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Dr. SALMAN AHMAD PROF. Department of Management Studies, University of Central Punjab, LAHORE.

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Presentation on theme: "Dr. SALMAN AHMAD PROF. Department of Management Studies, University of Central Punjab, LAHORE."— Presentation transcript:

1 Dr. SALMAN AHMAD PROF. Department of Management Studies, University of Central Punjab, LAHORE

2 ABSTRACT This paper analyzes the determinants of Foreign Direct Investment (FDI) in developing countries like Pakistan and examines why some countries like Pakistan have been relatively unsuccessful in attracting FDI despite policy reforms. This Study applies Auto Regressive distributed Lag (ARDL) Co-integration technique given by Pesaran. The study found GDP as a significant variable affecting low FDI inflows. The reason for small inflows is that Pakistan has small GDP and it is not growing at a higher rate showing a small size of market compared to India and China. The paper considers other variables like corruption, globalization, etc., but found them statistically insignificant taking into account Pakistan’s data for about 30 years.

3 ABSTRACT This study investigates the econometrically empirical evidence of the relationship between FDI and GDP in an ARDL framework for Pakistan. This study also examines causal linkages between the variables by applying the augmented Granger causality test of Toda- Yamamoto(1995). The results using data on Pakistan’s real GDP and FDI for the period 1980-81 to 2008-09 show cointegration between FDI and GDP when FDI is taken as the dependent variable. Furthermore, unidirectional Granger causality running from real GDP to real FDI has been found in bivariate causality framework.

4 IMPORTANCE FDI is crucial to a developing country like Pakistan as it provides the needed capital for investment. In addition, FDI brings with it employment, managerial skills and technology, and thus accelerates growth and development. The role of FDI as a source of capital has become increasingly important as IMF has started putting conditions and forced structural adjustments for loans.

5 INVESTMENT POLICY 1997 1.Previously only manufacturing sector was open to foreign investors. Now agriculture, services, infrastructure, and social sectors are also open for foreign investors on repatriable basis. 2.Manufacturing sector has been prioritized in four categories, namely a) Value added or export industries, the units which export 80 percent or more of their products in any one year or have minimum value addition of 40 percent of production value is treated as value added or export industries respectively.

6 Investment Policy b) High-tech, this includes information technology, solar technology, aerospace, defence production, etc. c) Priority industries, this includes engineering/capital goods industries, chemicals, and others. d) Agro-based industries, this includes production of quality/hybrid seeds, edible oil extracting/refining, livestock/poultry feeds, milk processing, etc. 3.Revision of labor laws in favor of industrialists.

7 LITERATURE REVIEW The size-of-market hypothesis is based on the assumption that an inadequate market size has retarded the specialization of productive factors. The argument holds that the size of the market has been insufficient to absorb efficiently the technology which the direct investor desires to introduce. Based on this and related assumptions that differences exist among nations in the level of technology; and that some nations are more able to mobilize financial capital than others, the size –of-market hypothesis is that foreign investment will take place as soon as the market is large enough to permit the capturing of economies of scale.

8 Literature review Bandra and White (1968) found market size to be a significant determinant of U.S. FDI. Schmitz and Bieri (1972) found the one-period lagged GNP of the EEC to be a significant variable in a FDI demand function. Lunn (1980) also found the one-period-lagged GNP of the EEC to be a significant explanatory variable for U.S. direct investment in Europe. For developing countries, Root and Ahmad(1979), Torrisi(1985), Schneider and Frey (1985), Petrochilas(1989), and Wheeler and Mody(1992) all find market size to be significant. For Pakistan, Muhammad Hanif

9 Literature Review Akhtar(2000) has used data from 1972 to 1996 and multivariate regression analysis to reveal that market size, relative interest rates and exchange rates are the major determinants of FDI in Pakistan. Zahir Shah and Qazi Masood Ahmad(2003) used cointegration technique to find that in the error correction model, tariff, per capita GNP and dummy for democracy were significant variables. Dar, et all (2004) have used the data for 1970-2002 to check causality and long-term relationship between FDI, Economic Growth and other socio- political determinants. They found economic growth, exchange rate, interest rates, unemployment, and political instability as having theoretically expected signs with two-way causality relationships.

10 HYPOTHESIS Three principal hypotheses have been proposed as to the motivation of foreign investment: size of market in the receiving area, economic growth, and tariff discrimination. These hypotheses are tested using the co- integration technique to determine their relative importance. The empirical data used in these tests relate to direct investment in Pakistan for the 1980-2009 period.

11 ARDL APPROACH The prosed ARDL approach to cointegration is developed by Pesaran and Pesaran(1997), Peasaran and Shin(1995, 1998) and further advanced by Pesaran et al. (2001). It is a unification of autoregressive models and distributed lag models. In an ARDL model, a time series is a function of its lagged values and current and lagged values of one or more explanatory variables. The traditional cointegration technique perform better only for large sample but this is more appropriate for 30 observations.

12 MODEL The measure of the size of market is the level of GDP shown by Y. The second category of hypotheses, the growth hypothesis, are fundamentally based on the relation between the level of aggregate demand and the total investment needed to satisfy this demand. Two variants of growth variables are used. The percentage rate of growth of Pakistan GDP is designated G1; and dY represents the absolute change in the Pak GDP.

13 Model The investment demand function incorporating the size-of- market, growth, and tariff-induced burden as arguments may be specified as: I = A0 + A1 Y+ A2 T+ A3 G-------------(1) Where: I = the annual book value of Direct foreign investment in Pakistan (in millions of dollars) Y = Pak Real GDP (1999-2000 constant factor cost in millions of rupees) T= Tariff revenue as a ratio of total tax revenue G = the general specification for the two variations of the growth hypotheses: Growth rate Gr, and change in real GDP dY

14 DATA FDI Real inflows of FDI are the annual inflows of FDI for the period 1980-81 to 2008- 09. Real value of the dependent variable is obtained by deflating the nominal values with GDP deflator at constant prices of 1999-2000. GDP Real GDP is used as a proxy to estimate the impact of existing market size in Pakistan on FDI. The data for this variable, being in millions of rupees at constant prices of 1999-2000, was converted into dollars using each year’s exchange rate of rupee per dollar. Economic Growth A high level of economic growth is a strong indication of market opportunities. The growth of the host market is deemed to be significant for expansionary direct investment. We have used two types of measures for growth. Real Growth rate of GDP is used to test the proposition that a growing Pakistani market attracts FDI.

15 DATA Exchange Rate An economy with a depreciating currency attracts more FDI as exporting from abroad becomes expensive, while it becomes cheaper to produce locally. Hence, exports by the home country are replaced through local production in the host country. Real exchange rate is used as a variable. It is the nominal exchange rate (rupees per US dollar) adjusted for relative changes in consumer price index (CPI) based on 2000 prices. Globalization

16 Data When one shifts to consider price linked determinants of foreign investment, direct influences on domestic prices should be distinguished from direct influences on international prices. Emphasis is placed on international price patterns to avoid domestic price change due to change in tariffs. It is said that foreign investment is undertaken to avoid obstacles to trade. An implication of this hypothesis is that trade liberalization as a result of WTO will allow goods to move more freely and thereby reduce the volume of international investment flow needed. We use government revenues from taxes on international trade, mainly import and customs duties. These revenues are divided by the total tax to compute the relative tax burden borne by the international sector.

17 EMPIRICAL FINDINGS To investigate the nature of any long-run relationship between FDI and the variables suggested in our model, we proceed to examine whether the series are cointegrated. Unless series are cointegrated, there is no equilibrium relationship between variables and inference is worthless.

18 Empirical Findings Unit Root Test The results of four different unit root tests, augmented Dickey-fuller(ADF), Phillips-Perron, Dickey-Fuller Generalised Least Square (DF-GLS), and Ng-Perron tests are employed for unit roots to find out whether the variables are integrated of the same order. Johansen- Juselius test for cointegration is employed followed by Error Correction Model to find short-run relationship of the variables. If not, then we use ARDL approach to test for long run relationship. Results of the ADF and Phillips- Perron tests are presented in Table 1.

19 VariablesAugmented Dickey-Fuller Test (ADF) Phillps-Perron Test (PP) InterceptIntercept & Trend InterceptIntercept & Trend Lx1-1.5341 (0.5020) -2.6657 (0.2569) -1.4808 (0.5283) -2.7374 (0.2304) DLx1-5.4522 (0.0001) -5.4533 (0.0001) Lx2-0.5813 (0.8566) -5.7460 (0.0005) -1.2030 (0.6587) -1.7989 (0.6781) DLx2-3.9483 (0.0055) Lx3-3.6020 (0.0123) -3.8615 (0.0278) -3.5532 (0.0138) DLx3-7.0140 ( 0.0000) Lx4-1.3640 (0.5845) -4.4203 (0.0080) -2.3650 (0.1602) -4.4033 (0.0083) DLx4 Lx5-3.6009 (0.0136) -1.5749 (0.4818) -1.6416 (0.7499) DLx5-5.8639 (0.0003)

20 VariablesDickey-Fuller Generalized Least Square Test (DF-GLS) InterceptIntercept & Trend Lx1-1.4648-2.7436 DLx1-5.4416 Lx20.4390-2.7736 DLx2-2.8076 Lx3-3.6663 DLx3 Lx4-1.3228-4.4877 DLx4 Lx5-2.0992 DLx5 TABLE 2

21 VariablesMZAMZTMSBMPT Lx1 with Constant-4.2439-1.28140.30195.9959 Lx1 Constant &trend-9.4247-2.16670.22999.6849 DLx1 with Constant-15.8455-2.74260.17301.8092 DLx1 Constant &trend Lx2 Constant-0.1183-0.07730.653126.9766 Lx2 Constant &trend-5094.22-50.46820.00990.0182 DLx2 Constant DLx2 Constant &trend Lx3 Constant-12.7850-2.33220.18242.6382 Lx3Constant &trend DLx3 Constant DLx3 Constant &trend Lx4 Constant-3.4389-1.19920.34877.0644 Lx4 Constant &trend-13.5126-2.59840.19236.7481 DLx4 Constant-10.2511-2.25960.22042.4064 DLx4 Constant &trend Lx5 Constant-51.2715-5.00720.09760.6158 Lx5 Constant &trend DLx5 Constant DLx5 Constant &trend 1% level of significance with constant-13.8000-2.58000.17401.7800 5% level of significance with constant-8.100-1.98000.23303.1700 10% level of significance with constant-5.7000-1.62000.27504.4500 1% level of significance with constant & Trend-23.8-3.420.1434.03 5% level of significance with constant & Trend-17.3-2.90.1685.48 10% level of significance with constant & Trend-14.20-2.620.1856.67 TABLE 3

22 variables ADFPP DF- GLS NG- PERRON Interc ept In te rc e pt & tr e n d Inte rcep t In te rc e pt & tr e n d Inter cept In te rc e pt & tr e n d Intercept In te rc e pt & tr e n d LX1I(1) I( 0) LX2 I( 0) I(1) I( 0) LX3I(1)I(0) LX4 I( 0) LX5I(0)I(1)I(0) TABLE 4 ORDER OF INTEGRATION

23 Table 5. Variable Addition Test (OLS case) ****************************************************************************** Dependent variable is DLX1 List of the variables added to the regression: LX1 LX2 LX3 LX4 LX5 27 observations used for estimation from 1983 to 2009 ****************************************************************************** Regressor Coefficient Standard Error T-Ratio[Prob] INPT -5.4185 7.5902 -.71388[.486] DLX1(-1) -.40578.20182 -2.0106[.062] DLX2(-1) -1.5150 2.6953 -.56209[.582] DLX3(-1).19199.15940 1.2044[.246] DLX4(-1).061084.11301.54054[.596] DLX5(-1).62421 2.5742.24248[.811] LX1.76126.16750 4.5449[.000] LX2.51600.67575.76360[.456] LX3.41967.19957 2.1028[.052] LX4 -.018261.17693 -.10321[.919] LX5 3.6916 1.2624 2.9244[.010] Joint test of zero restrictions on the coefficients of additional variables: Lagrange Multiplier Statistic CHSQ( 5)= 18.5838[.002] Likelihood Ratio Statistic CHSQ( 5)= 31.4733[.000] F Statistic F( 5, 16)= 7.0659[.001]

24 TABLE 6 Autoregressive Distributed Lag Estimates ARDL(2,0,2,0,1) selected based on Schwarz Bayesian Criterion ******************************************************************************* Dependent variable is LX1 27 observations used for estimation from 1983 to 2009 ******************************************************************************* Regressor Coefficient Standard Error T-Ratio[Prob] LX1(-1).60745.15191 3.9987[.001] LX1(-2) -.36779.19193 -1.9163[.072] LX2 -1.8355.61472 -2.9859[.008] LX3.18677.18721.99770[.332] LX3(-1) -.0090175.19288 -.046752[.963] LX3(-2).57923.18267 3.1710[.006] LX4.16931.14080 1.2025[.246] LX5 -7.8992 1.5840 -4.9867[.000] LX5(-1) 5.7617 1.1193 5.1474[.000] INPT 19.9557 6.7968 2.9361[.009] ************************************************************************ R-Squared.90367 R-Bar-Squared.85267 S.E. of Regression.31023 F-stat. F( 9, 17) 17.7199[.000] Mean of Dependent Variable 1.9084 S.D. of Dependent Variable.80826 Residual Sum of Squares 1.6362 Equation Log-likelihood -.46442 Akaike Info. Criterion -10.4644 Schwarz Bayesian Criterion -16.9436 DW-statistic 2.2851 ************************************************************************

25 Diagnostic Tests ************************************** ********************************** * Test Statistics * LM Version * F Version * ************************************** ************************************** *** * * * A:Serial Correlation*CHSQ( 1)= 3.3806[.066]*F( 1, 16)= 2.2901[.150]* * * * B:Functional Form *CHSQ( 1)= 1.3124[.252]*F( 1, 16)=.81743[.379]* * * * C:Normality *CHSQ( 2)=.72179[.697]* Not applicable * * * * D:Heteroscedasticity*CHSQ( 1)= 3.2248[.073]*F( 1, 25)= 3.3910[.077]* ************************************** ********************************** A:Lagrange multiplier test of residual serial correlation B:Ramsey's RESET test using the square of the fitted values C:Based on a test of skewness and kurtosis of residuals

26 CAUSALITY TEST TODA-YAMAMOTO AUGMENTED GRANGER CAUSALITY TEST The causal linkage among FDI, GDP, Growth rate, Trade restrictions are being tested by following the Granger causality procedures adopted by Toda and Yamamoto (1995) and interpreted and further expanded by Rambaldi and Doran(1996) and Zapata and Rambaldi(1997). Toda – Yamamoto Augmented Granger causality Test applied modified WALD test for restrictions on the parameters of a Seemingly Unrelated Regression (SUR). The following system of equations is being estimated to investigate the augmented Granger causality test.

27 Causality Test FDI t = α 1 + ∑ β FDI t-i + ∑ γ GDP t-i + u 1 GDP t = α 2 + ∑β FDI t-I + ∑ γ GDP t-i + u 2 The above system of two equations is estimated by SURE method. To explore that GDP does not Granger cause FDI, the null hypothesis will be H o : γ = 0. Likewise, the other null hypothesis for second equation is Ho: β =0, that is, the FDI does not Granger cause GDP. This was carried out by means of a Wald test with the null hypothesis that the values of the estimated coefficients β and γ are zero. The results of the Toda-Yamamoto test of augmented Granger causality are given in the table 7.

28 EquationNull hypothesisvalued.f.Prob. Equation 1 GDP does not Granger cause FDI 4.0061 0.045 Reject Ho Equation 2FDI does not Granger cause GDP 1.279510.258 Cannot reject Table 7. TODA-YAMAMOTA GRANGER CAUSALITY TEST

29 SUMMARY The results of ARDL approach to co-integration show cointegration between FDI and GDP when GDP, Foreign exchange rate, Indirect Taxes are taken as Independent variables.

30 CONCLUSIONS To date, most of the analysis of the determinants of foreign direct investment has been descriptive in form; there has been little work directed to statistically evaluating these determinants. In order to provide a more comprehensive empirical estimate of the determinants of foreign direct investment, this article used the ARDL cointegration technique and data for the 1980-2009 to test the size of market, growth, and tariff changes as explanations of why direct investment has been coming recently in large amounts.

31 CONCLUSIONS The study’s empirical tests lead to the conclusions that only the size-of-market hypothesis can be supported statistically. Negative findings were discovered for all variants of growth and tariff related imports; these hypotheses were rejected as not statistically significant. The results suggest that when dealing with policy problems relating to foreign direct investment, it is important to focus on the receiving country’s size of market as a major determinant of foreign direct investment flows.


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