This study examine the short run and long run relationship between foreign direct investment, GDP growth rate, exports of goods and services, real effective exchange rate (REER), custom duty on imports (TARR), trade openness (Trade) and whole sale price index (WPI) in case of Pakistan. The main object of this study is to explore the determinant of trade deficit by using Johansen co integration approach and Error correction model (ECM). The paper empirically identifies the determinants of growth in foreign direct investment (FDI) in Pakistan over the period 1980 to 2010. Time-series data is used in the study and data is collected from World Development Indicator (WDI). Pakistan aims to enhance the investment GDP ratio by attracting foreign direct investment (FDI). The foreign investors mostly from the developed dynamic centers are enhancing international production by investing in resource abundant economies. The analysis enabled identification of some economic determinants of FDI in Pakistan, like GDP growth rate, volume of exports, tariff on imports, and price index. The government should focus on export-oriented industries instead of encouraging FDI for domestic consumption.
I. Introduction
Foreign direct investment:
Foreign direct investment are the net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. This series shows net inflows (new investment inflows less disinvestment) in the reporting economy from foreign investors, and is divided by GDP. The significance of foreign direct investment (FDI) flows is well documented in literature for both the developing and developed countries. Over the last decade foreign direct investment have grown at least twice as rapidly as trade (Meyer, 2003). FDI protests against globalization involve a wide spectrum of discontents with modern life and market economies. They include the growth of international trade and specialization, and the established economic practices they entail. They include also the actions of intergovernmental agencies, such as the International Trade Organization (ITO), International Monetary Fund (IMF), the World Bank, and the regional development banks. And it is rare that multinational firms are not mentioned, as the presumed leaders and chief beneficiaries of globalization. There are also more specific accusations against multinationals. Many evils are alleged. They depress wages and employment at home by moving production abroad. They depress wages in their host countries by exploiting helpless workers. They stifle host-country growth by displacing local firms and obstructing their technological progress. In the context of transition economies, we expect that FDI will raise productivity. Hence foreign firms will be more productive than local ones. This is referred to as the direct effect of FDI. The positive direct effect of foreign ownership has been confirmed empirically in a large number of studies. Next to a direct effect, there exist a number of externality effects, by which FDI affects other firms in the same sector or even in other sectors. These indirect effects are commonly referred to as spillover effects. During early 1980s, the government in Pakistan has initiated market-based economic reform policies. These reforms began to take hold in 1988, and since then the government has gradually liberalized its trade and investment regime by providing generous trade and fiscal incentives to foreign investors through number of tax concessions, credit facilities, and tariff reduction and have also eased foreign exchange controls (Khan, 1999). In the 1990s, the government further liberalized the policy and opened the sectors of agriculture, telecommunications, energy and insurance to FDI. But the level of FDI remained low as compared to other developing countries due to rapid political changes and inconsistency in policies.
GDP Growth rate
GDP growth rate take the greater inflows of FDI than volatile economies ( Dasgupta and Rath 2000 and Durham 2002). Wei (2000) concluded that growth impart differ under different conditions of the economy. On the other hand, Asiedu (2002) narrated that economic growth has no impact on FDI.
GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Economic growth is the increase in value of the goods and services produced by an economy. It is conventionally measured as the percent rate of increase in real gross domestic product, or GDP. Historically, from 1952 until 2011, Pakistan's average quarterly GDP Growth was 5.00 percent reaching an historical high of 10.22 percent in June of 1954 and a record low of -1.80 percent in June of 1952. Pakistan's economy has suffered in the past from decades of internal political disputes, a fast growing population, mixed levels of foreign investment, and a costly, ongoing confrontation with neighboring India. However, IMF-approved government policies, bolstered by foreign direct investment (FDI) and renewed access to global markets, have generated solid macroeconomic recovery during the last decade.
Real Effective Exchange Rate
Real effective exchange rate is the nominal effective exchange rate (a measure of the value of a currency against a weighted average of several foreign currencies) divided by a price deflator or index of costs. Real Effective Exchange Rate (REER) is an extension of Nominal Effective Exchange Rate (NEER) index, which along with the exchange rates of the trading partner countries also takes in to account the CPI inflation of the respective countries. REER is mostly indicative of the long term equilibrium exchange rate and the competitiveness of a country and therefore has assumed greater importance within external sector macroeconomic indicators. The effect of exchange rates on FDI has been examined both with respect to changes in the bilateral level of the exchange rate between countries and in the volatility of exchange rates.
The importance of exchange rate in the determination of FDI flows to a country is a well studied topic in literature. (Froot and Stein, 1991 and Klein and Rosenger,1994) emphasized that when currency of a country devalue it will result in the reduction of the production cost, when measured in the foreign currency which will result in increase inflow of FDI as it will cause the wealth of foreign investors to grow. Froot and Stein (1991) provides empirical evidence of increased inward FDI with currency depreciation through simple regressions using a small number of annual US aggregate FDI observations, which Stevens (1998) finds is quite fragile to specification. More importantly the trend in July-April 2010-11 is not very much encouraging because most international currencies have bounced back against US dollar. Resultantly, as rupee depreciated against US dollar by just 0.27 percent, its depreciation against other currencies is massive. Grubert and Mutti (1991), Swenson (1994), and Kogut and Chang (1996), found consistent evidence that short-run movements in exchange rates lead to increased inward FDI, including with limited evidence that the effect is larger for merger and acquisition FDI ( Klein and Rosengren, 1994).
Exports of goods and services:
The key hypothesis from various theories is that gains from FDI are far higher in the export promotion (EP) regime than the import promotion regime. The theory proposes that import substitution (IS) regimes encourage FDI to enter in cases where the host country does not have advantages leading to extra profit and rent seeking activities. However in an EP regime, FDI uses low labor costs and available raw materials for export promotion, leading to overall output growth. Trade openness generally positively influences the export-oriented FDI inflow into an economy (Edwards (1990), Gastanaga et al. (1998), Housmann and Fernandez-arias (2000), Asidu (2001)).
Pakistan's trade policy focuses on boosting the exports that is connected with export-led growth policy of the country. Ejaz and Atif (2010), found that FDI is positively related with volume of exports from the country. The results support the hypothesis that gains from FDI are higher in the export promotion regime than the import promotion regime. In the export promotion regime FDI uses low labor costs and available raw material for export promotion.
Wholesale price index:
Ejaz and Atif (2010) found that the wholesale price index would be positive association with FDI as it stands to represent the movement of the economy towards boom, along with increased demand for goods and services. The foreign investors are concerned with the hot investment climate of the country. Wholesale price index refers to a mix of agricultural and industrial goods at various stages of production and distribution, including import duties. The Wholesale Price Index (WPI) is the price of a representative basket of wholesales goods. Some countries use WPI changes as a central measure of inflation. Some countries use the changes in this index to measure inflation in their economies. The Wholesale Price Index focuses on the price of goods traded between corporations, rather than goods bought by consumers, which is measured by the Consumer Price Index. The purpose of the WPI is to monitor price movements that reflect supply and demand in industry, manufacturing and construction. This helps in analyzing both macroeconomic and microeconomic conditions. Based on this individual movement, the WPI is determined through the averaging principle. The following method is used to compute the WPI. Laspeyres Formula (relative method): It is the weighted arithmetic mean based on the fixed value-based weights for the base period.The value for Wholesale price index (2005 = 100) in Pakistan was 191.51 as of 2010 .Over the past 50 years this indicator reached a maximum value of 191.51 in 2010 and a minimum value of 2.88 in 1960.
Custom duty on imports:
The link between FDI and trade protection in the form of tariff is seen fairly clear by most trade economists, that is higher trade protection should make firms more likely to substitute by producing in foreign country for domestic consumption to avoid the cost of trade protection. This is commonly termed tariff-jumping FDI. It is hypothesized that if foreign investors are interested of goods for domestic use then there should be a positive relationship between tariff on imports and FDI. It has been observed generally that foreign investors in Pakistan are investing in small units to meet the domestic demand. The examples are automobile industry, chemical industry and home-appliance industry. The relationship may be positive. Pakistan's customs tariffs get in the largest single share of national revenue. Most dutiable items are subject to ad valorem duties that range from 0% to 30%. In many cases, a 15% sales tax on imported goods (food, raw materials, and capital goods are exempt from this tax). Alcohol is levied at a rate up to 65%, but can be as high as 225%. These rates were significantly lowered in the late 1990s from an average high of 30% in the early 1990s. Customs Tariffs are levied on major items of export, but these rates are subject to modify as measures are taken to promote or discourage the export of raw materials. Exports of certain foods, used copper and brass utensils, and some hides and skins are disqualified. Trade with Israel, South Africa, and Taiwan is prohibited.
Openness:
It is empirically studied that a decrease in openness might be associated with more horizontal FDI, as investing firms might benefit from circumventing trade barriers through building production sites abroad. But Resmini (2000), studying manufacturing investment in Central and Eastern Europe, finds that these largely vertical FDI flows, benefit from increasing openness, as might be expected in a sector for which international trade flows in intermediate and capital goods are important. Singh and Jun (1995) also find that export orientation is very important in attracting FDI, and link this to the rising complementarily of trade and FDI flows.
II. Literature Review:
Shah and Ahmed (2003) have studied the determinants of FDI by categorizing into four parts i.e. market size factors, Cost factors, political and social factors. Time series data of Pakistan for the period of 1960-61 to 1999-00 is used. Co integration technique advanced by Johansen and Juselius (1990) is used to test the hypothesis. The regression result of the study showed that the market size has a positive effect on the inward FDI flow. A highly significant co-efficient of cost of capital, tariffs and cost of capital points toward the important role that the government can play toward attracting the FDI flow in Pakistan.
Khan (1997) has attempted to point out the reasons behind the low FDIs in Pakistan. The determinants that he believes are behind this discouraging trend of FDI are political instability, law and order situation and economic weakness of the country. He divides the determinants into four broader categories. These are concessions, cost, convenience and capability. Except for the concessions everything else is lacking in Pakistan
Hukro and Ghumro (2007) studied the factors that are responsible for the recent rise in FDI in Pakistan. The study takes in account the period of liberalization and studied all the variables of cost, investment macro-economic and risk and stability factors in the short as well as in the long run. VAR was used and the result suggested that the economic factors followed by the cost factors are the most important factors in determining the FDI.
Gastanaga, Nugent, and Pashamova (1998) and Asiedu (2002) focus on policy reforms in developing countries as determinants of foreign direct investment inflows. They find corporate tax rates and degree of openness to foreign direct investment to be significant determinants of FDI. They have also studied the impact of specific policy variables on FDI in the host countries. These policy variables include openness of trade, tariff, taxes and exchange rate.
The empirical literature suggests that FDI raises national welfare by increasing the volume and efficiency of investment through improved competitiveness, technological diffusion, accelerated spillover effects and the accumulation of human capital (Borensztein et al. 1998; Chakrabati, 2001; Asicdu, 2002; Durham, 2004). Overall, the flow of FDI to developing countries contributes to growth through two mechanisms, i.e., increasing total investment in the host country and increasing productivity through technology and management spillover (Mellow, 1999).
Capital inflows to a country are normally influenced by the domestic economic condition of the host country. Collins et. al (1999) suggested that the motives for FDI are different for different type of FDIs. The motive could be market seeking and efficiency seeking (Hanif, 2001).The study is about the locational determinants of the FDI in Pakistan. The findings of the regression analysis suggested that the economic factors are more significant determinants of the FDI than the political factors.
Ozturk et al (2007) pointed towards the deterioration of BOP when the profits are repatriated back. He argues that there is no universal positive association between the FDI flows and the economic growth. In case of LDC positive relationship exists but in case of DC no growth benefit was found. The relationship was studied by using Engle Granger co integration and causality test for the period 1975-2004.the result suggest that in case of Pakistan it is the GDP which is the main determinant of FDI.
Ahmed et al. (2003) have applied Granger's concept of causality on the data for the time period of 1972-2000, to examine the effect of export, production, domestic output, foreign income and exchange rate on inflow of FDI in Pakistan. They concluded that domestic output is the most powerful determinant of FDI. The domestic output is a micro-level concept, therefore Pakistan should stress on micro economic approach, which would increase domestic output of international standard
Ejaz and Nawaz (2010) have studied some economic determinants of FDI in Pakistan, like GDP growth rate, volume of exports, human population, tariff on imports, and price index and found that volume of exports has been emerged the most powerful determinant of FDI.
III. Data
The variables which are used for study are Foreign Direct Investment, Exchange rate, Growth Domestic Product, Wholesale Price Index, Exports, Import tariff and trade openess which is proxied by export plus import as percentage of GDP. The following symbols are used for the representation of the variables in the study.
Dependent variable is Foreign Direct Investment
Symbol used: Foreign Direct Investment (FDI)
Independent variables are Real Effective Exchange rate, Growth Domestic Product, Wholesale Price Index, Exports, Import tariff and trade openness.
Symbol used:-Real Effective Exchange Rate (REER)
Symbol used:-Growth Domestic Product (GDP)
Symbol used: -Exports of goods and services (EXP)
Symbol used:- Custom duty on imports (TARR)
Symbol used:- Wholesale Price Index (WPI)
Symbol used:- Trade Openess (TRADE)
In the study we want to investigate the correlation and regression/cointegraton relationship between FDI and Exchange rate, Growth Domestic Product, Wholesale Price Index, Exports, Import tariff and trade openess in Pakistan in long run as well as in short run. So, in the study the FDI is used as a dependent variable and Exchange rate (REER), Growth Domestic Product (GDP), Exports of goods and services (EXP), Custom duty on imports (TARR), Wholesale Price Index (WPI) and Trade Openess (TRADE) are studied as the independent variables. For the research the annual data will be used for the period of 1980 to 2010. The data of all above variables is taken from the international financial statistics (IFS), which is a publication of international monetary fund (IMF), from the publications of word bank development indicator's WDI and annual economic surveys by the government of Pakistan.
Model Specification
FDI = ß1 + ß2GDP + ß3REER + ß4EXP + ß5TARR+ ß6WPI+ ß7TRADE + UI
Where
FDI = Foreign Direct Investment
GDP = Annual growth rate of GDP
EXR = Annual average exchange rate as Rupees/Dollar
EXP = Exports of goods and services from Pakistan
TARR = Custom duty on imports in the country
WPI = General wholesale price index of the country
TRADE = Trade openess
IV. Methodology
The correlation is one of the most common and most functional statistics. A correlation is a single number that describes the degree of relationship between two variables. The correlation test will be done to check the correlation relationship between FDI and other independent variables in bivariate way.
Table 1:
Correlations
FDI
GDP
REER
EXP
TARR
WPI
TRADE
FDI
Pearson Correlation
1
-.181
.614**
.218
-.551**
.668**
.273
Sig. (2-tailed)
.331
.000
.239
.001
.000
.137
N
31
31
31
31
31
31
31
GDP
Pearson Correlation
-.181
1
-.466**
-.328
.465**
-.392*
.067
Sig. (2-tailed)
.331
.008
.072
.008
.029
.722
N
31
31
31
31
31
31
31
REER
Pearson Correlation
.614**
-.466**
1
.324
-.942**
.960**
-.321
Sig. (2-tailed)
.000
.008
.075
.000
.000
.078
N
31
31
31
31
31
31
31
EXP
Pearson Correlation
.218
-.328
.324
1
-.405*
.219
.296
Sig. (2-tailed)
.239
.072
.075
.024
.236
.106
N
31
31
31
31
31
31
31
TARR
Pearson Correlation
-.551**
.465**
-.942**
-.405*
1
-.832**
.417*
Sig. (2-tailed)
.001
.008
.000
.024
.000
.020
N
31
31
31
31
31
31
31
WPI
Pearson Correlation
.668**
-.392*
.960**
.219
-.832**
1
-.201
Sig. (2-tailed)
.000
.029
.000
.236
.000
.279
N
31
31
31
31
31
31
31
TRADE
Pearson Correlation
.273
.067
-.321
.296
.417*
-.201
1
Sig. (2-tailed)
.137
.722
.078
.106
.020
.279
N
31
31
31
31
31
31
31
Results
All variables FDI, REER, GDP, EXP, TARR, WPI and TRADE in this study are scale variables. After examining the values of Skewness and Std.Error of Skewness. The distributions were not highly skewed. But the distributions were approx. normally distributed. The scatter plots show the relationship that is not curvilinear. Now all the three assumptions for Pearson Correlation i.e. all variables are scale, neither of two distributions are highly skewed and scatter plots are not curvilinear. Based on these results it was appropriate to analyze the data with Pearson Correlation. Because of the seven variables were normally distributed and the assumption of linearity was not markedly violated, Pearson correlations were computed to examine the intercorrelations of the variables. Table: 1 shows that eleven of the twenty pairs of variables were significantly correlated. We are concerned only the correlation of FDI with the other variables. The strongest positive relation, which would be considered a moderate effect size, was between FDI and WPI, (r = - .67), p< .001. This means that FDI was highly positively affected by WPI. There was a moderate positive effect present between FDI and REER, (r= -.61), p<.001.There was also a negative moderate relation between FDI and TARR, (r =-.55), p<.05.Ther were no significant relations among FDI with GDP,EXR and TRADE openness.
Multiple Regression:
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
Durbin-Watson
1
.882a
.778
.722
.49856
1.533
a. Predictors: (Constant), TRADE, GDP, WPI, EXP, TARR, REER
b. Dependent Variable: FDI
ANOVA
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
20.894
6
3.482
14.010
.000a
Residual
5.965
24
.249
Total
26.859
30
b. Dependent Variable: FDI
TABLE:2 Coefficients
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant
-3.661
2.365
-1.548
.135
GDP
.061
.050
.140
1.222
.234
REER
-.023
.040
-.548
-.578
.569
EXP
-.190
.068
-.426
-2.814
.010
TARR
-.092
.033
-1.294
-2.824
.009
WPI
.009
.013
.434
.708
.486
TRADE
.295
.055
.840
5.375
.000
a. Dependent Variable: FDI
Results:
Simultaneous multiple regression was conducted to investigate the best predictor of Trade Balance. The intercorrelation can be found in Table: 1.The combination of variables to predict FDI from independent variables was not all statistically significant. Table: 1 showed that REER, TARR and WPI were correlated with FDI and GDP, EXP and TRADE were not statistically significant with FDI. In Model summary R Square indicated that 78% of the variance in the FDI can be predicted from the independent variables. The value of Durbin-Watson was 1.53 which lies between the ranges of 1.5 to 2.5; it showed that there was no Serial Correlation in the Model. In the ANOVA table the p-value was less than .01 indicated that the combination of these variables significantly predict the dependent variable. Table 2: showed that EXP, TARR and TRADE were significantly contributing and GDP, REER and WPI were not significantly contributing to the equation. However all the variables need to be included to obtain this result, because the overall F=14.01 value was computed with all the variables. F Statistic showed that overall model is good fit.
Histogram of Residual was nicely normally distributed associated with dependent variable, which also supported our model. Nothing problem with the residuals associated with dependent variable FDI.
The above plot was Normally P-P Plot Regression Standardized residual. This plot showed that the values were hugging with the line of greatest fit.
A more informative way to analyze variance of residuals associated with dependent variable FDI is a scatter plot of Regression Standardized Predicted Value on X-axis and Regression Standardized Residual on Y-axis.
A stationary time series has a constant mean, a constant variance and the covariance is independent of time. In order to explore the long run as well as short run determinant of trade deficit with reference to Pakistan Johansen co integration approach and Error correction model (ECM) are used. But first we check the stationary property of the variables.
To test the stationary property of all above series, the Augmented Dickey- Fuller (ADF) test will be used. After applying ADF all of the variables used in this study are non stationary at level form but stationary at first difference. Since p-values of ADF test for all the variables at first difference is less than one percent, null hypothesis of unit root is rejected in favor of alternative hypothesis of no unit root. Therefore all Variables are integrated of order of one I (1).These results lead to the co-integration test of the variables in case of full sample.
To establish order of integration, in next step, Johansen maximum likelihood cointegration method is used to investigate the presence of long run relationship among FDI and independent variables.
Table 3: Johansen Co integration test (Maximum trace value)
Unrestricted Co integration Rank Test (Trace)
Hypothesized
Trace
0.05
No. of CE(s)
Eigen value
Statistic
Critical Value
Prob.**
None *
0.914254
229.8385
125.6154
0.0000
At most 1 *
0.805878
158.6038
95.75366
0.0000
At most 2 *
0.776953
111.0649
69.81889
0.0000
At most 3 *
0.663638
67.55418
47.85613
0.0003
At most 4 *
0.552377
35.95674
29.79707
0.0086
At most 5
0.346398
12.64643
15.49471
0.1284
At most 6
0.010769
0.314000
3.841466
0.5752
Trace test indicates 5 cointegrating eqn(s) at the 0.05 level
TABLE 4:
1 Co integrating Equation(s):
Log likelihood
-319.5568
Normalized cointegrating coefficients (standard error in parentheses)
FDI
GDP
REER
EXP
TARR
WPI
TRADE
1.000000
-0.297528
-0.128780
0.567950
0.146311
0.066472
-0.859117
(0.05364)
(0.03239)
(0.05380)
(0.01957)
(0.01626)
(0.06635)
Table 3 shows that null-hypothesis of no cointegration among the variables is rejected because the trace statistic 229.84 exceeds the 95 percent critical value of the trace statistic (critical value is 125.62), therefore, present study rejects the null hypothesis of no cointegration vector, in favor of the general alternative hypothesis. As evident in Table 3, the null hypothesis of (At most 5) cannot be rejected at 5 percent level of significance. Consequently, we conclude that there is five co integration relationship involving variables FDI, GDP, REER, EXP, TARR, WPI and TRADE.
Johansen maximum likelihood co-integrated vector techniques indicate that there is a long run relationship among variables. Once long run relation is established, Error correction model can be used to examine short run distortion (shocks) in the model. To estimate the short run error correction model, we used general to specific approach [Hendry (1979)].
Error correction model (ECM)
Using Hendry general to specific approach, initially, two lags of the explanatory variables and one lag of the error correction term is incorporated. Later, insignificant variables are gradually eliminated. The estimated results of Error Correction Model (ECM) are presented in Table 5.
Table 5: Estimated Error Correction Model
variables
Coefficient
Std. Error
t-Statistic
Prob.
C
0.250227
0.103617
2.414924
0.0245
D(GDP)
0.003832
0.034786
0.110159
0.9133
D(REER)
-0.097024
0.028786
-3.370488
0.0028
D(EXP)
-0.186563
0.068990
-2.704216
0.0130
D(TAR)
-0.034593
0.031015
-1.115371
0.2767
D(WPI)
0.004526
0.010180
0.444573
0.6610
D(TRADE)
0.130259
0.037886
3.438195
0.0023
ECM(-1)
-0.247067
0.170553
-1.448627
0.1615
R-squared
0.648536
Mean dependent var
0.029000
Adjusted R-squared
0.536706
S.D. dependent var
0.540136
S.E. of regression
0.367647
Akaike info criterion
1.059794
Sum squared resid
2.973622
Schwarz criterion
1.433447
Log likelihood
-7.896909
F-statistic
5.799326
Durbin-Watson stat
1.787542
Prob(F-statistic)
0.000668
The coefficients of error correction model (ECM) are statistically significant at 5 percent level. It suggests the validity of long-run equilibrium relationship among the variables in Table 3 and Table 4. Thus, ECM is not only valid but also there is significant conservative force tendency to bring the model back into equilibrium whenever it strays too far. The results of diagnostic test indicate that the equation passes the test of serial correlation, normality and heteroscadasticity. The small sizes of coefficient of error-correction terms indicate that speed of adjustment is rather slow for equation to return to their equilibrium level once it has been shocked.
Estimated results of error correction model of response variable FDI is presented in Table 5and indicate that REER and EXPORTS has significant negative impact on FDI and Trade Openess has significant positive impact FDI in short run.
ECM suggests the validity of long-run equilibrium relationship among the variables in Table 3.
V. Conclusion
The important finding of the study is that export demand that is shown by the bulk of exports is major determinant of FDI in Pakistan. The national trade policy should focus on exports by increasing export processing zones, global market orientation and adjusting fiscal policies. A co-efficient of import tariff suggested an important role of the government in promoting the foreign investment in the country. It needs effective and encouraging import policies from the public sector to restore the confidence of the investors.
The results from the co integration estimation reveal that FDI and all its potential determinants have a long-run equilibrium relationship. The major determinants of FDI in Pakistan are Exports, imports tariff and trade openness. However the most significant and influential factors are market size and labor force growth. Overall, South Asian countries need to maintain growth momentum to improve market size, frame policies to make better use of their abundant labor forces, improve infrastructure facilities and follow more open trade policies for attracting more FDI.
The study clearly emphasizes the role of these policy variables in attracting FDI and determining its growth in both short and long run in Pakistan. The study also indicates a positive and significant impact of reforms on FDI in Pakistan.
