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The Effect of Corruption on FDI Inflow: Empirical Evidence from Panel
Data of East Asian Economies
Aye Mengistu Alemu (Ph.D)
Assistant Professor, SolBridge International School of Business, 151-13 Samsung 1dong, Dong-gu, Daejeon 300-814, Korea; E-Mail:;
Tel: +82-109818-2327
This study has made fresh insights and investigates the effects of corruption on FDI inflow for a
panel of 16 Asian economies from 1995-2009. The empirical result suggests a one unit increase
in the level of corruption triggers a decrease in FDI inflow by approximately 3.4 percentage
points. Thus, some of the arguments that corruption does not keep FDI out of those corrupt
countries were either flawed or invalid. In fact it is reasonable to claim that some of the countries
that are characterized by high level of corruption but also with a remarkable FDI inflow could
even double their inward FDI if they manage to reduce the present pervasive level of corruption.
Key Words:
Corruption; FDI Inflow; Panel Data; REM, FGLS; Economic Growth; Openness; Human Capital
Until recently, there was a strong consensus in the literature that multinational corporations
(MNCs) invest in specific locations mainly because of strong economic fundamentals in the host
countries for example, large market size, stable macroeconomic environment, availability of
skilled labor, infrastructure etc and thereby influence the attractiveness of the country to FDI
inflows (Dunning 1993, Globerman and Shapiro 1999, Shapiro and Globerman 2001). However,
the host country’s economic fundamentals may not be sufficient for inward FDI. Therefore it
now becomes important to study afresh what determines inflow of FDI. In this regard, one of the
most damaging risks that TNCs must consider in entering emerging market economies is the
threat of corruption, which undermines economic reform and, ultimately, national economic
stability. Moreover, corruption raises the cost of doing business, distorts the allocation of
resources and the price of goods and services for consumers, and discourages foreign direct
investment (Zhao, Kim, and Du, 2003). For instance, Surveys of private firms in Latin America
found that corruption negatively affects sales, investment, and employment growth, thereby
reducing firm competitiveness without producing any positive effects (Gaviria, 2002).
According to Myint (2000), corruption is defined as the use of public office for private
gain, or in other words, use of official position, rank or status by an office bearer for his own
personal benefit. Following from this definition, examples of corrupt behavior would include: (a)
bribery, (b) extortion, (c) fraud, (d) embezzlement, (e) nepotism, (f) cronyism, (g) appropriation
of public assets and property for private use, and (h) influence peddling. In this list of corrupt
behavior, activities such as fraud and embezzlement can be undertaken by an official alone and
without involvement of a second party. While others such as bribery, extortion and influence
peddling involve two parties –the giver and taker in a corrupt deal. Political corruption by public
officials can assume many forms, including bribery, embezzlement, extortion, nepotism, and
“graft” (where public officials either directly steal public funds or illegitimately benefit from
public funds). This freedom index is an indicator of the degree to which an economy is free of
such forms of corruption.
Similarly, the World Bank focuses on the abuse of public power for private benefits in
defining corruption (Tanzi, 1998). Similarly, Busse et al. (1996) define corruption as the use of
power by government and quasi-government officials and agents to extract quasi rents from
businesses for their own profit. Given this simple but broad definition, corruption is sometimes
all inclusive, taking into account bribes, bureaucratic and institutional inefficiency, and political
instability (Habib and Zurawicki, 2001).
Corruption exists throughout the world, in developed and developing countries alike. In recent
years there have been significant increases in the attention paid in corruption, in part due to (1)
series of high level corruption cases in industrialized countries, (2) due to an increasing
awareness of the costs of corruption throughout the world and, (3) due to the political and
economic changes which many countries are undergoing (Lawal, 2007). Corruption is worse in
countries where institutions, such as the legislature and the judiciary are weak, where rule of law
and adherence to formal rules neither are nor rigorously observed, where political patronage is
standard practice, where the independence and professionalism of the public sector has been
eroded and where civil society lacks the means to bring public pressure to bear (Lawal, 2007).
Generally speaking, corruption is a serious economic, social, political, and moral blight,
especially in many emerging countries. It is a problem that affects companies in particular,
especially in international commerce, finance, and technology transfer. And it is becoming an
international phenomenon in scope, substance, and consequences (Argandona, 2007). Corruption,
the abuse of public power for private gain, creates uncertainty regarding the costs of operation in
the country. It acts as an irregular tax on business, increasing costs, and distorting incentives to
invest (Shleifer and Vishny, 1993; Mauro, 1995; Wei, 2000a). Furthermore, it limits economic
growth because it reduces the amount of public resources, discourages private investment and
saving and impedes the efficient use of government revenue and development assistance funds.
The Linkage between Corruption and Investment
The effect of corruption on investment is still debatable. Many empirical studies provide support
for the idea that corruption in the host country is negatively related to FDI (Wei, 2000a, Wei,
2000b, Habib and Zurawicki, 2001; Lambsdorff, 2003). However, some scholars argue that
corruption can have a positive impact on investment by facilitating transactions in countries with
excessive regulations (Huntington, 1968; Leff, 1989; Wheeler and Mody, 1992; Henisz, 2000).
According to those researchers, some countries with high level of corruption, such as China,
India, or Nigeria, are the recipients of a great deal of FDI and hence corruption doesn’t keep FDI
out of very corrupt countries. This fact begs the question of just how corruption affects FDI.
Two broad presumptions can be made regarding the effect of corruption on the efficiency
of investment. First, corruption distorts the sectoral allocation of investible resources by
diverting resources from potentially productive sectors to unproductive sectors and thereby
decreasing the overall output-generating capacity of the investment. A good example of the
phenomenon in recent times has been the acquisition of large volumes of loans by many
entrepreneurs in South-East Asian countries by colluding with bank officials. These resources,
sometimes obtained by fraudulent means, were often invested in unproductive sectors or
activities and which contributed to the increase in non-performing loans and the eventual
contraction of GDP during the recent Asian economic crisis (Casserley and Gibb, 1999). RoseAckerman (1999) also notes that for business people in Eastern Europe and the Russian
Federation playoffs are often necessary to obtain credit. Thus investments are made not on the
basis of their rates of return but on the capacity of the entrepreneur to pay bribes. Second, bribes
which are often the major part in any act of corruption increase the cost of production which
ultimately gets reflected in a higher output price increase, reduction in demand and the eventual
reduction in the incremental output capital ratio for the activity.
If corruption is reduced, both the volume and productivity of investment will increase.
Resources spent on this area can be expected to yield rich dividends in the form of enhanced
economic performance. Hope (2000) argues that rent seeking activities tend to have the effect of
inflating the cost of doing business. Hope points that kickbacks and illegal commissions which
have to be paid to public officials are simply added to the final costs of contracts, equipment,
supplies and so on. The immediate consequences of such a situation is that entrepreneurs and
potential entrepreneurs withdraw from engaging in investment and the affected economy loses
the multiplier benefits that would have been forthcoming with those investments (Hope, 2000).
By the same token, corruption distorts the market by making regulatory controls ineffective and
acting as an arbitrary tax on FDI (Tanzi, 1998). The unpredictability in the level of corruption
adds to the arbitrariness and is especially problematic for foreign investment (Wei, 2000a).
Under this circumstances, investors will prefer not to invest and will likely divert the money to a
safer investment location. In the long run, the economy and its growth will suffer (Habib and
Zurawicki, 2001). Corruption erodes economic freedom by introducing insecurity and
uncertainty into economic relationships
Generally, corruption is a double edged sword; it reduces both the volume and efficiency
of investment and thus economic growth (Sarkar and Hasan, 2001). Pervasive corruption
increases MNCs operational costs and risks.
According to Quah (1999), the consequences of corruption can be minimized if
government has an effective anticorruption strategy and implements it impartially. Specifically,
the more effective anticorruption measures are, the greater their impact on the society in terms of
reducing the negative effects and the level of corruption. In doing so, Quah (1999) develops a
matrix of anticorruption strategies can be used to analyze the anticorruption efforts of several
Asian countries as shown in table 1 below.
Table 1: A Matrix of Anticorruption Strategies
Anticorruption Measures
Commitment of political leadership
Effective strategy
Ineffective strategy II
Ineffective strategy I
“Hopeless” strategy
Source: Quah (1982): 175
Table 1 shows four strategies for combating corruption, depending on the adequacy of the
anticorruption measures employed and the strength of political leaders' commitment. The
effectiveness of anti-corruption measures depends on two factors: (1) the adequacy of the measures in
terms of the comprehensiveness of their scope and powers; and (2) the level of commitment of
political leaders to the goal of minimizing corruption. In other words, for anticorruption measures to
be effective, they must be properly designed (to attack the causes of corruption), and they must be
sponsored and upheld sincerely by political leaders. In short, the most elaborate and well-designed
anticorruption measures will be useless if they are not enforced by the political leadership (Quah,
1982, 174-175).
Given the different corruption rates in Asian economies, Quah (1982) investigates the level of
corruption in those countries by applying the above matrix (Table 1) and apparently confirms only
Singapore and Hong Kong are the two least corrupt Asian city states that institutionalized both strong
commitment of political leadership and adequate anticorruption measures, and ultimately managed to
greatly minimize, if not eliminated, corruption. On the other hand, countries like Bangladesh, India,
Indonesia, Nepal, Pakistan, the Philippines, and Vietnam have neither strong commitment of political
leadership nor adequate anticorruption measures and as a result they fell in the category of the fourth
cells of anticorruption strategies as shown in Table 1. Likewise, the second and third cells of the
matrix of anticorruption strategies in table 1 represent two types of ineffective anticorruption
strategies (strategies 1 and 2). Ineffective strategy 1 occurs when anticorruption measures are
adequate but the political leadership's commitment is weak, thus resulting in the non enforcement of
anticorruption measures. This lack of political will can be seen in the ineffective anticorruption
strategies adopted in many of the South-East Asian economies. The third cell of ineffective strategy 2
is possible but unlikely in reality, as political leaders who are strongly committed to eradicating
corruption will probably improve inadequate anticorruption measures, instead of being satisfied with
inadequate anticorruption measures (Quah, 1982).
Other Important Factors Influencing FDI Inflow
In addition to our variable of interest, i.e. level of corruption, the following important factors are
also influencing FDI inflow and treated as control variables.
(i) GDP Growth in a sustainable way in a given country is an indicative of a vibrant
economy and hence a government that has generated impressive economic growth in
the past is likely to attract more foreign investors to its country. Because, in countries
with stable governments past policies are most useful in predicting the future.
Moreover, the market size hypothesis argue that inward FDI is a function of the size of
the host country market, usually measured either by its GDP or population growth.
While a large market size generates scale economies, a growing market improves the
prospects of market potential and thereby attracts FDI flows (Bhattacharya et al 1996,
Chen and Khan 1997, Mbekeani 1997). We use Log of GDP and growth rate of GDP
to capture the impact of this variable on FDI and expect this to have a positive impact
on inward FDI. The positive impact of GDP growth on FDI inflow was also previously
justified by Wheeler and Mody (1992), and Zhang and Markusen (1999). Thus, a
significant and positive relationship is expected between performance in GDP growth
and FDI inflow.
ii. Human capital captures both education and health. The stock of educated labor in turn
was proxied by the level of secondary school educational attainment. In line with this,
the health dimension of human capital is proxied by life expectancy in a given
country. Good health is an input to a healthy workforce necessary for economic
growth as well as an intrinsic measure of human development. States that fail to
ensure adequate health for their citizens are less likely to grow. Accordingly, the
differences in the level of countries' human capital lead to differences in their capacity
to attract FDI. In other words, enhanced human capital increases incoming FDI by
making the investment climate attractive for foreign investors. Thus, both education
and health (life expectancy) variables are assumed to be positively affecting FDI
iii. Infrastructure:-Cross-country studies by Canning and Bennathan (2000) indicates that
infrastructure; particularly telecommunications infrastructure significantly increases
economic growth. Likewise, Wheeler and Mody (1992) proves that good
infrastructure is a necessary condition for foreign investors to operate successfully.
Thus, infrastructure is expected to directly contribute to FDI inflow.
iv. Population size has two major implications in this study. First, it can be used as a good
proxy for market size in a sense that the more the country has large domestic market
size the more FDI inflow in that country. Because, foreign firms primarily target the
domestic market, and then towards regional and international markets for their
exports. Second, if a country has large population, it is expected to provide abundant
and relatively cheap labor for MNCs that would decrease their production costs,
compared to the country of origin. In both cases, therefore, population size matters
for FDI inflow.
v. Domestic Interest rate
The impact of cost of capital (i.e. lending interest rates) on FDI inflows is found to be
ambiguous in nature and statistically insignificant by many studies. On one hand, it
can be argued that higher lending rates may have a positive impact on FDI inflows,
i.e., higher the cost of capital in the host country the more capital is brought in by the
foreign firms. Alternatively, it can be argued that host country’s cost of capital impacts
directly on domestic consumption. Thus the lower the interest rates, the higher the
domestic consumption and hence higher the FDI inflows (Bende Nende, et al, 2000).
We do not hypothesis any particular relationship between the two.
Objective of the study
The effect of corruption on FDI Inflow is still debatable such that on one hand, some empirical
studies provide support for the idea that corruption in the host country may hinder FDI by
weakening investors’ confidence in the market systems and on political institutions. On the other
hand, there are still some scholars arguing that corruption can have a positive impact on
investment by facilitating transactions in countries and reported a positive relationship between
corruption and FDI. Therefore, the objective of this study is to verify the extent to which
corruption will have an effect on FDI under the context of Asian economies. As a result, this
study will contribute to create greater awareness and fresh insights into the problem and to
suggest concrete ideas and approaches on possible measures to combat it.
The Data and Stylized Facts
This study has made an intensive empirical analysis for a panel of 16 Asian economies from
1995 to 2009. The year 1995 was chosen as our starting point because it is a year for which we
have full, annual data coverage on the “freedom from corruption (FFC)” index which is derived
from the annual reports of the Index of Economic Freedom which has been published by the
Heritage Foundation, in partnership with the Wall Street Journal. The FFC index is measured
on a scale from 0 to 100, where 100 represent the highest level of freedom from corruption/or
the lowest level of corruption. However, the FFC index obtained from the Heritage Foundation
has been derived using statistics from organizations like the World Bank, the International
Monetary Fund and the Economist Intelligence Unit. List of countries included in this study as
shown in table 2 below. These countries are selected mainly because of the availability of data
throughout the years.
Table 2: Countries included in the study
Bangladesh, Cambodia, China, Hong Kong, India, Indonesia, Japan, Korea Republic, Malaysia,
Nepal, Pakistan, Philippines, Singapore, Thailand, Vietnam, and Sri Lanka.
Moreover, the independent variables, which were discussed in the preceding section and their
expected relationships with economic FDI inflow, are summarized in table 3 below.
Table 3:- Independent variables, their expected signs and data sources
Data Sources
Heritage Foundation
Economic Growth
WDI database
Population Size
WDI database
WDI database
WDI database
WDI database
Interest rate
WDI database
WDI database
The Level of Corruption and FDI Inflow in Asian Economies: Descriptive Statistics
From figure 1 below, it is possible to note that China is the major FDI destination economy in
Asia which receives an average FDI inflow of $60.6 billion every year. Meanwhile, Hong Kong,
Singapore, and India followed China at a distance with an FDI inflow performance of $30.1
billion, $15.85 billion, and $11.1 billion, respectively. Major economies in Asia such as Japan
and Korea republic have managed to attract only $ 7.5 billion and $ 5.3 billion FDI inflow,
respectively. At the opposite extreme, Nepal, Cambodia, and Bangladesh achieved the lowest
FDI inflow in the region with a record of $0.01 billion, $0.32 billion, and $0.53 billion,
respectively. Ironically, countries such as Philippines and Pakistan with a high level of trained
human capital and significant natural resources couldn’t manage to attract significant amount of
FDI. The reasons why economies like China, Hong Kong and Singapore achieved spectacular
performance in FDI inflow while others achieved a modest and still others a low level of FDI
inflow will be thoroughly investigated in the empirical analysis part of this study.
Figure 1: Average FDI Inflow into Asian Economies (1995-2009)
Figure 2 below explains to what extent countries are free from corruption using an index which
captures freedom from corruption (FFC) and measured on a scale from 0 to 100, where 100
represent the highest level of freedom from corruption/or the lowest level of corruption; while 0
represent the highest level of corruption. Accordingly, we can find Singapore, Honk Kong, and
Japan as the top three countries which enhanced a better and relatively corruption-free
economy’s. Particularly, Singapore and Hong Kong with an FFC index of 90 and 80 respectively
are very good examples of how economies that promotes low level of corruption are also able to
achieve high level of FDI inflow. In line with this, the level of corruption in some of the
economies such as Japan, Malaysia, and Korea are modest and they exactly achieved a modest
level of FDI inflow as it has been displayed in figure 1. Of course, there are some exceptions like
China and India with a high level of corruption but among the top FDI recipients in Asia.
However, those special cases do not change any of the assumptions that the more the country is
free from corruption the more the ability of the country to build confidence among foreign
investors and to be a major destination for FDI inflow. However, it can be argued that though
China and India are performing very well in attracting remarkable FDI inflow into their
respective economies, they are still performing well below-potential mainly because of the deeprooted corruption associated with their economies. As Vittal (2001) notes if China manages to
reduce red tape and corruption and enhance better rule of law and property protection, it
could potentially double its FDI. Similarly, he argues that if corruption levels in India come
down to those of Scandinavian countries, the GDP growth rate would increase by 1.5% and FDI
will grow by 12%. It is clear from figure 2 again that those countries with a high level of
corruption including Nepal, Bangladesh, Cambodia, Sri Lanka and the Philippines are also
experiencing a very low level of FDI inflow.
Figure 2: Average Index for ‘Freedom from Corruption (FFC)’ in Asian Economies (19952009)
Generally, figure 1 and 2 provide preliminary but factual observations to note the direct
association between level of corruption and FDI inflow in a given economy. However, this direct
relationship between corruption and FDI inflow and whether the theory matches with the reality
will be confirmed only by doing the appropriate econometrics analysis which is the main task of
the following section.
The Correlation between FDI Inflow and the Independent Variables
Before we move to the regression analysis, it is worth to examine the correlations that exist
between FDI Inflow and the independent variables. In other words, the correlation coefficients of
each variable determine the nature and strength of the relation between each factor including the
level of corruption and FDI inflow. Accordingly, correlation analysis not only helps to clarify
relations among variables but also often suggests directions for experimental research such as
regression analysis. From table 4, it has become evident that the more the economy is free from
corruption (having higher freedom from corruption index), the more the economy is able to
attract inward FDI.
Similarly, the correlation analysis also reveal that economic growth, human capital,
physical infrastructure, interest rates and openness have been directly and significantly
associated with FDI inflow in Asian economies. However, there was no evidence found for the
population variable to be correlated with FDI inflow.
Table 4: Partial Correlation of FDI Inflow with other Independent Variables
Freedom from Corruption (FFC)
Economic Growth
Population Growth
Education (secondary school enrollment ratio) 0.2878
Health (life expectancy)
Infrastructure (telephone /100 people)
Interest rate
Research Methodology
Given the panel structure of the data in this study, the model to investigate the effects of
corruption on FDI inflow was constructed for a balanced panel data of 16 Asian economies from
1995 to 2009 as follows:
FDIit = β0 + β1 FFCit + βj
Zit +αi + δt + εit
Where, index i refers to the unit of observation, t refers to the time period, FDI refers the ratio of
FDI to GDP, FFC refers freedom from corruption index, Z refers other control variables, αi refers
individual specific unobserved factors, δt refers time specific unobserved factors, and εit are
individual and time specific residuals. Since unobserved heterogeneity is the main problem of
un-experimental research, panel data estimation techniques can allow us to control for individual
unobserved heterogeneity. Furthermore, panel data give more informative data, more variability,
less collinearity among variables, more degrees of freedom and more efficiency (Gujarati, 2003).
In order to choose the most appropriate panel data estimation methods, first, the Hausman (1978)
specification test provides information about the appropriateness of the RE model versus FE
model, and the test confirms the suitability of random effect model instead of fixed effect model
by accepting the null hypothesis that individual specific unobserved effects are distributed
independently of the variables of interest Furthermore, a “Lagrange Multiplier (LM) test for
Random Effects” was performed and the result has led to choose the random-effect model
(REMO against the pooled OLS model . Thus, the Random Effect Model can be denoted as:
FDIit = β0 + β1FFCit + βj
Zit + δt + uit , where uit = αi + εit
In line with this, a White’s general test for heteroscedasticity was conducted and the result
rejected the null hypothesis of homoscedasticity. Similarly, Wooldridge’s tests for autocorrelation
in panel data were conducted and the null hypothesis that there is no first order autocorrelation
was not rejected. This implies that heteroscedasticity, but no serial correlation detected.
Furthermore, panel data unit root test was conducted using Levin-Lin-Chu test for FDI and the
result confirms the data is stationary.
According to Wooldridge (2002), if heteroscedasticity is detected but serial correlation is
not, then the usual heteroscedasticity-robust standard errors and test statistics can be used using
the appropriate estimation techniques, and in this case the random effect model (REM). In order
to verify the consistency of the results from REM, this study also used other appropriate panel
data analysis methods such as Feasible General Least Square Method (FGLS) and Regression
with Panels Corrected Heteroskedastic Standard Errors (PCSE) for the reason that
heteroskedastic models are usually fitted with feasible generalized least squares (EGLS or
heteroskedasticity and contemporaneous correlation of observations between the panels.
Accordingly, the main empirical results using the above mentioned panel estimation methods are
shown in table 5.
Regression Results and Main Findings
The correlation analysis in the preceding section only tells us whether or not individual
attributing factors are associated with “FDI inflow” without identifying the particular factors that
significantly affect FDI Inflow. Thus, this task can be accomplished using the appropriate model
and estimation techniques and in this case using REM, FGLS, and PCSE.
Accordingly, the empirical evidence from this study (table 5) has been found to be
consistent with the theory and the assumptions which were hypothesized at the outset. More
specifically, the empirical evidences based on the three panel estimation methods reveal that our
variable of interest, freedom from corruption (FFC) is statistically significant at 1 % level and
this provides a wake-up call for policymakers so as to give highest priority to curb corruption as
one of the main preconditions to create conducive atmosphere for attracting inward FDI into
their economies. For instance, based on the REM, it can be noted from table 5 that keeping other
factors constant, a 1 percent increase in the FFC index in an economy may raise FDI inflow by
3.5 percentage points. This is equivalent to say that if a country is able to decrease the level of
corruption by 1 percent, it may trigger inward FDI into the economy with an increase of 3.5
percentage points. Similarly, the empirical results derived from using FGLS and PCSE
estimation methods also verify that keeping other factors constant, a 1 percent improvement in
the FFC index may increase FDI inflow by 2.8 and 2.9 percentage points, respectively. This
disproves some scholars’ argument that corruption doesn’t keep FDI out of those corrupt
countries is flawed and in fact it is reasonable to claim that some of the countries such as China
and India that are characterized by high level of corruption but also with a remarkable FDI
inflow could even double their inward FDI if they manage to reduce the present pervasive level
of corruption in their respective countries. Therefore, the main implication of these findings is
that there is a crucial need for curbing the current level of deep-rooted corruption in many of
Asian economies by enhancing good governance and better economic institutions including
strengthening the effectiveness and predictability of the judiciary, enforceable contracts and the
rule of law, drying up the root causes of corruption and rent seeking, and developing an
environment where fair and predictable rules form the basis for social and economic interactions.
The regression results using the three estimation methods also confirm sustainable economic
growth in a country is one of the main positive attributing factors to promote inward FDI,
implying that an impressive growth record in the past may provide better confidence for foreign
investors to invest their capital in countries with better economic growth record. Because, in
countries with stable governments past policies are most useful in predicting the future. This
phenomenon has been observed in many countries such as China and India.
On the other hand, no evidence has been found for the population variable to affect FDI
inflow in Asian economies. This implies that MNCs are less bothered about the population
size/growth of the country, instead what it matters is the purchasing power of the population in
the case of domestic market size. In line with this, MNCs are much concerned not on the
population size as such, but they are more interested in the size of skilled and semi-skilled labor
force. This study verified that the two components of human capital: education which is proxied
by secondary school enrollment ration and health which is proxied by life expectancy are both
found to be to be a positive and significant determinants of FDI inflow as shown in table 5. It is
not surprising that one of the reasons for a relatively high movements of FDI into Asian
economies is because of the quality of skilled and semi-skilled labor that are abundantly
available in most of the countries. The strong statistical significance effect of education on FDI
inflow was confirmed using all the three panel estimation techniques employed in this study.
Likewise, the proposition that life expectancy affects FDI inflow was revealed from at least using
the FGLS and PCSE models. This verifies again a healthy workforce is one of the necessary
pull-factors for multinationals to be interested in investing their capital in host economies. To
sum up, countries that fail to ensure adequate education and health for their citizens are less
likely to attract significant FDI into their economies.
By the same token, infrastructure was found to be significantly influencing inward FDI in
Asian economies. This is in line with the findings of other previous researchers such as Wheeler
and Mody (1992), and Canning and Bennathan (2000) that infrastructure; particularly
telecommunications infrastructure is a necessary condition for foreign investors to operate
successfully. Another important variable revealed to be a significant determinant of FDI inflow is
the degree of openness of the economy. From table 5, it was evident that a 1 percent increase in
openness may raise inward FDI by 4.4, 3.7, and 4.3 percentage points, respectively. This is
because of the more the economy is open, the more substantial flows of intermediate inputs and
goods in and out of the host country which are highly required by the MNCs.
However, except using the PCRE estimation method, the evidence from REM and FGLS
found no evidence for domestic interest rate to influence the rate of FDI inflow in Asian
economies. This is perhaps because of on one hand; the higher the cost of capital in the host
country the more capital is brought in by the foreign firms. On the other hand, the lower the
interest rates, the higher the domestic consumption and hence higher the FDI inflows.
Table 5: The Effect of corruption and other control variables on FDI Inflow
(Coefficient/ Corrected Standard Error)
FDI Inflow
Freedom from Corruption (FFC)
(secondary school enrollment ratio)
Health (life expectancy)
(telephone /100 people)
Interest rate
Number of Observations
Number of Groups
Observation per group
Wald chi 2 (8)
Prob > chi 2
Economic Growth
Population Growth
On one hand, various empirical studies provide support for the idea that corruption in the host
country may hinder FDI inflow by increasing economic uncertainty, and thereby weakening
investors’ confidence in the market systems and on political institutions. On the other hand, some
scholars argue that corruption can have a positive impact on investment by facilitating
transactions in countries and reported a positive relationship between corruption and FDI. The
empirical evidence in this study generally confirms that corruption remains a significant problem
for inward FDI in Asian economies. This is equivalent to say that if a country is able to decrease
the level of corruption by 1 percent, it may trigger inward FDI into the economy with an increase
of 3.5 percentage points. Thus, some scholars’ argument that corruption doesn’t keep FDI out of
those corrupt countries is either flawed or invalid. In fact it is reasonable to claim that some of
the countries such as China and India that are characterized by high level of corruption but also
with a remarkable FDI inflow could even double their inward FDI if they manage to reduce the
present pervasive level of corruption in their respective countries. The level of corruption in
Asian economies and its main causes vary from one country to the next. The main contributing
factors for corruption in any country includes policies, programs and activities that are poorly
conceived and managed, failing institutions, poverty, income disparities, and inadequate civil
servants’ remuneration, lack of accountability and lack of transparency. Ultimately, all parts of
society must share the responsibility for containing corruption because all are willing or
unwilling participants. Each corrupt transaction requires a “buyer” and a “seller.” The
government is responsible for dealing with civil servants who engage in extortion and bribery but
it is businesses and individuals who offer bribes to civil servants to obtain certain advantages.
Thus, on one hand, governments need to introduce appropriate legislation to reduce corruption
and provide whatever means are necessary to ensure that appropriate steps are taken to build
systems of integrity and rule of law. On the other hand, educating and involving the public in
building integrity is the key to preventing corruption and thereby the key challenge and the
keystone of this holistic integrated strategy.
By the same token, the Singapore and Hong Kong’s experience demonstrate that whilst
corruption cannot be eradicated overnight, governments should have the obligation to take the
appropriate measures and at least to minimize the various forms of corruption through
strengthening effective economic and political institutions and good governance infrastructure.
To institute accountability and transparency in government and at least to minimize corruption,
there is a strong need for a combination of political will from the top and public pressure from
the base. According to Quah (1995), both Singapore and Hong Kong (China) have
institutionalized adequate anticorruption measures (Prevention of Corruption Act and the Corrupt
Practices Investigation Bureau in Singapore, and the Prevention of Bribery Ordinance and the
Independent Commission against Corruption in Hong Kong); both are blessed with political
leaders who are determined to remove the problem of corruption in their countries. Hence, it is
possible to minimize corruption if there is strong political will.
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