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This study focuses on the Assam Gramin Vikash Bank, which is at the forefront of the effort by the Regional Rural Banks (RRBs) to develop agriculture and the rural economy of Assam. As the foot soldiers of a bank, the branches are crucial in generating business. Thus, it is of paramount importance to measure the efficiency/inefficiency of the branches. This study analyzes the level of efficiency at which selected branches of the AGVB operate and investigates the cost savings potentiality. The AGVB was selected because of its large network spread over the different districts of Assam. The study uses economic modeling to identify the factors that are responsible for efficiency/inefficiency and it also determines the total factor productivity changes. The results show that on an average the branches operate at 67 percent efficiency level and that there remains scope of cost saving potentiality to the tune of 33 percent. The total factor productivity has grown by 13.4 percent, which indicates that the productivity growth of the branches is mainly driven by technological upgrades in the banking sector. The RRBs are exhibiting greater potentialities towards the financial inclusion of the rural areas as the branches of these banks are mainly located in places where few financial service providers are operating. Since the various schemes of the RRBs are modeled solely to uplift the rural masses, they are demonstrating a great impact on the lives of the people residing in rural Assam.


A thrust area of the Indian banking sector—in lockstep with the banking sector in transitional economies—is to bring larger numbers of people under formal banking coverage. As a result, the Indian government has taken several institutional initiatives to make banking services easily accessible mainly to people living in far-flung areas. In India one such category of bank, established with this objective of bringing the rural unbanked people under the purview of formal banking coverage, are the Regional Rural Banks (RRBs), which were established under the provisions of an ordinance passed on September 26, 1975.[1] The RRB Act of 1976 aimed to provide sufficient banking and credit facility for agriculture and other rural sectors. These banks were set up on the recommendations of the Narasimham Working Group during the Congress-led government with a view to include rural areas into the economic mainstream because at that time about 70 percent of the Indian population was of rural orientation. Over years these banks have expanded their operations through a wide network of branches.

The Prathama Bank, established in 1975, was the first RRB in the country. Though the emphasis of RRBs is to provide services to the priority sector and to serve the rural masses, over time the area of operation has extended to other urban and semi-urban centers like other commercial banks. However merely increasing a number of branches through the length and breadth of a country does not ensure the efficiency of a bank. Thus, it is important that such branches operate efficiently in such a manner that the overall efficiency of the bank as a whole increases. To ensure the overall efficiency of the bank, efficiency at the grassroots level—that is, at the branch level—is of paramount importance.

Against this backdrop, the study analyzes the branch level efficiency of one of the RRBs operating in the state of Assam, the Assam Gramin Vikash Bank, covering three of the thirty-three districts of Assam. The study identifies the factors responsible for efficiency or inefficiency, and it finds out the total factor productivity changes. Total-factor productivity (TFP), also called multi-factor productivity, is a variable which accounts for effects in total output growth relative to the growth in traditionally measured inputs of labor and capital.

The economy of Assam is mainly agriculture-based as about 69 percent of the state’s total population is engaged in this sector. According to the Indian census of 1971, out of the total workforce of Assam, 55.86 percent of the workers were cultivators and 9.92 percent were agricultural laborers. The overwhelming majority of 91.18 percent of the population of Assam lives in rural areas, while only 8.82 percent lives in urban areas. Alarmingly, 27.2 percent of rural households in the state reported indebtedness in comparison to total rural households in India as of June 1971.[2] Before the formation of rural banks in the state, private money lenders played a vital role in extending financial assistance to the rural people of Assam.

With abundant oil and natural gas resources, Assam produced 50 percent of the total onshore oil production of the country and was the sole oil producing state in India till 1960, when oil was discovered in Ankleshwar, Gujarat. Leaving aside Assam’s natural resources sector, the state lagged behind other Indian states in industrialization except for a few industries. Before the introduction of banking in the state, barter was used by every household to exchange goods and services with their neighbors, particularly to meet their requirement of rice and wheat. Such was the economic reality of rural Assam.

The need for a banking system in Assam was felt after nationalisation of 14 commercial banks in 1969. Though few banks were operational in different parts of the state, they were not sufficient to meet the needs of the rural population due to urban orientation of these banks. Although the deposits and advances of these banks increased after 1969, the credit-deposit ratio of the banks in the state was not satisfactory. In the normal course of business, the commercial banks were concentrating on industrial and commercial lending while discouraging priority sector lending in the state. It resulted in backwardness in the rural agricultural sector and limited industrialization. There were no bank branches in villages and small towns. In such a situation, the development of the rural economy was considered an integral part of the main strategy in the Sixth Five Year Plan of the country. The development of the rural areas was forced by the need to create social stability, economic growth and equality. In 1972, the Banking Commission under the chairmanship of R. G. Saraiya recommended the setting up of rural banks which would extend credit to the rural areas, which revolutionized the scope and extent of rural finance in Assam.[3]

Following the committee’s recommendation, on July 1, 1975, the Ministry of Finance, Government of India, constituted a working group to examine the rationale of creating rural banks to fulfill the credit needs of the rural people. On July 30, 1975 the working group submitted its report to the government which accepted the recommendations and passed the Regional Rural Bank Ordinance on September 26, 1975 to establish the RRBs. Later, in 1976 the ordinance was replaced by the Regional Rural Banks Act.[4] These initiatives led to the introduction of rural banks in Assam.


A Brief History of Assam Gramin Vikash Bank

In Assam, five RRBs began operating at different points of time, beginning with the Pragjyotish Gaonlia Bank. The proactive attitude and the rural bias of these banks culminated not only in inculcating thriftiness among the rural people but also in the dispensation of credit to them mostly for priority sector activities such as Kishan Credit cards, the PMEGP (Pradhan Mantri Employment Generation Scheme), etc. The government of India amalgamated the four RRBs sponsored by the United Bank of India in the state of Assam to form the Assam Gramin Vikash Bank on January 12, 2006 under notification No. F.1. (25)/2005.[5] The erstwhile RRBs amalgamated were the Pragjyotish Gaonlia Bank (established on July 6, 1976), Lakhimi Gaonlia Bank (July 29, 1980), Cachar Gramin Vikash Bank (March 31, 1981) and Subansiri Gaonlia Bank (March 30, 1982).

The AGVB covered the areas of operation of the pre-amalgamated RRBs with its head office at Guwahati. The bank covered 25 districts of the state through its network of 414 branches till June 2016. At present the bank has 473 branches spread over 33 districts of Assam after amalgamation with the Langpi Dehangi Bank, earlier sponsored by the State Bank of India.


Conceptual Framework


Efficiency can be simply defined as the ratio of output to input. More output per unit of input reflects relatively greater efficiency. If the greatest possible output per unit of input is achieved, a state of absolute or optimum efficiency has been attained and it is not possible to become more efficient without new technology or other changes in the production process.


Efficiency =

Typically, efficiency is used to analyze how well a bank uses its assets and its liabilities internally. The better the management of assets the larger will be the volume of income. It measures the efficiency or effectiveness with which a firm manages its resources. An efficient banking system contributes in an extensive way to higher economic growth in any country. Therefore, studies of banking efficiency are very important for policymakers, industry leaders and others who are reliant on the banking sector.


Efficiency Measurement Techniques

There are varied approaches to study efficiency that can be broadly categorized as an Accounting Approach and an Economic Approach. While the accounting approach relies mainly on accounting ratios, the economic approach centers around the measurement of input-output relationship.[6] Ratios are the most widely used and popular measure of efficiency. But they are subject to certain limitations which, at times, result in lower reliability. Thus, researchers are either departing from it or are using ratios in conjunction with some other techniques. The twentieth century has witnessed an increasing volume of literature on banking efficiency using parametric and non-parametric techniques. Among parametric, the popular techniques are Stochastic Frontier Analysis (SFA) and Free Disposal Hull (FDH), and in non-parametric techniques the most widely used is the Data Envelopment Analysis (DEA).[7]


Data Envelopment Analysis (DEA)

Originally developed by Charnes, Cooper and Rhodes in 1978 to evaluate nonprofit and public sector organisations, Data Envelopment Analysis is a very powerful service management and benchmarking technique.[8] Under this technique the most efficient unit located on the efficient line of units are those for which no other unit, or linear combination of units, has as much or more of every output or as little or less of every input.

The DEA determines a comparative ratio of the weighted sum of outputs to the weighted sum of inputs for each unit under evaluation. This relative score, expressed as a number between 0 and 1, provides an efficiency measurement comparable to that from parametric methods. Further, DEA’s ability to analyze multiple inputs and outputs at the same time is a strong advantage in evaluating a complex operation such as a bank or a bank branch. These non-parametric properties point to an easier, yet sophisticated, approach to tackle an industry problem, and DEA was judged to be particularly suitable for this study. DEA is a linear programming-based technique for measuring the performance efficiency of organizational units like banks. In DEA technique, every unit whose performance is to be measured is known as DMU or Decision Making Unit. In 1985, Sherman and Gold were the first to evaluate bank efficiency at the branch level using data from fourteen branches of an American bank—that too at a time when using DEA to measure efficiency was itself a novelty.[9]


Productivity Measurement

Productivity denotes human efforts to produce more and more output with less and less input in order to attain maximum distribution of benefits among the maximum number of people. However, Total-Factor Productivity (TFP) simply implies the changes in total output growth due to the growth in inputs. For measuring the total factor productivity changes, we have used the Malmquist Total Factor Productivity Index.


Malmquist Index

In the present study, we take the help of the Malmquist productivity index for measuring changes in output due to changes in inputs where there are more than one input and output. The advantage of the Malmquist Index over other Total Factor Productivity indices is that the former does not require information on the prices of input and output data. This makes the Malmquist Index a particularly suitable tool for the analysis of productivity change in the public sector, where output prices are not generally available. Another advantage of the Malmquist approach is that once the production technology is estimated, one can decompose TFP change into its component parts: efficiency change and technical change. Here decomposing denotes the separation of various constituent parts or elements, i.e. disintegration.


Technical Efficiency

Technical efficiency (TE) is the effectiveness with which a given set of inputs is used to produce an output. A firm is said to be technically efficient if it is producing the maximum output from the minimum quantity of inputs, such as labor, capital and technology. TE can be further categorized as: (1) Pure technical efficiency, and (2) Scale efficiency.[10]


Pure Technical Efficiency

Pure Technical Efficiency refers the managerial efficiency of an organization about how efficiently it manages its inputs to get its output. Pure Technical Inefficiency, however, results from using more inputs than necessary. Such inefficiency arises due to factors such as managerial errors arising from inertia and ignorance and poor quality of inputs, etc.


Scale Efficiency

A firm is said to be Scale Efficient (SE) when its size of operations is optimal so that any modifications to its size will render the unit less efficient. SE recognizes that economy of scale cannot be attained at all scales of production and that there is one most productive scale size where the scale efficiency is maximum at 100 percent.


Review of the Literature

The available literature on the subject is reviewed for the purpose of getting acquainted with the findings of the research conducted and thereby gaining insight into the topic. Idries Mohammad Al-Jarrah in 2007 employed the DEA approach to investigate the cost efficiency levels of banks operating in Jordan, Egypt, Saudi Arabia and Bahrain from 1992 to 2000.[11] The estimated cost efficiency was decomposed into technical and allocative efficiency at both variable and constant return to scale. Later on, the TE was further decomposed into pure technical and scale efficiency. The study revealed that cost efficiency scores ranged from 50 to 70 percent with some variations in scores depending on a bank’s size and its geographical location. Their results suggested that the same level of output could be produced with approximately 50 to 70 percent of their current inputs following the best practice.

Turning to the RRBs in India, an empirical study by Suresh Rao in 2014 on the performance of rural banks in India from 2001-2013, analyzed the data using various statistical techniques and parametric test like t-test, f-test and ANOVA.[12] T-test, f-test and ANOVA are parametric tests where it is hypothesized that the observed data are distributed from some well-known form to some unknown parameters on which the inference is made.[13] The findings of his study revealed that the performance of the RRBs in India since 1975 was good, even though the RRBs faced number of crises and competition with other banks. The negative results revealed in the study did not mean that the banks themselves did not perform well, but rather that the government of India took measures such as downsizing the banks to save the RRBs from crisis.  Suresh’s study also found that the state of Nagaland had been totally neglected as the RRBs had not been opened there. He suggested that measures should be taken for total coverage of districts by setting up RRBs.

A national level study by O.K. Gupta, et al (2008), analyzed the efficiency of the Indian banking sector using DEA methodology, found the determinants of efficiency using Tobit regression model.[14] Inputs and outputs were measured in monetary value and efficiency scores determined for the period 1999-2003. The study showed that the State Bank of India and its group had the highest efficiency, followed by private banks, and the other nationalized banks.


Research Objectives and Analysis

The objectives of this study are to, first; identify the level of efficiency at which the selected branches of the Assam Gramin Vikash Bank operate and to discover the potential cost savings at the individual branch level. Secondly, it aims to ascertain productivity growth and its decomposition. Thirdly, it seeks to determine the factors responsible for efficiency or inefficiency.


AGVB Branch Level Technical Efficiency and its Decomposition

This study is devoted to assessing the level of efficiency at which individual branches operate.


Breakdown Year-Wise TE Scores of Selected AGVB Branches

Table 1 succinctly presents the year-wise TE scores of the selected branches. It can be seen that the average score of TE is 67 percent. This implies that there exists cost saving potentiality on the part of sample branches to the extent of 33 percent (1-0.67) following the best practice. Thus, the overall technical inefficiency of branches came to almost 33 percent. This simply means that the branches can curtail their input expenditures on deposits, fixed assets and labor by 33 percent by adopting best practices. The branches exhibited a declining trend in TE for the initial three years from the year 2011 to 2013 followed by an increase in the last two years.


Table 1 Breakdown Year-Wise of TE Scores of Selected AGVB Branches


Technical Efficiency (TE) under BCC model

















Source: Own estimate using Data Envelopment Analysis Program 2.1 based on data pertaining to Branch Financial Statements (2011-2015).

Note:  BCC denotes Banker Charnes and Cooper, MAX denotes Maximum, MIN denotes Minimum.


Year-Wise Pure Technical Efficiency (PTE) Scores of Selected AGVB Branches

Table 2 reflects the PTE scores of the selected branches. It is seen that the average PTE scores of all the selected branches is about 76 percent, which indicates that there is a scope for potential cost saving to the tune of 0.24 percent (1-0.759) on the part of sample branches under study. This shows that 33 percent of TIE (Technical Inefficiencies) is explained by 24 percent of PTIE (Pure Technical Inefficiencies) which is due to the incapability of the management to utilize its resources. The remaining part of the TIE may be attributed to the fact that the banks are operating at below the optimal level. The branches exhibited a sharp decline in the PTE score from 2011 to 2015, except for the year 2014 where there is an increase in PTE score from 0.728 to 0.766.


Table 2: Year-wise PTE Scores of Selected AGVB Branches


Pure Technical Efficiency (PTE) under BCC model

















Source: Own estimate using Data Envelopment Analysis Program 2.1 based on data pertaining to Branch Financial Statements (2011-2015)

Note: BCC denotes Banker Charnes and Cooper, MAX denotes Maximum, MIN denotes Minimum.


Year-Wise Scale Efficiency (SE) Scores of Selected AGVB Branches

Table 3 below depicts the mean SE score at 88 percent for the years ranging from 2011 to 2015 which implies that average scale inefficiency (SIE) as much as 12 percent (1-0.88) is due to the choice of sub-optimal level of operation. The table depicts a fluctuating trend from 2011-2015, however, the highest increase in scale efficiency is for the year 2015.


Table 3: Year wise SE Scores of Selected AGVB Branches


Scale Efficiency (SE) under BCC model

















Source: Own estimate using Data Envelopment Analysis Program 2.1 based on data pertaining to Branch Financial Statements (2011-2015) 

Note: BCC denotes Banker, Charnes and Cooper, MAX denotes Maximum, MIN denotes Minimum


Productivity Growth of Selected AGVB Branches

Table 4 shows that the Total Factor Productivity Change index (TFPCH) has increased to the extent of 13.4 percent as compared to the base year 2011.

Decomposition of TFPCH index into Technical Efficiency Change Index (EFFCH) and Technological change index (TECHCH) components reveals that the TFPCH index is mainly driven by growth in the TECHCH index which shown a growth of 13.6 percent as compared to the base year, whereas there is a decline in the EFFCH index to the tune of 2 percent. This indicates that the increase in output was driven mainly by technological upgradation at the AGVB’s branches such as the introduction of Core Business System (CBS) in November 2011.

The decline in the EFFCH index is mainly due to an 18 percent plunge in the Pure Technical Efficiency Change Index (PECH) whereas the branches registered growth of 17 percent in its scale of operation. This implies the bank’s managerial inefficiency in utilizing its resources to its fullest extent.

Year-wise analysis of the index depicts that the TFPCH registered an improvement from the second year (2012) to the fourth year (2014), though in the last year under study (2015) the index declined by 16 percent. Again, in case of the EFFCH index, except for the fourth year, i.e. 2014, there was a sharp decline. In case of the TECHCH there was improvement all through the year except for the year fourth year (2014) which showed decline. Year-wise analysis of PECH depicts that the index grew spectacularly to 95 percent only in 2014 whereas in all the other years the index was below the base year index. Similarly, the year-wise analysis of the Scale Efficiency Change Index (SECH) showed growth in all the years except for the year 2013 where it registered a fall.


Table 4: Productivity Growth of Selected AGVB Branches


Technical Efficiency Change Index (Effch)

Technological Efficiency Change Index (Techch)

Pure Technical Efficiency Change Index (Pech)

Scale Efficiency Change Index (Sech)

Total Factor Productivity Change Index (Tfpch)































Mean# denotes Geometric Mean.        

Source: Own calculation using Malmquist Productivity Index.


Determinants of Efficiency/Inefficiency at Branch Level

Measuring efficiency/inefficiency is not the only tool to make a bank efficient; rather all the responsible factors need to be identified so that the reasons behind efficiency in one branch can be understood in order to remove the inefficiency of the other branches. Thus the efficiency score as obtained under TE, PTE and SE are used to identify the factors responsible for efficiency/inefficiency. For this purpose, the Tobit Regression model is used.


Selected Variables

The independent variables which are considered in the Tobit Model to estimate the factors which determine efficiency of the selected branches are salary, total assets, non-interest income, net interest income, non-performing assets (NPA), location1 and location 2. Location 1 and location 2 are two independent dummy variables indicating the area of the branches whether located at urban, semi-urban and rural areas. 


Table 6: Description of Variables for Tobit Model




Dependent Variable:

DEA Efficiency Scores obtained from Stage 1 of DEA

TE=Technical Efficiency Scores, PTE=Pure Technical Efficiency Scores, SE=Scale Efficiency Scores

Independent Variables:



1.     Total Assets

Volume of Total Assets

Large Sized Branches are more efficient.

2.     Salary

Employee cost

Increase in employee costs negatively impacts on efficiency

3.     Non-interest income

Income other than interest income

Increase in Non Interest Income positively impact on efficiency

4.     Net-interest income

Interest income deducted interest expended

More net interest income impacts positively on efficiency

5.     Location 1

Urban = 1


Urban and Semi-urban branches are more efficient than rural branches

6.     Location 2

Semi-urban=1, Others= 0

7.     NPA

Nonperforming assets over total advances

Nonperforming assets has a negative impact on efficiency

Source: Own calculation


Table 7 shows that Salary is found to have a negative impact on TE and PTE, which means that with an increase in employee cost, the efficiency of the branches decreases. Total Assets is found to have a positive impact on TE and PTE, which implies that an increase in asset size increases the efficiency of the branches. However, a paradoxical finding is that the relationship between Total Assets and SE shows a negative relationship, which leads us to infer that large sized branches account for a greater degree of scale inefficiency. Asset size is found to have the highest positive influence on Technical and Pure Technical efficiency. Non-interest income negatively influences the efficiency scores, which implies that income from diversified business did not give any advantage to the efficiency score of the banks possibly because of the inefficient management of the cross-selling business. In banking parlance, cross-selling business means offering or providing more than one product or service to customers according to their needs. Net interest income also negatively influenced the efficiency scores because the maximum number of branches under study exhibited negative interest spread, which means that interest expenditure was more than the interest income in the case of most branches under study. Non-performing assets, or NPAs, were found to have negative impact on the measures of efficiency, indicating that increase in NPAs over advances had adverse effect on efficiency of the bank as it hurts the bank’s interest income along with a negative growth in advance. The urban and semi-urban branches accounted for higher Technical Efficiency as compared to rural branches because urban and semi-urban branches accounted for higher deposit base compared to the rural branches which, in turn, resulted in higher scale of efficiency.


Table 7: Results of Tobit Model for AGVB



Dependent Variable TE


Dependent Variable PTE


Dependent Variable SE































** Statistically significant at 5 percent

Source: Own calculation using Stata2 software, Stata is the abbreviation of the word statistics and data.

SAL=Salary, TA=Total Assets, NOI=Non-interest income, NII=Net-interest income, NPA=Non-performing assets, LOC 1=Location1 (Urban branches), LOC 2= Location2 (Semi-urban branches)


Policy Prescription

  • Cost trimming: The AGVB should take proper measures to reduce its input cost to become fully efficient as this study shows that there exists cost saving potential within the sample branches to the tune of 33 percent, as demonstrated by the best practice branch.
  • The AGVB should consider reallocating its resources across branches as negative interest spread was occurring at a maximum number of branches, thus adversely affecting efficiency.
  • Efficient management of non-traditional business: Banks are currently involved in various activities rather than the traditional banking services, such as selling insurance products, net banking and new schemes as implemented by the government. Thus, it is seen that management is not able to handle the cross-selling business efficiently as a result of which non-interest income is negatively affecting their efficiency, as this study demonstrates. In this respect, the efficiency of the management needs to be improved for better performance of AGVB.
  • Proper autonomy should be given to the branch managers for taking various important decisions at the branch level so that they can properly utilize the input of the branches and remain accountable for the decisions so taken.


Impact of the Study and Conclusion

The study is expected to make a significant contribution to the existing research base and is likely to signal the bank managers about possible cost reallocation in their branch network. It cautions them about the possible cost curtailment prevailing in the branches under study following the best practice.

This study considers both the efficiency level as well as the cost saving potentiality of the selected branches of Assam Gramin Vikash Bank in the Silchar region. The analysis employs non-parametric DEA technique and it shows that the selected branches are operating at 67 percent efficiency level. Thus, there remains scope to increase the efficiency of the branches by reduction of input costs to the tune of 33 percent, following the best practice branch. It is also seen that the TIE (Technical inefficiency) is mainly driven by the Pure Technical Inefficiency, which means that the percentage of existing inefficiency is due to managerial incapability to maintain the branches efficiently. The productivity change index shows an increase in total factor productivity change, which is mainly due to technological improvement, indicating that the increase in productivity was caused by technological advancement in the banking sector.

We also identify the factors responsible for efficiency/inefficiency of the selected branches with the help of the Tobit Regression Model. The findings show that since an increase in salary cost inversely affects efficiency, therefore cost trimming has to be done by the bank. Total assets have a positive relationship with efficiency except SE, meaning that large sized branches are scale inefficient. Non-interest income as well as net interest income has shown negative impact on efficiency possibly because the bank is not efficient in the management of its diversified business or cross-selling business.

Nonperforming Assets have a negative impact on efficiency as expected. Branches at Location 1 and Location 2 have shown positive influence on efficiency. Thus, the management has to take measures to improve their managerial efficiency. The study, however, leaves ample scope for research in this direction. Further research is necessary to find out the lacunae in respect of both managerial inefficiencies as well as the scale inefficiency at branch level.

The motto of Assam Gramin Vikash Bank, “Rural development real development,” signifies that its target group is mainly the rural masses. As priority sector lending is the main target imposed by the government on the RRBs, whose proportion is 60:40 percent the bank bears great responsibility for upliftment of the poor people of the area (60 percent of the bank’s total lending is to the priority sector, and 40 percent is to the non-priority sector).Assam Gramin Vikash Bank has remarkable contribution in extending SHG (Self Help Group) loans compared to other commercial banks of the area, which help the poor people in self-development and employment generation in Assam. Along with the loans, various training programs and financial literacy camps are conducted by the various branches of the bank to disseminate financial awareness among the needy people who are yet to get the cream of banking services at an affordable cost.

Assam Gramin Vikash Bank is making a remarkable contribution towards the various flagship rural development programs of the Government of Assam and the Government of India such as Mudra loan, PMEGP (Pradhan Mantri Employment Generation Programme) loan, KCC (Kisan Credit Card) facilities, agricultural credits, SVAYEM (Swami Vivekananda Assam Youth Empowerment Yojana), RRY (Rajiv Rinn Yojana), Sarothi loan, etc. All these schemes were scrupulously followed and executed by this bank over the years.

From a modest beginning with 6 RRBs with 17 branches covering 12 districts in the country in December 1975, the number of RRBs increased to 196 with 14,446 branches spread over 518 districts across the country in March 2004.[15] The orientation of the RRBs has shifted from rural development towards commercialization over the years. As a result, along with the traditional banking activities of deposits and loans, now the RRBs are concentrating on offering their customers numerous products and services like the other commercial banks in India. Cross-selling activities of the RRBs are helping in generating revenue for their organizations. A study by Biswa Swarup Misra in 2006 revealed that during the financial year 2003-04, 163 RRBs earned profits to the tune of Rs.953 crore (Rs. 9.53 billion) whereas 33 RRBs incurred losses to the extent of Rs. 184 crore (Rs. 1.84 billion).[16] Recent trends in the performance of RRBs show that out of 56 RRBs up to the financial year 2017, 49 RRBs earned profits amounting to Rs. 2,604 crore (Rs. 26.040 billion).[17]

From the above analysis, it can be concluded that, as the responsibility of rural development is conferred upon the RRBs, the measurement of their performance is vital for their betterment. The results of this study will be helpful as an efficient-policy prescription for the management of the Assam Gramin Vikash Bank which is playing a crucial role in the upliftment of the poor rural people of the state.



Mohuya Deb Purkayastha is a PhD scholar at Assam University, Silchar. With M.Phil and M.Com degrees, her research interest is banking, and her major publications are “Branch Level Productivity of Assam Gramin Vikash Bank: A TFP Approach,” (co-author J. Deb), Prajnan, Vol. XLVII, No. 1, April-June 2019;  “Efficiency Measurement of Assam Gramin Vikash Bank: A Branch Level Analysis,” Think India Journal, Vol. 22, No 10 (2019); “A Case of Bank Striving for Network Efficiency,” (co-author with J. Deb), Wealth: International Journal of Money, Banking and Finance, Vol. 6, No. 2, July-December 2017; and “Profitability and Operational Efficiency of Indian Commercial Banks: A Comparative Study of Public and Private Sector Banks In India,” (co-author with J. Deb), Asian Journal of Research in Social Sciences and Humanities, Vol. IV, Issue VII, July 2014. She has presented papers on banks in India’s North East Region, women’s empowerment, the goods and services tax, and transition to a cashless economy.


Joyeeta Deb is an Assistant Professor in the Department of Commerce at Assam University, Silchar. With a PhD and M.Com, her areas of interest are banking and microfinance. Her book publications are Indian Banking System: Reforms, Reorganizations and Innovations (Evincepub Publishing, 2018); and Financial Performance and Efficiency of Co-operative Banks: Special Reference to Meghalaya Co-operative Apex Bank Ltd (Himalaya Publishing, 2018). Some of her major journal articles “Competition and Commercialization of Microfinance Institutions: Implications for Sector,” International Journal of Business Ethics in Developing Economies, Vol. 7 (2), December 2018; “Efficiency Determinants of Microfinance Institutions in India: Two Stage DEA Analysis,” (co-author S. Kar),Central European Review of Economics and Management, Vol.1, No. 4 (December 2017): 87-115; “Assessing Sustainability and its Determinants of Microfinance Institutions in India,” International Journal of Banking, Risk and Insurance, Vol. 5, No. 1 (2017): 1-9.  She has published book chapters such as Financial Reforms in India—Post Liberalization Experience, Department of Commerce, Assam University Silchar, 2017; and “The Determinants of Loan Repayment Performance: A Study on KCC Borrowers in the West District of Tripura, ”(co-author P. Chakraborty) in Rural Development and Management in India—Opportunities and Challenges (Nova Science publishers, New York).




[1] The Assam Gramin Vikash Bank, www.agvb.co.in.

[2] Census of India, 1971, Series I, India Part II A (i), General Population Table, p . 54.

[3] “Report on Trend and Progress of Banking in India,” Reserve Bank of India Bulletin, June 1982.

[4] P.K. Srivastava, Banking: Theory and Practice (Bombay: Himalaya Publishing House, 1983), 133.

[5] The official website of Assam Gramin Vikash Bank: www.agvbank.co.in.

[6] K.S. Gupta and K.R. Sharma, Financial Management Theory and Practice (New Delhi: Kalyani Publishers, 2007).

[7] R. Ramanathan, An Introduction to DEA: A Tool for Performance Measurement (New Delhi: Sage Publications, 2003).

[8] A. Charnes, W.W. Cooper, and E. Rhodes, “Measuring the Efficiency of Decision Making Units,” European Journal of Operational Research 2 (1978): 429-444.

[9] David H. Sherman and Franklin Gold, Measuring Operating Efficiency of Service Businesses with Data Envelopment Analysis: Empirical Study of Bank Branch Operations (Sagwan Press, 2015).

[10] Subhash C. Ray and Lei Chen, “Data Envelopment Analysis for Performance Evaluation: A Child’s Guide,” Indian Economic Review 45, No. 2 (2010): 79-124.

[11] M.I. Al-Jarrah, “The Use of DEA in Measuring Efficiency in Arabian Banking,” Banks and Bank Systems 2, No. 4 (2007).

[12] Suresh Rao, “Rural Banks of India and Its Performance: An Empirical Study,” International Journal of Academic Research1, No. 3 (2): 2014.

[13] S.P. Gupta, Statistical Methods (New Delhi: Sultan Chand & Sons Educational Publishers, 2006).

[14] Omprakash K. Gupta, Yogesh Doshit and Aneesh Chinubhai, “Dynamics of Productive Efficiency of Indian Banks,” International Journal of Operations Research 5, No. 2 (2008): 78-90.

[15] S.B. Misra, “The Performance of Regional Rural Banks in India: Has Past Anything to Suggest for Future?” Reserve Bank of India occasional papers, Vol 27, No. 1 and 2 (2006).

[16] Ibid.

[17] Sidharthi Sharma, Priyanka Goyal and Neha Mittal, “A Study on the Performance of Regional Rural Banks in India,” International Journal of Research and Analytical Reviews 6, No. 1 (2019): 441-445.