Total Investment, FDI and Growth Relationship

7/31/2019 Total Investment, FDI and Growth Relationship

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Presented by:

Irfan Ullah Tarar

Supervised by:

Dr. Nisar Ahmed

Total Investment, FDI and Growth

Relationship in Pakistan


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Agenda

Introduction

Review of Literature

Theoretical Framework

Data and Methodology

Empirical Results

Conclusion and Policy Implications


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Introduction

The FDI can be defined as: an investment made by a

resident of one economy in another economy and it is of a

long-term nature or of lasting interest, (UNCTAD, 2009)


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Importance of FDI Inflows

Promote Economic growth

Augment capital formation

Source of Human capital development

Diffused technology and spillovers

promote competition

Encourage Financial development

Source of bridging gap of saving-investment inLDCs(Source of external finance)


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Impact of FDI inflows on Total Investment

Complementary Effect (Crowding-in)

Substitution Effect (Crowding-out)

Neutral Effect (One-to-One Effect)


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Complementary Effect (Crowding-in Effect)

The TNCs can promote competition with domestic firms

Diffused modern technologies and positive spillovers

through FDI to domestic firms

Complement domestic firms production activities


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Substitution Effect (Crowding-out Effect)

The TNCs have comparative advantage over domestic

firms in managerial skills, financial resources, production

efficiency and technology

FDI inflows substitute domestic firms production activity,

it also leads to Crowding-out Effect


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Trends of FDI, Total Investment and

GDP Growth in Pakistan

FDI inflows during 1970s and 1980s are low

After restructuring of the economy in 1990s, FDI inflows

have moderate increase in 1990s and exponential growthduring 2003-2007

The FDI inflows and Gross Fixed Capital Formation ( TotalInvestment) have almost the similar trends during the studyperiod 1974-2009

The GDP Growth Rate fluctuates between 1.01% and10.2% during the study period 1974-2009


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FDI Inflows ($ million)

0

1000

2000

3000

4000

5000

6000

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

FDI Inflows($Million )

Source: UNCTAD(2011)


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FDI Inflows (As a Percentage of GFCF)

0

2

4

6

8

10

12

14

16

18

20

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

FDI Inflows (As a Percentage ofGFCF)

Source: UNCTAD(2011)


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Goss Fixed Capital Formation ($

Million)

0

5000

10000

15000

20000

25000

30000

35000

40000

Goss Fixed Capital Formation ($Million)

Source: World Development Indicators (2011)


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GDP Growth Rate (Annual %)

0

2

4

6

8

10

12

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

GDP Growth Rate (annual %)

Source: World Development Indicators (2011)


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Objectives of the Study

To test the short-run and long-run relationship among totalinvestment, FDI and economic growth in Pakistan

To check the crowding-in or crowding-out effect of FDI ontotal investment in Pakistan

To analyze the causal relationship among total investment,FDI and economic growth

To provide policy implications based on empirical results


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Literature Review

Van Loo (1977) examined the direct and indirect effects of

foreign direct investment on total investment in Canada for

the time period 1948-1966. The OLS and 2SLS techniques

were used to estimate the results. The coefficient of FDIwas 1.3981 which confirmed the evidence of

complementary effects ( crowding-in effect)

Lipsey and Kravis (1987) used cross- sectional and timeseries data and found that the growth rate was more closely

related to capital formation.


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Continued

De Long and Summers (1991,1992) used a cross-national

data from the 1950s to 1980s, and found a strong positive

causal relationship between capital investment and

economic growth.

Aitken and Hanson (1997) analyzed panel data on Mexican

manufacturing plants and found evidence of beneficial

spillovers from multinational enterprises to the Mexicaneconomy.


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Continued

Borensztein et al., (1998) analyzed the relationship betweenFDI and economic growth for a panel of 69 developingcountries. The minimum threshold of human capital wasnecessary for positive effects of FDI on economic growth.The study also found the supportive evidence of crowding-

in effect of FDI on domestic investment.

Agosin and Mayer (2000) developed total investmentmodel to analyze crowding-in or crowding-out effect of

FDI inflows for a panel of three developing regions (Asia,Africa and Latin America) and found crowding-in effect inAsia, but less so in Africa, and strong crowding-out in LatinAmerica.


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Continued Weinhold and Nair(2001) used causality tests for cross-

country panel of 24 developing countries to look at

dynamic relationship between FDI and economic growth

from 1971-1995 and found that there was a quite

heterogeneous effect of FDI on economic growth acrosscountries.

Chakraborty and Basu (2001) applied co-integration and

Error- Correction approaches to study long-run and short-run relationships between GDP and FDI in India for the

period 1974-1996. It was found that GDP in India had no

Granger causality by FDI, but causality from GDP to FDI

was present.


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Continued Misun and Tomsik (2002) used total investment model of

Agosin and Mayer (2000) to analyze crowding-in or

crowding-out for a panel of three countries; Czech

Republic, Hungary and Poland for time period 1990-2000.

The evidence of crowding-in was found in Hungary andCzeck Republic and crowding-out in Poland.

Kim and Seo (2003) analyzed the relationship among

inward FDI, economic growth and domestic investment forKorea over the time period 1985-1999. The VAR

methodology was used to estimate the results and found no

evidence of crowding-out effect of inward FDI on domestic

investment.


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Continued Backer and Sleuwaegen (2003) analyzed firms entry and

exit in Belgian manufacturing sector in the presence of FDI

and import competition and found the evidence of

crowding-out of domestic firms.

Choe (2003) examined the causal relationship among

economic growth, FDI and gross domestic investment

(GDI) for a panel of 80 countries over the period of 1971-

1995. There was a bidirectional causality between FDI andeconomic growth and the effects of economic growth were

stronger than FDI when the outliers were excluded for 80

countries from 1971-1995. In case of economic growth and

GDI, the causality ran in only one direction from economic

growth to GDI.


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Continued

Hansen and Rand (2006) analyzed the causal link between

FDI and economic growth for a panel of 31 developing

countries from Asia, Latin America and Africa over the

period of 1970-2000. The VAR methodology was used intheir study. The results supported the strong causal link

from foreign direct investment to economic growth in the

long run. It was also concluded that a higher ratio of FDI in

gross capital formation had positive effects on the level ofGDP and hence on economic growth in the long-run.


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Continued

Chowdhury and Mavrotas (2006) analyzed the causal

relationship between FDI and economic in a panel of Chile,

Malaysia and Thailand for the period of 1969-2000. The Toda-

Yamamoto test for causality analysis was used and found that

GDP Granger caused FDI in Chile and bidirectional causality

in Malaysia and Thailand.

Yang(2007) used panel data for 110 countries from 1973 to2002 to analyze the relationship between FDI and growth and

found the effect of FDI on growth was different over time and

across regions.


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Continued

Ozturk and Kalyoncu (2007) investigated the impact of FDI

on economic growth for Turkey ad Pakistan over the period

of 1975 to 2004 and found that it was GDP that caused FDI

in the case of Pakistan, while there was a bi-directionalcausality between the two variables for Turkey.

Sarkar (2007) used panel data of 51 less developed

countries for the period of 1981 to 2002 and time seriesdata from 1972-2002 and found mixed relationships

between FDI and economic growth across panel.


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Continued Tang et al., (2008) used quarterly data from 1981:1 to

2003:4 and analyzed the relationship among the FDI

inflows, domestic investment and economic growth in

China. The study found that FDI inflows complemented

domestic investment. The GDP and domestic investmenthad bidirectional causality relationship and FDI inflows had

unidirectional causality with domestic investment and GDP

Th i l F k d M d l


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Theoretical Framework and Model

Specification

A model of total investment in the presence of FDI by MNEsin the host country which is developed by Agosin and Mayer

(2000) is used in this study.

The model is based on the following identity:It =If,t+ Id,t (1)

Where,

It = Total investment in time period t

Id,t = Domestic investment in time period t

If,t = Investment by MNEs in host country in time

period t


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Continued

Domestic investment:

An accelerator model of investment is as:

Id,t= + 1GDPGt (2)

Where, GDPGtis GDP Growth Rate in time period t.

And Investment by MNEs:

If,t= f(FDIt) (3)


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Continued

Total Investment:

I = + 1GDPG + FDI (4)

Here , FDIis used for FDI inflows by MNEs

The more general form of total investment:

I = + 1GDPG + 2FDI (5)

The econometric model of Total Investment:

It= + 1GDPGt+ 2FDIt+ t (6)


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Continued

There are three possible outcomes:

(i) Crowding-in:

if, 2>0

(ii) Crowding-out:

if, 2


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Data and Methodology

Data description and sources

Variables DescriptionLRGFCF Natural log of real gross fixed capital formation ($ million)

LRFDI Natural log of real foreign direct investment inflows ($ million)

GDPG Growth rate of GDP (%)


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Sources of Data:

Online World Development Indicators (2011)

Online World Investment Report (UNCTAD,2011)


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Methodology

1. Unit root test (ADF-test and PP-test)

2. Cointegration ( Bound test Approach)

3. Error Correction Model in ARDL(p,q,r) Framework

4. Diagnostic Tests and Stability Analysis

5. Granger Causality tests


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The ARDL(p,q,r) Model

The General Form of Unrestricted ECM model in ARDL(p,q,r)formulation:

Here,

is the first difference operator

The coefficients of first part such as:i,i, and i, represent the

short run dynamics

The coefficients 1,2, and 3 represent the long run

relationships between the variables

And t is used for white noise error term in the model

(7)


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The Bound Test for Cointegration

It is the most suitable technique for small samples

It is applicable whether the variables areI(0), I(1) or mixed

There is no need for pre-testing for unit roots

Only a single reduced form equation is used for long runrelationships

Different variables have different optimal lags


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Continued

The bound test (F-test) is used to test the existence of long

run relationship between the variables.

Null Hypothesis:

H0 :1=2=3=0

Alternative Hypothesis:

Ha :10, or 20, or 30


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Continued

There are three possible outcomes:

1. If F-stat> upper bound (cointegration)

2. If F-stat< lower bound (no cointgration)

3. If F-stat lies between upper and lower bound(inconclusive)


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The Long Run ARDL(p,q,r) Model

If cointegration found in the general form of unrestricted

ECM model in ARDL(p,q,r) formulation, then the

following long-run ARDL(p,q,r) model will be estimated:

(8)


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The ECM Model

The following ECM model in ARDL(p,q,r) formulation is

used to estimate short-run relationships between total

investment, FDI and economic growth.

(9)


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The Empirical Results

Descriptive Statistics:Variable(s) LRGFCF LRFDI GDPGMaximum 5.5798 3.8741 10.2157Minimum 4.7677 -.80829 1.0144Mean 5.2763 1.7487 5.0974Std. Deviation .23810 1.0412 2.1106Skewness -.71770 -.18917 .16247Kurtosis - 3 -.75562 .25636 -.38687Coef of

Variation.045126 .59538 .41406


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Correlation Matrix

LRGFCF

LRGFCF LRFDI GDPG

1.0000 .12066 .51243

LRFDI .12066 1.000 -.12379

GDPG .51243 -.12379 1.0000


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Unit Root TestsADF-Test (intercept and no trend) PP-Test (intercept and no trend)

Variable At level At 1stdifference Critical Values At level At 1stdifference

LRGFCF -2.43 -3.44** 1% -3.63 -2.59 -3.08**

GDPG -2.57 -5.67*** 5% -2.94 -4.30*** -11.04***

LRFDI -1.34 -2.85* 10% -2.61 -2.64* -7.7***

Note: (***) significant at 1% level

(**) significant at 5% level

(*) significant at 10% level


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The Bound Test for Cointegration (F-test)

Dependent variable F-Statistics lag F-critical value* Conclusion

I(0) I(1)

F(LRGFCF/LRFDI,GDPG) F(3,19)= 7.67 3 3.793 4.855 Cointegration

F(LRFDI/LRGFCF,GDPG) F(3,19)=1.82 3 3.793 4.855 No Cointegration

F(GDPG/LRGFCF,LRFDI) F(3,19)=7.5 3 3.793 4.855 Cointegration

Note:(*) The critical value bounds are taken from Table CI(iii) of Pesaran et al.(2001)


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The Long-Run Results

ARDL(1,0,1) selected based on Schwarz Bayesian Criterion

Regressor Coefficient Standard Error T-Ratio[Prob]

LRFDI 0.037111 0.046303 .80148[.430]

GDPG 0.17081 0.031800* 5.3714[.000]

C 4.3337 0.21185* 20.4570[.000]

Note: (*) Significant at 1% level


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The ECM Results

Error Correction Representation for ARDL (1,0,1) selected based onSchwarz Bayesian Criterion

Regressor Coefficient StandardError

T-Ratio

[Prob]dLRFDI 012587 .015435 .81550[.421]dGDPG .026148 .0065873* 3.9695[.000]dC 1.4699 .34548* 4.2547[.000]

ecm(-1) -.33918 .068235* -4.9707[.000]Note: (*) significant at 1% level


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Diagnostic TestsTest Statistics LM Version F Version

A:Serial Correlation

B:Functional Form

C:Normality

D:Heteroscedasticity

R2 =.90457

R-bar Squared=.89093

DW-statistic=1.9885

Durbin's h-statistic=

.035971[.971]

CHSQ(1)= .43836[.508]

CHSQ(1)= 1.9263[.165]

CHSQ(2)= .62771[.731]

CHSQ(1)= 1.9058[.167]

F( 1, 27)= .36349[.552]

F( 1, 27)= 1.6738[.207]

Not applicable

F( 1, 31)= 1.9000[.178]


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Stability Tests

Cumulative Sum of Recursive Residuals ( CUSUM)

Cumulative Sum of Square of Recursive Residuals

(CUSUMSQ)


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Continued

CUSUM(Cumulative Sum of Recursive Residuals)

Plot of Cumulative Sum of Recursive Residuals

The straightlines represent critical bounds at5% significance level

-5

-10

-15

0

5

10

15

1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 20092009


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Continued

Cumulative Sum of Square of Recursive Residuals(CUSUMSQ)

Plot of Cumulative Sum of Squares of Recursive Residuals

The straightlines represent critical bounds at5% significance level

-0.5

0.0

0.5

1.0

1.5

1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 20092009


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Conclusion

The FDI inflows are statistically insignificant determinantof total investment in the short-run as well as in the long-

run during the study period 1974-2009.

The GDPG is highly significant determinant of total

investment for both short-run and long-run. The speed of adjustment (ECM coefficient) after

disequilibrium is moderate which is about -0.339

Bidirectional Granger causality exists between LRFDI and

LRGFCF (LRFDI LRGFCF) Unidirectional Granger causality between GDPG and

LRGFCF (GDPG LRGFCF)

No Granger causality between LRFDI and GDPG


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Policy Implications

GDP growth rate has statistically significant positive effecton total investment, therefore policy makers should adopt

those measures which will help to improve GDP growth

rate in Pakistan

The statistically insignificant effect of FDI inflows on total

investment may be due to small share of FDI inflows to

Gross Fixed Capital Formation

Need to attract FDI inflows in real sector

Need to overcome those factors which are responsible for

Documents

Total Investment, FDI and Growth Relationship