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International Review of Research in Emerging Markets and the Global Economy (IRREM)
An Online International Research Journal (ISSN: 2311-3200)
2016 Vol: 2 Issue: 2
Analysing the Effects of Financial Liberalization on
Zambia’s Economic Growth
Stephen Maimbika,
University of Namibia, Namibia.
E-mail: [email protected]
Johanna M. Mumangeni,
Department of Economics,
University of Namibia, Namibia.
E-mail: [email protected]
___________________________________________________________________________
Abstract
This paper investigated the effects of liberalization on economic growth from an interest rate
and capital formation perspective in Zambia from 1983 to 2013. The objective of the study is
to examine the impact of financial liberalisation on economic growth from an interest rate
and capital formation. Using time series data for the period 1983 to 2013, the paper
employed time series techniques, namely unit root test and cointegration. The order of
integration of the variables was tested and the variables were found to have the same order of
integration. The bounds test confirmed co-integration between GDP growth, real interest
rate and gross capital formation. The Autoregressive distributed lag model was used to
estimate the short and long run dynamics. The study used the coefficients form ARDL model
to calculate the long-run multipliers. It was seen that a 1 unit increase in the real interest
rate, which is being used as a proxy for financial liberalization leads to a 0.075824 percent
increase in growth. The empirical finding indicates that financial liberalization index and
economic growth are positively linked in the long run. On the other hand, financial
liberalization index and economic growth are inverse related and statistically insignificant in
the short run.
___________________________________________________________________________
Key Words: Financial Liberalization, Economic Growth, Interest rate, Capital, Augmented
Dickey-Fuller (ADF) Unit Root Test, Co-integration, Error Correction Mechanism (ECM),
Autoregressive- Distribution Lag Approach (ARDL), Zambia
JEL Classification: E43
840
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1. Introduction
The importance of the financial sector in developing countries and how it plays a vital
role in enhancing economic growth through resource mobilization and allocation has attracted
a lot of attention in the past two decades. Simatele (2004) argued that the perverse effects of
interest controls, overvalued exchange rates, controlled lending, and other controls have led to
a large volume of research relating to financial reform.
Financial liberalization or reform can be defined as a situation in which financial controls
on institutions and the economy as a whole are eliminated as was the case in Zambia in the
early part of 1991. The period prior to financial reform, Zambia’s financial sector was not
flexible and robust. The financial sector was riddled with controls implemented through direct
control over credit and interest rate. There were also substantial administrative controls in
place directing lending and international financial transactions (Simatele 2004).
The phase prior to Financial Reform was characterized by statutory interest ceilings,
where interest rate levels were set administratively by the prevailing government. The
Zambian government strived to keep the interest relatively low so as to encourage private
investment and boost capital accumulation. This view is supported by Ngugi and Kabubo
(1998) who argued that developing countries operated with low interest rates aimed at
increasing investment, improving the allocation of resources among sectors and keeping
financial costs down to avoid possible inflationary pressures. This approach towards
monetary policy was used since the inception of the Central bank in 1964 till calls for
financial reform were raised in 1989. The move towards financial liberalization came as a
result of private investment not increasing at the pace that was predicted. Hye and Wazarati
(2013) indicated that private sector investment could not increase as hoped because resources
were not used efficiently, due to governance problems and highly controlled financial system
by regulatory authority.
The endeavours to move to a more robust system in which monetary variables are
determined by market forces were motivated by the analysis of the sector as a whole by
financial liberalization proponents. They pointed out that financial development and
innovation that comes with financial liberalization can increase growth. Arguments were
raised on how the allocation of capital was more efficient in a competitive financial system
and that higher interest rates cause higher saving, thereby increasing the funds available to
finance investment in the economy. The liberalization case characterized the process of
macroeconomic adjustment in Zambia during the period 1989-1993. Launched in the wake of
the policy reversal in 1987 when Zambia unilaterally cancelled its International Monetary
Fund (IMF)-supported adjustment program, the 1989-1993 Economic Recovery Program
immediately faced difficulties. Domestic inflation rose sharply, from an average of 50% per
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annum in 1985-1990 to 170% per annum in 1990-1993 (Adam 1995). Financial liberalization
has multiple problems as well, in the sense that it induces excessive risk-taking, increases
macroeconomic volatility and may lead to a crisis post inception as was the case in Zambia in
the mid-1990s.
In the past decade, a lot of work has been done on how financial liberalization affects
economic growth and development through trade openness and capital inflow. Harvey and
Lumsdaine (2000) did some work on the benefits of financial liberalisation that come in the
form of increased foreign investment. Majority of literature available mainly focus on the
theoretical benefits that come as a result of liberalization ignoring the fact that it increases the
risk of speculative attacks and increases a country’s exposure to international shocks and
capital flight. The financial reforms carried out in several Latin American countries during the
1970s, aimed at ending financial repression, yet led to financial crises characterized by
widespread bankruptcies, massive government interventions, nationalization of private
institutions and low domestic saving (Diaz-Alejandro, 1985).
Though there are a multitude of papers surrounding the issue of financial reform, very
few work has been done on the adverse effects interest rate and capital formation have on the
overall aspect of growth and whether a robust financial system with a flexible interest rate is
better than a market comprised of a controlled interest rate and direct monetary instruments.
This paper will analyse how the flexible interest rate and the capital formation that come
as a result of liberalization affect growth. The objective of the study is to examine the impact
of financial liberalisation on economic growth in Zambia.
2. Literature Review
The idea of financial reform was brought about by Shaw and McKinnon (1973), many
economists and researchers alike have debated the dynamics of financial liberalization on
capital market and economic growth. This section encompasses the available theoretical and
empirical literature data on financial liberalization.
Shaw (1973) and McKinnon (1973) claimed that financial liberalization policies would
increase savings which consequently spurs investment and induce economic growth. They
argued that higher interest rates brought about by liberalization leads to a more efficient
allocation of resources, higher level of investment and economic growth. Their foundation
was based on the principle that interest rates have a positive relationship with economic
growth and that low interest rates would therefore impede growth.
McKinnon (1973) makes two basic assumptions. First that all economic units are selffinanced and secondly that investments have indivisibilities of considerable importance. The
implications of these assumptions are that an investor must accumulate money balances
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before he or she is able to invest. This accumulation is encouraged if there is a positive real
deposit interest rate.
A positive real interest rate lowers the opportunity cost of accumulating balances and
encourages individuals to deposit their money in the banks. This allows accumulation of
loanable funds from which investors can borrow. The indivisibilities of investment imply that
the demand for money is larger the larger the share of investment in total expenditures. In this
theory then, money and capital are complementary. It is often referred to as the
complementarity hypothesis. Without implying direction of causality, one can say increased
intermediation in this model leads to increased investment. The theory can be formalized as a
money demand function:
M
1
 f (Y , , d   e )
P
Y
Where Y is real GNP, I is gross investment, d, the nominal interest rate, 𝜋 𝑒 , expected
inflation (so that , 𝑑 − 𝜋 𝑒 is the real rate of interest), M is the stock of broad money and P is
the price level.
In the model, money has a first order impact on decisions to save and invest which are taken
to be one decision (because of the need to accumulate for investment).
The model can also be expressed as an investment function:
1
 f ( , d   e )
Y
Where 𝛾 is the average return to capital.
In Shaw's model, money is backed by productive investment loans to the private sector.
M2
When the money stock is large relative to the level of economic activity (say GDP ), the level
of intermediation between savers and investors is also larger. Thus this theory explicitly
emphasises the importance of financial intermediation. When a repressed economy reforms
its financial sector and removes controls, higher real rates of deposit increase intermediation
leading to financial development by providing incentives to savers. It also leads to a higher
average efficiency of investment by lowering the cost of borrowing through providing risk
diversification, accommodating liquidity preference, lowering informational costs and
increasing operational efficiency. Shaw's model is an inside money model and is often
referred to as the Debt Intermediation View (DIV). The money demand equation in the model
can be written as follows:
M
 f (Y ,  , d   e )
P
Where 𝛾 is a vector of opportunity costs in real terms of holding money.
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This theory relies on neo-classical market clearing assumptions. Specifically, the market
must work to equilibrate the demand and supply of loanable funds and the market must be
competitive. In this view then, when interest rates are kept artificially low, there will be low
incentives for saving hence little funds to lend, limiting investment and therefore the growth
of the economy. In this case, intermediation is repressed. The removal of controls in a
financially repressed economy therefore implies that the interest rates will increase resulting
in higher savings. These higher savings mean the availability of more investment capital and
hence an increase in output. The McKinnon-Shaw hypothesis is therefore the basic foundation
for financial liberalization.
According to Caprio, Atiyas and Hanson (1994), growth rates are regressed either on
measures of financial deepening, like ratio between widely defined money aggregates and
GDP or on real interest rates. Higher real interest rates are seen as a consequence of financial
liberalization.
Bekaert, Harvey and Lundblad (2001) have done extensive research on this area. In the
context of a dynamic panel of developed and developing countries they find that stock market
liberalization has had permanent impacts on growth. Wade (2001) warned that capital account
liberalization can be hazardous when the banks do not have exposure to international markets
and non-banks also borrow abroad. The hazards are even more horrendous when the
exchange rate is hanged and the financial sector is supported on bank borrowing instead of
equity finance.
Wyplosz (2002) finds financial liberalization destabilizing developing countries
economy more than the developed countries because the former tend to go through a boom
bust cycle. Singh et al. (2003) refuted the observation of the donors that the Asian crisis was
the result of imperfect structures of corporate governance and a poor competitive atmosphere
in the countries. He stated that the basic cause of the crisis was the precipitous capital account
liberalization. Henry (2004) argues that if a developing country opens its stock market to
foreign investors, aggregate dividend yield falls by 240 basis points, growth rate of output
increases by an average of 1.1 percentage points per year and growth rate of output per
worker rises by 2.3 percentage points per year.
Laeven (2000) constructed liberalization indexes for 13 developing countries since the
late 1980s and finds that the liberalization process in general has eased financial constraints
faced by large firms in these countries. Kasekende and Atingi-Ego (2003) examined the
impact of financial liberalization on the conduct of banking and its effect on real sector of
Uganda. They use quarterly data from 1987Q1 to 1995Q3 for the following variables GDP,
commercial bank credit to industrial sector, premium on official exchange rate, lending rate
and inflation rate, and they employed the Vector Autoregressive (VAR) method. The study
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found that financial liberalization has promote efficiency gains in the baking industry and
subsequently, the increased growth of credit to the private sector following financial
liberalization leads to economic growth.
Kaminsky and Schmuckler (2001) and Tornell, Westerman, and Martinez. (2004) carry
out similar studies, using panel data from emerging markets to show that liberalization results
in larger booms and crashes. Further work was done by Nair (2004) who suggested a
significant negative impact of financial liberalization index on the household saving.
Akpan (2004) conducted a study to analysis the effect of financial liberalization in the
form of an increase in real interest rates and financial deepening on the rate of economic
growth in Nigeria using the endogenous growth model. The study used time series annual
data over the period from 1970 – 2002. The study employed Error Correction model (ECM)
to capture both short and long run impact of the variables in the model. The results show a
low coefficient of the real deposit rate which implies that interest rate liberalization alone is
unlikely to accelerate economic growth. Overall, the results show a positive impact on the
economy of Nigeria.
Abu-Bader and Abu-Qarn (2005) examine the relationship between financial
development and economic growth in Egypt analysed time series annual data from 1960 to
2001 using VAR methodology on the following variables: GDP, ratio of money stock to
nominal GDP, ratio of bank credit to private sector to nominal GDP, ratio of credit issued to
non-financial private firms to total domestic credit. The study finds a rise in private
investment resulting from financial liberalization in 1990 which led to rebound in economic
performance of Egypt in the 1990s. Their result infers that there is a direct linkage between
financial development and financial liberalization. Mattoo, Rathindran, and Subramanian
(2006) found financial services liberalization having an affirmative and momentous effect on
economic growth in a sample of 59 countries.
Bashar and Khan (2007) evaluate the impact of liberalization on the economic growth in
Bangladesh, by analysing time-series data from 1974 to 2002, using co-integration and errorcorrection methods. The results suggest that long-run economic growth in Bangladesh is
largely explained by physical capital and real interest rates; while economic growth remains
unaffected by short-term changes in the labour force and the secondary enrolment ratio. They
conclude that the reason why financial liberalization has had a significant negative impact on
economic growth is that financial reforms have failed to attract new investment.
Kiyota, Peitsch, and Stern (2007) found the Ethiopian economy benefiting from the
opening of foreign banks and the related privatization of local banks. Khan and Qayyum
(2007) attribute long run growth in Pakistan to trade and financial liberalization. Ang and
Mckibbin (2007) reported financial liberalization having a positive effect in enhancing the
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development of the financial sector in Malaysia. Sulaiman, Oke and Azee (2012) assert the
effect of financial liberalization on economic growth of Nigeria. They employed Johansen
Co-integration test and Error Correction Mechanism (ECM). They found long-run
relationship among the variable. The study finds that financial liberalization has a growth
stimulating effect on Nigeria.
Based on the afore-mentioned literature, one can safely say the following: there are mixed
findings with regard to the relationship between financial liberalization and economic growth
ranging from those refuting and agreeing. There are also different methodological approaches
whether it is cross-country or individual country’s studies. There seem to be no study on
Zambia that has tested the effects of financial liberalization on Zambia economy. It is against
this background this study intends to fill the gap and add to empirical literature for Zambia.
3. Methodology
This section describes theoretical model, empirical model and the research design. The
research design reveals the type of data and the method of data collection
3.1 Econometric Framework and Model Specification
This paper follows Hye and Wazarati (2013) and specifies a modified model for real GDP
as the dependent variable with the following as explanatory variables, financial liberalization
index (FLI) and Gross fixed capital formation (K).
Yt   0  1 ( FLI )   2 (GCFt )   t
(1)
Where
Yt
is gross domestic product (GDP)
FLI is the financial liberalization index
GCFt is gross fixed capital formation
 t is the error term
A financial liberalization index has not yet been computed for Zambia. According to Fry
(2003) early empirical literature focused on the value of real interest as an indicator of
repression. The presumption was that countries with negative real interest rates were
financially repressed, while those with positive ones were liberalized. Therefore this study
will employ the model below using real interest rate as a proxy for the index.
Yt   0  1 RI t   2 GCFt   t
(2)
Prior expectations
RI > 0
K>0
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Where ( Yt ) is gross domestic product, (
RI t
) represents real interest rate and ( GCFt ) is gross
fixed capital formation at a certain period of time t; βo is the constant; and
 t is the stochastic
disturbance term.
3.2 Unit Root Test
The study used time series data and therefore, there was need to determine whether the
variables in question were stationary or non-stationary. A time series variable might be nonstationary i.e. its, mean, variance and covariance are not constant overtime. It is vital to test
for stationarity because as a regression analysis is made from a non-stationary data yield false
results (Granger and Newbold, 1974). The Augmented Dickey Fuller (ADF) test was used to
test for stationarity of the series.
3.2.1 Autoregressive- Distribution Lag Approach
To examine the relationship between focused variables, this study employed the
autoregressive distributed lag model (ARDL) suggested by Pesaran, Shin and Smith (2001),
for co-integration investigation data and error correction analysis. The ARDL has been
chosen since it can be applied for a small sample size.
Yt   0  1GCFt   2 RI t   t
(3)
This test method of co integration has certain econometric advantages in comparisons to other
co integration methods Dritsakis (2012). The autoregressive distributed lag (ARDL) model
has several advantages in comparison with other co-integration techniques. First, ARDL
model avoids endogeneity problems. Second, it estimates the long run and short run
parameters simultaneously. Third, pre-testing for unit roots is not required because the
methodology is appropriate whether the variables are I (0), I (1) or mutually integrated.
Lastly, all the variables are assumed to be endogenous.
Yt   0  1 K t   2 RI t   t
(4)
An ARDL representation of equation (2) can be formulated as follows:
n
n
n
i 1
i 1
i 1
Yt   0   1i Yt i    2i RI t i    3i GCFt i  1Yt 1   2 RI t 1   3GCFt 1   t (5)
Where: ∆ denotes the first difference operator, Β0 is the drift component, and ℯt is the usual
white noise residuals. The left hand side is the gross domestic product. The first until third
expression (𝛽1 − 𝛽3 ) on the right hand side correspond to the short-run dynamics of the
model. The remaining expression with the summation(𝛼1 − 𝛼3 ) represent the long- run
relationship of the model. The co integration testing procedure is based on the F-test.
According to Dritsakis (2012), the F-test is actually a test of the hypothesis of no co
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integration among the variables against the existence or presence of co integration among the
variable. In this case we can denote it as:
H0: 𝛽1= 𝛽2=𝛽3=0
H1: 𝛽1≠ 𝛽2≠ 𝛽3≠ 0
3.2.2 Error Correction Model
Co-integration tests have a short fall in analysing and establishing long-run relationships
because it is not applicable in cases of variables that are integrated of different orders i.e. I(1)
or I(0). According to Shittu, Yemitan and Yaya (2012), it is concerned with the analysis of
long run relationships between variable integrated of the same order and the speed of return to
equilibrium after a deviation is measured by the error correction model (ECM).
Equation (4) can be expressed in ECM as:
n
n
n
i 0
i 0
Yt   0  i  1 1Yt 1    2i GCFt i    3i RI t i  ECt 1   t
(6)
Where 𝜆 the speed of adjustment parameter and EC is is the residuals that are obtained from
the estimated co-integration model of equation (5).
Hence, by differencing and forming a linear combination of the non-stationary data, all
variables in an ARDL model are transformed equivalently into an ECM with stationary series
only (Shittu et al 2012).
3.3 Data and Data Sources
This study used annual time-series data covering the period 1983 to 2013. The variables
included are gross domestic product (GDP), gross capital formation (GCF) and real interest
rate (RI). The data is obtained from Zambia Central Statistics Office and Bank of Zambia
(BOZ).
4. Empirical Analysis and Results
4.1 Unit Root Test
This section discusses the empirical findings. This section examines the properties of the
time series data under review. Stationarity of the series is first tested using the Augmented
Dickey Fuller unit root test. Then the bounds testing procedure is used to test for cointegration amongst Gross Domestic Product (GDP), Gross Capital Formation (GCF) and
Real Interest Rate (RI). An ARDL model is used to estimate the short-run and long-run
parameters of the model. The computed F-statistics provided by Pesaran et al. (2001) for the
estimation of the ARDL model assumes that the variables are of I(0) or I(1). It is therefore
important to determine the order of integration of the variables to ensure that the order of
integration fall within this. To determine the order of integration for the time series, the
Augmented Dickey fuller test was used.
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Table 1: ADF test at levels and first difference
Augmented Dickey Fuller
Variables
Model
Levels
Specification
Critical
value
First
Critical
Order
at Difference
value at
of
5%
integration
5%
GDP
Intercept
-1.352
-2.968
-5.558**
-2.972
Trend &
-5.993**
-3.563
-5.456**
-3.581
Intercept
-1.471
-2.960
-5.405**
-2.964
Trend &
-2.033
-3.595
-2.382
-3.595
Intercept
-1.783
-2.960
-7.464**
-2.964
Trend &
-3.435
-3.563
-7.364**
-3.5684
1
Intercept
RI
1
Intercept
GCF
1
Intercept
Source: Author’s compilation and values obtained from Eview
**Denotes rejection of the of the null hypothesis at 5% significance level
This study used the Augmented Dickey Fuller (ADF) to test for unit root. The following
hypotheses were tested:
𝐻0 : Unit root present
𝐻1 : Unit root absent
Table 1 The ADF test results shown in table 1 reveal that all variables [GDP, GCF and
RI] are non-stationary and have a unit root in their level. However the ADF test results with
reverence to all variables in the first difference form are stationary at 5% level of significance.
These series are therefore integrated of order I (1).
4.2 Autoregressive-Distribution Lag Approach
An ARDL model is implemented using upper bound critical values for determination of
co integration. Before proceeding with checking for Cointegration, the model is checked for
optimal lag length, serial correlation and stability.
4.2.1 Lag selection
The selection of the lags that need to be used in the ARDL model is one of the most
crucial procedures. The optimal lag length that is eventually chosen should not possess serial
correlation so as affecting the overall estimates produced by the model
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Table 2: Lag Length Selection Criteria
Lag
LogL
LR
FPE
AIC
SC
HQ
0
-61.78381
NA
13.38823
5.422705
5.715235*
5.503841
1
-61.72674
0.082184
14.52144
5.498139
5.839425
5.592797
2
-61.20328
0.711911
15.20584
5.536262
5.926302
5.644443
3
-58.06078
4.022400*
12.94561
5.364862
5.803657
5.486565
4
-56.38576
2.010022
12.43209*
5.310861
5.798411
5.446086
5
-55.19133
1.337758
12.45212
5.295307
5.831612
5.444055*
6
-54.09942
1.135590
12.62962
5.287953*
5.873014
5.450224
Source: Author’s compilation obtained from Eview
*indicates lag order selected by the criterion
Various criteria can be used to determine the optimal lags to be employed. Table 2 shows
different criterion of lag selection, this paper used the Akaike information criterion (AIC) to
select the lag length to be used. Lag length six is chosen by the software based on the AIC
criteria because it possesses the lowest value.
4.2.2 Stability and Correlation Test
Figure 1: CUSUM plot
15
10
5
0
-5
-10
-15
1996
1998
2000
2002
2004
CUSUM
2006
2008
2010
2012
2014
5% Significance
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Table 3: LM Correlation Test
Lags
LM-Stat
Prob
1
1.443274
0.2296
2
5.049829
0.0246
3
0.305395
0.5805
4
3.853718
0.0496
5
0.149977
0.6986
6
3.263197
0.0709
Source: Author’s compilation
Figure 1 shows the results of the CUSUM test for stability and table 3 is the result of the
LM Correlation Test. Table 3 indicates the absence of serial correlation; therefore the model
is both stable and does not show any presence of serial correlation.
Table 4: Bounds Test Results
Test Statistic
Value
df
Probability
F-statistic
8.920147
(3, 3)
0.0527
Chi-square
26.76044
3
0.0000
The calculated F statistic for the model (GDP, GCF and RI) = 8.920147 is higher than the
upper bound Pesaran critical Value 4.35 at 5% significance level. This means that the null
hypothesis stating that there is no cointegration can be rejected thus implying the presence of
long-run relationship. The study proceeds to analyse the short- and long-run coefficients of
the variables.
Table 5: The long run model results
Regressor
GCF
Coefficient
0.357505
Std. Error
0.131556
t-Statistic
2.717505
Prob.
0.0110
RI
0.075824
0.034764
2.181128
0.0374
C
-2.574342
2.497221
-1.030883
0.3111
Source: Author’s compilation using Eviews
The long-run model above indicates that a 1 unit increase in the real interest rate, which is
being used as a proxy for financial liberalization leads to a 0.075824 percent increase in GDP.
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The coefficient of GCF is 0.357505, implying a positive relationship between GCF and GDP
in the long run. The increase in growth can be associated with the increased investment that
comes up as a result of increased returns on investment projects. This indicates that financial
liberalization is crucial for long run growth of the Zambian economy.
Table 6: Short run model results
C
D(GDP(-1))
Coefficient
0.653040
0.275348
Std. Error
0.537168
0.387300
t-Statistic
1.215710
0.710942
Prob.
0.2784
0.5089
D(GDP(-2))
0.643997
0.329904
1.952072
0.1084
D(GDP(-3))
1.157790
0.372700
3.106493
0.0267
D(GDP(-4))
1.409462
0.338589
4.162754
0.0088
D(GDP(-5))
0.741010
0.286545
2.586018
0.0491
D(GDP(-6))
0.433119
0.215648
2.008454
0.1008
D(GCF(-1))
-0.952429
0.308245
-3.089839
0.0272
D(GCF(-2))
-0.827114
0.305237
-2.709745
0.0423
D(GCF(-3))
0.311350
0.214420
1.452059
0.2062
D(GCF(-4))
-0.459346
0.193881
-2.369221
0.0640
D(GCF(-5))
-0.459501
0.186830
-2.459466
0.0573
D(GCF(-6))
-0.427434
0.214051
-1.996875
0.1023
D(RI(-1))
-0.005763
0.078890
-0.073057
0.9446
D(RI(-2))
-0.002004
0.055149
-0.036339
0.9724
D(RI(-3))
-0.022787
0.048691
-0.467988
0.6595
D(RI(-4))
-0.154365
0.060272
-2.561147
0.0506
D(RI(-5))
-0.082007
0.059383
-1.380985
0.2258
D(RI(-6))
-0.059808
0.050141
-1.192793
0.2865
ECT(-1)
-1.327075
0.367358
-3.612482
0.0153
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R-squared
0.979485
Adjusted R-squared
0.901526
S.E. of regression
1.480290
F-statistic
12.56412
Akaike info criterion
3.612916
Schwarz criterion 4.588016
Durbin-Watson stat
Prob(F-statistic)
1.319403
0.005357
Source: Author’s compilation using Eviews
The results of the short run dynamics of the model in Table 6 indicate that financial
liberalization has a negative and insignificant impact on economic growth in the short–run.
The coefficient of error correction term
is ( -1.32707)
i.e. it is negative statistically
significant, indicating that 132.7 percent discrepancy in the short run is adjusted in the long
run every year. The key regression statistics shows that R2 is high implying that overall
goodness of fit of the ARDL models is satisfactory.
The short-run coefficients show that financial liberalization brings about financial crises in
the short-run, since it brings changes in institutions supporting the functioning of the financial
market. This findings conform with that of Tornell et al (2004) argued that, financial
liberalization and crises affect growth through financial development indicating that the shortrun benefits of liberalization are minimal.
5. Conclusions
This study investigates the effects of financial liberalization on economic growth in
Zambia. This study used times series data covering the period 1983 to 2013. The study
utilized the following techniques, unit root and cointegration. The ADF Unit Root Test
revealed that the data was stationary of integration order I (1). The bounds testing procedure
was used to test for Cointegration. The results indicate that the null of no Cointegration
cannot be accepted when GDP is the dependent variable. We find long-run positive and
insignificant relationship between financial liberalization and growth, but there is a negative
and insignificant relationship in the short-run. The long-run elasticities of the relationship
between RI, GCF and growth give support to McKinnon-Shaw hypothesis that financial
liberalization spurs growth.
Overall, the results show that financial liberalization has been supportive but more needs
to be done for the economy to realise the full benefit. The study notes, however, that
excessively high interest rates will have unfavourable economic effects. Such a situation can
be avoided if the liberalization of the banking system takes place under appropriate
conditions, including monetary stability and the government supervision of the banks. Since
the results showed a negative relationship between economic growth and interest rate in the
short run the best policy for Zambia is to leave the determination of interest rate to the forces
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of demand and supply. Also measures that stimulate investments should be put in place with
fair lending rates so as to deepen the financial system of Zambia which will promote
economic growth.
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