4. Data Analysis and Interpretation

Transcription

1 CHAPTER 4 You manage what you measure. Unfortunately, performance assessment systems seldom evolve as fast as businesses do. - Andrew Likierman, Dean, London Business School (Harvard Business Review, October 2009)

2 Data Analysis and Interpretation The present study attempts to analyze the financial performance of sample commercial banks involved in mergers during the period 1994 to To evaluate the financial performance, statistical tools like ratio analysis, mean, standard deviation and t-test have been employed. 4.1 Evaluation of post-merger Performance of select commercial banks in India employing ratio analysis approach The financial performance of the 11 acquiring commercial banks (constituting the sample) before and after the merger has been analyzed below with the help of various financial ratios(please refer to Table 4.1) which characterize a commercial bank s performance. In order to test the validity of null hypotheses stated in chapter 1, the following parameters/ratios have been selected to test the results of pre and post -merger periods (Average of three years).

3 104 Table 4.1 Classification of financial ratios Class Code Variable/Parameter Business Parameters V1 Aggregate deposits V2 Average working funds (AWF) V3 Operating profits V4 Net profits(ni) Operational V5 Total Debt to Net worth Parameters V6 Interest income to AWF V7 Net interest income to AWF V8 Operating expenses to AWF V9 Capital adequacy ratio V10 Net interest Income to Average assets V11 Operating expenses to total expenses V12 Efficiency Ratio Profitability V13 Operating profit to AWF Parameters V14 Net profit to AWF V15 Net Profit to average net worth V16 Operating profit to average net worth V17 Asset utilization(au) V18 Equity multiplier(em) V19 Net Interest Margin (NIM) V20 Burden ratio V21 Earnings per share(eps) V22 Price-Earnings(PE) ratio Productivity V23 Business per employee Parameters V24 Business per branch V25 Operating profit per branch V26 Operating profit per employee V27 Assets per employee V28 Loans and Advances per employee V29 Net income per employee Source: Author s perspective

4 105 Table 4.2 Business Parameters (Rs in Crores) Business Parameter Analysis: Pre-Merger and Post-Merger Mean Parameter for acquiring banks Pre- Merger (3-year avg) Post- Merger (3-year avg) t-statistic (0.05 significance) p- values Aggregate Deposits (s) Average Working Funds(AWF) (s) Operating Profit Net Profit (s) Source: Results of data analysis The significance of the each parameter/ratio 3 is explained below by plotting a graph of the mean parameter/ratio (on vertical axis) and the relative time (in years) on horizontal axis. Aggregate Deposits (AD): Aggregate deposits include deposits from public (fixed, savings and current) and deposits from banks (fixed and current). From a different angle, aggregate deposits equal the total of all demand and time deposits. A high deposit figure signifies a bank s brand equity, branch network and deposit mobilization strength. 3 All the parameter/ratio values used for plotting graphs (4.1 to 4.27) are averages over three years before and after the merger year (financial year).

5 Average Deposits (Rs. crores) 106 Graph 4.1 Aggregate Deposits versus Relative Time T-3 T-2 T-1 T0 T+1 T+2 T+3 Relative Time (Yrs.) Source: Processed Data Average Working Funds (AWF): The average of the working funds at the beginning and at the close of an accounting year. Working funds are total resources (total liabilities or total assets) of a bank on a particular date. Total resources include capital, reserves and surplus, deposits, borrowings, other liabilities and provisions. A higher AWF shows a bank s total resource strength. This definition of working funds is in line with capital adequacy calculations to include all resources, not just deposits and borrowings and is more pragmatic.

6 AWF (Rs. Crs.) 107 Graph 4.2 Average Working Funds versus Relative Time T-3 T-2 T-1 T0 T+1 T+2 T+3 Relative Time (Yrs.) Source: Processed Data

7 Average Operating Profit (Rs. Crs.) 108 Operating Profit (OP): It is Net profit before provisions and contingencies. This is an indicator of a bank s profitability at the operating level. In other words, Operating Profit is a measure of a bank s operating efficiency. Graph 4.3 Operating Profit versus Relative Time T-3 T-2 T-1 T0 T+1 T+2 T+3 Relative Time(Yrs.) Source: Processed Data

8 Net Profit (Rs. Crs.) 109 Net Profit (NP): This is profit net of provisions, amortization and taxes. Net Profit is the basic indicator of a bank s profitability. Graph 4.4 Net Profit versus Relative Time T-3 T-2 T-1 T0 T+1 T+2 T+3 Relative Time (Yrs.) Source: Processed Data Analysis of Business Parameters: It would be observed that there is significant difference between average pre- and post-merger figures of Aggregate Deposits, Average Working Funds (AWF) and Net Profits at 5% level of significance, while it is not so in respect of Operating Profits(p-value=0.262). While the percentage growth between average pre and post merger aggregate deposits, average

9 110 working funds and Net profits is 110%, 102% and 142% respectively, the corresponding growth rate for Operating profit is only 19%, justified by p- value of In today s intensely competitive and increasingly deregulated financial markets, both the cost and amount of deposits with the banks are crucial in maintaining a sustainable competitive advantage. The financial management implication of the two features of the depositsstability and low cost source of funds- makes them the preferred source of funds by banks. All else being equal, banks with stronger deposit base are more valuable than those with a weak deposit base. The above advantages are reflected in the Net profit that has grown significantly though the Operating profit has not shown such a high growth rate.

10 111 Table 4.3 OPERATIONAL PARAMETERS Operational Parameter Analysis: Pre- Merger and Post Merger Mean Ratio for acquiring banks Pre- Post- Merger Merger t-statistic (3 years (3 year (0.05 avg. %) avg. %) significance) p-values Total Debt to Net Worth Interest Income to AWF Net Interest Income to AWF Operating Expenses to AWF * Capital Adequacy Ratio(CAR) Net Interest Income to Assets Operating expenses to total expenses * Efficiency Ratio * Source: Results of data analysis; * denotes that the variable in question is significant The significance of the each ratio is explained below by plotting a graph between the average ratio (on vertical axis) and the relative time (in years) on horizontal axis. Total debt to Net worth: This ratio is expressed as a number. The corresponding ratio in a manufacturing company is termed as debt- equity ratio. A higher ratio is a proof of bank s ability to leverage its net worth effectively. Debt-Equity Ratio is arrived at by dividing the total borrowings and deposits by shareholders net worth, which includes equity capital and reserves and surpluses less revaluation reserves and miscellaneous expenses not

11 Total Debt to Networth Ratio (times) 112 written off. This is one of the measures of capital adequacy under the highly popular CAMEL Model, a world-renowned model for evaluating the financial health of a bank. Graph 4.5 Total Debt to Net worth versus Relative Time T-3 T-2 T-1 T0 T+1 T+2 T+3 Relative Time (Yrs.) Source: Processed Data

12 Interest Income to AWF 113 Interest Income to AWF: Expressed as a percentage, this ratio shows bank s ability to leverage its average total resources in enhancing its main stream operational interest income. Graph 4.6 Interest Income to Average Working Funds versus Relative Time T-3 T-2 T-1 T0 T+1 T+2 T+3 Relative Time (Yrs.) Source: Processed Data

13 Net Interest Income to AWF 114 Net Interest Income to AWF: It is a measure of bank s operational profitability as a percentage of average working funds. Graph 4.7 Net Interest Income to Average Working Funds versus Relative Time T-3 T-2 T-1 T0 T+1 T+2 T+3 Relative Time (Yrs.) Source: Processed Data

14 Operating Expenses to AWF 115 Operating expenses to AWF: The operating expense to AWF ratio explains the overall operational efficiency of a bank. In fact, this ratio is one of the indicators of the operating profitability of a bank. Graph 4.8 Operating Expenses to Average Working Funds versus Relative Time T-3 T-2 T-1 T0 T+1 T+2 T+3 Relative Time (Yrs.) Source: Processed Data

15 Capital Adequacy Ratio % 116 Capital adequacy Ratio (CAR): This ratio relates a bank s core net worth to its risk weighted assets. This ratio is an internationally accepted risk- driven measure of a bank s degree of capitalization. This ratio indicates the risk exposure of the bank, the quality of assets and the capacity of the bank s capital to sustain the risk level. A higher ratio indicates that a bank is well capitalized vis-à-vis its perceived risks. It is an excellent indicator of a bank s long term solvency. The minimum CAR prescribed by the RBI is 9%. Graph 4.9 Capital Adequacy Ratio versus Relative Time T-3 T-2 T-1 T0 T+1 T+2 T+3 Relative Time (Yrs.) Source: Processed Data

16 Net Interest Income to Avg.Total Assets 117 Net Interest Income (NII) to Assets: Net interest income is equal to the interest received minus the interest paid. The NII when expressed as a percentage of earning assets gives the NIM (Net interest margin) of the bank. This is an extremely important measure in evaluating a bank s ability to manage interest rate risk. Graph 4.10 Net Interest Income to Avg.Total Assets versus Relative Time T-3 T-2 T-1 T0 T+1 T+2 T+3 Relative time (yrs.) Source: Processed Data

17 Operating expenses to Total Expenses 118 Operating expenses to total Expenses: Operating expenses equals non-interest expenses. It is also called overhead expense. This can be decomposed into components like establishment expenditure etc which as a percentage of total overhead expense indicate where cost efficiencies are being realized or where a bank has a comparative disadvantage. Non-interest expenses vary between banks and are a function of the composition of liabilities. (Timothy W. Koch & S. Scott Macdonald, 2003) Graph 4.11 Operating Expenses to Total Expenses versus Relative Time T-3 T-2 T-1 T0 T+1 T+2 T+3 Relative Time (Yrs.) Source: Processed Data

18 Efficiency Ratio (times) 119 Efficiency Ratio: Efficiency ratio measures a bank s ability to control non-interest expense relative to adjusted operating income. This is given by the formula Efficiency Ratio = Non-interest expense/ (NII+Non-interest income) Banks use this ratio to measure the success of efforts to control noninterest expense while supplementing earnings from increasing fees. The smaller the efficiency ratio, the more profitable is the bank, all other factors being equal (Timothy W. Koch & S. Scott MacDonald, 2003). Graph 4.12 Efficiency Ratio versus Relative Time T-3 T-2 T-1 T0 T+1 T+2 T+3 Relative Time (Yrs.) Source: Processed Data Analysis of Operational Parameters: Of the eight operational parameters explained above, a significant difference has been observed only in respect of three i.e average i) Operating Expenses to AWF ii) Operating expenses to total expenses and

19 120 iii) Efficiency ratio. It may be further observed that average operating expenses to AWF ratio has declined from 3.67% to 3.39% (t=1.842, p=0.005) in the post-merger situation. The other operating performance ratio that has registered a marginal improvement is the efficiency ratio which has declined from 79.92% to 77.76% (t=0.697, p=0.008) in the post-merger situation. However, the average operating expenses to total expenses ratio has slightly increased (from 35.93% to 37.18%) in post merger period (t=-0.545, p=0.049). There is no significant difference in respect of other parameters i.e. average i) Total Debt to Net worth ii) Interest income to AWF iii)net Interest income to AWF iv) Net interest income to assets and v) Capital adequacy ratio, at 5% level of significance. These results suggest that commercial bank mergers in India have, on balance, resulted in a slight decline in operating efficiency. The bank mergers have also not significantly impacted the Interest and Net interest income to Average Working Funds ratios. Net-interest income (NII) = Interest income minus interest expense, highlights a few basic risks in banking. It maps into interest rate risk, liquidity risk and prepayment risk. (Joseph.S.Sinkey Jr, 2002). The efficiency ratio is quite popular and measures a bank s ability to control non-interest expense relative to adjusted operating income [Non-interest expense/(nii+ Non-interest income)]. Conceptually, it indicates how much a bank pays in noninterest expense for one rupee of operating income. The smaller the

20 121 efficiency ratio, the more profitable is the bank, all other factors being equal. (Timothy W. Koch & S. Scott MacDonald, 2003) Table 4.4 PROFITABILITY PARAMETERS Profitability Parameter Analysis : Pre-Merger and Post-Merger Mean Ratio for acquiring banks Post- t- Pre- Merger Merger (3 statistic (3 year avg. year avg. (0.05 %)# %)# sig) p-values Operating Profit to AWF Net Profit to AWF(ROA) * Net Profit to Avg Net Worth(ROE) Operating Profit to average Net Worth Asset Utilization(times) Equity Multiplier(Times) * Net Interest Margin(NIM) Burden ratio Earnings per Share(EPS)(Rs.) * PE ratio** Source: Results of data analysis; ** Valuation ratio; #unless stated otherwise; * denotes the variable in question is significant. The significance of the each ratio is explained below by plotting a graph between the average ratio (on vertical axis) and the relative time (in years) on horizontal axis.

21 Op. Profit to AWF 122 Operating Profit to AWF: Operating profit is net profit before provisions and contingencies. This ratio is a measure of a bank s operating efficiency. Profitability of the bank and also its ability to earn consistently can be easily determined by its earning quality measures. The ratio PBDITATA (OP/AWF) measures the effectiveness of the bank in employing its working funds to generate profits. This measure also finds place in the world-renowned CAMEL model generally adopted to evaluate the financial performance of the commercial banks. CAMEL stands for Capital Adequacy, Asset Quality, Management, Earnings Quality and Liquidity. Working funds is computed as the average of total assets during the year. (For Indian Bank this ratio was 2.61%, topping the list among PSBs in ). Graph 4.13 Operating Profit to AWF versus Relative Time T-3 T-2 T-1 T0 T+1 T+2 T+3 RELATIVE TIME (YRS) Source: Processed Data

22 NP to AWF 123 Net Profit to AWF: This ratio is a foolproof indicator of excellent utilization of resources and optimum leveraging of funds. Graph 4.14 Net Profit to AWF versus Relative Time T-3 T-2 T-1 T0 T+1 T+2 T+3 RELATIVE TIME (YRS) Source: Processed Data

23 NP to Avg. NW 124 Net Profit to Average Net worth: This ratio is the equivalent of the return on net worth ratio used in other industries. It is a good indicator of profitability and return on shareholder s funds. Graph 4.15 Net Profit to Average NW versus Relative Time T-3 T-2 T-1 T0 T+1 T+2 T+3 RELATIVE TIME (YRS) Source: Processed Data

24 Op. Profit to ANW 125 Operating Profit to Net worth: This ratio is corollary to the NP/ANW ratio and another indicator of the shareholder s returns. Graph 4.16 Operating Profit to ANW versus Relative Time T-3 T-2 T-1 T0 T+1 T+2 T+3 RELATIVE TIME (YRS) Source: Processed Data

25 Asset Utilization 126 Asset Utilization: A bank s ROA is composed of asset utilization (AU), the expense ratio(er) and the tax ratio. ROA = AU ER TAX where AU= Total Revenue /Average total assets. The greater the AU and lower are ER and TAX, the higher is the ROA. Graph 4.17 Asset Utilization versus Relative Time T-3 T-2 T-1 T0 T+1 T+2 T+3 RELATIVE TIME (YRS) Source: Processed Data Equity Multiplier: We have ROE= ROA * EM. A bank s equity multiplier compares assets with equity such that large values indicate a large amount of debt financing relative to stockholders equity. EM thus measures financial leverage and represents both a profit and risk measure. EM influences a bank s profits as it has a multiplier effect on ROA in determining a

26 Equity Multiplier (times) 127 bank s ROE. Financial leverage works in bank s favor when the earnings are positive, but the other side is that it also magnifies the negative impact of losses. EM is also a risk measure because it reflects how many assets can go into default before a bank becomes insolvent. A high EM raises ROE when net income is positive but also implies a high solvency or capital risk. Graph 4.18 Equity Multiplier versus Relative Time T-3 T-2 T-1 T0 T+1 T+2 T+3 Relative Time (Yrs.) Source: Processed Data

27 128 Net interest margin (NIM): NIM is a summary measure of the net interest return on income producing assets. Spread, which equals the average yield on earnings assets minus the average cost of interest bearing liabilities, is a measure of the rate spread or funding differential. These two measures are extremely crucial in evaluating a bank s ability to manage interestrate risk. Graph 4.19 NIM versus Relative Time Source: Processed Data Burden Ratio: NIM and spread must be large enough to cover burden, loan loss provisions, securities losses and taxes for a bank to grow profitably. The burden ratio measures the amount of non-interest expense covered by fees, service charges, securities gains, and other income as a fraction of

28 129 average total assets. The greater is this ratio, the greater the non-interest expense exceeds non-interest income for the bank s balance sheet size. A bank is obviously better off with a smaller burden ratio, ceteris paribus. Graph 4.20 Burden Ratio versus Relative Time Source: Processed Data Earnings per share (EPS) and PE ratio While the EPS has increased from Rs.9.22 to Rs (over 100%) postmerger and the rise is statistically significant, the Price-Earnings ratio has increased marginally from 7.26 to 9.83 and the change is statistically not significant Analysis of Profitability Parameters It is observed that while three profitability ratios are significant, the remaining seven are not at 5% level of significance. The ratios which show significant difference in performance between pre and post-merger

29 130 situations are i)net profit to AWF ii) Equity Multiplier and iii)the EPS. While the ratio of Net profit to AWF (ROA) has shown a 10% decline from 1% to 0.90%, the Equity multiplier (EM) (Total assets/total equity) and the EPS have increased from 13.7 to (t= , p=0.007) and from Rs 9.23 to Rs respecively. The increase in EM is psioitive and significant. A high EM increases ROE when net income is positive but is also indicative of a high solvency or capital risk.as regards EPS, it is observed that while the EPS has risen by over 100% post-merger, the valuation ratio PE has not kept pace with it in as much as it has increased by only Rs.2.57 post-merger and the change is not significant. The interest spread to AWF ratio, which shows how well a bank is managing and matching its interest income and interest expenditure effectively has increased only slightly by 0.40% (1.90% to 2.30%, p=0.983). Spread management is critical in successful bank management because of its impact on the bottom-line. Similarly, the operating profit to AWF has also come down from 1.8% to 1.60%. The change in average net profit to average net worth (ROE)(increase by about 2%) is also not significant( p=0.978) as also the Operating profit to average net worth.(p=0.495). The change in AU ratio is not significant in as much as it has come down by just 1% (p=0.211). The changes in NIM and burden ratios are not significant. While the Net interest margin (NIM), which is a summary measure of the net interest return on income producing assets, has not changed significantly (p=0.983), the burden ratio, has increased

30 131 from 0.8 to 1.6 (p=0.640) not indicating clearly towards improved performance in the post-merger scenario. Table 4.5 PRODUCTIVITY PARAMETERS (Rs. in Crores) Productivity Parameter Analysis: Pre-Merger and Post-Merger Mean for acquiring banks Pre Merger Post Merger t-statistic (3 years (3 year (0.05 avg.) avg.) significance) p-values Business Per employee * Business per branch Operating Profit per branch Operating profit per employee * assets per employee * Loans per employee** * Net income per employee Source: Results of data analysis; **Loans & Advances per employee; *denotes that the variable in question is significant. The significance of the each ratio is explained below by means of a graph between the average ratio (on vertical axis) and the relative time (in years) on horizontal axis.

31 Business Per employee 132 Business per Employee: This ratio indicates the degree of labor (employee) productivity of banks. This reflects the contribution of employees towards the business growth which in turn impacts the organizational growth. Graph 4.21 Business per Employee versus Relative Time T-1 T-2 T-3 0 T+1 T+2 T+3 RELATIVE TIME (YRS) Source: Processed Data

32 Business per branch 133 Business per Branch: This ratio indicates how well a bank s branches are being managed and reflects the degree of branch productivity of banks. The commercial banks over the years have been mainly concentrating on deposit mobilization and credit deployment activities. Of late there has been a marked shift towards non-fund based activities to supplement the income streams. Graph 4.22 Business per Branch versus Relative Time T-1 T-2 T-3 0 T+1 T+2 T+3 RELATIVE TIME (YRS) Source: Processed Data

33 Operating Profit per branch 134 Operating Profit per Branch: This ratio indicates how well a bank s branches are being managed and reflects the degree of branch productivity of banks. Graph 4.23 Operating Profit per Branch versus Relative Time T-1 T-2 T-3 0 T+1 T+2 T+3 RELATIVE TIME (YRS) Source: Processed Data

34 Operating profit per employee 135 Operating Profit per Employee: This ratio indicates the degree of labor (employee) productivity of banks. In a service industry like banking, human resources play a crucial role in extending quality services needed for overall development and making the banking profitable and enduring. Graph 4.24 Operating Profit per Employee versus Relative Time T-1 T-2 T-3 0 T+1 T+2 T+3 RELATIVE TIME (YRS) Source: Processed Data

35 Assets per employee 136 Assets per Employee: (Self Explanatory) Graph 4.25 Assets per Employee versus Relative Time T-1 T-2 T-3 0 T+1 T+2 T+3 RELATIVE TIME (YRS) Source: Processed Data

36 Net income per employee Loans per employee 137 Loans and advances per employee: (Self Explanatory) Graph 4.26 Loans& Advances per Employee versus Relative Time T-1 T-2 T-3 0 T+1 T+2 T+3 RELATIVE TIME (YRS) Source: Processed Data Net Income per Employee (Self Explanatory) Graph 4.27 Net Income per Employee versus Relative Time T-1 T-2 T-3 0 T+1 T+2 T+3 RELATIVE TIME (YRS) Source: Processed Data

37 Analysis of productivity parameters: Out of the seven productivity parameters that have been considered for analysis, the improvement in four of them has been found to be statistically significant, post- merger. These are i) Business per employee ii) Operating profit per employee iii) Assets per employee and iv) Loans and advances per employee. The business per employee ratio has increased from Rs.2.75 crores to Rs crores (t = , p= 0.014) i.e by about % which is quite impressive and indicative of the significant contribution made by the employees of the banks towards business growth in the post-merger period. While the operating profit per employee has grown from Rs crores to Rs crores (t= , p=0.003) (by about 26.05%), the Assets per employee ratio has grown from Rs crores Rs crores (by about % which is quite overwhelming) and the increase is statistically signifcant. The Loans per employee ratio has registered a massive increase of over 135% (from Rs to Rs crores) (t=-2.235,p=0.049). The other two productivity ratios which have shown an increase are i) Business per branch(bpb) and ii) Net income per employee. The increase in BPB is not significant possibly because of the speed of branch expansion to meet the competition and enhance the reach & the need to meet regulatory requirements of the RBI in the post liberalization/merger period. The marginal decline in operating profit per branch can also be attributed to these factors. The net income per employee ratio has increased

38 139 marginally from to possibly due to the fact that growth in NI (PAT) has not kept pace with the increase in the number of employees. On balance it can be concluded, that the productivity of commercial banks has shown a healthy increase in the post-merger period. 4.2 Evaluation of post-merger efficiencies of select commercial banks in India using Data Envelopment Analysis (DEA) approach The impact of mergers on the Technical (TE= crste), Pure Technical (PTE= vrste) Scale (SE-se), Cost(X-or CE) and Profit (PE) efficiencies(annexure-b) of the acquiring Indian commercial banks is investigated below, merger-wise. The tables 4.6 to 4.21 summarize DEA TE, PTE, SE, CE and PE scores for 6 public sector and 2 private sector commercial banks as acquiring banks in the respective commercial bank mergers constituting the sample. This could help shed some light on the sources of inefficiency of the Indian banking sector in general as well as to differentiate between the public and private sector banks in terms of their relative efficiencies. DEA analysis has been conducted using the computer program (DEAP version 2.1) written by Professor Tim Coelli (1996). This program has been used to construct DEA frontiers for the calculation of various efficiency scores and also for the calculation of Malmquist Total factor productivity (TFP) Indices.

39 140 DEA data analysis Table 4.6 Oriental Bank of Commerce (OBC) -Bari Doab Bank (BDB) Merger (Technical Efficiency) Total Sample Technical Efficiency MODEL1 Year Pre-merger Merger Year Post-merger Mean premerger efficiency Mean postmerger efficiency TE PTE SE Technical TE Efficiency PTE MODEL 2 SE Source: Appendix A, Tables A1, A2 Total Sample Table 4.7 Oriental Bank of Commerce (OBC) -Bari Doab Bank (BDB) Merger (Cost & Profit Efficiencies) Year Pre-merger Merger Year Post-merger Mean premerger efficiency Mean postmerger efficiency Cost(X-) Efficiency CE Profit Efficiency PE Source: Appendix A, Tables A3, A4 DEA model decomposes Technical Efficiency (TE) in two parts, one due to Pure technical efficiency (PTE) and the other due to Scale efficiency (SE). Pure technical efficiency refers to the firm s (bank s) ability to avoid waste by producing as much output as input usage allows, or by using as little

40 141 input as output production allows. Scale efficiency refers to the ability of the firm to operate at its optimal scale.(coelli, 1998). The above results indicate that the Oriental Bank of Commerce-Bari Doab Bank Ltd merger led to an increase in mean post-merger TE and SE of the acquiring bank (OBC) under both the Models, 1 and 2(see table 4.6). A similar improvement has been observed post-merger in regard to the CE and PE also (see table 4.7). While the PTE has remained at 100% all along under the Model1, it has gone up from 94.00% to 95.40% under model 2 post-merger. The scale efficiency has increased from 87.30% to 99.60% under Model, 1 and from 61.80% to 67.70% under Model, 2. The cost and profit efficiencies of the acquiring bank (OBC) have improved from 96.10% to 98.70% and 96.30% to 98.40% respectively. The results suggest that the acquiring bank has performed relatively well in transforming expenditure into income under Model 1.This follows from the mean TE scores under Model 1,which are 87.30% pre-merger and 99.60% post-merger. This also indicates that the acquiring bank has reduced the input waste by 12.30% post-merger. The results compare favorably with Chu and Lim (1998) where the average overall efficiency of Singapore banks was found to be 95.30% during the period The results also compare well with the14%-25% average input wastes exhibited by Indian commercial banks (Bhattacharyya et al, 1997) and the study of Fukuyama (1993) on Japanese banks (14%). However,

41 142 under Model 2, the mean technical efficiency of the acquiring bank has gone up by only 5.30% post-merger. While the PTE hovered around 95% during the period under consideration, the mean SE which was 61.80% per-merger had gone up to only 67.70% post-merger. It may therefore be concluded that the primary cause of marginal increase in mean TE under Model 2 was SE only and the acquiring bank was pure-technically fairly efficient (95%) during the period under consideration as could be seen from the above table. The merger has also resulted in a post-merger increase of 2% %( from 96% to 98.5%) in both mean Cost(X-) and Profit efficiencies for the acquiring bank(models 3 and 4 respectively). Large banks may be more X-efficient than small banks if they are better able to attract and retain capable managers, and because they tend to be located in highly competitive metropolitan areas where competitive pressures are higher (Robert De Young, 1997). OBC s X-efficiency at a very high level of 98.50% post-merger, is somewhat in line with this proposition. The mean profit efficiency of OBC (which was 96.30% before merger) had gone up by about 2.50% to 98.40%, which is quite impressive. However the marginal increase in PE of OBC may be attributed to the very small size of BDB in comparison with that of OBC. Hence the impact of merger on cost and profit efficiencies of the acquiring bank OBC does not appear to be significant.

42 143 Total Sample Technical Efficiency MODEL1 Table 4.8 Oriental Bank Of Commerce (OBC)-Global Trust Bank (GTB) Merger (Technical Efficiency) Year Pre-merger Merger Year Post-merger Mean premerger efficiency Mean postmerger efficiency TE PTE SE Technical TE Efficiency PTE MODEL 2 SE Source: Appendix A, Tables A1, A2 Table 4.9 Oriental Bank Of Commerce (OBC)-Global Trust Bank (GTB)Merger (Cost & Profit Efficiencies ) Total Sample Year Pre-merger Merger Year Post-merger Mean premerger efficiency Mean postmerger efficiency Cost(X-) Efficiency CE Profit Efficiency PE Source: Appendix A, Tables A3, A4 The table 4.8 clearly indicates that the mean TE of the acquiring bank (OBC) under Model 1 has remained stable at 98.70% even after the merger. The mean PTE has declined slightly from 99.50% to 98.70% post-merger despite the increase in mean SE from 99.20% to 100%. Under Model 2, there is a quantum jump in mean TE and mean PTEs of the acquiring bank from 45.80% to 82.10% & from 52.80% to 82.90% respectively post-merger. The mean SE of the acquiring bank OBC has jumped from 85.80% to 99.10% post-merger under the Model 2.

43 144 The mean cost and profit efficiencies have declined by 4.60% and 6.20% respectively. Hence the increased mean PTE has a significant role in enhancing the mean TE under Model 2. The Cost and profit efficiencies(models 3 and 4 respectively) at a considerably high level of around 98% before merger and at around 93% post-merger (the first two years average being 96.50%) (See table 4.9) point to the superior managerial capabilities displayed in running the organization. However, the merger does not seem to have helped the acquiring bank (OBC) in improving its mean X-efficiency or Profit efficiency. OBC had very strong fundamentals. As on March 31, 2004 it had a deposit base of Rs.35, 674 crore, advances amounting to Rs.19,681 crore, and total assets worth Rs.41,701 crore. Its gross non-performing assets (NPAs) were Rs.1, 211 crore and it had no net NPAs. Its operating profit was Rs.1, 533 crore. Its net profit for the said period was Rs.686 crore. Its investment to deposit ratio worked out to 47.08% and spread to assets ratio stood at 3.55%. Both its business per employee at Rs.4.16 crore and profit per employee at Rs.5.10 crore were quite impressive. Based on these statistics, one could have forecasted that OBC s operational efficiency would not be unduly affected post-merger. In fact it consolidated its position in the southern and western parts of the country by leveraging on the GTB s branch net work, strong ATM base and excellent customer service synergies. Further the merger added one

44 145 million retail deposit holders to OBC s tally. While the profitability of the acquiring bank (OBC) could have taken a hit to some extent on account of additional NPA provisioning, the increase in technical efficiency could be attributed to the fact that both banks had the same technology platform, Finacle from Infosys, which facilitated the smooth transition and integration of operations in good time. Total Sample Technical Efficiency MODEL1 Technical Efficiency MODEL 2 Table 4.10 Bank Of Baroda (BOB)-Bareilly Corporation Bank Ltd (BCB) Merger (Technical Efficiency) Year Pre- merger Merger Year Post -merger Mean premerger efficiency Mean postmerger efficiency TE PTE SE TE PTE SE Source: Appendix A, Tables A1, A2 Total Sample Table 4.11 Bank Of Baroda (BOB)-Bareilly Corporation Bank Ltd (BCB) Merger (Cost & Profit Efficiencies) Year end Pre merger Merger Year Post merger Mean premerger efficiency Mean postmerger efficiency Cost(X-) Efficiency CE Profit Efficiency PE Source: Appendix A, Tables A3, A4

45 146 The Bareilly- headquartered bank(bcb) established in 1927, had a Rs 307 crore deposit base and a Rs.344 crore asset base at the time of merger(i.e for the financial year ). The bank s net profit was Rs lakhs in as against Rs.25 lakh in the year before. The bank despite two successive profit years, had recorded an accumulated loss of Rs 3 crore. The rationale given by a senior executive of BOB for the merger of BCB with BOB was that it (BCB) was not viable as an independent unit. BCB s Capital adequacy ratio (CAR) was as low as 3% against the RBI stipulated CAR of 8%. Post the merger, the mean TE and mean SE of the acquiring bank BOB have increased by 7.10% and 7.70% respectively under Model 1.However the PTE has declined by a marginal 0.70%. Under Model 2, there is a steep decline in mean TE and PTE levels i.e of the order of 25.60% and 36% ( see table 4.10). The results therefore do not show convincingly that the merger has resulted in improvements in TE and PTE scores of the acquiring bank (BOB). However, the mean X-efficiency and Profit efficiency scores have registered a slender increase in the range of 2%-4% (see table 4.11). One way of looking at the results is that the target bank is very small in size (in terms of assets and deposits) to make a significant impact on the efficiency of the acquiring bank BOB.

46 147 Table 4.12 Bank Of Baroda (BOB)-Banaras State Bank(BSB) Merger (Technical Efficiency) Total Sample Technical Efficiency MODEL1 Technical Efficiency MODEL 2 Year Pre-merger Merger Year Post-merger Mean premerger efficiency Mean postmerger efficiency TE PTE SE TE PTE SE Source Appendix A, Tables A1, A2 Table 4.13 Bank Of Baroda (BOB)-Banaras State Bank (BSB) Merger (Cost & Profit Efficiencies) Total Sample Cost(X-) Efficiency Profit Efficiency Year CE PE Pre-merger Merger Year Post-merger Mean premerger efficiency Mean postmerger efficiency Source: Appendix A, Tables A3, A4 Banaras State Bank was the second beleaguered UP-based bank to be merged with BOB, the first being the Bareilly Corporation Bank, in accordance with the scheme of amalgamation drawn up by RBI under Section 45 of the Banking Regulation Act. BOB gained 105 branches across the country following the merger, taking its branch network to over 2,500. As on March 31, 2001 BOB s deposits accounted for Rs

47 148 53,985 crore, it had an advance portfolio of Rs.27, 420 crore and an investment portfolio of Rs.19, 857 crore. The asset base of BOB stood at Rs.62, 462 crore as on 31 st March, In contrast, BSB had assets worth Rs.1, 134 crore and its deposits, advances and investments amounted to Rs.1, 031 crore, Rs.230 crore and Rs.631 crore respectively. BSB had posted a net loss of Rs crore as on March 31, As on the date of amalgamation 19 th june 2002, BSB s deposits were Rs Crore and advances Rs. 151 Crore. The bank had a total branch network of 105 of which 91 were located in UP and Uttaranchal while that of BOB s strength in those two states was 554 branches before the merger approval in June DEA analysis(see table 4.12 ) indicates that under Model 1, the mean TE of the acquirer had declined by 3.10% due to around 1%-2% decline in mean PTE and SE. Under Model 2 (table 4.12), the mean TE of the acquiring bank had improved by about 4.30% despite the decline in mean PTE by 18.60% due to a massive increase in mean SE by over 30%. This lends credence to the hypothesis that mergers can result in scale and scope economies. While the mean cost efficiency declined postmerger by about 4%, the mean profit efficiency declined by 6% (see table 4.13). This could be explained by the fact that the merger was more in the nature of a rescue exercise under the mandate of the RBI, to salvage an ailing bank and it is possible that the positive effects of merger from the marginally increased size and reach would have been experienced by

48 149 the merged bank only in the long run in terms of increase in cost and profit efficiencies. Total Sample Technical Efficiency MODEL1 Technical Efficiency MODEL 2 Year Table 4.14 Union Bank Of India (UBI)-Sikkim Bank (SB) Merger (Technical Efficiency) Pre -merger Merger Year Post-merger Mean premerger efficiency Mean postmerger efficiency TE PTE SE TE PTE SE Source: Appendix A, Tables A1, A2 Table 4.15 Union Bank Of India (UBI)-Sikkim Bank(SB) Merger (Cost & Profit Efficiencies) Total Sample Cost(X-) Efficiency Profit Efficiency Year CE PE Pre-merger Merger Year Post-merger Mean premerger efficiency Mean postmerger efficiency Source: Appendix A, Tables A3, A4 Under the merger scheme Union Bank of India (UBI) was required to absorb the accumulated losses of Sikkim Bank (SB) as well as their total staff. SB s entire loan outstandings of Rs.60 crore had turned bad. Its net worth was negative at Rs crore. The only attraction to UBI

49 150 in the merger proposition was that Sikkim bank had 8 branches in the North-East and this could give UBI the needed foothold in the North Eastern region where it did not have a significant presence. On the other hand, UBI was among the top public sector banks in India in terms of business mix and customer profile, with a net profit of Rs crore for the financial year ended It may be observed from the above table, that under Model 1(see table 4.14), the mean TE has increased by 3.8% which is accounted for by a marginal increase of 4.10% in mean SE. The mean PTE remained high all along at around 99.50%, an impressive feature in its own right. But under Model 2 (see table 4.14), (inputs: Deposits and Employee compensation and outputs: Loans and Advances & Non-interest income), the mean Technical Efficiency (TE) had received a major hit, declining as it did, by about 14% prompted by the decline in mean PTE by a whopping 30.50%. However, the mean SE under Model 2, had gone up by 8.30% and under the Model 1, it had increased by 4.10%, which may be attributed to the impact of merger. The pre- and post-merger Cost and profit efficiencies had remained stable at around 97%. Though the figure appears to be healthy in itself, the absence of any increase in this regard might be attributed to the fact that the target bank was a small and ailing bank with just 8 branches that too in the North Eastern region of India besides having accumulated losses leading to a negative net worth.

50 151 Table 4.16 Punjab National Bank (PNB)-Nedungadi Bank Ltd (NB) Merger (Technical Efficiency) Total Sample Technical Efficiency MODEL1 Technical Efficiency MODEL 2 Year Pre-merger Merger Year Post-merger Mean premerger efficiency Mean postmerger efficiency TE PTE SE TE PTE SE Source: Appendix A, Tables A1, A2 Table 4.17 Punjab National Bank(PNB)-Nedungadi Bank Ltd(NB) Merger (Cost & Profit Efficiencies) Total Sample Cost(X-) Efficiency Profit Efficiency Year CE PE Pre-merger Merger Year Post-merger Mean premerger efficiency Mean postmerger efficiency Source: Appendix A, Tables A3, A4 Public sector Punjab National Bank (PNB) took over Kozhicode (Kerala) based troubled Nedungadi Bank Ltd (NB) the oldest private sector bank in Kerala, along with its 1,619 employees under a scheme of amalgamation prepared by the RBI in the year The merger added 173 additional branches to PNB s branch network taking it to around 4,000. Of the 173 branches of Nedungadi bank, 110 branches were in

51 152 Kerala with a pool of NRI accounts. Nedungadi Bank (NB) had a deposit base of about Rs.1, 400 crore and advances of over Rs.750 crore as on March 31, On the other hand, PNB s deposits and advances figures stood at Rs.66, 680 crore and Rs.34, 450 crore respectively. The merger added only 2.24% to PNB s business which was over Rs.1, 00,000 crore at the time of merger. The meagre addition of 2.24% to the acquiring bank (PNB) s business from the merger explains the around 1 % (relatively small) change in mean cost and profit efficiencies of PNB post-merger. Under Model 1 (see table 4.16), while the mean TE had not changed much, the mean PTE change had placed the bank on the efficient frontier post-merger. However, there was an insignificant decline in the mean SE of PNB to the extent of 0.07%. Referring to Model 2 (see table 4.16), we find that the mean SE had increased by a massive 26%, which would speak well of the scale economies that are theoretically expected to result from the merger. This had in turn resulted in an increase of mean TE of the acquiring bank (PNB) by 11.60% despite a drop in mean PTE of PNB by 3.70%. The marginal decline in mean PTE post-merger reflects the inability of the merged bank in converting the deposits and employee potential into Loans and Advances and Non-interest income (fee-based income) on a substantial basis. It is observed from the table 4.17 that the cost and profit efficiencies have increased from 96.5% and 96.2% respectively to 97.6% and 96.7% respectively post-merger indicating a marginal, though

52 153 positive, impact of the merger on the cost and profit efficiencies of the acquiring bank(pnb). Table 4.18 ICICI Bank (ICICIB)-Bank Of Madura (BOM) Merger (Technical Efficiency) Total Sample Technical Efficiency MODEL1 Technical Efficiency MODEL 2 Year Pre-merger Merger Year Post-merger Mean premerger efficiency Mean postmerger efficiency TE PTE SE TE PTE SE Source: Appendix A, Tables A1, A2 Table 4.19 ICICI Bank(ICICIB)-Bank Of Madura(BOM) Merger (Cost & Profit Efficiencies) Total Sample Cost(X-) Efficiency Profit Efficiency Year CE PE Pre-merger Merger Year Post-merger Mean premerger efficiency Mean postmerger efficiency Source: Appendix A, Tables A3, A4 The merger between ICICI Bank and Bank of Madura (BOM) was a remarkable one. This merger was the first between an old generation private sector bank and a new generation private sector bank.

53 154 Pre merger status of ICICI Bank: Assets: Rs.12, 063 crore; Deposits: Rs.9, 728 crore; Equity market capitalization: Rs.2, 466 crore; Capital adequacy ratio: 17.59%; Branch network and extension counters: 106; Net worth: Rs.1, 219 crore; Number of employees: 1,700; one of the most tech-savvy and fastest growing private sector banks in the country. Pre-merger status of Bank of Madura (BOM): Assets: Rs.3, 988 crore; Deposits: Rs.3, 395 crore; capital adequacy ratio: 15.80%; Equity market capitalization: Rs.100 crore; Branch net work: 263; Number of employees:2,700;a 57-year old South India based private sector commercial bank. Synergies expected from the merger: ICICI Bank was looking at a branch network of , which would have taken 4 to 5 years to achieve, given the pace of branch expansion. This merger provided the much needed network immediately and also provided opportunities to ICICI Bank to spread its network to several other states. BOM had a customer base of 1.20 million. Hence the merger enabled ICICI Bank to have an aggregate customer base of 2.70 million on an asset base of Rs.16,000 crore(providing the needed economies of scale and scope) in addition to cross selling opportunities for assets and other products & services, like cash management services. The merger was also expected to be favorable to BOM shareholders in term of value creation besides providing technology based and sophisticated banking services to the customers.bom looked at the merger favorably because size was a major

54 155 consideration in the highly competitive banking scenario emerging in India in the aftermath of economic reforms launched by the Government of India. Size (critical mass) was a necessity in the context of compliance with the capital adequacy norms stipulated by the RBI and the risk management measures to be put in place by the commercial banks as mandated by the Basel committee. It is observed from the above table that under Model 1(see table 4.18), the mean TE came down by 1% due to the decline in mean SE by 1%.However, post the merger, while the mean TE came down by just 1%, the PTE continued to remain at 100%, an impressive performance in deed. Even under Model 2(see table 4.18), the mean TE, PTE and SE remained at 100% level post the merger as was the case before merger. Even the cost(x-) and profit efficiencies, remained at the level of 100% (see table 4.19) post-merger. These facts clearly show that ICICIB was an efficient bank (it was on the cost and profit frontiers) and could gainfully exploit the synergies predicted before the BOM s merger with itself.

55 156 Total Sample Technical Efficiency MODEL1 Technical Efficiency MODEL 2 Year Table 4.20 HDFC Bank (HDFCB)-Times Bank (TB) Merger (Technical Efficiency) Pre-merger Merger Year Post-merger Mean premerger efficiency Mean postmerger efficiency TE PTE SE TE PTE SE Source: Appendix A, Tables A1, A2 Total Sample Year end Table 4.21 HDFC Bank (HDFCB)-Times Bank (TB) Merger (Cost & Profit Efficiencies) HDFC BANK(HDFCB)-TIMES BANK(TB) MERGER Merger Pre-merger Year Post-merger Mean premerger efficiency Mean postmerger efficiency Cost(X-) Efficiency CE * Profit Efficiency PE * Source: Appendix A, Tables A3, A4 *Efficiency could not be calculated for the values of input or output for the said period. (Please refer limitations). The takeover of the Times Bank (TB) by HDFC Bank (HDFCB) was unique in the sense that it was the first merger deal between two new generation private sector banks. In a milestone transaction in the Indian

56 157 banking sector, Times Bank Ltd promoted by Bennett, Coleman &Co (Times Group) was merged with HDFC Bank effective February 26, 2000.The shareholders of the Times Bank received 1 share of HDFC Bank for every 5.75 shares of Times Bank. The merger with Times Bank had catapulted HDFC Bank into a different league, providing it with greater muscle in terms of retail client base as well as mid-market corporate clientele. The bank had nearly 8.5 lakh retail accounts post-merger. While the lending focus continued to be on top-end corporate clientele, it had an added advantage (diversification benefits) of serving the mid-market clientele that came as a part of the Times Bank baggage. Times Bank had an asset base of Rs.3, crore;deposits:rs crore, Capital adequacy ratio:9.97;advances: Rs.1, crore; Fee based income to total income ratio:24.58% and Credit-deposit ratio:44%;investment-deposit ratio:35% as on HDFC Bank: The bank s total assets increased almost three fold postmerger to Rs.11, crore. Pre-merger investment/deposit ratio: 58.23%; Assets: Rs crore Deposits: Rs crore Advances: Rs crore; Paid-up capital: Rs crore. Synergies expected from the merger: As per the scheme of amalgamation issued by the HDFC Bank to its shareholders, the following synergies were expected to be realized from the deal:

57 158 Branch network to increase by over 50% Increase geographical coverage and ATM numbers which allow multibranch access to retail clients. Increase in retail customer base and improvement in product portfolio Increase in shareholders wealth Cost savings from centralized processing and scale and scope economies Complementary business practices Improved infrastructure facilities While the mean TE under Model 1 (see table 4.20),declined slightly by 0.70% due to decline in mean PTE by 0.80%,the mean SE remained steady at 96.80%( a healthy figure) post-merger. Under Model 2 (see table 4.20), the mean TE, PTE and SE dropped by 9.80%, 5.60% and 6.80% respectively. Hence it would appear that the merger had not improved the PTE and SE under Model 2, which involved conversion of deposits &compensation to employees into Advances and Non-interest income. Hence the merger could not leverage the resource base available in terms of employee potential and deposits for the acquiring bank, HDFCB. Coming to the Cost and Profit efficiencies (see table 4.21), they had remained at 100% both pre and post merger which could be construed as the hallmark of efficiency. The ability to sustain the cost and profit efficiency post-merger could be attributed to the three fold increase in size, increase in geographical and improved access to retail clients through increased ATM numbers.

58 Analysis of Technological and Technical Efficiency changes post-merger employing DEA Malmquist Productivity Index Malmquist index of total factor productivity (TFPCH) examines whether firms (banks) are using the resources efficiently to produce goods and services, and if they are using the existing technology to produce goods and services. Values greater than one means increases in productivity, while values less than one indicate decreases in productivity over time. Farrell et al (1992) decomposed this index into sub indexes measuring changes in technical efficiency and changes in technology: TFPCH= TEFFCH * TECHCH The first term on the right hand side of the above equation represents the change in technical efficiency (TEFFCH); and the second term is the change in technology (TECHCH). A value greater than one means increases in output technical efficiency, value less than one means decrease and a value of one indicates no change. The second term represents the technological change. Using the data envelopment analysis computer program written by Coelli (1996), the input oriented Malmquist Total Factor Productivity Change (TFPCH) index has been computed.

59 160 The table 4.22 provides the summary of efficiency scores of mean TEFFCH, TECHCH and TFPCH before and after merger of the merged banks under Model 1. Table 4.22 DEA Malmquist Productivity Index (TFPCH) (Model 1) Merged banks(acquiring bank in brackets) Mean pre-merger efficiency change Mean post-merger efficiency change TEFFCH TECHCH TFPCH TEFFCH TECHCH TFPCH OBC-BDB(OBC) OBC-GTB(OBC) BOB-BCB(BOB) BOB-BSB(BOB) UBI-SB(UBI) PNB-NB(PNB) ICICIB-BOM(ICICIB) HDFCB TB(HDFCB) Source: Data processed It is observed from the table 4.22 that the Malmquist Productivity Index (MPI) of the acquiring bank post-merger has increased substantially in respect of to five out of eight bank mergers listed above under Model 1. On further examination by decomposing the MPI into its components, Technical efficiency change (TEFFCH) and Technological change (TECHCH)(also known as Frontier Shift), it follows that the technical efficiency change has declined post-merger in six out of eight cases and in one case, it has remained stable. However, the rate of technological change has increased in seven out of eight cases. Fare et al.(1992) define that MPI>1indicates productivity gain; MPI<1 indicates productivity loss; and MPI=1 means no change in productivity from time t to t+1. They also

60 161 state that a value of TECHCH greater than one indicates a positive shift or technical progress where as a value of TECHCH less than one indicates a negative shift or technical regress, and a value of TECHCH which equals is indicative of no shift in technology frontier. From this perspective, it may be inferred that the total factor productivity index (TFPCH) has increased in five out of eight cases. Hence, on an average, it is found that there has been an increase in MPI post-merger prompted more by a technological frontier shift rather than technical efficiency change. The table 4.23 provides the summary of efficiency scores of mean TEFFCH, TECHCH and TFPCH before and after merger of the merged banks under Model 2. Table 4.23 DEA Malmquist Productivity Index (TFPCH)(Model 2) Merged banks(acquiring bank in brackets) Mean pre-merger efficiency change Mean post-merger efficiency change TEFFCH TECHCH TFPCH TEFFCH TECHCH TFPCH OBC-BDB(OBC) OBC-GTB(OBC) BOB-BCB(BOB) BOB-BSB(BOB) UBI-SB(UBI) PNB-NB(PNB) ICICIB-BOM(ICICIB) HDFCB TB(HDFCB) Source: Data processed

61 162 It is be observed from the table 4.23 that the total factor productivity change (TFPCH) has increased post-merger in seven out of eight mergers listed above. While the TEFFCH has increased in four cases, it has declined in the remaining four cases. The technological frontier has shifted positively in five out of eight cases. Hence the change in MPI postmerger cannot be solely attributed to either technical efficiency change increase or positive frontier shift change. Both have played their role Tobit Analysis Model specification To determine the influence of different factors on the efficiency estimated using Data Envelopment Analysis (DEA), Tobit model has been employed. Important variables considered for the analysis (based on the literature review) exclude those considered as input and output variables for determining the respective efficiencies i.e. Technical Efficiency, Cost Efficiency(X-Efficiency) and Profit Efficiency using DEA methodology. The variables chosen are both qualitative and quantitative in nature. Qualitative variables are included to capture the effect of merger/likelihood of merger and the ownership (the bank in question is a public sector bank or a private sector bank).

62 163 Table 4.24 Selected Quantitative and Qualitative Variables Predictor Symbol Description Size(Market share of SIZE ln (Average total assets) business) Return on net worth RONW Net income/average total equity Return on capital employed ROCE Net income/average capital employed Capitalization SHFATA Shareholders fund / Average total assets Employee(Staff) cost COE ln(compensation to employees) Level of fee-based activity NIITI Non- interest income / Total income Proxy for fee-based NIINI Non interest income / Net income activity Earning power or Operating Profitability PBDITATA Profit before depreciation, interest and tax / Average total assets A measure of fund s cost INTEXPTE Interest expenses/total expenses ( Cost Of funds) Significance of Bank DSECTOR Public / Private sector ownership Significance of the year DYBMY Year before merger year before merger year Significance of the year after merger year DYAMY Year after merger year Source: Author s perspective Factors influencing the Technical Efficiency (TE) of commercial banks in India (Model 1) The following Tobit model has been employed to develop an average relationship between the technical efficiency scores obtained under CRS (Model 1) and the factors affecting it. Y it = α + β 1 SIZE it +β 2 RONW it +β 3 ROCE it + β 4 ANWATA it +β 5 COE it +β 6 NIITI it +β 7 PBDITATA it +β 8 DSECTOR it + β 9 DYBMY it +β 10 DYAMY it + ε it

63 164 Y it (Dependent variable) = Technical efficiency score obtained by i-th (acquiring) bank in time period t under CRS under Model 1. SIZE it = Natural logarithm of average total assets of the i-th bank in time period t. RONW it = Return on net worth of the i-th bank in time period t. ANWATA it = Capitalization (Shareholders equity ratio) of the of the i-th bank in time period t. Capital refers to the Tier-I capital of the commercial bank computed in accordance with the Basel norms circulated by the RBI. COEit = Natural logarithm of compensation to employees (salaries, wages and bonus) paid by the i-th bank in time period t. NIITI it = Non-interest income to Total income ratio of the i-th bank in time period t. PBDITATA it = Profit before depreciation, interest and tax to average total assets ratio of the i-th bank in time period t. DSECTOR it= 1 if i-th bank in time period t is a public sector bank otherwise zero. DYBMY it = 1 if the time period t represents the year before merger year for the i-th bank, otherwise zero. DYAMYit = 1 if the time period t represents the year after merger year for the i-th bank, otherwise zero.

64 165 α, β1, β2.. β10 are the regression parameters to be estimated by using the Tobit regression model. And ε it is the error term. It is expected that all the explanatory variables except dummies for public sector banks and the year before merger year will have positive impact on the technical efficiency of the bank. Results of Tobit Analysis The results of the Tobit estimation using the STATA software are presented in the Tables 4.25 to In suggested specifications, nominal values of the variables are used. As inflation has proportionate impact on the values of input and output of the commercial banks, it is not necessary to adjust the effect of inflation while computing the efficiency scores using DEA methodology. For the efficiency estimation of commercial banks, those banks with negative values of considered input and output variables have been excluded. Quantitative explanatory variables which characterize the commercial banks are considered along with dummies (to capture over time the performance characteristics of the commercial banks) in the suggested Tobit models. These quantitative explanatory variables exclude those variables which are considered as input and output variables in determining the efficiency in the respective specification(coelli et al,1998), as otherwise they would be highly correlated with input and output variables of DEA leading to biased results. In a similar fashion, in Tobit models, quantitative

65 166 explanatory variables have been transformed to remove the skewness in the distribution and to accomplish this objective, logarithmic transformation is applied on these variables. This transformation is also intended to compress the difference among the values of these variables. Four models (TE1, TE2, CE and PE) are presented in the following tables 4.25 to For each of the four models used, the Prob > χ 2 is zero, implying that the set of independent variables considered together satisfactorily explain the variations in the dependent variable. The results of Tobit regression for Model 1 are presented in Table Table 4.25 Model 1: Technical Efficiency Explanatory Variables Coefficient Std. Error z- statistic P> z SIZE * RONW -4.48E E ROCE 5.36E E COE NIITI * ANWATA * PBDITATA * DSECTOR * DYBM DYAM Constant No. of observations = 24*15 = 360 * Significant at 1% level Prob > χ 2 = Log likelihood = Source: Appendix A, Table A1 4 None of these four models indicate the existence of muti-collinearirty among the independent variables.

66 167 A positive regression coefficient implies an efficiency increase whereas a negative coefficient reflects a decline in efficiency. Size has a highly significant positive effect on technical efficiency of the bank indicating that larger banks on an average would be more technically efficient, possibly because of the scale economies derived from the bank merger. This is in line with the efficiency theory of Mergers and Acquisitions (Weston 2000). Capitalization variable (ANWATA) s impact is positive and significant in explaining the technical efficiency. Theoretically, better capitalized banks should enjoy a higher level of efficiency (Sufian et al, 2007). The variables (ratios) Non-interest income to total income (NIITI) & PBDIT to average total assets (PBDITATA) are highly significant and the signs of their regression coefficients are positive indicating that they have a positive influence on the technical efficiency of the commercial banks. The dummy variable, ownership of the bank (DSECTOR) is also highly significant with its regression coefficient taking a negative sign. This implies that the impact of owner ship on the technical efficiency of the bank, though highly significant, is negative. To state differently, private sector banks are more technically efficient than public sector banks, which is in line with our earlier findings. The other explanatory variables are, however, not significant in their contribution to the technical efficiency of the bank under Model 1.

67 Factors influencing the Technical efficiency (TE) of commercial banks in India (Model 2) The following Tobit model has been employed to develop an average relationship between the technical efficiency scores obtained under CRS (Model 2) and the factors affecting it. Y it = α + β 1 SIZE it +β 2 RONW it +β 3 ROCE it +β 4 ANWATA it +β 5 NIITI it +β 6 PBDITATA it +β 7 INTEXPTEit +β 8 DSECTOR it +β 9 DYBMY it +β 10 DYAMY it + ε it Y it (Dependent variable) = Technical efficiency score obtained by i-th bank in time period t under CRS under Model 2. SIZE it = Natural logarithm of average total assets of the i-th bank in time period t. RONW it = Return on net worth of the i-th bank in time period t. ANWATA it = Capitalization (Shareholders equity ratio) of the of the i-th bank in time period t. Capital refers to the Tier-I capital of the commercial bank computed in accordance with the Basel norms circulated by the RBI. NIITI it = Non-interest income to Total income ratio of the i-th bank in time period t. INTEXPTEit = Interest expenses to total expenses ratio of the i-th bank in time period t. PBDITATA it = Profit before depreciation, interest and tax to average total assets ratio of the i-th bank in time period t.

68 169 DSECTOR it= 1 if i-th bank in time period t is a public sector bank otherwise zero. DYBMY it = 1 if the time period t represents the year before merger year for the i-th bank, otherwise zero. DYAMYit = 1 if the time period t represents the year after merger year for the i-th bank, otherwise zero. α, β1, β2.. β10 are the regression parameters to be estimated by using the Tobit regression model and ε it is the error term. Table 4.26 Model 2: Technical Efficiency Explanatory Variables Coefficient Std. Error z-statistic P> z SIZE * RONW E ** ROCE ** INTEXPTE NIITI ANWATA ** PBDITATA ** DSECTOR * DYBM ** DYAM Constant No. of observations = 24*15 = 360 * Significant at 1% level;**significant at 5% level Prob > χ 2 = Log likelihood = Source: Appendix A, Table A2

69 170 It is expected that all the explanatory variables except dummies for public sector banks and the year before merger year will have positive impact on the technical efficiency of the bank. Analysis of results Bank size has a positive and highly significant impact on it s technical efficiency computed under, Model 2 as was the case under Model 1.While the Return on net worth (RONW) and Capitalization (ANWATA) ratios have been found to be significant in determining the technical efficiency of commercial banks under Model 2, the former has a negative impact and the latter has a positive impact on the technical efficiency of the banks as seen from the above table. That a better capitalized bank will have a higher level of technical efficiency is in line with the theory. The negative impact of RONW on technical efficiency though, is counter intuitive; it can be treated as negligible because of the extremely small value of the corresponding regression coefficient. The negative sign of the regression coefficient RONW can be explained as under: ROE=ROA X EM where ROA stands for the Return on Assets and EM for equity multiplier or the financial leverage. The ROA of the banks is around 1% which is very low. To earn a decent ROE the banks have to increase the EM to say, 15% to 20%. According to finance theory, financial leverage is a double edged sword. In good times it supercharges the profit and in bad times its effect is just reversed. The profit does not fall, but plummets. Further in their effort to achieve a healthy ROA if the

70 171 banks push up the EM, it might result in negative consequences to the bank if the level of debt is sub-optimal, despite the increase in ROE or RONW. The dummy variable DSECTOR has been found to be significant but its regression coefficient has a negative sign. This implies that public ownership of a commercial bank though significant, is associated with a decline in technical efficiency as was observed under Model 1. Though the last explanatory variable DYBM is significant in explaining technical efficiency under Model 2, its regression coefficient is negative. This can be explained by the theories of merger motives which state that acquisition of complementary resources(synergies) implying the absence of certain resources which are crucial for continued survival and growth is inferred from the bank s characteristics before merger in the year preceding the year in which the bank merger has taken place. The other explanatory variables have not been found to be significant in their contribution to the technical efficiency of the bank under Model Factors influencing the Cost Efficiency (CE) of commercial banks in India The following Tobit model has been employed to develop an average relationship between the Cost efficiency(x-efficiency) scores obtained under CRS (Model 3) and the factors affecting it.

71 172 Y it = α + β 1 SIZE it +β 2 RONW it +β 3 ROCE it +β 4 ANWATA it +β 5 COE it +β 6 NIINI it +β 7 PBDITATA it +β 8 DSECTOR it +β 9 DYBMY it +β 10 DYAMY it + ε it Y it (Dependent variable) = Cost efficiency score obtained by i-th bank in time period t under CRS under Model 3. SIZE it = Natural logarithm of average total assets of the i-th bank in time period t. RONW it = Return on net worth of the i-th bank in time period t. ANWATA it = Capitalization (Shareholders equity ratio) of the of the i-th bank in time period t. Capital refers to the Tier-I capital of the commercial bank computed in accordance with the Basel norms circulated by the RBI. COEit = Natural logarithm of Compensation to Employees (COE: Salaries, wages and bonus) paid by the i-th bank in time period t. NIINI it = Non-interest income to Total income ratio of the i-th bank in time period t. PBDITATA it = Profit before depreciation, interest and tax to average total assets ratio of the i-th bank in time period t. DSECTOR it= 1 if i-th bank in time period t is a public sector bank otherwise zero. DYBMY it = 1 if the time period t represents the year before merger year for the i-th bank, otherwise zero.

72 173 DYAMYit = 1 if the time period t represents the year after merger year for the i-th bank, otherwise zero. α, β1, β2.. β10 are the regression parameters to be estimated by using the Tobit regression model and ε it is the error term. It is expected that all the explanatory variables except dummies for public sector banks and the year before merger year will have positive impact on the Cost efficiency of the bank. Table 4.27 Model 3: Cost Efficiency(X-Efficiency) Explanatory variables Coefficient Std. Error z- statistic P> z SIZE ** RONW * ROCE COE NIINI ** ANWATA PBDITATA * DSECTOR ** DYBM DYAM Constant No. of observations = 24*15 = 360 * Significant at 1% level;**significant at 5% level Prob > χ 2 = Log likelihood = Source: Appendix A, Table A3

73 174 Results analysis It is observed from the table 4.27 that while SIZE, RONW, NIINI and PBDITATA have positive and significant influence on the Cost Efficiency(X-Efficiency) of the banks, RONW and PBDIATA have highly significant influence on the CE of the banks. The dummy variable DSECTOR is also significant but the negative sign of the corresponding regression coefficient indicates that public sector banks are less cost efficient as compared to the new generation banks in the private sector Factors influencing the Profit Efficiency (PE) of commercial banks in India The following Tobit model has been employed to develop an average relationship between the Profit efficiency scores obtained under CRS (Model 4) and the factors affecting it. Y it = α + β 1 SIZE it +β 2 RONW it +β 3 ROCE it +β 4 ANWATA it +β 5 COE it +β 6 NIITI it +β 7 PBDITATA it +β 8 DSECTOR it +β 9 DYBMY it +β 10 DYAMY it + ε it Y it (Dependent variable) = Profit efficiency score obtained by i-th bank in time period t under CRS under Model 4. SIZE it = Natural logarithm of average total assets of the i-th bank in time period t. RONW it = Return on net worth of the i-th bank in time period t.

74 175 ANWATA it = Capitalization (Shareholders equity ratio) of the of the i-th bank in time period t. Capital refers to the Tier-I capital of the commercial bank computed in accordance with the Basel norms circulated by the RBI. COEit = Natural logarithm of Compensation to Employees (Salaries, wages and bonus) paid by the i-th bank in time period t. NIITI it = Non-interest income to Total income ratio of the i-th bank in time period t. PBDITATA it = Profit before depreciation, interest and tax to average total assets ratio of the i-th bank in time period t. DSECTOR it= 1 if i-th bank in time period t is a public sector bank otherwise zero. DYBMY it = 1 if the time period t represents the year before merger year for the i-th bank, otherwise zero. DYAMYit = 1 if the time period t represents the year after merger year for the i-th bank, otherwise zero. α, β1, β2.. β10 are the regression parameters to be estimated by using the Tobit regression model and ε it is the error term. It is expected that all the explanatory variables except the dummies for public sector banks and the year before merger year will have a positive impact on the Profit efficiency of the bank.

75 176 Table 4.28 Model 4: Profit Efficiency Explanatory variables Coefficient Std. Error z- statistic P> z SIZE RONW E * ROCE E COE NIITI ANWATA PBDITATA * DSECTOR ** DYBM DYAM Constant No. of observations = 24*15 = 360 * Significant at 1% level;**significant at 5% level Prob > χ 2 = Log likelihood = Source: Appendix A, Table A4 Analysis of the results Both the explanatory variables RONW and PBDITATA are highly significant and positive in their impact on profit efficiency. This is of course intuitive. It is interesting to note that the variable Compensation paid to Employees (COE) though not significant has a negative impact on the profit efficiency, which is again intuitive. The last significant explanatory variable is the dummy variable DSECTOR which has a negative impact on the profit efficiency of the banks thereby indicating

76 177 public sector banks are less profit efficient as compared to the new generation banks in the private sector like HDFC Bank. This is also borne out of our observations in the context of other efficiencies referred to above. 4.3 Marketing implications of commercial bank mergers The data has been analyzed for significant association between the variables influencing the customer perception of bank mergers (items in the questionnaire) and the Demographic/Behavioral Variables (DBV) of the respondents employing Chi-square test.

77 178 Relationship between Demographic (S.Nos: 1-4)/Behavioral Variables (S.Nos:5&6) (DBV) and customer perception of Service quality in the face of bank mergers in India The results of analysis are summarized below, item (variable) wise. Cohran recommends that, while performing Chi-square test, at least 80% of the expected cell count be five or more and that no expected cell count be less than one(cohran s criterion) Table 4.29 Relationship between DBV and customer perception regarding mergers of commercial banks improving dependability of service S. NO. DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV) CHI- SQUARE VALUE P- VALUE 1 Gender (s) 2 Age ( years) Educational Qualification Yearly Income(Rs. lakhs) (s) 5 Association with Bank (years) (s) 6 Monthly Transaction Frequency Source: Appendix C, Tables C1 to C6 The table 4.29 provides a summary of broad perception of customerrespondents on the impact of bank mergers on dependability of customer service. It is observed that there is highly significant association between the two sets in terms of three out of six demographic/behavioral

78 179 variables. These are gender, yearly income and the length of association with bank (in years). There is however no significant relationship between the opinions of individuals and their age based on the results of the Chi-square test (p=0.102).this is in line with the observation of Urban &Prat (2000) in their studies in US. A similar conclusion follows in respect of the other variables Educational qualification and Monthly transaction frequency also. There is no significant difference between the within group male and female respondents who are highly optimistic that merged banks serve better. However, there is significant discrimination among the respondents who are pessimistic. Only 2.5% of the female respondents feel that there will be very insignificant change in the service quality of merged banks. On the other hand, 19% of the male respondents perceive the same. While among those respondents with an income level of Rs 1.50 to Rs.2.50 lakhs, only 45% opined that the service quality would improve after merger, respondents from all other income classes expressed in favor of bank mergers improving their service quality. It is possible that middle income groups are more conservative in expressing their views as compared to the younger and older generation respondents. Among those respondents who have more than 10 years of association with the bank, 58.6% feel that the dependability of service of the banks would improve after merger, whereas 20.7% of the people think otherwise

79 180 and the same percentage of people remain neutral in their perceptions. A significant observation is that among the respondents with less than 2 years of association with the bank, 88.2% of the people feel that merged banks provide more dependable service to the customers. It has also been found that the frequency of monthly transactions is also a significant study variable. 59.7% of those with higher number of bank transactions are optimistic about improvement in dependability of the banking service with mergers as compared to 14.9% who perceive the improvement in dependability of service post-merger as insignificant. These relationships are further investigated below. Table 4.30 Relationship between DBV and customer perception regarding the increase in number of banking services provided post-merger S. NO. DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV) CHI- SQUARE VALUE P - VALUE 1 Gender Age ( years) (s) 3 Educational Qualification Not calculated *** 4 Yearly Income( Rs.lakhs) Association with Bank (years) (s) 6 Monthly Transaction Frequency (s) ***Cohran s criterion not satisfied. Source: Appendix C, Tables C7 to C11 It is observed from the above table 4.30, that there is significant association between the respondents opinions and their age, length of their association with the bank and their monthly transaction frequency.

80 181 It is observed that 42.1% of the customers who have optimistic views in this regard are of the age group of more than 45 years and 29.3% of the people are of the age group of years. Further, the impact of customer s association on their perception is very much on the expected lines, as customers whose association with the bank is long would generally be more knowledgeable about the great variety of products and services offered by the bank and perceive the implications of bank mergers on customer service equally well. It is however, interesting to note that while of those with over 10 years association 75% strongly opined that bank mergers result in increase in the number of services provided, a much higher percentage i.e. about 83% to 93% of those customers with an association of 10 years or less agreed with this view strongly. 85% of the total respondents feel that a merger results in an increase in the number of services provided, in contrast to the 5.7% of respondents who think that a merger is unlikely to prompt an increase in the number of new services. 9.3% of the respondents have a neutral opinion on the issue. Another interesting observation is that out of the 84.3% of ihe respondents whose length of association influences their perception as to the increase in number of services provided post-merger; only 15% have more than 10 years of association with their respective banks.

81 182 Table 4.31 Relationship between DBV and customer perception regarding the increase in range of banking products available post-merger S. NO. DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV) CHI- SQUARE VALUE P - VALUE 1 Gender Age ( years) Not calculated *** 3 Educational Qualification Not calculated *** 4 Yearly Income( Rs.lakhs) Association with Bank (years) Not calculated *** Monthly transaction 6 Frequency (S) ***Cohran s criterion not satisfied. Source: Appendix C, Tables C12 to C14 The above table 4.31 indicates significant association between the respondents monthly transaction frequency and in perceiving the implications of bank mergers on customer service in so far as the range of products available is concerned. 88.1% of the respondents with a transaction frequency of more than 5 opine that the transaction frequency brings about new product introductions by the banks in contrast to the 12% of the people who either think this is unlikely or remain neutral. Also, a larger percentage of respondents (65.4%) agree with this view compared to the 7.7% of respondents who do not agree and 26.9% who remain neutral. 35% of the customers who agree with the above mentioned view fall in the income slab of up to Rs. 2.5 lakh, 25.7% of the people under Rs lakh and 19.3% come under less than Rs. 5 lakh income category. This

82 183 is an indication that more people from low income groups expect bank mergers to give rise to new products. Table 4.32 Relationship between DBV and customer perception regarding the increased size of bank loan limits post-merger S. NO. DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV) CHI- SQUARE VALUE P-VALUE 1 Gender Age ( years) Educational Qualification (S) 4 Yearly Income(Rs. lakhs) (S) 5 Association with Bank (years) (S) 6 Monthly Transaction Frequency Source: Appendix C, Tables C15 to C20 Mergers are supposed to enhance the ability of the banks to lend more because of the enhanced financial and other resources of the merged entity. Again we find(see Table 4.32) that the customers educational qualification in addition to their yearly income and length of association with the bank are significant in influencing their perception of the merged bank s ability to offer larger loan limits. It is observed that only 9.3% of the respondents strongly agreeing with the above view have an association of more than 10 years with the bank. Further, while 40% of the professionals strongly agree with the above opinion, 48% of the post-graduates do not entertain this view. A widely varying view of perception emerges in terms of variation in annual incomes as well.

83 184 Table 4.33 Relationship between DBV and customer perception regarding the increased accessibility to conventional bank services (not online) post-merger S. NO. DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV) CHI- SQUARE VALUE P- VALUE 1 Gender (S) 2 Age ( years) Not calculated *** 3 Educational Qualification (S) 4 Yearly Income( Rs. lakhs) (S) 5 Association with Bank (years) (S) 6 Monthly Transaction Frequency (S) Source: Appendix C, Tables C21 to C25 S***Cohran s criterion not satisfied In regard to the improvement in the accessibility to conventional banking (not online) services post-merger, the above table 4.33 indicates significant association between all the demographic variables except age and the perception of customers. It is generally expected that accessibility to conventional banking services(not online) would improve after merger because of the merged entity s increased human, financial and technological resources which if deployed intelligently would enable it to take it closer to this goal. However, it is found that the perception of customers is influenced significantly by their Gender and other demographic/behavioral variables. 65% of the male respondents and 55% of female respondents feel that accessibility to conventional banking services improves significantly after mergers. However, it is interesting to note that out of the 62.1% people

84 185 who agreed with this opinion, only 15.7% were female respondents and 46.4% were male respondents. While, a majority of the male respondents (65%) agreed with the view, only 12% of them were strongly opposed to this line of thinking. 69% of the respondents who had more than 10 years of association with the bank strongly agreed with the above view while 10.3% felt otherwise. 61.2% of the respondents who had greater frequency of transaction also agreed that access to banking services would improve after merger. Another important observation is that out of the total respondents, who agreed with the view, 17.1% were professionals, 38.6% were post graduates and 6.4% were graduates. 62.9% of the respondents with income exceeding Rs. 5 lakhs also opined in favor of this view. Table 4.34 Relationship between DBV and customer perception regarding improvement in Online Banking Services after merger S. NO. DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV) CHI- SQUARE VALUE P- VALUE 1 Gender Age ( years) Educational Qualification (S) 4 Yearly Income( Rs. lakhs) (S) 5 Association with Bank (years) (S) 6 Monthly Transaction Frequency Source: Appendix C, Tables C26 to C31 Online banking services which require deployment of advanced and latest technology & more financial resources are generally expected to

85 186 improve after the merger as the combined entities are better off because of the possible realization of financial and technological synergies. However, it is observed from the above table 4.34 that there is significant association between customer s educational qualification, yearly income and length of association with the bank, and their perception of the improvement in merged entity s ability to provide improved online banking services. 48.6% of the respondents opine that online banking improves with mergers. It is observed that among these, 10% have more than 10 years of association with the banks concerned, 16.4% have 6-10 years of association and remaining 22.2% have less than 6 years of association. It is interesting to note that as high as 60% of the respondents whose transaction frequency is as low as once in a month have strongly supported the view that online banking services will improve/expand post-merger.

86 187 TABLE 4.35 Relationship between DBV and customer perception regarding the reduction in service time of bank after merger S. NO. DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV) CHI- SQUARE VALUE P- VALUE 1 Gender Age ( years) (S) 3 Educational Qualification (S) 4 Yearly Income( Rs.lakhs) (S) 5 Association with Bank (years) (S) 6 Monthly Transaction Frequency (S) Source: Appendix C, Tables C32 to C37 Again strong association has been observed (Table 4.35) between all the demographic variables except gender and the perception of customers on the reduction of service time post merger. It is generally expected that the perception in this regard should not be influenced by the demographic variables. But the above results of Chi-square test show significant relationship between the two. Out of the 29% of people who agree with the statement that service time is reduced after merger, it is interesting to note that an overwhelmingly high percentage i.e. 25% of them are in the age group of This shows that the younger generation is more optimistic in their perception. Significant divergence of opinion has also been observed in terms of the differences in the length of association, educational qualification, yearly income and the frequency of transaction.

87 188 Table: 4.36 Relationship between DBV and customer perception regarding increased safety of deposits after merger S. NO. DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV) CHI- SQUARE VALUE P- VALUE 1 Gender (S) 2 Age ( years) Educational Qualification (S) 4 Yearly Income(Rs. lakhs) (S) 5 Association with Bank (years) (S) 6 Monthly Transaction Frequency (S) Source: Appendix C, Tables C38 to C43 Safety of deposits is one of the primary factors about which all the bank customers are concerned. It is normally expected that the safety of the deposits of the customers will improve post merger because of the enhanced capital base and other financial resources of the combined entity. It is however found (Table 4.36) that there is significant association between all the variables in question except age and the perception of customers about the increased safety of their deposits postmerger. 47.1% of the respondents are of the view that safety of deposits increases after merger. Out of this, it is significant to note, that 37.1% are male and only 10% are female. It is also interesting to note that 23.6% of the respondents who share this view are post graduates. While about 67.5% of the professionals have opined that safety of deposits is enhanced after

88 189 the merger, an equally high percentage i.e. 63% of the respondents whose income is in the range of Rs lakh have strongly veered around this view. 60% of the respondents with frequency of transaction as low as once in a month have also exuded optimism in this regard. Table: 4.37 Relationship between DBV and customer perception regarding bank mergers resulting in less competitive interest rates S. NO. DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV) CHI- SQUARE VALUE P- VALUE 1 Gender Age ( years) (S) 3 Educational Qualification (S) 4 Yearly Income( Rs.lakhs) (S) 5 Association with Bank (years) (S) 6 Monthly Transaction Frequency Source: Appendix C, Tables C44 to C49 It is hypothesized that bank mergers result in less competitive interest rates in view of the reduction in number of banks that follows leading to greater monopolistic tendencies which bring in their wake systemic rigidities. But the same line of thinking is not visible across the different demographic groups as seen from the above table Educational qualification, age, association with the banks and yearly income differences seem to be strongly influencing the perception of customers in regard to the movement of interest rates of the banks in the post-merger scenario.

89 % of the people, who are of the age group 45 years and more, opine that bank mergers result in less competitive interest rates. This shows that elderly respondents are less positive in their approach. While 78.9% of the post-graduates and 61.5% of graduates entertain this perception, a lesser percentage i.e about 45% of the professionals agree with this view. Further as high as 68.2% of the respondents whose income level is Rs lakh (middle income groups) are supportive of this view. However, on taking a comprehensive view, it is observed that only 47.90% of the total respondents strongly entertain the opinion (52.10% do not subscribe strongly to this view) that the bank interest rates will become less competitive post-merger. The rationale for this view is that a larger bank can better exploit financial synergies post-merger and will be in a stronger position to raise cheaper funds which in turn will enable it to quote more competitive rates of interest. Table: 4.38 Relationship between DBV and customer perception regarding fee reduction for different banking services post-merger S. NO. DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV) CHI- SQUARE VALUE P - VALUE 1 Gender (S) 2 Age ( years) Educational Qualification (S) 4 Yearly Income(Rs. lakhs) (S) 5 Association with Bank (years) Monthly Transaction Frequency Source: Appendix C, Tables C50 to C55

90 191 Mergers are broadly expected to result in fee reductions following the merged bank s ability to achieve economies of scale and scope. It is however observed that the respondents opinion in this regard is shaped by their demographic segmentation. The gender, educational qualification and income levels seem to be strongly associated with their thinking in this regard. More educated respondents (and probably they belong to higher income groups) might possess deeper understanding of the implications of mergers and hence are probably in a better position to look at the issue from other perspectives as well. It is observed that 60.7% of the respondents think that mergers do not result in fee reduction for different services. Out of this, 39.3% of them are males. It is however significant to note that 75% of the female respondents entertain a similar view.68.2% of the graduates 60% of the l professionals and 78.4% of the respondents whose income is less than Rs 1.5 lakh feel that fee reduction for banking services is not likely to follow bank mergers.

91 192 Table 4.39 Relationship between DBV and customer perception regarding enhancement in Goodwill of the bank post-merger S. NO. DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV) CHI- SQUARE VALUE P- VALUE 1 Gender Age ( years) Educational Qualification (S) 4 Yearly Income(Rs. lakhs) (S) 5 Association with Bank (years) (S) 6 Monthly Transaction Frequency Source: Appendix C, Tables C56 to C61 The thinking of the customers in regard to the enhancement of goodwill post-merger appears to be very strongly influenced by their educational qualification, annual income and length of association with the bank as can be seen from the above table % of the respondents who have an association of more than 10 years opine that the goodwill of the bank is enhanced significantly after the merger in contrast to only 19.5% of them who think otherwise and 12.2% who have neutral views. While 67.9% of the post graduates and 50% of the professionals are very optimistic about the improvement in the goodwill of the bank, only 23% of the graduates strongly entertain this view. In terms of the annual income levels also significant divergence in opinion has been observed.

92 193 Table: 4.40 Relationship between DBV and customer perception regarding bank s technological advancement after merger S. NO. DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV) CHI- SQUARE VALUE P - VALUE 1 Gender (S) 2 Age ( years) Not calculated *** 3 Educational Qualification (S) 4 Yearly Income(Rs. lakhs) (S) 5 Association with Bank (years) (S) 6 Monthly Transaction Frequency (S) ***Cohran s criterion not satisfied Source: Appendix C, Tables C62 to C66 Banks are generally expected to be technologically sound after merger because of the improved access/ability of the merged bank to access the financial markets, state of the art technologies and human talent. The above table 4.40 indicates that there is a strong relationship between the demographic/behavioral variables, except age and the opinion of the respondents as to the technological soundness of the bank post-merger. While 61% of the male respondents are of the opinion that there will be a rapid technological advancement in the banks after merger, only 13.6% of the female respondents strongly entertain this view. Out of the 57.1% of the respondents who share this view, 26.4% of them account for maximum frequency in transactions (more than five per month). It is also significant to note that a sizeable percentage i.e. 70% of the professionals and 50% of the post-graduates are optimistic in this regard.

93 194 Table: 4.41 Relationship between DBV and customer perception regarding the increased availability of bank s ATM services after merger S. NO. DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV) CHI- SQUARE VALUE P- VALUE 1 Gender Age ( years) Educational Qualification Yearly Income( Rs. lakhs) (S) 5 Association with Bank (years) (S) 6 Monthly Transaction Frequency (S) Source: Appendix C, Tables C67 to C72 The above table 4.41 shows significant association between the yearly income, length of association of the customer with the bank, monthly frequency of transaction and the perception of the customers in regard to improved customer service following an increase in number of ATMs that may be made available post-merger. 84.6% of the respondents have opined that a rise in ATM number post- merger is a positive sign in improving customer service. 90% of the respondents who have up to 5 years of association with the banks feel that a greater number of ATMs after merger help in improving customer service. 87.8% of people having 6-10 years of association have also expressed their agreement with this view. Again, 91% of the respondents, whose frequency of transaction is more than ten, are also of the same view. It is also significant to note that 91.5% of the respondents, whose income is less than Rs 2.5 lakh (low income group), also share this

94 195 opinion. Low income groups generally draw small amounts and quite often because of the nature of their needs. Their opinion is expected to carry more weight in view of their more intense use of ATMs as compared to others. Table: 4.42 Relationship between DBV and customer perception regarding the quality of bank s customer relationship management (CRM) after merger S. NO. DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV) CHI- SQUARE VALUE P- VALUE 1 Gender Age ( years) Educational Qualification (S) 4 Yearly Income( Rs. lakhs) (S) 5 Association with Bank (years) (S) 6 Monthly Transaction Frequency Source: Appendix C, Tables C73 to C78 A very strong association has been observed (Table 4.42) between the variables, educational qualification, yearly income & the length of association with the bank and the customer perception regarding the improvement in Customer Relationship Management (CRM) after the merger. However, the gender, age and the monthly transaction frequency do not seem to significantly influence the opinions of the customers in this regard.

95 % of the respondents having an association of 6-10 years are of the opinion that customer relationship management gets better after merger, compared to only 9.1% of them who think otherwise. As regards the impact of income differentials, it is found that about 25% of the total of 45% expressing themselves strongly in favor the improvement in CRM post-merger are those with income levels below Rs.1.5 lacs & Rs.5 lacs and above in equal proportion. While 55% of the professionals have expressed strongly in favor of this view, only 48.7% of the post-graduates and much less i.e. 13.6% of the graduates seem to veer around this view indicating that the level of education is impacting the customer perception significantly. Table: 4.43 Relationship between DBV and customer perception regarding the impact of change of staff members of the bank after merger S. NO. DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV) CHI- SQUARE VALUE P- VALUE 1 Gender Age ( years) Educational Qualification (S) 4 Yearly Income( Rs. lakhs) Association with Bank (years) (S) 6 Monthly Transaction Frequency (S) Source: Appendix C, Tables C79 to C84

96 197 The general human tendency is the preference for continuous dealing with the bank s staff members whom they know well as polite and knowledgeable over time. It is however revealed from the results of Chisquare test (Table 4.43) that there is significant association between respondents educational qualifications, their length of association with the bank, monthly transaction frequency and their perception as to whether mergers result in change of staff giving an impersonal feel. 76.5% of the respondents having less than 2 years of association with the banks are of the opinion that mergers do not result in change of staff and hence the personal feel is not lost, when compared to the 17.6% of respondents who think otherwise. It is of interest to note that 70% of the respondents with minimum frequency of monthly transactions also share the same opinion. Table: 4.44 Relationship between DBV and customer perception regarding change in competition scenario of banks post-merger S. NO. DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV) CHI- SQUARE VALUE P- VALUE 1 Gender Age ( years) (S) 3 Educational Qualification Yearly Income(Rs. lakhs) (S) 5 Association with Bank (years) (S) 6 Monthly Transaction Frequency Source: Appendix C, Tables C85 to C90

97 198 Mergers are generally expected to result in big players resulting in monopolistic tendencies and reduced competition. The above table (Table 4.44) clearly hints at strong association between the customer perceptions in this regard and their age, yearly income levels, the length of association with the bank in years and their annual income levels. But no significant association has been observed between the customer perception in this regard and the gender, monthly transaction frequency and educational qualifications. 57.3% of the respondents of the age group and 52.4% of the respondents of age group opine that mergers result in big players and significantly reduce competition. While a significant proportion, as high as, 68.3% of respondents with 6-10 years of association with banks concerned also share this opinion, 52.9% of the respondents having less than 2 years of association with banks think otherwise. Further, a sizeable proportion i.e 60.9% of the respondents having income of Rs lakh (middle income groups) are of the view that mergers result in reduced competition and emergence of big players.

98 199 Table: 4.45 Relationship between DBV and customer perception regarding the increase in opportunities for the merged bank to cross-sell banking products post-merger S. NO. DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV) CHI- SQUARE VALUE P- VALUE 1 Gender Age ( years) (S) 3 Educational Qualification Yearly Income(Rs. lakhs) (S) 5 Association with Bank (years) (S) 6 Monthly Transaction Frequency Source: Appendix C, Table C91 to C96 Mergers facilitate cross-selling of products of the merging banks which will result in exploitation of synergies arising out of complementary strengths/resources. It would appear from the above table(table 4.45) that there is significant association between the respondents age,yearly income, & their association with bank and their opinion in regard to the merged entity acquiring added ability to cross sell products and thereby enrich its product offerings. While 76.5% of the respondents having an association of less than 2 years with the bank and 54.1% of respondents having income less than Rs 1.5 lakh (low income groups) are of the view that merged bank will be able to better cross-sell, the corresponding percentages are relatively less ranging between 30% to 40% for other categories determined by annual income levels and association with the bank (in terms of years).

99 200 Table: 4.46 Relationship between DBV and customer perception regarding the improvement in innovative ability of the bank after merger S. NO. DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV) CHI- SQUARE VALUE P- VALUE 1 Gender Age ( years) Educational Qualification (S) 4 Yearly Income( Rs. lakhs) Association with Bank (years) (S) 6 Monthly Transaction Frequency Source: Appendix C, Tables C97 to C102 As depicted in the above table 4.46, there is very strong association between the respondents educational qualification (in years) and their customer perception of improvement in innovative ability of banks following merger. 63.4% of the respondents having 6-10 years of association with the banks do not opine that merged banks provide innovative products and redefine the way banks interact with customers, while 64.7% of the respondents having less than 2 years of association have a neutral opinion. Also, it is significant to note that 60% of the professionals also do not opine that merged banks redefine the way the bank interacts with the customers. Of the total respondents, 53.60% were of the view (medium to strong) that mergers could improve the innovative ability of the banks and redefine the way they interact with the customers. This makes the reasearcher conclude that the customer opinion in this regard is evenly divided. This calls for further exploration

100 201 by the banks as to the reasons and how best they can convince their customers of their ability to research and innovate in the post-merger scenario. Table: 4.47 Relationship between DBV and customer perception regarding the importance of communication about merger to the bank customers S. NO. DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV) CHI- SQUARE VALUE P- VALUE 1 Gender (S) 2 Age ( years) (S) 3 Educational Qualification (S) 4 Yearly Income( Rs. lakhs) Association with Bank (years) (S) 6 Monthly Transaction Frequency (S) Source: Appendix C, Tables C103 to C108 Communication to the bank customers about the merger is considered to be important so that they are not exposed to undue stress and strain in perceiving the implications of the merger to the safety and security of their deposits and the lasting relationships which they have come to develop with the bank s employees and the bank itself over the years. It may be inferred from the above table 4.47 that there is significant association between the customer perceptions in this regard and the variables listed above, except for yearly income. 69% of the male respondents and 62.5% of the female respondents opine that communication about mergers to customers is not very important % of the respondents having less than 2 years of association feel

101 202 that it is important to communicate to the customers about the merger. However, 75.5% of respondents having 2-5 years association and 75.6% of respondents with 6-10 years of association have responded otherwise. Another important observation is that 72.3% of respondents with 3-4 transactions and 67.2% of respondents with more than 5 transactions per month feel that the communication on bank merger to the customers is not generally important. This view has been supported by 75% of the professionals. On balance, while it makes the researcher conclude that, a major chunk of the respondents opine that communication to bank customers about impending merger is not very important, it is also possible that a majority of the customers could not have fully captured/appreciated the intricacies or importance of this communication. Table: 4.48 Relationship between DBV and customer perception regarding his/her switching preference after the bank s merger S. NO. DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV) CHI - SQUARE VALUE P - VALUE 1 Gender Age ( years) Educational Qualification Yearly Income(Rs. lakhs) (S) 5 Association with Bank (years) (S) 6 Monthly Transaction Frequency Source: Appendix C, Tables C109 to C114

102 203 It is observed from the above table 4.48 that the opinions of the customers as to whether they will continue with their present bank after merger or not is influenced by their association with the bank in years and their yearly income. The other demographic variables like gender and age etc do not have significant association with the opinion of the bank customers on this issue. 63.4% and 65.5% of respondents with 6-10 and more than 10 years of association respectively prefer to switch to some other bank if their bank gets merged. However, only 9.3% of the total respondents opined against switching to another bank, while 35% of them are unsure about it. 67.6% of respondents with income less than Rs 1.5 lakh and 60.9% of respondents with income in range Rs lakh were in favor of switching to another bank in case of merger. These switching tendencies evidenced by bank customers should be examined and analyzed by the banks concerned if they are not to lose sizeable chunks of customers in the wake of bank mergers as profitability and customer loyalty are strongly linked.

103 204 Table: 4.49 Difference in Customer Perception of Service Quality based on Whether Customer s Bank Has Experienced Any Merger S. NO VARIABLES Mergers improve dependability of bank service(say, improvement in after sales service) CHI- SQUARE VALUE P VALUE It results in increase in number of services provided It increases the range of products available It results in larger loan limits After merger better accessibility to services is possible Online banking does not improves after merger Service time is not reduced after merger Safety of deposits increases after merger Bank mergers result in less competitive interest rates Mergers generally do not result in fee reduction for different services Goodwill of the bank is not enhanced after merger Post merger, banks get quickly technologically advanced

104 More number of ATMs after merger help in improving customer service ( s) Customer relationship management does not get better after merger Mergers result in change of staff which gives an impersonal feel Mergers results in big players and reduces competition I don t like cross selling (ex: banks selling insurance products) undertaken by the merged bank Merged banks provide innovative products and redefine the way banks interact with customers. Communication about merger to the customers in general is not very important I prefer to switch to some other bank if my bank gets merged Source: Processed Data It is clear from the above table 4.49 that except for a larger number of ATMs helping in improved customer service after merger, there is no significant association between the customer perception about the marketing implications of commercial bank mergers and whether the respondent is a customer of the bank which has undergone any merger or not. This conclusion has significant implications to our study as quite a few respondents in our sample belong to commercial banks which have not gone through any merger.

105 206 Table: 4.50 Difference in Customer Perception Of Service Quality Based On The Nature Of Bank s Ownership (Public Sector /Private Sector) S. NO VARIABLES Mergers improve dependability of bank service(say, improvement in after sales service) CHI - SQUARE VALUE P - VALUE It results in increase in number of services provided It increases the range of products available (s) It results in larger loan limits (s) After merger better accessibility to services is possible Online banking does not improves after merger (s) Service time is not reduced after merger Safety of deposits increases after merger Bank mergers result in less competitive interest rates (s) Mergers generally do not result in fee reduction for different services Goodwill of the bank is not enhanced after merger (s) Post merger, banks get quickly technologically advanced More number of ATMs after merger help in improving customer service (s)

106 Customer relationship management does not get better after merger Mergers result in change of staff which gives an impersonal feel Mergers results in big players and reduces competition (s) I don t like cross selling (ex: banks selling insurance products) undertaken by the merged bank Merged banks provide innovative products and redefine the way banks interact with customers. Communication about merger to the customers in general is not very important I prefer to switch to some other bank if my bank gets merged (s) Source: Processed Data In respect of eight out of twenty items listed in the questionnaire (Table 4.50), it is found that there is significant association between the customer perception of bank service quality post-merger and the nature of ownership of the bank of the customer, i.e. whether he/she is a customer of a public or private sector bank. It is possible that the customer perception is broadly influenced by the diversified range of products and services, tech-savvy nature and the promptness in services of the private sector banks as compared to those in the public sector where the process of technological upgradation started relatively late.

107 208 Graph 4.28 Influence of merger on the commercial bank services: Customer response wise break-up Get Better Remain about the same Get worse Don t know 2% 15% 21% 62% Source: Processed Data While 62% of the respondents opined that the bank service would get better after the merger, 21% were of the view that it would remain about the same. Only 15% of the respondents were pessimistic in this regard stating that it would worsen after merger (Graph 4.28). On balance, it can be seen that the customers of the banks expect an improvement in service quality post-merger.

108 209 Graph: 4.29 Customers classified based on their opinion on the strategy to be followed in commercial bank mergers in India PSU Banks should merge with private sector banks PSU Banks should merge with PSU banks Private sector Banks should merge with private sector banks Merger should not take place at all 14% 4% 44% 38% Source: Processed Data It is interesting to note that only 4% of the respondents feel that mergers of banks should not take place. While 44% of the respondents expressed the view that the merged bank (Combined entity) should be a private sector bank, 38% maintained that public sector banks should be preferably merge with public sector banks only (Graph 4.29).