ARDL Approach for Determinants of Foreign Direct Investment (FDI) in Pakistan ( ): An Empirical Study

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1 Global Journal of Quantitative Science Vol. 3. No.2. June 2016 Issue. Pp.9-14 ARDL Approach for Determinants of Foreign Direct Investment (FDI) in Pakistan ( ): An Empirical Study Zahid Iqbal 1, Adil Mahmood 2 1 Dr. Zahid Iqbal, Assistant Prof., Department of Mathematics and Statistics, International Islamic University, Islamabad 1 Department of Mathematics and Statistics, International Islamic University. Abstract The objective of this study is to empirically investigate the determinants of FDI inflows in Pakistan for the duration of the time 1961 to The Foreign Direct Investment (FDI) is taken as dependent variable, whereas Gross Domestic Product (GDP), Exchange Rate (EX), Inflation (INF), Interest Rate (IR) and Trade Openness (TROP) are used as independent variables. The ADF unit root analysis indicates that some of the variables like lnfdi, lnex and lninf are integrated of order one and variables like lngdp, lnir and TROP are become stationary after taking their first difference. Thus, we use ARDL approach for the long run and the short run association among variables. Keywords: VECM, FDI, Pakistan, Unit Root, Co-Integration Introduction The FDI defined as an individual or group of individuals outside of the country invests their cash on any kind of the business or production activity in the host country. For the economic growth of any country the FDI has a basic part and the growing FDI inflow considered as an image of the developing economy. There are several elements in the research literature which has an impact on the FDI like trade openness, real interest rate, political instability, infrastructure, exchange rate, market size and labor cost (Mughal and Akram, 2011). Many countries in the world are searching for the policies to enhance FDI for the economic growth in their country. According to the World Investment Report in 2015, the global FDI inflows was 1.47 trillion dollar, while the global FDI inflow in 2014 was 1.23 trillion dollars, which is 16% below as compared to The fall of the global FDI inflow described that the condition of the global economy is going down because of the strategic uncertainty for the foreign investors and higher political risk. But the investment report 2015 claimed that the global FDI inflow is expected to 1.4 trillion dollar in 2015, 1.5 trillion dollars in 2016 and 1.7 trillion dollars in (World Investment Report 2015). Figure 1 FDI Inflows of Different Economies *Corresponding author. z.iqbal@iiu.edu.pk

2 Zahid Iqbal et al. ARDL Approach for Determinants of FDI in Pakistan Page 10 In the developing countries FDI inflows were 681 billion dollars and this group of economy got 55% of the total global FDI inflow. The telecommunication sector remained the main beneficiary of the FDI and trailed by the financial and energy sector in Pakistan. However, the FDI inflow remained low in other sectors because of diverse factors like security issue, the poor condition of infrastructure, the arbitrary administration of laws and regulations, the non-respect of intellectual rights and administration struggle to open its economy (Stander Trade Portal). In 2014, the FDI inflow remained under 1 billion US dollar. According to the report of UNCTAD that the ability of investment charm in Pakistan lowered than India, but similar to Bangladesh and Sri-lanka. Because of the negative image on an international level the FDI inflows are not increasing in Pakistan. Pakistan s relationship with the foreign countries is worsened because of terrorism, so the growth of FDI is not improving. Despite of this situation, many agreements and cooperation signed recently with China in the field of energy and defense (Stander Trade Portal). From the Figure 1.2 we noticed that FDI in Pakistan going down in last 6-7 years. Figure 2 FDI inflows in Pakistan (Thousand $) SOURCE: WORLD DEVELOPMENT INDICATOR (WDI) Literature Review There are many factors present in different research paper which influencing on FDI in an economy. Kamel et al. (2015) used time series data from They used the ARDL bounds testing approach and ECM technique for data analysis. They concluded that macroeconomic and political stability are not enough to attract FDI in Algeria. Acheampong and Osei (2014) inspected the essential components of the FDI in Ghana for the period 1980 to The essential point of the study is to take a glimpse at the effect of the infrastructure and natural resources on the FDI in Ghana. The study discovered that in the short run natural resources absolutely pull the FDI, however over the long run natural resources adversely impacted on the FDI inflow in Ghana. The study inferred that the better transportation and more political strength had a positive and critical influenced on the FDI inflow in Ghana. Though, the market size discovered insignificant in Ghana. Sober Mall (2013) applied the ARDL approach using time series data ranging from In his study, he found that the infrastructure had a positive and significant impact on the FDI whereas financial market, GDP growth and inflation had an insignificant impact on FDI in Pakistan. In the short run only the infrastructure had a significant impact on FDI. Mughal and Akram (2011) used time series data ranging 1984 to 2008, the basic objective of their research work is to inspect the influenced of market size (proxy GDP current US $), exchange rate (official exchange rate) and corporate taxes on the FDI inflow in Pakistan. The ARDL approach to

3 Zahid Iqbal et al. ARDL Approach for Determinants of FDI in Pakistan Page 11 cointegration and ECM technique is utilized to examine the relationship among the variables. In their study, they concluded that the market size plays a key role to fascinate the FDI in Pakistan. Azam (2010) used log form of the economic model, he considered three countries Armenia, Kyrgyz Republic and Turkmenistan and employed lest square method for analysis. In his study he determined that market size and official development assistant had a progressive effect on the FDI while inflation had a harmful impact on the FDI. He proposed that market size and official development assistant required to encourage and inflation must be controlled to attract the FDI. Materials and Method Source of Data There are two sources used for the data collection of this study the World Development Indicator (WDI) and International Financial Statistic (IFS) for the period 1961 to The variables like Gross Domestic Product (GDP), Exchange Rate (Ex), Trade openness (TROP) and Inflation (INF) is obtained from the WDI and the data for the IR is collected from the IFS. Model Specification Ioannatos (2003) used log linear specification to estimate the coefficient of the variables. The mathematical model is represented by: FDI=f{Gross Domestic product(gdp), Exchange Rate(EX), Trade Openness(TROP), Interest Rate(IR) and Inflation(INF)}. The econometric model is as: (1) Where LnFDI =Log of Foreign Direct Investment LnGDP =Log of Gross Domestic Product LnIR =Log of Interest rate LnINF =log of Inflation LnEX =Log of Exchange Rate LnTORP =Log of Trade Openness μ = Error Terms Results and Discussion Philips Perron Test The ADF employed for this study for the stationary conditions of the variables. Table 1: ADF stationary test of variables Variables AT LEVEL AT DIFFERENCE With Intercept With Trend With Intercept With Trend LnFDI * * * LnGDP * * LnEX * * * LnIR * * LnINF ** * * TROP * -8.93* *Significant at 1 % and ** significant at 5 % In Table 1 the ADF test demonstrated that lnir, lngdp and TROP are non-stationary at a level and turn into stationary at 1% in their first difference. The variables like lnex and lnfdi is stationary at a level with the trend and the first difference of lnex and lnfdi are also stationary at 1% level of significance. The variable lninf is stationary at 5% in level with the trend and the first difference of lninf is also stationary at 1%. Model Selection In this study Akaike Information Criteria (AIC) used for the ARDL specification by using eviews9. On the basis of AIC the optimal lag for the variables is ARDL (101000). Bounds Test for Cointegration The bounds test based on the F-statistic with the null hypothesis of no cointegration. First we estimate the model (1) given below by using Least Square Method and results are given in Table Pesaran et al. (2001) named the model (1) as Conditional Error Correction (CEC) in his paper.

4 Zahid Iqbal et al. ARDL Approach for Determinants of FDI in Pakistan Page (2) Table 2: Conditional EC Representation of ARDL (101000) Model Variables Coefficients Std. Error t-statistic Prob. D(lnEX) D(lnEX(-1)) * D(lnIR) *** lnfdi(-1) * LnGDP(-1) * lnex(-1) ** lnir(-1) lninf(-1) *** TROP(-1) C ** * significant at 1% ** significant at 5% *** significant at 10% We ignored the insignificant coefficient of D(lnGDP), D(TROP) and D(lnINF) to avoid the over parametrized and run again the above model (Pesaran et al 2001). Next, we make sure that the above model is serially uncorrelated and dynamically stable. The Breusch-Godfrey (BG) LM test used for serial correlation and results are given in Table.2. The results showed that there is no problem of serial correlation. Table 3: BG Test for Conditional EC representation of ARDL (101000) F-statistic Prob. F(2,39) Obs*R-squared Prob. Chi-Square(2) For the stability of the model Cumulative Sum (CUSUM) test is used from Figure 7 we concluded that CUSUM statistic lies between the critical bounds at the 5% level of significance. Figure 3 CUSUM Test for Conditional EC of ARDL (101000) Model CUSUM 5% Significance

5 Zahid Iqbal et al. ARDL Approach for Determinants of FDI in Pakistan Page 13 Now we can conduct the F-statistic test to check the significance of the coefficient of the variables at a level with lag one. Table 3: Wald Test for Conditional EC of ARDL (101000) Model Test-Statistic Value Lags F-statistic Lags(101000) Critical values Bounds Pesaran et al (2001) Significance I0 Bounds I1 Bounds 5% % As the value of F-statistic in Table 4.18 greater than the critical upper value bound, so we concluded that there is long run association among the variables. ARDL (101000) Long Run Coefficient The long run coefficients of the model ARDL (10100) by using Least Square (LS) are given in Table 4. Table 4: ARDL (101000) Long Run coefficient Variables Coefficients Std.Error t-statistic Prob. lngdp * lnex * lnir lninf * TROP C * significant at 1% ** significant at 5% From Table 4 we noticed that lngdp, lnex, and lninf have a significantly influenced on lnfdi with expected sign. From the coefficient of lngdp we concluded that 1 % increase in the level of GDP can increase the lnfdi by 6.91 percent in the long run. While l % up in the level of EX can boost up the FDI inflow in Pakistan by 4.77 percent. From the negative sign of INF we make inferences that one percentage point increase in the level of inflation can reduce the FDI inflow in Pakistan by 8.69%. The sign of lnir is positive and TROP is negative, but both are insignificant. Table 5: ARDL (101000) ECM Estimates Variables Coefficients Std.Error t-statistic Prob. D(lnFDI(-1)) D(lnGDP) * D(lnEX) D(lnIR) * D(lnINF) * D(TROP) ECT(-1) * * significant at 1% ** significant at 5% *** significant at 1% R-squared Mean dependent var Adjusted R-square S.D dependent var S.E of regression Akaike info criterion Durbin Watson stat Schwarz info criteria Hannan Quinn Criter As in Table 5 we observed that the coefficient of Error Correction Term (ECT) is negative and significant the coefficient showed that the FDI adjust to restore 87 percent of the disequilibrium from the previous year to the current year. In the short run LnGDP has a positive and significant influenced on the FDI. While lnex and LnINF has a negative and significant impact on FDI in the short run with lag one. The coefficient of lnir is positive in the short run estimates and influenced on LnFDI

6 Zahid Iqbal et al. ARDL Approach for Determinants of FDI in Pakistan Page 14 significantly. From the negative sign of TROP we concluded that in the short run trade openness has a negative impact on the FDI. Table 6: Diagnostic Test ARDL (101000) ECM Estimates Test Type of Test Null Hypothesis Test Statistics Conclusion Normality Test (Jarque Bera) Residual terms are normal JB Reject Null (0.000) Serial Correlation (Breusch Godfrey Errors are not correlated Chi-sq. Accept Null Test L.M) 3.128(0.20) Heteroscedasticity Breusch Pagan Residuals are Chi-sq Accept Null Test Godfrey Homoscedasticity (0.073) From Table 6 wo observed that there is no problem of serial correlation and heteroscedasticity but JB test reject the null hypothesis that error are normal. Conclusion In this study, we investigated the effects of variables GDP, EX, lnir, INF and TROP on the FDI inflow in Pakistan for the period of 1961 to 2013 From the stationary conditions of the variables in ADF test, we noticed that all the variables become stationary at their first difference but INF at 5% of the level of significance is stationary at level, likewise FDI and GDP are stationary at a level with time trend. We employed the ARDL approach for the long run and short run behavior of the variables after accepting that the variables are stationary at different levels. On premise of AIC criteria with maximum lag one the selected best model in Eviews 9 is ARDL (101000). From the long run coefficient of ARDL (10100) model we concluded that GDP & EX positively and significantly boost up FDI inflow in Pakistan, while INF has a negative impact on the FDI. From the ARDL short run estimates, we noticed that GDP and IR has a positively expand the FDI whereas INF has a negative and significant impact in the short run. The problem in ARDL (101000) model is that residuals are not normal. References Acheampong, P. and Osei, V. (2014). Foreign Direct Investment inflows into Ghana: should Focus Be on Infrastructure or Natural Resources? Short Run and Long Run Analyses, International Journal of Financial Research, vol.5. Azam, M. (2010). Economic Determinants of Foreign Direct Investment In Armenia, Kyrgyz Republic and Turkmenistan: Theory and Evidence, Eurasian Journal of Business and Economic. Ioannatos, P. E, (2003). The Demand Determinants of Foreign Direct Investment: Evidence from Non Nested Hypothesis. The Asymmetrical Economy: Growth, investment and public policy, Studies in Economic Transformation and Public policy, pp Johansen, S. and Juselius, K (1990). Maximum Likelihood Estimation and Inference on Cointegration with Application to the Demand for Money, Oxford Bulletin Of Economic and Statistics, vol 52, pp Kamel, S. M., Benhabib, A., Lazrag, M. and Zennagui, S (2015). The Effect of Foreign Direct Investment on Algeria Economy International Journal of Economics, commerce and management Vol.3. Mughal, M. M. and Akram, M. (2011). Does Market Size affect FDI, Interdisciplinary Journal of Contemporary Research in Business, vol 2. UNCTAD (2105), World Investment Report, New York and Geneva: United Nations. Pesaran, M.H., Shin, Y. and Smith, R. J. (2001). Bounds Testing Approaches to the Analysis Of level Relationship Journal of Applied Econometrics. Stander Trade Portal (2015) retrieved from counterparts. Sober mall (2013). Foreign Direct Investment Inflows in Pakistan: A Time Series Analysis Unit with ARDL Approach International Journal of Computer Application vol.78.