Economic freedom, exchange rates stability and FDI in South Asia.
Nasir, Zafar Mueen ; Hassan, Arshad
This study empirically examines the role of economic freedom,
market size and exchange rates in attracting foreign direct investment
in south Asian countries for the period 1995-2008 by employing panel
data analysis in fixed effect setting. Results clearly indicate the
presence of significant positive relationship between economic freedom
and FDI inflows in South Asian countries during the peri6d of study. The
real effective exchange rate was having negative association with it
indicating that depreciation in host country currency negatively
influences the inflow of FDI to that country. Therefore, monetary policy
should focus on providing stability to currencies of host countries. The
model explains approximately 90 percent of total variation in FDI. The
paper concludes that South Asian countries should make concerted efforts
in devising polices that improve level of economic freedom. In other
words, they should provide more investment friendly climate, trade
openness, efficient monetary and fiscal policies and freedom from
corruption. This can help to attract more foreign direct investment in
the South Asian countries.
I. INTRODUCTION
Foreign direct investment (FDI) plays an important role in the
economic development by enhancing growth and bringing foreign funds, new
technology and skills to the host country. The FDI also shows a
long-term interest in a local entity by an investor operating in another
country. Flow of FDI to specific country is based upon macroeconomic
factors, government policies, and long term corporate strategies of
multinational corporations. Empirical research provides evidence that
size of market, legislative and incentive structure, availability of
human capital, reliability and efficiency of financial system, natural
resources, macroeconomic environment, governance perception, law and
order situation, and physical infrastructure are the some basic
determinant for attracting FDI. Economic and fiscal environment are also
critical factors for attracting FDI along with a favourable business and
investment milieu based on political and legal framework. Considering
the importance of these factors the Heritage Foundation developed the
Economic Freedom Index (EFI) based on these policy parameters. They
included business freedom, investment climate, trade openness, monetary
and fiscal environment in the index. This index is widely used by
investors in selecting the destination for their investment decisions.
Heritage Foundation defines Economic Freedom as "aspect of
human liberty that is concerned with the material autonomy of the
individual in relation to the state and other organised groups. The
highest form of economic freedom provides an absolute right of property
ownership, fully realised freedoms of movement for labour, capital, and
goods, and an absolute absence of coercion or constraint of economic
liberty beyond the extent necessary for citizens to protect and maintain
liberty itself." Gwartney, et al. (1996) defined economic freedom
for individuals to acquire property without the use of power, fraud or
theft and protected from physical invasion by others. The owners of the
property are free to use, trade, or offer their belongings as long as
their actions do not infringe the same privileges of others. Economic
freedom is considered as antithesis of centralised planning and
governmental control mechanism. It is replication of democratic
political pluralism in economic world. It is a philosophy that promotes
entrepreneurship and decentralises economic power and decision making
across the economy.
South Asian countries recognise that FDI is potential source of
much needed capital, knowledge, technology and access of international
markets. To facilitate greater inflows of FD1, many countries in the
region have taken important steps to create a more favourable investment
climate. Genesis of change can be traced back to late 1970s. In 1977,
Sri Lanka started the process of liberalisation and other south Asian
countries follow the suit. Pakistan's efforts to attract FDI can be
traced back to the advent of deregulation, privatisation, and
liberalisation policies initiated at the end of the 1980s. However, this
liberalisation process is found uneven across countries and can be
termed as hesitant liberalisation. This process is accelerated in the
region in early 1990s with the initiation of liberalisation process in
India.
FDI to South Asian economies increases from US$ 204 million in 1980
to US$ 3 billion in 1995 and around US$ 9.8 billion in 2005. The
increase in FDI inflow, however, is insignificant and share of south
Asia in global FDI inflows in 2005 is observed as just 1.1 percent. By
2008, FDI inflows to South Asian economies has crossed US$30 billion
mark, however it was still around one percent of the global FDI inflows.
Historical patterns of FDI to South Asian region are shown in Figure
1.1.
[FIGURE 1.1 OMITTED]
The FDI flows in Pakistan have increased from mere US$250 million
in 1990 to US$5.2 billion in 2008. (1) However, track record of FDI
inflows to Pakistan is not attractive. Number of factors can be
identified that contributed in discouraging the foreign investors in
investing in the country. These include law and order conditions, power
shortage, poor governance, political instability, inefficient fiscal and
monetary policies, corruption in higher echelons of government, trade
barriers, inconsistency in economic policies etc.
It is worth mentioning that despite market liberalisation process,
South Asian countries are not fully realising there latent growth
potential. It may be due to the fact that South Asia is still one of the
overregulated regions for multinational corporations. These trade
restrictions limit potential trade, inflow of FDI and economic growth.
In order to accelerate growth and attract FDI to bridge the resource
gap, South Asian nations are required to integrate into world economy
through liberalisation, privatisation and deregulation to achieve
competitiveness. This means more economic freedom is needed to promote
investment. The international experience suggests that economic freedom
is prime vehicle for attracting FDI and achieving sustainable growth.
Specially, freedom of trade, business and investment accelerate the
process of domestic and foreign investment in the country and stimulate
the process of sustainable economic growth.
Most of the empirical work on relationship between economic freedom
and economic growth is focused on developed markets and no significant
study on relationship between economic freedom and FDI in south Asian
region is available. Similarly, number of studies exists that explore
the role of various variables independently and jointly. However no
study captures the joint effect of these policy parameters through an
index. The present study is an effort to bridge this gap. The study has
the following objectives;
* To study the role of market size and exchange rate in attracting
FDI inflows in South Asian countries,
* To explore the role of economic freedom in attracting FDI inflows
in South Asia,
* To provide policy guideline for designing effective macroeconomic
policies.
This study will help the economic managers of South Asian nations
to identify the factors and design policies that are vital for
attracting FDI in the region/countries. The paper is organised as
follows. Section II provides an overview of literature on the
relationship among variables of interest. Section III discusses data and
methodological issues. Section IV presents empirical results and the
conclusions are provided in the last section.
II. LITERATURE REVIEW
A number of studies focusing on the determinants of countries'
attractiveness to FDI inflow highlighted the role of democracy,
governance and unwavering macroeconomic environment. For example, Hines
(1995) studied the dynamics of US FDI in foreign countries and found
that US FDI to corrupt countries declined over time. However, the
relationship between corruption and FDI was found insignificant.
Okeahalam and Bah (1998) confirmed the results by examining the
relationship between corruption and FDI flows. Ayal and Georgios (1998)
examined the impact of components of economic freedom on growth rate,
output and investment by using OLS method. Results indicated that
economic freedom accelerated economic growth through accelerating
capital accumulation process. Further, these factors contributed
positively in enhancing total factor productivity. El-Naggar (1990) and
Collier and Gunning (1999) focused on the role of institutions. This
study emphasised that efficient tax regime, property rights and rule of
law were some major factors in mobilising foreign as well as and
domestic investment.
Wheeler and Mody (1992) explored relationship between host country
risk factor and FDI but no significant relationship was observed.
Chakrabarti (2001) found that size of market, cost of inputs, trade and
non trade barriers, trade openness, growth rate, stability of foreign
exchange were major determinants of FDI. Lipsey (1999) included size of
market, growth rate, real per capita GDP, a distance variable and a
measure of tax rates to examine the determinants of the location of US
affiliates in Asia. His findings were in line with Chakrabarti (2001).
Wei (2000) investigated the dynamics of bilateral FDI flows between
12 investing countries and 45 host countries. Results indicated that
corruption was significantly negatively related to the volume of FDI.
Similarly, Bengoa and Sanchez-Robles (2003) found significantly positive
relationship between economic freedom and FDI in Latin American
countries. Harms and Ursprung (2001) explored the relationship of
political rights and civil liberties with FDI and concluded that
significant positive relationship exists among these variables. Adkins,
Moomaw and Savvides (2002) concluded that higher economic freedom leads
to improved economic performance and that augmented economic freedom had
helped countries to move closer to the production frontier. Asiedu
(2002) reports that infrastructure development, rate of return, trade
openness and country risk factors were important determinant of FDI
inflows.
Janicki and Wunnava (2004) found significant role of economic
growth, political risk, trade openness and labour cost to explain the
flow of FDI to Central and Eastern European countries. Kyrkilis and
Pantelidis (2003) examined the determinants of FDI in developing and
developed countries and discovered that real GNP, effective exchange
rate, and human capital were important determinants of FDI flows.
However, openness was found insignificantly related to FDI. Bengoa,
Marta, and Sanchez-Robles (2003) examined the relationship between
economic freedom and foreign direct investment for 18 Latin American
countries for the period 1970 to 1999 by employing panel data analysis.
Results showed that economic freedom contributed positively towards
inflow of FDI. The economic growth was also found positively related
with FDI. Study suggested that human capital, economic stability and
liberalised markets may be helpful in attracting long-term capital
flows.
Cole (2003) compared various theories of economic growth and
analysed the impact of economic freedom on economic growth by employing
economic freedom index. He found the relationship significant and robust
under different diversified theoretical framework. Similarly, Scully
(2002) examined the contribution of economic freedom in determining
economic growth and in the distribution of market income by employing
structural models. Study analysed the role of government policy in
advancing economic progress and effect of economic progress on the
distribution of market income. Results revealed that economic freedom
promotes economic growth as well as equity. He also found a positive
trade-off between economic growth and income inequality. However, this
trade off was found small and insignificant in magnitude. Gordillo,
Manuel, and Alvarez (2003) investigated the dynamic causal relationship
economic freedom, political freedom, democracy and economic growth by
employing Kiviet method. Results suggested that economic freedom
fostered economic growth but impact of political freedoms on economic
growth was insignificant. Similarly, study concluded that democracy
accelerates economic growth and economic freedom and in response
economic prosperity supports democratisation process.
Chan and Gemayel (2004) reported that economic, financial,
political risks and instability related with each risk were critical
determinants of FDI in the Middle East. Sekkat and Veganzones-Varoudakis
(2007) found that trade openness and investment climate had significant
impact on FDI flows to Middle East. This study also found that GDP and
GDP growth rate were insignificant in determining FDI inflows to
developing countries, including the Middle East. Doucouliagos and
Ulubasoglu (2006) studied the interplay of economic freedom and economic
growth through a comprehensive literature review of 45 different studies
conducted during in recent past. Study revealed that significant
positive association exist between economic freedom and economic growth
and studies of economic growth that do not include economic freedom as
determinant of economic growth are bound to arrive at biased results.
Study also suggest that physical investment also influences the
explanatory power of economic freedom as exclusion of a measure of
investment in physical capital augments the anticipated effect of
economic freedom on economic growth.
The review of the literature clearly indicates that economic
freedom along with other macroeconomic variables does play a role in
attracting FDI flows. This merits investigation of economic
freedom's role in attracting FDI in South Asian countries. In the
following section, the framework of the study is explained along with
the description of the data.
III. DATA DESCRIPTION AND METHODOLOGY
This study examines the relationship among FDI inflows, market size
and index of economic freedom for the period 1995-2008 by employing
annual time series data. Market size is measured by using GDP. Index of
Economic freedom reported by heritage foundation is used as measure of
economic freedom. Index of Economic Freedom comprises of a comprehensive
set of measures of policy parameters like business freedom, trade
freedom, fiscal freedom, government size, monetary freedom, investment
freedom, financial freedom, property rights, freedom from corruption and
labour freedom. Each component of economic is ranked on 0-100 scale.
Highest score may be 100 which is an indicator of most conducive
environment for economic freedom. Index of Economic freedom is an
equally weighted index of above components. Equal weights are used to
avoid biased behaviour toward any specific policy parameter.
1. Methodology and Econometric Model
This study employs multivariate regression analysis in a panel data
framework to explore the dependence of foreign direct investment on
economic freedom and other factors like market size, investment climate
and foreign exchange rate. The panel data analysis helps to explore of
cross-sectional and time series data simultaneously. Panel data analysis
has been used with assumption of constant coefficients as well as in
fixed and random effect setting.
Constant coefficient model assumes that the intercept and slope
terms are constant and there are no differences among the data matrices
of the cross sectional dimensions. The model of the study is presented
in the following equation.
[LNFDI.sub.i,t] = [[beta].sub.0] + [[beta].sub.1] [LNEF.sub.i,t] +
[[beta].sub.2] [LNGDP.sub.i,t] + [[beta].sub.3][REER.sub.i,t] +
[[[micro].sub.t]
Where [LNFDI.sub.i,t] is natural logarithm of foreign direct
investment in country "i" for the year "t" and
[LNEF.sub.i,t] is natural logarithm of index of economic freedom for
country "i" for the year "t". REER is real effective
exchange rate and LNGDP captures the market size. The [[micro].sub.t]
represents the error term.
Common constant method is quite restrictive so more insight can be
achieved through inclusion of fixed and random effects in the method of
estimation. In the fixed effect method the constant is treated as
section-specific so fixed effect model allows for different constants
for each section. The applicability of fixed effect model has been
tested by using Standard F test. The null hypothesis is that all the
constants are same and therefore common constant model can be used.
F = {([R.sup.2.sub.FE]-
[R.sup.2.sub.CC])/N-1}/{(1-[R.sup.2].sub.FE])/(NT-N-K)}
If calculated value is greater than F critical value, we reject the
hypothesis that all constants are same. In fixed effect model the cross
sectional effect is captured through dummy [D.sub.i] which represents
the countries.
[LNFDI.sub.i,t] = [[beta].sub.0] [[beta].sub.1][LNEF.sub.i,t] +
[[beta].sub.2][LNGDP.sub.i,t] + [[beta].sub.3][REER.sub.i,t] +
[ZIGMA][D.sub.i] + [[micro].sub.t].
An alternative method of estimation is random effect model which
assumes that the constants for each section are not fixed but are
random. Fixed effect model assumes that each country differs in its
intercept term whereas random effect model assumes that each country
differs in error term.
[LNFDI.sub.i,t] = [[beta].sub.0] [[beta].sub.1][LNEF.sub.i,t] +
[[beta].sub.2][LNGDP.sub.i,t] + [[beta].sub.3][REER.sub.i,t] +
([v.sub.i] + [[micro].sub.t])
The choice between fixed effect and random effect model is made
through Hauseman Test (1978). That is based on the idea that under the
hypothesis of no correlation, both OLS and GLS are consistent and OLS is
inefficient, while under the alternative, OLS is consistent but GLS is
not.
H =([[beta].sup.FE] - [[beta].sup.RE]) '[(Var([[beta].sup.FE])
- Var([[beta].sup.RE])][sup.1]([[beta].sup.FE] - [[beta].sup.RE]) ~ [chi
square](k)
If the value of H statistic is large, the difference between
estimates is significant, so null hypothesis that random effect model is
consistent is rejected and fixed effect estimators are used. If the
value of H statistics is small then random effect estimators is more
appropriate.
IV. EMPIRICAL RESULTS
Table 4.1 exhibits the statistical properties of time series data.
Descriptive statistics indicates that India attracts highest average
foreign direct investment during 1995-2009 which is more than $7154
million. Average foreign direct investment in Pakistan during said
period is $1630 billion and Bangladesh remains at third position with
$312 million per year. In 2007-2008 south Asian countries receive
highest FDI inflows, India crossed $ 22950 million and Pakistan touched
$ 5409 million which is the highest level in its history.
With reference to economic freedom, Sri Lanka ranks the highest
with an average score of 62 and Pakistan stands second with 55.9.
Bangladesh is placed at last position in the region. India is also found
comparatively over regulated market in the region as its index of
economic freedom is lower than average of South Asian region. With
reference to size, India is the largest market whereas Sri Lanka is the
smallest market.
Results of common effect model are reported in Table 4.2 which
indicates that LNEF, LNGDP and REER can explain 43.8 percent of the
total variation in FDI inflow.
As Common Constant Method is quite restrictive so Fixed and Random
Effects models have also been tested. The null hypothesis is that all
the constants are same is tested by using Standard F-test. Here
calculated value of F =70.53 is greater than F-critical value at 95
percent confidence level so null hypothesis is rejected. Therefore Fixed
effect model is better model.
Finally, In order to make a choice between Fixed Effect Model and
Random Effect Model, Hausman test has been applied and results are
reported in Table 4.3 below.
Above table shows that the value of H statistics is high which
indicates that difference between estimates is significant at
a=0.05.Therefore null hypothesis that random effect model is consistent
is rejected and fixed effect estimators are considered most appropriate.
Results of fixed effect model are reported in Table 4.4
Results clearly indicate the presence of significant positive
relationship between economic freedom and FDI inflows in south Asian
countries during period of study. This relationship has economic
rationale as economic freedom captures the impact of components like
business freedom, trade freedom, fiscal freedom, government size,
monetary freedom, investment freedom, financial freedom, property
rights, freedom from corruption and labour freedom for a country.
Therefore a country that offers the right to create, operate, and close
an enterprise without interference from the state and permits
individuals and businesses to keep and control their income and wealth
for their own benefit and use will definitely attract the foreign direct
investment. Similarly, trade openness and low corruption levels also
provide confidence to foreign investors and effect their decision
regarding location of business. LNGDP is also significantly positively
associated with foreign direct investment at 95 percent confidence
interval which shows that large markets attract more FDI. Therefore, in
South Asia, India attracts maximum FDI followed by Pakistan, Bangladesh
and Sri Lanka. Real effective exchange rate is found significantly
negatively related to FDI indicating that depreciation in host country
currency negatively influences the inflow of FDI to that country. As
above studied variables are able to capture most of the important
dimensions of decision parameters of investors regarding FDI so above
model is able explain approximately 90 percent of total variation in
FDI.
V. CONCLUSION
The magnitude of FDI in South Asia remains relatively low. The
region despite a supportive macroeconomic environment and financial
sector stability has attracted very low amount of FDI inflows. Its share
in the world inflows as well as proportion of its GDP is negligible. The
reasons are many but in my opinion one major reason is limited Economic
Freedom.
In south Asian countries, economic freedom is found significantly
positively related to FDI. As Economic Freedom is an important catalyst
in attracting FDI in the region, so through Freedom of trade, business
and investment these countries can accelerate the process of domestic
and foreign investment in the country and stimulate the process of
sustainable economic growth. These countries should also improve
governance mechanism and control corruption which is necessary to
improve Economic Freedom in the country. Therefore, key policy
implications for South Asian countries attempting to attract FDI are to
create a better investment climate by improving Economic Freedom. The
real effective exchange rate (REER) is found statistically significant
and negatively related to FDI indicating that these countries should
design and develop such policies that provide stability to their
currencies. The significant positive relationship between Market Size
and FDI inflows is quite logical and indicative of the fact that large
markets have more attraction and potential for foreign investment.
The main conclusion of the paper is that by providing a stable,
consistent, and transparent regulatory framework along with stable
macroeconomic environment, these countries can attract more FDI inflows.
It is worth noted that these countries should focus not only on policies
to attract FDI but also on the policies that are necessary for FDI to
generate a positive development impact in the recipient country.
REFERENCES
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Comments
The paper is a good attempt to find the determinants of FDI in
South Asia using Panel data. However, if they focus on the following
points that can improve their paper. First of all there is no
theoretical framework of the model, which is necessary for any economic
explanations. There is lots of literature available on FDI.
Authors have decided to use fixed effect model after confirming
from Haussmann test. But coefficients of Random effect model and fixed
effect model are drastically different which shows definite error in the
model. Is it due to specification problem or spurious nature of
regression, needs to be checked. Moreover, all the variables, i.e.,
foreign direct investment, gross domestic product, economic freedom
index and real effective exchange rate are expected to be non-stationary
at level except for economic freedom index which does not show much
variation. Thus authors should try Panel cointegration approach, which
may resolve various problems in the regression and authors may get
robust estimates.
Muhammad Ali Kemal
Pakistan Institute of Development Economics, Islamabad.
(1) Besides attractive incentive structure for investment,
privatisation of public sector corporations contributed to higher FDI
flows in the country.
Zafar Mueen Nasir <zfrnasir@yahoo.com> is Chief of Research
at the Pakistan Institute of Development Economics, Islamabad. Arshad
Hassan <aarshad.hasan@gmail.com> is Head of Business Department at
the Muhammad Ali Jinnah University, Islamabad.
Table 4.1
Descriptive Statistics
(for the Period 1995-2008)
Std
Mean Median Deviation Minimum Maximum
Pakistan
FDI 1629.88 772.80 1765.73 308.00 5409.80
Econ Freedom 55.74 55.89 1.73 53.02 58.42
GDP 89732.00 69430.90 44097.04 52201.09 185429.3
REER 57.33 57.13 7.01 45.02 69.94
India
FDI 7154.24 3955.65 7275.17 2143.60 22950.00
Econ Freedom 50.49 50.70 2.70 45.09 54.20
GDP 471572.8 380772.6 215594.5 281122.5 960297.0
REER 45.37 45.64 3.31 39.03 50.28
Bangladesh
FDI 312.78 235.05 259.51 1.90 692.00
Econ Freedom 48.88 49.92 3.73 38.72 52.90
GDP 47668.33 42174.95 13588.91 34120.60 76931.36
REER 59.97 60.52 4.76 52.16 66.66
Sri Lanka
FDI 258.43 231.00 136.63 56.00 529.00
Econ Freedom 62.28 62.49 2.44 58.41 66.00
GDP 17495.90 14339.91 7366.27 10172.61 36368.41
REER 100.00 99.60 10.22 76.54 113.33
Table 4.2
Panel Data Analysis
Common Effect Model
Variable Coefficient Std. Error t-Statistic Prob.
34.059 7.205 4.727 0.000
LNEF_? -6.592 1.616 -4.079 0.000
LNGDP_? 0.240 0.200 1.199 0.236
REER_? -0.034 0.013 -2.638 0.011
Adjusted [R.sup.2] 0.4384
F statistics 13.5318
F significance 0.0000
Table 4.3
Correlated Random Effects
Hausman Test
Chi-Sq.
Statistic Chi-Sq. d.f. Prob.
H Statistics 271.8767 3 0
Cross-section Random Effects Test Comparisons
Variable Fixed Random Var(Diff) Prob.
LNEF_? 2.4837 -6.5915 1.0502 0
LNGDP_? 2.8869 0.2397 0.0787 0
REER_? -0.0288 -0.0341 0.0001 0.52
Table 4.4
Panel Data Analysis
Fixed Effect Model
Variable Coefficient Std. Error t-Statistic Prob.
LNEF_? 2.4837 1.2138 2.0461 0.046
LNGDP_? 2.8869 0.2920 9.8868 0.000
REER_? -0.0288 0.0097 -2.9601 0.005
BAN__C -9.8881 4.6029 -2.1482 0.037
IND__C -12.7837 4.7577 -2.6869 0.010
PAK__C -20.2733 5.0310 -4.0296 0.000
SLK__C -14.0955 4.9576 -2.8431 0.006
Adjusted [R.sup.2] 0.9142
F statistics 87.0632
F significance 0.0000
