51

Chapter 5

Impact of Capital Adequacy Ratio on the Performance of the Indian Banking Sector

5.1 Introduction

The primary function of a banking sector comprises of three main objectives: the operation of

the payment system, the mobilization of payment system and the allocation of savings to

investment projects. The liberalization has brought in the inception of competition in the

Indian banking sector by weakening entry barriers and introducing the concept of

differentiation after decades of financial repression. The blueprints of the reform were the

Narasimham Committee report published in 1991 followed by the banking sector reforms of

1998. Despite the period of liberalization coinciding with the implementation of the Basel

norms elsewhere on the globe, it was not possible for the Indian Banking sector to embrace

these norms after the decades of financial repression. The Narasimham Committee report and

the banking sector reforms report paved the way for gradual implementation of Basel Norms

in India. Among the few significant changes brought in by the reforms was the

implementation of regulatory capital which fosters a good measure for efficient capital

allocation within the bank.

The foreign banks operating in India were stipulated achieve a CRAR of 8% by March 1993,

while Indian banks with branches abroad were to comply with the norm by March 1995. All

other banks were to achieve a capital adequacy norm of 4% by March 1993 and the 8% norm

by March 1996. In its midterm review of monetary and credit policy in October 1998, on the

basis of the banking sector reforms report, which progressively synchronised the go-forward

scenario of aligning the Indian banking industry with Basel framework, the RBI raised the

minimum regulatory CAR requirement to 9%, and banks were advised to attain this level by

March 31, 2009.

52

In this era of holistic convergence, the weakening of the entry barriers brought in the

inception of competition in the sector along with technological advancements and challenged

the adaptive potential of the existing banks. Compliance to these norms and in the run for its

existence in the competitive environment, the banks objectively incubated diverging portfolio

of products and services leading to a change in the capital allocation and business operations

of the banks.

5.2 Research Objective

In Chapter 2, we have discussed the impact of CAR on various aspects of economy and the

banking sector in detail. The existing set of literature documents that the change in the

portfolio composition of the banks by an increased government investments, limited loans

and advances to achieve a balance between the profitability and stability. The existence of the

empirical evidence pertaining to this effect remains a comparatively unexplored in the global

context or for any particular economy. In Chapter 3, we have discussed about the changes in

the various performance indicators for the different eras of economic reforms. The change in

the business strategies of the banks brought in by these regulations is likely to impact the

performance of the banks. In this chapter we shall try to empirically establish the impact of

these regulation on the performance of the Indian banking sector.

5.3. Behavioural Pattern of CAR in the Indian Banking Sector

Before the financial liberalization and the onset of the Basel norms , the Indian banks were

required to maintain a high Cash Reserve Ratio (CRR) of 15% and a high Statutory Liquidity

Ratio (SLR) of 38.5 % in the form of Government debts. The requirement was reduced to a

great extent following the reforms with an introduction of prudential norms which required

the maintenance of 8% CAR with the onset of the financial liberalization (Roland, 2005;

Tanan, 2001). The CAR was increased to 9% following the banking sector reforms of 1998.

53

However a drastic reduction in the CRR and SLR and the introduction of the prudential

norms posed problems for the capital composition of the banks in India (Nag and Das, 2004).

In our study, which starts from 1999, we find that most of the banks strived and have

maintained the minimum CAR of 9% leaving out a few exceptions. However there is a wide

variation in the CAR over the banks across the years in our time period. A snapshot of the

CAR trends across different ownerships is graphically plotted in Figures 5.1 -5.4 for better

illustration.

54

Figure 5.1 CAR Trends of SBI and Associates

0

2

4

6

8

10

12

14

16

STATE BANK

OF BIKANER

AND JAIPUR

STATE BANK

OF

HYDERABAD

STATE BANK

OF INDIA

STATE BANK

OF INDORE

STATE BANK

OF MYSORE

STATE BANK

OF PATIALA

STATE BANK

OF

SAURASHTRA

STATE BANK

OF

TRAVANCORE

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

55

Figure 5.2 CAR Trends of the Nationalised Banks

0

5

10

15

20

25

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

56

Figure 5.3 CAR Trends of the Private Sector Banks

0

5

10

15

20

25

30

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

57

Figure 5.4 CAR Trends of the Foreign Banks

0

10

20

30

40

50

60

70

80

90

100

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

58

5.4 Data and Framework

We shall analyze the impact of CAR on the performance of the Indian banking sector with

reference to its profitability, efficiency, productivity and asset quality across the different

ownership structure of the Indian Banking sector (Nationalised banks, Foreign Banks, Private

Sector banks, SBI and associates) under a set of control variables to account for size, priority

sector advances, off balance sheet exposures using Panel Data Analysis.

Sarkar, et. al. (1998) classified the performance of the banking sector via profitability and

efficiency. However in view of the objective of the Basel accords of empowering the bank

portfolio by minimizing risky assets from its portfolio through a set of incentivized

framework, we include the asset quality and productivity along with profitability and

efficiency as performance measures in our study. The framework of the study is the same as

that discussed in Chapter 3. However in terms of efficiency, we shall include the overall

technical efficiency scores to account for the inefficiency arising out of managerial

incompetence as well as improper size into our framework. The framework is illustrated in

Table 5.1 below.

Table 5.1 Framework of the Study

Dependent Variables Independent Variables Control Variables

Performance

Indicator

Measure Ownership Dummy

Variables: D1SBI,

D1Pvt, D1Foreign

Era Dummy variables:

D2Basel I and D2Basel

II

CAR

Total assets (size- Total

Assets)

Priority sector advances

to total advances

(Regulated Business -

PSATA)

Non-interest income to

total income (Off balance

sheet exposure-

OBALEX)

Profitability Return on Assets

(ROA)

Return on Equity

(ROE)

Operating Profit

Ratio (OPRTA)

Efficiency Technical

Efficiency of the

Banks(TE)

Productivity Business per

employee(BPE)

Asset Quality Net NPA/Net

Advances(NPANA)

59

The sample of our study in this chapter is the same as that of Chapter 3 and the time period is

also broken up into three eras as per the rationale discussed in Chapter 2. However in

addition to the data that has been used for depicting an overview of the performance

indicators in Chapter 3, which were directly available from the RBI database, we include the

overall technical efficiency scores calculated in Chapter 4.

We have used log of the total assets instead of total assets as a control variable in our study

for the purpose of scaling down the values. The other correction that needed to be done was

of technical efficiency. Since technical efficiency ranges from 0 to 1, we had to transform the

values to include the whole range. However, a simple logistic transformation was not

possible in this case as the technical efficiency scores of many banks were 1. We corrected

the scores on the basis of the correction of extreme values as suggested by MacMillan and

Creelman (2005) by subtracting 1/2n,( where n denotes the number of observations) from the

cases of extreme values (i.e. TE=1) followed by a logistic transformation (log𝑇𝐸

1−𝑇𝐸 ) on the

whole set.

5.5. Methodology - Panel Data Analysis

Our dataset comprises of data of the performance of 62 banks divided across four ownership

structures for a time period of 14 years (1999-2012) divided across three regulatory regimes.

Thus, there lies a high probability of heterogeneity across these units. As a reason, we prefer

Panel data regression over ordinary least square linear regression estimation. There has been

many mergers in the Indian banking sector during this period and many banks have come into

business during this period. Thus our data set comprises of an unbalanced panel.

Mathematically, a panel data regression model can be expressed as:

60

Yit =β1 + β2X2it + β3X3it+ .......+ βkXkit + εit

where i= 1,2,....n and t= 1,2,.....T

The two well known methods for estimating a panel data are Fixed Effects (FE) model and

Random Effects (RE) model. FE explores the relationship between predictor and outcome

variables within an entity (Greene, 2012) e.g. banks in our case. When using FE we assume

that something within the individual may impact or bias the predictor or outcome variables

and we need to control for this. This is the rationale behind the assumption of the correlation

between entity‘s error term and predictor variables. FE removes the effect of those time-

invariant characteristics from the predictor variables so we can assess the predictors‘ net

effect.

Another important assumption of the FE model is that those time-invariant characteristics are

unique to the individual and should not be correlated with other individual characteristics.

Each entity is different therefore the entity‘s error term and the constant (which captures

individual characteristics) should not be correlated with the others. If the error terms are

correlated then FE is no suitable since inferences may not be correct and we need to model

that relationship using RE.

In RE the variation across entities is assumed to be random and uncorrelated with the

predictor or independent variables included in the model (Greene, 2012). RE is used in cases

where the differences across entities is assumed to have some influence on the dependent

variable. Thus an advantage of RE is that the time variant variables can be included which are

otherwise absorbed by the intercept in case of FE. RE assumes that the entity‘s error term is

not correlated with the predictors which allows for time-invariant variables to play a role as

explanatory variables.

61

In order to decide the choice of RE or FE, we generally run a Hausman's test where the null

hypothesis is that the preferred model is random effects vs. the alternative the fixed effects. It

basically tests whether the unique errors are correlated with the regressors, the null

hypothesis is they are not.

5.6. Analysis- Impact of CAR on the Performance of the Indian Banking Sector

Capital adequacy norms restrict the banks in their freedom of capital structure. Rationally a

bank invests in opportunities which seem to be profitable. From the portfolio theory, we

know that risk is directly proportional to return. Thus at the very outset, we can say that the

enforcement of capital adequacy ratio would have a negative impact on the profitability

(OPRTA, ROA, ROE) of the banks. This is in agreement with Calomiris and Kahn (1991),

which documents that agency costs between managers tend to inflate owing to higher capital

ratio due to the imposing of debt repayment in order to satisfy the capital adequacy norms.

However, Holmstrom and Tirole, (1997); Allen et al, (2011) ; Mehran and Thakor, (2011) are

of the view that increased surplus generated by better bank-borrower relationship and

improved monitoring laid down by the capital adequacy norms would bring in a positive

impact on the banks' profitability. Further the capital adequacy norms aims at stability of the

banks and as a result, it tries to minimize the riskiness of the assets in the portfolio of the

banks. From this point of view, it is very obvious that the enforcement of the capital

adequacy norms would bring in an improvement on the asset quality of the banks. Thus in

our case, we expect a negative relation between CAR and the ratio of Non Performing Assets

to Net Advances. There are some evidences in literature about the impact of CAR on the

efficiency and productivity of the banks. However they offer contrasting views. For example,

Berger and Patti (2006), using a parametric distribution free approach, documents that higher

capital ratios have a negative impact on the efficiency of the banks in US. However,

Fiordelisi et al. (2011) documents that the less efficient banks tend to take on more risk and

62

that better capitalized banks perform better in terms of efficiency. We thus need an empirical

investigation before arriving at any conclusion.

5.6.1. Results

Based on the framework as discussed in Table 5.1, we have computed both the models for the

fixed effects as well the random effects on the set of dependent variables OPRTA, ROA,

ROE, TE, BPE, NPANA separately. However, we have reported only the results of the model

which was found to have a significant impact at 5% level of significance on the basis of

Hausman Test. The results are summarized in Table 5.2 below.

We find CAR to have a positive impact in case of all the three cases of the profitability

regresssions i.e. OPRTA, ROA and ROE and in case of technical efficiency regression. In the

other cases CAR was not found to have any significant impact.

In case of operating profit ratio regression the dummy variables pertaining to the time period

was found to have a positive impact and the control variable: ratio of priority sector advances

to total advances was found to have a negative impact. Similar results were observed in case

of ROA regression. However in case of ROE regression, none of the dummy variables or the

control variables were found to have any significant impact.

In case of the Technical efficiency regression, we find that there no impact of the dummy

variables pertaining to time period. However the dummy variable pertaining to foreign banks

was found to have a positive impact. Among the control variables, total assets was found to

have a negative impact on technical efficiency while the ratio of priority sector advances to

total advances was found to have a positive impact.

The R2 of the ROE regression was found to comparatively low since only CAR was found to

be a significant variable in this case. Further studies can be conducted by including a set of

other control variables like debt equity ratio, write offs, cost per borrower for better results.

63

Table 5.2 - Results from Panel Data Regression

Independent

Variables

Dependent Variables

OPRTA

(FE-0.001)

ROA

(FE-0.001)

ROE

(FE-0.001)

TTE

(RE-0.099)

BPE

(RE-0.287)

NPANA

(FE-0.001)

Intercept 3.387

(0.005)***

0.687

(0.477)

-6.786

(0.481)

2.772

(0.092)*

2.597

(0.886)

6.265

(0.051)*

D1SBI 0

(Omt)

0

(Omt)

0

(Omt)

0.388

(0.426)

2.628

(0.631)

0

(Omt)

D1Pvt 0.744

(0.230)

0.616

(0.216)

13.572

(0.006)

0.541

(0.169)

2.517

(0.566)

-2.259

(0.171)

D1Foreign 0

(Omt)

0

(Omt)

0

(Omt)

2.568

(0.001)***

28.797

(0.001)***

0

(Omt)

D2Basel1 0.437

(0.001 )***

0.37

(0.001)***

0.392

(0.698)

0.0218

(0.917)

9.127

(0.001)***

-4.096

(0.001)***

D2Basel2 0.426

(0.022)**

0.41

(0.006)***

-1.336

(0.367)

-0.421

(0.145)

17.138

(0.001)***

-4.264

(0.001)***

Log_TA -0.091

(0.263 )

-0.002

(0.971)

0.987

(0.129)

-0.223

(0.033)**

0.589

(0.607)

0 .117

(0.587)

PSATA -3.232

(0.020)**

-2.43

(0.03)**

-4.966

(0.655)

6.777

(0.001)***

-93.342

(0.001)***

-10.847

(0.003)***

OBALEXP -0.0086

(0.135)

-0.002

(0.706 )

-0.031

(0.507 )

0.004

(0.730)

-0.08

(0.465)

0.018

(0.242)

CAR 0.026

(0.006)***

0.027

(0.001)***

0.307

(0.001)***

0.065

(0.001)***

-0.088

(0.615)

0.007

(0.775)

R2: Within

0.418 0.539 0.036 0.045 0.076 0.378

R2: Between 0.171 0.265 0.009 0.472 0.529 0.006

R2: Overall 0.263 0.296 0.009 0.248 0.284 0.235

p- value in parenthesis

*** denotes significance at 1% level of significance

** denotes significance at 5% level of significance

* denotes significance at 10% level of significance

Omt denotes the variable that have been omitted from the final model

The choice of fixed effect or random effect is specified in the first row of the table

corresponding the performance indicator along with the p-value of the Hausman test.

64

5.7. Conclusion

The CAR was found to have positive impact on the profitability and the efficiency of the

banks in the Indian banking sector. The positive impact on the profitability can be discussed

on the basis of the fact that the maintenance of quality capital in the balance sheet and

restricting the flow to loans to highly risky ventures by the improved monitoring standards

has reduced the bad debts in the banks' portfolio. The amount of funds generated from the

these restricted but safe investments turned out to be greater than that generated when the

lending of the banks were not enforced by risk based on the probability of default of the

borrower. Although the CAR has no significant impact on NPANA regression, we can assert

this view on the basis of the reduction in the ratio of NPANA after the adoption of BASEL

norms. In case of efficiency, we measured it in monetized terms and as a result we have used

only the different sources of income of the banks as our output vectors. Hence with the

reduction in bad debts, we found a positive impact of CAR on the efficiency of the Banks as

well.