Sunday, May 6, 2012

Impact of Foreign Capital Inflows on Economic Growth in Pakistan

Jurnal Internasional PUSREFIL
Pusat Referensi Ilmiah
Bidang: Ilmu-Ilmu Ekonomi, Keuangan dan Administrasi
Edisi Maret 2012
_________________________________________________________________________________________________________________



Aurangzeb
HOD, Business Administration, Dadabhoy Institute of Higher Education

Anwar Ul Haq
Research Scholar

Abstract
This study investigates the impact of foreign capital inflows on economic growth of Pakistan. The data used in this study were collected from the period of 1981 to 2010. Unit root test confirms the stationary of all variables at first difference. The multiple regression analysis technique is used to identify the significance of different factors. Results indicate that the all three independent variables are having positive and significant relationship with economic growth (GDP). The Granger-Causality test confirms the bidirectional relationship between remittances and external debt, GDP and external debt, foreign direct investment and external debt, and foreign direct investment and remittances. On the other side we found unidirectional relationship from gross domestic production to foreign direct investment. It is concluded that the foreign capital inflows are very important for the growth of any economy. It is recommended that the policy makers should focus on the foreign cash flows to increase the flow of money in economy in the sense of investment and increasing consumption.

Keywords: Foreign Capital Inflows, Foreign Direct Investment, Remittances, Gross
Domestic Production, External Debt

Introduction
Over the past few decades, the emphasis on foreign direct investments has gained considerable attention, with even faster growth in international transactions. The share of net FDI in world GDP has grown five-fold through the eighties and the nineties. This paper attempts to make a contribution in this context, by analyzing the existence and nature of causalities, if any, between FDI and economic growth.

The literature on FDI and economic growth generally points to a positive relationship between the two variables, and offers several, standard explanations for it. In principle, economic growth may induce FDI inflow when FDI is seeking consumer markets, or when growth leads to greater economies of scale and, hence, increased cost efficiency. On the other hand, FDI may affect economic growth, through its impact on capital stock, technology transfer, skill acquisition, or market competition.

FDI and growth may also exhibit a negative relationship, particularly if the inflow of FDI leads to increased monopolization of local industries. Empirically, the positive effect of economic growth on FDI and also the positive and negative effects of FDI on economic growth have been identified in the literature.

On the one hand, FDI flows can be induced by host country economic growth if the host country offers a sizeable consumer market, in which case FDI serves as a substitute for commodity trade or if growth leads to greater economies of scale and cost efficiency in the host country. On the other hand, FDI itself may contribute to host country economic growth, by augmenting the country’s capital stock, introducing complementary inputs, inducing technology transfer and skill acquisition, or increasing competition in the local industry. Empirically, the positive effect of host country economic growth on FDI inflow has been confirmed by various studies (Barrell and Pain, 1996; Taylor and Sarno, 1999; Trevino et al., 2002). The effects of FDI on subsequent economic growth has been shown to be both positive (Ericsson and Irandoust, 2000; Trevino and Upadhyaya, 2003) and negative (Moran, 1998).

Overall, though, FDI turns out to be associated with greater domestic investment, not
smaller. Moreover, this positive association between FDI and domestic investment tends to be greater than that between foreign portfolio investment and domestic investment (Bosworth and Collins, 1999, Rachidi & Saidi,2011).

Recently the inflow of worker’s remittances has increased many folds, since a large part of this has now been channelized via the banking sector, this has help document the inflow more accurately and has resulted significantly in improving the foreign reserves situations as well as increasing the buying power of the peoples. So, theoretically this is an indirect investment in the society as a whole.

Generally FDI decisions depend on a variety of characteristics of the host economy, in addition to its market size. These include the general wage level, level of education, institutional environment, tax laws, and overall macroeconomic and political environment. Turning to economic growth, the standard determinants include the rate of capital accumulation and variables that raise total factor productivity, such as education level, institutional quality, macroeconomic stability, political environment, and, potentially, trade openness.

External debt plays duel role in shaping the economy, in particular of developing countries. It act as a positive catalyst when used for capital expenditures, but it can be a disaster if the same is wasted on non developmental and person expenditures. Low level of external debt effects the economic growth positively, which becomes negative with a high external debt. External borrowing leads to a current account deficit and appreciation of real exchange rate and sometimes to a balance of payments
crisis (if foreign resources are run down) or an external debt crisis (if debt is too high).

It is important to highlight that the relation of these variables is not simple, and each variable (GDP, FDI, exports etc) has a theoretical foundation to effect the other. Without knowing about them properly, will affect the policy making and hence hamper the economic growth. Therefore it is important to investigate the relation between these variables for effective policy making.

Literature Review
Atique et al, (2004) studied the effect of foreign direct investment on economic growth of Pakistan. Five variables GDP, FDI ,labor, gross capital formation as percentage of GDP. Education expenditure as a percentage of GDP and ratio of total merchandise trade to GDP are tested using Engle-Granger and Hansen methods. Data for this study covers the period from 1970-2001. The analysis concluded that the impact of FDI tends to be greater for an export oriented trade regime than that of Import substitution trade regime. Furthermore , Pakistan’s capacity to progress more would depend upon how attractive its policies are towards foreign investment.

Falki, (2009) studied the effect of FDI on economic growth in Pakistan for the period 1980-2006.A simple OLS methodology is adopted for this study. Other variables that were tested include domestic capital, foreign aid capital, and labor force. The results showed a negative and insignificant relation between GDP and FDI for the period under review. This meant that FDI despite showing an upward trend in Pakistan has not been able to contribute meaningfully in the economic growth, in comparison to other variables. It was recommended that Pakistan should do more in order to attract
more FDI for increasing the contribution of it in economic growth.

Carkovic and Levine, (2000) in their paper “Does FDI promote development” studied 72 countries in a panel for a period 1960-1995.they used two econometric methods in their study, first a simple OLS model and the second is the use of dynamic panel procedure with data average over 5 years period. The results concluded that the FDI inflows do not exert an independent influence on economic growth. While sound economic policies may facilitate in increasing both FDI and Economic growth. This result is inconsistent with the findings that FDI exerts a positive impact on growth
independent of other growth determinants.

Chakraborty & NunnenKamp, (2006) analyzed the effect of foreign direct investment and economic reforms in India. The study focused on industry specific FDI and its growth, by using Granger Causality and panel cointegration. The results showed that the growth effects of FDI vary widely across different sector. There was no casual relationship found in case of Primary sector. While only transitory effect of FDI on output was found In the service sector. These differences in FDI growth relation suggests that FDI is unlikely to make wonders in India if only remaining regulations
are relaxed and still more industries are opened up.

Fortanier, (2007) studied the role of investor country in the event of foreign investment and growth. A panel data comprising of six major investor and 71 host countries for the period of 1989-2002 was used. The results showed that growth consequence of FDI differs by country of origin, and the effect of origin country also varies depending upon the host country characteristics.

Rachidi & Saidi, (2011) analyzed the effect of foreign direct investment and portfolio
investment for both developed and developing countries. The panel data covers the period of 1999-2009 and comprises of 100 countries. Popular methods of poled, random effect and fixed effect models have been used in the study. Results suggested that FDI has a significant positive impact on real per capita growth. Also no evidence was found that Portfolio Investment enhances output growth in developing countries.

However this is positive and significant for developed countries, when the GMM estimator is used. For random effect the coefficient of FDI remains positive but statistically insignificant, and the portfolio investment remains negative and insignificant for all the countries.

Duasa, (2007) studied the relation of FDI and growth with respect to stability in Malaysia. Quarterly data from the first quarter of 1990 to fourth quarter 2002 is collected. GARCH and causality are applied to analyze the impact of FDI on the stability of economic growth, and examine any causal relationship between FDI and growth respectively. The study didn’t find any strong causal relationship between FDI and economic growth. However it was found that flow of FDI contributes towards less volatility of economic growth and vice versa. This means that in Malaysia FDI does not causes economic growth but it does provide stability to economic growth.

Hameed et al, (2008) analyzed the impact of external debt on economic and business growth in Pakistan. By applying co integration and error correction model on the annual data from 1970-2003, the empirical results were analyzed. The results showed that debt servicing has a negative relation with labor and capital hence affect economic growth adversely. It was also concluded that a negative relation exists between debt servicing and GDP, which in the long run reduces the debt servicing
ability of the country. A short run and long run causal relation was also established running from debt to service to GDP.

Yousuf et al, (2008) evaluated the economic impact of foreign direct investment in Pakistan. The research studied the impact of FDI on imports and exports of Pakistan. Time series data from 1973-2002 is used in this paper. By applying cointegration and error correction techniques, it was concluded that FDI positively impact real demand for imports both in short and long run. The results for export model showed that FDI has a negative relation in short run while in the long run it impacts positively real exports.

Mohamed & Sidiropoulos, (2010) studied the effect of workers remittance on economic growth. For this study data from seven MENA countries were collected for the period of 1975-2006. Both fixed effect and random effect models are used for empirical analysis. The results showed support for fixed effect models, and proved that remittances have a positive impact on economic growth both directly and indirectly through their interaction with financial and institutional channels.

Malik et al, (2010) explored the relationship of external debt and economic growth in Pakistan for the period of 1972-2005. A simple OLS model was used for testing, the results showed a negative and significant relation between external debt and economic growth. Same stands good for the relation between debt servicing and economic growth.

Tiwari & Mutascu, (2011) analyzed the relationship between economic growth and FDI forAsian countries using Panel data approach. The sample period for this purpose comprises 1986 to 2008, and it included data of 23 countries .it was concluded from the study that both foreign direct investment and exports enhances the growth process. In addition labor and capital also play a significant role in economic growth.

Data & Methodology
Multiple regression analysis is used to find the long run relationship between the variables. In this research we have focused on secondary type of data, all data is collected from the official economic survey of Pakistan. In this study we have used the data of gross domestic production as a dependent variable. The data of remittances, foreign direct investment and external debt are collected as independent variables for the period of 1981 to 2010. After selection of the above variables we can
describe the economic growth function of Pakistan in the following way:
EG = f (REM, FDI, ED)
Where EG is the economic growth, f represents the function of and REM, FDI, ED represent respectively, remittances, foreign direct investment and external debt. After specifying the economic growth function in linear form with an addition of error term, we can write in following way:
EG = α + β1 REM + β2 FDI + β3 ED + ε
This research is based on the following hypothesis that clearly defines the research criterion.
H1: External Debt has no significant impact on Economic Growth
H2: Remittances has no significant impact on Economic Growth
H3: Foreign Direct Investment has no significant impact on Economic Growth

Result Analysis
Table 4.1: Descriptive Statistics

GDP
ED
FDI
REM
Mean
Maximum
Minimum
Std. Dev.
Observations
3182.31
6004.41
1346.38
1347.69
30
1024.64
3459.84
86.78
922.75
30
59.31
338.35
0.35
99.80
30
137.87
746.33
20.95
180.72
30

The table 4.1 represents the descriptive statistics of the model. In the above table GDP is a dependent variable and FDI, REM and ED are independent variables. The sample size comprises of 30 observations from the period of 1981 to 2010. The minimum and maximum value of EG (1346.38) &  (6004.41) respectively, whereas the mean value is (3182.31) and standard deviation is (1347.69). ED having minimum value (86.78), maximum value (3459.84), mean value (1024.64) and standard deviation (922.75). FDI having minimum value (0.35), maximum value (338.35), mean value (59.31) and standard deviation (99.80). REM having minimum value (20.95), maximum value (746.33), mean value (137.87) and standard deviation (180.72).

Study in the mentioned subject of econometrics indicates that various macroeconomics variables data are found non stationary. The finding was drawn from regression (integrated in different order) proceeds non sense or spurious regression. Thus it is essential to analysis the stationary of the data before drawn the long run association among the variables.

Table 4.2: Stationary Test Results

Variables
Augmented Dickey Fuller test
Philips Perron test
Level
First Difference
Level
First Difference
Intercept
Trend & Intercept
Intercept
Trend & Intercept
Intercept
Trend & Intercept
Intercept
Trend & Intercept
GDP (Gross
Domestic
Production) ED (External
Debt) 
FDI (Foreign
Direct
Investment) REM
(Remittances)
1.685 


-0.058  

2.108   

2.227   

2.227   
-0.540


-1.839

-1.876

-1.128

-1.128
-3.891


-4.567

-3.992

-3.893

-3.893
-4.531


-4.666

4.512

-4.472

-4.472
-0.866


-0.045

0.225

1.897

1.897
-1.571


1.284

0.877

-1.343

-1.343
-4.326


-4.758

-4.321

-3.893

-3.893
-4.740


-5.879

-5.214

-4.472

-4.472



Table 4.2 highlighted the finding of Augmented Dickey Fuller (ADF) test and Philip Perron unit root test. The impacts of result shows that the non stationary in all variables at level. Here equation is used to check stationary in the data first with intercept and then with trend and intercept. Here null hypothesis means non stationary in the data and alternative hypothesis means stationary in the data. All the given variables are non stationary at level. Analyzing the stationary in the data at level consequently checking stationary at first difference the result indicates that all the variables are stationary at first difference. All the variables are checked at the lag length of one. All the given variables are integrated at order one.


Table 4.2: Results of OLS
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
REM
FDI
ED
R-Square
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
1739.882
2.122
3.756
1.476
0.968
0.964
256.329
1708312.000
-206.815
1.682
76.996
0.707
0.855
0.127
22.597
-3.000
4.391
11.620
0.000
0.006
0.000
0.000
3182.309
1347.689
14.054
14.241
258.549
0.000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)



In the above table EG is a dependent variable and ED, REM and FDI are independent variables. Table 4.3 gives us the R-value, which represents the correlation between the observed values and predicted values of the dependent variable. R-Square is called the coefficient of determination and it gives the adequacy of the model. Here the value of R-Square is 0.968 that means the independent variable in the model can predict 97% of the variance in dependent variable. The p-value is given by 0.000 which is less that 0.05, which shows the significance of our model. The values of Durbin-Watson statistics for dependent variables in our case is very near to 2.00, this indicates that there is no autocorrelation exists in our study and the regression models assume that the error deviations are uncorrelated.

The Beta value shows the relationship between the variables in the model, if the value of coefficient is positive it means that independent variables have positive relation with dependent variable i.e. increase in dependent variable is caused by increase in independent variable and if the value of coefficient is negative than independent variables are having negative relation with the dependent variable i.e. decrease in dependent variable is caused by increase in dependent variable. The values of coefficients beta and constant are used to construct the regression model, the model is shown below:
GDP = 1739.88 + 1.48 (ED) + 2.12 (REM) + 3.76 (FDI)
Foreign direct investment (3.76) is having positive and significant impact on economic growth (GDP), that’s mean if the FDI is increase than gross domestic production will also increase.

Remittance (2.12) is having positive and significant effect on GDP, that’s mean if remittance is increase then gross domestic production will also increase. External debt (1.48) is having positive and significant impact on the Economic Growth (GDP) because the p-value is less than 0.05, that’s mean if ED is increase then the GDP will also increase. The findings of remittances, foreign direct investment and external debt are very logical and consistent with past studies & with economic theories. The increases in foreign capital inflows positively affect the level of investment; the consumption level is also increase because of foreign capital inflows. The increase in investment and consumption is lead to increase the gross domestic production.


Table 4.3: Results for Causality
Null Hypothesis:
Obs
F- Statistic
Probability
REM does not Granger Cause ED
ED does not Granger Cause REM
GDP does not Granger Cause ED 
ED does not Granger Cause GDP
FDI does not Granger Cause ED 
ED does not Granger Cause FDI
GDP does not Granger Cause REM 
REM does not Granger Cause GDP
FDI does not Granger Cause REM 
REM does not Granger Cause FDI
FDI does not Granger Cause GDP
GDP does not Granger Cause FDI
28

28

28

28

28

28

3.86300
2.81792
7.21934
4.46446
11.81730
3.56227
0.59773
1.93617
6.30215
9.38266
1.73827
8.35724
0.03577
0.08043
0.00369
0.02300
0.00029
0.04490
0.55838
0.16705
0.00657
0.00105
0.19814
0.00187


The Granger Causality approach to the problem of whether ‘x’ causes ‘y’ is to see how much of the current ‘y’ can be explained by past values of ‘y’ and then to see whether adding lagged values of  ‘x’ can improve the explanation. ‘Y’ is said to Granger-Caused by ‘x’ if ‘x’ helps in the prediction of  ‘y’ or equivalently, if the coefficients on the lagged x’s are statistically significant.

After applying the causality test we found the bidirectional relationship between remittances and external debt, GDP and external debt, foreign direct investment and external debt, and foreign direct investment and remittances. On the other side we found unidirectional relationship from gross domestic production to foreign direct investment.

Conclusion
This study examines the impact of foreign capital inflows on the economic growth of Pakistan by using the yearly data for the period of 1981 – 2010. Multiple regression technique is used to analyze the relationship between dependent variable (gross domestic production) and independent variables (external debt, Remittances, and foreign direct investment). It is concluded all independent variables have positive and significant impact on economic growth of Pakistan. This finding is consistent with theoretical literature because Pakistan is developing country, so increases in foreign capital inflows positively affect the level of investment; the consumption level is also increase because of foreign capital inflows. The increase in investment and consumption is lead to increase the gross domestic production.

This study provides in depth detail of relationship between foreign capital inflows and economic growth. The policy makers should focus on the foreign cash flows to increase the flow of money in economy in the sense of investment and increasing consumption. Foreign direct investment is a key for the growth of any economy. It is recommended that the government and policy makers should make those policies which increase the foreign direct investment in Pakistan.*****



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