Determinants of Foreign Direct Investment in Pakistan over the period ...

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DECEMBER 2012

INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS

VOL 4, NO 8

Determinants of Foreign Direct Investment in Pakistan over the period 1975-2011 *Muhammad Kashif Rasheed **Dr. Hazoor Muhammad Sabir *** Safdar Hussain Tahir ****Muhammad Umar Farooq Abstract The main objective of this study was to examine the impact of determinants like gross domestic product, indirect taxes, transport storage and communication, exchange rate and trade openness on the foreign direct investment in Pakistan. To fulfill the objective of the study, time series data spreading over the last 37 years (1975-2011) were taken from World Development Indicator. The Johansen co-integration approach was applied to examine the sensitivity of foreign direct investment by changes in gross domestic product, indirect taxes, transport storage and communication, exchange rate and trade openness in the long-run. The estimated coefficients of gross domestic product (GDP), indirect taxes (IT), trade openness (TO) and exchange rate (ER) were found significantly affect the foreign direct investment by 0.309, 0.912, 0.122, -0.204 percent respectively. While the coefficients of the transport storage (TS) and Communications were found statistically insignificant. The most significant factor identified, having impact upon the foreign direct investment was the indirect taxes. It was contributing 0.912 percent toward the foreign direct investment. Key words: Johansen Co-integration, ECM, GDP, indirect taxes, exchange rate, transport storage and communication, trade openness

1. Introduction Foreign direct investment (FDI) is a measure of foreign ownership of productive assets, such as factories, mines and land. Increasing foreign investment can be used as one measure of growing economic globalization. Foreign direct investment (FDI) plays an extraordinary and growing role in global business. It can provide a firm with new markets and marketing channels, cheaper production facilities, access to new technology, products, skills and financing. For a host country or the foreign firm which receives the investment, it can provide a source of new technologies, capital, processes, products, organizational technologies and management skills, and as such can provide a strong impetus to economic development.

*M. Phil Scholar (Economics) G.C University Faisalabad. **Associate professor, Economics Department G.C University Faisalabad. *** Assistant Professor, Banking & Finance G.C University Faisalabad. ****M. Phil Scholar (Economics) G.C University Faisalabad

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Foreign direct investment, in its classic definition, is defined as a company from one country making a physical investment into building a factory in another country. The direct investment in buildings, machinery and equipment is in contrast with making a portfolio investment, which is considered an indirect investment. In recent years, given rapid growth and change in global investment patterns, the definition has been broadened to include the acquisition of a lasting management interest in a company or enterprise outside the investing firm’s home country. In 1947 when Pakistan gained independence, the average economic growth rate was higher than that of the world economy during the period. During the 1960s, Pakistan was thought to be a model of economic development around the world, and achieved much admires for its economic progression. Afterwards, economic mismanagement and imprudent economic policies caused a large volume of public debt which led to slower growth in the 1990s (Wikipedia, nd). In the last two decades, Pakistan Government realized the necessity of changing to their economic policy to compete globally. 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. Worker remittances have increased rapidly, growing to more than three times the value of Official Development Assistance (ODA), being second only to FDI flows in developing countries. Worker remittances in developing countries reached US$280.8 billion in 2009; they accounted for 42.1 percent of all external sources of financial flows, including official and FDI flows to developing countries (UNCTAD, 2011). In 2010, remittances received by developing countries reached US$325 billion, and they are expected to grow to US$346 billion by 2011 and US$374 billion by 2012 (World Bank, 2011). The present study adds to the existing literature by empirically examining the response of FDI to selective policies, namely tax and tariff policy, fiscal incentives offered and exchange rate policies in Pakistan. More specifically, the objective of this study is to find out the effectiveness of these policies during the reform period. From this study we would be able to see which specific government policy is attracting or distracting FDI in Pakistan. This study would be of interest to policy makers in many developing countries where structural reforms are being implemented.

1.2 Objectives The main objectives of the study are as follow: 1. To explore the Long run and short run association among the variables like foreign direct investment, gross domestic product, indirect taxes, trade openness, transport storage and communication and exchange rate over the period 1975-2011. 2. To analyze factors which encourage or discourage foreign direct investment inflows in Pakistan. 3. To suggest policy implications to obtain better and sustainable economic growth.

2. Literature Review There had been various factors influencing FDI in an economy. Asiedu et al. (2002) found trade openness, return on investment and GDP (proxy variable for market size) as significant variables for FDI fostering, while infrastructure and political risk insignificant. Fuat et al. (2002) examined location associated factors that persuade FDI inflows into the Turkish economy. They revealed that infrastructure (proxy by share of transportation, communication and energy expenditures in GDP), the size of the host country’s market and the COPY RIGHT © 2012 Institute of Interdisciplinary Business Research

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openness of the economy (as considered by the ratio of exports to imports) were positively interrelated to FDI inflows. The results further discovered that together economic instability and exchange rate instability (considered by interest rate) had negative effects on FDI. Naeem et al. (2005), used time series data from 1970-71 to 1999-2000 for Pakistan and found the main economic factors as market size, indirect taxes, inflation, and external debt, domestic investment, trade openness. Aqeel et al. (2005) empirically identified the determinants of foreign direct investment (FDI) in Pakistan for the period 1961 to 2003. Main interest was to study how different variables reflecting trade and financial sector liberalization attracting FDI in Pakistan. The study used the error-correction techniques and Co integration to identify the variables for explaining the FDI in Pakistan. Ahmad et al. (2009) analyzed the determinants of Foreign Direct Investment (FDI) in developing countries like Pakistan and examined why some countries like Pakistan had been relatively unsuccessful in attracting FDI despite policy reforms. Mughal et al. (2009) studied the effects of FDI flows on the Pakistani economy over the period 1961-2005 utilizing the Johansen co-integration method and the Vector Error Correction Model. They concluded that FDI does comprise a positive effect on growth rate and further economic variables, principally in the short term. Foreign investment was found to contain a less important role than local investment.

3. METHODOLOGY 3.1 Data Annual time series secondary data for 1975 to 2010 were collected for all different variables like foreign direct investment, Gross domestic product, Exchange rate, Trade openness, Indirect taxes and Transport storage and communication. The data were obtained from different resources like hand book of statistics and WDI on the economy of Pakistan.

3.2 Functional Form Ioannatos (2003), and Aseidu (2005), Azam et al. (2009), and BardhylDauti (2009) used log linear specification to estimate the coefficient of various variables for two reasons; firstly, the relationship between different variables were not linear and secondly, in case of log models, the value of different coefficients could be explained in term of percentage rather than in units. It is true that we have various units to measure various things. So, to avoid this problem, log linear regression model was applied for analysis. For basic objectives of the study, the Log linear regression model used to measure the link between foreign direct investment and Gross domestic product, Exchange rate, Trade openness, Indirect taxes and Transport storage and communication. The functional form of the model can be written as below: FDI = f (Gross domestic product, Exchange rate, Trade openness, Indirect taxes and Transport storage and communication)

3.3 Estimation Procedure For time series analysis, the decision of the appropriate econometric technique depends upon the level of integration of the variables. We, for this, used the Augmented Dickey Fuller (ADF) unit root test. If all the selected variables find stationary at level, then OLS can be used. COPY RIGHT © 2012 Institute of Interdisciplinary Business Research

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However, in the presence of unit root, OLS technique in most of the cases will not be appropriate due to the chances of existence of spurious regression. On the contrary, if all variables are nonstationary at level and become stationary at the first difference, then the most commonly used econometric technique is VAR based Johansen Co-integration technique. Now, Johansen cointegration technique can be carried out through following steps:

3.3.1 Unit root test-test for stationarity It is necessary to test for stationarity to check that the process by which data could have been generated is a stochastic one. This was done by using Augmented Dickey Fuller Test, as it had been utilized by Rubio and Rivero (1994), on their analysis of foreign direct investment in Spain. Therefore, in conducting the Dickey Fuller test on Equation, it was assumed that the error term u is uncorrelated. In the cases when u was correlated, Dickey and Fuller had developed a test, known as the augmented Dickey Fuller (ADF) test. The initial point in unit root test is:

The null hypothesis in the Augmented Dickey Fuller test was that the undertaking process which generated the time series was non-stationary. This was tested against the alternative hypothesis that the information of time-series of interest was stationary. The series is stationary, If the null hypothesis is rejected i.e. it is integrated to order zero. On the other hand, if the series is non-stationary, it is integrated to higher order and must be differenced till it becomes stationary. The order of integration of a time series data shows that the number of times, the series has to be differenced before it becomes stationary (Gujarati, 2003). We wanted to find out When testing for unit root whether a in the Equation was equal to 1. If a is smaller than 1, the series is stationary. On the other hand, If a is greater than 1, then it will be an explosive series.

Subtract from both sides of Equation which was estimated by Augmented Dickey Fuller test. Since the null hypothesis is that a is equal to 1, it must be that b is equal to zero. Hence, there is unit root when b is zero and we have not sufficient evidence to reject the null hypothesis for non-stationarity. We have to difference the variables in order to test for the stationarity of time series.

3.3.2 Johansen Co-integration This study used the co-integration to measure the empirical long term relationship between key factors of the economy like foreign direct investment, Gross domestic product, indirect taxes, Exchange Rate, trade openness, and transport, storage and communication in case of Pakistan. The main reason of utilizing Johansen co-integration was the most consistent one approach. The main benefit of this approach is to measure numerous co-integration associations among the variables in the same period. Two statistical techniques were used for the cointegration, named by the Trace (Tr) test, the Maximum Eigen value (λ max) test. In Johansen co-integration technique, basic two types of statistics (Trace statistics and maximum Eigen value were to be calculated for estimating the co-integration among the variables.

Tr  T

n

 ln(1  ˆ)

i  r 1


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n

max  T  ln(1  r 1 ) i  r 1

Here T is the sample size. The trace test estimates the null hypothesis of r co-integrating vectors against the alternative hypothesis of n co-integrating vectors. On the other hand, the maximum Eigen value test, estimates the null hypothesis of r co-integrating vectors against the alternative hypothesis of r +1 co-integrating vectors.

3.3.3 Error correction mechanism We applied the second step of estimation process for dynamic modeling in order to make the analysis of co-integration approach, suggested by Engle and Granger (Engle and Granger 1987). Hence, when estimating the final short run model, in order to make the long run dynamics, suggested by Augmented Dickey Fuller test, we considered the hypothesis of the lagged residuals as an error correction term, gained from the OLS estimation of Equation. Following this technique, we estimated the co-integration regression, which cleared the presence of long run relationships between the independent variables (Gujarati, 2003) the error correction model is as follows. Yt   0  1 X t   2 (Yt 1  C  X t 1)   t (Error Correction Mechanism) where u t 1  (Yt 1  C  X t 1)  

Firstly, we estimated Error Correction Model from Co-integrating regression; we lagged it, and then became the following regression. n

FDI t  1    2 FDI I 1

n

   6 LNTOP I 1

t 1

n

t 1

   3 LNGDP I 1

t 1

n

   7 LNER I 1

n

n

I 1

I 1

   4 Tt 1    5 TSC

t 1

 ECM t 1   t 1 t 1

 t 1 Denote the error correction term. If there is existence of long run relationship among the variables, it means that the variables under observation move together over time. Any instability is corrected, if occurred, from the long run trend. Next, we found out for the optimal lag length which used in the analysis through Johansen co-integration technique The Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC) used to choose the optimal lag length on the basis of minimum value of these criterions.

4. RESULTS AND DISCUSSIONS The study elaborated empirical relationship between foreign direct investment and explanatory variables like trade openness indirect taxes, gross domestic product, transport storage and communication and exchange rate during 1975-2010 on the basis of an appropriate estimation approach like Johansen Co-integration technique.


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4.1ADF Unit Root Test Results variables

ADF at Level

Results

ADF at First difference

Results

LnGDP

-2.5

Non-stationary

-5.91*

Stationary

T/GDP

-1.41

Non-stationary

-6.50*

Stationary

TSC/GDP

-2.65

Non-stationary

-6.68*

Stationary

FDI/GDP

1.24

Non-stationary

-6.86*

Stationary

LnTOP

-1.50

Non-stationary

-7.08*

Stationary

LnER

-2.91

Non-stationary

-5.69*

Stationary

*identified the significance level at 1 %

ADF unit root test results indicated that all variables were non-stationary at the level, but they were stationary at the first difference. In such situation, we Applied Johansen Co-integration technique.

4.2 Co-integration Firstly, for the appropriate co-integration result, feasible lag length was selected. The minimum value of AIC (Akaike Information Criterion ) and SC ( Schwarz Criterion) was utilized to check the fitness of the model at the specific lag length. On the basis of minimum -8.08 and 7.77values of AIC and SC, one lag length was selected for the co-integration technique. After the selection of the lag length, the next activity was to find out the number and existence of cointegrating vectors among the variables in the model. For this objective, two tests were used; trace test; maximum Eigen value test.

4.2.1 Johansen Co-Integration Rank Tests Hypothesized No. of

Max-Eigen Statistic

CE(s)

None *

Critical Value

Trace Statistic

[Eigen] at 5%

42.88360

40.07757

Critical Value [Trace] at 5%

108.4344

95.75366

* denotes rejection of the null hypothesis at the 0.05 level

Max-Eigen value test indicated 1 co-integrating equation at the 0.05 level Trace test indicated 1 co-integrating equation at the 0.05 level Johansen Co-integration Rank Test results were presented in table 4.2 which explained that long run co-integration existence between endogenous and exogenous variables at 5 % significance level because the value of the trace statistics was above its critical value at the 5 % significance level.Johansen Co-integration Rank Test results were presented in table 4.2 which explained that long run co-integration existence between endogenous and exogenous variables at 5 % significance level because the Eigen value statistics was well above its critical value at the five percent significance level.


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4.3 Empirical Long Run Relationship between foreign direct investment and Explanatory Variables The results regarding long term relationship between dependent and independent variables were presented as follow: Variables

Coefficients

T-statistics

LnTOP

0.122

4.29

T/GDP

0.912

3.23

TSC/GDP

-0.009

0.099

LnER

-0.024

-3.69

LnGDP

0.309

4.86

FDˆ I/GDPt  1.45  0.122 LnTOPt  0.912T/GDPt - 0.024LnERt  0.309 LnGDPt  0.009TSC / GDPt It concluded that the estimated coefficient of gross domestic product, trade openness, indirect taxes and exchange rate were significant at 1% level. Gross domestic product, trade openness and indirect taxes had displayed a positive impact on the foreign direct investment. The estimated coefficient of the transport storage and communication was found statistically insignificant. Transport storage and communication and FDI witnessed a negative relationship in the long run. The estimated coefficient of exchange rate was appeared negative and statistically significant at 1% significance level. The positive estimated co-efficient of the gross domestic product exhibited that averagely, 0.309 percent rise in foreign direct investment had been resulted from 1 % increase in gross domestic product. The estimated coefficient of the trade openness was 0.413 which exerted that one percent increase in trade openness had led to 0.122 % rise in the foreign direct investment. The estimated coefficient of the indirect taxes was 0.912 which displayed that one percent rise in the indirect taxes had led to 0.912 % increase in the foreign direct investment during the time period under consideration. The estimated coefficient of the transport storage and communication revealed that on average, one percent rise in the transport storage and communication had brought about 0.009% fall in the foreign direct investment in the long term but study showed statistically transport storage and communication had no impact on foreign direct investment. The estimated coefficient of exchange rate disclosed that one percent increase in the annual average exchange rate had brought about 0.024 % fall in the foreign direct investment in the long time period because currency depreciation caused a negative contribution to the foreign direct investment.

5. CONCLUSIONS AND SUGGESTIONS The major focus of this paper was to investigate the effectiveness and efficiency of the economic determinants of FDI in Pakistan. We found that gross domestic product, indirect taxes, trade openness and exchange rate were strongly influenced on foreign direct investment and statistically significant. The empirical outcome of the error correction model for foreign direct investment highlighted that every year 59 % of the disequilibrium would be adjusted in the economic system in the short time period. In some aspects conclusions were different from the work of other researchers such as we found that increase in exchange rate led to fall in FDI. The COPY RIGHT © 2012 Institute of Interdisciplinary Business Research

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reason was that it could be negative if investors were expecting a higher return on their investment. Our study also revealed that improvement in infrastructure did not contribute to FDI. In distinction this study is more appropriate to the public policies and their effects on the inward FDI flows in Pakistan. Our results may make available a prospect frame for some policy implications. The results of the paper confirmed that enhancement in GDP had positive effect on inflows of FDI in Pakistan. Therefore, the government should positively deliberate on maximum deployment of resources to improve GDP. Government should make effective monetary and fiscal policies to stabilize annual exchange rate and not spend more on infrastructure to attract FDI. To increase more FDI into Pakistan, the management authorities of the country require ensuring stable economic growth, maintaining exchange rate, encourage the volume of taxes, reduce trade barriers and consistency in the government policies for the reason that these all are the key determinants for potential investors in building investment choices.


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