Download A CAUSALITY BETWEEN CAPITAL FLIGHT AND ECONOMIC

A CAUSALITY BETWEEN CAPITAL FLIGHT AND ECONOMIC GROWTH:
A CASE STUDY INDONESIA
Setyo Tri Wahyudi
Department of Economics-Brawijaya University INDONESIA
[email protected]; [email protected]
Ghozali Maski
Department of Economics-Brawijaya University INDONESIA
[email protected]
Abstract
Foreign capital is a source of financing for investment and consumption, as well
as to strengthen the country's foreign exchange reserves. The phenomenon
indicates that in Indonesia the flow of foreign capital in the form of Foreign
Direct Investment (FDI) was dominated the composition of foreign capital since
1990, which was largely dominated by the flow of portfolio capital in the form of
bonds, stocks, equity, and other short term instruments compared with the flow
of capital in form of FDI itself. These conditions have indicated the presence of
capital flight in Indonesia.
The purpose of the study is to describe and identify the causal relationship
between capital flight and Indonesia's economic growth over the period 20002009. Although the results of study was indicated that economic growth in
Indonesia moves towards positive economic growth rate is quite high, but on the
other hand, Indonesia has problems enough attention, which is still a high level
of capital flight out of the country. Furthermore, the causality test results show
that the capital flight have impact on economic growth and not vice versa.
Keywords: Capital flight, Economic growth.
JEL classification: F32, F43
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BACKGROUND
The economic crisis that was hit southeast Asia in 1997-1998 has brought a
significant influence especially on the economic structure in the region. For Indonesia,
the impact of the economic crisis has resulted in fundamental changes to the economy.
The impact can be seen from several aspects such as the fall of the Rupiah up to a level
of Rp.17.000 per US dollar, high inflation, the number of banks that had to be closed
down, many workers is "laid off", and various other problems. This suggests that the
economic structure of Indonesia is still very vulnerable to protect changes in external
factors.
Recently, although the current macroeconomic conditions of Indonesia had
steadily improved, but the shadow of economic crisis that had devastated the economic
structure still continue to haunt. Moreover, the facts have shown that the process of
economic recovery in post-crisis in Indonesia as very slow compared to the neighboring
country which was also experiencing a similar crisis, such as Malaysia and Thailand.
Therefore, it is not surprising if confidence of the investor to invest in Indonesia is still
very low, even be said that Indonesia is no longer a country that is safe as an investment
destination. Further, the survey regarding investment climate of Indonesia that has been
done by World Bank and International Financial Corporation was shown that the
position of Indonesia is the lowest when compared with other countries in Southeast
Asia. In other hand, although the survey from Doing Business 2010 which show that
Indonesia has raised the position of the order of 129 to 122, but the position is still a
long way when compared with neighboring countries like Singapore which are able to
maintain a great image, perched in the first rank. Meanwhile, Thailand is ranked at
position 12, Malaysia in position 23, Vietnam ranked 93, and Brunei Darussalam
ordered at 96, while, Indonesia's position just ahead of the Philippines which are in the
order of 144 and Laos in position 167. The results of this ranking indicated that, the
ease of investing in Indonesia is very low; further, this also means that Indonesia is not
an attractive investment destination in Southeast Asia.
The implementation of free foreign exchange system that began in 1967 was
support the Indonesia's financial system to be integrated with world financial system.
As a result is more increasingly open the flow of foreign capital in term of free exit and
entry. If the capital outflows very high, the phenomenon indicates that there has been a
capital flight. Generally, the phenomenon of foreign capital flight is usually indicated by
the type of short-term investments such as investment in portfolio. Investments in
portfolio could be affecting the domestic financial market with transaction forms such
as equity and cash securities. For developing countries like Indonesia, the flow of
foreign capital is important as the source of financing for investment and also
consumption, further to strengthen the country's foreign exchange reserves. The
purpose of this study was to (1) describe the capital flight in Indonesia during the period
2000-2009, and (2) identify the causality between capital flight and economic growth.
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Concept of Capital Flight
The main problem when describing the capital flight is that there is no clear
consensus on the definition of the phenomenon. Although, it was agreed that the
phenomenon is a response to both economic and political uncertainty, but based on the
literatures, no further consensus regarding this concepts. Several studies have tried to
define and declare that the capital flight with capital outflows, while others argue that it
is only a part of all outflows. Therefore there are two categories of definitions of capital
flight: 1) those who distinguish between capital flight and 2) those who are no
distinguished between capital flight. Schneider (2001) gives two main characteristics of
the concept of capital flight are:
Capital flight - A response to the handling of domestic capital
Schneider (2001) argues that capital movements can occur in response to a perceived
change and uncertainty are not always captured by portfolio theory, as summarized as
follows: Capital flight is part of the deployment of international asset or portfolio
adjustments in response to an unusual decline in the perceived risk/reward profile
associated with the assets located in certain countries, faced with the conflict between
asset holders and the government. Two-way capital flows due to different effects
experienced by domestic and foreign investors, arising due to several factors such as
information asymmetry, risk, return, and the impact of political risk.
Capital Flight - An illegal transaction
Capital flight is often defined as an illegal transaction that occurs when traders get
foreign capital by way of falsified trade documents. Capital flight can be done in a way
that intentionally makes transactions without invoice exports and imports.
Ways thus is easily detected by comparing the trading partner country statistics. Capital
flight is defined as occur only when the foreign exchange traders illegally transfer funds
out of the country in hopes to avoid the domestic market. A serious drawback of the
definition is that for the calculation of the transfer mechanism may include income that
is stored outside the country to avoid quotas and tariffs, as well as income from criminal
activity and smuggling that does not have to be included in the concept. However, the
concept of capital flight which assumes that this is an illegal transaction is a good
indicator for the effort to prevent such activities. The question that arises is whether we
should limit the occurrence of capital flight is only for illegal transactions? Does that
not mean that the use of the concept, we will underestimate the actual capital flight? In
fact, there is evidence that the illegal flight of capital as a transaction should be
considered as part of the total capital flight and should be included in the calculation of
capital flight despite using different definitions.
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Causality between Capital Flight and Economic Growth
A wide range literature regarding the causality between capital flight and
economic growth in around the world. This section will review the relevant empirical
studies linking capital flight and economic growth. Li and Liu (2005), on the other
hand, uses the panel data of 84 countries to investigate the influence of FDI on growth.
The study found a significant relationship between FDI and economic growth.
Additionally, a stronger relationship was extracted when FDI interacted with human
capital. This is because stronger human capital poses better absorptive capacities due to
the complementary nature of the FDI and the human capital, most importantly for the
developing countries. In contrast, Akinlo (2004) investigated the impact of FDI on
economic growth in Nigeria using the ECM showed an insignificant negative influence
of FDI on growth. The author further argued that extractive FDI might not extract
significant impact on growth compared to the FDI in manufacturing sector.
Additionally, FDI may influence growth negatively once there is an evidence of the
foreign investors transferring profits, or other investment gains to their home country.
Kadochnikov (2005) analyzes the determinants and effects of capital flight on
the Russian economy by using an institutional approach that is rarely raised in the study.
The New Institutional Economics approach as basis for analyzing the impact of capital
flight. To support his analysis, he used a modification of non-Granger Causality test to
determine whether capital flight dynamics have a causal effect on interest rates, and vice
versa. The study concluded that the handling of capital flight in Russia do not require a
strict policy of capital, which in fact it will worsen the condition, because of some
policy tightening will not increase investment opportunities, reduce the quality of
project financing, the accumulation of bad debt, and may be cause crisis. Thus, the
policy of restrictions on capital flight in the case is not part of the pro-growth policies.
Instead, it takes only an increase in institutional functions where it can improve the
investment process and encourage investment activity, the impact of capital flight would
likely decline.
Ayadi (2008) investigates the linear determinants of capital flight in Nigeria
utilizing the ordinary least squares (OLS) and the error correction method (ECM). The
study found that the validity of the portfolio theory which postulates how risk-averse
investors can build portfolios in order to optimize or maximize expected returns given a
level of market risk. Further, the study confirmed that capital flight is caused by the
interest rates deferential both in the short and in the long run. In addition, Ayadi found
that exchange rate depreciation significantly increases capital flight in Nigeria. Output
growth which measures the domestic opportunity cost of flight in Nigeria is negative
and significant in the short-run indicating that non performance of domestic resources
can trigger capital flight.
Recent study by Ogundipe and Aworinde (2011) explored the causality between
Foreign Direct Investment and economic growth in Nigeria using Granger causality.
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The study, using annual data covering the period between 1970-1985, 1986-2007 and
1970-2007, showed causality relationship from economic growth (GDP) to FDI in the
prederegulation era, which implies that there is causality relationship from economic
growth to FDI. In the post-deregulation era there is no casual relationship between
GDP and FDI. However, in the whole period 1970-2007 economic growth (GDP) is the
cause of FDI in the pre-deregulation era, which implies that there is causality
relationship from economic growth to FDI. In other words, there is a one-way
relationship between FDI and economic growth. Other noteworthy studies examining
the influences of FDI employs the Granger causality test (Knoldy, 1995; Nair –Reichert
and Weinhold, 2001) but the results vary according to country, method used and time
frame under study.
METHODOLOGY
Data and Sample
The data used in the study is secondary quarterly time series from the first
quarter 2000 to third quarter 2009. The data collected from International Financial
Statistics, World Development Indicators, financial statistical data issued by Bank
Indonesia and the Center of Statistical Office (BPS).
Model Specifications
In this study Granger causality test will be used in order to test the hypotheses
regarding the presence and the direction of causality between Capital Flight and
Economic Growth. The models suggested for this test are as follows:
..........................................(1)
.......................................(2)
The methods and procedures testing is Causality Test Model. The test procedure
is as follows:
(1) Stationarity Test
Stationarity test used to see whether the observed data are stationary or not.
Although it was just a natural test of Granger causality test, but if the results show that
the observed data is stationary, this will improve the accuracy of the analysis of Granger
causality. As a consequence of the use of time series data, the stationarity test will give
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a profit, this is because the data analysis has been to eliminate the variables are nonstationary in the model. This means that the outcome would avoid biased estimates of
standard error. If the estimate was biased, it could lead to the conventional criteria used
to justify the causality between two variables becomes invalid. This means that
estimation using a variable that has the data non-stationary (unit root) can result in
incorrect conclusions because of the regression coefficient estimator is inefficient.
In order to apply Granger causality test, the series that belong to variables should
be stationary. Therefore; it is necessary to make test for unit roots to examine whether
the series for these two variables are stationary or not. Macroeconomic time series are
usually not stationary. Such series are made stationary by calculating logarithms or
taking first or second differences. There are many tests used to determine stationary. In
this study, the stationary of the variables will be tested by using Augmented DickeyFuller unit root test. Technically, procedures in the ADF test is based on MacKinnon
critical values instead of t-test, the t-ratio is compared with critical value of t-statistics
in ADF table in order to determine the presence or absence of unit roots. If the
hypothesis is accepted, the variable was not stationary, and is necessary to test the
degree of integration. Test the degree of integration is intended to look at the degree or
order difference to how the observed data be stationary.
(2) Granger Causality Test
The direction of causality determines the direction of the relationship among
variables and Granger causality test has three different directions for these purposes:
a) One way causality: In a single equation model, Y is the dependent variable and X
independent. Here, there is a causality relationship from X towards Y Independent
variable is the cause and causes a one-way effect on dependent variable, which shows
the presence of one-way causality and the relationship is determined as Y on X
b) Two-way causality: There can be a reciprocal effect between variables.
c) Lack of Causality: There is no relationship among variables, therefore no causality.
To find out the possible existence of various forms of causality as mentioned in
equations (1) and (2), the F-test performed for each regression model. Null hypothesis
is:
Test F-test using the formula:
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where: SSRr = Sum of squared residuals for the restricted equation; SSRu = Sum of
squared residuals for the unrestricted equation; w = number of regressors in the equation
n = Number of observations; k = number of regressors in the equation. Based on the
Granger causality test
model, the hypotheses to be tested are:
H0: Economic growth does not affect the capital flight
Hi: Economic growth affects capital flight
H0: Capital flight does not affect economic growth
Hi: Capital flight affecting economic growth
Here, H0 hypothesis are tested by comparing the value obtained in this test with the
values calculated by Dickey-Fuller. Null Hypothesis shows that series is not stationary
and has a unit root (Ho: γ=0), and alternative hypothesis shows that series is stationary.
If the absolute value of calculated statistics is higher than the absolute value of critical
values, we cannot reject the hypothesis which shows that series is stationary. However,
if this value is lower than critical value, time series is not stationary (Gujarati, 2004).
RESULTS AND ANALYSIS
Overview of Indonesia’s Economic Growth
An indicator of a country’s economic growth is represented by the process of the
production capacity of an economy that embodied in the form of increased national
income. The development of economic growth in Indonesia during the period 20072009 at current prices (ADHB) and at constant prices 2000 (ADHK) are shown in Table
1. In 2009, Indonesia's economy was grown by 4.5 percent compared to 2008. The
value of GDP at constant prices (ADHK) in 2009 reached Rp2,177 billion, while in
2008 and 2007 is Rp2,082 trillion and Rp.1,964 trillion, respectively. When viewed by
current prices (ADHB), GDP in 2009 rose by Rp662 billion, from Rp4,951.4 trillion in
2008 amounted to Rp5,613.4 trillion in 2009.
During 2009, all economic sectors experiencing growth. The highest growth
occurred in transport and communications sector which was reached 15.5 percent,
followed by sector electricity, gas and clean water (13.8 percent), construction sector
(7.1 percent), services sector (6.4 percent), the financial, real estate, and company
services (5.0 percent), mining and quarrying (4.4 percent), agricultural (4.1 percent, and
trade, hotel and restaurant (1.1 percent). GDP growth in oil and gas in 2009 reached 4.9
percent.
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Table 1: Indonesia’s GDP, 2007-2009
No
Sectors
1
Agriculture
2
Mining
3
Manufacture
Electrical, Gas, and
Clean Water
Construction
4
5
6
7
8
Trade, Hotel, and
Restaurant
Communications
and
Transportations
Financial, Real
Estate, and
Company services
Services
9
GDP
GDP Non-Migas
Current Prices
(Billion Rupiah)
2007
2008
2009
531.9
716.1
858.3
Constant Prices 2000
(Billion Rupiah)
2007
2008
2009
271.5
284.6
296.4
440.6
540.6
591.5
171.3
172.4
180
1,068.7
1,380.7
1,480.9
538.1
557.8
569.5
34.7
40.9
46.8
13.5
15
17.1
305
419.6
555
121.8
131
140.2
592.3
691.5
750.6
340.4
363.8
367.9
264.3
312.2
352.4
142.3
165.9
191.7
305.2
368.1
404.1
183.7
198.8
208.8
398.2
3940.9
3,534.4
481.7
4951.4
4,427.2
573.8
5613.4
5,146.5
181.7
1964.3
1,821.8
193
2082.3
1,939.5
205.4
2177
2.035.1
Source: BPS, 2010
Indonesia's GDP by nine sectors of economic activities during 2007-2009 period
shows that the largest contributing sector is manufacturing, followed by agriculture and
services sectors. While the sector with smallest contribution is electricity, gas, and
water. The contribution of agriculture sector continued to decline, it demonstrates the
ongoing structural transformation in Indonesia (Table 2). Compared with 2007 and
2008, in 2009 there was an increase in some sectors except: Industry Sector, Trade
Sector, Hotel and Restaurant, Mining and Quarrying, and the Financial Sector, Real
Estate and Business Services. The Role of Agriculture Sector increased from 14.5
percent to 15.3 percent, services sector from 9.7 percent to 10.2 percent, construction
sector from 8.5 percent to 9.9 percent, while the Transport and Communications Sector
and the Sector Electricity, Gas and Water respectively provide the same role from 2008
that is equal to 6.3 percent and 0.8 percent. While the Manufacturing sector fell from
27.9 percent to 26.4 percent, Trade Sector, Hotel and Restaurant down from 14.0
percent to 13.4 percent, Mining and Quarrying sector decreased from 10.9 percent to
10.5 percent, and the Financial Sector, Real Estate and Business Services dropped from
7.4 percent to 7.2 percent. Furthermore, if viewed in total, the role of oil and gas GDP
rose from 89.4 percent in 2008 to 91.7 percent in 2009.
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Table 2: The Structure of Indonesia’s GDP (%)
No.
1
2
3
4
5
6
7
8
Sectors
Agriculture
Mining
Manufacture
Electrical, Gas, and Clean Water
Construction
Trade, Hotel, and Restaurant
Communications and Transportations
Financial, Real Estate, and Company
services
Services
2007
13.7
11.2
27
0.9
7.7
15
6.7
9
GDP
GDP Non-Migas
2008
14.5
10.9
27.9
0.8
8.5
14
6.3
2009
15.3
10.5
26.4
0.8
9.9
13.4
6.3
7.7
7.4
7.2
10.1
100
89.5
9.7
100
89.4
10.2
100
91.7
Source: BPS, 2010
Causality Test Results
This section discusses the results of causality testing between capital flight and
economic growth. Causality testing stages are starting with a unit root test that aims to
determine the stationarity of variables in the model. If the stationarity test results
concluded that the variables in the study were stationary at the same degree, and then
can be continue to Granger Causality test.
(1) The results of stationarity tests
Testing stationarity aims to test the stationarity of data due to the use of time
series data in the research. This must be done in order to avoid the problem of model
bias estimation or spurious model. In this study, the unit root test conducted by using
Augmented Dickey-Fuller test (ADF). The criteria is when the ADF test statistic is
smaller than the Mackinnon critical values, it is said that the variable are stationary.
Meanwhile, if the test results concluded that the data are not stationary, then the
differentiation procedure done, which is further analyzed to obtain data that are
stationary. The results of the testing unit roots using the ADF test are shown below:
Table 3: Stationarity Test Results
Variable
ADF statistics
Degree
CF
1.034664
Level
GDP
-1.502480
Level
Note: Mackinnon critical values are -4.420595 (1% level); -3.259808 (5% level); -2.771129 (10%
level).
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As the presented in Table 3, it is known that the ADF statistic for both variables
(CF and GDP) is smaller than the Mackinnon critical values then it can be concluded
that the variables stationary on the same degree of integration of 0 or the I(0). That is,
all variables used in this study at degree level stationary are significant.
(2) The results of Granger Causality Test
Granger causality test is used to look at the relationship between two variables
statistically, the capital flight and economic growth in Indonesia. Through this test can
be seen whether the two variables are unidirectional relationship, two-way (mutual
influence), or have absolutely no linkage (not affect each other). Granger causality test
results can be seen in Table 4 below:
Table 4: Granger Causality Test Results
Pairwise Granger Causality Tests
Sample: 2000 2009
Lags: 2
Null Hypothesis:
GDP does not Granger Cause CF
CF does not Granger Cause GDP
Obs
F-Statistic
Prob.
8
0.78936
0.5304
17.2931
0.0225
To find out the relations between the two variables, hypothesis testing is done as
follows:
a) H0: Economic growth (GDP) does not affect the capital flight (CF)
Hi: Economic growth (GDP) affects the capital flight (CF)
b) H0: Capital flight (CF) does not affect economic growth (GDP)
Hi: Capital flight (CF) affects economic growth (GDP)
If the probability of the hypothesis is smaller than the errors then both decided to
reject H0, so that interpretation is economic growth and capital flight interplay
(Causality). Conversely, if only one hypothesis H0 is rejected, then the relationship
between economic growth and capital flight is only a one-way causal relationship.
Based on the test results for both hypotheses, obtained the following results:
a) H0: Economic growth (GDP) does not affect the capital flight (CF)
Hi: Economic growth (GDP) affects the capital flight (CF)
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Granger test indicates the probability of F-statistic is 0.5304. Probability value is
greater than the tolerable error (α = 5%). Means to accept the null hypothesis (Ho),
namely economic growth (GDP) does not affect the flight of capital (CF).
b) H0: Capital flight (CF) does not affect economic growth (GDP)
Hi: Capital flight (CF) affects economic growth (GDP)
Granger test indicates the probability of F-statistic is 0.0225. Probability value is less
than the tolerable error (α = 5%). It means to reject the null hypothesis (Ho), the
capital flight (CF) effects on economic growth (GDP).
Thus, it can be concluded that the causal relationship between capital flight and
economic growth in Indonesia did not show a two-way relationship, but only one-way
relationship, which influence the direction of capital flight (CF) effects on economic
growth (GDP) and not vice versa.
CONCLUSION
The study has shown the causality between capital flight and economic growth
in Indonesia. During 2000 to 2009, Indonesia's macroeconomic show that Indonesia’s
economic growth moves towards positive rate which is quite high. On the other hand,
Indonesia is still high dependence on foreign capital loans as one of the engine to
promote economic growth.
The causality test concluded that capital flight have impact on economic growth
and not vice versa. It indicates that Indonesia is also experiencing problems enough
attention, which is still a high level of capital flight out of the country. Further,
Indonesia is still very vulnerable to external shocks, particularly from short-term foreign
loans. As a result, Indonesia's economic growth is interrupted.
Several suggestions and recommendations regarding the study are as follows: 1)
the need for appropriate policies that were taken and run the government of Indonesia to
prevent capital flight. Improvement of the investment and licensing procedures in
Indonesia should be reexamined, so that capital flight can be prevented so as not to
interfere with the process of economic growth. 2) Necessary efforts to promote
economic growth in Indonesia. Although Indonesia's economic growth performance
has been pretty good, but needs to be improved and maintained so that growth occurs
truly reflect the level of welfare of society as a whole. Several attempts to do to
improve economic growth is to maintain the stability of inflation and exchange rates,
increased domestic production and encourage export activities. And 3) reduce
dependence on foreign aid or financing. The higher foreign debt, the high economic
growth will never be enjoyed by the public, otherwise used for debt repayments and
interest from time to time due to the increasingly burdensome in the long run,
fluctuations in inflation rates, as well as the exchange rate is difficult to control.
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References
Akinlo, A.E., (2004). Foreign Direct Investment and Growth in Nigeria: An Empirical
Investigation. Journal of Policy Modelling, Vol 26, pp 627-639.
Ayadi, F.S. (2008). Econometrics analysis of capital flight in developing countries: a
study of Nigeria. Paper presented at 8th Global Conference on Business &
Economics. October, 18-19th, Italy.
Bank Indonesia. (2011). Indonesia’s Foreign Debts Statistics. Jakarta.
Gujarati, D. (2004). Basic Econometrics. McGraw-Hill Company: New York.
Kadochnikov, D.V. (2005). Economic Impact of Capital Flight from Russia and its
Institutional Context: Why Capital Controls cannot be a Part of a ProGrowth Policy. Working Paper, June.
Knoldy, S. (1995). Causality Between Foreign Direct Investment and Spillover
Efficiency. Applied Economics, 27(8), pp 745-749.
Li, X. and Liu, X., (2005). Foreign Direct Investment and Economic Growth: An
Increasingly Endogenous Relationship. World Development, vol 33(3), pp
393-407.
Nair-Reichert, U., and Weinhold, D. (2001). Causality Tests for Cross-country Panels:
A New Look at FDI and Economic Growth in Developing Countries.
Oxford Bulletin of Economic and Statistics, 363(2), pp 153-171.
Ogundipe, M.A., and Aworinde, O.B. (2011). An analysis of causality between
Economic Growth and Foreign Direct Investment in pre- and postderegulated Nigerian economy (1970-2007). European Journal of
Scientific Research, 53 (3), pp.317-325.
Schneider, B. (2001). Measuring Capital Flight: Estimates and Interpretations. London:
Overseas
Developement
Institute.
(www.odi.org.uk/publications/working_papers/wp194_maintext.pdf)
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