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Impact of FDI & Remittances On economic Growth OF Pakistan 2013 RESEARCH PAPER Impact of Foreign Direct Investment & Remittances on Economic Growth of Pakistan. 1 | Iqra University Gulshan Campus

Syed Zaidi

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Impact of FDI & Remittances On economic Growth OF Pakistan 2013

RESEARCH PAPER

Impact of Foreign Direct Investment & Remittances on

Economic Growth of Pakistan.

Instructor Mr. Tehseen Jawed.

By S.M Zaid (5450)

Raheel Roshan Ali (5912 )

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Impact of FDI & Remittances On economic Growth OF Pakistan 2013

Hina Shareef (7441)

Nizar Rajwani ( )

Sana Shareef (8496)

AcknowledgementFirst of all we are very thankful to almighty ALLAH who makes us able to perform this research, we are also thankful to our Teacher MR. Tehseen Jawaid for the valuable information and great support throughout the research.

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Impact of FDI & Remittances On economic Growth OF Pakistan 2013

Thanks Allot

INTRODUCTION:Economic growth of a country is influenced by a large number of multiple factors especially for developing countries, out of which Federal direct investment and the remittances are two major sources of cash inflow they are observed and argued as a significant determinant of economic growth in too many researches by various researchers. GDP is a sum of market price of all goods and service produced in a country within a specific period of time1, Foreign direct investment (FDI) is defined as "investment made to acquire lasting interest in enterprises operating outside of the economy of the investor." 2.There are so many arguments for and against of the relationship between foreign direct investment and the economic development of a country FDI some time shows different relation with different GDPs Anwar Hossain and Kamal Hossain (2012)3 it mean it is not necessary that the increade in FDI will always have positive impact on economic growth. According to Chien and Zhang (2012)4 there is a strong bidirectional relation between FDI & remittance Both FDI and GDP also contributed significantly and positively in explaining each other4. The argument behind researches are the flows of FDI could fill the gap between desired investment and the domestically saving which will intended to use for investment purpose, Many scholar widely believe that the FDI benefits not only include the investment but also provide the acquisition of new technology, employment, enhancing domestic investment, and increasing tax revenue generated by FDI.Now what is the impact of FDI on

1 Principles of Macroeconomics by John Taylor Page 137.

2 Foreign Direct Investment by Harrison G. Blaine 2009 Page 7

3 Empirical Relationship between Foreign Direct Investment and Economic Output in South Asian Countries by Anowar Hossain & Kamal.

4 FDI of Vietnam; Two-Way Linkages between FDI and GDP, by Chien and Zhang

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economic growth of Pakistan the following graph shows the relationship between

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Fdi & GDP of Pakistan.

but if we analyze this scatter plot we can’t conclude any result for the relationship between FDI on economic growth of Pakistan similarly in the same way the remittances is the second most powerful source of cash inflow as observed by researchers its flows have increasingly become one of the most important sources of external financing in many developing countries. Sharp increase in remittance in recent decades claims the investigation of effectiveness of such flow on economic growth of the recipient countries. According to various researches there should be a positive long run relationship between remittances and GDP Das5 and Chowdhury6 (2011). A Negative relationship between remittance and economic growth was also predicted by Chami et al. (2003). According to Rajan and Subramanian (2005) there is no positive impact of remittances on long-term growth of a country, Das (2012) argues that remittances can bring a favorable outcome even if it is used for consumption. For four developing countries over 1975 to 2006, Das (2012) showed that remittance can have a positive impact on economic growth either through consumption or investment 7Following is the scatter plot of Remittances and GDP rate of Pakistan.

5 Anupam Das, PhD. Assistant Professor, Department of Policy Studies, Mount Royal University, 6 Murshed Chowdhury, PhD Candidate. Department of Economics, University of Manitoba,

7 Das, A. (2012), “Remittance Behavior of Migrants and its Macroeconomic Effects in Four Developing Countries”, International Journal of Applied Behavioral Economics,

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0

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We can see that there is not a consistent trend in the scatter plot therefore we can’t conclude any such result from the scatter plot it neither show a positive nor negative impact of Remittances on economic growth of Pakistan.

Theoretical Background:

The theoretical link between FDI and economic growth can be found out from by a number of theories out of which some are as follow.

Modernization theory

Modernization theory suggests that FDI could promote economic growth under the principle that growth requires capital investment8. However, the new growth theories emphasize the role of technology transfer through FDI because developing countries has a lacks of necessary infrastructure

Dependency Theory

dependency theory which is derived from the work of Karl Marx according to this theory Foreign investment is harmful for the long-term in developing countries it suggest that dependency on foreign investment is expected to produces negative impact on growth and income distribution because FDI creates monopolies in

8 Understanding a Diverse Society by Margaret L. Andersen, Howard Francis Taylor- Page 252

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industrial sector, which leads to underutilization of domestic resources. This implies that the economy is controlled by foreigners9.

Industrialization Theory on FDI

Industrial theory derived from the work of Hymer’s in 1976 according to which FDI represents not simply a transfer of capital, but the transfer of a package in which capital, management, and new technology are combined, termed as industrialization theory of FDI .FDI entails a cross-border transfer of resources including, process and product technology, managerial skills, marketing and distribution know-how and human Capital.10

Neoclassical Economic Theory of FDI

According to the Bergten, Horst & Moran (1978) the neoclassical economic theory FDI contributes positively to the economic development and increases the level of social well-being, because foreign investors are bringing capital in to the host country, thereby influencing the quality and quantity of capital formation.

According to the World Bank, remittances are one of the least volatile sources of foreign exchange earnings for developing countries Ratha (2003). Although, Capital flows tend to rise during favorable economical cycles and fall in bad times, remittances appear to show remarkable stability over time. The research on the subject has shown expatriates remittances tend to be stable or even counter cyclical in response to political crises, economic turndown, or even in natural disasters in the recipient country (Mohapatra, Joseph and Ratha 2009; Ratha 2003, World Bank 2006a). Although several studies have demonstrated that the importance of both host and home country factors in determining remittances flows, it was not clear how remittances would behave in response to a significant economic or financial turn down in the host countries (Mohaptara and Ratha 2010). 11

Based on these mixed theoretical views, many empirical studies have been carried out to examine the relationship between FDI and economic growth.

9 Understanding a Diverse by Margaret L. Andersen, Howard Francis Taylor page 253

10 Hymer, S. H., 1976. The International Corporations of National Firms: A Study of Direct Foreign.11 Book: Migration and Remittances during the Global Financial Crises and Beyond Pg: 365 to 371

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Literature Review:

Husain and Terrace,(2012) investigates the relationship between Foreign Direct Investment (FDI) and the economic growth in Pakistan during the period of 1972to2008 by using three econometric models, 1) Augmented Dickey-Fuller (ADF) test 2) Engle-Granger two-step co-integration test, and the third is Vector error correction mechanism (VECM). This study also used Granger Causality (GC) to find the directional relationship between FDI and GDP. They find no co-integration between FDI and GDP in Bangladesh and India. But they find the co-integration between them in Pakistan.

Chien and Zhang, (2012).Investigates the impact of foreign direct investment (FDI) in the North Central Area and South Central Coast of Vietnam in the period from 2000 to 2010. By using panel data and Ordinary Least Square (OLS) Method results found that: 1) there is a strong bidirectional relationship between FDI and GDP in this area of Vietnam. Both FDI and GDP also contributed significantly and positively in explaining each other in the provinces, 2) There is no strong competition between provinces in attracting FDI 3) Ability to access information and infrastructure quality of provinces affects significantly and positively to attract FDI in this region.

Abbas, Akbar, Nasir, Aman, Naseem, (2011). Investigates the impact of foreign direct investment on economic Growth of SAARC countries. They test this relationship by applying multiple regression.gdp is taken as dependent variable while FDI and inflation are considered as independent variables. The data used for this is ranging from year 2001 to 2010 of SAARC Countries. There is a positive and significant relationship between GDP and FDI while they found an insignificant relationship between GDP and inflation.

Noormamode, (2008).investigates an updated analysis of the causality between foreign direct investment (FDI) and growth, the data of 58 countries is used for the period of 1980to2004 Period. Variables are real GDP, percapita GDP and FDI. A panel VAR model is used. The results of this study provide no clear cut evidence on the growth rate due to the change in FDI.

Anupam Das and MurshedChowdhury, (2011).investigates about the impact of remittance on growth they take the data of 11 top remittance-recipient developing countries. These countries are: Bangladesh, Dominican Republic, El Salvador, Gambia, Guatemala, Honduras, Jamaica, Lesotho, Philippines, Senegal and Sri Lanka. Using recently developed econometric techniques, panel co integration and

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pooled mean group (PMG) approach results support a positive long run relationship between remittances and GDP.

Laura Alfaro, (2003) investigates the effect of foreign direct investment on growth. An analysis using cross-country data for the period 1981to1999.the result suggests that total FDI have a significant effect on growth. Foreign direct investments in the primary sector, however, tend to have a negative effect on growth, while investment in manufacturing a positive one. Evidence from the service sector is ambiguous.

Chien, Zhong&Giang (2012) investigates the impact of FDI on economic growth of Vietnam for the period of 2000 to 2010. They use effects estimation method for econometric models, their results show that FDI has a positive impact on economic growth of Vietnam. The effect in those provinces which has better socio - economic conditions was stronger as compare to those which has worse socio- economic conditions. The study also examines the impact of FDI on economic growth by different regions in Vietnam. The results show that FDI has a positive impact on economic growth in the period 2000 to2010.

Adegbite and Ayadi (2010) investigate the relationship between Foreign Direct Investment (FDI) flows and Economic Growth in Nigeria by using simple OLS regression analysis and Econometrics test. GDPGR (Real GDP growth rate %), LPGROW (Labor Productivity Growth Rate %), GRCS (Growth rate of real Capital Stock %), TFPG (Total Factor Productivity Growth Rate %), TRADO (Volume of trade/RGDP) and FDIGR (FDI Growth Rate) variables have been used. This study concluded that indeed, FDI encourages economic growth and hence the need for more infra structural development, excellent macroeconomic environment as well as human capital development is essential for FDI productivity boosting.

Agrawal and Khan (2011) investigate the effect of FDI on economic growth (GDP) of China and India by using OLS (Ordinary Least Square) method of regression. The factors included in the model are GDP, Human Capital, Labor Force, FDI and Gross Capital formulation, where GDP is dependent and all others are independent. The study indicates that 1% increase in FDI results in 0.07% increase in GDP of China and 0.02% that of India. FDI effects China’s growth more than India. India should learn from China for better FDI utilization.

Coung and Mont (2012) analyze the Economic Impact of international remittances on different household welfare indicator in Vietnam by using Fixed-Effect Regressions using Vietnam House hold living standard surveys 2006 and 2008.

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Child education, assets, durable goods and reservation wages of other working age household member’s variables have been used. The Study suggests that the current international remittances are not an effective measure for poverty reduction in the short run in Vietnam.

Methodology:

We apply regression analysis tests to find out the relationship between the GDP FDI and Workers remittance, this test is run by ECONOMETRIC VIEWS (E-VIEWS) which find the relationship between them with minimum chances of error as it works with OLS or Least square method.

Modal

GDP rate (f) α + β1 (FDI) +β2 (remittance).

F=function

α = change in GDP due to other factors except FDI& Remittances it is constant of eqn.

β1& β2 are coefficients

FDI and remittances are independent variable

GDP rate is dependent variable

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If FDI and remittances are increases by 1 unit GDP will affected by β1& β2 times other things remains constant.

DATA:

The new data set of 36 year for PAKISTAN is collected from Central Bank State Bank Of Pakistan SBP site, to evaluate the relationship between FDI and remittances on economic growth of Pakistan it include GDP rate,GDP,FDI and remittances in millions dollar 12.

Regression Analysis (OLS)

Ho=FDI & Remittances does not affect GDP

H1=FDI & Remittances effect GDP

Table: 1 Regression Analysis i

Variable Constant FDIREMITTANCE

Coefficient 2.56E+10 15.41673 9.272257t-Statistic 3.804702 4.659389 3.132542Prob. 0.0007 0.0001 0.0041R-squared 0.808049 F-statistic 56.83052 Prob. 0 Adjusted R-squared 0.793831 Durbin-Watson stat 0.369804

12 See www.sbp.org.pk.; www.worldbank.com.

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Interpretation:

According to the equation:

GDP rate (f) α + β1 (FDI) +β2 (remittance).

GDP=2.56E+10 +15.41(FDI) +9.27(REMITTANCES)

Where C is constant FDI & REMITTANCES are independent variables and GDP is dependent variable Coefficient sign are positive it mean that 1 unit increase in FDI will increase the GDP by 15.41 times and 1 unit increase in remittance GDP will increase by 9.27 times other things remain constant.Prob value for all independent variables is less than 0.1 reject Ho all the independent variables have significant effect on GDP.

The value of R2 in the result indicates that our model can capture 79.3% deviation of the upcoming environment.

F-statistic is 56.83 shows combined effect of independent variable over the dependent variable and its Prob is 0 shows highly significance. So the FDI & Remittances combined effect on GDP is significant.

Durbin Watson value 0.369 shows that our model has a positive auto correlation, for further confirmation of autocorrelation we will run LM test.

Table 2 LM Test Result ii :

F-statistic 45.35249PROB 0

Interpretation: H0 = no auto correlation.But the Prob value of F-statistic is less than 0.1 reject h0There is auto correlation in the model hence confirmed by our LM test result.We need to eliminate auto correlation at this step as its illness will change the significance of our model so first we will transpose all variables and then again run the LM test for confirmation.

Table 3 LM test result for transposed variables iii

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Durbin-Watson stat 1.953394F-statistic 2.451657Prob. 0.13

Now we can see that after transposing the variables Durbin Watson value improves to 1.95 which is almost equal to 2 hence no auto correlation similarly the Prob value of LM test also confirms that auto correlation is almost removed from this model.

Table 4 Heteroskedasticity Result iv :

F-statistic 5.547256Prob. 0.0017

Interpretation: H0 = No HeteroskedasticityThe Heteroskedasticity Test shows that the Prob. F 0.0017 which mean there is a chance of Heteroskedasticity in our Model. So we reject our claim that No Heteroskedasticity at Prob value of 0.0017.

Stability AnalysisThe stability of relationship is imperative for the effectiveness of coefficients not data CUSUM and CUSUM of square of recursive residual have been used to check the stability of coefficients results are as follow.

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According to the cosum test graph the results of data are stable hence there is no break point so we can assume that there is no year in data in which data has a Sevier fluctuation. While on the other hand According to the cosum square test graph the data has breakpoints in the year of 1989 and it remains senior until 2007 hence there is a sevior fluctuation between 1989 to 2007. To confirm the stability of results we will apply chow break point test

Table 5 chow break point testv

F-statistic14.394

01Prob. 0

H0 =No structural break.

Interpretation:The claim is that β is not inconsistent. But Prob Value is 0.000 which is less than 0.1 so the claim is rejected so there is a structural break these results support the cosum of square result.

Stationary test:

Some time any category of data will have a very power full trend that’s why it will have a savior impact on other variables and show an overall over fitted model although in reality the relation between variables is week as compare to their apparent result to check the stationary test we use unit root test the results are as follow

TABLE 6Augmented Dickey Fuller (ADF)

AT LEVEL INTERCEPT

level and trend & intercept

first difference and intercept

first difference and trend & intercept

VARIABLES PROB. PROB. PROB. PROB.GDP 1 0.9985 0.0427 0.0262FDI 0.0269 0.0092 0.0044 0.0107REM 0.9999 0.9998 0.0467 0.0266

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Ho= data is not stationary

Now we can see that at level intercept the claim against GDP and Remittances are accepted so there is a trend in this data but in FDI the claim is rejected so FDI data has no trend while on the other hand at first difference all three variables trend is removed for graphical presentation of data.vi

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i Table 1 OLS result

Dependent Variable: GDPMethod: Least SquaresDate: 11/25/13 Time: 10:41Sample: 1980 2009Included observations: 30

Variable Coefficient Std. Error t-Statistic Prob.  

C 2.56E+10 6.73E+09 3.804702 0.0007FDI 15.41673 3.308744 4.659389 0.0001

REM 9.272257 2.959979 3.132542 0.0041

R-squared 0.808049    Mean dependent var 6.57E+10Adjusted R-squared 0.793831    S.D. dependent var 3.97E+10S.E. of regression 1.80E+10    Akaike info criterion 50.16234Sum squared resid 8.77E+21    Schwarz criterion 50.30246Log likelihood -749.4351    Hannan-Quinn criter. 50.20717F-statistic 56.83052    Durbin-Watson stat 0.369804Prob(F-statistic) 0.000000

ii TABLE 2 LM TEST RESULT

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 45.35249    Prob. F(1,26) 0.0000Obs*R-squared 19.06836    Prob. Chi-Square(1) 0.0000

Table 3 lm test result after transposing variables

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iii

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 2.451657    Prob. F(1,25) 0.1300Obs*R-squared 2.589936    Prob. Chi-Square(1) 0.1075

Test Equation:Dependent Variable: RESIDMethod: Least SquaresDate: 11/25/13 Time: 11:27Sample: 1981 2009Included observations: 29Presample missing value lagged residuals set to zero.

Variable Coefficient Std. Error t-Statistic Prob.  

C 5.34E+08 1.66E+09 0.322730 0.7496TFDI 0.551391 1.846137 0.298673 0.7677

TREM -1.01E-10 1.43E-10 -0.705896 0.4868RESID(-1) 0.358650 0.229056 1.565777 0.1300

R-squared 0.089308    Mean dependent var 5.92E-07Adjusted R-squared -0.019975    S.D. dependent var 6.36E+09S.E. of regression 6.42E+09    Akaike info criterion 48.13159Sum squared resid 1.03E+21    Schwarz criterion 48.32018Log likelihood -693.9081    Hannan-Quinn criter. 48.19066F-statistic 0.817219    Durbin-Watson stat 1.953394Prob(F-statistic) 0.496543

iv Table 4 Heteroskedasticity Test: White

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Heteroskedasticity Test: White

F-statistic 5.547256    Prob. F(5,23) 0.0017Obs*R-squared 15.85359    Prob. Chi-Square(5) 0.0073Scaled explained SS 17.79443    Prob. Chi-Square(5) 0.0032

Test Equation:Dependent Variable: RESID^2Method: Least SquaresDate: 11/25/13 Time: 11:37Sample: 1981 2009Included observations: 29

Variable Coefficient Std. Error t-Statistic Prob.  

C -9.81E+18 2.22E+19 -0.440762 0.6635TFDI -5.59E+10 6.83E+10 -0.818238 0.4216

TFDI^2 38.81052 20.48161 1.894896 0.0707TFDI*TREM -4.48E-09 3.47E-09 -1.290463 0.2097

TREM 13.68320 5.159613 2.651983 0.0142TREM^2 -3.75E-19 1.62E-19 -2.305870 0.0305

R-squared 0.546675    Mean dependent var 3.91E+19Adjusted R-squared 0.448127    S.D. dependent var 6.64E+19S.E. of regression 4.93E+19    Akaike info criterion 93.71036Sum squared resid 5.60E+40    Schwarz criterion 93.99325Log likelihood -1352.800    Hannan-Quinn criter. 93.79896F-statistic 5.547256    Durbin-Watson stat 1.890997Prob(F-statistic) 0.001706

v Table 5 chow breakpoint test result

Chow Breakpoint Test: 1989 Null Hypothesis: No breaks at specified breakpointsVarying regressors: All equation variablesEquation Sample: 1980 2009

F-statistic 14.39401 Prob. F(3,24) 0.0000Log likelihood ratio 30.88056 Prob. Chi-Square(3) 0.0000Wald Statistic  43.18202 Prob. Chi-Square(3) 0.0000

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