This document discusses a study examining the price effects of mergers and acquisitions in the Indian banking sector. The study estimates a fixed effects regression model using data from 1987 to 2015 to analyze how mergers affect deposit rates and interest rates offered by banks in both the short-run and long-run. The results show that in the short-run, mergers lead to insignificant decreases in deposit rates as banks do not gain much market power. However, in the long-run deposit rates of merged banks rise by up to 0.3 basis points as efficiency gains are realized. No significant or consistent effects on interest rates are found. The study concludes that efficiency gains dominate market power effects in the long-run, making mergers beneficial for consumers.

1. EFFICIENCY VS MARKET POWER
Evidence From India
Akash Gandhi
U1558189
September 14, 2016
University of Warwick

2. Abstract
It is a well documented fact that mergers and acquisitions are wealth-increasing events for
shareholders but in the process harm consumers through adverse price effects. The aim of this
paper is to examine price effects associated with mergers in the Indian Banking sector and in-
vestigate how they effect deposit rates offered to customers both in short run and long run. The
paper also studies the significance of interest rate charged on loans while studying the effects
of consolidation in the banking sector. This study estimates a fixed effect regression using data
obtained from Bankscope data source from the years 1987 to 2015 and finds that mergers lead
to very small decrease in deposit rates in the short run, but in the long run merged entities offer
higher deposit rates hence, more favourable prices to consumers. Also I do not find evidence of any
effects on interest rate due to mergers. I conclude from this research that the efficiency gains effect
dominates the market power effect in the long run and mergers can be beneficial for consumers.
i

3. Contents
Abstract i
1 Introduction 1
2 Review of Literature 4
2.1 The Banking Sector and The Market Background . . . . . . . . . . . . . . . . . . 5
2.2 Market Power and Efficiency Gains . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3 Data and Methodology 11
3.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4 Results 15
4.1 The Entire Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.2 The Sub-Samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
5 Robustness Checks 20
6 Conclusion 23
Appendix 26
Tables 27
References 34

4. 1 Introduction
Mergers and acquisitions (M&As) are often employed as a part of a strategic approach used by
many firms to achieve various objectives. The value of global M&As deals has grown approximately
from 1.71 trillion U.S. dollars in 2009 to 4.28 trillion U.S. dollars in 2015. In 2015, the United States
proved to be the largest M&A market worldwide, with M&A deals amounting to approximately
1.97 trillion U.S. dollars. As far as the industry is concerned, the highest value of M&A deals was
signed in the energy, mining and utilities sector.
There is a plethora of literature studying the motivation behind mergers, and the two most
commonly discussed motives are the following: First, M&As lead to efficiency gains via economies
of scale, decreases in average costs and better management. This in turn results in decreasing
the price by the merged firm which is beneficial to consumers. Second, M&As lead to an in-
crease in market power through market expansion, decreasing the competition or increasing the
concentration. It is difficult to determine the underlying motives of M&A participants, but there
is evidence suggesting that some M&As are designed to increase market power (Berger, Demsetz
and Strahan 1999). Though rational for the firms, M&As are not free of pitfalls and lead to the
following basic competitive problems specially if the motive behind is to gain market power. The
first is the elimination of competition between the merging firms, which, depending on their size,
could be significant. The second is that M&As can create substantial market power and might
give the merged entity the power to raise prices as a result worsening the situation for consumers.
The third problem is that, an increase in concentration in the relevant market, might induce the
entities to engage in secret collaboration and result in tacit coordination of behaviour in their
pricing and output decision.
The purpose of this paper is to analyse the pricing effects of M&As in the Indian banking
sector and to see whether the merging entities realise the efficiency gains and transfer the gains to
consumers or they end up using the market power and putting burden on consumers. Prices can
increase due to market power effect or decrease due to efficiency gains hence we examine the change
in direction of prices to study the overall pricing effects. The banking sector provides an ideal
1

5. sample for this study because the banking sector offers the perfect commodity to study the price
effect i.e. bank deposits. There are several advantages associated with studying the impact on
deposit rates offered by banks (Focarelli and Panetta, 2003). Deposit rates are highly standardised
and some of their characteristics are set by law, making comparison over time and across different
banks relevant and plausible. Also as the competition is taken at local level, it provides an
opportunity to study the pricing effects of mergers in markets with varying characteristics, while
holding industry constant.
The majority of previous studies done on this topic have concluded that mergers lead to less
competitive pricing and the downside of the mergers is borne by the consumers (Kim and Singhal,
1993; Prager and Hannan, 1998). But these studies only study the process in a short time period
and have not explored the long-term repercussions of the M&As. While the consolidated firms
can exercise the market power immediately after the merger, it takes time for efficiency gains to
be realised (I will discuss this briefly in the section 2.2). Focarelli and Panetta (2003) is one study
which considers a longer period after the merger and finds that, in the long run, M&As benefit
consumers in the form of more favourable prices.
This study adds to the previous literature in three ways. Firstly, by considering the bank
loans offered by the banks, I also study the effect of M&As on the interest rate. Focarelli and
Panetta (2003) and Prager and Hannan (1998) have voiced the concern that the exclusion of
interest rate might affect the results in their studies. Since mostly literature focuses solely on
deposit rates, I feel that it would be interesting to also explore the effect on interest rates due
to M&As. Secondly, this paper studies both the short term and long-term impacts of M&As,
by separating the short-run period from long-run period. This allows merging entities to fully
realise the efficiency gains effect and pass on the benefits of M&A to consumers in the form
of more favourable prices. Thirdly, I am doing this study on a developing country like India.
Although numerous studies analyse M&As in developed economies, a much smaller number of
studies focus on M&As in emerging economies. To my knowledge, there hasn’t been any previous
study done on India or on a developing country on this topic. Also there are significant differences
in institutional environments, corporate governance practices, and markets between developed
2

6. countries and developing countries, therefore existing knowledge on acquisitions can be extended
by this study.
This study examines the price effects of the recent bank mergers in India using the deposit rate
offered by the banks as the price measure for a period of 29 years (1987-2015). The tangential
purpose of this paper is to see the effects on the interest rate charged by the banks on loans
and their relevance. The data needed for such a study is generally unavailable and it might be
the reason that there is no previous study done on this topic in India. Using the data from the
Bankscope database, I find that in the short run, banks fail to generate much market power hence
the effect on deposit rate is insignificant. And the results for long run analysis shows that the
deposit rate of the merged banks rises and eventually reaches up to 0.3 basis points above its
pre-merger level. I also discover that the results on the effects on interest rates are insignificant
and inconsistent with the hypothesis.
My results have important implications for the Indian Competition Act (2002) and policy-
makers. Since the inception of globalization, M&As have become a common phenomenon in
developing countries. In 1991, Monopolies and Restrictive Trade Practices (MRTP) Act relating
to licensing for expansion of enterprises, amalgamation and takeover of business enterprises, and
acquisition of foreign technology and foreign investment was removed in India (P.L. Beena, 2014).
This was done in the belief that such restrictions hampered the expansion, diversification, and ad-
vancement of technology required for global competitiveness, which had become imperative with
the opening up of the economy. So this would have led to an increase in M&A activities in India.
A primary goal of any antitrust policy in the world is to prevent mergers that would lead to a
significant increase in market power. However, most often, no special analysis was undertaken
by the government regarding the effect of the merger on the public interest before deciding on
the application to allow or reject a potential merger (Khurana 1981). This paper gives a more
accurate picture of pricing effects of M&As and helps in understanding the motives behind M&As.
The paper is organised as follows. Section 2 reviews the literature, Section 3 describes the
data and explains the methodology. Section 4 presents and discusses the results and is followed
3

7. by robustness checks in section 5. Finally, section 6 concludes and contemplates opportunities for
further research.
2 Review of Literature
There are two main strands of literature that are relevant to this paper. The first part talks
about the relevance of the Indian Banking Sector and its background for this research and it is
being discussed in the section 2.1. The second part consists of studies that talk about the two
main effects observed after mergers i.e. the market power effect and the efficiency gain effect. The
review of the latter is relatively more extensive as it lays the foundations for my paper and it is
discussed in the section 2.2.
2.1 The Banking Sector and The Market Background
The banking industry provides an ideal setting for studying the effects of mergers on changes in
prices. As in the case of airline industry, which was the focus of several previous studies, banking
(at least for some banking products) is characterised by many different local markets within a
single industry (Focarelli and Panetta, 2003). This implies that price changes registered by firms
operating in markets affected by mergers can be compared with price changes registered by the
reference group which comprises of those firms that are not operating in such markets, allowing
us to draw inferences concerning the impact of mergers on prices.
The banking sector in India can be divided into two eras i.e. pre-liberalization era and post-
liberalisation era since 1991. The post-liberalisation era has seen tremendous changes as a result
of the embarkation of the policy of liberalisation by the then Narasimha Rao government. Licences
were given to small number of private banks like Global Trust Bank, which later amalgamated
with Oriental Bank of Commerce, Axis Bank (formerly UTI Bank), ICICI Bank and HDFC Bank.
This move, in conjunction with the overall rapid growth of the Indian economy, had augmented
the growth in the Indian banking sector.
4

8. Globalisation and liberalisation lead to various consequences in Indian Banking Sector in terms
of market regulations and structure. Post liberalisation, the exposure to both domestic and inter-
national competition increased for the Indian industries. With the changing environment, many
different strategies had been adopted by the banking sector to remain efficient and to surge at the
forefront in the global arena with M&As being the most important and popular strategy.
This led to several studies being done on various topics related to M&As in the Indian banking
sector. Panwar (2011) studies the ongoing merger trends in Indian banking from the viewpoint
of the stockholders and managers. The findings shows that the trend of consolidation in Indian
banking industry has so far been mainly confined to restructuring of weak banks and harmonization
of banks and financial institutions. Also Vyas, Narayanan and Rammanathan (2012) investigate
the determinants of M&As in the Indian pharmaceutical industry. They analyse the determinants
with the use of logit regression analysis and state that large and multinational firms are investing
more in M&A activities.
The Indian banking sector offers several other attractive features that make it an appropriate
and suitable sample to do this research. First, as mentioned above, after stepping in the post-
liberalisation era, we saw new banks entering the market and the big incumbent banks acquiring
other banks as a part of their growth strategy to face increased competition. Therefore, this
period provides a good sample for our research. Second, with starting of the globalization process,
Indian enterprises started facing the heat of competition from domestic players as well as from
global giants, which called for a level playing field and investor-friendly environment. Therefore,
the new scenario demanded the competition laws to shift the focus from curbing monopolies to
encouraging companies to invest and grow, thereby promoting competition while preventing any
abuse of market power. This led to a relative relaxation of competitive laws and had a positive
impact on the M&A activities all over the country, especially in the banking sector. Third, in the
second phase of WTO commitments commencing from April 2009 warrants that, inter alia, foreign
banks may be permitted to enter into M&A transactions with any private sector bank in India
subject to the overall investment limit of seventy four percent (RBI, 2005). This may have led to
further consolidation of the banking sector. Fourth, as India is a developing country, it gives us
5

9. an opportunity to study the impact of M&A in a developing country context and juxtapose these
findings with the results from the studies on more developed economies.
2.2 Market Power and Efficiency Gains
M&As are considered one of the most attractive business strategies that are increasingly ad-
opted and utilized among companies today. Companies try to improve their competitive position
in a global market place through M&As. There are various motives for M&As and these extend
from maximising shareholder value to economies of scale to managerial motives. Financial service
firms can maximise their value through two main ways after consolidating- increasing their market
power or by increasing their efficiency.
There are various ways to increase efficiency through consolidations. The increase in efficiency
can come from economies of scale or scope, increases in managerial efficiency, diversification,
improving their production techniques, tax considerations or other synergistic gains. The acquirer
firm after the merger may spread their fixed costs over a larger base or get acquainted to cost-
saving technologies which will help in reducing the average costs. They also gain access to a wider
customer base and may enter in new markets.
However, there is no guarantee of the efficiency gains being passed on to the consumers via lower
prices even though the merged firm may become more efficient. This happens because M&As can
increase the market power hence allowing the consolidated firm to raise profit by charging higher
prices to its customers. Market power effect depends on various factors like barriers to entry for
new competitors, geographical scope of the markets involved and characteristics of the deal such
as in-market or out-of-market merger. Therefore the direction of the change in the market price
as a consequence of a merger will be ambiguous and will depend on whether the market power
effect or the efficiency gain effect dominates.
There are several papers that examine the effects of local market concentration on financial
institution’s profits and prices in a static framework. Berger and Hannan (1989 and 1997) and
Hannan (1991) found that banks in more concentrated markets charge higher rates on small
6

10. business loans and pay lower rates on retail deposits. However, there are several caveats related to
the efficacy of results from the static literature. First, we cannot compare the consolidating firm
with other firms because the pricing policies adopted by consolidating firms may be completely
different given that they are opting for M&As. Second, there might be problem of endogeneity.
For example, concentration might increase because of the expansion of the most efficient firms,
with favourable effects on prices (Focarelli and Panetta, 2003).
On the other hand, dynamic market analyses is the direct way to examine the effects of the
M&As on prices and profits while incorporating all other effects like transition cost and market
concentration effects. However, few studies have done dynamic analysis due to lack of adequate
and good quality data.
Previous literature that has examined the effect of M&As on the change of market prices in
a dynamic framework has reached contradicting results. Prager and Hannan (1998) analyse the
price effects of recent US bank mergers by using the deposit rates that banks offer their customers
as the price measure over the 1991-1994 time period. They find that the deposit rates offered
by participants in substantial mergers declined by a greater percentage than did deposit rates
offered by banks not operating in markets in which such mergers took place. This shows that
mergers lead to increased market power. These results were consistent with the study conducted
on the airline industry by Kim and Singhal (1993) where they examine price changes associated
with airline mergers during 1985-1988, a sample period in which mergers were not contested by
the government, comparing the changes in the airfares on routes affected by mergers with those
not affected. They find that the mergers lead to raised airfares by 9.44 percent relative to the
routes unaffected by the merger, creating wealth transfers from consumers. According to their
paper, the impact of efficiency gains on airfares is more than offset by exercise of increased market
power. In addition to this, by using substantially different methods, Severin Borenstein (1990) too
finds the evidence of increased market power following the two controversial airline mergers that
occurred in the U.S. in the fall of 1986. He takes the evidence on price changes, market shares and
changes in service from 1985 to 1987 and finds a significant increase in relative airfares on routes
affected by the Northwest-Republic mergers, but no evidence of fare increases associated with the
7

11. TWA-Ozark merger.
One problem that is common with all these studies is that the examined post-merger period
is too short to capture the full effect on market prices. This is an important issue because of the
different characteristics of the two effects- market power effect and efficiency gains effect. Market
power can be realised and exercised immediately after the deal as it only requires the consolidated
firm to update its pricing strategy to exploit the larger market share. Efficiency gains can take
time to show its effects. This means that all the above mentioned studies have failed to capture
the efficiency gains effect and hence overestimate the adverse price change.
There are several factors that can delay the efficiency gains experienced from mergers. First,
cost-cutting takes time. It takes time for new plans to be implemented and firms to restructure the
infrastructure. Laying off staff in order to cut costs is also a long process especially in businesses
where human capital is important (Pulvino, 1998). Second, merging workforces from different
companies is a lengthy task with a lot of obstacles like different work ethic, attitude, communica-
tion style and corporate culture (Kole and Lehn, 2000). Third, it might take time to improve the
performance, rationalising branches, integrating data processing systems and operations, training
the employees of the target bank and getting them the taste of their new owner’s product. Fourth,
resignation of key executives and employee turnover may cause loss of information. Berger, Saun-
der, Scalise and Udell (1998) mention in their paper that three years are enough for the gestation
period which is needed by a consolidated firm to restructure and start realising the efficiency gains.
Despite this, only a limited number of studies have taken such lags into consideration. One
study worth noting is by Focarelli and Panetta (2003). They examine whether the deposit rates
of the consolidated banks diverge from those of the control sample, distinguishing temporary from
permanent changes. They analyse the Italian market using the data from three different sources
for a period of nine years (1990-1998) by estimating a fixed-effects regression. They find that
the deposit rate lowers by 16.6 basis points in the year of the merger showing that consolidation
increases the market power in the short run. However, in the long run the deposit rate of the
merged bank rises, eventually reaching 13 basis points above its pre-merger level. This finding is
8

12. consistent with the notion that in long run efficiency gains effect dominates market power effect
and mergers can be beneficial for consumers.
However, all the research on the banking sector on this topic has assumed that the banks
only have deposit rate at their disposal. But in reality deposit rates are just one of the products
banks offer to their customers. They have two different rates that they can play with- deposit
rate and interest rate. The deposit rate refers to the amount of money paid out in interest by a
bank or financial institution on cash deposits by the customers and the interest rate is the rate
charged by the bank on the loans given to the customers. So the consolidating bank can use
different strategies for marketing their products to customers. For example, a consolidating firm
can increase the deposit rate but also increase the interest rate charged on loans along with it.
As no previous study talk about the effects on interest rate, it is important to study and also
investigate its relevance for this research.
Thus, to gain further insights into the motivations behind the M&A activities adopted by the
firms and explore whether they actually transfer gains to the consumers, this paper studies the
effects of mergers on both deposit rate and interest rate offered by the banks. By adopting the ap-
proach advocated by Focarelli and Panetta (2003), this paper examines how market price changes
after consolidation and which effect is dominant in the Indian banking sector- The efficiency gains
or the market power?
3 Data and Methodology
3.1 Data
The paper uses twenty-nine years (1987-2015) of panel data obtained from Bankscope database,
which I access through knowledge management centre (KMC) portal under the International
Centre for Education in Islamic Finance (ICEIF). Bankscope is a comprehensive, global database
containing information on public and private banks. As mentioned previously, lack of data is one
of the main reasons that we have limited studies on this topic and this is the only data source
9

13. available that enables me to construct my dataset according to the needs of the paper. It has the
advantage of providing information on banks that were dissolved in the past because of various
reasons including M&As.
The data set contains a total of 169 banks that are currently operating or once operated in
India from which 131 banks are currently active and 21 banks were dissolved through mergers.
Data on M&As are drawn from the website of Reserve Bank of India (RBI). Table 1 lists the
names of the acquired and acquiring firms and the year of merger. From Table 1, I observe that
twenty-one bank mergers have happened in India in the last twenty years (1996-2015) and thirteen
different banks have been involved in acquiring other banks in these mergers with, ICICI Bank
acquiring the most (Five banks). Almost all of the mergers happened in the twenty-first century
with only one merger happening before the year 2000.
With twenty-nine years of data in our hands, we have two big advantages. First, we can
examine long run impact of mergers on the changes of prices. To separate the effect of efficiency
gains from the market power effect, we need to consider longer period in order to form two sub
groups: the transition period and the completion period. Transition period measures the short
run effect and completion period measures the long run impact (discussed later in section 3.2).
This is one of the major requirements of the paper. Second, we can analyse and see the results
using different time periods which can be used as a robustness check.
The data sample includes information on a variety of bank characteristics like size, efficiency,
growth, performance and place. The bank prices employed in this paper are the deposit rates and
the interest rates that are offered and charged by the banks to their customers respectively. Due to
the unavailability of the actual values, this paper uses proxy values for them. From the database I
obtain information on bank’s total assets, total interest income on loans and total interest expense
on customer deposits, which allow me proxy for interest rate and deposit rate by dividing both
total interest income on loans and total interest expense on customer deposits by total assets
respectively. This is one of the limitation that might lead to measurement error as the proxy
values are different from the actual values. But the proxy values are not a misrepresentation of
10

14. the actual values and especially when the proxy values are being normalised. In general, the data
used in this study is not the ideal data. Some important variables (for example deposit rates and
interest rates) are missing from the dataset which limits the scope and expansion of this research.
There are missing values for different variables for some periods which leads to inconsistency in
number of observations for each variable (as can be seen in table 2) and hence can affect the
results. But still, data is good enough to conduct this research.
Table 2 shows summary statistics on the reporting banks. The mean values of our dependent
variables i.e. proxy for deposit rate and interest rate are 4.9% and 6.2% respectively. The mean
value of variables used to compute our dependent variables are $528.9 million for total interest
expense on customer deposits and $ 678.9 million for total interest income on loans. The mean
size measured by the log of total assets is $14.4 thousand while the ratio of operating costs to
gross revenues (a standard indicator of efficiency) is 52.7%. I also report to use data on the growth
of total assets, growth of gross loans, profit before tax and ratio of non-performing loans to gross
loans, which are indicators of growth and performance.
3.2 Methodology
In this section I describe the econometric methodology used in this empirical study. The
paper follows the methodology developed by Focarelli and Panetta (2003) to examine the effects
of mergers on changes in prices. To identify price changes that can be attributed to mergers, I
compare changes in the deposit rate offered by the merging banks relative to the reference group
consisting of banks that were not involved in any M&A activities. According to the theory, if
consolidation increases local market power, it should increase the prices offered by the consolidating
company. Hence, in our case it should decrease the deposit rate offered to consumers. On the
other hand, if mergers only lead to an increase in efficiency, the price should decrease thereby
increasing the deposit rate. Therefore the effect on prices is ambiguous as mergers could influence
both market power and efficiency. Thus for market power effect to dominate the efficiency gains,
the deposit rate should move downwards. On the contrary, increase in the deposit rate would
11

15. indicate that the efficiency gains prevail over the market power effect. I also study the effects on
the interest rate in a similar way.
To separate the impact of market power effect and efficiency gains, and to take the long run
impact of mergers into consideration, I analyse two sub-periods: transition period and completion
period (Kim and Singhal, 1993). This analysis will help in separating the short run effects from
long run or permanent effects of M&As. As mentioned in section 2.2, gestation period required by
a consolidated firm is about three years, so our transition period covers three years that includes
the year of the merger and the next two years. The completion period includes all the subsequent
years. According to our hypotheses, market power should increase in the transition period (short
run) thereby decreasing the deposit rate or increasing the interest rate offered by merged banks
relative to non-merged ones. And during the completion period (long run), when companies have
fully realised their efficiency gains, there should be a rise in the deposit rate or a decline in the
interest rate relative to the reference group.
Following Focarelli and Panetta (2003), I estimate the following fixed-effects regression:
ri,t = α + β0−2InMerger0−2
i,t + β3+InMerge3+
i,t + δBanki,t + dt + i,t (1)
where ri,t is my dependent variable. I am using two different dependent variables i.e. deposit
rate and interest rate. In the case of deposit rate, it is the total interest expense on the deposits
held by customers in year t by bank i divided by total assets in year t by bank i. In case of interest
rate, I take the total interest income on the loans given to customers in year t by bank i divided
by total assets in year t by bank i. InMerge0−2
i,t is a dummy that is equal to 1 if in year t or in the
previous two years (the transition period) bank i merged with a target. InMerge3+
i,t is a dummy
that is equal to 1 if the merger took place three or more years before (the completion period).
Banki,t is a set of time varying bank-specific control variables and dt is a set of time dummies.
Finally, I include a zero-mean random error i,t. I use calendar year fixed effect to control for
cyclical pattern common to all banks. The bank variables capture the relationship of deposit rates
or interest rates and the banks’ characteristics and I avoid simultaneity by including the lagged
12

16. values. I also include growth of gross loans and profit before tax to control for bank specific
characteristics like size, performance and efficiency. Lagged value of growth of total assets is also
used as a control for deposit rate while impaired loans (non-performing loans) is an additional
control for interest rate.
According to my hypotheses, the predicted value for β0−2 should be negative for deposit rate
and positive for interest rate as the coefficient β0−2 represents the market power effect and in
the short run, mergers increase the market power. The value for β3+ will help us in determining
which effect dominates in the long run. A negative value for β3+ for the deposit rate analysis, and
a positive one for interest rate study would indicate that market power dominates the efficiency
gains where as a positive value for β3+ while studying the effect on deposit rate and negative one
for interest rate will imply that efficiency gains outweigh the market power effect.
4 Results
4.1 The Entire Sample
Table 3 reports the main results of the effect of M&As on deposit rate and interest rate in
panel A and panel B respectively, by estimating equation (1). Panel A shows the impact on
deposit rate of the consolidating banks is very modest/ negligible after mergers in the transition
period relative to non-merging banks. The deposit rate increases by 0.06 basis points post-merger
(ceteris paribus) but this coefficient is statistically insignificant. While in the completion period,
the deposit rate increases by 0.3 basis points (significant at a 1% level), being consistent with the
hypothesis that in the long run M&As are beneficial to the consumers. The gains for the consumers
are not trivial even though they are not huge. A potential explanation for the insignificance of the
coefficient of transition period is that the mergers examined in the sample are not able to produce
substantial market power. It can be because the consolidating bank might be operating in a very
large area with very high level of competition or the mergers are too small to affect competitive
conditions. Efficiency gains are not affected by this thereby giving significant results.
13

17. The results reported in panel B of table 3 shows that the interest rate have the same effect
after mergers both in the transition and the completion period, increasing by approximately 0.2
basis points relative to non-merging banks (ceteris paribus). However both these coefficients are
statistically insignificant. According to our hypothesis, during the transition period, we should
have seen an increase in the interest rate whereas in the completion period interest rate should
have decreased due to the realisation of the efficiency gains experienced by mergers in the long
run. My results are contrary to my hypothesis, the coefficients however, are insignificant. This can
be explained by the fact that interest rates are different from deposit rates as they are not affected
by the market conditions and M&As the same way as deposit rates. Deposit rate acts as a better
product for studying the effects of M&As on the pricing policy in the banking sector than interest
rate because of the following reasons (Focarelli and Panetta, 2003). Firstly, deposit rate market
has a volume of local players (there are more people who have bank accounts than people who take
loans) in comparison to the interest rate market, making deposit rate market more competitive
and also more vulnerable to the effects of M&As. Secondly, barriers to entry exist in local deposit
markets (cost of opening branches) which implies that M&As can alter competitive conditions for
deposit rate market. Whereas for interest rate, people do not have to necessarily open an account
in the bank or be a regular customer to get a loan from a bank.
Another alternative hypothesis can be that long-run rise in the deposit rates of the merged
banks does not reflect efficiency gains but a relatively poorer performance by banks. Merging
firms can face difficulties in integrating which can lead to deterioration in the quality of services.
This may induce the merged banks to compensate for the poor quality with higher deposit rates.
However, it can be shown that the post-merger deposit rate changes are not explained by proxies
for service quality but instead banks that are successful in reducing costs after merger are the
ones who raise the deposit rate. But due to data limitation (as mentioned in section 3.1), I could
not gather information for proxies for service quality, successful mergers and unsuccessful mergers.
Focarelli and Panetta (2003) use appropriate data and find that the long run rise in the deposit
rates of merged banks reflects efficiency gains, while quality is not able to explain these price
changes.
14

18. 4.2 The Sub-Samples
As discussed in the previous section, mergers in my sample are unable to generate substantial
market power, so now I am going to explore the effects of M&As on deposit rates on sub-samples
of customers and markets that are more prone to an increase in market power. This might help
in finding a negative coefficient for the transition period (i.e. evidence of market power in short
run), while deposit rate should rise in the completion period as found earlier.
Given that India is a very big country, it might be a useful exercise to analyse subgroups. It
seems logical to account for the concentration of banks around a given bank. A merger involving
a big bank in a high concentration area makes it more likely for the merging entity to gain enough
market power so that the consumers bear the cost of merger in the form of lower deposit rates.
Therefore, I split my sample into two sub-group, North India and South India. There are 51 banks
situated in North India and 115 banks in South India. Although South India has more number of
banks, but South is also home for a lot of big banks that have a larger portion of market share.
I chose the two on the basis of concentration of banks where North forms the less concentrated
group and South forms the more concentrated group.
Panel A of table 4 reports the results of estimating equation (1) for the North India and panel
B of table 4 shows it for South India. As can be seen from the table, the results are consistent
with the entire sample results for the completion period (0.4 basis points for both North and
South as against 0.3 basis points for the entire sample). In the transition period, for North India
the coefficient is still positive though small (0.1 basis points). For South India the coefficient is
negative but very modest, (-0.06) indicative of some market power effect. Both the coefficients,
however, are insignificant.
Even after dividing India into two halves, the halves are still really big. For South India the
coefficient became negative but was still close to zero and insignificant. Therefore to gain further
insight, I now study the effects of M&As on deposit rates in two of the biggest and financially most
important states of South India- Maharashtra and Tamil Nadu. According to the hypothesis, a
more concentrated market should have a greater reduction in the deposit rate in the transition
15

19. period. These two states are an excellent representation of a highly concentrated market as they
have the biggest banks and around 75% of all mergers took place in these two states.
The results, reported in panel A of table 5, indicate that in the transition period the reduction
in the deposit rates is much more than we found previously but it is still insignificant (-0.1 basis
points). The results for the completion period are again significant and consistent with the previous
studies: in the long run the mergers increase deposit rates by 0.6 basis points (ceteris paribus).
As mentioned above, I considered the two biggest and financially most important states of
India. Now I will consider the two most important cities of India- Delhi and Mumbai. Delhi is
the capital of India and Mumbai is termed as the financial capital of India. Both the cities are
involved in three mergers of banks each, which is maximum by any city. I estimate the equation
(1) for these two cities combined and test the same hypothesis mentioned above.
The results are reported in panel B of table 5 for the effects of M&As on deposit rate considering
only the mergers that took place in Delhi and Mumbai. Consistent with the results of previous
sections, I find that in the short run mergers decrease the deposit rate by 0.06 basis points (still
insignificant), whereas in the completion period the mergers lead to an increase in deposit rate by
0.8 basis points (significant at a 1% level).
4.3 Discussion
I started by taking the entire sample and the results for deposit rates were consistent with the
last study (Focarelli and Panetta, 2003) done on this topic. I found that the coefficient for interest
rate is insignificant for both transition and completion period and it cannot explain the effects of
M&As on pricing policy. The reasons as mentioned above justify the exclusion of interest rates
in previous literature. Then to explore further in, I run different regressions using sub-samples.
We can always expect the effect of mergers to be different in different areas due to the difference
in concentration levels, market conditions and area specific characteristics like population. The
market power effect is more vulnerable to these area specific differences. The transition period’s
coefficient for the sub-samples turns negative (except for North India) indicating some effects of
16

20. market power. I can conclude that the market concentration in India is not that high. Even
M&As in the big cities and states, with a lot of big banks operating in those places, could not
manage to generate enough market power to take advantages in terms of transferring wealth from
consumers by decreasing deposit rates. It goes in line with the findings of Panwar (2011) that the
trend of consolidation in Indian banking industry is to restructure by merging with weak banks
(as mentioned above in section 2.1), so it can be a reason why merging banks do not generate
substantial market power. There can be other reasons also for the insignificance of market power
coefficient for example there might be issues with data as discussed in section 3.1. One issue can
be a small number of banks were involved in M&As in comparison to the banks that were not
involved in any M&As activities. Using the proxies could have also affected the results.
For the completion period, all the results that I find in the sub-sample groups are similar
to the one for the entire sample. The deposit rates are always positive and strongly significant.
This shows that efficiency gains take some time to manifest but when they are realised, they
make M&As beneficial to the consumers. However, the gains to the consumers are not too big
(maximum 0.8 basis points) in comparison to the increase in deposit rate found in a previous
study (Focarelli and Panetta, 2003). Still I can confirm that efficiency gains effect overshadows
the market power effect in long run and I can infer that M&As are not harmful for the consumers.
5 Robustness Checks
I undertake several measures to assess the robustness of my results. I consider different samples
based on the different characteristics of banks for example bank size and different time period to
check the impact on my study. Then I use alternative methodology to see the effects on deposit
rate for long run.
In this section I analyse the pricing effect of mergers excluding small banks. I define size of
the banks through the total assets owned by a bank and take the log value of total assets and
categorise a bank as small if the value of log total assets is less than 13 (the average value of
log total assets is 14.5). This will help me in excluding the small banks as small banks can offer
17

21. favourable deposit rate to attract customers or to compensate for other facilities that they are
unable to provide (which are provided by big banks) and in the process introducing a bias in our
results. I check whether removing small banks affect our results.
As reported in the panel A of table 6, there are no changes in the results if I exclude small
banks. The results are consistent with the results of the entire sample results. I see a 0.3 basis
points of increase in deposit rate in the completion period while in the transition period, it’s a
very small positive coefficient and it is also insignificant.
From Table 1 (in section 3.1), I observe that almost all the mergers took place in the years
after 1999. So I analyse a sample of different time period to see any changes in our results. I
will consider a time period of sixteen year i.e. 2000-2015 and estimate equation (1). I find the
results for the completion period are consistent with my main regression (0.3 basis points increase
in deposit rate relative to non-merging banks), however, the coefficient for the transition period
is negative but still close to zero and insignificant (see panel B of table 6).
Another potential underlying cause of the results being contrary to our hypothesis can be that
the consolidating banks can update their pricing policy even before the merger. As discussed in
section 2.2, market power could be exercised immediately, therefore firms can modify their pricing
even before merging. If this is true, our analysis might underestimate the transition effect of
M&As. There to address this issue I construct another dummy (InMerge1−
i,t ) that identifies the
year before merger and then estimate equation (2). From panel C of table 6 I can see that this
dummy is insignificant and its inclusion in the regression does not affect my results.
ri,t = α + β0−2InMerger0−2
i,t + β3+InMerge3+
i,t + δBanki,t + β1−InMerge1−
i,t + dt + i,t (2)
I also estimated different versions of equation (1) by combining different time periods and
different bank characteristics (bank size) with the sub-sample groups mentioned in section 5.2. In
all cases, the results (unreported) are similar and consistent with the results discussed above.
18

22. As a final robustness check I use standard difference-in-difference (DiD) method to see the
long run effect on deposit rates after M&As. The DiD model measures the difference between the
outcomes of the control group and the treatment group before and after the treatment (Blundell
and Dias, 2009). The treatment group consists of all the banks that acquired some banks in my
sample and the control group is made up of all the banks that did not participate in any M&A
activities. Table 7 reports the results showing that mergers lead to a 0.2 basis point increase in
the deposit rate. This is consistent with my main results. The effect confirms that mergers in the
long run lead to efficiency gains and hence a favourable pricing policy for the consumers. It is
important to note that a possible limitation with DiD approach is that if the two types of banks
(that participate and do not participate in M&As in the said markets) are fundamentally different
and they chose to participate or not based on that difference then the results are not too well
founded as we are not comparing like for like.
6 Conclusion
M&As continue to be a highly popular form of corporate development. A wide range of
literature has tried to examine the consequences of M&As on consumers in terms of pricing policy
adopted by the merged entities. Previous studies have found that mergers generate substantial
market power especially in the short run but there can be benefits to consumers in the long run
through lower pricing policy adopted by merged firms. In this study, I have sought to look directly
at the effect of M&As on pricing behaviour of banks by studying recent mergers in the Indian
banking system.
By using panel data from the years 1987 to 2015 obtained from Bankscope database, I study
the effects of M&As on deposit rates offered by the banks, separating short run from long run
effects. I also inspect how interest rates are affected by M&As. Consolidating firms are able to
utilise the increased market power immediately hence charging higher prices from customers in
the short run. In the long run, the prices may also reflect the effects of improved efficiency, which
may cause prices to be more attractive to the consumers. Depending on which effect dominates,
19

23. we can determine whether M&As are beneficial for consumers or not. In the case of deposit
rates examined for this study, a rise in the deposit rate (relative to the reference group) may be
interpreted as the efficiency effect dominating over the market power effect, hence benefitting the
consumers, while a decrease may be interpreted as consistent with dominance of the market power
effect over the efficiency effect and in the process harming the consumers.
Consistent with the previous research, I find that in the long run, the deposit rates of the banks
involved in the M&A activities rise relative to the reference sample. However, in the short run,
there is no evidence for increase in market power leading to lower deposit rates. This is because
the mergers in our sample were too small to generate considerable market power. Although when
I consider a sub-sample that is more susceptible to an increase in market power, I find a negative,
though statistically insignificant, effect on deposit rate for the short run effects. So I can say
that the recent M&A activities in the Indian banking sector are not harmful for the consumers
even though the gains are not huge. Finally, I find that the interest rates are inconsequential
in explaining the effects of M&As on pricing policy. Both short run and long run effects are
insignificant. The possible reason for this is the exogenous nature of the interest rate.
This study has important implications for further research and policy analyses. From the
research perspective, this study can be done in other developing countries especially one with
a higher number of mergers (a better chance to exploit the effects of market power) and see if
the results are consistent. Another interesting field to venture into would be to study the long
term effects of M&A using the extended data sources of the previous studies like Prager and
Hannan (1998) or Kim and Singhal (1993) to better understand the effects of merger and check
if the results change with consideration of longer period. There is scope of testing alternative
hypothesis in which analysis can be extended to study a bigger range of effects but only if we
manage to get a better sample of data. As deposit rate is only one of many types of products
offered by the merging banks, alternative hypothesis can test how other products are affected for
example the quality of services. Better data that includes the actual values of deposit rates will
also help in seeing if there is any measurement error in this study as I proxy for deposit rate. Also
as this is the first study to see the effects of mergers on interest rate, there is scope for further
20

24. investigation. We can examine them in the financial environment of a developed country and see
if they are still insignificant.
From a policy standpoint, policymakers need to be aware of the motives behind mergers before
approving a deal. They should know that efficiency gains would take time to show any kind of
impact therefore should be skeptical of claims of rapid efficiency improvement by firms proposing
consolidations. But given the conditions in India and their concentration level in the banking
industry, as seen from the insignificant coefficient of market, there is potential for more mergers
of banks, at least till the time consolidated banks are not able to generate substantial market
power to harm consumers. Therefore the policymakers need to keep both short term and long
term impact in mind, as while approving or rejecting a merger.
21

25. Appendix
The DiD model measures the difference between the outcomes of the control group and the
treatment group before and after the treatment. The observed difference in this is the DiD
estimator, which is the casual effect of interest. The choice of control group is extremely important.
The control group must be appropriate for comparison with the treatment group, therefore it shares
some commonalities with the treatment group- they should be similar enough that the outcome
trend in the absence of the treatment would be same in both groups. This is the key assumption
of the DiD model, the ”common trend” assumption (Angrist and Pischke, 2009). This paper looks
at the changes in the deposit rates by the banks who are involved in M&A activities, compared to
the groups that were not involved in any M&As. In econometric terms, this amounts to running
a DiD regression of the following form:
ri,t = α + β1Treatmenti,t + β2Interactioni,t + δBanki,t + dt + i,t (3)
where ri.t is the deposit rate offered by the banks. α is the mean outcome variable of the
control group before the treatment. Treatmenti,t is a treatment variable, which is equal to 1 if
the bank is involved in M&As. The coefficient β2 of the interaction term is the casual effect of
interest and is the DiD estimate. Banki,t is a set of time varying bank-specific control variables
and dt is a set of time dummies. Finally, I include a zero-mean random error i,t.
22

26. Tables
Table 1: M&As happened in India since 1987
Name of The Acquiring Bank Bank Acquired Year
ICICI Bank SCICI Bank 1996
HDFC Bank Ltd. Times Bank Ltd. 2000
ICICI Bank Bank of Madura 2001
Citi Citicorp Finance Ltd. 2001
ICICI Bank ICICI Ltd. 2002
Punjab National Bank Nedungadi Bank Ltd. 2003
Oriental Bank of Commerce Global Trust Bank Ltd. 2004
Centurion Bank Ltd. Bank of Punjab 2005
IDBI Ltd. IDBI Bank Ltd. 2005
Federal Bank Ganesh Bank of Kurandwad 2006
IDBI Ltd. United Western Bank Ltd. 2006
ICICI Bank Sangli Bank Ltd. 2007
Indian Overseas Bank Bharat Overseas Bank 2007
Centurion Bank of Punjab Lord Krishna Bank Ltd. 2007
HDFC Bank Centurion Bank of Punjab 2008
State Bank of India State Bank of Saurashtra 2008
ICICI Bank Bank of Rajasthan Ltd. 2010
State Bank of India State Bank of Indore 2010
State Bank of India SBI Commercial & Int. Bank Ltd. 2011
Indiabulls Housing Finance Indiabulls Financial Services 2013
Kotak Mahindra ING Vysya Bank Ltd. 2015
Notes: Data is collected from the website of Reserve Bank of India. Table 1 shows
the M&As history in the Indian Banking sector since 1987.
23

27. Table2:SummaryStatistics
PanelAPanelBPanelC
VARIABLESObservationsMeanStandardDeviation
DepositRate1,5890.04890.0203
InterestRate1,8820.0623.0298
LogvalueofTotalAssets2,08614.480572.121687
TotalInterestExpenseonCustomerDeposits1,589522703.11426005
TotalInterestIncomeonLoans1,882627890.61680802
OperatingcosttogrossRevenue1,97252.6855828.80194
GrowthofGrossLoans1,88126.4201761.9815
GrowthofTotalAssets(Laggedby1year)1,89023.8740248.22905
ProfitbeforeTax2,086170757.8595740.5
ImpairedLoans(NPLs)/GrossLoans1,1405.9767018.628438
Notes:Table2presentsdescriptivesummarystatisticsforthevariablesusedinthispapertorunthe
regression.DataisobtainedfromBankscopedatasourcefrom1987to2015.Thetableshowsthemean
valueandthestandarddeviationofthesevariables.
24

28. Table 3: Baseline Results
Panel A Panel B
Variables Deposit Rate Interest Rate
InMerge1 0.000581 0.00238
(0.00118) (0.00167)
InMerge2 0.00342*** 0.00246
(0.00127) (0.00216)
GrowthofGrossLoans -3.51e-06 -0.000169***
(8.26e-06) (2.95e-05)
GrowthofTotalAssetsL1 -2.23e-05* -
(1.23e-05)
ProfitbeforeTax -3.76e-09*** -1.49e-09
(7.55e-10) (1.08e-09)
ImpairedLoansNPLsGrossLo - -0.000547***
(0.000138)
Observations 1,338 1,064
R-squared 0.862 0.870
Notes: Table 3 reports the main results of the effect of M&As
on deposit rate and interest rate. Data is obtained from Bankscope
data source from 1987 to 2015. Panel A shows the result for deposit
rates and Panel B shows the results for interest rates. InMerge1 is
a dummy that is equal to 1 for a bank if the bank has merged with
a target in the year of consideration or in the previous two years.
InMerge2 is a dummy that is equal to 1 if the merger took place
three or more years before. Growth of gross loans, lagged value of
growth of total assets and profit before tax are the controls used
for deposit rates whereas growth of total assets, profit before tax
and non- performing loans are used as controls for interest rates to
control for bank specific characteristics like size, performance and
efficiency. The outcome is an indicator of the direction of change
in deposit rate and interest rate, in respective panels, in short run
period (InMerge1) and in long run period (InMerge2). Standard
errors are robust to heterogeneity. Significance levels: *p<0.10
**p<0.05 ***p<0.01
25

29. Table 4: North India vs South India
Panel A Panel B
VARIABLES North India South India
InMerge1 0.000975 -0.000626
(0.00203) (0.00140)
InMerge2 0.00407** 0.00413**
(0.00198) (0.00179)
GrowthofGrossLoans -0.000048* 2.63e-06
(0.000026) (8.60e-06)
GrowthofTotalAssetsL1 2.39e-06 -0.000027**
( 0.00003) ( 0.000013)
ProfitbeforeTax -3.49e-09* -3.90e-09***
(1.85e-09) (8.62e-10)
Observations 332 976
R-squared 0.873 0.858
Notes: Table 4 shows the findings of the effect of M&As on
the deposit rate. The data obtained from Bankscope data
source is used as dataset and it consists of 29 years of data
(1987-2015). Panel A shows the impact of consolidation on
deposit rate in North India and panel B shows the impact
of consolidation on deposit rates in South India. InMerge1
is a dummy that is equal to 1 for a bank if the bank has
merged with a target in the year of consideration or in the
previous two years. InMerge2 is a dummy that is equal to
1 if the merger took place three or more years before. Both
regressions include controls such as growth of gross loans,
growth of total assets and profit before tax. The outcome
is an indicator of the direction of change in deposit rate in
short run period (InMerge1) and in long run period (In-
Merge2) and shows separately for North India and South
India. Standard errors are robust to heterogeneity. Signi-
ficance levels: *p<0.10 **p<0.05 ***p<0.01
26

30. Table 5: Sub-Samples
Panel A Panel B
VARIABLES Maharashtra and Tamil Nadu Delhi and Mumbai
InMerge1 -0.00127 -0.000629
(0.00161) (0.00157)
InMerge2 0.00642*** 0.00832***
(0.00231) (0.00213)
GrowthofGrossLoans 7.01e-06 8.56e-06
(8.14e-06) (8.68e-06)
GrowthofTotalAssetsL1 -3.05e-05** -2.94e-05*
(1.34e-05) (1.51e-05)
ProfitbeforeTax -3.32e-09*** -2.27e-09**
(9.11e-10) (9.50e-10)
Observations 714 523
R-squared 0.850 0.844
Notes: Table 5 demonstrates the findings for sub-samples. Data is obtained from
Bankscope data source from 1987 to 2015. Both panels are same regressions with
a focus on different areas. Panel A shows the result for the impact of M&As in
Maharashtra and Tamil Nadu on the deposit rates and Panel B shows the result for
the impact of mergers on the deposit rates in Delhi and Mumbai. InMerge1 is a
dummy that is equal to 1 for a bank if the bank has merged with a target in the year
of consideration or in the previous two years. InMerge2 is a dummy that is equal
to 1 if the merger took place three or more years before. Both regressions include
controls such as growth of gross loans, growth of total assets and profit before tax.
The outcome is an indicator of the direction of change in deposit rate in short run
period (InMerge1) and in long run period (InMerge2). Standard errors are robust to
heterogeneity. Significance levels: *p<0.10 **p<0.05 ***p<0.01
27

31. Table6:RobustnessCheck
PanelAPanelBPanelC
VARIABLESExcludingSmallBanksTimePeriod:2000-2015IncludingTheYearBeforeMerger
InMerge10.000740-0.0001380.000454
(0.00116)(0.00129)(0.00116)
InMerge20.00345***0.00299**0.00301**
(0.00125)(0.00142)(0.00117)
InMerge3---0.00204
(0.00208)
GrowthofGrossLoans-3.30e-05**4.66e-06-3.60e-06
(1.57e-05)(8.35e-06)(8.27e-06)
GrowthofTotalAssetsL14.99e-067.11e-06-2.23e-05*
(2.06e-05)(2.57e-05)(1.23e-05)
ProfitbeforeTax-3.71e-09***-3.66e-09***-3.71e-09***
(7.59e-10)(7.41e-10)(7.65e-10)
Observations1,1551,0061,338
R-squared0.8580.8490.863
Notes:Table6showstheresultsfortherobustnesschecksdoneinthispaper.DataisobtainedfromBankscopedatasource
from1987to2015.PanelAreportstheresultsbyexcludingsmallbanksfromthesample.PanelBshowstheresultsby
takingadifferenttimeperiodintoconsiderationi.e.2000-2015.PanelCshowstheresultsbytakingtheyearbeforethe
mergerintoconsiderationaswell.InMerge1isadummythatisequalto1forabankifthebankhasmergedwithatarget
intheyearofconsiderationorintheprevioustwoyears.InMerge2isadummythatisequalto1ifthemergertookplace
threeormoreyearsbefore.Bothregressionsincludecontrolssuchasgrowthofgrossloans,growthoftotalassetsandprofit
beforetax.Theoutcomeisanindicatorofthedirectionofchangeindepositrateinshortrunperiod(InMerge1)andin
longrunperiod(InMerge2).Standarderrorsarerobusttoheterogeneity.Significancelevels:*p<0.10**p<0.05***p<0.01
28

32. Table 7: Difference-in-Difference
(1)
VARIABLES Deposit Rate
Treatment 0.0133***
(0.00209)
Interaction 0.00267*
(0.00137)
GrowthofGrossLoans 5.48e-06
(7.72e-06)
GrowthofTotalAssetsL1 1.49e-06
(2.30e-05)
ProfitbeforeTax -3.72e-09***
(7.47e-10)
Observations 1,171
R-squared 0.865
Notes: Table 7 reports the results by using
Difference-in-Difference methodology. Data
is obtained from Bankscope data source from
1987 to 2015. Treatment group includes all
the banks that acquired some banks dur-
ing this period and control group consists of
all the banks that are not involved in any
M&A activity. Both regressions include con-
trols such as growth of gross loans, growth
of total assets and profit before tax. The
outcome is an indicator of the direction of
change in deposit rate in the long run period
(Interaction). Standard errors are robust to
heterogeneity. Significance levels: *p<0.10
**p<0.05 ***p<0.01
)
29

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