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Productivity Growth in Regional Rural Banks

This paper examines total factor productivity technical and scale efficiency changes in regional rural banks by using data from 192 banks for the period 1996 to 2002. Rural banks showed significant economies of scale in terms of assets and number of branches under each bank. Total factor productivity growth of rural banks was higher in profitability than in service provision during liberalisation. Banks located in economically developed as well as low banking density regions exhibited significantly higher productivity growth. Overall there is a convergence of efficiency of rural banks during the study period. Parent public sector banks have no influence on the efficiency and productivity growth of rural banks. There is a justification for opening new banks in low banking density regions as efficiency and productivity growth of rural banks in these areas are high. There is also a case for mergers and enlargement of the asset base and the number of branches under each rural bank.

Productivity Growth in Regional Rural Banks

This paper examines total factor productivity technical and scale efficiency changes in regional rural banks by using data from 192 banks for the period 1996 to 2002. Rural banks showed significant economies of scale in terms of assets and number of branches under each bank. Total factor productivity growth of rural banks was higher in profitability than in service provision during liberalisation. Banks located in economically developed as well as low banking density regions exhibited significantly higher productivity growth. Overall there is a convergence of efficiency of rural banks during the study period. Parent public sector banks have no influence on the efficiency and productivity growth of rural banks. There is a justification for opening new banks in low banking density regions as efficiency and productivity growth of rural banks in these areas are high. There is also a case for mergers and enlargement of the asset base and the number of branches under each rural bank.

A AMARENDER REDDY

I Introduction

C
urrently, rural credit system comprises of about 30 state cooperative banks and 369 district central cooperative banks (DCCBs) with 13,635 branches, one lakh primary agricultural credit societies (PACS) and 196 regional rural banks (RRBs) with more than 15,000 branches spread throughout rural India. In addition to these exclusive rural bank networks, about 30,000 rural and 14,000 semi-urban branches of commercial bank branches form institutional credit. The institutional credit accounts for about 60 per cent of total rural credit needs. The remaining 40 per cent of rural credit gets taken care of by noninstitutional sources like traditional moneylenders, shopkeepers and relatives. All the rural credit dispersing agencies work under the direction and guidance of the National Bank for Agriculture and Rural Development (NABARD).

RRBs were established as low cost alternatives to commercial banks and cooperative banks to cater to the needs of rural credit under a separate statute – the Regional Rural Banks Act of 1976. These banks were sponsored by a public sector bank (PSB), which owned 35 per cent of the share capital and provided technical and managerial support to the banks. The sponsor bank also provided the key personnel to the RRB. The government of India owns 30 per cent of the share capital and the state government owns 15 per cent. With the UPA government’s emphasis on agriculture/rural areas and a plan to double rural credit within three years, both academicians and policy-makers are showing renewed interest in the functioning of rural credit institutions, especially RRBs.

Agricultural loans account for more than 45 per cent while that for non-agricultural purposes account for the remaining 54 per cent of the total loans of RRBs, with almost all being dispersed in rural and semi-urban areas. The aggregate deposits of RRBs are about Rs 43,220 crore and aggregate credit disbursed is Rs 18,373 crore with a credit deposit ratio of 42 per cent in 2002 and a significant and growing share of rural credit market.

However, RRBs suffered losses since their inception. Some studies have estimated that RRBs in their present set-up incur losses of about Rs 5,00,000 per day, despite their lower costs of operation in early 1990s [Ghosh 1992]. As a response to this dismal performance of RRBs, the Indian government and NABARD initiated corrective measures in RRBs in 1994-95. The NABARD started implementing development action plans (DAPs) for each of the rural finance institutions. The DAP exercise was also facilitated by deregulation in the banking sector initiated in 1994 by both the Reserve Bank of India and Indian government. Under the DAP, specific directions of credit flow were withdrawn to give flexibility to RRBs in running their business, although broad sectoral targets for agriculture and other priority sectors were retained. The emphasis on subsidised credit programmes also ended. Banks were given the autonomy to devise and market their saving and credit products. RBI permitted the banks to relocate loss-making branches to better business locations/centres and allowed conversion of loss-making RRBs into satellite/ mobile offices without impairing the performance of service areas. The asset quality of RRBs has improved significantly since the inception of liberalisation and the DAP. This has been possible due to their improved performance and recovery. With these initiatives, number of profitable regional banks increased from 46 in 1996 to 168 out of 196 during 2002. The aggregate loss of RRBs was about Rs 581 crore in 1996 and with this turnaround RRBs made an aggregate profit of about Rs 610 crore in 2002. Non-performing assets (NPAs) as per cent of total assets reduced from more than 35 per cent in 1996 to about 16 per cent in 2002. In addition to liberalisation and the DAP, rapid technological advances and free entry of new private banks accelerated the process of change.

The literature on costs, efficiency and productivity of financial institutions is voluminous. Many papers analysed the cost structure of public/private banks by examining the technical efficiency of commercial banks [Reddy 2004]. With the above approach total factor productivity growth (TFP) and its decomposition into technical change (shift in production frontier) and efficiency change (catching up) has not been studied, the only exception being Mohan and Ray (2002), who studied TFP growth by using the Malmquist index for commercial banks. Even they did not decompose TFP growth into technical change and technological efficiency change. In the current context of the liberalised era, with increasing scope of mergers and acquisitions and rapid technological progress, there is a need to look at technical change, scale efficiency and pure technical efficiency by further decomposing TFP growth. Moreover, no study has been undertaken on the functioning of regional rural banks in terms of efficiency and TFP growth and its decomposition.

To bridge this research gap, this study on the efficiency and productivity of RRBs has been undertaken. The study is concerned with the period of 1996-2002. In this period, RRBs underwent revolutionary changes due to the impact of overall economic liberalisation coupled with vigorous financial sector liberalisation, technological progress and the DAP of NABARD. As a result, RRBs were able to produce new services and increase the quality of their productive and organisational systems, with a corresponding effect on productivity and production costs. However, not all RRBs are able to take advantage of the new technologies. From a technological standpoint, some of them are not efficient. This study to some extent also examines factors, which influence these differences in efficiency and TFP growth.

The rest of the paper proceeds as follows. Section II sets the modelling stage with the specification of our data envelopment analysis (DEA) models for calculating the Malmquist TFP index and its three constituent components, as well as discusses the data set used for the study. In Section III, results are discussed. Section IV ends with conclusions and policy implications.

Bhattacharya et al (1997) studied the impact of the limited liberalisation initiated before the deregulation of the 1990s on the performance of Indian commercial banks. Their study covered 70 banks in the period 1986-91. The authors used advances, investments and deposits as outputs and interest expenses and operating expenses as inputs. They found public sector banks had the highest efficiency compared to private and foreign banks. However, the efficiency of public sector banks declined after 1987, private banks showed no change while foreign banks showed a sharp rise in efficiency. They did not consider technical change explicitly in the model.

While studying efficiency of Indian banks Das (1997) measured overall efficiency as well as technical, allocative and scale efficiency of PSBs during 1990 to 1996. The study concluded that there was a decline in all efficiency measures during that period. However, this study also did not explicitly measure technological progress (TFP growth) of the banks in the period.

Reddy (2005) observed that the share of public sector banks in the aggregate assets of the banking sector came down from 90 per cent in 1991 to around 75 per cent in 2004. The share of wholly government-owned public sector banks sharply declined from about 90 per cent to 10 per cent of aggregate assets of all scheduled commercial banks during the same period. Diversification of ownership has led to greater market accountability and improved efficiency.

Leeladhar (2005) pointed out that foreign banks and new private sector banks have embraced technology right from the inception of their operations and, therefore, they have adapted themselves to changes in technology easily, whereas the PSBs and old PSBs have not been able to keep pace with these developments. Added to these woes, the PSBs were also saddled with some non-viable and loss-making branches, thanks to the social banking concept thrust upon them by the regulatory authorities in 1960s.

Mohan (2005) stated that internationally, a return of 1 per cent on assets is considered as outstanding. India’s banking system in 2002 was the second most profitable in the world after that of the US. Most recent research places the optimal size of banks between $ 10 bn and $ 25 bn in the US. It is possible that technological progress as well as deregulation has resulted in a larger size being optimal than before. The average size of Indian banks in comparison to international banks is quite small, for example SBI, India’s largest bank ranks 82nd amongst the top global banks.

II Model Specifications

Even though studies from the developing country perspective on costs, efficiency and productivity of financial institutions are few, a voluminous literature is available for developed countries. There are two approaches to study productivity change: one is the parametric (econometric approach) and the other is the nonparametric approach. In the econometric approach there is a need to estimate cost, production and revenue functions, while the non-parametric approach requires the construction of index numbers by using non-parametric techniques. This study adopts the latter approach as it does not require a specific functional form for the structure of production technology, as is the case with the econometric approach. This approach is the most widely used and accepted method in measuring the efficiency of banks. Specifically, the Malmquist TFP index has been used in this study.

In the current study, we employ the non-parametric frontier method of data DEA to compute the Malmquist TFP indexes. The reasons for choosing Malmquist (1953) index over Fisher and Tornquist (1936) indexes are manifold; it also does not require price data and is capable of accommodating multiple inputs and outputs without worrying about how the inputs and outputs have been aggregated. It also does not make any restrictive value/behaviour assumptions for the economic units, such as cost minimisation or profit maximisation, as required by the Törnquist and Fisher indexes. Most importantly, this technique allows further decomposition of the Malmquist TFP index into three components: (a) shifts in production technology, (b) pure changes in technical efficiency, and (c) effects of economies of scale. The decomposition of the Malmquist TFP index is informative: the component of technological progress represents the degree of innovation potential, and the component of pure technical efficiency change reflects the potential of a banking firm to catch up with other leading benchmarks [Arcelus and Arozena 1999; Grifell-Tatje and Lovell 1999].

The original MPI assumes constant returns to scale for the production process. As a result, the original MPI typically overestimates productivity change if the production process displays decreasing returns to scale (or underestimates it for increasing returns to scale). To cope with the issue of variable returns to scale, Färe et al (1994) recommended the use of a generalised MPI that includes an additional component, called scale index to represent such an effect of economies of scale on productivity. We will also include such scale factors in our analysis. A brief mathematical account has been given below.

MPI Decomposition

A production process, which employs input vector Xt to produce output vector Yt at time t, can be defined by using an output set [Shao and Shu 2002]:

Pt(Xt) = {Yt: Xt can produce Yt}. (1)

The output set Pt (Xt) is assumed to be closed, bounded and convex, and it satisfies the strong disposability of inputs and outputs [Coelli et al 1998]. The output distance function is defined on the output set as:

Dt(Xt, Yt) = min {ρ : (Yt/ρ) ∈ Pt (Xt)} (2-1) = [max {ρ : (ρYt) ∈ Pt (Xt)}]–1. (2-2) The formula in equation (2-2) indicates that the distance function

Dt is the inverse of the measure of Farrell’s output-oriented technical efficiency [Farell 1957] and it facilitates the computation of an output distance function through the methods of efficiency measurement like DEA. The value of the output distance function ranges from 0 to 1, with a higher score indicating a location closer to the boundary of the output set (i e, the production frontier).

According to Färe et al (1994), the MPI can be defined as:

1

+1 +1 t+1 +1 +12

t tt +1 t +1 ⎡ Dt ( Xt , Yt )D ( Xt , Yt ) ⎤

M ( Xt , Y , X , Y ) =⎢×⎥

Dt XtYt Dt +1 Xt Yt

⎣ (,) (,) ⎦

1 Dt +1 Xt +1 Yt +1 ⎡ Dt Xt +1 Yt +1 Dt XtYt ⎤ 2

(,) (,) (,)

= ⎢×⎥(3)

+1 +1 +1 +1

Dt ( Xt , Yt ) ⎣ Dt ( Xt , Yt ) Dt ( Xt , Yt ) ⎦

In equation (3), the ratio outside the brackets is equal to the change of technical efficiency between time t and t + 1. In other words, it represents the change in the relative distance of the observed production from the maximum potential production. The component inside the brackets of equation (3) is the geometric mean of the two productivity indexes and represents the shift in production technologies (technical change) between timetand t + 1.

That is, technical efficiency change + t +1 t+

TEC = Dt 1(X , Y 1) (4) tt

Dt (X , Y ) Technical change

1 t+1 t+ tt 2

TCH= ⎡ Dt (X , Y 1) Dt (X , Y ) ⎤ (5)

×

⎢⎣Dt+1(Xt +1, Yt +1) Dt +1(Xt , Yt ) ⎥⎦

Technical efficiency change (TEC) in equation (4) can be further decomposed as the product of two components – pure technical efficiency change and scale change – as follows [Fare et al 1994]:

+ t+1 t +

Dt 1(X , Y 1)

=

Dtt t

(X , Y )

(6)

Dt +1(Xt+1, Yt +1) ⎡Dt +1( t+1 t+1) Dt (Xt , Yt ) ⎤ rX , Y r

×

ttt

,D (XY ) ⎢⎣ Dt (Xt , Yt ) Dt +1(Xt +1, Yt +1) ⎥⎦

rr

The ratio outside the brackets in equation (6) represents the pure change of technical efficiency, subject to a distance function

(D) with variable returns to scale, between time t and t + 1 and

ris denoted by PEC hereafter. In other words, Pure technical efficiency change

t+1( t +1 t +1)

D X , Y

PEC = r(7) ttt

D (X , Y )

r

The component inside the brackets of equation (6) represents the effects of economies of scale on productivity and is expressed as SCH. It is noted that SCH can be readily derived by dividing TEC of equation (4) by PEC of equation (7) and would not involve its own computations of additional output distance functions. That is, scale change

SCH = TEC / PEC (8)

After incorporating equation (6) – (8) into equation (3), we obtain the complete decomposition of the MPI:

Mt (Xt , Yt , Xt+1, Yt +1)= (TCH) × (TEC)

= (TCH) × (PEC) × (SCH) (9)

Another merit of defining the MPI using the output distance function Dt is that the MPI and its corresponding components (TCH, TEC, PEC and SCH) are all calculated in an index form and have a threshold value of one. In other words, if a derived value is equal to one, it indicates that a bank’s performance remains unchanged in that performance measure. A value greater than one represents an improvement, and a value less than one indicates a decline. The product of the index components of TCH, PEC and SCH then amounts to the final MPI.

DEA Models

DEA is a non-parametric and linear programming-based method for calculating a Farrell-like measure of technical efficiency [Banker et al 1984; Charnes et al 1978]. Due to the computational linkage specified by equation (2-2), the optimal values obtained by the corresponding output-oriented DEA models are equal to the reciprocal of the output distance functions in computing the final MPI.

To determine the final MPI, a close examination of equations

(3) and (6) reveal that we have to compute TCH, TEC and PEC, and then derive SCH by dividing TEC by PEC. Each output distance function corresponds to one particular output-oriented DEA linear programme. Among TCH, TEC and PEC, there are six output distance functions and, thus, a total of six different DEA models have to be formulated and solved:

t +1 t +1 t +1 t +1 tt ttt

D (X , Y ), D (X , Y ), D (X , Y ),

tt+1 t +1 t +1 t+1 t +1 ttt

D (X , Y ), D (X , Y ), and D (X , Y ).

rr

For easy understanding, the study denotes superscripts u and v to represent the possible combinations of t and t + 1 in formulating our DEA models (i e, u, v ∈ {t, t + 1}). As a result, as an alternative of six linear programmes, the study presents only two general DEA models to compute the MPI for the banking industry of a particular bank m (where I is the number of inputs, J is the number of outputs, and N is the number of banks):

vv −1Du

[ (X ,Y )] = max qm

N

vu

subject to −∑ p ≥0, i=1, ..., I

xmi mnxni

n=1 N

vu

−q +∑ p ≥0, j=1, ..., J

m ymj mn ynj

n=1 (10)

p ≥0, n=1, ..., N

mn

qm unrestricted in sign

and

uuu −1

[Dr (X ,Y )] = max qm

uu

subject to − N ∑ p ≥0, i=1, ..., I

xmi mn xni

n=1 N

uu

−q +∑ p ≥0, j=1, ..., J

m ymj mn ynj

n=1 N

∑ p =1, (11)

mn n=1

p ≥0, n=1, ..., N

mn qm unrestricted in sign

Data used in this study contains 192 out of 196 RRBs and 27 parent public sector banks for the period 1996 to 2002.The data for the remaining four RRBs is not available. The data has been collected from the RBI data set ‘Statistics Relating to Indian Banks’. As stated earlier, the inputs and outputs considered in this study were based on both profit generation and service provision. The inputs considered for the study were interest expenses plus operating expenses, the outputs considered were liquid assets, total advances, total deposits and total income (descriptive statistics of inputs and outputs have been given in Appendix 3). The criteria for considering these inputs/outputs have been given in the following section.

Definitions and Measurement of Bank Inputsand Outputs

There are three common approaches –the intermediation, user cost and production approach (value added approaches) in measuring outputs and inputs in the banking industry. A brief description about each method has been given below.

The first method is the intermediation approach in which bank deposits are regarded as having being converted into loans. While this approach seems to be appropriate for banks that purchase their funds in big chunks from other banks and large institutional depositors, it may not be so for the rural banks. Most rural banks provide value added services for their deposit holders, such as safekeeping of funds and other transaction services. Most importantly, accepting deposits from the vast majority of the rural population spread over widely dispersed and remote villages is an important social objective assigned to rural banks. This makes their deposits an output rather than an input and therefore the intermediation approach underestimates the overall value addition of the RRBs in serving rural India.

The second method is the user-cost approach. The user-cost approach considers an asset as an output, if the financial returns are greater than the opportunity cost of funds. Similarly, a liability item is regarded as an output if the financial costs are less than the opportunity cost. If neither of these conditions is satisfied, the assets or the liability is classified as an input [Berger and Humpherey 1992]. The user-cost approach is usually attributed to Hancock (1986). According to Hancock, user costs can be calculated for all the assets and liabilities on the balance sheet. However, an important weakness of this approach is that the assignment of assets and liability items as inputs or outputs may change with movements in interest rates and services charges.

The alternative is the production approach (value added approach) where banks are regarded as using labour and capital to produce deposits, loans and income [Mester 1987; Berger and Humpherey 1992]. Under this approach, high value creating activities such as loans, taking deposits and income are classified as outputs and measured in dollar terms, whereas labour and physical capital are classified as inputs [Wheelock and Wilson 1995].

Taking into account the advantages and disadvantages of each method, the value added (production) approach was employed, which enabled the classification of inputs and outputs based on their perceived value addition. This method turns out to be attractive as it allows differentiation between the various functions performed by banks. In analysing the functions performed by commercial banks, Bergendhal (1998) mentions five fundamental goals of efficient bank management: profit maximisation, risk management, service provision, intermediation and utility provision. David and Manole (2002) reclassified the above five functions into two broadly defined ones: profit maximisation (combining features of Bergendhal’s profit maximisation and risk management) and service provision (combining elements of service provision, intermediation and utility provision). In reality any bank operation combines the elements of the above two functions, since it is hard to imagine a bank which is not trying to produce (if not maximise) profits or establish a good rapport with its clients (service provision in terms of deposit mobilisation and making loans).

In this paper, two separate models were evaluated (both based on production approach) one with emphasis on profitability and the other with service provision. The inputs considered were interest expenses plus operational expenses excluding provisions. The three outputs considered in the first model (model I) were liquid assets, total advances and total income (interest income plus non-interest income). And the three outputs considered in the second model (model II) are liquid assets, total advance and total deposits.

The above mix of outputs and inputs were selected by considering the broader objectives such as profitability (total income; model I), service provision (deposits; model II) in addition to the usual total liquid assets and total advances as outputs with interest expenses and operating expenses as inputs. (The same approach has been followed in David and Manole (2002) and Wheelock and Wilson (1995). In the context of rural India, taking deposits (mobilising savings) from the vast mass of the population (who were dispersed in a wide area and not within the reach of capital markets to invest their savings) is an equally important function (in line with the social/national objectives) as that of giving loans (advances) to the needy; hence, both deposits and advances are considered as outputs in line with the study by David and Manole (2002) using the service approach. In both the models, advances and liquidity are taken as outputs as maintaining adequate liquidity and providing loans is an important and natural function of rural banks.

III Results of DEA

In line with the objectives of RRBs, mean efficiency is higher in service provision than in earning profits (Table 1). The average technical efficiency of all RRBs increased from 0.71 to 0.81 from 1996 to 2002 in model I (profitability), while average efficiency rose from 0.81 to 0.86 in the same period measured by model II (service). Mean efficiency in profitability also increased by about 10 per cent during 1996 to 2002 with initiatives such as the DAP and liberalisation. The efficiency of RRBs in devel-Figure 1: Technical Efficiency of RRBs by Size (Total Assets) oped regions is higher compared to less developed regions in both profitability and service provision. 0.95

In general banks located in the southern region are more

0.9

efficient in terms of both service provision and profitability, while

0.85

banks located in eastern India have the lowest efficiency (Table 2).

0.8

Bottom quartile Lower middle quartile Top middle quartile Top quartile

Technical Efficiency Technical Efficiency

But, RRBs located in the north-east region have the highest

efficiency in service provision (91 per cent). The high efficiency of

the south and north-east regions may be attributed to the high literacy

rate in this region, along with a well-developed banking culture.

Banks with a larger asset base have higher efficiency in both

the years and in both service provision and profitability; banks

0.75

0.7

0.65

0.6

Total Assets

—e—Model I (1996) —*—Model I (2002)

with a small asset base also performed better compared to medium

—D— Model II (1996) —+—Model II (2002)

size banks (Figure 1, Table 3). Economies of scale were observed conspicuously after attaining a certain size in all RRBs in both Figure 2: Technical Efficiency of RRBs by No of Branchesprofitability and service provision. The interrelationship between the number of branches per RRB and efficiency also showed 0.9

inverted “U” shape (Figure 2). Banks functioning in lower

Table 1: Technical Efficiency of RRBs by Development of Zone

Technical Efficiency Profitability Services (Model I) (Model II)

0.8

Zone-Development 1996 2002 1996 2002
Developed 0.77 0.86 0.83 0.89
Medium 0.66 0.78 0.81 0.84
Less 0.68 0.72 0.77 0.85
Total 0.71 0.81 0.81 0.86

0.7

Table 2: Technical Efficiency of RRBs by Regions

Technical Efficiency
Profitability Services
(Model I) (Model II)
Zone-Geographical 1996 2002 1996 2002
North 0.66 0.80 0.79 0.83
North-east 0.76 0.74 0.80 0.91
East 0.62 0.76 0.80 0.84
Central 0.72 0.84 0.83 0.89
West 0.72 0.80 0.77 0.83
South 0.86 0.88 0.84 0.87
Total 0.71 0.81 0.81 0.86

Table 3: Technical Efficiency of RRBs by Size (Total Assets)

Technical Efficiency
Profitability Services
(Model I) (Model II)
Total Assets 1996 2002 1996 2002
Bottom quartile 0.72 0.82 0.79 0.86
Lower middle quartile 0.66 0.78 0.77 0.84
Top middle quartile 0.66 0.80 0.80 0.85
Top quartile 0.82 0.85 0.90 0.89
Total 0.71 0.81 0.81 0.86

Table 4: Technical Efficiency of RRBsby Banking Density of Region

Technical Efficiency

Banking density Profitability 1996 2002 Services 1996 2002
Bottom quartile Lower middle quartile Top middle quartile Top quartile All 0.76 0.74 0.67 0.73 0.73 0.84 0.81 0.76 0.81 0.82 0.89 0.79 0.79 0.87 0.82 0.86 0.84 0.83 0.89 0.87

0.6 Bottom quartile Lower middle quartile Top middle quartile Top quartile

Bank Grouping based on No of Branches —+—Profit (1996) —

—Profit (2002) —,— Service (1996) —*—Service (2002)

banking density regions were more efficient than banks functioning in higher banking density regions. This illustrates shows the economic justification of opening of more regional banks in low banking density areas which not only meets social objective but also enhances the efficiency and productivity of rural banks.

Comparison of TFP Growth in RRBs and PSBs

Average TFP growth per annum for RRBs is 1.21 in profitability (model I) and 1.14 in service provision (model II) and for PSBs the same figures are 1.03 and 1.22 respectively (Table 6). TFP growth of RRBs in profitability was higher than in service provision, while for PSBs TFP in service provision was higher than that in profitability. These figures indicate that productivity growth measured in terms of profitability have increased significantly for RRBs, compared even to their parent PSBs in the study period (descriptive statistics of productivity growth have been given in Appendix 1). This shows that in the liberalisation period the emphasis of RRBs shifted to profitability from pure service orientation, while the hitherto emphasis on profitability of PSBs changed to service provision in rural areas. TFP growth was higher for RRBs and PSBs in developed regions than in less developed regions even from a higher base as reported in Table 1. The decomposition of TFP growth into technical change and technical efficiency change indicates that for both RRBs and PSBs contribution of technical efficiency change (catching up) is higher than technical change (technical progress). Further, the contribution of pure efficiency change is higher than scale efficiency change in technical efficiency change. Technical change (technical progress) is significantly higher for RRBs (1.03)

compared to PSBs (0.33), especially in service provision irre-Figure 1 depicts the relation between TFP growth of RRBs spective of development of region of operation (see Table 5). and PSBs derived from both model I and model II. Only the TFPs

RRBs in north and central India have high TFP growth, while growth derived from model I and model II for RRBs aresignificant RRBs located in the south and north-eastern region exhibit low positive influences on each other. The efficiency of the parent TFP growth. These figures indicate that RRBs located in backward PSB’s TFP growth does not have any significant influence on regions in terms of literacy and banking culture are picking up the TFP growth of RRBs in either in the profitability or service their productivity levels, while banks located in educationally well provision approach (top 20 RRBs in terms of TFP growth have developed regions are facing slow growth in TFP, which indicates been given in Appendix 2). convergence of efficiency in RRBs as well as PSBs across geographical regions. This is also indicated in the low level of technical Table 6: TFP Growth of RRBs by Geographical

Location, 1996-2002

change (less than one) and high level of technical efficiency change (catching up) in north-eastern and southern banks (see Table 6). Efficiency indicator North North-east East Central West South Total Banks belonging to the middle and lower quartile according

Total factor productivity growthto size of assets showed significantly high TFP growth than top RRBs-M1 1.28 1.05 1.24 1.28 1.17 1.07 1.21 and bottom quartile banks, however, these banks exhibited a low RRBs-M2 1.18 1.14 1.13 1.20 1.10 1.03 1.14

PSBs-M1 1.01 1.07 1.05 1.01 0.99 1.06 1.03

level of technical efficiency in 1996 as well as in 2002 (Table 7).

PSBs-M2 1.71 1.03 1.18 1.29 0.97 1.08 1.22This indicates that even though middle size banks showed signi-Technical change

ficant TFP growth during the study period, they have not reached RRBs-M1 1.04 1.01 1.01 1.06 1.02 1.03 1.03 RRBs-M2 1.10 0.89 1.02 1.08 1.01 0.98 1.03

the technical efficiency level of the top quartile banks. The same

PSBs-M1 1.00 0.99 1.00 0.99 1.01 1.02 1.00is true for bank groups based on the number of bank branches, PSBs-M2 0.34 0.31 0.31 0.34 0.33 0.34 0.33

but due to the paucity of space the tables are not given here. Table 8 Technical efficiency change RRBs-M1 1.23 1.04 1.23 1.21 1.14 1.03 1.17

depicts relation between TFP growth of RRBs and banking density

RRBs-M2 1.07 1.28 1.11 1.12 1.10 1.05 1.10

of the region of operation. These figures clearly indicate that banks

PSBs-M1 1.01 1.07 1.05 1.02 0.98 1.03 1.03 operating in low banking density areas showed significantly PSBs-M2 5.06 3.30 3.79 3.85 2.96 3.13 3.71 higher TFP growth than banks operating in higher banking density Pure efficiency change

RRBs-M1 1.21 1.00 1.24 1.18 1.13 1.02 1.15

regions, even though these banks showed a higher level of technical

RRBs-M2 1.05 1.15 1.05 1.06 1.07 1.03 1.06efficiency in the base year of the study period (see Table 4). These PSBs-M1 1.05 1.10 1.08 1.04 1.00 1.04 1.05

figures indicate that the location of operation of RRBs plays an PSBs-M2 2.49 1.73 1.86 1.70 1.78 1.81 1.86 Scale change

important role in productivity level as well as growth in the study

RRBs-M1 1.01 1.04 0.99 1.02 1.01 1.01 1.01period. That is, banks operating in less dense populated areas are RRBs-M2 1.03 1.11 1.06 1.05 1.02 1.02 1.04

functioning at a higher level of efficiency and exhibited high TFP PSBs-M1 0.96 0.98 0.97 0.98 0.98 1.00 0.98 PSBs-M2 2.03 1.91 2.03 2.27 1.66 1.73 2.00

growth in the study period. This clearly reveals the justification for expanding RRBs (or similar regional banks) in low banking Notes: M1: model I (profit approach); M2: model II (service approach) density regions, not only for social and equity goals but also to RRBs: regional rural banks; PSBs: public sector banks. increase the productivity and profitability of regional banks.

Table 7: TFP Growth of RRBs by Bank Size Groups,1996-2002

Table 5: TFP Growth Per Annum of RRBs by Development of Region, 1996-2002

Total Bank Assets (RRBs) Indicator Bottom Lower Middle Top Middle Top Total Developed Medium Less Total

Quartile Quartile Quartile Quartile Total factor productivity growth

Total factor productivity growthRRBs-M1 1.22 1.22 1.10 1.21

RRBs-M1 1.22 1.20 1.29 1.13 1.21

RRBs-M2 1.17 1.11 1.10 1.14

RRBs-M2 1.13 1.14 1.16 1.12 1.14

PSBs-M1 1.02 1.03 1.05 1.03

PSBs-M1 1.04 1.02 1.02 1.04 1.03

PSBs-M2 1.32 1.16 1.07 1.22

PSBs-M2 1.13 1.35 1.19 1.22 1.22

Technical change Technical changeRRBs-M1 1.07 1.01 1.01 1.03

RRBs-M1 0.99 0.99 1.06 1.09 1.03

RRBs-M2 1.06 1.03 0.94 1.03

RRBs-M2 0.99 1.02 1.04 1.08 1.03

PSBs-M1 1.01 0.99 0.99 1.00

PSBs-M1 1.00 1.00 0.99 1.00 1.00

PSBs-M2 0.34 0.32 0.33 0.33

PSBs-M2 0.33 0.34 0.33 0.32 0.33

Technical efficiency change Technical efficiency changeRRBs-M1 1.14 1.21 1.09 1.17

RRBs-M1 1.22 1.21 1.21 1.04 1.17

RRBs-M2 1.10 1.08 1.18 1.10

RRBs-M2 1.14 1.12 1.12 1.03 1.10

PSBs-M1 1.01 1.04 1.06 1.03

PSBs-M1 1.03 1.01 1.03 1.03 1.03 PSBs-M2 3.89 3.65 3.23 3.71

PSBs-M2 3.47 3.97 3.59 3.81 3.71 Pure efficiency change

Pure efficiency changeRRBs-M1 1.12 1.20 1.08 1.15

RRBs-M1 1.19 1.19 1.21 1.04 1.15 RRBs-M2 1.07 1.04 1.09 1.06

RRBs-M2 1.11 1.07 1.06 0.99 1.06 PSBs-M1 1.03 1.06 1.08 1.05

PSBs-M1 1.05 1.04 1.04 1.06 1.05 PSBs-M2 1.99 1.80 1.57 1.86

PSBs-M2 1.73 1.95 1.76 2.00 1.86 Scale efficiency change Scale changeRRBs-M1 1.02 1.01 1.01 1.01 RRBs-M1 1.03 1.02 1.00 1.01 1.01 RRBs-M2 1.03 1.05 1.08 1.04 RRBs-M2 1.03 1.04 1.06 1.04 1.04 PSBs-M1 0.98 0.98 0.99 0.98 PSBs-M1 0.98 0.98 0.99 0.97 0.98 PSBs-M2 1.95 2.03 2.06 2.00 PSBs-M2 2.00 2.04 2.04 1.91 2.00

Notes: M1: model I (profit approach); M2: model II (service approach) Notes: M1: model I (profit approach); M2: model II (service approach) RRBs: regional rural banks; PSBs: public sector banks. RRBs: regional rural banks; PSBs: public sector banks.

RRBs-1 RRBs-2 PSBs-1 PSBs-2 RRBs-1RRBs-2

RRBs-1=.257+ .851*RRBs-2; R^2=.415 RRBs-2=.551+ .487*RRBs-1; R^2= .415 RRBs-1=1.791- .533*PSBs-1; R^2=.014 RRBs-2=1.862- .685*PSBs-1; R^2= .04 RRBs-1=1.165+ .053*PSBs-2; R^2=.045 RRBs-2= 1.127+ .02*PSBs-2; R^2= .011 RRBs-1and PSBs-1 are based on profitability (Model I) RRBs-2 and PSBs-2 are based on service (Model II)

Figure 3: Scatter Diagram of TFP Growth of RRBs by TFPThe above finding clearly reveals that development of the

Growth of PSBs, 1996-2002

region, geographical location, banking density of the region, bank size indicators such as total assets under operation and number of branches played a greater role in determining the efficiency and TFP growth of RRBs than did the efficiency of the parent PSBs.

Tables 9 and 10 reveal that there is an ongoing process of convergence of efficiency of RRBs in both profitability as well as service provisioning, as reflected in the high growth of TFP in banks with less base year technical efficiency and low growth of TFP in bank with high base year technical efficiency during the DAP and liberalisation periods. Banks that have least efficiency showed the highest TFP growth and banks that have the highest efficiency in initial years exhibited the least TFP growth during 1996 to 2002.

IV Conclusion

The technical efficiency of rural banks is higher in service provision than in the parent public sector banks. The efficiency of rural banks is higher in economically and socially developed regions as well as in low banking density regions. Rural banks

Table 8: TFP Growth of RRBs by Banking Density of Region,

showed significant economies of scale in terms of assets and number

1996-2002

of branches under each bank. Total factor productivity growth

Bank Groups Based on Bank Density

of rural banks is higher in profitability than service provision

(Population Per Branch) Parameter Bottom Lower Middle Top Middle Top Total Quartile Quartile Quartile Quartile Table 10: Initial Level of Technical Efficiency by TFP Growth(Service)

Total factor productivity growth TFP (Service) Growth of RRBs between 1996 and 2002

RRBs-M1 1.20 1.15 1.20 1.28 1.21 Technical Efficiency Lower Upper

RRBs-M2 1.11 1.10 1.11 1.20 1.14

1996 (Profit) Least Middle Middle Upper TotalPSBs-M1 1.02 1.03 1.05 1.02 1.03 PSBs-M2 1.33 1.09 1.02 1.40 1.22

Least 7 10 13 18 48 Technical change Lower middle 9 12 15 12 48 RRBs-M1 1.06 1.03 1.02 1.03 1.03 Upper middle 14 10 14 10 48 RRBs-M2 1.04 1.01 1.03 1.04 1.03 Upper 18 16 6 8 48 PSBs-M1 1.00 1.01 1.00 1.00 1.00

Total 48 48 48 48 192

PSBs-M2 0.33 0.34 0.31 0.32 0.33

Technical efficiency change Appendix 1: Descriptive Statistics of TFP GrowthRRBs-M1 1.14 1.12 1.18 1.23 1.17 and Its Components RRBs-M2 1.08 1.09 1.08 1.15 1.10

Bank Group Minimum Maximum Mean Std Deviation PSBs-M1 1.02 1.02 1.05 1.03 1.03 PSBs-M2 4.00 3.17 3.30 4.34 3.71

TFP growthPure efficiency change RRBs (M1) 0.556 2.767 1.242 0.297 RRBs-M1 1.12 1.12 1.18 1.20 1.15 RRBs (M2) 0.646 2.197 1.156 0.225 RRBs-M2 1.04 1.07 1.05 1.07 1.06 PSBs (M1) 0.823 1.266 1.031 0.065 PSBs-M1 1.03 1.03 1.08 1.06 1.05 PSBs (M2) 0.456 5.003 1.449 1.190

PSBs-M2 1.84 1.77 1.66 2.07 1.86

Technical change RRBs (M1) 0.652 1.351 1.043 0.138

Scale efficiency change RRBs (M2) 0.663 1.410 1.038 0.129

RRBs-M1 1.01 1.00 1.00 1.03 1.01

PSBs (M1) 0.938 1.120 1.003 0.038

RRBs-M2 1.04 1.02 1.03 1.08 1.04

PSBs (M2) 0.274 0.542 0.333 0.053 PSBs-M1 0.99 0.99 0.98 0.97 0.98

Technical efficiency changePSBs-M2 2.17 1.79 1.98 2.10 2.00 RRBs (M1) 0.499 2.517 1.198 0.283 RRBs (M2) 0.687 2.282 1.117 0.193

Notes: M1: model I (profit approach); M2: model II (service approach) PSBs (M1) 0.808 1.130 1.029 0.052

RRBs: regional rural banks; PSBs: public sector banks.

PSBs (M2) 0.841 17.132 4.558 4.203 Pure efficiency changeTable 9: Initial Level of Technical Efficiency by TFP Growth (Profit) RRBs (M1) 0.498 2.567 1.184 0.286 RRBs (M2) 0.679 1.975 1.069 0.162

TFP (Profit) Growth of RRBs between 1996 and 2002 PSBs (M1) 0.808 1.187 1.051 0.066 Technical Efficiency Lower Upper PSBs (M2) 0.854 6.519 2.206 1.565 1996 (Profit) Least Middle Middle Upper Total Scale efficiency change

RRBs (M1) 0.818 1.357 1.015 0.071 Least 3 6 11 28 48 RRBs (M2) 0.866 2.282 1.047 0.111 Lower middle 8 11 18 11 48 PSBs (M1) 0.888 1.030 0.981 0.043 Upper middle 11 17 13 7 48 PSBs (M2) 0.984 3.256 2.125 0.712 Upper 26 14 6 248 Total 48 48 48 48 192

Notes: M1: model I (profit approach); M2: model II (service approach) RRBs: regional rural banks; PSBs: public sector banks.

during the liberalisation period. Banks located in economically developed as well as low banking density regions exhibited significantly higher productivity growth. Overall there is a convergence of efficiency of rural banks during the study period. There is no influence of parent public sector banks on the efficiency and productivity growth of rural banks. The decomposition of productivity into technical progress and technical efficiency

Appendix 2: Top 20 RRBs in Terms of TFP Growth in Profitability and Service Provision

RRB Name TFP (P) TFP (S) Parent State Growth Growth Bank

Top 20 RRBs TFP growth (profitability)

Patliputra Gramin Bank 2.77 1.68 PNB Bihar Saran Kshetriya Gramin Bank 2.07 1.52 CBI Bihar Alwar Bharatpur Gramin Bank 2.05 1.32 PNB Rajasthan Shivpuri Guna Kshetriya Gramin Bank 1.90 1.33 SBI MP Parvatiya Gramin Bank 1.89 1.97 SBI HP Samastipur Kshetriya Gramin Bank 1.87 1.11 SBI Bihar Raigarh Kshetriya Gramin Bank 1.80 1.44 SBI Chhattisgarh(MP) Baitarani Gramin Bank 1.80 1.11 BOI Orissa Sri Ganganagar Kshetriya Gramin Bank 1.78 1.31 SBBJ Rajasthan Dhenkanal Gramin Bank 1.76 1.61 IOB Orissa Kutch Grameena Bank 1.75 2.20 Dena Gujarat Bareilly Kshetriya Gramin Bank 1.75 1.30 BOB UP Bastar Kshetriya Gramin Bank 1.74 0.85 SBI Chhattisgarh(MP) Allahabad Kshetriya Gramin Bank 1.73 1.38 BOB UP Subansiri Gaonlia Gramin Bank 1.69 1.09 UBI Assam

(United) Dewas Shajapur Kshetriya Gramin Bank1.63 1.22 BOI MP Pratapgarh Kshetriya Gramin Bank 1.63 1.47 BOB UP Damoh Panna Sagar Kshetriya

Gramin Bank 1.62 1.50 SBI MP Kapurthala Firozpur Kshetriya

Gramin Bank 1.60 1.18 PNB Punjab Rushikulya Gramin Bank 1.60 1.39 Andhra Orissa Top 20 RRBs TFP growth (service) Kutch Grameena Bank 1.75 2.20 Dena Gujarat Bundelkhand Kshetriya Gramin Bank 1.52 2.06 SBI MP Parvatiya Gramin Bank 1.89 1.97 SBI HP Golconda Gramin Bank 1.32 1.93 SBH AP Patliputra Gramin Bank 2.77 1.68 PNB Bihar Mizoram Rural Bank 1.15 1.67 SBI Mizoram Dhenkanal Gramin Bank 1.76 1.61 IOB Orissa Basti Gramin Bank 1.48 1.60 SBI UP Gorakhpur Kshetriya Gramin Bank 1.29 1.53 SBI UP Saran Kshetriya Gramin Bank 2.07 1.52 CBI Bihar Nagaland Gramin Bank 1.39 1.51 SBI Nagaland Damoh Panna Sagar Kshetriya

Gramin Bank 1.62 1.50 SBI MP Pratapgarh Kshetriya Gramin Bank 1.63 1.47 BOB UP Shahajahanpur Kshetriya Gramin bank 1.48 1.46 BOB UP Etawah Kshetriya Gramin Bank 1.32 1.44 CBI UP Raigarh Kshetriya Gramin Bank 1.80 1.44 SBI Chhattisgarh(MP) Pithoragarh Kshetriya Gramin Bank 1.43 1.44 SBI Uttaranchal(UP) Farrukhabad Gramin Bank 1.53 1.42 BOI UP Rushikulya Gramin Bank 1.60 1.39 Andhra Orissa Allahabad Kshetriya Gramin Bank 1.73 1.38 BOB UP

Appendix 3: Descriptive Statistics of Inputs and Outputsin 2002 and 1996

(In Rs lakh at 1996 prices)

Mean Minimum Maximum Std Deviation
Net loan 6088 (3444) 174 (39) Liquid assets 8381 (3812) 461 (302) Deposits 15452 (7227) 450 (245) Total income 1929 (771) 67 (43) Interest expenses 1155 (543) 30 (25) Operating expenses 506 (371) 36 (28) Profit/loss 212 (-297) -719 (-2424) 42391 (24827) 6044 (3695) 64988 (40501) 7382 (4989) 62561 (35317) 11125 (5961) 8620 (5267) 1492 (792) 4366 (2951) 825 (460) 2438 (1543) 402 (293) 1977 (1038) 368 (458)

Note: Figures in parenthesis are for the year 1996.

growth indicated that technical efficiency change contributed more to productivity growth than technical progress in both rural banks and parent PSBs, however, comparatively contribution of technical progress is higher for rural banks than parent PSBs. There is a justification for opening new banks in low banking density regions as efficiency and productivity growth of rural banks in these areas is high. There is also a case for mergers and enlargement of the asset base as well as the number of branches under each rural bank as there exist economies of scale.

rr;

Email: aareddy12@email.com

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