how important is ownership in a market withfacultyresearch.london.edu/docs/dp24.pdfderegulation, and...

CENTRE FOR NEW AND EMERGING MARKETS

Discussion Paper Series Number 24

HOW IMPORTANT IS OWNERSHIP IN A MARKET WITH

LEVEL PLAYING FIELD?

THE INDIAN BANKING SECTOR REVISITED*

Sumon Kumar Bhaumik** London Business School

Ralitza Dimova

Catholic University at Leuven

May 2002

Contact details: Anna M Malaczynska Tel: +44 (0)20 7706 6964 Fax: +44 (0)20 7724 8060 www.london.edu/cnem © London Business School, 2002

* The authors would like to thank Subrata Sarkar and Jayati Sarkar about useful discussions on the Indian banking sector, and Saul Estrin for useful discussions about the rationale for and effects of privatization. The usual disclaimers apply. ** Corresponding author. Address: Centre for New and Emerging Markets, London Business School, Sussex Place, Regent’s Park, London NW1 4SA, UK. Phone: +44 20 7262-5050, extension 3386. Fax: 44 20 7724-8060. Email: [email protected].

http://www.london.edu/cnem

Abstract:

It has long been argued that private ownership of firms leads to better firm performance.

However, data from emerging markets do not always support this hypothesis. This has given rise

to the argument that in these countries the extent of competition drives firm performance more

than ownership. In India, banking sector reforms were initiated in 1992, leading to entry

deregulation, and a level playing field for all banks. Data for 1995-96 through 1997-98 suggest

that by 1997-98 ownership was no longer a significant determinant of performance; competition

(and regulations) may have played a bigger role in determining the banks’ performance than

ownership.

Keywords: banking sector reforms, performance, competition, ownership, convergence

Journal of Economic Literature Classification Codes: D21, G21, G28, L32, L33

1

Non-technical Summary

It has long been argued that private ownership of firms leads to better intra-firm allocation of

resources, and leads to the existence of more efficient firms. However, it is by no means

guaranteed that privately owned firms would necessarily outperform public sector firms. The

separation of ownership and management that often accompanies existence of privately owned

firms can give rise to agency problems that undermine the performance of firms. At the same

time, the empirical literature on mergers and acquisitions suggest that there is no strong evidence

to suggest that subsequent to takeover a firm would necessarily perform better. Finally, it can be

argued that if firms are subjected to competitive forces, they would perform efficiently

irrespective of whether they belong to the private or public sector.

In other words, the impact of ownership on performance, which manifests the extent to which a

firm is efficient, is somewhat ambiguous, and, therefore, provides the basis for interesting

empirical exercises. The Indian banking industry can provide the setting for a particularly

interesting empirical exercise because of its heterogeneity and the financial reforms that have

been implemented in India since 1991. The banking sector in India comprises of both privately

owned and government controlled domestic banks, as well as foreign banks. Further, some of the

domestic banks are listed in the stock exchanges, thereby subjecting them to some degree of

market discipline, while others are either fully government owned or owned by a handful of

private agents. Finally, the banking sector was thrown open to competition in 1992, and since

then all banks, private and government controlled, domestic and foreign, have been subjected to

prudential norms and other regulations like cash reserve ratio, thereby creating a level playing

field for all banks. Hence, the Indian banking industry provides a setting that allows one to verify

whether, in a level playing field, a privately owned bank necessarily performs better than a

government owned/controlled bank, and whether competition reduces over time any divergence

that might exist between these two types of banks.

In the only existing study linking performance to ownership in the Indian banking sector, Sarkar,

Sarkar and Bhaumik (1998) [SSB] found that, in so far as profitability is concerned, there was a

clear hierarchy among the Indian banks: foreign banks outperformed domestic banks, domestic

2

private traded banks outperformed other domestic banks, and there was no discernible difference

between domestic private non-traded and public sector banks. They concluded that while the

results were in harmony with the logic of market discipline, the relationship between ownership

and performance was somewhat weak in the Indian banking sector. Further, they argued that

there was no evidence to suggest that the weak relationship between ownership and profitability

was on account of competitive forces that, in principle, induce all banks/firms to behave

optimally, irrespective of their ownership.

However, the SSB study was incomplete on four different counts. First, the study used data for

the financial years 1993-94 and 1994-95 when the impact of deregulation had not fully set in.

Second, sample used for the study did not include “new” private sector banks, the de novo

component of the Indian banking industry. Third, the results in the study may have been

influenced by outliers, as identified later by the Varma Committee set up by the Reserve Bank of

India in 1999. Finally, the SSB study included in the sample all the foreign banks, most of whom

were niche players and hence did not compete with the mainstream of the banking industry.

Using pooled data from 1995-96 through 1997-98, obtained from the Performance Highlights of

Indian banks published by the Indian Banks’ Association and Prowess data set of the Centre for

Monitoring the Indian Economy, this paper explores the determinants of inter-bank variability in

performance, as measured by the return on assets (ROA). The null hypothesis is that foreign

banks and de novo private banks are inherently more profitable than the state owned banks and

the “old” private banks, once size, portfolio composition and labour quality are controlled for.

Empirical analysis suggests the following hierarchy with respect to the inherent profitability of

Indian banks:

Foreign ≡ “New” Private > “Old” Private > State Owned

This is consistent with the findings of the SSB study of 1998. However, unlike in the SSB study

of 1998, this result comes with a very important caveat: there has been a significant convergence

of performance of public and private sector banks, and domestic and foreign banks, between

1995-96 and 1997-98. This evidence in favour of this convergence has been robust with respect

to the econometric methodology and choice of sample.

3

Does this empirical finding suggest that competition alone would lead to enhancement of

performance in the Indian banking sector, and that, therefore, privatisation of the public sector

banks is not required for enhancement of performance? The paper argues that the empirical

analysis has taken into account only the short run, thereby excluding Schumpeterian dynamics

from the ambit of the analysis. Specifically, the data does not explicitly highlight the

heterogeneity in the extent of innovation across banks, and the causal relation (or even

correlation) between ownership status and the extent of innovation. It is a stylised fact that

private owners have more incentive to innovate and move ahead of the competition, whereas

state owned firms try to keep up with the others. In an era of globalisation, the ability of an

industry in any country to prosper depends on its ability to innovate, and adopt and improve

upon global best practices quickly and efficiently. This path is much more likely to be adopted

by privately owned firms than by state owned firms, and, hence, the paper concludes that the

rationale for privatisation of the state owned banks in India remains undiluted.

4

1. Background

It has long been argued that private ownership of firms leads to better intra-firm allocation of

resources, and leads to the existence of more efficient firms. Public choice theorists, for example,

have argued that government officials maximize their own utility and since their objectives are

not necessarily consistent with profit maximization of the firms they manage, government

ownership and management of firms can lead to persistence of X-inefficiencies (Niskanen, 1971;

Levy, 1988). This problem gets further aggravated when the government itself changes the

firms’ objectives frequently to accommodate the interests of different pressure groups (Estrin and

Perotin, 1991).

The aforementioned arguments have found support among economists like Alchian (1965) and

de Alessi (1980) who have argued that private ownership of a firm provides undisputed property

rights and this, in turn, ensures that the firm is run more efficiently than a public sector firm,

none of whose stakeholders have a clear right over its assets and profits. This property rights

hypothesis has been further bolstered by Manne (1965) and Fama (1980) who pointed out that

the managers of a privately owned firm would always be induced to perform efficiently because

they would otherwise become vulnerable to takeovers, leading to loss of control for the

incumbent management.

However, it is by no means guaranteed that privately owned firms would necessarily outperform

public sector firms. The separation of ownership and management that often accompanies

existence of privately owned firms can give rise to agency problems that undermine the

performance of firms (Jensen and Meckling, 1976). At the same time, the empirical literature on

mergers and acquisitions suggest that there is no strong evidence to suggest that subsequent to

takeover a firm would necessarily perform better (Bhaumik, 1999). Finally, it can be argued that

if firms are subjected to competitive forces, they would perform efficiently irrespective of

whether they belong to the private or public sector (Caves and Christensen, 1980; Borcherding et

al., 1982; Millward, 1988).

5

In other words, the impact of ownership on performance, which manifests the extent to which a

firm is efficient, is somewhat ambiguous, and, therefore, provides the basis for interesting

empirical exercises. The Indian banking industry can provide the setting for a particularly

interesting empirical exercise because of its heterogeneity and the financial reforms that have

been implemented in India since 1991. The banking sector in India comprises of both privately

owned and government controlled domestic banks, as well as foreign banks. Further, some of the

domestic banks are listed in the stock exchanges, thereby subjecting them to some degree of

market discipline, while others are either fully government owned or owned by a handful of

private agents. Finally, the banking sector was thrown open to competition in 1992, and since

then all banks, private and government controlled, domestic and foreign, have been subjected to

prudential norms and other regulations like cash reserve ratio, thereby creating a level playing

field for all banks [see Bhaumik and Mukherjee (forthcoming) for details]. Hence, the Indian

banking industry provides a setting that allows one to verify whether, in a level playing field, a

privately owned bank necessarily performs better than a government owned/controlled bank, and

whether competition reduces over time any divergence that might exist between these two types

of banks.

In the only existing study linking performance to ownership in the Indian banking sector, Sarkar,

Sarkar and Bhaumik (1998) [SSB] found that, in so far as profitability is concerned, there was a

clear hierarchy among the Indian banks: foreign banks outperformed domestic banks, domestic

private traded banks outperformed other domestic banks, and there was no discernible difference

between domestic private non-traded and public sector banks. They concluded that while the

results were in harmony with the logic of market discipline, the relationship between ownership

and performance was somewhat weak in the Indian banking sector. Further, they argued that

there was no evidence to suggest that the weak relationship between ownership and profitability

was on account of competitive forces that, in principle, induce all banks/firms to behave

optimally, irrespective of their ownership.

6

However, the SSB study was incomplete on four different counts. First, the study used data for

the financial years 1993-94 and 1994-95. However, since the deregulation and reform of the

banking sector began in 1992, the impact of competitive pressures on the Indian banks were not

apparent until much later. Indeed, Sarkar and Bhaumik (1998) concluded that as of 1994-95 the

deregulation of entry had not had much effect on the extent of competition in the Indian banking

sector. Hence, results obtained using data for 1993-94 and 1994-95 are unlikely to reflect the role

of competition in disciplining banks in India.

Second, the second half of the nineties saw the emergence of “new” private sector banks in India.

Unlike the “old” private sector banks, which were largely closely held and/or controlled, the

“new” private sector banks were widely held and listed in stock exchanges, and the equity of

many of these banks were heavily traded, thereby exposing them to the possibility of takeovers.

Further, unlike the public sector banks which had cornered an overwhelmingly large share of the

market by virtue of ownership and branch networks, and “old” private sector banks which were

well entrenched in their niche markets, the “new” private sector banks had to compete for

deposits and business with all the banks, very much like the foreign banks. Hence, the omission

of these banks from the sample may have not allowed the SSB study to effectively compare the

relative effects of ownership – foreign versus domestic, in this case – and competition on

performance.

Third, the Varma Committee set up by the Reserve Bank of India in 1999 clearly identified three

public sector banks – Indian Bank, United Bank of India, and United Commercial Bank – as

clear outliers in terms of performance. Hence, the clubbing of these three banks with the public

sector banks may have biased the measure of profitability/performance against the public sector

banks, thereby pre-determining the nature of the empirical results in the SSB study.

Finally, the SSB study included in the sample all the foreign banks, then numbering 26.

However, an examination of the branch information and asset portfolios of the foreign banks

indicate that only the 10 largest banks like Citibank, Standard Chartered Bank and ANZ

7

Grindlays compete with the domestic banks both in the deposit and product markets.1 The other

banks’ portfolios comprise mostly of trade credit and off balance sheet “assets” like letters of

credit.2 As we shall see later, the average ROA of foreign banks nearly double once the rates of

return of these “trading” banks are taken into account, and hence inclusion of these banks in the

sample clearly biases upwards the profitability of foreign banks, even as these banks are clearly

not in competition with the domestic banks, certainly not in the deposit market and only

marginally in the credit market.

The aim of this paper is to take a fresh look at the relationship between ownership and

performance in the Indian banking sector, after correcting for the above lacunae. The paper uses

data for all banks – domestic and foreign, private and public sector, listed and non-listed, “old”

and “new” – for the financial years 1995-96, 1996-97 and 1997-98, and an improved

econometric specification to re-examine the aforementioned relationship. The results indicate

that public sector banks recorded improvement in profitability/ performance during the 1995-96

and 1997-98 financial years such that, by the end of 1997-98, the variation in performance

among the banks, as measured by their returns on non-fixed asset (ROA), could no longer be

explained by the differences in their ownership.

The organization of the paper is as follows: Section 2 describes the data and sets out the

econometric specification. Section 3 discusses the results. Section 4 concludes.

2. Data and Specification

The data was obtained from publication entitled Performance Highlights of Indian banks

published by the Indian Banks’ Association. The data contains information taken from the

balance sheets and profit and loss accounts of the banks, as well as information on their branch

network and staff composition. The data about the “listing” status of the domestic banks (at

Indian stock exchanges) was obtained from the Prowess data set of the Centre for Monitoring the

Indian Economy. 1 The other 7 banks are HSBC, Bank of America, American Express, Deutsche Bank, ABN Amro, Bank of Tokyo-Mitsubishi, and British Bank of Middle East. 2 In 1996-97, for example, the net deposits collected by the 10 largest foreign banks operating in India was Rupees 5,798 crore (1 crore = 10 million), while that collected by all other foreign banks was Rupees 1,167 crore. The corresponding figures for 1997-98 were Rupees 4,925 crore and Rupees 425 crore respectively.

8

The descriptive statistics obtained from the data [Tables 1a through 1c] highlight several

patterns, some obvious and others interesting. First, the public sector banks are significantly

larger than the domestic private sector banks and the foreign banks. As expected, a significant

proportion of the domestic banks are rural, a legacy of the branching regulations prior to 1992.

Second, all banks have a significant exposure to government securities, not surprising given the

existence of the statutory liquidity ratio, and the limits on equity exposure of the banks.3

However, contrary to expectations, foreign banks have a larger exposure, on average, to

government securities, as a percentage of total investments, despite the fact that the return on

such securities is lower than returns on other forms of assets. This is a manifestation of the fact

that in India there are few securities apart from those carrying sovereign guarantee that have

acceptable risk-return ratios.4 At the same time, in keeping with expectations, foreign banks, on

average, have smaller exposures to the priority sector – largely agriculture and small industries –

than the domestic banks.

Third, foreign banks earn a significantly greater share of their revenue from non-interest sources,

by way of activities like derivatives trading and merchant banking. This difference can have two

possible explanations. It may manifest the difference in the skill set or know how between

foreign and domestic banks, with the foreign banks having greater know how in areas like

merchant banking and derivatives trading. It may also be a manifestation of the fact that banks

with relatively clean balance sheets in India are often reluctant to lend to non-blue chip corporate

entities for fear of accumulating non-performing assets in an environment that de facto does not

allow foreclosure. In such an event, the interest income of the banks would be low relative to

non-interest income accrued from fee-based activities.

Fourth, the “new” private banks have profiles that are more similar to those of the foreign banks

than those of other domestic banks. Their ROA is significantly higher than those of the other

domestic banks, and similar to the ROA of the 10 largest foreign banks which are comparable to 3 All banks are supposed to keep 25 percent of their deposits in the form of approved liquid assets, and government securities are by far the most liquid of the approved assets which do not include equity. The banks can invest up to 5 percent of incremental deposits into equities. 4 The biggest risk associated with corporate debt instruments is that, except for AAA and AA+ rated securities, they have high liquidity risks associated with them.

9

the domestic banks. Further, while the “new” private banks have many more rural branches than

the foreign banks, as a proportion of their total branches, this proportion for the “new” private

banks is nearly a third of the proportion of rural branches owned and operated by other domestic

banks. Also, these “new” banks have much smaller exposure to the priority sectors than the other

domestic banks, their exposure being similar to the exposure of the foreign banks.

Finally, the data suggests that not only should one distinguish between “old” and “new”

domestic private banks, and create a separate category for the 3 banks which were deemed as

chronically under-performing by the Varma Committee, but also one should distinguish between

the State Bank of India (SBI) group and the other nationalized banks.5 Even apart from the

difference in market power, the differences between the SBI group and the nationalized banks

are manifest in the summary statistics [Tables 1a through 1c]. For example, while the

nationalized banks recorded positive ROA, on average, during 1995-96, the SBI group recorded

negative ROA, on average. The difference between the ROAs of the SBI group and the

nationalized banks was also significant in 1997-98.

The regression specification for the model with ROA as the dependent variable should, therefore,

control for 5 of the following 6 groups of banks: SBI group (STBANK), the 3 “bad” nationalized

banks identified by the Varma Committee (BADNAT), the other/“good” nationalized banks

(GOODNAT), “old” private banks (OLDPRIV), “new” private banks (NEWPRIV), and (the 10

largest) foreign banks (FOREIGN). Since the SSB study had indicated that the foreign banks

clearly outperformed the domestic banks, and since comparison of the relative performances of

the different categories of domestic banks would be more instructive than comparison among

domestic and foreign banks, the foreign banks would be treated as the omitted category.

Following the logic of the SSB study, the explanatory variables include (logarithm of) non-fixed

assets (LASSET), share of government securities in total investment (GSC2TINV), share of the

priority sector in the total credit advanced by the banks (PRT2TADV), and the share of non- 5 The SBI group comprises of SBI, the largest bank in India, and its 7 affiliates. While SBI alone accounts for 20 percent of the deposit market, the 8 banks together accounted for 25 percent of this market in 1997-98. The market share of the largest nationalized bank is about 5 percent. Given this degree of market power, it is reasonable to assume that the SBI group is significantly different from the other public sector banks, namely, the nationalized banks. Hence, one should distinguish between the SBI group and the nationalized banks in the regression analysis.

10

interest income in the total revenue of the banks (NIT2TINC). However, instead of assuming that

ROA varies linearly with the (logarithm of) non-fixed assets, this paper assumes a quadratic

relationship. The rationale for this functional form lies with the hypothesis that an increase in the

scale of operations can initially be associated with economies of scale and scope, while

diseconomies might set in once the scale of operations exceeds some threshold, thereby making

the organization more complex.6 Further, the number of rural branches in the SSB specification,

which is highly correlated with the extent of priority sector exposure of the banks, has been

replaced with the ratio of non-officers to officers (NOCR2OCR), a stylized indicator of labor

quality.

The regression model, therefore, is given by

ROA = β0 + β1*LASSET + β2*LASSET2 + β3*GSC2TINV + β4*PRT2TADV

+ β5*NIN2TINC + β6*NOCR2OCR + β7*STBANK + β8*GOODNAT

+ β9*BADNAT + β10*OLDPRIV + β11*NEWPRIV + ε [1]

The estimates of β1 and β2 are expected to yield an inverted U relationship between (logarithm

of) non-fixed assets and ROA, and hence one of them should have a positive sign and the other a

negative sign. Since the yield on government securities is lower than yield on alternative forms

of financial assets, large exposure to government securities is expected to have a negative impact

on ROA, and hence β3 is expected to be negative. Similarly, given that loans to the priority

sectors may be in the form of soft loans, and given that the political economy of these sectors

may make recovery of these loans difficult, ROA is expected to be negatively correlated with the

banks’ exposure to the priority sector, and hence the expected sign of β4 is negative.

A priori it is difficult to form expectations about the signs of β5 and β6. As explained above, a

high share of non-interest income in a bank’s revenue may be the manifestation of the bank’s

expanding skill set, from credit based activities to fee based activities like merchant banking and

derivatives trading, but it also may manifest credit market failure in the presence of a large

proportion of low-quality borrowers. If high NIN2TINC manifests higher levels of skill or know

how, β5 will be positive, while if it signals credit market failure β5 may well be negative.

6 Note, however, that this is a hypothesis that has to be tested in the context of the Indian banking sector.

11

Similarly, a high ratio of non-officers to officers may indicate a relatively low level of labor

quality, and hence have negative implications for a bank’s ROA. But if the bank is overstaffed to

begin with, a high ratio of non-officers to officers may reflect lower average labor cost and,

therefore, have positive implications for the ROA.

Finally, in keeping with the findings of the SSB study and the Varma Committee, one can expect

β7 through β10 to be negative, with β9 more negative than the others. It is not obvious as to

whether β11 should also be negative, and it may be reasonable to hypothesize that these “new”

private banks, which have had to compete with the incumbent banks for market share in both the

deposit and credit markets, may have performed as well as the foreign banks. In other words, the

relevant null hypothesis would be that β11 equals zero.

3. Regression Results

3.1 Preliminary Results

To begin with, model [1] is estimated separately for each year [Table 2]. The estimation results

indicate that greater exposure to government securities can translate itself into lower ROA (β3 <

0), and that scale of operations can indeed have a non-linear impact on the ROA of a bank (β1 <

0 and β2 > 0). Also, the ratio of non-officers to officers seems to be positively correlated with the

ROA of the banks (β6 > 0), indicating that the banks are possibly overstaffed, a conclusion that is

consistent with the stylized beliefs about Indian banks.

However, one has to take into consideration an important caveat: the above results do not hold

for all the years. Indeed, for 1997-98, none of these results hold. In other words, the portfolios of

the banks, and other attributes like their size and labor quality has different impact on the ROA

of banks in different years. Hence, if one uses a pooled 3-year sample for the analysis, a la the

SSB study, the specification must not only control for the differences in the overall economic

condition prevailing during the 3 years using year dummies, these dummy variables must also be

interacted with the variables reflecting the portfolios of the banks and their labor quality. Despite

the abovementioned results, however, (logarithm of) assets will not be interacted with the year

dummy variables; there is no compelling theoretical/analytical argument to support the

hypothesis that the impact of size on ROA is not independent of time. The specification of the

12

SSB study included interactions between the relevant year dummy and the ownership dummy

variables, but it did not include interactions between year dummies and banks’ characteristics.

This is a possible econometric lacuna of the SSB study which this paper addresses adequately.7

Aside from the fact that the impact of ownership on performance varied across the years, the

result which immediately comes into focus is that except in 1995-96 the good domestic banks –

all except the 3 “bad” nationalized banks identified by the Varma Committee – are no worse than

the foreign banks in terms of profitability. Further, while the “bad” nationalized banks are worse

than the foreign (and other domestic) banks during all three years, the negative coefficient for the

relevant dummy variable is significant at the 5 percent level for 1996-97, and at the 10 percent

level for 1997-98, as opposed to at the 1 percent level for 1995-96. In other words, there seems

to have been a remarkable convergence in the performance of the banks – domestic and foreign,

private and government controlled, “good” and “bad” – over time, when performance is

measured by ROA. If one makes the reasonable assumption that competitive pressures in the

Indian banking sector increased over time, however marginally, as more banks competed for

market share in both the deposit and credit markets, the equalization of the ROAs of foreign and

all except the “bad” domestic banks, at least in the statistical sense, perhaps indicates that, at

least in the Indian banking sector, the level playing field for all banks seems to have been a

bigger driver of performance than ownership.8

3.2 Pooled Time Series-Cross Section Data

It is obvious from the above discussion that, if the analysis is extended in keeping with the

methodology of the SSB study, which had used pooled time series-cross section data for 1993-94

and 1994-95, specification [1] has to be extended to include the year dummies, as well as the

7 The SSB study also does not mention corrections for heteroskedasticity. Appropriate tests indicate that the data for 1995-96, 1996-97 and 1997-98, as well as the pooled data, have heteroskedasticity, and hence the standard errors of the regression coefficients were corrected using White’s robust variance-covariance matrix. 8 Data on share of the credit market has to be adjusted for the quality of the credit portfolios of the various bank groups, before one can draw any meaningful conclusion from them. However, since the ability of a bank to lend depends on its ability to mobilize deposits, competition in the deposit market is usually a reasonable proxy for competition among banks (Sarkar and Bhaumik, 1998). As seen in Figure 1, the domestic private banks’ share of the deposit market rose sharply after the entry of the “new” private banks, while the share of the foreign banks rose significantly after the abandonment of branching restrictions early in the nineties. However, by 1997-98, the public sector banks were able to regain a significant part of the market share it had lost to the foreign and domestic private banks, primarily at the expense of the foreign banks. [Note that the market shares have been calculated on the basis of annual flows, and not on the basis of the stock of deposits outstanding on March 31 of each year.]

13

interactions between the year dummies and the ownership dummies, and the year dummies and

many of the control variables. Specifically, the extended specification has been estimated using

two different samples: a sample of all banks (i.e., all domestic banks and the 10 largest foreign

banks) with foreign banks as the omitted category, and a sample of all domestic banks with

“new” private banks as the omitted category. The specification for the latter regression exercise

includes a dummy variable with value unity if a (domestic) bank was listed on a stock exchange

during a given year, thereby allowing us to control for market discipline. The rationale for using

the two different samples has been explained below. The estimates of the regression coefficients

of this extended model are presented in Table 3.

The estimates obtained using the sample of all banks and the extended specification indicates

that all domestic banks except for the “new” private sector banks are significantly inferior to the

foreign banks in terms of profitability. Specifically, the hierarchy of the banks in terms of

profitability is as follows:

Foreign ≡ “New” Private > “Old” Private > “Good” Nationalized

> State Bank of India Group > “Bad” Nationalized

Clearly, the difference between the different categories of banks in 1995-96 was strong such that

the above hierarchy is preserved even after the data for the three years are pooled.

Given that the performance of the “new” private banks were equivalent to that of the foreign

banks during the three years in question, the reduced sample of only domestic banks was used to

re-estimate the extended specification, with a control for stock exchange listing/market discipline

added to the specification. The omission of the foreign banks allows one to bypass the possibility

that, even though all the foreign banks are listed outside India, their operations in India, which

form a small part of their global operations, may not be significantly influenced by the discipline

imposed on them by their shareholders. The regression estimates indicate that while the above

hierarchy is preserved as such, in 1997-98 the SBI group and both the “good” and “bad”

nationalized banks had significantly narrowed the gap between themselves and the “new” private

banks. This is consistent with the results highlighted in Section 3.1.

14

3.3 Convergence: Further Evidence

It is evident from the above analysis that there was some degree of convergence among the

different types of banks over the three years in question. In order to further verify this

phenomenon, in the next stage of the analysis, the of the change in the ROA of the banks

between 1995-96 and 1996-97, 1996-97 and 1997-98, and 1995-96 and 1997-98 is regressed on

its possible determinants: changes in GSC2TINV, PRT2TINV, NIT2TINC and NOCR2OCR,

and the ownership dummies. The resultant regression model is given by

∆ROA = γ0 + γ1*∆GSC2TINV + γ2*∆PRT2TADV + γ3*∆NIN2TINC

+ γ4*∆NOCR2OCR + γ5*STBANK + γ6*GOODNAT + γ7*BADNAT

+ γ8*OLDPRIV + γ9*NEWPRIV + ε [2]

As before, specification [2] has been estimated using two sample, the first including all banks,

and the second including only the domestic banks. Once again, the omitted category for the

former sample is foreign banks, while that for the latter sample is “new” private banks. Further,

the specification for the latter sample has been expanded to include a dummy variable indicating

whether or not a bank is listed at a stock exchange. The estimates of γ are presented in Tables 4a

and 4b.

The regression estimates clearly indicate that the public sector banks improved their

profitability/performance over the three years in question, to narrow the gap between themselves

and the “new” private banks and foreign banks. More importantly, the improvement in

performance was most significant for the “bad” nationalized banks. At the same time, the “old”

private banks were unable to bridge the gap between themselves and the better performing “new”

private banks and foreign banks. These results, together with the fact that a reasonably long time

had elapsed between the initiation of banking sector reforms and deregulation of entry in 1992

and the years included in the analysis, thereby allowing competition to emerge in the Indian

banking sector, support the aforementioned hypothesis that the convergence of performance of

the different types of banks in India is perhaps more on account of level playing field, i.e.,

greater competition, than on account of ownership differences.

15

3.4 Other Observations

The analysis so far has focused largely on the impact of ownership (and competition) on the

profitability of Indian banks. The empirical evidence has suggested that ownership has perhaps

mattered less than level playing field, and hence competition, in determining the performance of

these banks over time. However, this focus on bank/ownership specific impact on ROA takes

ones attention away from the impact of systemic issues/problems on the banks’ profitability. For

example, banks are forced to lend to the priority sectors, and are obligated to invest in low yield

government securities by way of the statutory liquidity ratio. Further, cyclical and regional

factors which affect the ability of banks to effectively participate in the credit market may impact

different banks in different ways, and hence may not be fully captured by the year dummies.

In order to estimate the impact of these systemic issues on the profitability of Indian banks, panel

data models were estimated using the extended specification which includes year dummies, and

interactions between year dummies and the banks’ portfolio characteristics. As before, the

regression estimates have been obtained using two samples: one including all banks, and the

other including only domestic banks. The results are presented in Tables 5a and 5b. The

Hausman test statistics indicate that the fixed effects model better fits the former sample, and the

random effects model better suits the latter sample.9

The regression estimates indicate that regulations, and perhaps credit market failure which may

have resulted in substitution of credit by investment in government securities in the banks’

portfolios, have had negative impact on the profitability of the banks. However, exposure to

government securities, which has had a negative impact on ROA in general, has had positive

impacts on the ROA of banks in 1997-98, especially for the domestic banks. These results are

consistent with the ones obtained from the pooled cross-section time series analysis. The positive

impact of exposure to government securities on the ROA during 1997-98 can be explained by

rising interest rates that was a policy reaction against the South East Asian currency crisis.

9 The choice of the appropriate model is very important when the time dimension is small and the cross-sectional dimension is large, as is the case with the samples used in this paper (Hsiao, 1986, p. 41).

16

Finally, the size of the banks seem to have had a negative impact overall on their performance.

Simulations using coefficients of Table 5b suggest that the relationship between (logarithm of)

non-fixed assets and ROA of banks in India is U shaped and the relationship turns positive once

logarithm of assets exceeds 19.5. Two things should be noted in this context. First, the U shaped

relationship is contrary to the logic that expansion of scale of operations initially generates

economies of scale and scope, but that expansion may have a negative impact on profitability

once the organization becomes too complex and agency problems set in at different levels.10 The

U shape suggests that, as a bank expands its scale of operations, the costs associated with this

expansion dominates economies of scale and scope, and that these economies bear fruit only

when the size of the bank exceeds some threshold. Second, the logarithm of non-fixed assets of

even the largest bank operating in India is less than 9, suggesting that Indian banks will not be

able to benefit from expansion of scale of operations unless there is a significant consolidation of

the banking sector, leading to the emergence of a few large banks.

4. Concluding Remarks

The performance of the banks in India, as reflected by their ROAs, is in harmony with the

stylized hypotheses that private firms perform better than public sector firms, and, in the context

of emerging markets, foreign firms perform better than domestic firms. However, unlike in the

SSB study of 1998, this result comes with a very important caveat: there has been a significant

convergence of performance of public and private sector banks, and domestic and foreign banks,

between 1995-96 and 1997-98. This evidence in favor of this convergence has been robust with

respect to the econometric methodology and choice of sample.

Since the empirical analysis controls for the scale of operations, nature of the portfolio, and labor

quality, the convergence between the performance of the different types of banks, by way of

improved performance by public sector banks (and domestic banks in general) as opposed to

significant decline in the profitability of private sector banks (or foreign banks, as the case may

be), suggests that the banking sector reforms that leveled the playing field in the Indian banking

sector, and thereby heralded competition, is the likely force spurring this convergence over time.

10 Indeed, this line of argument suggests that the relationship between ROA and scale of operations should have an inverted U relationship.

17

This argument finds further support from two sources. Even though foreign banks continue to

outperform the public sector banks and “old” private banks, despite the convergence, the

performance of the “new” private banks, which have had to innovate and compete to gain market

share at the cost of the incumbent banks. are not significantly different from the performance of

the foreign banks. Further, while public sector banks have narrowed the gap between themselves

and the “new” private sector banks and foreign banks since 1996-97, the “old” private banks

have not been able to catch up significantly with the industry leaders.

Does this empirical finding, therefore, suggest that competition alone would lead to enhancement

of performance in the Indian banking sector, and that, therefore, privatization of the public sector

banks is not required for enhancement of performance? As we have already seen, an increase in

the size of an average Indian bank may allow it to reap benefits on account of scale and scope,

and privatization may be the optimal way to bring about such consolidation. More importantly,

however, we have to note that the above empirical analysis has taken into account the short run,

thereby excluding Schumpeterian dynamics from the ambit of the analysis. Specifically, the data

does not explicitly highlight the heterogeneity in the extent of innovation across banks, and the

causal relation (or even correlation) between ownership status and the extent of innovation. It is a

stylized fact that private owners have more incentive to innovate and move ahead of the

competition, whereas state owned firms try to keep up with the others.11 In an era of

globalization, the ability of an industry in any country to prosper depends on its ability to

innovate, and adopt and improve upon global best practices quickly and efficiently. This path is

much more likely to be adopted by privately owned firms than by state owned firms, and, hence,

the rationale for privatization of the state owned banks in India remains undiluted.

In sum, the banking sector reforms which began in India in 1992, and which has led to greater

competition in this sector (apart from reducing systemic risks by way of prudential norms),

seems to have had a positive effect on the performance and viability of banks. However, in the

face of globalization and increasing competition from foreign banks, the reforms agenda has to 11 Anecdotal evidence, for example, suggests that foreign and de novo private banks in India have been much more successful in adopting and effecting complex trading strategies in the foreign exchange market than the public sector banks, with the latter relying more on vanilla instruments to hedge exposures of their clients. Further, while all foreign and de novo private banks in India have national ATM networks, public sector banks are still not able to provide such service to their customers.

18

be taken a step forward, thereby enabling the domestic banks exploit all ways of enhancing

efficiency and realizing economies of scale and scope. More importantly, reforms would have to

take into consideration the different long term effects of alternative forms of ownership on

dynamism and innovation at the bank level. Indeed, privatization is the next and unavoidable

step in India’s banking reforms.

19

References

Alchian, Armen A., “Some Economics of Property Rights,” Il Politico, 30, 816-829, December

1965.

Bhaumik, Sumon K., “Mergers and Acquisitions: What Can We Learn from the Wave of the

1980s?” Money and Finance, pp. 39-58, October-December, 1999.

Bhaumik, Sumon K. and Paramita Mukherjee, “The Indian Banking Industry: A Commentary,”

in P. Banerjee and F-J. Richter (eds.) Economic Institutions in India, Macmillan,

forthcoming.

Borcherding, Thomas E., Werner W. Pommerehne and Friedrich Schneider, “Comparing the

Efficiency of Public and Private Production: The Evidence from Five Countries,” Journal

of Economics, 0:0, Supplement 2: 127-156, 1982.

Caves, Douglas W. and Laurits R. Christensen, “The Relative Efficiency of Public and Private

Firms in a Competitive Environment: The Case of Canadian Railroads,” Journal of

Political Economy, 88, 5:958-976, 1980.

de Alessi, Louis, “The Economics of Property Rights: A Review of the Evidence,” In Richard O.

Zerbe, Ed., Research in Law and Economics: A Research Annual, Volume 2, pp. 1-47,

Greenwich, CT: Jai Press, 1980.

Estrin, Saul and Virginie Perotin, “Does Ownership Always Matter?” International Journal of

Industrial Organization, 9, 1:55-72, 1991.

Fama, Eugene, “Agency Problems and the Theory of the Firm,” Journal of Political Economy,

88, 2:288-307, 1980.

Hsiao, Cheng, Analysis of Panel Data, Cambridge University Press, 1986.

Jensen, Michael C. and William H. Meckling, “Theory of the Firm: Managerial Behavior,

Agency Costs and Ownership Structure,” Journal of Financial Economics, 3, 4:305-360,

1976

Levy, Brian, “A Theory of Public Enterprise Behavior,” Journal of Economic Behavior and

Organization, 8, 1:75-96, 1987.

Manne, Henry G., “Mergers and the Market for Corporate Control,” Journal of Political

Economy, 73, 2:110-120, 1965.

Millward, Robert, “Measured Sources of Inefficiency in the Public and Private Enterprises in

20

LDCs,” In Paul Cook and Colin Kirkpatrick, Eds., Privatization in Less Developed

Economies, Chapter 6, pp. 143-161, New York: St. Martin’s Press, 1988.

Niskasen, William, “Bureaucrats and Politicians,” Journal of Law and Economics, 18, 3:617-

643, 1975.

Reserve Bank of India, Report on the Working Group on Restructuring Weak Public Sector

Banks (Varma Committee Report), 1999.

Sarkar, Jayati, Subrata Sarkar and Sumon K. Bhaumik, “Does Ownership Always Matter?

Evidence from the Indian Banking Industry,” Journal of Comparative Economics, 26,

262-281, 1998.

Sarkar, Jayati and Sumon K. Bhaumik, “Deregulation and the Limites to Banking Sector

Competition: Some Insights from India,” International Journal of Development Banking,

16, 2:29-42, 1998.

21

Table 1a

Summary Statistics for 1995-96

Public Sector

Nationalized Banks

Private Sector

Foreign Owned

State

Bank

Group

Good Bad Old New All Top 10

Return on

assets

-0.0013

(0.0186)

0.0002

(0.0132)

-0.0374

(0.0278)

0.0116

(0.0085)

0.0194

(0.0085)

0.0281

(0.0294)

0.0168

(0.0064)

Non-fixed

assets

21876.38

(45082.38)

16718.38

(10634.27)

13102.67

(3765.92)

1457.64

(1374.04)

978.22

(731.57)

1337.69

(1914.04)

3554.60

(2193.90)

Government

securities as

% of total

investment

75.09

(4.62)

68.62

(6.50)

76.93

(2.79)

68.85

(9.96)

77.57

(18.59)

84.35

(18.91)

80.09

(12.61)

Priority

sector

lending as

% of total

advances

12.73

(3.93)

32.95

(5.25)

31.37

(5.89)

32.44

(9.84)

15.63

(9.02)

25.38

(17.41)

20.77

(9.91)

Non-interest

income as

% of total

income

14.88

(1.93)

10.67

(1.85)

10.86

(0.39)

12.09

(4.29)

15.72

(5.59)

17.86

(12.55)

18.12

(6.64)

Ratio of

non-officer

to officer

3.21

(0.41)

2.50

(0.56)

2.48

(0.50)

2.71

(0.57)

0.13

(0.62)

0.85

(1.06)

1.56

(0.87)

Non-urban

branches as

% of total

branches

69.92

(4.75)

65.51

(4.79)

67.09

(1.75)

59.45

(17.97)

7.77

(15.63)

0.00

(0.00)

0.00

(0.00)

Table 1b


Public Sector

Nationalized Banks

Private Sector

Foreign Owned

State

Bank

Group


Return on

assets

0.0071

(0.0028)

0.0070

(0.0038)

-0.0159

(0.0079)

0.0163

(0.0257)

0.0159

(0.0072)

0.0189

(0.0157)

0.0130

(0.0047)

Non-fixed

assets

24773.38

(50483.61)

18855.75

(12070.70)

13540.00

(2563.19)

1720.36

(1639.07)

1774.55

(928.93)

1436.60

(2267.03)

4420.50

(2700.95)

Government

securities as

% of total

investment

76.57

(4.07)

68.05

(7.88)

75.94

(1.66)

68.28

(9.57)

67.73

(17.57)

80.88

(21.14)

79.55

(8.35)

Priority

sector

lending as

% of total

advances

34.44

(3.53)

34.14

(4.87)

34.39

(2.96)

32.70

(8.58)

22.48

(9.81)

24.52

(16.77)

16.63

(9.44)

Non-interest

income as

% of total

income

12.92

(1.88)

10.23

(1.62)

9.96

(1.92)

11.49

(4.39)

15.31

(4.74)

18.94

(15.04)

18.23

(7.07)

Ratio of

non-officer

to officer

3.15

(0.36)

2.51

(0.50)

2.51

(0.58)

2.69

(0.56)

0.13

(0.32)

0.82

(1.00)

1.46

(0.85)

Non-urban

branches as

% of total

branches

68.82

(5.57)

62.72

(8.72)

65.75

(1.94)

58.47

(17.92)

12.20

(16.53)

0.00

(0.00)

0.00

(0.00)

1

Table 1c


Public Sector

Nationalized Banks

Private Sector

Foreign Owned

State

Bank

Group


Return on

assets

0.0100

(0.0030)

0.0081

(0.0034)

-0.0074

(0.0091)

0.0073

(0.0086)

0.0149

(0.0063)

0.0285

(0.0439)

0.0433

(0.0809)

Non-fixed

assets

27985.66

(57635.62)

22478.07

(14125.49)

15430.22

(2829.08)

2279.29

(1995.26)

2879.58

(1117.01)

1639.72

(2682.85)

5158.01

(3222.90)

Government

securities as

% of total

investment

75.46

(4.96)

66.13

(8.32)

73.47

(8.15)

66.08

(8.67)

63.14

(11.33)

77.08

(16.65)

74.15

(12.01)

Priority

sector

lending as

% of total

advances

36.07

(5.06)

33.70

(5.34)

31.75

(5.06)

34.31

(9.19)

20.83

(7.19)

25.86

(15.80)

17.21

(9.49)

Non-interest

income as

% of total

income

14.03

(2.10)

11.29

(1.94)

10.85

(1.89)

13.78

(5.84)

19.54

(4.52)

22.80

(15.32)

21.58

(7.11)

Ratio of

non-officer

to officer

3.10

(0.30)

2.47

(0.53)

2.45

(0.51)

2.56

(0.80)

0.13

(0.33)

0.83

(1.03)

1.18

(0.91)

Non-urban

branches as

% of total

branches

69.33

(6.85)

63.04

(5.11)

64.67

(3.30)

68.68

(44.82)

25.16

(13.37)

0.14

(0.86)

0.53

(1.69)

2

Table 2 Yearly Determinants of ROA for Banksa 1995-96 1996-97 1997-98 Constant 0.0304

(0.0303) 0.2149 * (0.0657)

- 0.0016 (0.0763)

Logarithm of net fixed assets - 0.0035 (0.0064)

- 0.0514 * (0.0153)

0.0107 (0.0159)

Square of logarithm of net fixed assets 0.0003 (0.0004)

0.0027 * (0.0008)

- 0.0008 (0.0010)

Government securities as % of total investment

- 0.0002 ** (0.0001)

0.0001 (0.0001)

0.0007 (0.0008)

Priority sector lending as % of total advances

0.0001 (0.0001)

0.0001 (0.0002)

- 0.0007 (0.0007)

Non-interest income as % of total income

0.0002 (0.0003)

0.0008 (0.0005)

- 0.0017 (0.0016)

Ratio of non-officer to officer 0.0048 ** (0.0024)

0.0035 (0.0037)

0.0045 (0.0076)

Dummy variable for State Bank group - 0.0278 * (0.0090)

- 0.0071 (0.0071)

- 0.0360 (0.0307)

Dummy variable for good nationalized banks

- 0.0263 * (0.0071)

0.0007 (0.0083)

- 0.0317 (0.0284)

Dummy variable for bad nationalized banks

- 0.0693 * (0.0151)

- 0.0222 ** (0.0090)

- 0.0567 *** (0.0329)

Dummy variable for old private banks - 0.0119 * (0.0038)

- 0.0088 (0.0058)

- 0.0381 (0.0304)

Dummy variable for new private banks 0.0114 ** (0.0045)

0.0044 (0.0077)

- 0.0153 (0.0144)

R-square 0.58 0.61 0.34 N 68 69 70

Note: 1. Sample: a) all domestic public and private sector banks, and ten largest foreign banks 2. The numbers within parentheses indicate standard errors. 3. *, ** and *** indicate significance at the 1 percent, 5 percent and 10 percent levels.

Table 3 Determinants of ROA for Banks (Pooled Data) Model 1a Model 2b Constant 0.1152 ***

(0.0655) 0.1290 ** (0.0569)

Characteristics of banks: Logarithm of net fixed assets - 0.0193

(0.0143) - 0.0221 *** (0.0133)


0.0012 *** (0.0007)

Government securities as % of total investment (GSC2TINV)

- 0.0003 * (0.0001)

- 0.0003 ** (0.0001)

Priority sector lending as % of total advances (PRT2TADV)

- 0.00003 (0.0001)

- 0.00003 (0.0002)

Non-interest income as % of total income (NIT2TINC)

0.0006 (0.0004)

0.0005 (0.0005)

Ratio of non-officer to officer 0.0043 (0.0034)

0.0043 ** (0.0023)

Ownership variables: Dummy variable for State Bank group (STBANK)

- 0.0228 ** (0.0190)

- 0.0277 ** (0.0115)

Dummy variable for good nationalized banks (GOODNAT)

- 0.0151 *** (0.0077)

- 0.0215 ** (0.0179)

Dummy variable for bad nationalized banks (BADNAT)

- 0.0500 * (0.0154)

- 0.0564 * (0.0179)

Dummy variable for old private banks (OLDPRIV)

- 0.0146 * (0.0054)

- 0.0173 ** (0.0069)

Dummy variable for new private banks (NEWPRIV)

0.0074 (0.0077)

Market discipline: Dummy variable with value unity if bank is listed at a stock exchange

- 0.0006 (0.0018)

Controls for years: Dummy variable for year 1996-97 (Y9697)

- 0.0239 (0.0246)

- 0.0213 (0.0287)

Dummy variable for year 1997-98 (Y9798)

0.0058 (0.0494)

- 0.0145 (0.0188)

Interaction terms: GSC2TINV * Y9697 0.0003

(0.0002) 0.0003 (0.0002)

GSC2TINV * Y9798 0.0010 (0.0007)

0.0001 (0.0002)

PRT2TADV * Y9697 - 6.94E-06 (0.0002)

- 0.0001 (0.0003)

PRT2TADV * Y9798 - 0.0007 (0.0006)

- 0.00004 (0.0002)

NIT2TINC * Y9697 - 0.0002 - 0.0002

1

(0.0006) (0.0009) NIT2TINC * Y9798 - 0.0022

(0.0019) 0.0001 (0.0005)

STBANK * Y9697 0.0136 (0.0088)

0.0094 (0.0082)

STBANK * Y9798 - 0.0130 (0.0244)

0.0171 ** (0.0086)

GOODNAT * Y9697 0.0113 (0.0085)

0.0076 (0.0081)

GOODNAT * Y9798 - 0.0176 (0.0273)

0.0121 *** (0.0071)

BADNAT * Y9697 0.0233 (0.0161)

0.0195 (0.0166)

BADNAT * Y9798 - 0.0056 (0.0354)

0.0334 ** (0.0164)

OLDPRIV * Y9697 0.0092 (0.0083)

0.0054 (0.0061)

OLDPRIV * Y9798 - 0.0233 (0.0230)

0.0004 (0.0060)

NEWPRIV * Y9697 0.0050 (0.0067)

NEWPRIV * Y9798 - 0.0163 (0.0196)

R-square 0.39 0.47 N 207 177 Note: 1. Sample: a) all domestic public and private sector banks, and ten largest foreign banks b) only domestic public and private sector banks 2. The numbers in the parentheses are standard errors. 3. *, ** and *** indicate significance at the 1 percent, 5 percent and 10 percent levels.

2

Table 4a Determinants of Changes in ROA of Banks (All Banksa) Change between

1995-96 & 1996-97 Change between 1996-97 & 1997-98

Change between 1995-96 & 1997-98

Constant - 0.0052 (0.0039)

0.0521 (0.0350)

0.0336 (0.0248)

∆ GSC2TINV - 0.0002 (0.0001)

0.0001 (0.0004)

0.0003 (0.0005)

∆ PRT2TADV - 0.0002 (0.0003)

- 0.0011 (0.0010)

- 0.0012 (0.0010)

∆ NIT2TINC 0.0011 (0.0009)

- 0.0052 (0.0027)

- 0.0029 (0.0022)

∆ NOCR2OCR - 0.0086 (0.0112)

0.0135 (0.0114)

0.0008 (0.0074)

STBANK 0.0207 *** (0.0120)

- 0.0408 (0.0299)

0.0052 (0.0179)

GOODNAT 0.0128 ** (0.0053)

- 0.0451 (0.0319)

- 0.0220 (0.0229)

BADNAT 0.0284 ** (0.0108)

- 0.0407 (0.0348)

- 0.0018 (0.0241)

OLDPRIV 0.0060 (0.0063)

- 0.0430 (0.0274)

- 0.0274 (0.0183)

NEWPRIV 0.0016 (0.0066)

- 0.0321 (0.0249)

- 0.0146 (0.0314)

R-square 0.23 0.28 0.30 N 67 68 68

Note: 1. Sample: a) all domestic public and private sector banks, and ten largest foreign banks 2. The numbers within parentheses indicate standard errors. 3. *, ** and *** indicate significance at the 1 percent, 5 percent and 10 percent levels.

3

Table 4b Determinants of Changes in ROA of Banks (Domestic Banks)

Change between

1995-96 & 1996-97

Change between

1996-97 & 1997-98

Change between

1995-96 & 1997-98

Constant 0.0014

(0.0055)

0.0073

(0.0056)

- 0.0040

(0.0056)

∆ GSC2TINV - 0.0001

(0.0002)

- 0.0002

(0.0001)

- 0.0001

(0.0001)

∆ PRT2TADV - 0.0003

(0.0003)

- 0.00005

(0.0002)

2.45E-06

(0.0002)

∆ NIT2TINC 0.0012

(0.0010)

- 0.0022

(0.0015)

0.00007

(0.0003)

∆ NOCR2OCR - 0.0068

(0.0113)

0.0054

(0.0043)

0.0073

(0.0045)

STBANK 0.0188 **

(0.0099)

- 0.0019

(0.0042)

0.0174 **

(0.0070)

GOODNAT 0.0079

(0.0053)

- 0.0041

(0.0045)

0.0130 **

(0.0057)

BADNAT 0.0222 **

(0.0106)

0.0028

(0.0048)

0.0039 **

(0.0126)

OLDPRIV 0.0011

(0.0046)

- 0.0074

(0.0053)

0.0011

(0.0040)

LISTED - 0.0031

(0.0045)

0.00009

(0.0021)

- 0.0023

(0.0043)

R-square 0.23 0.37 0.36

N 57 58 58

Note:

1. The numbers within parentheses indicate standard errors.

2. *, ** and *** indicate significance at the 1 percent, 5 percent and 10 percent levels.

4

Table 5a Determinants of ROA for Banks (All Banksa)

Fixed Effects Random Effects

Constant 0.0945 (0.1176)

0.1494 * (0.0419)

Characteristics of banks: Logarithm of net fixed assets 0.0380

(0.0366) - 0.0227 ** (0.0095)

Square of logarithm of net fixed assets - 0.0050 *** (0.0028)

0.0010 *** (0.0005)


- 0.0006 ** (0.0002)

- 0.0004 *** (0.0002)


- 0.0003 (0.0002)

- 0.0002 (0.0002)


- 0. 0013 *** (0.0007)

0.0005 (0.0005)


- 0.0004 (0.0019)


- 0.0274 (0.0247)

- 0.0299 (0.0258)


- 0.0207 (0.0269)

- 0.0299 (0.0258)

Interaction terms: GSC2TINV * Y9697 0.0004

(0.0002) 0.0003 (0.0003)

GSC2TINV * Y9798 0.0013 * (0.0003)

0.0013 * (0.0003)

PRT2TADV * Y9697 0.0003 (0.0003)

0.0002 (0.0003)

PRT2TADV * Y9798 - 0.0010 * (0.0003)

- 0.0009 * (0.0003)

NIT2TINC * Y9697 - 0.00003 (0.0006)

- 0.0003 (0.0006)

NIT2TINC * Y9798 - 0.0011 *** (0.0006)

- 0.0017 * (0.0006)

Other parameters σu 0.0599 0.0107 σe 0.0158 0.0158 ρ 0.9342 0.0134 F (fixed effects), Chi-square (random effects) (Prob > F/Chi-square)

7.13 (0.00)

85.18 (0.00)

R-square: Within Between Overall

0.45 0.10 0.08

0.34 0.28 0.27

N 207 207 Hausman Chi-square (Prob > Chi-square)

23.76 (0.04)

Note: 1. Sample: a) all domestic public and private sector banks, and ten largest foreign banks 2. The numbers in the parentheses indicate standard errors. 3. *, ** and *** indicate significance at the 1 percent, 5 percent and 10 percent levels.

5

Table 5b Determinants of ROA for Banks (Domestic Banks)

Fixed Effects Random Effects

Constant 0.2099 * (0.0689)

0.1733 * (0.0284)

Characteristics of banks: Logarithm of net fixed assets - 0.0284

(0.0224) - 0.0312 * (0.0062)


0.0016 * (0.0003)


- 0.0005 * (0.0001)

- 0.0004 * (0.0001)


- 0.0001 (0.0001)

- 0.0001 (0.0001)


- 0.0001 (0.0005)

0.0006 (0.0004)


- 0.0001 (0.0013)


- 0.0311 *** (0.0163)

- 0.0270 (0.0177)


- 0.0219 (0.0188)

- 0.0196 (0.0191)

Interaction terms: GSC2TINV * Y9697 0.0005 *

(0.0001) 0.0004 ** (0.0002)

GSC2TINV * Y9798 0.0006 * (0.0002)

0.0004 (0.0002)

PRT2TADV * Y9697 0.0001 (0.0002)

0.00003 (0.0002)

PRT2TADV * Y9798 - 0.0001 (0.0002)

- 0.00004 (0.0002)

NIT2TINC * Y9697 - 0.0001 (0.0004)

- 0.0001 (0.0005)

NIT2TINC * Y9798 - 0.0004 (0.0004)

- 0.0003 (0.0004)

Other parameters σu 0.0226 0.0068 σe 0.0088 0.0088 ρ 0.8674 0.3728 F (fixed effects), Chi-square (random effects) (Prob > F/Chi-square)

3.65 (0.00)

64.37 (0.00)

R-square: Within Between Overall

0.33 0.21 0.14

0.23 0.39 0.26

N 177 177 Hausman Chi-square (Prob > Chi-square)

15.14 (0.36)

Note: 1. The numbers within parentheses indicate standard errors. 2. *, ** and *** indicate significance at the 1 percent, 5 percent and 10 percent levels.

6

Share of the Deposit Market

0.0010.0020.0030.0040.0050.0060.0070.00

1990-91 1991-92 1992-93 1993-94 1994-95 1996-97 1997-98

Financial Year

Perc

ent

SBI & Associates Nationalised Foreign Domestic Private

Figure 1

how important is ownership in a market withfacultyresearch.london.edu/docs/dp24.pdfderegulation, and...

Documents