THE RELATIONSHIP BETWEEN REAL AND BANKING SECTORS IN JORDAN ALAWIN, Mohammad* AL-MUHAISSEN, Tariq
MERZA, Ebrahim Abstract
This paper investigates the interrelation between real side and banking side in Jordan, using real GDP as an indicator for the real side, changing in private debt and capital adequacy index as indicators for the banking side for the period 1980-2011. The results indicate that there is a positive relationship between the two sides in the short run.
Impulse Response Function (IRF) verified that changing in the private debt affects RGDP significantly, suggesting that the banking side affects the real side. Also, the IRF verified that capital adequacy index affects RGDP, suggesting that the strength of banking system also affects the real side too.
JEL Codes:
Keywords: Private Debt, Banking System, Capital Adequacy index, cointegration, Jordan.
1. Introduction
During the past two decades, the banking system in Jordan had been growing rapidly.
By the end of 2011, the number of banks totaled to 26 commercial and Islamic banks, with a total asset equaling JD37686.4 million which represents about 184% of the Gross Domestic Product (GDP). At the same year, the total deposits raised to be JD24377.9 million, about 119% of GDP (Central Bank of Jordan, 2012).
The banking sector is connected to the real sector; investment is regarded as one of the main aspects that show the connection between these two sectors. According to the economic theory, investment is associated with the ability of banks to grant loans. So, it is possible to link the volume of loans granted by the banking sector, particularly, those which are granted to the private sector, with the size of GDP (Bortis, 2010).
Growth in loans to the private sector or the expansion of the lending process may be caused by either the lending policy in the commercial banks and other specialized banks or the expansionary monetary policy from the central bank by reducing the interest rate.
Is it possible for these two factors to explain the behavior of the lending to the private sector in Jordan?
It is well known that the Jordanian banks pursue prudent lending policies, making first reason for the growth of loans to the private sector unlikely. On the other hand, the second reason mentioned above may be excluded; the reason is that interest rates were almost constant and stable over the past decade. Interest rate took an upward trend during the first decade of the new millennium because of the high rates of inflation. So, growth in loans to the private sector may be attributed to the presence of the Investment Promotion Law, which allowed the flow of investments from abroad and attracted international companies which were able to fulfill lending conditions, set by the domestic
* Mohammad Alawin, The University of Jordan, Mohammad Alawin <[email protected]>, Tariq Al-Muhaissen, The University of Jordan; Ebrahim Merza, Kuwait University
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commercial banks. Despite the growth in the volume of loans, commercial banks continue suffering from high levels of liquidity and that up to about 55% or more during the last decade (Association of Banks in Jordan, 2011).
Due to this growth along with the desire of the central bank of Jordan to maintain the strength of the banking system, the central bank decided to force commercial banks to apply the standards of the Basel Committee III through the application of capital adequacy standards primarily.
This study demonstrates the interrelationships between the real side of the economy and the banking side, which clearly has an impact on the economy's growth and development. The study examines the relationship between the growth of the banking side on the real side through demonstrating the impact of the strength of the banking system on RGDP. It relies on the indicators of the strength of the banking system by linking loans to the private sector beside the capital adequacy ratio to the real sector (RGDP).
This study is of importance because it relies on whether the banking system (the largest sector in Jordan compared to other sectors) will have a significant impact on the real side or not? The study purports to answer that concern in details.
The rest of this study is organized as follows: section II represents the literature review and section III explains the data and model variables. Section IV demonstrates the empirical work. Finally, section V presents the conclusion.
2. Literature Review
There are many theoretical and empirical studies related to the relationship between real side of the economy and the banking side. For example, Bortis (2010) investigated theoretically the interaction between the real sector and financial sector through a classical – Keynesian perspective. He investigated the role of both real and financial sectors of a monetary production economy. The conclusion of the study represented a policy to explain the real and financial sectors interaction. Further, the paper included three conclusions related to the financial sector; the necessity of employment and income policy, financing old age pension’s policy, and reorganization of the financial sector.
Sendeniz–Yuncu et al. (2008) studied the Credit View Hypothesis and the casual link between the banking sector and real sector in 11 OECD countries. Using time series analysis on a quarterly data between 1987 and 2003, the study found out that there is a significant link between the banking sector and the real sector in many countries of OECD. The variant decomposition of Vector Error Correction Mechanism (VECM) backed the Credit View Hypothesis in some countries. However, the impulse-response function backed Credit View Hypothesis in most of the countries in the sample chosen.
A non-linear Vector Autoregressive model (VAR) is used by Puddu (2010) to examine the dual relationship between the real sector and banking sector in the United States and Switzerland as a country specific analysis. Moreover, Puddu used pooled data analysis, including quarterly data between 1983 to 2009 for a sample of countries including the U.S. and Switzerland. The study investigated the real effect and the feedback effect. The variables used in this study are: output gab, interest rate, inflation rate and non- performing loans. The results supported the existence of both real and feedback effects.
They proved that the output gap and interest rate are the main transition channels of real and feedback effect. However, the inflation rate played a marginal role in the transition.
Dehkordi et al. (2012) analyzed the long-run interrelation between financial and real sector in Iran for the period 1981 – 2010. The study used the economic growth rate represented by change in RGDP as indicator of Iran real sector, change in balance of commercial and specialized banks granted loans and banks Capital Adequacy index as financial sector indicators. The Vector Error Correction Model used, and the results show that there are no casual relations between variables in the short-run .Besides, there is no evidence for a mutual relation between any of the two variables.
Using panel VAR methodology, Monnin and Jokipii (2010) investigated the impact of banking sector stability on the real economy. The study used quarterly data consisting of the market value of the outstanding debt between 1980 and 2008 as an indicator for the financial sector, real GDP growth and inflation rate as indicators for the real sector. A sample of 521 banks covering 18 OECD countries was used in the study. The results show that there was a positive link between the banking sector stability and the real output growth.
3. Data and Model Variables
In order to investigate the relationship between the real economy sector and the banking system; we need indicators for each side. This study uses real GDP as an indicator for the real side during the period (1981–2011).
However, for the banking side, this study uses two indicators:
1. The Change in Private Debt (CPD): The change in loans granted to private sector during the period (1981 – 2011).
2. Capital Adequacy Ratio (CAP): One of the financial indices that measures the performance of banks. By using Basel III classification, this ratio is calculated as follows: the numerator is represented by Capital and Reserves (which banks hold to face the risk of defaults on loans and other investment activities). For the denominator, the study employs the risk weighted – assets, as shown in Table (1):
Table (1): Risk Weighted - Assets
Item Weighted coefficient
Cash in the banks 0%
Government debt 0%
Legal reserves 0%
Public and Semi-public institutions debt 50%
Private debt 100%
Foreign Currency 20%
*Source: The Central Bank of Jordan 4. Empirical Work
This paper applies several econometric tests such as Augmented Dickey Fuller (ADF) test in order to examine stationarity of variables and Johansen’s cointegration test to state the cointegration relationship among the variables. In order to test causality between variables, the Granger causality test will be applied. Then, according to the
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results of the previous tests, the VECM will be employed in order to determine the adjustment process between the short-and long-run equilibrium.
a) Unit root test: ADF test is used to identify the stationarity of the time series data.
The results maintain that all variables are stationary at the first difference. Table (2), in the Annex reported ADF results.
b) Cointegration: Following Dehkordi et al. (2012)’s methodology for cointegration, the Johansen’s cointegration test is implemented. On this test, the null hypothesis states that there is no cointegration relationship among the variables. The results indicate that the null hypothesis (of no cointegration) can be rejected at 5% level of significance. The trace and max-eigenvalue statistics indicate that at least there is one cointegration vector, existing at the 5% level of significance. These results are reported in Table (3) in the Annex.
c) Granger Causality test: On this test, it is possible to determine the causal direction between the variables (Hamilton, 1994). The results, reported in Table (4), indicate that the relationship between RGDP and the Change of Private Debt (CPD) is a unidirectional causality, and it goes from RGDP to CPD that is, RGDP causes CPD, but CPD does not cause RGDP. The same result appears between RGDP and Capital Adequacy ratio (CAP), where RGDP causes CAP, but CAP does not cause RGDP. Finally, the results show that CAP causes CPD, and CPD does not cause CAP.
Table (4): Granger Causality Test Results
Variables Null Hypothesis F-statistic Prob. Result CPD does not Granger Cause RGDP 1.68788 0.2061 Reject RGDP & CPD
RGDP does not Granger Cause CPD 3.77299 0.0376 Accept CAP does not Granger Cause RGDP 2.16342 0.1368 Reject RGDP & CAP
RGDP does not Granger Cause CAP 3.59999 0.0429 Accept CAP does not Granger Cause CPD 2.54575 0.0994 Accept CAP & CPD
CPD does not Granger Cause CAP 0.80535 0.4586 Reject d) Vector Error Correction Model (VECM): As known as Vector Error Correction Model (VECM), which is used to provide an approximate value for the speed of converge between the long-run and short-run equilibrium between the variables.
According to the previous results, which state the existence of cointegration between the variables, this gives the reason to use VECM (Cujarati, 2003). The results of VECM regression are reported in Table (5). From Table (5), the coefficient of CPD from cointegrating equation equals (-3.37) and the cointegrating coefficient on CAP equals (- 19402.63). The adjustment coefficient on cointegration equation 1 for RGDP is -8.8%.
Table (5): Vector Error Correction Model Results
Cointegrating Eq. CointEq1 Error Correction: D(RGDP) D(CPD) D(CAP)
RGDP(-1) 1
CPD(-1) -3.371327
CAP(-1) -19402.63
CointEq1 -0.088376 (0.06051)
0.402544 (0.10740)
0.00000839 (0.0000071)
d) Variance Decomposition: Variance decomposition is considered as a way of characterizing the dynamic behavior of the model. This method breaks down the variance of the forecast error for each variable into components, attributed to each of the endogenous variables (Dehkordi et al., 2012 and Sendeniz–Yuncu et al., 2008). Variance decomposition results are reported in Table (6).
Table (6): Variance Decomposition Results
RGDP CPD CAP
t
S.E RGDP CPD CAP S.E RGDP CPD CAP S.E RGDP CPD CAP 1 191.9 100 0 0 340.6 30.9 69.1 0 0.02 19.6 14.3 66.1 2 355.9 97.3 2.6 0.1 454.7 52.2 47.5 0.3 0.03 17.1 19.7 63.2 3 543.6 96.5 3.1 0.4 532.4 61.6 38.1 0.3 0.03 14.7 24.2 61.1 4 758.4 94.3 4.8 0.9 600 65.2 31.6 3.2 0.03 12.6 28.4 59 5 1000.9 94.1 4.7 1.2 640.6 67.4 27.8 4.8 0.04 10.9 27.8 61.3 6 1254.4 94.3 4.4 1.3 672.9 68.5 25.7 5.8 0.04 10.8 24.9 64.3 7 1508.3 95.1 3.7 1.2 739.2 65.2 29.9 4.9 0.04 13.5 21.2 65.3 8 1763.7 95.4 3.4 1.2 814.1 66.3 29.6 4.1 0.05 17.7 18.6 63.7 9 2024.3 95.4 3.4 1.2 894.4 69.5 27.1 3.4 0.05 22.2 17.3 60.5 10 2297.1 95.1 3.6 1.3 957.6 72.9 23.6 3.5 0.05 25.9 16.9 57.2
The results show that RGDP is highly related to the variable itself instead of other two variables in the first period. While for 10 periods forecast of RGDP, 95.1% of the forecast variance will be mainly attributed to RGDP shocks, 3.6% to CPD shocks and 1.3% to CAP. The change in Private Debt took downward trend in the ten periods. In the first year for forecasting CPD, 69.1% of the forecast variance will be attributed to CPD shocks and 30.9% to RGDP shocks, the CAP effects on CPD fluctuated between 3.2%
and 5.8%. For 10 periods forecast of CPD, 72.9% of the forecast variance will be attributed for RGDP shocks, 23.6% to CPD shocks and the rest to CAP shocks. For Capital Adequacy ratio at the first period, 66.1% of the forecast variance will be attributed to CAP shocks, 14.3% to CAP shocks and 19.6% to RGDP shocks. Moving to the tenth period 57.2% of the forecast variance attributed to CAP shocks, 16.9% to CPD shocks and 25.9% to RGDP shocks. The CPD forecast variance fluctuated during the ten periods.
e) Impulse Response Function (IRF): The Impulse Response Function (IRF) traces out the response of one variable in the system to shocks in the error term. One shock of the standard deviation of the variable may affect the other variables (both) positively and (or) negatively. The IRF traces the response of the endogenous variable overtime to a shock in that variable and in every another endogenous variable in the system. IRF results reported in Table (7).
Table (7): Impulse Response Function Results
RGDP CPD CAP
Period
RGDP CPD CAP RGDP CPD CAP RGDP CPD CAP 1 191.9 0 0 189.6 282.9 0 -0.009 -0.008 0.018 2 294.1 57.6 -10.7 268.2 134.8 -25.9 -0.006 -0.009 0.012 3 402.2 77.1 33.9 258.5 98.5 11.6 0.002 -0.008 0.008 4 507.2 134.1 66.2 245 -77.2 -103.3 0.001 -0.009 0.009 5 633.1 140 79.1 205.2 14.5 -89.6 0.001 -0.006 0.011 6 736.2 147.7 88.7 181.9 48.4 -83.3 0.004 -0.003 0.012 7 823.2 128.9 85.2 215.3 216.7 17.4 0.008 -0.002 0.014 8 896.6 151.1 95.5 288.9 180.5 17.5 0.011 -0.004 0.013 9 971.6 177.1 109.1 340.9 143.9 17.9 0.013 -0.006 0.012 10 1053.4 224.1 138.2 335.7 -9.1 -65.2 0.014 -0.007 0.011
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The results indicate that one standard deviation in RGDP innovation affects CPD and CAP positively in the whole periods, except for the first period for which CPD effect equals zero, first and second period for CAP, zero and negative effect, respectively. One standard deviation CPD innovation affects RGDP positively, and affects CAP negatively for second, fourth, fifth, sixth and tenth periods, and positively for the rest of periods except for the first period the affect equals zero. Finally, one standard deviation CAP innovation affects CPD and RGDP positively, except for the first and second periods for RGDP.
5. Conclusions
This study investigates the interrelation between the real side of the economy and the financial side, using annual data for the period 1981 - 2011. The unit root test ensures that the variables are stationary at the first difference. Thus, we applied cointegration test to verify the cointegration relationship between the variables. The causality test verified a unidirectional causality between the variables, that is, RGDP causes CPD, but CPD does not cause RGDP, RGDP causes CAP, but CAP does not cause RGDP, Finally, CAP causes CPD and CPD does not cause CAP, which indicates the short-run causal relation between the variables. Vector Error Correction Model (VECM) was used to identify the relation between the real and financial sides of the economy, the adjusting speed of short run towards the long run for RGDP is -8.8%.
The Impulse Response Function results verified that one standard deviation CPD innovation affect RGDP positively for all periods, suggesting that the banking side affect the real side, that is, there is no such a relation between the two sides. Also, the results verified that one standard deviation CAP innovation affect RGDP positively nearly for all periods, suggesting that the strength of the banking system has a clear impact on the real side.
References
Association of Banks in Jordan, (2011). Annual report.
Bortis, H.. (2010). The real and financial sector of a monetary production economy in the perspective of classical-Keynesian political economy, VII International Colloquium, the University of Brasilia and the University of Paris.
Central Bank of Jordan, Annual reports.
Central Bank of Jordan, Monthly reports.
Central Bank of Jordan, (2004), Yearly Statistical Series (1964 – 2003), Special Issue.
Dehkordi, S.; Sameti, M.; and Dehkordi, S.. (2012). Analyzing the Long run Interrelation between Banking System and Real sector. Function in Iran Economic through Simulation of Capital Adequacy Index. Economic and Finance Review, Vol. 2(6), pp. 68–76.
Gujarati, D. (2003). Basic Econometrics, Fourth Edition, McGraw–Hill, New York.
Hamilton, J. (1994), Time Series Analysis, Princeton University Press, USA.
Monnin, P. and Jokipii, T. (2010). The Impact of Banking Sector Stability on the Real Economy. Swiss National Bank, Switzerland.
Sendeniz–Yuncu, I.; Akdeniz, L.; and Aydogan, K.. (2008). Interdependence of the Banking Sector and the Real Sector: Evidence from OECD Countries. Applied Economics, Vol. 40(6), pp. 749–764.
Puddu, S.. (2010). Real Sector and Banking System: Real and Feedback Effects. A Non- Linear VAR Approach, HEC Lausanne Switzerland.
Annex
Table (2): Augmented Dickey Fuller Test Results RGDP
Statistic
CPD Statistic
CAP Statistic
Level 3.81 -1.75 -0.47
Intercept
First Difference -4.01* -5.52* -5.74*
Level -0.18 -2.84 -2.83
Trend and
intercept First Difference -4.02* -5.45* -5.84*
* Significant at 5% level.
Table (3): Johansen Cointegration Test Results Hypothesized
No. of CE(s)
Eigen value
Trace statistic
0.05 critical value
Prob.
Max- Eigen.
statistic
0.05 critical value
Prob.
None 0.533 31.33 24.27 0.0055 22.12 17.79 0.0105
At most 1 0.207 9.21 12.32 0.1570 6.76 11.22 0.2711
At most 2 0.810 2.45 2.45 0.1386 2.45 4.12 0.1386
YEAR Pub. &
semi Pub.
Debt
privat debt
Forgien.
Liab.
RGDP 50% of (Pub. &
semi Pub.
D.)
100%
(private.
Debt)
20%
(Forgien.
Liab.) adjusted
The Denominator
1980 1 2 3 1+2+3
**
1984
** 149.9 1176.1 303.9 3604.1 ** 74.95 303.9 ** 60.78 ** 439.63
**
1985
** 180.8 1241.6 331.9 3506.5 ** 90.4 331.9 ** 66.38 ** 488.68
**
1986
** 210.8 1346.9 349.5 3699.5 ** 105.4 349.5 ** 69.9 ** 524.8
**
1987
** 269.3 1405.7 384.2 3785.5 ** 134.65 384.2 ** 76.84 ** 595.69
**
1988
** 296.4 1492 535 3840.8 ** 148.2 535 ** 107 ** 790.2
**
1989
** 284.9 1599.2 646.7 3428.7 ** 142.45 646.7 ** 129.34 ** 918.49
**
1990
** 284.6 1703 712.4 3419.3 ** 142.3 712.4 ** 142.48 ** 997.18
**
1991
** 271.3 1841.8 1573.3 3474.3 ** 135.65 1573.3 ** 314.66 ** 2023.61
**
1992
** 274.7 2076.4 1964 3967.3 ** 137.35 1964 ** 392.8 ** 2494.15
**
1993
296.8 2316.2 1819.9 4151.1 ** 148.4 1819.9 ** 363.98 ** 2332.28
**
1994
341.9 2769.9 1995.4 4358.1 ** 170.95 1995.4 ** 399.08 ** 2565.43
**
1995
362.2 3200.1 2277.9 4627.7 ** 181.1 2277.9 ** 455.58 ** 2914.58
**
1996
408.9 3362.9 2352.5 4724.3 ** 204.45 2352.5 ** 470.5 ** 3027.45
**
1997
425.3 3535.3 2311.2 4880.5 ** 212.65 2311.2 ** 462.24 ** 2986.09
** 303.1 3839.6 2632.9 5027.5 ** 151.55 2632.9 ** 526.58 ** 3311.03
136
1998
**
1999
307.9 4032.1 2821 5198 ** 153.95 2821 ** 564.2 ** 3539.15
**
2000
317.9 4211.9 3224.3 5418.7 ** 158.95 3224.3 ** 644.86 ** 4028.11
**
2001
284.3 4695.6 3517.6 5704.2 ** 142.15 3517.6 ** 703.52 ** 4363.27
**
2002
261.3 4847.8 3835.1 6034.2 ** 130.65 3835.1 ** 767.02 ** 4732.77
**
2003
278 5015.5 3886.5 6285.2 ** 139 3886.5 ** 777.3 ** 4802.8
**
2004
483.2 5885 4685.4 6823.7 ** 241.6 4685.4 ** 937.08 ** 5864.08
**
2005
528.1 7668.7 4754.8 7379.6 ** 264.05 4754.8 ** 950.96 ** 5969.81
**
2006
520.7 9546.4 5164.8 7976.9 ** 260.35 5164.8 ** 1032.96 ** 6458.11
**
2007
625.7 11003.3 5370.1 8629 ** 312.85 5370.1 ** 1074.02 ** 6756.97
**
2008
652.6 12533.5 4754.1 9253.3 ** 326.3 4754.1 ** 950.82 ** 6031.22
**
2009
482.1 12693.4 4433.4 9760 ** 241.05 4433.4 ** 886.68 ** 5561.13
**
2010
516.5 13612.7 4887.6 9985.4 ** 258.25 4887.6 ** 977.52 ** 6123.37
**
2011
513.8 14925 5258.8 10244 ** 256.9 5258.8 ** 1051.76 ** 6567.46
YEAR The numerator PC&R&A CAP G- P D G-RGDP ∆CAP
** 1984 187.8 ** 0.427177
** 1985 201.2 ** 0.411721 ** 0.092135 ** -0.027080 ** -0.015456
** 1986 214.3 ** 0.408346 ** 0.053028 ** 0.055040 ** -0.003375
** 1987 229.2 ** 0.384763 ** 0.099284 ** 0.023246 ** -0.023582
** 1988 251.1 ** 0.317767 ** 0.392503 ** 0.01460 ** -0.066996
** 1989 280.8 ** 0.305719 ** 0.208785 ** -0.107295 ** -0.012048
** 1990 312.4 ** 0.313283 ** 0.101592 ** -0.002741 ** 0.007564
** 1991 348.6 ** 0.172266 ** 1.208450 ** 0.016085 ** -0.141017
** 1992 348.5 ** 0.139726 ** 0.248331 ** 0.141899 ** -0.032539
** 1993 492.7 ** 0.211252 ** -0.073370 ** 0.046328 ** 0.071525
** 1994 582.8 ** 0.227174 ** 0.096433 ** 0.049866 ** 0.015921
** 1995 701.7 ** 0.240755 ** 0.141575 ** 0.061861 ** 0.013580
** 1996 771 ** 0.254669 ** 0.032749 ** 0.020874 ** 0.013914
** 1997 1047.7 ** 0.350860 ** -0.017555 ** 0.033063 ** 0.096190
** 1998 1181.3 ** 0.356777 ** 0.139191 ** 0.030119 ** 0.005917
** 1999 1316.6 ** 0.372010 ** 0.071442 ** 0.033913 ** 0.015233
** 2000 1377.9 ** 0.342071 ** 0.142963 ** 0.042458 ** -0.029939
** 2001 1436.2 ** 0.329156 ** 0.090965 ** 0.052687 ** -0.012914
** 2002 1545.1 ** 0.326468 ** 0.090260 ** 0.057852 ** -0.002688
** 2003 1623.2 ** 0.337969 ** 0.013402 ** 0.041596 ** 0.0115010
** 2004 1874.3 ** 0.319623 ** 0.205557 ** 0.085677 ** -0.018345
** 2005 2252.6 ** 0.377331 ** 0.014811 ** 0.081466 ** 0.057708
** 2006 3183.3 ** 0.492915 ** 0.086228 ** 0.080939 ** 0.115583
** 2007 3523 ** 0.521387 ** 0.039749 ** 0.081748 ** 0.028472
** 2008 3803.5 ** 0.630635 ** -0.114709 ** 0.072349 ** 0.109247
** 2009 4374.8 ** 0.786674 ** -0.067457 ** 0.054758 ** 0.156039
** 2010 4949.7 ** 0.808329 ** 0.102449 ** 0.023094 ** 0.021654
** 2011 5397.2 ** 0.821809 ** 0.075947 ** 0.025897 ** 0.013479
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