Kashif Imran University of Karachi, Karachi Mohammed ... · Mohammed Nishat, PhD Institute of Business Administration (IBA), Karachi Abstract . Determinants of Bank Credit in Pakistan

Determinants of Bank Credit in Pakistan 1

Proceedings of 2nd

International Conference on Business Management (ISBN: 978-969-9368-06-6)

DETERMINANTS OF BANK CREDIT IN PAKISTAN

“Determinants of Bank Credit in Pakistan: A Supply Side Approach”

Kashif Imran

University of Karachi, Karachi

Mohammed Nishat, PhD

Institute of Business Administration (IBA), Karachi

Abstract


Proceedings of 2nd


This study empirically identifies the factors which explain the bank credit to the businesses in

varying financial environments and emerging global challenges. The growth in bank credit to the

private sector is used as dependent variable whereas growth of liabilities from abroad, growth in

domestic deposits, money market rate, M2 as percentage of GDP, real economic growth, inflation

and the exchange rate are identified as major explanatory variable to explain the behavior of

bank credit.. With the major focus on the supply side this study uses the ARDL econometric

approach using annual data from the period 1971 to 2008 for Pakistan. The empirical results

indicate that the foreign liabilities, domestic deposits, economic growth, exchange rate, and the

monetary conditions have significant impact on banks credit to the private sector in Pakistan,

particularly in long run. Whereas the inflation and money market rate do not affect the private

credit. Moreover, in short run the domestic deposit does not influence private credit. The reason

may be that the banks do not issue immediate loan from currently deposited amount by account

holders. The results also infer that the financial health and liquidity of the banks play a

significant and vital role in the determination of loan. A strong economic condition measured by

GDP, as motivating factor to banks has statistically significant impact on issuance of more

private credit to businesses. Results also indicates that the long run relationship is stable and any

disequilibrium formed in the short run will be temporary and get corrected over a period of time

with a high speed of 53.5 percent per year. This study does not statistically distinguish the

behavior of bank credit during non-financial (1971-1989) and financial reforms periods (1990-

2008) in Pakistan

JEL Classification: G21, E44, E51

Key Words: Bank Credit, Reforms, Pakistani


Proceedings of 2nd


1. Introduction

The finance is a backbone of every business, as business grows; it needs more capital to assist its

various operational and non-operational activities. A most basic question in financial economics

is that how businesses get financing to fund their operations? There are two main sources for a

business to raise capital; interna1 and external. The loan from financial institutions like banks is

a key source among the major external sources for a business. In order to fulfill their financial

needs investors, mostly don’t build their capital structure entirely with their internal funds,

especially small firms who have limited sources of raising capital [White and Cestone (2003),

Golar and Zeira (1993)]. The existing literature shows that the availability of bank credit play a

crucial role to boost up the economic growth especially in emerging markets, so to find the

determinants of bank credit is a more important issue to discuss because of the growing trend of

bank loan in the world economies.

A strong financial system is essential for economic growth, whereas a developed economy

support to sturdy financial system, thus it becomes a two way process. A well developed

businesses as well as economic growth raise demand for credit and lead to credit growth. The

mild monetary conditions and a vigorous banking sector tend to enhance more credit. The

underlying financial market imperfections create borrowing constraints, hence lower economic

and credit growth. In the case of Pakistan the domestic credit by banking sector has declined

from 51.1 percent of GDP in 1971 to 46.8 percent in 2010 (World Development Index, 2011). As

shown in figure 1a (the trend line exhibits a declining pose). The various factors influence on

banking decision to allocate the credit in the economy beside of investors’ own characteristics

e.g. unstable political environment of the country, unstable government economic policies, and


Proceedings of 2nd


the legal risk. The figure 1b show that before the financial liberalization in Pakistan (before

1990) the credit by banks was increased (as trend line show upward slope), however, in post

liberalization period the credit ratio was declined (downward slope can be seen in figure 1c). So,

it can be concluded that the financial liberalization/ reforms has negative association with credit

growth in the case of Pakistan.

Figure 1a: Domestic credit provided by banking sector (% of GDP) from 1971-2010

Figure 1b: Domestic credit provided by banking sector (% of GDP) from 1971-1989


Proceedings of 2nd


Figure 1c: Domestic credit provided by banking sector (% of GDP) from 1990-2010

In general, the bank credit can be viewed from two perspectives, the demand side (firms or

individual's access to credit) and the supply side (financial intermediaries like banks). The

present study identified the supply side factors that affect the credit growth.

To assess the growing trend of bank credit before and after boom and bust cycles observed in

most economies it is crucial to identify the factors which determine the bank credit especially

from the bank side. Most of the studies, done for Pakistan considering the demand side approach

[Qayyum (2002); Afzal and Mirza (2010); Awan (2009); Khawaja (2007); and Ali et al. (2011)].

Not any study is undertaken considering the supply side variables of bank credit in Pakistan. The

main objective of present study is to identify the determinants of bank credit from supply side.

The study also empirically identifies if the bank credit behavior is different during financial

reform and non-reform period. The rest of the paper is organized that section 2 presents review

of the existing literature, followed by data and methodology in section 3. The, estimation and

discussion of results is presented in section 4. The summary and concluding remarks are

presented in section

Literature Review


Proceedings of 2nd


In a recent study Guo and Stepanyan (2011) indicated that domestic and foreign funding are

positively associated with the credit growth. The stronger economic growth leads to higher credit

growth, whereas higher inflation lessens the real credit growth in the economy. Monetary policy

has also a significant impact on the credit growth, the soft monetary conditions of the country as

well as global, lead a more credit, and finally the strong banking sector positively influence on

the credit growth. Chernykh and Theodossiou (2011), by using a sample of Russian banks, found

that the median banks assign only 0.5 percent of its total assets in terms of long-term loans to

business and there is large cross-sectional disparity in this ratio among banks. They argued that

the bank’s capacity to expand long-term business loans depends on various factors including its

capitalization, size and the availability of long-term liabilities, however, the ownership of banks

did not matter. They also concluded that the banks hesitated to issue business loans with more

than three years maturity. Their results exhibit that the banks with lower level of capital, the

banks having lower funding for long term loans and banks in most competitive areas are

reluctant to supply long term loans. They considered weak creditor rights protection,

enforcement and the low creditworthiness of risky borrowers as other hurdles in providing long-

term loans to firms.

According to Aisen and Franken (2010) prior to financial crisis the bank credit growth was larger

as compared to post crisis period. Using a sample over eighty countries they also concluded that

the countercyclical monetary policy and liquidity position of the banks played a crucial role and

lessened the bank credit reduction in the post crisis era. These findings advocate that the

countries should follow the appropriate institutional and macroeconomic structure favorable to

countercyclical monetary policies.They also found that the countries responding differently in


Proceedings of 2nd


various regions of the world, due to diversity in countries’ structural characteristics e.g. financial

depth and integration etc.

Takats (2010) studied the bank lending behavior and empirically found that during the financial

crisis the cross-border bank lending declined sharply. Using the data of twenty-one emerging

markets, he concluded that during the financial crises the demand and supply factors contributed

to the fall in bank lending, but the impact of supply factors were dominated. However, both the

factors appear to have more balanced effects in pre-crisis period. Furthermore, supply shock was

the key determinant of slowdown in cross border lending to emerging economies. The credit

growth before the crisis was vastly different across countries and regions.

During the post crisis period the emerging markets experienced a considerable slowdown in

credit growth (Guo and Stepanyan 2011) compared to pre crisis period. The Pakistan also face

slowdown in post crises period (as shown in figure 2)

Figure 2. Domestic credit provided by banking sector (% of GDP) in post crises period

Theoretically there is a long term relationship between bank health and the foreign bank credit

growth in emerging markets. The negative shocks to bank health created slowdowns in credit

growth. Moreover, the financial crises badly hit the banks health which leads to a lower credit


Proceedings of 2nd


growth. Empirical studies indicated that the reduction in bank credit during financial crisis left a

negative impact on the real side of those emerging economies, which relied heavily on bank

financing (McGuire and Tarashev 2008). Over the last two decades, most of the fastest growing

economies of the developing nations have experienced lending booms and financial crises

(Ranciere et al. 2003). Kamil and Rai (2010) found that the countries rely more on external

finance suffered the most during the crisis era. Bakker and Gulde (2010) considered external

factors as key reasons for credit booms and busts in new European Union members. The pre-

crisis boom and slowdown in partner economies were the fundamental determinants of credit

growth during the crisis. It is argued that the banks which faced ultimate liquidity stress lost their

ability to lend more (Aisen and Franken 2010).

3. Data And Estimation Techniques

The present study investigates the factors determine the bank credit in the case of an emerging

economy like Pakistan. The annual data used for econometric analysis, spanning the period of

forty years from 1971 to 2010. The data obtained from various sources e.g. Banking Statistics of

Pakistan, World Development Index (WDI) and the International Financial Statistics (IFS).

The model used in the study is as follows

𝑃𝐶𝑡 = 𝛽0 + 𝛽1𝐹𝐿𝑡 + 𝛽2𝐷𝐷𝑡 + 𝛽3𝐶𝑃𝐼𝑡 + 𝛽4𝐺𝐷𝑃𝑡 + 𝛽5𝐸𝑅𝑡 + 𝛽6𝑀𝑀𝑅𝑡 + 𝛽7𝑀2𝑡 + 𝜇𝑡 … . (1)

Where

PC is Private Credit, FL is Foreign Liabilities, DD is Domestic Deposit, CPI is Consumer Price

Index, GDP is Real Gross Domestic Product, ER is Exchange Rate, MMR is Money Market

Rate, M2 is M2 as percentage of GDP and µt is the error term.


Proceedings of 2nd


All variables are taken in natural logarithm form.

By concluding the existing literature and according to the theory the liabilities from abroad,

deposits by the domestic businesses and individuals, inflation rate, economic growth, exchange

rate and the monetary conditions of the economy create a positive impact on the growth of

private credit from the supply side, whereas the money market rate decreases the private credit.

To find the long run relationship between variables, in terms of methodology this study used the

robust econometric technique Autoregressive Distributed Lag (ARDL) by Pesaran (1997),

Pesaran and Shin (1995, 1999) and Pesaran et al. (1996). The ARDL has several advantages

upon other conventional methods of cointegration like Johansen (1998) and Johansen and Julius

(1990). The ARDL method can make a peculiarity between dependent and independent

variables. One major advantage of ARDL approach is that the estimation is possible even the

explanatory variables are endogenous (Pesaran and Shin 1999; Pesaran et al., 2001). Another

advantage is that it can be applied whether the variables are integrated at level or at order one or

fractionally co integrated (Pesaran and Pesaran 1997). The empirical results are usually very

sensitive to the method and diverse alternative choices are available in the estimation procedure

(Pesaran and Smith 1995). Moreover, it is possible with ARDL that different variables can have

different number of lags. So this study also used ARDL approach for cointegration analysis and

the follow-on error correction mechanism (ECM).

Firstly to find the long run relationship estimates the following equation:


Proceedings of 2nd


∆𝑃𝐶𝑡 = 𝛼0 + 𝛽𝑖∆𝑃𝐶𝑡−𝑖 + 𝛾𝑖∆𝐷𝐷𝑡−𝑖 + 𝜑𝑖∆𝐶𝑃𝐼𝑡−𝑖 + 𝛿𝑖∆𝐺𝐷𝑃𝑡−𝑖 +

𝑝

𝑖=0

𝑝

𝑖=0

𝑝

𝑖=0

𝑝

𝑖=1

𝜋𝑖∆𝐸𝑅𝑡−𝑖

𝑝

𝑖=0

+ 𝜏𝑖∆𝑀𝑀𝑅𝑡−𝑖

𝑝

𝑖=0

𝜃𝑖∆𝑀2𝑡−𝑖 + 𝜆1𝑃𝐶𝑡−1

𝑝

𝑖=0

+ 𝜆2𝐷𝐷𝑡−1 + 𝜆3𝐶𝑃𝐼𝑡−1 + 𝜆4𝐺𝐷𝑃𝑡−1 + 𝜆5𝐸𝑅𝑡−1 + 𝜆6𝑀𝑀𝑅𝑡−1

+ 𝜆7𝑀2𝑡−1 + 𝜖𝑡 … . . (2)

Where p is the optimal lag length and Δ is first difference of the concerned variables. To test the

existence of long run relationship F test is used. The null hypothesis for no cointegration among

variables is

H0: λ1 = λ2 = λ3 = λ4 = λ5 = λ6 = λ7= 0 and alternative is H1: λ1 ≠ λ2 ≠ λ3 ≠ λ4 ≠ λ5 ≠ λ6 ≠ λ7 ≠ 0

If the long run relationship exists in the variables, the following long run model is performed:

𝑃𝐶𝑡 = 𝛼1 + 𝛽1𝑖𝑃𝐶𝑡−𝑖

𝑝

𝑖=1

+ 𝛾1𝑖𝐷𝐷𝑡−𝑖

𝑝

𝑖=0

+ 𝜑1𝑖𝐶𝑃𝐼𝑡−𝑖 + 𝛿1𝑖𝐺𝐷𝑃𝑡−𝑖 + 𝜋1𝑖𝐸𝑅𝑡−𝑖

𝑝

𝑖=0

+ 𝜏1𝑖𝑀𝑀𝑅𝑡−𝑖

𝑝

𝑖=0

𝑝

𝑖=0

𝑝

𝑖=0

+ 𝜃1𝑖𝑀2𝑡−𝑖 + 𝜇𝑡 … . (3)𝑝𝑖=0


Proceedings of 2nd


The lag orders are selected on the basis of Akaike Information Criterion (AIC). For short run

dynamics the ECM has constructed. The ECM version of ARDL approach can be written as.

Δ𝑃𝐶𝑡 = 𝛼2 + 𝛽2𝑖∆𝑃𝐶𝑡−𝑖

𝑝

𝑖=1

+ 𝛾2𝑖∆𝐷𝐷𝑡−𝑖 + 𝜑2𝑖∆𝐶𝑃𝐼𝑡−𝑖 + 𝛿2𝑖∆𝐺𝐷𝑃𝑡−𝑖 + 𝜋2𝑖∆𝐸𝑅𝑡−𝑖 +

𝑝

𝑖=0

𝑝

𝑖=0

𝑝

𝑖=0

𝑝

𝑖=0

𝜏2𝑖Δ𝑀𝑀𝑅𝑡−𝑖 + 𝜃2𝑖Δ𝑀2𝑡−𝑖 + 𝜓𝐸𝐶𝑀𝑡−1 + 𝜐𝑡 … . (4)

𝑝

𝑖=0

𝑝

𝑖=0

Where ECMt-1 is the error correction term and ψ represents the coefficient of correction in

disequilibrium.

4. Discussion Of Empirical Results

Descriptive statistics is highlighted in table 1.

Although, it is not necessary for the variables to be integrated at the same level for the

application of ARDL but the use of unit root test will prove us whether or not the ARDL model

should be used. In order to check the level of integration of variables, this study used Augmented

Dickey Fuller (ADF) and Phillips-Perron (PP) unit root tests. Table 2 shows a mixture of level

I(0) and I(1) of underlying variables, hence we can proceed the ARDL methodology.

Table 1: Descriptive Statistics of the Variables


Proceedings of 2nd


PC FL DD CPI GDP ER MMR M2

Mean

419070

.9

5035.8

5 787878.6

9.2735

9

52297.8

8

30.352

4

8.45153

8

40.346

67

Median

155794

.2 2524.6 234378.2 7.9 49820 21.71 8.63 40.08

Maximum

235964

0

21806.

1 4230479 26.66 104074 82 12.33 47.49

Minimum

11579.

1 255 13143.8 2.9 17221 4.7619 2.14 30.72

Std. Dev.

584969

.4

5492.4

8 1106155

5.5008

32

26665.1

2

21.826

5

2.36038

5

3.8286

23

Jarque-

Bera

39.020

89

12.474

7 26.78459

21.208

36

2.60280

2

4.2880

7

2.35844

7

0.6755

38

Observatio

ns 40 40 40 40 40 40 40 40

Table 2:

Unit Root

Test

Results

Variables

ADF PP

Level 1st Diff. Level 1st Diff.

PC 3.616 -4.796* 12.671 -7.380*


Proceedings of 2nd


FL -2.513 -7.748* -2.513 -7.769*

DD 6.430 -11.222* 19.459 -9.763*

CPI -3.797* -5.863* -3.239** -7.281*

GDP 4.087 -4.369* 4.310 -4.330*

ER 0.385 -4.268* 2.433 -5.124*

MMR -2.248 -5.762* -2.464 -5.764*

M2 -4.693* -5.827* -2.395 -3.765*

ADF and PP statistics with trend and intercept

* and ** are statistically significant at 1 and 5 percent level of significance

respectively

The first step after checking the level of integration is to find the long run relationship among

variables. In order to decide the number of optimal lags Akaike Information Criterion (AIC) is

used. The results of long run relationship are presented in table 3. The growth of liabilities from

foreign (FL), domestic deposit (DD), real GDP (GDP), exchange rate (ER) and the M2 as

percentage of GDP (M2) are significantly affecting the private credit (PC). Whereas consumer

price index (CPI) and money market rate (MMR) does not affect the private credit. As banks get

loan from foreign financial institutions their assets as well as their liquidity goes up, as a result

they can lend more at domestic level. Similarly the same impact imposed by the domestic

deposits. Increase in real GDP boost up the manufacturing sector’s income as well as the general

peoples earning, which leads to higher domestic deposits, hence increase the liquidity of banks

and they can lend more for investment needs, so the GDP has a positive association with private

credit. Exchange rate also positively associated with private credit in the case of Pakistan, which

confirms that private credit in terms of domestic currency appear to pick up some valuation


Proceedings of 2nd


effect of foreign currency credit. Inflation level does not significantly impact the private credit

but a positive sign indicates that private credit also increases with the inflation level. The M2

considers as an alternative gauge of monetary conditions. As monetary conditions of the country

going up, the credit to private sector also enhances. The insignificant impact of money market

rate on private credit does not seem strange because the money market rate does not show a

noteworthy value of standard deviation (as can be shown in table 1), it shows a smooth pattern

throughout the whole study period. Most of our results support to Guo and Stepanyan (2011).

Another factor which can affect the private loan is financial liberalization reforms. Pakistan

adopted these reforms in 1990 to promote his financial sector. In case of Pakistan these reforms

do not pose a significant impact on the private credit1.

Table 3: Long Run Model (1, 2, 2, 1, 2, 1, 1, 1)

Dependent Variable PC

Variables Coefficient

Constant

-381291

(-1.026)

1 To capture the impact of financial liberalization reforms on private credit, we estimated several models

consisting on a dummy variable having value 1 from 1990 to 2010 and 0 otherwise, and the interaction term of some

independent variables with financial liberalization dummy. The results can be seen in table 1 and 2 in appendix. The

results do not exhibit some different pattern


Proceedings of 2nd


FL

11.591***

(1.937)

DD

1.264.***

(1.986)

CPI

7233.607

(1.220)

GDP

65.983*

(3.113)

ER

27688.35*

(2.929)

MMR

1396.959

(0.101)

M2

26967.33**

(2.330)

R-squared 0 .999

Durbin-Watson stat 1.696

t-statistics are in parenthesis

*, ** and *** are statistically significant at 1, 5 and 10 percent respectively

The results of error correction model are given in table 4. Most of the results are similar in both

long run and short run. However, little difference exists like in short run domestic deposits have

not significant relationship with private credit. The reason could be that banks do not issue loan

immediately from the currently deposited amount by account holders. The money market rate


Proceedings of 2nd


has positive impact on private credit in the short run. However, the empirical evidences do

support significant effect of money market on the private credit in the long run. Results of

remaining variables are same in short run as in the case of long run. The coefficient of ECt-1 (-

0.535) is indicates that the above said long run relationship is stable and any disequilibrium

formed in the short run will be temporary and get corrected over a period of time with a high

speed of 53.5 percent per year. A number of diagnostic tests are also applied in current study.

The results of those diagnostic tests evidenced that there is not serial correlation,

heteroskedasticity and the Autoregressive Conditional Heteroskedasticity (ARCH) effect in the

disturbances.

Table 4: Error Correction Model (2, 2, 1, 2, 2, 1, 1, 2)

Dependent Variable ΔPC

Variables Coefficient

Constant

-8103.52

(-0.670)

ΔFL

2.443***

-1.994

ΔDD

0.105

-1.016


Proceedings of 2nd


ΔCPI

1330.059

-1.172

ΔGDP

9.965**

-2.335

ΔER

2423.236***

-1.766

ΔMMR

5613.966***

-2.123

ΔM2

4472.377***

-1.793

EC(-1)

-0.535***

(-1.788)

R-squared 0.978

Durbin-Watson stat 1.672

Diagnostic Tests

LM Test 0.485

ARCH Test 0.241

Heteroskedasticity Test 0.422

t-statistics are in parenthesis

*, ** and *** are statistically significant at 1, 5 and 10 percent

respectively


Proceedings of 2nd


F-statistics of Breusch-Godfrey Serial Correlation LM Test, ARCH Test

and White Heteroskedasticity Test

5. Summary and Concluding Remarks

This study empirically identifies the factors which determine the bank credit by using time series

data from 1971 to 2010 in Pakistan. For empirical estimation the study used auto regressive

distributed lag (ARDL) model, concerning its various advantages over traditional conintegration

methods. Our results show that the foreign liabilities, domestic deposits, economic growth,

exchange rate, and the monetary conditions of the country are the factors which significantly

affect the banks for credit issuance to the private sector in the long run. Whereas the inflation and

money market rate does not affect the private credit. Moreover in the short run domestic deposit

does not associated with private credit, it may that the banks does not issue immediate loan from

currently deposited amount by account holders. Similar to long run the inflation does not play a

significant role in issuance of private loan but in the both cases it is positively associated. Our

analysis also shows that the financial health and liquidity of the banks play a vital role in the

determination of loan. This liquidity may be in the form of domestic deposit or foreign liabilities.

A strong economic condition creates more demand for goods and services which lead to more

investment in different sectors hence increase the per capita income as well as the savings,

collectively these factors convince to banks to issue more private credit. The coefficient value

and sign of ECt-1 (-0.535) confirms that the long run relationship is stable and any disequilibrium

formed in the short run will be temporary and get corrected over a period of time with a high

speed of 53.5 percent per year.


Proceedings of 2nd


References

Aisen, A., and Franken, M. (2010) “Bank Credit during the 2008 Financial Crisis: A Cross-

Country Comparison” IMF Working Paper WP/10/47

Afzal, A and Mirza (2010) “The Determinants of interest Rate Spread in Pakistan Commercial

Banking Sector”, CREB Working Paper No. 01-10

Ali, K., Akhtar, M. F., and Ahmed, H. Z. (2011) “Bank Specific Macroeconmic Indicators of

Profitability: Empirical Evidence from Commercial Bankins in Pakistan”, International

Journal of Business and Social Science, vol. 2(6): 235-46.

Awan, A. G. (2009) “Comparison of Islamic and Conventional Banking in Pakistan”, Proceeding

2nd

CBRC, Lahore.

Chernykh, L., and Theodossiou, A. K. (2011) “Determinants of Bank Long-term Lending

Behavior: Evidence from Russia”, Multinational Finance Journal, vol. 15(3/4): 193–216.


Proceedings of 2nd


Golar, O., and Zeira, J. (1993) “Income Distribution and Macroeconomics”, Review of Economic

Studies, 60(1): 35-52.

Government of Kenya (2005) “Development of Micro and Small Enterprises for Wealth and

Employment Creation for Poverty Reduction”, Sessional Paper No. 2, Government

Printers, Nairobi.

Guo, K., and Stepanyan, V. (2011) “Determinants of Bank Credit in Emerging Market

Economies” IMF Working Paper WP/11/51.

Johansen, S. (1998) “Statistical Analysis of Cointegration Vectors”, Journal of Economic

Dynamics and Control, Vol. 12: 231-54.

Johansen, S., and Juselius, K. (1990) “Maximum Likelihood Estimation and Inference on

Cointegration with Applications to the Demand for Money”, Oxford Bulletin of

Economics and Statistics, Vol. 52: 169-210.

Khawaja, I. (2007), “Determinants of Interest Spread in Pakistan” PIDE Working Paper,

2007:22.

McGuire, P., and Tarashev, N. (2008) “Bank Health and Lending to Emerging Markets”, BIS

Quarterly Review, December 2008: 67-80.

Pesaran M. H., and Shin, Y. (1995) “Autoregressive Distributed Lag Modelling Approach to

Cointegration Analysis”, DAE WP 9514, Department of Applied Economics, University

of Cambridge.

Pesaran, M. H., and Smith, R. (1995) “Estimating Long-run Relationship from Heterogeneous

Panels”, Journal of Econometrics, vol. 68: 79-113.


Proceedings of 2nd


Pesaran M. H., Shin, Y., and Smith, R. (1996) “Testing the Existence of A long-run

Relationship”, DAE WP 9622, Department of Applied Economics, University of

Cambridge.

Pesaran, M. H. (1997) “The Role of Economic Theory in Modelling the Long-run”, Economic

Journal, vol. 107: 178-91.

Pesaran M. H., and Pesaran, B. (1997) “Working with Microfit 4.0: Interactive Econometric

Analysis”, Oxford University Press

Pesaran M. H., and Shin, Y. (1999) “An autoregressive Distributed Lag Modelling Approach to

Cointegration Analysis”, Chapter 11 in Econometrics and Economic Theory in the 20th

Century: The Ragnar Frisch Centennial Symposium, Strom S. Cambridge University

Press: Cambridge.

Phillips, P. C. B., and Perron, P. (1988) “Testing for Unit Roots Time Series Regression”,

Biometrika, vol. 75(2): 335-46.

Qayyum, A. (2002) “Demand for Bank Lending by Private Business Sector in Pakistan”, The

Pakistan Development Review, 41:2 (Summer), pp. 149-159.

Ranciere, R., Tornell, A., and Westermann, F. (2003) “Crises and Growth: A Re-Evaluation”,

NBER WP 10073.

Said, E. S., and David, A. D. (1984) “Testing for Unit Roots in Autoregressive Moving Average

Models of Unknown Order”, Biometrika, vol. 71(3): 599–607

Takats, E. (2010) “Was it Credit Supply? Cross-Border Bank Lending to Emerging Market

Economies during the Financial Crisis”, BIS Quarterly Review, June 2010: 49-56.

White, L., and Cestone, G. (2003) “Anti-Competitive Financial Contracting: The Design of

Financial Claims”, Journal of Finance, 58(5): 2109-42.


Proceedings of 2nd


Appendix

Table 5A: Results based on ARDL with financial liberalization dummy

Varia

bles Model 1 Model 2 Model 3 Model 4

LR SR LR SR LR SR LR SR

Const

ant

-

140808

(-

1.236)

-13397

(-0.709)

-

137398

(-

1.214)

-13048.4

(-

0.68214)

-109847

(-

1.023696

)

-13047.8

(-

0.694714

)

-

156731.7

(-

1.437822

)

-

12897.38*

(-

0.953715)

PC(-1)

0.793*

(4.085)

0.272

(0.916)

0.770*

(4.199)

0.328656

(1.09322)

0.63774*

(3.11804

5)

0.224585

(0.68794)

0.815241

*

(4.55641

2)

0.501449

(3.19873)

PC(-2) - -0.199 - -0.16734 - -0.26583 - -0.301679


Proceedings of 2nd


(-0.685) (-

0.564754

)

(-

0.814787

)

(-

1.522812)

FL

4.504*

**

(1.984)

4.529***

(2.095)

4.352*

**

(1.987)

4.24265*

**

(1.94170

1)

-1.0884

(-

0.367745

)

5.3739**

*

(1.97044

2)

5.230374

**

(2.40216

3)

5.62063*

(3.842234

)

FL(-1)

-1.371

(-

1.058)

2.103

(1.064)

-1.387

(-

1.069)

1.882755

(0.9486)

-4.65756

(-

0.790158

)

2.553353

(1.16435

8)

-

1.260623

(-

1.003308

)

3.80209**

(2.604728

)

FL(-2)

-5.709

(-

2.832)

-1.77

(-0.820)

-

5.738*

*

(-

2.811)

-1.75498

(-

0.79928)

0.340034

**

(-

2.26442)

-1.77956

(-

0.824433

)

-

5.918456

*

(-

3.082283

)

-1.58020

(-

1.016533)

DD

0.403*

*

(2.116)

0.210

(1.211)

0.420*

*

(2.112)

0.192146

(1.09504

4)

0.4305**

*

(1.82728

9)

0.283931

(1.30242

7)

0.672299

**

(2.33278

6)

1.053742*

(3.900342

)


Proceedings of 2nd


DD(-

1)

0.415*

**

(2.029)

0.299

(1.086)

0.426*

**

(2.076)

0.261802

(0.91309

6)

-

0.6245**

*

(2.07935

1)

0.297452

(1.11388

1)

0.40700*

**

(2.05357)

0.808045*

(3.413969

)

DD(-

2)

-

0.8102

*

(-

2.984) -

-

0.829*

*

(-

2.861) -

1440.226

**

(-

2.503357

) -

-

0.900684

*

(-

3.352378

) -

CPI

2424.7

6

(1.377)

1298.206

(0.643)

2396.9

92

(1.362)

1555.606

(0.75642

8)

2115.189

(0.74467)

1747.877

(0.97220

2)

2617.438

(1.54361

8)

3365.762*

*

(2.548869

)

CPI(-

1)

2633.4

1

(1.584)

1515.801

(0.951)

2631.9

37

(1.577)

1631.029

(1.00345

5) -

1730.982

(1.13550

2)

2783.69*

**

(1.74711

1)

2999.582*

*

(2.649014

)

CPI(-

2) -

1524.777

(0.966) -

1434.909

(0.88934

6) -

1024.849

(0.66783

3) -

-311.0175

(-

0.265726)


Proceedings of 2nd


GDP

20.314

*

(3.186)

16.280**

(2.404)

20.093

*

(3.175)

15.95383

**

(2.32124

7)

17.27436

**

(2.65969

5)

15.4826*

*

(2.33503

4)

20.38392

*

(3.39649

7)

8.19929

(1.590415

)

GDP(-

1)

-11.212

(-

1.599)

0.074

(0.009)

-11.281

(-

1.597)

-0.15942

(-

0.020895

)

-8.88072

(-

1.26838)

2.16387

(0.27216

1)

-

11.7453*

**

(-

1.74568)

1.600954

(0.295355

)

GDP(-

2)

-10.277

(-

1.493)

-9.996

(-1.491)

-9.946

(-

1.440)

-9.83342

(-

1.444346

)

-9.60448

(-

1.374784

)

-11.0947

(-

1.63214)

-

11.23723

(-

1.678365

)

-

16.41287*

(-

3.206783)

ER

8822.9

*

(2.987)

4284.135*

**

(1.895)

8867.8

1*

(2.954)

4038.92*

**

(1.78350

4)

7563.519

**

(2.73388)

3661.449

(1.71746

4)

9514.796

*

(3.29984

5)

9187.639*

(4.287232

)

ER(-1)

-

7415**

(-

2.820)

-1835.78

(-0.538)

-

7567.4

**

(-

2.841)

-2276.7

(-

0.670905

)

-

7753.42*

*

(-

2.812546

-2711.62

(-

0.863397

)

-

7063.07*

*

(-

2.793113

-67.76508

(-

0.028428)


Proceedings of 2nd


) )

MMR

1461.6

14

(0.350)

10205.23*

*

(2.214)

1230.8

2

(0.299)

9761.05*

**

(2.03478

3)

1177.003

(0.28454

6)

7719.52*

**

(1.81730

8)

2576.658

(0.62228)

1285.46

(0.359561

)

MMR

(-1)

-

2543.8

3

(-

0.556)

2713.925

(0.488)

-

2464.3

7

(-

0.539)

1918.875

(0.34471)

-1804.38

(-

0.398078

)

3020.981

(0.53695

7)

-

2240.835

(-

0.513474

)

-345.7535

(-

0.097836)

M2

8752.6

**

(2.445)

7281.133*

**

(1.855)

8672.4

**

(2.430)

7165.93*

**

(1.79716

9)

6817.19*

**

(1.91201)

6872.27*

**

(1.77427

2)

9215.712

**

(2.70679

1)

5069.07**

*

(1.788715

)

M2(-

1)

-

7021.2

**

(-

2.582)

353.9425

(0.082)

-

6957.8

**

(-

2.568)

130.7064

(0.02992

4)

-4751.85

(-

1.518467

)

-318.566

(-

0.075337

)

-

7266.545

**

(-

2.874134

)

-2690.503

(-

0.862637)

M2(-

2) -

704.052

(0.240) -

577.3621

(0.19114 -

-192.188

(- -

-4029.334

(-


Proceedings of 2nd


8) 0.067983

)

1.740347)

DUM

-27592

(-

0.814)

-17184.5

(-0.767) - - - - - -

LOAN

*DUM - -

-

1057.8

9

(-

0.779)

-453.906

(-

0.483046

) - - - -

DUM*

FL - - - -

7.268078

(0.68489

5)

-1.98785

(-

0.829937

) - -

DUM*

DD - - - - - -

-

0.198506

(-

1.373436

)

-

0.186411*

(-

3.655965)

EC(-1) -

-0.385

(-1.065) -

-0.44598

(-

1.240002 -

-0.31544

(-

0.783354 -

-

0.43327**

*


Proceedings of 2nd


) ) (-

1.969266)

Table 5B: Results based on ARDL with financial liberalization dummy

Variables Model 5 Model 6 Model 7

LR SR LR SR LR SR

Constant

-156071.3

(-1.359155)

-14168.8

(-

0.759051)

-147621.6

(-

1.092786)

-12978.4

(-0.686888)

-153532.5

(-1.258318)

-12885.23

(-0.678395)

PC(-1)

0.804465*

(4.291476)

0.222244

(0.742245

)

0.78242*

(3.554049

)

0.283881

(0.964865)

0.77658*

(4.148299)

0.288821

(0.922254)

PC(-2) -

-0.228944

(-0.78956) -

-0.103984

(-0.390117) -

-0.183111

(-0.622315)

FL

4.787151**

(2.160418)

4.551004*

*

(2.217437

)

3.783174

***

(1.882684

)

4.141201**

*

(2.083585)

4.410959**

*

(1.958489)

4.317371**

*

(2.027371)

FL(-1)

-1.335371

(-1.042408)

2.114831

(1.109525

-1.465369

(-

1.85658

(0.980299)

-1.378457

(-1.061811)

1.9178

(0.985261)


Proceedings of 2nd


) 1.110715)

FL(-2)

-5.791171*

(-2.936026)

-1.819136

(-0.8506)

-

5.575614

**

(-

2.653338)

-1.791926

(-0.824707)

-

5.674968**

(-2.812293)

-1.805413

(-0.825577)

DD

0.440478**

(2.242183)

0.25934

(1.378912

)

0.374507

***

(1.996148

)

0.168183

(1.032879)

0.409632**

*

(2.106813)

0.207428

(1.159827)

DD(-1)

0.409116**

*

(2.020481)

0.341255

(1.227126

)

0.414487

***

(1.998548

)

0.281094

(1.052044)

0.422471**

*

(2.056437)

0.282257

(0.99806)

DD(-2)

-0.854969*

(-3.109649) -

-0.761006

(-

2.786481) -

-0.816673*

(-2.899496) -

CPI

2558.708

(1.463359)

1174.396

(0.600113

)

2451.875

(1.2858)

1212.743

(0.574704)

2396.106

(1.360039)

1384.053

(0.666834)

CPI(-1)

2675.801

(1.642694)

1369.683

(0.862817

2442.3

(1.472419

1613.123

(1.029192)

2750.97

(1.598139)

1577.342

(0.980733)


Proceedings of 2nd


) )

CPI(-2) -

1432.33

(0.941919

) -

1716.333

(1.021638) -

1518.772

(0.940443)

GDP

20.84064*

(3.2919)

15.99867*

*

(2.423961

)

19.87415

(3.023191

)

16.61508**

(2.405355)

20.09935**

*

(3.169798)

15.94536**

(2.35122)

GDP(-1)

-11.43252

(-1.657692)

0.624247

(0.083749

)

-10.83851

(-

1.508387)

-0.725442

(-0.095424)

-11.23874

(-1.592202)

-0.06392

(-0.00843)

GDP(-2)

-10.68709

(-1.568466)

-10.15193

(-

1.534673)

-10.10465

(-

1.448559)

-10.38388

(-1.541613)

-9.988781

(-1.446231)

-9.966771

(-1.475083)

ER

9187.389*

(3.103324)

4396.238*

**

(1.980946

)

8585.617

**

(2.754573

)

4830.543**

*

(1.83441)

8787.229*

(2.960249)

4186.832**

*

(1.83453)

ER(-1)

-7214.416**

(-2.793946)

-1327.253

(-

0.382918)

-

7462.065

**

(-

-1122.433

(-0.284762)

-

7390.778**

(-2.807137)

-1947.837

(-0.559539)


Proceedings of 2nd


2.755369)

MMR

1913.767

(0.459189)

9982.98**

(2.323284

)

2256.78

(0.406277

)

10955.57**

*

(2.091855)

1397.055

(0.334864)

9916.444**

*

(2.132512)

MMR(-1)

-2402.227

(-0.536569)

3039.608

(0.563682

)

-2354.175

(-

0.505625)

2607.69

(0.468085)

-2361.741

(-0.518555)

2346.308

(0.418523)

M2

8946.622**

(2.554403)

6986.464*

**

(1.820712

)

8607.683

**

(2.216121

)

7483.84***

(1.876594)

9025.978**

(2.39417)

7174.221**

*

(1.817038)

M2(-1)

-7152.863**

(-2.712912)

283.3513

(0.067143

)

-

6827.127

**

(-

2.323688)

832.7066

(0.184913)

-

7014.113**

(-2.550308)

402.8137

(0.091548)

M2(-2) -

726.926

(0.254994

) -

756.4671

(0.255184) -

641.548

(0.21604)

DUM*GDP

-0.678495

(-1.045959)

-0.380235

(-

0.980419) - - - -


Proceedings of 2nd


DUM*MM

R - -

-2158.757

(-

0.482935)

-1797.711

(-0.725175) - -

DUM*M2 - - - -

-693.3747

(-0.767027)

-349.8095

(-0.619213)

EC(-1) -

-0.355014

(-

1.008607) -

-0.428013

(-1.262838) -

-0.416146

(-1.149414)

Note: In model 1 to 7 the variables financial liberalization dummy, loan to private

sector*dummy, liabilities from abroad*dummy, domestic deposit*dummy, GDP*dummy, money

market rate*dummy, and M2*dummy are included respectively.

LR represents long run results whereas the SR represents short run results. In parenthesis the t-

statistics values are given. *, ** and *** represent statistically significant at 1, 5 and 10 percent

respectively. Dependent variable is growth of private credit. Lags are selected on the basis of

AIC.

Documents

Kashif Imran University of Karachi, Karachi Mohammed ... · Mohammed Nishat, PhD Institute of Business Administration (IBA), Karachi Abstract . Determinants of Bank Credit in Pakistan