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Scholarly Research Journal for Humanity Science & English Language, Online ISSN 2348-3083, SJ IMPACT FACTOR 2019: 6.251, www.srjis.com PEER REVIEWED & REFERRED JOURNAL, OCT-NOV, 2019, VOL- 7/36

EFFICIENCY MEASUREMENT OF SCHEDULED COMMERCIAL BANKS STATE-WISE: A REGIONAL ANALYSIS Amit Khatri Abstract One of the principal goals of the Indian banking sector reforms was to enhance the efficiency of the banking industry. This article contributes to the banking eﬃciency literature by measuring the technical eﬃciency of scheduled commercial banks at the state level. This study estimates the technical efficiency of scheduled commercial banks (SCBs) at the state level for a time span of fifteen years using data envelopment analysis (DEA). Data for the same is obtained from the RBI website from 1996-97 to 2010-11. It is found that technical efficiencies of different states are different during different time periods indicating that some states are prompt, while, other states are slow in adopting the banking reforms. Super efficiency estimated for each state year-wise reveals that Uttar Pradesh is the leading state rank wise followed by Maharashtra, Rajasthan, and Gujarat. While the comparison of the rank of states with the gross state domestic product indicates that the states with higher banking efficiency are better in development.

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1. Introduction With a rapidly changing domestic and global economic environment, the banking sector in India has undergone a sea change since the commencement of the banking reforms introduced from the year 1992. One of the prime objectives of the banking liberalization program was to inculcate greater competition in the banking sector thereby enhancing the profitability, greater amount of funds utilized in better ways, service quality for consumers and greater safety in terms of improved capital buffer in absorbing risk (Berger et al., 1993). The efficiency of banks is necessary to sustain trust, confidence, and soundness in the banking system (Zeitun&Benjelloun, 2013). Also, greater efficiency in the banking system leads to greater financial stability, product innovation and access of households and firms to financial services, which in turn affects economic growth (Egesa, 2010). With the purpose to impart greater efficiency to the resource allocation process in the banking system, the policymakers gradually implemented a series of reform measures like the dismantling of administrated interest rate structure, reduction in statutory pre-emptions in the form of reserve requirements, and liberal entry of private banks and foreign banks. After

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liberalization, different states in India have witnessed growth and development at varying levels. A study by Ghosh, B.and De, P.(2004)shows that interstate disparities in physical, social and economic infrastructure facilities have remained at an alarmingly high level. Another study by Bhattacharya and Sakthivel (2004) reveals that Industrial states are growing much faster than backward states, and there is no evidence of convergence of growth rates among states. This is a cause of concern for the government. Hence, the government is eager to trap the factors responsible for inequality in economic growth and the development of various states. One of the key factors for growth and development has been infrastructural development. A good banking system by itself is a major financial infrastructure required for development. The studies so far on the efficiency of banks in India have concentrated on the macro level i. e. at the banks on all India basis. However, with regional development becoming a core necessity for maintaining a higher growth of 7% or more at the national level, the development of an efficient banking system at the state/regional level is inevitable. As such, this study aims at analyzing the efficiency of scheduledcommercial banks which, along with other factors cater to the development of states as a whole between states and within the state. 2. RESEARCH PROBLEM Impediments to efficient functioning were still aplenty. Over the decades, the public sector banks in India have been overshadowed by governmental bureaucratic clutches rather than being driven by the market rationale. The State bank of India (SBI), being the largest public sector bank, comprises none other than government nominees to constitute its board of directors thus leaving the bank with little or no room for independent functioning. This, in turn, obstructs the bank to achieve optimum productivity and efficiency in its overall operations. Same is the story with other banks, as well. Occasionally these banks are overstaffed and ill-managed, thereby adversely affecting the banksâ€™ profitability. The entrant of new private and foreign banks has added fuel to the fire. There are many challenges for the banking sector in India, hence there are particularly more challenges for a new foreign entrant in the Indian banking system.A lot of foreign banks have already burnt their fingers in the retail segment and have decided to move out of a few retail segments completely. Most of the public sector banks are either looking to pick up a smaller bank or waiting to be picked up by Copyright ÂŠ 2017, Scholarly Research Journal for Interdisciplinary Studies

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a larger bank. The central government also seems to be encouraging public sector banksto merge or acquire other banks. In the aftermath of liberalization, restructuring, and deregulation of financial markets not only in India but also around the world, the performance evaluation of financial institutions has become an issue of great research interest. Particularly the performance of commercial banking has been the cynosure of all eyes. The public sector banks (PSB) are not willing to lose the race. On the contrary, they have evolved sufficient flexibility in their system to embrace the cutting-edge technological changes in international commercial banking. The result of these initiatives can be noticed in the growth of net profit of the PSBs, which has nearly doubled during the financial year 2001-02 as compared to the financial year 2000-01 (Rs.4,316.94 crore in 2000-01 to Rs.8,301.24 crore). However, during the same period, the net profit for the new private sector banks increased from Rs.639.41 crore to Rs.774.62 crore, a meager increase of 21 percent. Thus measuring the efficiency of scheduled commercial banks which includes public sector banks, private sector banks, and foreign banks after banking liberalization may result in interesting outcomesat regional level. 3. Main Results The analysis reveals that technical efficiency in the state of Maharashtra and Rajasthan was best among all states selected for study whereas the performance of scheduled commercial banks was worst in the state of Kerala. Regionally the technical efficiency of the remaining states needs improvement.Super efficiency of scheduled commercial banks reveals Uttar Pradesh, Maharashtra, Gujarat and Rajasthan were the four top ranking states. Comparison of super efficiency ranks with the gross state domestic product ranking of states year wise, reveals convergence between the two in case of Uttar Pradesh, Maharashtra, Gujarat, Rajasthan for almost entire study period. 4. Review of literature In the pre-1991 period, the commercial banking sector in India operated under a regime of financial repression where the allocation of resources and pricing of deposits and loans were, to a large extent, administered by the government. In the nineties, however, the banking environment experienced radical transformation consequent to the dismantling of entry barriers, rate deregulation, the introduction of prudential accounting norm and the implementation of Basel I capital adequacy norms. These changes brought a competitive scenario in the Indian banking sector that compelled the commercial banks to provide Copyright ÂŠ 2017, Scholarly Research Journal for Interdisciplinary Studies

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unprecedented attention towards the reduction in operating cost. Inter alia, improvement of asset quality became an important agenda for the commercial banks during the reform period. This gained further momentum in the following years, given the introduction of Basel II and Basel III norms. The literature on the use of DEA for measuring banking efficiency can be broadly classified as the comparison between public sector banks, private banks, and foreign banks. Studies by Bhattacharyya et al. (1997b), Galagedera and Edirisuriya (2005), Sathye (2003), Ram Mohan and Ray (2004a) showed that technical efficiency of public sector banks was far better than private banks and foreign banks. Work done by Mukherjee et al. (2002), Chatterjee and Sinha (2006), Sinha and Chatterjee (2008), Sinha (2008a), Sinha (2008b), and Kumar and Gulati (2009a) revealed that technical efficiency of private banks were more than both the public sector banks and foreign banks. On the other hand studies by Ketkar et al. (2003), Ataullah and Le (2006), Zhao et al. (2008), Sarkar et al. (1998), Chakrabarti and Chawla (2005), Das et al. (2005), Debasish (2006), Sanjeev (2006), Dash and Charles (2009) depicts that the foreign banks were more efficient as compared to private banks and public sector banks.It needs to be mention here that most of the authors have used input and output variables to their restricted choice and various studies in the literature depict approaches that are distinct to the theme of the research work. 4.1. New work in Indian Banking literature Substantial research on efficiency and productivity of banking sector prevails in the literature. Research work on individual banksâ€™ efficiency, branch efficiency, cross- country bank efficiency, comparative study of banking performance on basis of the ownership group, impact of banking reforms on efficiency, financial liberalization and banking performance, the impact of financial crises on banking performance and productivity. However, in all the work done so far, no specific study deals with the regional analysis of banks at the state level and comparing the performance of states via banking efficiency. In the present study, banking efficiency is estimated for various states over fifteen years from 1996 to 2011. Further, in the present study, the super efficiency of scheduled commercial banks is computed regionally to rank accordingly

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5. Banking reforms The first major step towards banking reforms was Nationalization of Banks in the year 1969. Further, in line of banking reforms were the Narasimham committees I and Narasimham committee II recommendations. 5.1. Nationalization of Banks The banking system had made some progress in terms of deposit growth in the 1950s and the 1960s, but its spread was mainly concentrated in the urban areas. This had raised the number of scheduled bank branches under government control from 31 percent from 84 percent (Chakraborty, 2006, p.156). The rapid increase in deposits concerning to their owned capital enabled the industrialist shareholders to enjoy immense leverage which sidelined the agricultural sector then. This converged in barely any development in rural India. So the government was forced to go for the nationalization of banks in the year 1969. The impact of nationalization of banks can be seen intable no. 1 Statistics of Credit and Deposit pre and post Nationalization Tables No. 1 Year 1951 1969 1991

No. of Banks No. of Branches Deposit Credits 566 89 277

4151 8187 59752

908 547 4646 3599 201199 121865

Source:Banking Commission 1971and Statistical Tables Relating to Banks in India (various issues, Rupees in crore) During the post nationalization period, the banking sector suffered serious erosion in its efficiency and productivity (Dhar, 2003). Moreover, the sound banking system had been disturbed by the system of directed credit operation in the form of subsidized credit flow in the under-banked and priority areas, IRDP lending, loan festival, etc. The operational expenditure of the public sector banks had tremendously increased due to rise in the number of branches, poor supervision, rising staff level and high unit cost administering loan to the priority sector.

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5.2. Narasimham Committee Report on Banking Reforms Banking issues were many post nationalization of banks mentioned above and the financial crisis of 1991 compelled the government to move towards reform for the financial sector. As a counteraction to this, the government introduced financial sector reforms in the year 1991. Banking reforms were subsequently initiated by RBI in the year 1992. The process of liberalization of the banking sector and the policies changes commenced during 1992 and had continued their onwards with the goals to create a more diversified, profitable, efficient and robust banking system (Government of India,(1991), Narasimham Committee Report-I). The Narasimham Committee Report I was aimed at bringing operational flexibility and functional autonomy to enhance efficiency, productivity, and profitability of the banking industry. The Narasimham Committee Report II focused on bringing structural changes to strengthen the banking system to counter the financial crisis globally as well as at the regional level. The second generation of financial sector reforms was initiated by the Committee on Banking Sector Reforms (BIS) formed in 1998 under the chairmanship of M. Narasimham. The purpose was to review the banking reform progress and design a programme for further strengthening the financial system of India. The committee focused on various areas such as capital adequacy, bank mergers, and bank legislation. The highlighting point of Narasimham committee report II was that Central Bankâ€™s role should be separated from being the monetary authority to that of the regulatory body for the banking sector of the country. As seen from Table No. 2, the number of offices reveals a gradual increase in all states except Bihar. There has been a decline in the number of employees for SCB from 1996 to 2010 indicating that the operational efficiency of banks has improved. Also, implementation of computers and related technology in the banking sector in later 1990s has helped to get the right set of employees with the right skills (trained) which have also improved performance of banking sector. A substantial upsurge in deposits of all states signifies mobilization of deposits has transpired as recommended by Narasimham committee. The escalation in credit outstanding among various states suggests that SCBs were observed as the prime source of fund raising institutes for economic, social and domestic requirements.

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6. Data for study The focus here is on the source of data, selection of data and selection of states used for research work. Empirical work on the state-wise measurement of efficiency and performance analysis requires input and output variables. Using the intermediate approach, the funds that come to banks should be utilized as financial resources for economic and social sectors. In this context, the input variables include deposits and borrowings, salary and other expenses. The output variables include loans and advances and investments. 6.1. Sourceand Selection of Data: Data for Scheduled Commercial Banks (SCBs) are taken from Basic Statistical Returns (BSR) from RBI website. As the study is to assess the efficiency of these banks in comparison to its counterparts in other states, the need is to look for those variables that will help in this assessment. This study being on the banking sector, the rationale is to see the funds available to banks and the funds utilized by banks for various economic and social activities. In this context, both deposits and borrowings are treated as input variables. Also, the resources needed for operational processes are treated as input variables for the banks. 6.2. Variables for the Study Going into the details of these variables, the study considers two input variables namely deposits and borrowings and second salary and other expenses.The funds utilized by banks are treated as output variables for the present study. In this context, the two output variables considered for the study are Investments1, while the second being Loans2 and advances. Here, it needs to be informed that the present study considers all Scheduled Commercial Banks of a state together, so the consolidated data for all Scheduled Commercial Banks in a state form a DMU for that state. As an example, the output variable investment for SCB showing the data for the year 1996-97 for the state of Maharashtra is taken by adding the investments for all SCB banks in the state of Maharashtra for the year 1996-97. Likewise, values the other variable, namely loans and advances for each state yearwise are received from the same source using an identical method of adding. This data is available from RBI website state-wise and year wise for SCBs. A similar job is carried out

Investment comprises of Investments in Government Securities and Investments Other than Government approved securities. 2 Loan comprises of demand loans and term loans. Advances include overdraft facilities and cash credits for various economic and socio-economic activities.

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for all input variables and output variables. Thus all data for the present study are necessarily secondary by choice. State-wise Statistics of Scheduled Commercial Banks for the study period Table No. 2

Source: RBI Basic Statistics, various years. Deposits* show the figures are in lacs of rupees Credits Outstanding* show the figures are in lacs of rupees. 6.3. Selection of Number of States: The results of DEA are influenced by the size of the sample set i.e. DMUs. Cooper et.al. (2007) provides two rules to decide the number of DMUs that are given as follows: j ≥ max( i , p ) , j ≥ 3 * ( i + p) where, j = number of states selected for study (DMUs), i = number of input variables (two for the study) and p = number of output variables (two for the study). Care has been taken to see to it that the number of states satisfies these norms.

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6.4. Selection of States: The selection of states in the present study is dependent on the distribution of banks over the study period. We use the location quotient index (LQ 3 index) as it gives an idea about the penetration level of banks in a geographical region. With regard to the values of LQ index, the states selected for Scheduled Commercial Banks include Andhra Pradesh, Bihar, Delhi, Goa, Gujarat, Haryana, Himachal Pradesh, Jammu &Kashmir, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Orissa, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh and West Bengal. 7. Methodology In methodology we discuss the theoretical aspect of DEA and the model used for computation of technical efficiency. 7.1 Theoretical Aspect of Efficiency Measurement DEA methodology is a linear programming approach used to develop an efficient frontier, which is then used to generate relative efficiency of states. DEA calculates specific efficiency score for a state (DMU) relative to other DMUs and not an absolute value, unlike the stochastic approach. This is a non-parametric technique that requires no assumptions about the form of production technology or function used. It develops a frontier by enveloping all the observed input and output values.The DEA model employed in this study uses variable returns to scale (VRS) approach. The use of VRS specification helps to compute technical efficiency (TE) devoid of the influence of scale efficiency (SE). Technical efficiency refers to the conversion of physical inputs of banks, such as deposits and borrowings, into outputs like investments and loans and advances relative to the best-practice. Technical efficiency focuses on the ability of a bank to produce an existing level of output with the minimum inputs (input-oriented DEA4) or to produce maximal output from a given set of inputs (output-oriented DEA). In this study output-oriented DEA is used to measure technical efficiency. Thus, technical efficiency relates to the productivity of inputs (Sathye 5, 2001). Consequently, the technical efficiency of a bank is viewed as its ability to transform

3 4

LQ index depicts the penetration level of banks state wise for the present study. Although there are two ways of measuring it, the present study uses output-oriented TE and input-oriented CE.

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multiple resources into multiple financial services (Bhattacharyya 6 et al., 1997). A bank is technically inefficient if production occurs within the interior of the frontier generated. 7.2 Computation of Technical Efficiency for Scheduled Commercial Banks Following are the sequence of steps to compute the VRS technical efficiency (i.e. VRS TE): (1) Finding the radial efficiency for each state by solving the following LPP: đ?œƒ0 âˆ—đ?‘‰đ?‘…đ?‘† = min đ?œƒ đ?‘‰đ?‘…đ?‘† 18

Îťj xij â‰¤ đ?œƒđ?‘Ľio ,

đ?‘– = 1,2

đ?œ†đ?‘— đ?‘Śđ?‘žđ?‘— â‰¤ đ?‘Śđ?‘žđ?‘œ ,

đ?‘ž = 1,2

đ?‘— =1 18

đ?‘— =1 18 đ?‘— =1

đ?œ†đ?‘— = 1,

đ?œ†đ?‘— â‰Ľ đ?‘œ,j = 1 ... â€Śâ€Ś18

(Equ. 1)

where is the technical efficiency of Oth state, are coefficients, represents the ith input of the jth state, is the ith input of Oth state, is the qth output of the jth state, and is the qth output of the Oth state. This is done for step1 and the value is stored in, 1. (2) In the second step maximize the sum of slacks + đ?’Žđ?’‚đ?’™ đ?’ˆ đ??€, đ?’”âˆ’ đ?’Š , đ?’”đ?’’ = âˆ’ âˆ’ + đ?’? đ??€,đ?’”đ?’Š ,đ?’”đ?’’

đ?&#x;? đ?’’=đ?&#x;?

đ?’”đ?’’+ +

đ?&#x;?

đ?’”âˆ’ đ?’Š=đ?&#x;? đ?’Š

Subject to constraints: 18

đ?œ†đ?‘— đ?‘Śđ?‘žđ?‘— âˆ’ đ?‘ đ?‘ž+ = đ?‘Śđ?‘žđ?‘œ đ?‘— =1 18

đ?œ†đ?‘— đ?‘Ľđ?‘–đ?‘— + đ?‘ đ?‘–âˆ’ = đ?‘?đ?‘Ľđ?‘–đ?‘œ đ?‘— =1 18

đ?œ†đ?‘— = 1 đ?‘— =1

đ?œ†, đ?‘ đ?‘–âˆ’, đ?‘ đ?‘ž+ â‰Ľ 0 (Equ. 2)

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where represent slack term for input access that remains in input i of state â€˜oâ€™ after the radial contraction was applied to state oâ€™s inputs and is the slack term for the output shortfall in the (production) servicing of state â€˜oâ€™, refers to ith input of the oth state which has been contracted by a factor of as derived from step 1. Step 1 and 2 helps us to find efficient and inefficient states. All states with efficiency score and no slacks are the efficient states. States with at least one non-zero slack variable(s) form the slack set for further analysis. (3) In this step, the focus is on finding all those states in the slack set in which input variables may have some slacks. This is done by solving i linear programming problems one for each input. đ?’Žđ?’Šđ?’? đ?œ˝ Subject to constraints: 18 đ?‘’đ?‘“đ?‘“

đ?œ†đ?‘— đ?‘Ľđ?‘–đ?‘—

â‰¤ đ?œƒđ?‘?đ?‘Ľđ?‘–đ?‘œ ,

đ?‘– = 1,2

đ?‘— =1 18

đ?‘’đ?‘“đ?‘“

đ?‘— =1,đ?‘–â‰ đ?‘—

đ?œ†đ?‘— đ?‘Ľđ?‘–đ?‘—

â‰¤ đ?‘?đ?‘Ľđ?‘–đ?‘œ ,

đ?‘— = 1, â€Ś â€Ś â€Ś â€Ś .17 ,

18 đ?‘’đ?‘“đ?‘“

Îťj đ?‘Śđ?‘žđ?‘—

â‰¤ đ?‘Śđ?‘žđ?‘œ ,

đ?‘ž = 1,2

đ?‘— =1

Îťj â‰Ľ o.

đ?‘— = 1, â€Ś â€Ś â€Ś â€Ś .18

(Eq. 3)

where is the ith input of the jth state, being the efficiency score calculated in step 1, is the ith input of oth state which has been contracted by a factor of as expressed in step1. The main purpose of step 3 is to identify the input dimensions in which slacks exist. So no actual changes occur in the projected points (computed in step 1). This information turns out to be handy in the subsequent step. Initially, it was thought that one may be able to determine the potential directions of slack by simply noting down the non-zero slack variables from step 2 results (and hence one could discard step 3). However, it became evident, that the slacks identified in step 2 need not identify all dimensions in which potential slack exists. Hence, step 3 is a necessity. Here it also needs to be noted that step 3 can generate some difficulty in the computation if some inputs are zero.

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(4) In this step (for the ith state in the slacks set) run the LP which seeks a radial reduction in all inputs identified as having potential slack (in step 3). The LP, in this case, is expressed as follows: đ?’Žđ?’Šđ?’? đ?œ˝ Subject to constraints: 18 đ?‘’đ?‘“đ?‘“

đ?œ†đ?‘— đ?‘Ľđ?‘–đ?‘—

â‰¤ đ?œƒđ?‘?đ?‘Ľđ?‘–đ?‘œ ,

đ?‘– = 1,2

đ?‘— =1 18 đ?‘ ,đ?‘’đ?‘“đ?‘“

đ?‘ â‰¤ đ?‘?đ?‘Ľđ?‘–đ?‘œ ,

đ?‘– = 1,2

đ?‘›đ?‘ ,đ?‘’đ?‘“đ?‘“

đ?‘›đ?‘ â‰¤ đ?‘Śđ?‘žđ?‘œ ,

đ?‘ž = 1,2 .

đ?œ†đ?‘— đ?‘Ľđ?‘–đ?‘— đ?‘— =1, 18

Îťj đ?‘Śđ?‘žđ?‘— đ?‘— =1

Îťj â‰Ľ o(Eq.4) where the superscript s indicates the subset of inputs consisting of the potential slacks and ns indicates the remaining inputs. The starting point of radial reduction is the projected point (as depicted by step 1). (5) Having performed the radial reduction in step 4, itâ€™s likely that there may still exist some slacks in a few input variables. So taking the projected points identified in step 4 (for the oth state) and repeat steps 3 and 4 until no slacks remain in any input variables. (6) Considering projected point from step 5 (for the oth state) and repeating steps 3-5, and conducting radial expansion in output slack dimensions until no output slack remains. The final projected point will be on the efficient surface. The peers of the state can then be identified from the Îťâ€™s of the final projected point and the slacks may be calculated by subtracting the final projected point from the projected point as given in step 1. The multi-stage DEA approach outlined above selects more appropriate peers compared to stage one and stage two DEA approach. In this approach, one seeks efficient projection points by moving along the surface of the facet radially. However, in case of some states the technical efficiency shows a score of 1 for a few years. Thus, to compare the efficiency of states, itâ€™s necessary to estimate super efficiency. This is done using the DEA solver for each year to get rankings of states year-wise.

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8. Analysis and Results As the study period is vast, to get a deeper understanding of regional growth in the banking sector, the spectrum of banking efficiency is divided into three periods. The first period covers 5 years from 1996-97 to 2000-01, the second period consists of 6 years from 2001-02 to 2006-07 while the third period has four years starting from 2007-08 to 2010-11. The first period is selected to view the implementation process of banking reforms brought in by the first Narsimham Committee in the year 1992 and its subsequent impact. Literature also calls this as the Basel I period. Therefore, this could be considered as the effects of Basel I norms. The second period which starts from 2001-02 to 2006-07 is selected to comprehend the effects of reform process of second Narsimham Committee report, recommendations of which commenced from 1998-99 onwards. Here again, as the period is sighted as Basel II process, the period could analyze the impact of Basel II norms. Lastly, the third period consisting of the years 2007-08 to 2010-11, looks into the post-Basel II period. The outcome of distribution of technical efficiency for Scheduled commercial banks can be seen in table no. 3A, 3B and 3Cfor the first, second and third period respectively. Observing table no. 3A for the first period, Maharashtra, Orissa, Rajasthan, and Uttar Pradesh have been consistently efficient throughout the first period, while Jammu and Kashmir and Gujarat were found to be efficient for 85% and 71% of the period respectively. Again, while Haryana and Madhya Pradesh were efficient for more than half of the first period, Andhra Pradesh and Himachal Pradesh were efficient for little less than half of the first period. Similarly, states like Bihar and Goa were efficient for little more than one-fourth of the first period. Delhi and Karnataka were efficient for only a little more than one-tenth of the first period. Remaining states like Kerala, Punjab and West Bengal were found to be inefficient throughout the first period. Looking at table no. 3B for the second period, states like Gujarat, Haryana, Himachal Pradesh, Maharashtra, Rajasthan, and Uttar Pradesh were found to be efficient for the entire second period whereas Delhi and Orissa were efficient for less than three fourth of the period. Andhra Pradesh and Bihar were efficient only for 33% and 16% of the period respectively. Inefficient states for the entire second period were Goa, Jammu & Kashmir, Karnataka, Kerala, Madhya Pradesh, Punjab, Tamil Nadu, and West Bengal. For the third period, table no.3C show that Himachal Pradesh, Maharashtra, Rajasthan, and West Bengal were efficient while Andhra Pradesh, Gujarat, and Haryana were Copyright ÂŠ 2017, Scholarly Research Journal for Interdisciplinary Studies

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efficient for three fourth of the third period. Goa and Uttar Pradesh were efficient for half of the third period although Madhya Pradesh, Orissa, and Punjab were efficient for only 25% of the period. Among the inefficient states for the entire third period were Bihar, Delhi, Jammu and Kashmir, Karnataka, Kerala, and Tamil Nadu. Technical Efficiency 1996-97 to 2000-01 Table No: 3A Freq of STATES States with % Time â–ź TE =1 Andhra Pradesh 3 60 Bihar 0 0 Delhi 1 20 Goa 1 20 Gujarat 3 60 Haryana 3 60 Himachal Pradesh 3 60 Jammu & Kashmir 4 80 Karnataka 0 0 Kerala 0 0 Madhya Pradesh 2 40 Maharashtra 5 100 Orissa 5 100 Punjab 0 0 Rajasthan 5 100 Tamil Nadu 0 0 Uttar Pradesh 5 100 West Bengal 0 0 Freq. of Years TE=1 % Years Ave. Over States

Distribution of TE from 1996-97 to 2000-01 1996-971997-98 1998-99 1999-00 2000-01 0.817 0.817 1 1 1 0.991 0.991 0.898 0.752 0.899 0.274 0.274 0.297 0.303 1 0.592 0.592 1 0.393 0.042 1 1 0.961 0.844 1 0.534 0.534 1 1 1 1 1 0.284 0.229 1 1 1 1 1 0.247 0.803 0.803 0.854 0.794 0.706 0.62 0.62 0.638 0.617 0.578 1 1 0.898 0.83 0.839 1 1 1 1 1 1 1 1 1 1 0.502 0.502 0.602 0.596 0.565 1 1 1 1 1 0.782 0.782 0.952 0.95 0.92 1 1 1 1 1 0.815 0.815 0.924 0.919 0.88 8 8 8 7 9 44.4444 44.44444 44.4444 38.88889 50 0.81833 0.818333 0.85044 0.790389 0.8153333

Ave,. over Years 0.9268 0.9062 0.4296 0.5238 0.961 0.8136 0.7026 0.8494 0.792 0.6146 0.9134 1 1 0.5534 1 0.8772 1 0.8706

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Technical Efficiency 2001-02 to 2006-07 Table No: 3B Freq of STATES States with â–ź TE =1 Andhra Pradesh 2 Bihar 0 Delhi 4 Goa 0 Gujarat 6 Haryana 6 Himachal Pradesh 6 Jammu & Kashmir 0 Karnataka 0 Kerala 0 Madhya Pradesh 0 Maharashtra 6 Orissa 4 Punjab 0 Rajasthan 6 Tamil Nadu 0 Uttar Pradesh 6 West Bengal 0 Freq. of Years TE=1 % Years Ave. Over States

Distribution of TE from 2001-02 to 2006-07 Ave,. % Time 2001-022002-032003-042004-052005-062006-07over Years 33.3333 0 66.6667 0 100 100 100 0 0 0 0 100 66.6667 0 100 0 100 0

0.987 0.948 0.931 0.949 0.872 1 0.734 0.808 1 1 1 1 0.042 0.043 0.047 0.053 1 1 1 1 1 1 1 1 1 1 1 1 0.258 0.304 0.264 0.244 0.701 0.681 0.627 0.675 0.566 0.583 0.559 0.555 0.832 0.802 0.72 0.753 1 1 1 1 1 1 0.983 0.831 0.562 0.535 0.504 0.469 1 1 1 1 0.87 0.834 0.811 0.884 1 1 1 1 0.924 0.915 0.899 0.862 8 9 7 7 44.444 50 38.8889 38.889 0.8119 0.8136 0.78217 0.7824

1 1 0.702 0.681 0.321 0.327 0.415 0.494 1 1 1 1 1 1 0.677 0.71 0.703 0.689 0.702 0.69 0.807 0.798 1 1 1 1 0.58 0.592 1 1 0.956 0.998 1 1 0.897 0.872 8 8 44.444 44.4444 0.82 0.82506

0.96917 0.7995 0.77467 0.18233 1 1 1 0.4095 0.67933 0.60917 0.78533 1 0.969 0.54033 1 0.89217 1 0.89483

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Technical Efficiency 2007-08 to 2010-11 Tables No: 3C Freq of Distribution of TE from 2007-08 to 2010-11 Ave. STATES States with% Time 2007-08 2008-09 2009-10 2010-11 over â–ź TE =1 Years Andhra Pradesh 3 75 0.974 1 1 1 0.9935 Bihar 0 0 0.929 0.658 0.666 0.681 0.7335 Delhi 0 0 0.932 0.359 0.362 0.352 0.50125 Goa 2 50 1 0.6 0.536 1 0.784 Gujarat 3 75 1 1 1 0.97 0.9925 Haryana 3 75 1 0.892 1 1 0.973 Himachal Pradesh 4 100 1 1 1 1 1 Jammu & Kashmir 0 0 0.988 0.79 0.72 0.733 0.80775 Karnataka 0 0 0.999 0.7 0.684 0.678 0.76525 Kerala 0 0 0.932 0.722 0.721 0.657 0.758 Madhya Pradesh 1 25 0.965 1 0.8 0.741 0.8765 Maharashtra 4 100 1 1 1 1 1 Orissa 1 25 1 0.864 0.885 0.846 0.89875 Punjab 1 25 1 0.69 0.722 0.762 0.7935 Rajasthan 4 100 1 1 1 1 1 Tamil Nadu 0 0 0.983 0.914 0.977 0.969 0.96075 Uttar Pradesh 2 50 1 1 0.896 0.795 0.92275 West Bengal 4 100 1 1 1 1 1 Freq. of Years TE=1 10 8 7 7 4 % Years 55.556 44.4444 38.889 38.889 Ave. Over States 0.9834 0.84383 0.8316 0.8436

Note: (1) Frequency of states is viewed as the number of times a state is efficient (TE = 1) for the period. (2) Fequency of years indicate the total number of states that are efficient (TE = 1) for a year. (3) %Time is the percent of time span for the period during which TE=1. (4) % states is the percent of states among all states for which TE = 1. During the entire study period from 1996 to 2011 which is a combination of the three periods, Maharashtra and Rajasthan were seen as efficient states (table no.5). Further, Uttar Pradesh and Gujarat were found to be efficient for 88% and 82% of the study period respectively. Both Haryana and Himachal Pradesh were efficient for marginally above three fourth of the entire study period. Orissa was observed to be technically efficient for almost 70% of the study period. States like Andhra Pradesh and Jammu & Kashmir were efficient for less than half of the study period. Each of the states Delhi and Madhya Pradesh were found efficient for slightly above one-fourth of the study period whereas Goa and West Bengal were efficient for a period below one-fourth of the study period. The state of Bihar was seen to be technically efficient for only 17% of the study period. On the other hand, Karnataka, Punjab and Tamil Nadu were barely efficient throughout the study period. Kerala was the only inefficient state for the entire study period.

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Summary: Distribution of TE (Percentage time) period wise Table No.: 5 % Time 100%

First period Maharasthra, Oriisa Rajasthan Uttar Pradesh

Second Period Gujarat, Haryana Himachal Pradesh Maharashtra Rajasthan Uttar Pradesh 99%-66% Jammu & Kashmir Delhi Gujarat Orissa

Third Period Himachal Pradesh Maharashtra Rajasthan West Bengal

65%- 50% Haryana Madhya Pradesh 5%-25% Andhra Pradesh Hiachal Pradesh Bihar, Goa <25% Delhi Karnataka Tamil Nadu o% Kerala Punjab West Bengal

Goa Uttar Pradesh Madhya Pradesh Orissa, Punjab

Andhra Pradesh

Andhra Pradesh Gujarat, Haryana

Bihar

Goa, Karnataka, Kerala Jammu & Kashmir Madhya Pradesh Punjab, Tamil Nadu West Bengal

Bihar, Delhi Jammu & Kashmir Karnataka, Kerala Tamil Nadu

Change in (Period wise) performance of States with regard to Technical Efficiency score Table No. 6.

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Table No. 7

8.1. State wise Super Efficiency As seen from the table no.7, Uttar Pradesh is the leading state followed by Maharashtra, Rajasthan and Gujarat with regard to the super efficiency rank. Next in line are Orissa, Haryana and Himachal Pradesh. Surprisingly, states like Punjab Karnataka and Kerala are at the lower end of ranking. 8.2. Super Efficiency of Banks and Status of States Banks are one of the leading sources of finance in the development of a state or a region. As such when a state develops and has higher ranking in the status of states/regions, the efficiency of banks could be a source for this status. Comparing the efficiency of banks in different states and their development status, one could arrive at the extent to which the banks could play a role in the development of states. Therefore, this study compares the super efficiency of banks in different states and the status of states. For this the efficiency of banks has been ranked using super efficiency and states have been ranked on the basis of the GSDP at factor cost. These have been tabulated to form 15 different tables one each for each of the 15 years of the study period. The following subsection gives an analysis of matching between the super efficiency and gross state domestic product. Copyright ÂŠ 2017, Scholarly Research Journal for Interdisciplinary Studies

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Table No. 8 Super Efficiency Scheduled Commercial Banks Years → 1996−1997−1998−1999−2000−2001−2002−2003−2004−2005−2006−2007−2008−2009−2010− 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 States ↓

Andhra Pradesh10

SE 3

Bihar

Delhi

Goa

Gujarat

Haryana

Himachal Pradesh 6

Jammu & Kashmir 6

Karnataka

Kerala

Madhya Pradesh 5

Maharashtra

Orissa

Punjab

Rajasthan

Tamil Nadu

Uttar Pradesh 1

West Bengal 11

SE →Super Efficiency Rank

8.2Comparison of Super Efficiency of Scheduled commercial banks and Status of States Table No. 8(A) Comparing the Ranking of Super Efficiency and GSDP of States for SCB TE 1996-97 1996-97 States

GSDP

Rank

Rank 3 14 12 18 5 13 17 16 7 11 9 1 15 10 8 4 2 6

Andhra Pradesh 10 Bihar

Delhi

Goa

Gujarat

Haryana

Himachal Pradesh 6 Jammu & Kashmir 6 Karnataka

Kerala

Madhya Pradesh 5 Maharashtra

Orissa

Punjab

Rajasthan

Tamil Nadu

Uttar Pradesh 1 West Bengal

Number of Matching States

1997-98 1997-98 States

GSDP

Rank

Rank 3 14 12 18 5 13 17 16 7 11 9 1 15 10 8 4 2 6

Andhra Pradesh 10 Bihar

Delhi

Goa

Gujarat

Haryana

Himachal Pradesh 6 Jammu & Kashmir 6 Karnataka

Kerala

Madhya Pradesh 5 Maharashtra 3 Orissa

Punjab

Rajasthan

Tamil Nadu

Uttar Pradesh1 West Bengal 11

1998-99 1998-99 States

GSDP

Rank

Rank 2 14 12 18 5 13 17 16 7 11 9 1 15 10 8 4 3 6

Andhra Pradesh 4 Bihar

Delhi

Goa

Gujarat

Haryana

Himachal Pradesh 18 Jammu & Kashmir 5 Karnataka

Kerala

Madhya Pradesh 12 Maharashtra 2 Orissa

Punjab

Rajasthan

Tamil Nadu 10 Uttar Pradesh1 West Bengal 11

1999-00 1999-00 States

GSDP

Rank

Rank 5 14 12 18 6 13 17 16 7 10 9 1 15 11 8 4 2 3

Andhra Pradesh 4 Bihar

Delhi

Goa

Gujarat

Haryana

Himachal Pradesh 18 Jammu & Kashmir 5 Karnataka

Kerala

Madhya Pradesh 11 Maharashtra 3 Orissa

Punjab

Rajasthan

Tamil Nadu 8 Uttar Pradesh2 West Bengal 9

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Observing the table no. 8(A), 8(B), 8(C) and 8(D), it is found that in these 15 years, while one year showed 8 states match with the top 10 ranks of both status of states and bank efficiency for 4 years, 7 states match with top 10 rankings of both status and bank efficiency. Again for 6 and 3 years each, 6 and 5 states respectively match with top 10 rankings of both status of states and bank efficiency only for 1 year and 4 states match with top 10 rankings both status and banking efficiency. This clearly indicates that bank efficiency plays a major role in the development of the states. Better is the efficiency higher is the status of the states in development. Thus banks play a key role in the development of regions. The matching states where Andhra Pradesh, Gujarat, Madhya Pradesh, Maharashtra, Rajasthan, Tamil Nadu, Uttar Pradesh, and West Bengal. While Maharashtra matched the status of states and banking efficiency for all the 15 years, Andhra Pradesh, Gujarat, Rajasthan, and West Bengal matched the two rankings for 14 years of the study. West Bengal, Tamil Nadu, and Madhya Pradesh matched the two rankings for 10, 6 and 3 years respectively. Table No. 8(B) Comparing the Ranking of Super Efficiency and GSDP of States for SCB TE 2000-01 2000-01 States

GSDP

Rank

Rank 4 14 12 18 6 13 17 16 7 11 9 1 15 10 8 3 2 5

Andhra Pradesh9 Bihar

Delhi

Goa

Gujarat

Haryana

Himachal Pradesh 6 Jammu & Kashmir 17 Karnataka

Kerala

Madhya Pradesh13 Maharashtra

Orissa

Punjab

Rajasthan

Tamil Nadu

Uttar Pradesh 1 West Bengal

Number of Matching States

2001-02 2001-02 States

GSDP

Rank

Rank 7 13 11 18 14 12 17 16 9 2 1 3 15 8 10 6 5 4

Andhra Pradesh 9 Bihar

Delhi

Goa

Gujarat

Haryana

Himachal Pradesh 6 Jammu & Kashmir 17 Karnataka

Kerala

Madhya Pradesh 13 Maharashtra 2 Orissa

Punjab

Rajasthan

Tamil Nadu

Uttar Pradesh1 West Bengal10

2002-03 2002-03 States

GSDP

Rank

Rank 4 14 13 18 6 12 17 16 7 9 10 1 15 11 8 5 2 3

Andhra Pradesh 10 Bihar

Delhi

Goa

Gujarat

Haryana

Himachal Pradesh 7 Jammu & Kashmir 17 Karnataka

Kerala

Madhya Pradesh 13 Maharashtra 2 Orissa

Punjab

Rajasthan

Tamil Nadu

Uttar Pradesh 1 West Bengal 11

2003-04 2003-04 States

GSDP

Rank

Rank 3 14 13 18 6 12 17 16 7 10 9 1 15 11 8 5 2 4

Andhra Pradesh 9 Bihar

Delhi

Goa

Gujarat

Haryana

Himachal Pradesh 5 Jammu & Kashmir 17 Karnataka

Kerala

Madhya Pradesh 13 Maharashtra 3 Orissa

Punjab

Rajasthan

Tamil Nadu

Uttar Pradesh1 West Bengal 10

Amit Khatri (Pg. 9612-9637) Table No. 8(C ) Comparing the Ranking of Super Efficiency and GSDP of States for SCB TE 2004-05 2004-05 States

GSDP

Rank

Rank 7 14 11 18 5 13 17 16 6 9 10 1 15 12 8 3 2 4

Andhra Pradesh 8 Bihar

Delhi

Goa

Gujarat

Haryana

Himachal Pradesh 5 Jammu & Kashmir 17 Karnataka

Kerala

Madhya Pradesh13 Maharashtra

Orissa

Punjab

Rajasthan

Tamil Nadu

Uttar Pradesh 1 West Bengal

Number of Matching States

2005-06 2005-06 States

GSDP

Rank

Rank 7 15 11 18 4 12 17 16 6 9 10 1 14 13 8 3 2 5

Andhra Pradesh 5 Bihar

Delhi

Goa

Gujarat

Haryana

Himachal Pradesh 7 Jammu & Kashmir 15 Karnataka

Kerala

Madhya Pradesh 11 Maharashtra 2 Orissa

Punjab

Rajasthan

Tamil Nadu 9 Uttar Pradesh1 West Bengal10

2006-07 2006-07 States

GSDP

Rank

Rank 7 15 11 18 4 12 17 16 6 9 10 1 14 13 8 3 2 5

Andhra Pradesh 5 Bihar

Delhi

Goa

Gujarat

Haryana

Himachal Pradesh 7 Jammu & Kashmir 12 Karnataka

Kerala

Madhya Pradesh 11 Maharashtra 2 Orissa

Punjab

Rajasthan

Tamil Nadu 9 Uttar Pradesh1 West Bengal10

2007-08 2007-08 States

GSDP

Rank

Rank 7 15 11 18 4 13 17 16 6 9 10 1 14 12 8 3 2 5

Andhra Pradesh 14 Bihar

Delhi

Goa

Gujarat

Haryana

Himachal Pradesh 8 Jammu & Kashmir 12 Karnataka

Kerala

Madhya Pradesh 15 Maharashtra 1 Orissa

Punjab

Rajasthan

Tamil Nadu

Uttar Pradesh2 West Bengal 3

Table No. 8(D) Comparing the Ranking of Super Efficiency and GSDP of States for SCB TE 2008-09 2008-09 States

GSDP

Rank

Rank 7 15 11 18 4 12 17 16 6 9 10 1 14 13 8 3 2 5

Andhra Pradesh5 Bihar

Delhi

Goa

Gujarat

Haryana

Himachal Pradesh 8 Jammu & Kashmir 12 Karnataka

Kerala

Madhya Pradesh 7 Maharashtra

Orissa

Punjab

Rajasthan

Tamil Nadu

Uttar Pradesh 6 West Bengal

Number of Matching States

2009-10 2009-10 States Andhra

GSDP

Rank

Rank 7 15 12 18 4 11 17 16 6 9 10 1 14 13 8 3 2 5

Bihar

Delhi

Goa

Gujarat

Haryana

Himachal

J& K

Karnataka

Kerala

Madhya

Maharashtra 1 Orissa

Punjab

Rajasthan

TamilNadu

UttarPradesh 2 WestBengal 6

2010-11 2010-11 States

GSDP

Rank

Rank 8 14 12 18 4 11 17 16 6 9 10 1 15 13 7 3 2 5

Andhra Pradesh 3 Bihar

Delhi

Goa

Gujarat

Haryana

Himachal Pradesh 5 Jammu & Kashmir 14 Karnataka

Kerala

Madhya Pradesh 13 Maharashtra 1 Orissa

Punjab

Rajasthan

Tamil Nadu 9 Uttar Pradesh11 West Bengal 4

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9.Conclusions Looking at the technical efficiency state-wise period wise as seen from table no. 6, Maharashtra and Rajasthan are the best performers. Uttar Pradesh, Gujarat, Haryana, Himachal Pradesh, Andhra Pradesh, and West Bengal are the states with moderate improvement in technical efficiency scores from the first period to the third period. Punjab and Tamil Nadu show a marginal improvement in technical efficiency scores from the first period to the third period. Madhya Pradesh, Jammu & Kashmir, Bihar, Delhi, Goa, Karnataka, Kerala, and Orissa depict a decline in technical efficiency score from the first period to the third period. Further, the comparison between super efficiency and gross state domestic product depicts that Uttar Pradesh, Maharashtra, Gujarat, Rajasthan, Andhra Pradesh, and West Bengal are matching the ranks for a longer duration for the study period. This indicates that the states have been able to capitalize on the banking reform process much better than the other states. 10. Limitations of the Study and Scope of Further Research The effect of banking reforms varies in different states based on their adoption time and implementation period and exposure of banks to the agriculture sector, industry sector service sector and socio-economic sector. This study does not focus on the effect of banking reforms on the movement of non-performing assets (NPA) regionally as well as the digitalization of banking processes regionally. Hence, it may be considered for further research. Another outlook for the future study could be to evaluate the performance of Indian banks regionally by taking into account loan commitments, securitization, and derivatives with a larger set of the data sample. References Berger et al., (1993), Bank efficiency derived from the profit function, Journal of Banking and Finance, issue: 17, pp. 317-347, North-Holland. Zeitun, R. and H. Benjelloun, (2013), Efficiency of banks and the financial crisis in a

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economy: The case of Jordan. Journal of Finance, Accounting and Management, 4(1): pp. 120. Egesa, Kenneth Alpha, (2010), Financial Sector Liberalization and Productivity Change in Ugandaâ€™s Commercial Banking Sector - AERC Research Paper 202, African Economic Research Consortium Nairobi Kenya 2010. Copyright ÂŠ 2017, Scholarly Research Journal for Interdisciplinary Studies

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