Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 5(3):305-315
© Scholarlink Research Institute Journals, 2014 (ISSN: 2141-7024)
jetems.scholarlinkresearch.org
Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 5(3):305-315 (ISSN: 2141-7016)
The Effect of Lax Environmental Laws on Foreign Direct Investment
Inflows in Developing Countries
Hoda Hassaballa
The Department of Economics,
The American University in Cairo, Egypt.
AUC New Cairo, AUC Avenue, P.O. Box 74, New Cairo 11835.
_______________________________________________________________________________________
Abstract
This paper investigates the effect of lax environmental laws on foreign direct investment (FDI) inflows in
developing countries. Lax environmental laws in host countries may attract polluting FDI that usually escapes
from stringent environmental laws in home countries. This is examined in a dynamic panel data model. For that,
a fixed effect panel data model with homogenous slope is used. Besides the traditional determinants of FDI
inflows, the effect of human capital and environment on FDI is tested. The empirical results indicate that a) lax
environmental laws are a statistically significant determinant of FDI inflows. b) Lax environmental laws are the
most influential determinants of FDI inflows in developing countries. c) Human capital is not a statistically
significant determinant of FDI inflows in developing countries. In addition to these results, policy implications
for developing countries are given. This is very essential in order to solve the ongoing dilemma of how to
promote FDI inflows without leading to environmental degradation.
__________________________________________________________________________________________
Keywords: foreign direct investment, lax environmental laws, developing countries, dynamic panel data model.
(1) shows the increase in FDI inflows from 19952009.
INTRODUCTION
For a long period of time FDI was seen as the rescuer
of developing countries from low rates of growth
through its effect on filling the saving gap, increasing
managerial abilities and decreasing foreign exchange
shortages (Aliyu, 2005). Accordingly, increasing
trade liberalization and free movement of capital will
stimulate growth. Yet, trade liberalization and FDI
are becoming a real environmental threat. In
particular, FDI may reduce welfare through
increasing polluting emissions level and resource
depletion. This happens when polluting FDI are
concentrated in developing countries as a result of lax
environmental laws. According to the pollution haven
hypothesis, there is a positive relationship between
FDI inflows and loose environmental laws. This is
because the freer the trade and movement of capital
is, the greater the shift of polluting industries from
countries with stringent environmental laws to
countries with loose environmental laws will be.
Aliyu (2005) showed that there are three dimensions
for this hypothesis. The first dimension is that based
on the comparative advantage theory, developing
countries may impose loose environmental laws to
attract FDI and hence specialize in polluting
industries. The second dimension is that as a result of
stringent environmental laws, developed countries
will damp their polluting wastes through FDI in
developing countries. The third dimension is the
immense depletion of developing countries resources
such as petroleum, forests and timber by giant
corporations. These effects are magnified when
considering the rising trend of FDI inflows. Figure
The scale of FDI has increased rapidly during the
period 1980- 2000. As reported by Fredriksson
(2003), nominal FDI inflows worldwide increased by
18 % per year during 1987-1997. This result is also
assured by the figures of the Organization for
Economic Co-operation and Development (OECD)
of both FDI inflows and outflows for OECD
countries. However, FDI
Source: Calculated using data from UNCTAD World
Investment Report, 2010.
Figure (1): FDI Inflows by Region (1995-2009) US$
Billions
305
Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 5(3):305-315 (ISSN: 2141-7016)
composition and the relative importance of its
determinants have changed across time (Noorbakhsh
et al, 2001). For instance, FDI was mainly in the
primary sector, and natural resources were the most
influential determinant of FDI in the 50’s (McKern,
1996; UNCTAD, 1998). The picture has changed
since the 60’s as FDI was more directed to the
industrial sector with a decline in natural resource
importance. Escaping trade barriers can be a possible
explanation for changes in FDI flows. Furthermore,
market size and economic growth have become more
attractive to foreign investors (UNCTAD, 1998).
However, since the 80’s FDI inflows were directed to
services and technology based manufacturing. For
example, FDI inflows in the services sector
constituted 60% of FDI inflows in 1990 (UNCTAD,
1993). The growing importance for the services
sector is also true for the case of FDI inflows in
developing countries. However, the petroleum sector,
construction, chemicals production and transportation
are still major recipients of FDI inflows in developing
countries (UNCTAD, 2012). Hence, polluting
industries are still directed to developing countries
via FDI. Table (1) shows FDI inflows to developing
countries by sector.
developing countries among the world largest 20
recipients of FDI inflows in 2012.
Table 1: FDI Inflows to Developing Countries by
Sector (US$ Billions)
Period
1989-1991
2005-2007
Primary
3.9
46.8
Manufacturing
16.1
121.0
In billions of dollars, (x) = 2011 ranking.
Source: UNACTD, 2013
Figure (2): Top 20 FDI host economies in 2012
Services
9.3
161.4
Source: Calculated using data from UNCTAD, World
Investment Report, 2009.
Among developing countries, China has been the
major recipient of FDI inflows since 1992.
Furthermore, China is the second largest recipient in
the world after the US. In 2012, East and South East
Asia constituted 46.4% of FDI flows to developing
countries. Latin America and the Caribbean came
second with 34.7% of FDI flows to developing
countries. On the other hand, Africa received only
7.1% of FDI inflows to developing countries
(UNCTAD, 2013). More details are found in figure
(3) which illustrates FDI inflows to developing
countries in 2012.
Examining the recent trends of FDI inflows in
developing countries by region and by sector
highlights the importance of studying the
determinants of FDI inflows in these economies. For
that, the objective of this paper is to investigate the
effect of lax environmental laws and human capital
on FDI inflows in developing countries. This is
examined in a dynamic panel data model for 22
developing countries over the period 1990-2010.
Accordingly, a fixed effect panel data model with
homogenous slopes is used. The rest of the paper is
organized as follows: Section 2 describes the flow of
FDI by location to developing countries; section 3
provides a quick theoretical background with
empirical evidences; section 4 discusses the empirical
model; finally, conclusion and policy implications are
presented in section 5.
The growth figures of global FDI slowed down
recently from $1652 billion in 2011 to $1351 billion
in 2012 (UNCTAD, 2013). Weak economic growth,
deficiencies in stock markets and institutional
obstacles are among the causes that may lead to this
slow down (Mihci et al., 2005). In addition, the Arab
Spring since 2011 has led to political and economic
instability which negatively affected FDI inflows to
the Middle East. North Africa was the major
recipient of FDI inflows to Africa. In 2011, FDI
inflows to the continent decreased by 50% to reach
$7.69 billion with an insignificant flows to Egypt and
Libya (UNCTAD, 2012).
THE FLOW OF FDI TO DEVELOPING
COUNTRIES
In 2012, the share of FDI inflows to developing
countries was 52% of the world FDI inflows
surpassing flows to developed countries by $142
billion for the first time (UNCTAD, 2013). Not only
this, but as shown in figure (2) there were 9
306
Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 5(3):305-315 (ISSN: 2141-7016)
macroeconomic stability, trade openness, market size,
labor cost and energy availability. Table (2)
summarizes the empirical evidences with respect to
the effect of the traditional determinants on FDI
inflows.
Recently, the effects of human capital and the
environment on FDI inflows are considered. The
economic literature addressed the importance of
human capital as a determinant of FDI inflows. For
instance, Lucas (1990) deduced that lack of human
capital decreases FDI inflows. In addition, Dunning
(1988) showed that labor skills and education affect
the size of FDI inflows and the type of production by
TNCs. Furthermore, Zhang and Markusen (1999)
formulated a model that showed that human capital
affects both the volume and the destination of FDI
inflows.
Source: Calculated from data given by UNCTAD
World Investment Report, 2013.
Figure (3): FDI Inflows in Developing Countries in
2012
Table 2: Determinants of FDI – Summary of the
literature
Determinants
of FDI
Openness
THEORATICAL BACKGROUND AND
EMPIRICAL EVIDENCES
To understand the nature of the relationship of FDI
and the environment from a theoretical point of view,
it is appropriate first to discuss briefly the theory of
FDI. The classical explanation of FDI depends
mainly on capital arbitrage phenomenon. Differences
in marginal rate of returns is what causes FDI; that is,
capital moves from low rates of return countries to
relatively higher marginal rates of return ones. Carius
(2002, p.4) showed that economic and political
conditions are what affect FDI location. Political
conditions include such factors as political stability,
environmental laws and administrative capacities. On
the other hand, economic conditions include factors
such as GDP growth rate, policies governing trade,
macroeconomic stability, infrastructure, cost and
types of production. The market-size hypothesis
states for instance that FDI will not be initiated unless
certain size of the market is reached. This market size
is essential to achieve economic efficiency (Mihci et
al., 2005).
Infrastructure
quality
Positive
Edwards (1990)
Gastanaga et al
(1998),Hausmann
& FernadezArias(2000)
Wheeler & Mody
(1992),Kumar
(1994), Loree&
Guisinger (1995)
Schnelder & Frey
(1985), Tsai
(1994) & Lipsey
(1999)
Edwards
(1990)
Jasperson,
Aylward,
& Knox
(2000)
Labour cost
Wheeler & Mody
(1992)
Schnelder
& Frey
(1985)
Political
instability
Insignificant
Tsai (1994),
Loree&
Guisinger
(1995) &
Lipsey
(1999)
Real GDP per
capita
Taxes and
tariffs
Besides political and economic conditions, there are
other factors that affect FDI. Among these factors,
there are the promotions offered by developing
countries to attract FDI. Tax incentives, exemption
from strict environmental laws, or any other
regulations imposed on domestic firms are examples
of these promotions. Finally, the decision of FDI can
be very much affected by trade barriers conditions as
it can be more profitable sometimes to be engaged in
FDI rather than trade as suggested by the tariff
jumping hypothesis (Mihci, et al., 2005).
Negative
Loree&
Guisinger
(1995),
Gastanaga
et al
(1998)&
Wei
(2000)
Schnelder
& Frey
(1985) &
Edwards
(1990)
Loree&
Guisinger
(1995),,Wei
(2000)
Hausmann &
FernandezArias (2000)
Tsai (1994),
Loree&
Guisinger
(1995) &
Lipsey
(1999)
Wheeler &
Mody (1992)
&
Lipsey(1999)
Loree&
Guisinger
(1995),
Jasperson,
Aylward, &
Knox (2000)
&
FernandezArias (2000)
Source: Asiedu (2002)
On the empirical level, the effect of human capital on
FDI inflows is a debatable issue. For instance, Root
Accordingly, FDI inflows are affected by many
factors. The classical or traditional factors include
307
Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 5(3):305-315 (ISSN: 2141-7016)
developed countries. Likewise, there is no strong
evidence to support the hypothesis that loose
environmental laws form an attraction to FDI
inflows. In addition, Copeland and Taylor (2003)
stated that strict environmental laws do not affect the
direction of FDI but rather it is the type of
instruments used. They also argued that there can be
a pollution haven effect and not a hypothesis.
and Ahmed (1979) and Schneider and Frey (1985)
showed that human capital is not a significant
determinant of FDI inflows. On the other hand,
Hanson (1996) showed that human capital affects
FDI inflows but not as political stability and security
of property rights. Noorbakhsh et al (2001) also
concluded from their research effort that human
capital is not only a significant determinant of FDI
inflows, but also it is one of the most influential
factors and its importance is increasing across time.
Another remarkable finding was reached by the
OECD (1997) which discovered that most polluting
industries in developed countries are directed via FDI
to other developed and not developing countries. In
addition, the polluting FDI inflows directed to
developing countries represented a lesser proportion
of total FDI receipts in 1992 than in 1972.
Concerning environment as a determinant of FDI
inflows, the classical trade perspective of
comparative advantage considers environment as
another factor of production where stringent
environmental laws increase production costs.
Accordingly, developed countries with stringent
environmental laws will have relatively high
production costs. Consequently, these countries will
not have comparative advantage in polluting
industries as they cannot compete. While developing
countries with loose environmental laws will have
comparative advantage in polluting industries due to
relatively lower production costs. Hence, developing
countries will specialize in polluting industries and
their lax environmental laws will attract polluting
FDI.
On the other hand, Kolstad and Xing (1998)
conducted an empirical analysis to test the effect of
stringency of the environmental laws in destination
countries on the location of dirty industries. They
discovered that US FDI chemical industry outflows
and the stringency of environmental laws of the
foreign destination country exhibit a statistically
significant negative linear relationship. Nevertheless,
this relationship is not really apparent for less
polluting FDI industries.
The evidence is stronger that loose environmental
laws form a source of attraction to polluting FDI
flows. This result was also achieved by Co et al.
(2004) in their study of US FDI outflows to
developed and developing countries. They studied
two manufacturing industries in a panel data model
from 1982-1992. Their results confirmed that the
stringency of environmental laws influences
investment decisions as there is an inverse
relationship between environmental standards and
FDI flows for the average developing countries
despite of some possible exceptions for this finding.
Furthermore, Smarzynska and Wei (2001) found out
that there could be a support for the pollution haven
hypothesis if the country’s participation in
international environmental agreements was taken as
a measure for the environmental standards of a
country. In their research, 543 major multinational
corporations in 24 countries in Central/Eastern
Europe and the former Soviet Republics were studied
using firm level data instead of the country/industry
level data on investment.
According to the pollution haven hypothesis, there is
a positive relationship between FDI inflows and loose
environmental laws. This is because the freer the
trade and movement of capital is, the greater the shift
of polluting industries from countries with stringent
environmental laws to countries with loose
environmental laws will become. Two empirical
results could be deduced from this hypothesis. First,
there is a positive relationship between lax
environmental laws and FDI inflows in developing
countries. Also, there is a positive relationship
between FDI outflows and stringency of
environmental laws in developed countries.
Opposite to this, the neo-technology trade perspective
states that FDI and the environment exhibit a positive
relationship. For example, the pollution halo
hypothesis believes that FDI inflows can have a
positive effect on the environment. This is through
the transfer of environmental friendly techniques of
production via FDI from developed countries to
developing countries that rely on environmental
damaging techniques (Doytch, 2012).
In addition, Mihci et al. (2005) formed a model that
was founded on the integrated approach of Dunning
(1981; 1988) to study the FDI-environment
relationship. They constructed several equations to
examine the effects of various factors on FDI inflows
and outflows. Many samples were used to examine
the consistency of the independent variables such as
FDI between developed and developing OECD
countries, FDI in bilateral agreements between all
On the empirical level, FDI-environment relationship
is indistinct. For example, Levinson (1996) provides
an empirical literature survey on how sensitive FDI to
environmental regulations in US is at the
international and domestic levels. He discovered that
after more than twenty years of empirical research,
the evidence is weak to support that stringent
environmental laws push polluting FDI away from
308
Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 5(3):305-315 (ISSN: 2141-7016)
OECD countries and total inflows to OECD
countries. The most remarkable finding was the effect
of the environmental variable on FDI in most of the
samples. The index of environmental sensitivity
performance in the reporter country (IESP) was used
as a proxy of strict environmental laws. The main
result was that IESP in the reporter country and FDI
outflows had a positive significant relationship.
Lastly, Aliyu (2005) constructed an econometric
model for the period of 1990-2000 to study the effect
of environmental laws on FDI outflows in 11
developed OECD countries. His study also evaluated
the effects of FDI inflows on pollution emissions in
14 non OECD developing countries. The results
showed that there is a positive correlation between
FDI outflows of polluting industries and the
stringency of environmental policies in developed
countries.
becomes an important issue in the case of presence of
many explanatory variables.
The Choice of Variables
Many proxies have been used to assess the stringency
or laxity of environmental laws in previous studies.
These include: the degree of participation in
international environment treaties (Smarzynska and
Wei, 2001), the index of environmental sensitivity
performance IESP (Mihci et al., 2005), carbon
dioxide emissions (Hoffmann et al., 2005) and
environmental tax (Aliyu, 2005). Environmental tax
is a more reliable proxy for the stringency of
environmental laws if compared to other proxies.
This is because participating in international
environmental treaties does not necessarily mean that
they are enforced in practice. In addition, data for
IESP is available for very short time series. Still, due
to the availability of data, carbon dioxide emissions
are the chosen proxy variable for lax environmental
laws in this research. It could be argued, then, that
high carbon dioxide emissions are a reflection of lax
environmental laws which could be a source of
attraction for FDI. Accordingly, the relationship
between FDI inflows and carbon dioxide emissions is
expected to be positive.
Following this review of empirical studies on FDIenvironment relationship, it could be deduced that
this area of research is controversial and a hot
debatable issue in the field of economics. Hence, the
research
problem
is
the
FDI-environment
controversy. Further empirical research is needed to
have a clearer picture. For that, this research work
forms another step forward to have a better
comprehension of the nature of FDI-environment
relationship.
Higher level education is the most influential element
of human capital (OECD, 1998; World Bank, 1998).
Thus, correct policies with respect to higher
education are a precondition for improving the skills
of human capital. This in return will give the host
country a locational advantage (Noorbakhsh et al,
2001). For that, secondary school enrollment ratio is
used in this study to measure human capital. This
variable was used before by many researchers such as
Root and Ahmed (1979), Schneider and Frey (1985)
and Noorbakhsh et al (2001). It is expected then to
have a positive relationship between FDI inflows and
secondary school enrollment ratio.
THE EMPIRICAL MODEL
The empirical model investigates the effect of lax
environmental laws on FDI inflows in developing
countries. This research uses a dynamic panel model
that is founded on the econometric work of
Noorbakhsh et al (2001). For this purpose, the model
uses a panel data set for developing countries over
the period 1990-2010 for which data is available. The
list of countries is shown in appendix A.1. Missing
data were calculated through the use of linear
interpolation.
In order to guarantee high profits and efficiency,
certain size of the host country market must be
reached as suggested by the market size hypothesis.
Many studies such as that of Root and Ahmed (1979),
Schneider and Frey (1985) and UNCTC (1992)
showed that the market size in the host country is an
influential determinant of FDI inflow. Theoretically
speaking, the bigger the market and the higher the
rate of growth of GDP are, the more investment
opportunities are available. Therefore, the rate of
growth of GDP is used to reflect the market size. This
was used before in many studies such as Lim (1983),
Singh & Jun (1995) and Torrisi (1985). Following
this logic, it is expected to have a positive
relationship between FDI inflows and the rate of
growth of GDP.
The Econometric Approach
Consider the following fixed effect panel data model
with homogenous slopes:
FDIit = α Envit + β ' X it + λi + ε it
(1)
For a country i at time t, FDI is the net FDI inflows as
a percentage of GDP; Env is the measure of lax
environmental laws; X is a vector of explanatory
variables which includes determinants of FDI inflows
other than environment; λ is the fixed effect dummy
variable for individual unobserved effects and ε is the
error term.
Using panel data enlarges the sample size for better
estimation and improves the power of the test
statistics. Furthermore, panel data is preferred
because it increases degrees of freedom. This
Labor cost is another determinant of FDI inflows
especially in labor intensive industries. However
309
Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 5(3):305-315 (ISSN: 2141-7016)
factors include economic conditions,
availability or even technological abilities.
when tested empirically, there are evidences with and
others against the aforementioned hypothesis. For
instance, Flamm (1984) and Lucas (1993) showed
that wage cost variable is a significant determinant of
FDI inflows. Others like Kravis and Lipsey (1982)
showed that this result is not always true. A possible
explanation for this discrepancy in results is that
sometimes if high wages are used as proxy for skills,
FDI inflows may be attracted to destinations of high
wages. There are many measures for labor cost such
as the wage differential as used by Noorbakhsh et al
(2001) or the growth rate of the labor force used by
Dunning (1973). Due to data availability, this paper
uses the growth rate of the labor force. The logic here
is that the more available the labor is, the lower his
cost and the more FDI inflows will be.
factors
Lagged change in FDI inflows is included to test
whether any persistence or correction is happening
with respect to FDI. Foreign direct investors usually
carry on calculation measures regarding continuity of
their investment based on conditions in host and
home countries. Previous FDI inflows contain
information regarding the conditions in host countries
(Noorbakhsh et al, 2001). In addition, Johanson and
Wiedersheim Paul (1993) showed that investors
prefer investing in countries that they were
introduced to them before.
It is worth mentioning that other variables such as
democracy, risk and natural resource availability can
be used as explanatory variables. Nevertheless, the
sample period is 1990-2010 which marks the decline
in the importance of natural resource availability for
FDI inflows (UNCTAD, 1998). Also, studies by
Loree and Guisinger (1995), Jasperson, Aylward, and
Knox (2000), as well as Hausmann and FernandezArias (2000) showed that political instability has
insignificant effect on FDI inflows. In addition,
studies like Noorbakhsh et al (2001) showed that risk
variable is insignificant. Agarwal (1980) referred that
to the variation of different guarantees for political
risk in home countries. Also, the variables used to
reflect democracy or risk are based on subjective
assessment and may not reflect properly the
measured variable (Noorbakhsh et al, 2001). For that,
these variables are not included in the model.
Trade openness is another important determinant of
FDI inflows. The logical reason behind this is that the
more liberalized the trade is, the more confidence the
investors will have in this market. Following previous
studies as UNCTAD (1999) and Noorbakhsh et al
(2001), trade openness is measured by the ratio of the
sum of exports and imports to GDP. The sign of the
trade openness coefficient is expected to be positive.
Macroeconomic stability is another source of
attraction to FDI inflows. Financial liberalization is
used as a proxy for macroeconomic stability by many
researchers such as Root and Ahmed (1979) and
Noorbakhsh et al (2001). The logic behind this choice
as suggested by Fry (1997), and Easterly and
Schimidt-Hebbel (1993) is that besides financial
liberalization’s attraction of FDI inflows, it is a
necessary condition for macroeconomic stability.
This is due to the fact that the increase in private
sector share of domestic credit is usually coupled
with inflation stabilization. For that, the share of
domestic credit to the private sector in GDP is used
as a proxy for macroeconomic stability in this study.
Estimation
To examine the effect of lax environmental laws on
FDI inflows in developing countries, a dynamic panel
data model is used. The sample studied is for 22
developing countries over the period 1990-2010.
Hausman test between fixed effect and random effect
models was conducted. Accordingly, Random effect
model was rejected. Hence, a fixed effect panel data
model with homogenous slopes was used as shown in
equation 1. Table (3) shows the results of Panel least
squares estimation of equation 1 where at time t for
country i, FDI inflows is regressed on lagged change
of FDI (∆FDI-1), carbon dioxide emissions (CO2),
labor availability (AVL), market size (MS), trade
openness (TO), macroeconomic stability (MAC),
secondary school enrollment ratio (SSE), net energy
imports (ENG) and a trend term.
Cheap energy source is another vital determinant of
FDI inflows. According to the UNCTAD (1998), the
availability of energy sources that are accessible is
one of the biggest concerns of foreign investors.
Following Noorbakhsh et al (2001), net energy
imports is the variable chosen to reflect energy
availability. Net energy imports can be defined as the
difference between energy use and energy production
as a percentage of energy use. The coefficient of the
net energy imports is expected to be negative since it
is a measure of shortages of energy.
Most explanatory variables including carbon dioxide
emissions were significant with the exception of
labor availability and market size. All of them got the
expected signs with the exception of labor
availability and human capital. However, testing for
heteroskedasticity through white test indicated its
presence. Accordingly, White correction was
implemented to achieve heteroskedasticity-consistent
In addition to these explanatory variables, a trend
term and lagged change in FDI inflows are also
considered. A trend term is used to reflect
unobserved components such as promotion efforts,
decrease in corruption, administration efficiency or
even a combined supply side factors that reflect
conditions in home countries. These supply side
310
Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 5(3):305-315 (ISSN: 2141-7016)
estimation. Table (4) shows the results of the
estimation after White correction.
coefficient of carbon dioxide is positive and
significant at the 5% level. The coefficients of
macroeconomic stability and the trend term are
positive and significant at 5% level as well. In
addition, the positive significant coefficient of the
lagged change in FDI at 1 % level indicates that there
is persistence in FDI inflows. This shows that
investors prefer investing in markets they were
introduced to before. The coefficient of energy is
significant and negative at 10% level. This highlights
the importance of cheap energy sources as a
precondition for FDI inflows. The coefficients of
human capital, trade openness and market size are all
insignificant.
Table 3: Empirical Results of Equation 1a
Dependent variable
FDI
∆FDI -1
CO2
AVL
MS
TO
MAC
SSE
ENG
Trend
Constant
Adjusted R Squared
a
Coefficients
0.257478
(4.814655)*
1.113600
(2.368246)**
-0.051762
(-0.832071)
0.047227
(1.700945)
0.021207
(2.106012)**
0.040133
(3.856280)*
-0.032202
(-2.265641)**
-0.016140
(-3.505768)*
0.126640
(4.400143)*
-2.371303
(-1.868084)
0.502500
As a robust check, the model has been re-estimated
after applying general least squares (GLS) weights
estimation as an alternative to White correction for
heteroskedasticity. Table (5) shows the results of this
re-estimation.
Table 5: Empirical Results of Equation 1 a
(General Least Squares Weights)
Dependent variable
FDI
∆FDI -1
t- values are in parentheses
* Significance at 1% level
** Significance at 5% level
CO2
AVL
Table 4: Empirical Results of Equation 1 a
(White Correction)
Dependent variable
FDI
∆FDI -1
CO2
AVL
MS
TO
MAC
SSE
ENG
Trend
Constant
Adjusted R Squared
MS
Coefficients
TO
0.257478
(4,147305)*
1.113600
(2.254594)**
-0.051762
(-0.422550)
0.047227
(1.215299)
0.021207
(1.098492)
0.040133
(2.453759)**
-0.032202
(-1.067181)
-0.016140
(-1.956948)***
0.126640
(2.049607)**
-2.371303
(-1.950125)***
0.502500
MAC
SSE
ENG
Trend
Constant
Adjusted R Squared
Coefficients
0.244528
(5.099925)*
1.031815
(3.608723)*
0.017934
(0.472639)
0.035865
(1.920831)***
0.018788
(2.801079)*
0.032742
(4.645176)*
0.005929
(0.637127)
-0.010082
(-3.404768)*
0.044899
(2.267673)**
-3.171310
(-3.969344)*
0.634665
a
t- values are in parentheses
* Significance at 1% level
** Significance at 5% level
*** Significance at 10% level
After applying general least squares weights, the
empirical results indicate the following: Most
explanatory variables are found significant and with
the correct signs with few exceptions. Among these
exceptions are the insignificant coefficients of labor
availability and human capital. In addition, the
energy coefficient is significant and negative similar
to the white corrected estimation. Again, this
indicates that the more the shortages of energy are,
the less FDI inflows will be. The coefficient of
carbon dioxide is significant at 1% level and positive.
a
t- values are in parentheses
* Significance at 1% level
** Significance at 5% level
*** Significance at 10% level
After adopting white correction, the empirical results
of the model show that lax environmental laws are a
significant determinant of FDI inflows. The
311
Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 5(3):305-315 (ISSN: 2141-7016)
correcting for heteroskedasticity, after adopting
White correction and for general least squares
weights estimation. The empirical results in all cases
indicated that lax environmental laws are a
statistically significant determinant of FDI inflows
and that it is one of the most influential determinants
of FDI inflows in developing countries. This finding
in particular represents the contribution of this paper
as it emphasizes the effect of the environment on FDI
inflows.
This indicates that lax environmental laws attract FDI
inflows in developing countries. Also, the
coefficients of macroeconomic stability and trade
openness are significant and positive at 1% level.
This indicates that economic stability and the extent
of free trade increase foreign investors’ confidence in
host countries’ markets. In addition, the coefficient of
market size is positive and significant at 10% level.
This moves in line with the market size hypothesis.
With respect to the trend term, it was found positive
and significant at 5% level. Finally, there is
persistence in FDI inflows as suggested by the
positive significant lagged change in FDI coefficient.
Surprisingly enough, the coefficient of human capital
was only significant in the first regression. This
contradicts with the results of Noorbakhsh et al.(
2001). Accordingly, the effect of human capital on
FDI inflows is still a debatable issue. A possible
explanation though for insignificant human capital
effect is the lack of policies needed to develop human
capital. Poor education and lack of skilled labor are
common features in many developing countries.
One can deduce then that lax environmental laws
attract FDI inflows. It would be interesting as well to
determine its relative importance in attracting FDI
inflows. This is more clarified in table (6).
Examining the relative importance of each of the
explanatory variables shows that lax environmental
laws are the most influential determinant of FDI
inflows in developing countries. This is because it
has the largest estimated beta coefficients of 0.20986
and 0.1024 for White corrected estimators and
weighted GLS respectively. Second in importance is
the change in lagged FDI inflows, followed by the
trend term, macroeconomic stability and market size,
trade openness and finally energy shortages.
Most other traditional explanatory variables were
found significant and with the correct signs with the
exception of labor availability. With respect to labor
availability, it was found insignificant in all the three
estimations. This reflects the lack of an effective
policy related to labor. The energy coefficient was
significant and negative. This shows that cheap
energy sources are attract FDI. Macroeconomic
stability and lagged change in FDI were always
significant and positive in the three regressions which
highlight the importance of economic stability and
the persisting nature of FDI inflows. Finally, trade
openness and market size were among the significant
determinants of FDI after adopting general least
squares weights estimation. In addition, the trend
term was always positive and significant in the three
regressions.
Table 6: Relative Importance of Explanatory
Variables in Attracting FDI Inflows
(Estimated Beta Coefficients)*
Dependent
Variable: FDI
∆FDI -1
White Correction
GLS Weights
0.0061
0.004
CO2
0.20986
0.1024
AVL
-0.0024
0.000236
MS
0.0007
0.000232
TO
0.000156
0.000044
MAC
0.00025
0.00008
SSE
-0.00037
0.00002
ENG
-0.00005
-0.00001
Trend
0.00298
0.0003
Since the empirical results showed that lax
environmental laws attract FDI inflows in developing
countries, certain policies have to be put in action.
This has to be tackled with delicacy. This is because
if this process continues, we will end up with a
depletion of developing countries’ resources, increase
in pollution level and environmental degradation of
developing countries. However, if this process stops
at once, developing countries will lose huge volume
of FDI inflows which is a cornerstone for their
development. Hence, to take the maximum benefit of
this with the least cost, the following policy
implications are suggested:
1. It is very essential for developing countries to
get rid of corruption.
2. Spread firm’s and public awareness about the
importance of preserving the environment.
3. Be updated with the new production
techniques and ensure their accessibility.
4. Ensure
transparency
and
information
availability.
* The estimated beta coefficients (units free) are
equal to the product of the estimated coefficient of
the explanatory variable and the ratio of standard
deviation of the explanatory variable to that of the
dependent variable.
CONCLUSION & POLICY IMPLICATIONS
This paper examined the effect of lax environmental
laws and human capital on FDI inflows in developing
countries in a dynamic panel data model.
Accordingly, besides the traditional determinants of
FDI inflows, carbon dioxide emissions and secondary
school enrollment ratios were included in the
regression. A fixed effect panel data model was used.
This was carried for three regressions: Before
312
Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 5(3):305-315 (ISSN: 2141-7016)
Aliyu, Mohammed Aminu (2005). Foreign direct
investment and the environment: pollution havens
hypothesis revisited. Annual Conference on Global
Economic Analysis, Germany.
5. Adopt stringent environmental laws and ensure
their enforcement and compliances. This can
be done for instance by adopting a policy mix
between command and control approach and
economic
incentive
approach.
These
regulatory approaches are explained briefly in
appendix A.2.
6. Make sure that FDI and the environment are
complementing each other and get rid of their
conventional treatment as substitutes.
7. Make the maximum benefit of other influential
determinants of FDI inflows such as
macroeconomic stability, cheap energy sources
or the persisting nature of FDI. This is to form
a source of attraction for FDI inflows that will
substitute the role played by lax environmental
laws.
8. Offer more incentive, promotions, free zones
and tax breaks that encourage FDI inflows.
9. Adopt the correct policies with respect to labor
in general and human capital in particular to
form another source of attraction for FDI. This
can be done through better education,
spreading
knowledge,
research
and
development, offering training programs to
labor to improve their skills,…etc.
10. Encourage public private partnership (PPP) to
encourage FDI.
Asiedu, E. (2002).On the determinants of foreign
direct investment to developing countries: is
Africa different? World Development, 30, 107-119.
Carius, A. (2002). Environmental impacts of foreign
direct investment in the mining sector in the newly
independent states, Conference Paper, OECD Global
Forum on: International Investment: Conference on
Foreign Direct Investment and the Environment.
“Lessons to be learned from the Mining Sector”
February 7-8, Paris, France
Co, Catherine Y., List, J.A. and Qui, L.D. (2004).
Intellectual
property
rights,
environmental
regulations and FDI. Land Economics 80, 153-173.
Copeland, Brain R. and Taylor, M.S. (2003). Trade,
growth & the environment, National Cambridge:
Bureau of Economic Research working paper,
No.9823.
Doytch, N. (2012). FDI halo vs. pollution haven
hypothesis. 65th Annual Meeting NYSEA
Proceedings, Volume 5.
All in all, there is a strong relationship between FDI
and the environment. This was confirmed by the
results of this research work. Putting this in mind
enables us to have a clearer picture on how to tackle
the FDI-environment dilemma in order to promote
FDI inflows and at the same time preserve the
environment in developing countries. The suggested
policy implications open the door for a possible path
to tackle this matter. However, a lot is still needed to
ensure their applicability in practice. For that, I
suggest conducting research on FDI-environment
relationship on a country level basis. This is because
the main limitation of this study is the unavailability
of data for some of the countries which made it
difficult to use a fixed effect panel data with
heterogeneous slopes. Using heterogeneous slopes
would have allowed suggesting policy implications
for individual countries. Hence, collecting reliable
data on macro and micro levels should be among the
major concerns of governments and research centers
of developing countries. If so, it is more advisable to
study this relationship on an industrial level as well.
http://nysea.bizland.com/nysea/publications/proceed/
2012/Proceed_2012_p047.pdf
Dunning, J. (1973). The determinants of international
production. Oxford Economic Papers, 25, 289-336
Dunning, J. (1981). International Production and the
Multinational Enterprise. Sydney: George Allen and
Unwin.
Dunning, J. (1988). Explaining
production. London: Unwin Hyman.
international
Easterly, W. and Schmidt-Hebbel, K. (1993). Fiscal
deficits and macroeconomoc performance in
developing countries. World Bank Research
Observer, 8, 211-237.
Edwards, S. (1990). Capital flows, foreign direct
investment and debt – equity swaps in developing
countries. NBER working paper no. 3497,
Cambridge, MA: NBER.
ACKNOWLEDGEMENT
The author would like to express her deep gratitude
to Dr. Fatma El Diwany for her valuable advice.
Flamm, K. (1984). The volatility of offshore
investment. Journal of Development Economics,16,
231-248.
REFERENCES
Agarwal, J. (1980). Determinants of foreign direct
investments: a survey. Weltwirtschaftliches Archiv,
116, 739-777.
313
Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 5(3):305-315 (ISSN: 2141-7016)
Lim, D. (1983). Fiscal incentives and foreign direct
investment in less developed countries. Journal of
Development Studies, 19, 207-212.
Fredriksson, P. G., List, J.A. and Millimet, D.L.
(2003). Bureaucratic corruption, environmental
policy and inbound US FDI: theory and evidence.
Journal of Public Economics, 87,1407- 1430.
Fry, M.(1997). In favor of financial liberalization.
Economic Journal, 107, 754-770.
Lipsey, R.E. (1999). The location and characteristics
of US affiliates in Asia, NBER working Paper, no.
6876.
Gastanga, V., Nugent, J.B. and Pashamova, B.
(1998). Host country reforms and FDI inflows: how
much difference do they make?. World Development,
26, 1299-1314.
Loree, D.W. and Guisinger, S. (1995). Policy and
non-policy determinants of US equity foreign direct
investment. Journal of Business Studies, 26, 281299.
Hanson, J. R., Jr. (1996). Human capital and direct
investment in poor countries. Explorations in
Economic History, 33, 86-106.
Lucas, R.B. (1993). On the determinants of direct
foreign investment: evidence from East and Southeast Asia. World Development, 21, 391-406.
Hausmann, R.and Fernandex-Arias, E. (2000).The
new wave of capital inflows: sea change or just
another title ?. Inter-Amercian Development Bank
working paper, 417.
Lucas, R.E., Jr. (1990). Why does not capital flow
from rich to poor countries. American Economic
Review, 80, 92-96.
Lydon, R. and Williams, M. (2005). Communications
network and foreign direct Investment in developing
countries. Communications & Strategies, 58, 43-60.
Hoffmann, R., Chew-Ging Lee, Ramasamy, B. and
Yeung, M. (2005). FDI and pollution: a Granger
causality test using panel data. Journal of
International Development 17, 311-317.
McKern, B. (1996). Transnational corporations and
the exploitation of natural resources. InTransnational
corporations and world development. UNCTADDTCI, London: Routledge.
Jaspersen, F.Z, Aylward, A.H. and Knox,
A.D.(2000). The effects of risk on private investment:
Africa compared with other developing areas. In P.
Collier & C. Pattillo (Eds.), Investment and risk in
Africa (pp. 71-95). New York: St. Martin's Press.
Mihci, H., Cagatay, S. and Koska, O. ( 2005). The
impact of environmental stringency on the foreign
direct investment of the OECD countries. Journal of
Environmental Assessment Policy and Management
7, 679-704.
Johanson, J. and Wiedersheim Paul, F. (1993). The
internationalization of the firm: four Swedish Cases.
In P.Buckley, & P. Ghauri (Eds.), The
internationalization of the firm. London: Academic
Press.
Noorbakhsh, F., Paloni, A. and Youssef, A. (2001).
Human capital and FDI inflows to developing
countries: new empirical evidence. World
Development, 29, 1593-1610.
Karvis, I. and Lipsey, R.(1982). The location of
overseas production and production for exports By
US multinational firm. Journal of International
Economics, 12, 201-223.
OECD. (1997).Foreign direct investment and the
environment: an overview of literature, DAFFE/ MAI
(97) 33, 1.
Kolstad, C. and Xing, Y. (1998). Do lax
environmental regulations attract foreign investment?
Economics Working Paper, No.1078, Department of
Economics, UC, Santa Barbara.
OECD. (1998). Human capital investment: an
international comparison. Paris: OECD Centre for
Educational Research and Innovation.
Kumar, N. (1994). Determinants of export orientation
of foreign production by US multinationals: an intercountry analysis. Journal of International Business,
25, 141-156.
Root, F. and Ahmed, A.(1979). Empirical
determinants of manufacturing direct foreign
investment in developing countries. Economic
Development and Cultural Change, 27, 751-767.
Levinson, A. (1996). Environmental regulations and
manufacturer’s location choices: evidence from the
census of manufacturing. Journal of Public
Economics, 61: 5-29.
Schndeider, F. and Frey, B.S.(1985). Economic and
political determinants of foreign direct investment.
World Development 13, 161-175.
314
Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 5(3):305-315 (ISSN: 2141-7016)
financially rewarded for reducing pollution further
below the legal levels. Finally, non mandatory
approach relies on voluntary actions of firms to
upgrade
their
environmental
performance.
Environmental self regulation by firms assumes that
self interest is the reason behind firms’ adoption of
voluntary action to reduce pollution. For example,
firms are afraid of losing public acceptance as a
reaction to the disclosure of environmental
information about the firms.
Singh, H. and Jun, K.(1995). Some new evidence on
determinants of foreign direct investment in
developing countries. Policy Research Working
Paper, No. 1531, World Bank, Washington, DC.
Smarzynska, B. K. and Wei, S-J. (2001). Pollution
havens and foreign direct investment: dirty secret or
popular myth? NBER Working Paper, No. 8465.
Torrisi, C. (1985). The determinants of direct foreign
investment in a small LDC. Journal of Economic
Development, 10, 29-45.
Tsai, P.L. (1994). Determinants of foreign direct
investment and its impact on economic growth.
Journal of Economic Development, 19, 137-163.
UNCTAD (1993, 1998, 1999, 2009, 2010, 2012,
2013). World investment report. Geneva: UNCTAD.
UNCTC.(1992). The determinants of foreign direct
investment: a survey of evidence. New York: United
Nations.
Wei, S.J. (2000). How taxing is corruption on
international investment location decisions: the case
of US firms. Journal of International Economics, 33,
57-76.
Wheeler D. & Mody, A. (1992). International
investment location decisions: the case of US firms.
Journal of International Economics, 33, 57-76.
World Bank. (1998). Knowledge for development:
World development report 1998/1999. New York:
Oxford University Press.
Zhang, K. and Markusen, J. (1999). Vertical
multinationals and host-country characteristics.
Journal of Development Economics, 59, 233-253.
APPENDIX
A.1. The list of countries includes: Albania, Algeria,
Cameroon, Chile, China, Colombia, Costa Rica,
Ecuador, Egypt, Guatemala, India, Indonesia, Iran,
Jordan, Malaysia, Mexico, Morocco, Nepal,
Philippines, South Africa, Tunisia and Venezuela.
A.2. There are three different approaches of
environmental regulations: Command-and-control
approach, economic incentive approach and non
mandatory approach. In command-and-control
approach, the government decides on standards for
each pollution emissions and the most suitable
technology to be used in production that ensures that
these standards are met. On the other hand, in the
economic incentive approach producers are free to
choose their appropriate methods of production or
technologies as long as pollution levels are within the
legal standards. Not only this, but also producers are
315