Does Financial Liberalization affect Credit
allocation to Private and Public Sector in Pakistan?
Isbah
Naim,
MBA Scholar,
Institute of Management Science, University of Balochistan,
Email: isbah.naim@hotmail.com
Corresponding Author
Kaneez
Fatima,
Assistant Professor, IMS, University of Balochistan, Quetta, Balochistan, Pakistan, fatima.sohail@hotmail.com
Jamil
Ahmed,
Assistant Professor, IMS, University of Balochistan, Quetta, Balochistan, Pakistan, jamil.ahmed@um.uob.edu.pk
Abdul
Naeem Khan,
, Associate
Professor, IMS, University of Balochistan, Quetta, Baluchistan, Pakistan, naeemyz@yahoo.com
Financial liberalization is considered as an important prerequist for economic growth and development of an economy. Theories suggest that it is one basic requirement for an efficient allocation of capital. This is achieved when liberalizing allow markets to function freely and set a suitable market adjusted interest rate which then stimulate saved funds, cash holdings and less productive selfinvestments to freely position themselves hence facilitating financial intermediation inclusively. This study aims to investigate the impact of Financial Liberalization on the credit allocation to the private and public sectors in Pakistan. The index of financial inclusion index is computed using Principal component analysis. Financial inclusion, trade openness, inflation rate, interest rate and exchange rate are used as control variables. All these variables are integrated at order one I(1).Therefore, the Vector Error Correction (VECM) approach is used to estimate the impact of financial liberalization on allocation of credit to private and public sector for the period of 1990 to 2014. It is found that financial liberalization has a negative and statistically significant impact on the private sector credit. However, positive and significant relationship exists between financial liberalization and public sector credit of Pakistan.
Evidence
from the countries worldwide presents that credit allocation has remained a
government controlled task (Ahmad and Islam,
2009). In Pakistan also, credit allocation was initially controlled by
the government. Later, keeping in view the benefits of financial liberalization,
many countries including Pakistan liberalized its financial activities to
achieve efficient allocation of credit across the sectors(He, 2012; Abaid, Oomes, and Ueda, 2005; Muhammad,
A., & Wizarat, S. 2011). Pakistan liberalized its financial system in late
90s. Studies on Financial liberalization signifies that a healthy liberalized
financial system can accelerate efficient allocation of credit (Abaid,Oomes, and Ueda, 2005). Under the
liberalized financial system, the intermediaries functions to stimulate saving
and hence channelize them in a manner that they are taken back from the less
efficient industries to the industries yielding higher contributions towards
overall productivity in the economy (Beck, Levine,
and Loayza, 2000).
Financial
Liberalization basically comprises of three major reforms: freeing the flow of
capital, deregulation of lending and deposit rates to allow interbank
competition and opening the national financial market to the international
financial market (Muhammad, 2016). Literature highlights
that countries, in the wake of this Liberalization vibe, “eliminated the
interest rate ceilings, lowered reserve‐ratio
requirements, reduced government interference in credit allocation decisions,
and privatized many banks and insurance companies”. Some countries also induced
the development of local stock markets and relaxed the procedures to actively
allow foreign financial intermediaries to enter in the domestic financial
market (Richey,
2019).It is a viable way to bring financial market at its competitive
equilibrium by setting the interest rate free to adjust accordingly. Which then
stimulate saved funds, cash holdings and less productive selfinvestments to
freely position themselves across borders or in other words improving financial
intermediation inclusively (Khan and Aftab,
1993; Chaudhry, 2007). Moreover, this potential granted to financial
intermediaries has resulted in rapid industrialization as they get chance to
easily and freely participate in allocating the investments to the projects
promising long term permanent gains (Kabango and
Paloni, 2011; Beck, Levine, and Loayza, 2000). In various cases it has
been observed that ever since the controlled regime been replaced by the
financial liberalization policies, the process of allocation of resources has
affected automatically (Wang and Giouvris, 2019;
GuhaKhasnobis and Bhaduri S. N, 2000; Schiantarelli, Galindo, and Weiss, 2007).
Pakistan
economy has observed extreme apices of growth trends since its inception. That
is the years of tremendous growth in the sixty’s decade and also an era of low and
slow growth in 19982000.Several
policies were enacted by the government to increase growth rate in the past few
years. Financial Liberalization is one of those policies (Sheera
& Ashwani, 2019). Considering the Government shift towards financial liberalization,
this study is aimed to analyze the extent to which financial liberalization has
effected the credit allocation across the public and private sectors in
Pakistan. Similar proposition is hypothesized in various studies which
highlights that Financial Liberalization has achieved favorable allocation of
credit towards the sectors with best productivity (Hamdaoui
& Maktouf, 2019). However, this study hypothesizes that financial
liberalization positively impact credit allocation to Private Sector.
To investigate the
impact of financial liberalization on allocation of credit to private and
public sector in Pakistan, the
sample period covers24 years (19902014).The relevant annual data is collected
from The State Bank of Pakistan, Economic Survey of Pakistan published by
Economic Adviser’s Wing, Finance Division, Government of Pakistan, Islamabad
and the Global Economy Website.The
dependent variables are credit allocated to private and public sector measured
as bank credit to public and private sectors as percentage
of GDP respectively.To measure
the financial liberalization, we use the Financial Liberalization
Index computed by Querashi, (2018) using principal
component analysis. Similar PCA technique was also used by Sheera & Ashwani
(2019) in their study over Financial Deepening in South Asian Countries. The
measures of financial liberalization to compute the index include privatization
reforms, institutional strengthening, nonperforming loan, debt management, monetary
management measures, exchange payment, capital market reforms, banking reforms,
and prudential regulations taken as the number of a policy measures implemented
in respective.
Apart
from financial liberalization, we consider a small set of control variables. We
restricted our choice of control variables to financial inclusion, trade
openness, exchange rate and interest rate in order to avoid the excessive loss
of degree of freedom as we have a small size. This is so because Pakistan
started taking measures of financial liberalization in early 1990s (Querashi,
2018). Financial Inclusion is defined as a phenomenon of building a culture of
inclusive financial system in a country(Nanda, 2017;
Dhungana and Kumar, 2019).Its index is computed using principal
component analysis equation as follows;
Where,
FI is a measure of Financial Inclusion, NBB is the number of bank branches
taken over 1000 adults, AA is number of advance accounts as percent of GDP and NDA
is Number of Deposit Accounts calculated as rupee Deposits as percent of adult
population. The
The Trade openness (to)is measured as
ratio of sum of export and import to real GDP, (export + import/real GDP). It
is taken as control variable for a reasons that it can act as a proxy of
bringing allocative efficiency (Abaid, Oomes,
and Ueda, 2005). The change in exchange
rate is the real effective exchange rate of the time t and is calculated as
log (reer_{t}/reer_{t1}). It effects the investment to the
private sector positively (Harchaoui, Tarkhani,
and Yuan, 2005). Lastly, the interest rate(IR) is taken as a proxy of
cost of financing. It is calculated as average of monthly call money rate
averages and is taken as control variable in the model as it directly effects
the investment tendency i.e. higher the interest rate lower will the investment
take place and vice versa(Keynes, 1936).
We consider the following model to
investigate the impact of financial liberalization on allocation of credit to
private and public sector;
Equation
(2) and (3) will be computed separately.
Results and Discussion
Before, we move to estimate the model 3 and 4, we run the unit root test to examine the stationarity of the variables.
Augmented
Dicky Fuller test is used to test the stationarity and the results are produced
in Table 1.
Table 1:Unit Root Test
Variables 
I(0) 
I(1) 
Private/GDP 
0.467282 
3.761193^{***} 
Public/GDP 
0.400588 
5.659881^{***} 
Financial Liberalization Index 
3.401472 
3.990042^{***} 
Financial Inclusion Index 
1.330317 
4.340368^{***} 
Interest Rate 
2.090362 
4.298901^{***} 
Real Effective Exchange rate 
2.059881 
5.323598^{***} 
Trade Openness 
2.499409 
6.627797^{***} 
Note:
The level of significance at 1% is shown by ***, 5 % is by ** and 10% by *.
The
results in the above table show that all variables are stationary at first
difference. In this case we may run regression with all the variables by taking
first difference. However, this will remove the long run dynamics of the
variables. These variables may be cointegrated i.e. they may observe long run
relationship. To test for cointegrating relationship, we run the Johansen cointegration
test (Johansen,
1988). The selection of lag length is
based on AIC criterion. The optimal lag length is 2 for model specified in
equation 2 and 1 for the model specified in equation 3.
The
results of cointegration test (reported in Appendix Table A1) suggests that
the variables are cointegrated implying that they observe long run relationship.
According to the criteria for the Johansen cointegration test, the max statistics
is checked to be lesser than 5% critical value. Results given in the tablespecifies
that there exist3 cointegrating equations in case of private credit to GDP (equation
2) and 1 cointegrating equation in case of public credit to GDP (equation 3).
These findings suggest that there exists a long run relationship between the
variables. The variables deviate from the long run relationship in short run,
however they converge to long run equilibrium with certain speed known as speed
of adjustment. The appropriate model in
this situation is the vector error correction model.
This
model is run on the series integrated at order I(1). It adjusts with sufficient
numbers of lags (p1) in the model and estimates the shortrun relationship
between the variables and deviations from their equilibrium. The coefficient of
the ECM is the speed of adjustment of the shortrun disequilibrium to the
longrun equilibrium(Andrei and C. Andrei, 2015).
Equation in the VECM model is as follows:
Where,
Table 2: Estimated results of
VECM (private credit as dependent variable)
Regressor 
Coefficient 
tstats 
C 
7.048479 
1.60796^{*} 
D(Fin. Liberalization) 
8.329916 
2.48432^{**} 
Trade Openness 
0.315179 
2.31959^{**} 
Interest Rate 
0.407844 
2.10980^{**} 
Fin. Inclusion 
0.305029 
1.04679 
CointEq1 
0.018911 
1.20312 
RSquare 
0.669770 

Fstatistics 
5.408513** 
Note:
The level of significance at 1% is shown by ***, 5 % is by ** and 10% by *.
The
estimated VECM is reported in Table 2 and 3. To be precise and relevant we are
only reporting the results estimated for equations where dependent variable is
private credit (for equation 2) and for equation 3 where dependent variable is
public credit. The results showed a negative and significant effect of financial
liberalization on private credit whereas positive and significant effect on
public sector credit allocation.
Our
results shown in Table 2 may be explained as the empirical evidences available
in the literature on financial liberalization states that in developing
countries, financial liberalization has not brought positive results towards
achieving high level of economic development through it channel of private credit
allocation in the short run. This also clearly identifies that any such policy
which covers an overall country domain does not show positive effects on
private credit allocation right after the policy is implemented as financial
market in the transition phase cannot achieve efficient allocation of credit in
the early years (Arestis and de Paula, 2008). Another reason of significantly
negative relationship of financial liberalization with private credit is that
the increase in the investment in the private sector depends upon the available
saved fund remained after the public sector allocation at a certain time.
However, in the case of developing economy of Pakistan such funds turns to be
the short. Also, since the relationship of financial liberalization with
interest rate is negative, a conclusion can be generalized that negative
private credit coefficient is due to fever deregulation of interest rate in
Pakistan (Munir, Awan, and Hussain, 2010).
This point also leads to the conclusion that since the private sector
credit is negatively influenced by the interest rate, therefore the higher the
interest rate lower will the credit allocation towards private sector take
place. It shows that the private sector credit in the country is constrained by
the cost of financing at a time (Majeed and
Khan, 2013).
The
Cointegration coefficient for model1 (equation2) is 0.018911 and is
insignificant. The negative sign with the cointegration coefficient is rightly
specified and if the series deviates from its equilibrium then it comes back to
its equilibrium at a speed of 1.89%. The probable reason of this small magnitude
of adjustment of 2% is the small sample size. Also, because of the sluggish
nature of Pakistan economy this small percentage seems justified.
Table 3: Estimated results of
VECM (public credit as dependent variable)
Regressor 
Coefficient 
tstats 
C 
3.068198 
1.68774^{*} 
(Fin. Liberalization) 
11.21775 
3.35459^{***} 
Exchange Rate 
61.09612 
2.39721^{**} 
Interest Rate 
0.410684 
2.12550^{**} 
Fin. Inclusion 
0.421018 
1.38753^{*} 
CointEq1 
0.002017 
4.25639^{***} 
RSquare 
0.594299 

fstatistics 
4.980548^{**} 
Note:
The level of significance at 1% is shown by ***, 5 % is by ** and 10% by *.
The
results for the model when public sector credit allocation in Pakistan is taken
as dependent variable are reported in Table 3. This shows positive relationship
at five percent level of significance. The results lead to the conclusion that
financial liberalization has positively impacted the public sector credit.
Nonetheless, there are the following reasons withstanding the results for this
model. Firstly, the issue that remained
unsettled in the prereform period is that in
Pakistan the flow of “directed credit” to the selected sectors in the postreform period was discontinued
but the same is still in practice through various other ways (Khalid and
Nadeem, 2017). Secondly, it can also be concluded that public sector
credit has always been favored in terms of credit allocation as the government
has been serving this sector by continuous credit injections for achieving
various long term plans (Majeed and Khan, 2013).
The
Cointegration coefficient for model2 (equation2) is 0.002017 and is significant.
The negative sign is a satisfactory and it can be interpreted as when the
series deviates from its equilibrium, it comes back to its equilibrium at a
speed of 0.207%.
Models
are diagnosed for the heteroscedasticity, serial correlation, normality and misspecification.
For
heteroscedasticity, White (1980) test is used. The null hypothesis of this test
is that there is no heteroscedasticity (Narayan, 2003). For checking the serial correlation, BreuschGodfrey
Serial Correlation LM test is used. Serial Correlation defines that the
residual values of the model are correlated with each other. Lagrange
Multiplier test also known as BreuschGodfrey Serial Correlation LM Test is
used to test the serial correlation.
For testing normality,
Jarque Bera (1980) test is used. The null hypothesis of this test is that the
residual series is normal.
The diagnostic test results shown in table 4 indicates that the
models are well specified. None of the statistics shown in the table are
significant.
Table 4: Diagnostic Tests
Dependent Variable 
LM test 
White test 
JB test 
Private/GDP 
0.911043 (0.4722) 
36.60910 (0.4404) 
3.019437 (0.5546) 
Public/GDP 
1.325382 (0.2849) 
24.96839 (0.7266) 
7.707874 (0.1029) 
LM
as BrueschGodfrey test for Serial Correlation
White
test for heteroscedasticity
JB
as Jarque Bera test for normality
4. Conclusion:
This
study was conducted to investigate the effect of financial liberalization on
credit allocation towards private and public sectors for the economy of
Pakistan over the period of 1990 to 2014. The study used Augmented Dicky Fuller
test of unit root to test for the stationarity of data. Next, the Johansen
Cointegration test was used to check for the long run cointegration of the
model. VECM model was then used to check for the short run dynamics of the
models since the variables were stationary at order I(1). The results showed
that financial liberalization has a negative and statistically significant
impact on the private sector credit in the short run. However, positive and significant relationship exists
between financial liberalization and public sector credit.
The
negative coefficient in the case of private sector credit and a positive
coefficient in the case of public sector credit implies that financial
liberalization as an output of improving the private sector credit was not a
success story and is contrary to the hypothesis presented for financial
liberalization. The necessary measure in this regards should be a proper
implementation of the policies encompassing financial liberalization in the
country. Which means, it is necessary to allocate a separate proportion of
private sectors credit out of the total credits available. There is also a
requirement of making concerted efforts towards strengthening the financial
institutions to function favorably towards supporting the private sector and
also to attracting the external funds. Additionally, in the developing economy
of Pakistan, there is an intense requirement of settling a relatively favorable
interest rate in order to boost up the private sector.
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Appendix
Table A1: Johansen Co
integration Test
Test for equation 2 (for private credit) 
Test for equation 3 (for public credit) 

Hypothesized No. of CE(s) 
Max Statistic 
0.05 Critical Value 
Hypothesized No. of CE(s) 
Max Statistic 
0.05 Critical Value 
None * 
46.16935 
27.58434 
None * 
43.57814 
40.07757 
At most 1 * 
43.91338 
21.13162 
At most 1 * 
30.94676 
33.87687 
At most 2 * 
15.94064 
14.2646 
At most 2 * 
28.82804 
27.58434 
At most 3 
1.938737 
3.841466 
At most 3 
16.3268 
21.13162 



At most 4 * 
4.721839 
14.2646 
Maxeigenvalue test
indicates 3 cointegrating equations at the 0.05 level 
Maxeigenvalue test indicates 1 cointegrating equation
at the 0.05 level 






