately translates themselves
to private investments. The higher level of disposable income allows the
households to spend more on health and education, through which the
overall labour productivity increases, raises their standard of living
and ultimately reduces poverty. Calderon, et al. (2008) found negative
impact of remittances on poverty and inequality for their study on 10
Latin American Countries [Zaman, et al. (2008)]. Jongwanish, (2007)
found that there is direct and significant impact of remittances on
poverty reduction through higher per capita income and ease of credit
constraints.
[FIGURE 4 OMITTED]
Some studies on the issue found positive relationship between
remittances and growth [Stark and Lucas (1988); Taylor (1992); and Faini
(2002)]. On the contrary, Chami, et al. (2003) found negative and IMF
(2005) found no impact of remittances on economic growth. Brown (1994)
found positive relationship between remittances and savings and
investment in Tonga and Samoa basing on micro-level analysis. Yang
(2004) found that remittances improves child enrolment in schools and
increases education expenditure. Mesnard (2004) for Tunisia using a
life-cycle model found that remittances ease the credit constraint of
workers whose access to the financial market is limited. In terms of
poverty, Adams and Page (2005) studied the impact of remittances on
poverty in 71 developing countries and found that remittances do help in
poverty reduction. Stahl (1982) however argues that while remittances
acts as a blessing to the household, there is a cost associated with it.
The most obvious one is of migration itself. Since migration is not
cheap, poor are least likely to be recipient of remittances from abroad
hence the impact may be negligible on poverty of it may even increase
the levels of poverty and inequality in the country [Jongwanish (2007)].
Adams (2002) found positive impact of remittances on the savings in
Pakistan during the 1980s and early 1990s. The marginal propensity to
save out of international remittances was found to be 0.71 compared to
the marginal propensity to save out of rental income of just 0.085.
Nishat, et al. (1991) analysed the impact on remittances on
economic growth in Pakistan for the period 1959-60 to 1987-88. The
results indicated a strong positive impact of remittances on GNP,
consumption, investment and imports. They argue that remittances
increase the dependency on imports through increase in consumption of
imported goods and worsen balance of payments problems.
IV. ECONOMETRIC MODELLING
Time series data usually suffer from the unit root problem thus
involving a serious violation of assumptions of ordinary least square
method of estimation. Keeping this in view, the data was first checked
for stationarity before applying conventional Ordinary Least Square
method of estimation.
Augmented Dicky-Fuller (ADF) test uses following equation to test
whether there is unit root in the time series:
[DELTA][Y.sub.t]= [[beta].sub.t] [alpha][y.sub.t-1] [gamma]
[SIGMA][DELTA][y.sub.t-1]+[[epsilon.sub.t] ... ... ... ... ... (1)
Where [[epsilon].sub.t] is white noise error term and t represents
time trend. The null hypothesis in ADF test is that variable has unit
root.
In addition to ADF, the Phillips-Perron (PP) [1988] unit root test
is also used in the study, which is a nonparametric system of
controlling for serial correlation while testing for the stationarity of
variables. The PP method estimates the following equation:
[Y.sub.t] = [[alpha].sub.0] + [[alpha].sub.1] [y.sub.t-1] +
[[alpha].sub.2] (t- n/2)+ [[epsilon].sub.t] ... ... ... ... ... (2)
Where [Y.sub.t] is the corresponding time series, n is the number
of observations and [[epsilon].sub.t] is the error term. The null
hypothesis of a unit root is [H.sub.0]: [[alpha].sub.1] = 1.
After testing for stationarity our next step would be to
investigate the long run and short run relationship between the
variables. There are several econometric techniques available to study
such relationship. Uni-variate co-integration includes Engle- Granger
(1987) and Fully Modified Ordinary Least Squares (FMOLS) of Philips and
Hansen (1990); and multivariate co-integration techniques includes
Johansen (1988); Johansen and Juselius (1990); and Johansen's
(1995). Although these tests are most commonly used to test for
con-integration but in recent years, the Autoregressive Distributed Lag
(ARDL) model approach, developed by Pesaran and Shin (1996 and 1988),
Pesaran, et al. (1996) and Pesaran, et al. (2001), has become more
popular and preferred to other conventional co-integration approaches.
The ARDL technique has become so popular particularly because it
can be applied irrespective of the order of integration i.e., purely
I(0), purely I(1) or mutually co-integrated (and in small samples) while
other co-integration techniques require all variables be of equal degree
of integration i.e. either purely I(0) or I(1) (and largo samples). All
the variables are assumed to be endogenous in the said approach. In this
study we employed the Pesaran, et al. (2001) approach to investigate the
existence of a long-run relationship in the form of unrestricted error
correction model. We will try to find long run relationship of the
variables through following equations:
Poverty Equation
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (3)
[Modified version of the model employed by Castejon, et al.
(2006)].
Infant Mortality Equation
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (4)
Total Enrolment Equation
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (5)
Female Enrolment Equation
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (6)
Health Expenditure Equation
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (7)
Education Expenditure Equation
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (8)
Where lnPOV is the per capita poverty headcount in natural log, A
is the set of Foreign Inflows, Foreign Assistance, Remittances and
Foreign Direct Investment used separately which splits Equation 3 in
four different equations. lnEDEX is the federal education expenditure in
natural log, lnTENR is natural log of total enrollment in schools, lnER
is natural log of exchange rate, lnHEEX is natural log of federal health
expenditure, lnIM is natural log of Infant Mortality, lnFENR is natural
log of Female Enrollment, lnY is natural log of per capita GDP and
[[epsilon].sub.1] is the white noise error term. The parameters
[y.sub.i] where i = 1, 2, 3, 4 are the corresponding long-run
multipliers, [[beta].sub.i] where i=1, 2, 3, 4 are the short dynamic
coefficients of the underlying ARDL model. We test the null hypothesis
of no co-integration i.e., [H.sub.0] : [[gamma]sub.i] = 0 or
[[gamma]sub.1] = [[gamma]sub.2] = [[gamma]sub.3] = [[gamma]sub.4] = 0 in
Equation 3, against the alternative using the F-test with critical
values tabulated by Pesaran and Pesaran (1997) and Pesaran, et al.
(2001).
If there is evidence of long-run relationship in the model then in
order to estimate the long run coefficients, the following long run
model will be estimated:
Poverty Equation
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (9)
Infant Mortality Equation
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (10)
Total Enrolment Equation
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (11)
Female Enrolment Equation
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (12)
Health Expenditure Equation
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (13)
Education Expenditure Equation
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (14)
If we find the evidences of long run relation then in the 3rd step
we utilise the following equation to estimate the short run
coefficients:
Poverty Equation
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (15)
Infant Mortality Equation
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (16)
Total Enrolment Equation
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (17)
Female Enrolment Equation
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (18)
Health Expenditure Equation
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (19)
Education Expenditure Equation
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (20)
Where ([[phi].sub.1] is the error correction term in the model
which indicates the pace of adjustment towards long run equilibrium
following a short run shock, [ECM.sub.t-1] represents the error
correction term derived from long-run con-integration equation through a
newly developed technique of ARDL, [[beta].sub.i](i =1, 2, 3, 4) are
constant terms, and [[delta].sub.i] is the serially uncorrelated random
disturbance term with mean zero. Long-Run relationship can also be
verified through the model specified in Equation (5), with the
significance of the lagged ECM by t-test.
The ARDL approach involves two steps for estimating the long run
relationship Pesaran, et al. (2001), first step is to investigate the
long run relationship among the variables specified in the equation, and
the second step is to estimate short run causality. The second step is
only applied when existence of long run relationship is found in the
first step [Narayan, et al. (2005)]. Two sets of asymptotic critical
values are provided by Pesaran and Pesaran (1997) and Pesaran, et al.
(2001). The first set assumes that all variables are I(0) while the
second based on the assumption of I(1). The null hypothesis of the no
co-integration will be rejected if the calculated F-statistic is greater
than the upper bound critical value, implying that there exists long run
relationship among the variables. If the computed statistics are less
than the lower bound critical values, we cannot reject the null
hypothesis. Lastly, if the computed F-statistics falls within the two
bound critical values discussed above, the result will be inconclusive.
In addition to the ARDL approach for the investigation of a long
run relationship between the variables in multivariate models, the
Johansen co-integration technique will also be used in this study.
Johansen (1988) and Johansen and Juselius (1990) presented the method to
estimate the maximum likelihood estimators in multivariate models [Yuan,
et al. (1994)]. They also present two likelihood ratio tests, one based
on maximal eigenvalue with Ho that the number of co-integrating vectors
is less than or equal to r against the H1 of r+1 co-integrating vectors
and other test based on trace test with the same null hypothesis and HI
that there are at least r+1 co-integrating vectors. In order to apply
Johansen co-integration technique, it is necessary that the variables
should be stationary at I(1) [Ahlgren, et al. (2002)].
V. DATA AND VARIABLE DESCRIPTION
Data has been taken from various different sources for the period
of 1973-2008. Ideally, literacy rate and Human Development Index would
be the better indicators of wellbeing but due to unavailability of time
series data, we used infant mortality and school enrolment as proxy
variables. Brief information about the variables and their source is
given in the following table.
GDP Gross Domestic Product, Handbook of Statistics on
at Constant Prices of Pakistan Economy 2005,
2000-01 in Million PKR. updated with Annual
Reports of SBP.
FI Foreign Inflows Handbook of Statistics on
(FDI+FA+Rem) in Million Pakistan Economy 2005,
PKR. updated with Annual
Reports of SBP and
Economic Survey of
Pakistan various issues.
FDI Foreign Direct Investment Handbook of Statistics on
in Million PKR. Pakistan Economy 2005,
updated with Annual
Reports of SBP and
Economic Survey of
Pakistan various issues.
FA Foreign Assistance Handbook of Statistics on
(Foreign Grants + Foreign Pakistan Economy 2005,
Debt) in Million PKR. updated with Annual
Reports of SBP and
Economic Survey of
Pakistan various issues.
Rem Remittances in Million Handbook of Statistics on
PKR. Pakistan Economy 2005,
updated with Annual
Reports of SBP and
Economic Survey of
Pakistan various issues.
IM Infant Mortality, Deaths Handbook of Statistics on
per 1000 persons. Pakistan Economy 2005,
updated with Annual
Reports of SBP and
Economic Survey of
Pakistan various issues.
Data for some years was
filled in using Quadratic
Interpolation.
TENR Total School Enrolment in 50 Years of Pakistan
thousands. Economy in Statistics.
Pakistan Statistical Year
Book 2008, Economic
Survey of Pakistan
2008-09.
FENR Female School Enrolment 50 Years of Pakistan
in thousands. Economy in Statistics.
Pakistan Statistical Year
Book 2008, Economic
Survey of Pakistan
2008-09.
POV Poverty headcount ratio. Jamal, H. (2006),
Economic Survey of
Pakistan 2008-09, ratio
for 2008 was taken from
an article of business
recorder and for the year
2007 it was calculated
using cubic-spline
function.
HEEX Federal Expenditure on Annual Budget Statements
Health in Million PKR. (Various Issues).
EDEX Federal Expenditure on Annual Budget Statements
Education in Million PKR. (Various Issues).
ER Exchange Rate of Pakistan Handbook of Statistics on
in Term of US Dollars. Pakistan Economy 2005,
updated with Annual
Reports of SBP.
VI. EMPIRICAL RESULTS
Unit Root Test
Table 2 presents the results of units root tests. As discussed
before, we used Augmented Dickey Fuller test and Philip-Perron test to
do the unit root analysis. The results suggest that most of the
variables are not stationary at level therefore we cannot apply
traditional OLS techniques for our estimation. The results of ARDL
estimation are given in next section.
Estimated Coefficients
The long-run and short-run results of poverty equation are
presented in Tables 3 and 4. For the FI variable as a whole, it was
found that foreign inflows actually increase the poverty in Pakistan
both in long-run and short-run. More specifically, in long-run one
percent increase in foreign inflows bring about 0.6 percent increase in
poverty while in short-run, 1 percent increase in foreign inflows brings
about 0.4 percent increase in poverty. The other variables, education
expenditure, total enrolment, exchange rate and per capita GDP found to
be contributing to poverty alleviation policies in both long and
short-run. The coefficient for health expenditure was however found to
be having insignificant impact on poverty. Similar results were found
for foreign assistance variable where FA positively affects poverty and
test of the variables significantly and negatively affects poverty
except for the health expenditure variable. The reason for this could be
the improper utilisation or underlying agenda of the donor country which
played its role in restricting the impact of assistance on poverty
[Masud, et al. (2005)]. We also found that remittances had insignificant
impact on poverty reduction reason may be, as discussed by Jongwanish,
(2007), the cost associated with migration due to which poor are not
usually the beneficiaries it foreign remittances. Similarly for FDI, the
coefficient was found to be insignificant suggesting that FDI has no
direct relationship with poverty, neither in long-run nor in short-run.
In order to capture the forward linkages of poverty on different
socio-economic variables like health and education, which are also the
determinants of poverty, we estimated few more equations. For instance
Tables 5 and 6 represents the results of infant mortality equations. We
found that poverty has no relationship with infant mortality in
short-run but in long-run, poverty increases infant mortality. We also
found that, both in long-run and short-run, health expenditures have no
impact on infant mortality, suggesting that the crucial component of
public spending is either misallocated or being a victim of poor
governance. Hence it not translating itself in improvement of important
health sector indicator; infant mortality. The relationship between
female enrolment and infant mortality was found to be negative,
suggesting that ah educated mother can take care of her child better
than an uneducated mother.
The long and short-run impacts of foreign inflows on public health
expenditure are given in Tables 7 and 8 respectively. Results showed
that both FI and FA had negative impact on health expenditure in
long-run suggesting that with increased magnitude of inflows, priority
of the government diverts to other areas. FI however had insignificant
impact on health expenditure in the short-run. We have already seen that
health expenditures had insignificant impact on infant mortality and
poverty which gives us the implication that in addition to the fact that
foreign assistance is negatively influencing the health expenditure, the
expenditure itself is not correctly allocated. The other two components
of the inflows, remittances and FDI, had positive relationship with
health expenditure in both long-run and short-run. Poverty showed
negative relationship with health expenditure in both time-scales,
suggesting that with increase in poverty, the indicators with direct
influence on poverty become government's priority expenditures and
hence less is left to be allocated to health.
We attempted to capture the impact of poverty and inflows on
education sector through total enrolment, female enrolment and
government expenditure on education. Tables 9 and 10 presents the result
of total enrolment equation. Results suggest that poverty has no
influence on total enrolment in the long-run however it may negatively
affect it in the short run. All inflow variables except for remittances
showed positive and significant impact of total enrolment in the
long-run while in the short run, only aggregated FI variable had
positive and significant relationship with total enrolment.
Similarly, the impact of poverty and inflows was analysed on female
school enrolment. The results (Tables 11 and 12) suggest that both FI
and FA have positive and significant relationship with total enrolment
while poverty had negative relationship with female enrolment in both
long and short-run. The impact of remittances and FDI on female school
enrolment was also found to be positive and significant. We also found
positive relationship between government expenditure and female
enrolment.
For the equation of education expenditure, we found that FI and FA
had negative relationship with education expenditure in the long-run but
in short-run the impact of aggregated FI variable had insignificant
impact on education expenditure (Tables 13 and 14). Remittances had
positive while FDI had insignificant impact on education expenditure
both in long-run and short-run. Poverty negatively influenced education
expenditure in long-run but in short-run the impact was relatively
insignificant.
VII. CONCLUSION
In this study we tried to find out the direct and indirect impacts
of foreign inflows and poverty in economy. Foreign Inflows generally
supplement resources of the recipient countries to promote economic
growth and eliminate poverty. We attempted to test this argument in this
study and found that foreign inflows, specifically foreign assistance,
have actually increased poverty in Pakistan both in long-run and
short-run through direct and indirect channels. We used infant mortality
tate and enrolment rates as a proxy to capture welfare impacts. We found
that poverty increases infant mortality in Pakistan. Earlier in this
study, the relationship of foreign assistance is already shown to be
positive with poverty, hence an increase in foreign assistance would not
only increase poverty but also infant mortality therefore we need
concrete policy measures that can make sure of the positive feedback of
foreign assistance on infant mortality in Pakistan. We also found that
all the foreign inflow variables in disaggregated forms had positive
impact on both female and total enrolment in Pakistan suggesting
beneficial impact of foreign inflows in education.
Another interesting finding of this paper was the insignificant
impact of government health expenditure on poverty and infant mortality.
The impact could be because of improper allocation of resources or
inability of these finances to reach the critical geographic areas. As
far as the policy recommendations are concerned, in light of this
analysis we can see that there is a need of proper allocation of
resources in the country. The inflows are somewhat continuous and
increasing with time but their results are not as significant as they
should be. Proper allocation of resources would not only reduce poverty
but also improve other indicators such as infant mortality and female
school enrolment.
Comments
The author has made a good attempt to determine the impact of
foreign inflows on poverty in Pakistan through the channel of health,
education and other indicators related to human development. This is a
good paper with an excellent review of literature as the author has
tried to establish a coherent story.
The author has also examined the composition of inflows in Pakistan
and suggests the highest share of foreign debt in the total inflow
variable at 76.5 percent followed by remittances at 12.5 percent, FDI at
9.96 percent and grants at 1.02 percent. I think there appears to be
some confusion as according to my knowledge inflows of foreign loans
plus grant were US$2.5 billion in FY05 while inflows of FDI and workers
remittances were US$1.5 billion and US$ 4.1 billion. Probably, the
author has included stock of foreign debt rather than flows in this
variable which seems to be incorrect. A stock variable is measured at
one specific time which may have been accumulated in the past while a
flow variable is measured over an interval of time. Thus, it is
important to revise it as it can alter the conclusion.
While the results are not consistent with the perception and other
studies, I have doubt on poverty variable in terms of consistency of
poverty estimates over time as author has used poverty variable which
was computed till 2001 using Malik (1988) poverty line. This is not
consistent with the official poverty estimates based on official poverty
line announced by Planning Commission in 2002 and onward.
The authors' results that exchange rate found to be
contributing to poverty alleviation policies in both long and short-run
are surprising. But exchange rate depreciation leads to increase in
inflation and there is no doubt that inflation increases poverty. I
agree on insignificant coefficient for health expenditure which has no
impact on poverty. It is worrying that the country spends too little on
health and even this meager government spending at 0.5 percent of GDP
seems to be poorly targeted which is not beneficial for the poor.
However, this result may have been due to only taking federal
expenditure on health and education and ignoring the provincial
expenditure. Health and education areas are provincial subject and thus
author should include provincial expenditure.
In addition, I would like to comment that running expenditure
merely on nominal expenditure does not capture the policy shift or
emphasis. Therefore, the right approach to capture the weight of the
policy is to take expenditure as percent of GDP over time and then run
regression. Furthermore, the author should explain that why remittances
have not shown positive and significant impact of total enrolment in the
long- run while it has an impact in the short run. Finally, some of the
references are missing which I am pointing out.
Talat Anwar
Canadian International Development Agency (CIDA), Programme Support
Unit, Islamabad.
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Muhammad Ali <alionline83@yahoo.com> is M.Phil Student,
Applied Economics Research Centre, University of Karachi, Karaehi.
Muhammad Nishat <mnishat@iba.edu.pk> is Professor, Institute of
Business Administration, Karachi.
Table 1
Composition and Shares of Foreign Inflows
Share in Total Inflow
Share of Share of Share of Share of
Remittances FDI Grant Debt
Obs
1975 17.9% 1.2% 5.2% 75.7%
1980 53.2% 0.9% 7.7% 38.2%
1985 62.9% 1.8% 9.8% 25.5%
1990 42.0% 4.7% 11.6% 41.7%
1995 37.3% 8.8% 6.1% 47.8%
2000 32.8% 15.7% 4.2% 47.4%
2005 50.8% 18.6% 4.3% 26.3%
2008 42.5% 33.9% 3.5% 20.1%
Percentage of GDP
Rem FDI Grant Debt
Percent of Percent of Percent of Percent of
Obs GDP GDP GDP GDP
1975 2.1% 0.1% 0.6% 8.7%
1980 8.2% 0.1% 1.2% 5.9%
1985 8.7% 0.3% 1.4% 3.5%
1990 5.5% 0.6% 1.5% 5.4%
1995 3.4% 0.9% 0.6% 4.4%
2000 1.4% 0.7% 0.2% 2.1%
2005 4.0% 1.5% 0.3% 2.1%
2008 4.1% 3.3% 0.3% 1.9%
Source: Hand Book of Statistics 2005, Economic Survey
(Various Issues).
Table 2
Results of the Unit Root Tests
Level 1st Difference
Variable t-statistic p-value t-statistic p-value
lnFL -2.45 0.35 -7.18 0.00
lnPOV -1.14 0.91 -11.68 0.00
lnFA -1.15 0.91 -5.93 0.00
lnREM -3.17 0.11 -4.07 0.02
lnFDI -3.42 0.07 -7.65 0.00
lnER -2.41 0.37 -4.22 0.01
lnY -4.06 0.02 -5.43 0.00
lnTENR -1.86 0.65 -4.73 0.00
lnFENR -1.83 0.67 -5.80 0.00
lnIM -1.30 0.87 -4.29 0.01
lnEDEX -4.92 0.00 -3.65 0.04
lnHEEX -1.77 0.70 -6.64 0.00
Level 1st Difference
Variable t-statistic p-value t-statistic p-value
lnFL -2.35 0.40 -8.41 0.00
lnPOV -0.87 0.95 -3.98 0.02
lnFA -1.44 0.83 -5.93 0.00
lnREM -2.20 0.47 -4.11 0.01
lnFDI -3.41 0.07 -8.07 0.00
lnER -2.32 0.41 -4.24 0.01
lnY -4.07 0.02 -5.55 0.00
lnTENR -2.05 0.55 -4.61 0.00
lnFENR -1.84 0.67 -5.80 0.00
lnIM -1.37 0.85 -4.13 0.01
lnEDEX -4.31 0.01 -3.65 0.04
lnHEEX -1.67 0.74 -7.61 0.00
Table 3
Estimated Long-run Coefficients using the ARDL Approach
Dependent Variables lnFI lnFA lnREM lnFDI lnEDEX
lnPOV -0.623 -0.571
(0.007) -- -- -- (0.00)
0.566 -0.543
-- (0.031) -- -- (0.00)
-0.377 -0.469
-- -- (0.195) -- (0.02)
0.099 -0.389
-- -- -- (0.365) (0.119)
Dependent Variables lnTENR lnER lnHEEX lnY
lnPOV -0.583 -0.712 -0.097 -0.882
(0.005) (0.001) (0.443) (0.00)
-0.635 -0.874 -0.134 -1.084
(0.02) (0.003) (0.361) (0.00)
-0.169 -0.377 -0.027 -0.874
(0.907) (0.195) (0.904) (0.009)
0.009 -0.546 -0.181 -0.914
(0.962) (0.088) (0.549) (0.017)
Table 4
Estimated Short-run Coefficients using the ARDL Approach
Dependent
Variables [DELTA]lnFI [DELTA]lnFA [DELTA]lnREM
[DELTA]lnPOV 0.428
(0.022) -- --
0.344
-- (0.051) --
0.02
-- -- (0.674)
-- -- --
Dependent
Variables [DELTA]lnFDI [DELTA]lnEDEX [DELTA]lnTENR
[DELTA]lnPOV -0.393 -0.401
-- (0.004) (0.018)
-0.329 -0.385
-- (0.01) (0.04)
-0.214 -0.465
-- (0.132) (0.053)
-0.027 -0.148 -0.497
(0.379) (0.238) (0.033)
Dependent
Variables [DELTA]lnER [DELTA]lnHEEX [DELTA]lnY Ecm(-1)
[DELTA]lnPOV -0.489 -0.067 -0.606 -0.687
(0.014) (0.454) (0.006) (0.00)
-0.53 -0.081 -0.657 -0.606
(23) (0.379) (0.006) (0.001)
-0.172 -0.013 0.399 -0.457
(0.239) (0.904) (0.078) (0.024)
-0.207 -0.069 0.347 -0.379
(0.173) (0.545) (0.161) (0.027)
Table 5
Estimated Long-run Coefficients using the ARDL Approach
Dependent Variables lnFENR lnHEEX lnEDEX lnPOV lnY
LnIM -0.909 -0.688 1.155 1.754 -1.428
(0.019) (0.139) (0.000) (0.029) (0.024)
Table 6
Estimated Short-run Coefficients using the ARDL Approach
Dependent
Variables [DELTA]lnFENR [DELTA]lnHEEX [DELTA]lnEDEX
[DELTA]LnIM -0.148 -0.112 -0.069
(0.153) (0.152) (0.459)
Variables [DELTA]lnPOV [DELTA]lnY Ecm(-1)
[DELTA]LnIM -0.002 -0.233 -0.163
(0.99) (0.085) (0.018)
Table 7
Estimated Long-run Coefficients using the ARDL Approach
Dependent
Variables lnFI lnFA lnREM lnFDI lnPOV 1nER
lnHEEX -1.224 -1.475 0.71
(0.084) -- -- -- (0.00) (0.007)
-1.021 -1.367 0.912
-- (0.014) -- -- (0.00) (0.00)
0.264 -1.227 0.937
-- -- (0.071) -- (0.00) (0.002)
0.236 -1.095 0.277
-- -- -- (0.039) (0.002) (0.413)
Dependent
Variables LnY
lnHEEX 1.48
(0.029)
1.204
(0.002)
0.014
(0.944)
0.191
(0.155)
Table 8
Estimated Short-run Coefficients using the ARDL Approach
Dependent Variables [DELTA]lnFI [DELTA]lnFA [DELTA]lnREM
[DELTA]lnHEEX -0.481
(0.114) -- --
-0.462
-- (0.027) --
0.098
-- -- (0.068)
-- -- --
Dependent Variables [DELTA]lnFDI [DELTA]lnPOV [DELTA]lnER
[DELTA]lnHEEX -0.579 0.279
-- (0.008) (0.095)
-0.619 0.413
-- (0.003) (0.026)
-0.454 0.3456
-- (0.022) (0.054)
0.095 -0.444 0.112
(0.054) (0.034) (0.478)
Dependent Variables ANY Ecm (-1)
[DELTA]lnHEEX 0.581 -0.393
(0.053) (0.006)
0.544 -0.453
(0.007) (0.002)
0.005 -0.37
(0.944) (0.006)
0.077 -0.406
(0.166) (0.005)
Table 9
Estimated Long-run Coefficients using the ARDL Approach
Dependent Variables lnFI lnFA lnREM lnFDI lnEDEX
lnTENR 0.595 -0.281
(0.001) -- -- -- (0.083)
0.449 -0.165
-- (0.003) -- -- (0.262)
-0.205 0.216
-- -- (0.698) -- (0.774)
0.278 -0.226
-- -- -- (0.00) (0.045)
Dependent Variables lnY lnPOV
lnTENR 0.55 -0.822
(0.002) (0.131)
-0.622 -0.502
(0.002) (0.306)
1.694 -1.403
(0.286) (0.728)
0.899 -0.26
(0.000) (0.264)
Table 10
Estimated Short-run Coefficients using the ARDL Approach
Dependent Variables [DELTA]lnFI [DELTA]lnFA [DELTA]lnREM
[DELTA]lnTENR 0.134
(0.077) -- --
0.092
-- (0.149) --
0.0102
-- -- (0.769)
-- --
Dependent Variables [DELTA]lnFDI [DELTA]lnEDEX [DELTA]lnY
[DELTA]lnTENR 0.058 0.125
-- (0.508) (0.025)
-0.034 0.127
-- (0.252) (0.044)
0.108 0.085
-- (0.838) (0.267)
0.017 -0.068 -0.271
(0.45) (0.054) (0.002)
Dependent Variables [DELTA]lnPOV Ecm (-1)
[DELTA]lnTENR -0.196 -0.226
(0.014) (0.079)
-0.103 -0.205
(0.0123) (0.128)
-0.07 -0.05
(0.45) (0.563)
-0.314 -0.302
(0.017) (0.005)
Table 11
Estimated Long Run Coefficients Using the ARDL Approach
Dependent Variables lnFI lnFA lnREM lnFDI lnPOV
lnFENR 0.987 -1.004
(0.03) -- -- -- (0.011)
0.891 -0.451
-- (0.07) -- -- (0.002)
1.064 -2.032
-- -- (0.021) -- (0.021)
1.02 -0.243
-- -- -- (0.02) (0.03)
Dependent Variables lnY lnER lnEDEX
lnFENR 0.12 -0.123 0.981
(0.03) (0.00) (0.002)
0.871 -0.101 0.876
(0.00) (0.031) (0.001)
1.203 -0.004 1.271
(0.006) (22) (0.034)
0.923 -2.03 0.35
(0.031) (0.032) (0.031)
Table 12
Estimated Short-run Coefficients using the ARDL Approach
Dependent Variables [DELTA]lnFI [DELTA]lnFA [DELTA]lnREM
[DELTA]lnFENR 0.211
(0.00) -- --
0.103
-- (0.05) --
0.329
-- -- (0.03)
-- -- --
Dependent Variables [DELTA]lnFDI [DELTA]lnPOV [DELTA]lnY
[DELTA]lnFENR -0.432 0.72
-- (0.005) (0.004)
-0.219 0.21
-- (0.001) (0.014)
-0.482 0.56
-- (0.018) (0.02)
0.045 -0.018 0.09
(0.05) (0.06) (0.01)
Dependent Variables [DELTA]lnER [DELTA]lnEDEX Ecm (-1)
[DELTA]lnFENR -0.94 0.022 -0.333
(0.031) (0.016) (0.014)
-0.439 0.41 -0.323
(0.001) (0.001) (0.026)
-0.591 0.34 0.012
(0.591) (0.00) (0.007)
-0.21 0.69 -0.016
(0.045) (0.935) (0.003)
Table 13
Estimated Long-run Coefficients using the ARDL Approach
Dependent Variables lnFI lnFA lnREM lnFDI lnY
lnEDEX -1.052 2.609
(0.035) -- -- -- (0.001)
-0.879 2.184
-- (0.007) -- -- (0.00)
0.649 -0.401
-- -- (0.013) -- (0.525)
0.219 0.628
-- -- -- (0.353) (0.214)
Dependent Variables lnPOV lnER
lnEDEX -1.305 -0.77
(0.005) (0.066)
-1.196 -0.396
(0.002) (0.155)
-1.053 1.017
(0.028) (0.103)
-0.756 -0.427
(0.393) (0.45)
Table 14
Estimated Short-run Coefficients using the ARDL Approach
Dependent Variables [DELTA]lnFI [DELTA]lnFA [DELTA]lnREM
[DELTA]lnEDEX 0.328
(0.377) -- --
-0.259
-- (0.015) --
0.162
-- -- (0.009)
-- -- --
Dependent Variables [DELTA]lnFDI [DELTA]lnY [DELTA]lnPOV
[DELTA]lnEDEX 0.640 -0.32
-- (0.014) (0.114)
0.642 -0.352
-- (0.005) (0.076)
0.809 -0.263
-- (0.031) (0.177)
0.043 0.122 0.147
(0.328) (0.355) (0.512)
Dependent Variables [DELTA]lnER Ecm (-1)
[DELTA]lnEDEX -1.126 -0.246
(0.012) (0.008)
-0.576 -0.294
(0.08) (0.00l)
0.265 -0.249
(0.456) (0.005)
-0.696 -0.195
(0.064) (0.037)
Fig. 1. Share of Each Component in Total Inflow Variable (2008)
Share of Grants, 3.50%
Share of
Remittances, 42.50%
Share of FDI, 33.90%
Share of Debt, 20.10%
Source: Author's Estimates based on Hand Book of Statistics 2005,
Economic Survey (Various Issues).
Note: Table made from pie chart.
