Efficiency of Shadow Banks in India and Its Drivers

Abstract

With the growth of shadow banking (SB) across the globe, their importance as an inevitable segment of the financial system is already established. Therefore, along with banking institutions, the assessment of the efficiency of this segment has become imperative. This article tries to look into the aspects of their efficiency in the Indian context. With the data set of all the listed companies in the SB category for the past 10 years, the efficiency score is ascertained and the drivers of efficiency are identified. Data envelopment analysis and Malmquist Index are employed to ascertain the efficiency score and analyse the efficiency change, respectively. Tobit regression is employed to identify the drivers of efficiency. The findings reveal that many of the firms are operating at the efficiency frontier with the maximum score of 1, and few others are yet to achieve the same. The size of the institution, earning capacity and liquidity drive the efficiency further, whereas the quality of the assets drags it down. There is a need to improve the managerial efficiency for inefficient firms to achieve economies of scale and optimum efficiency. As size has emerged as an important driver, small SBs need to scale up their operation. Furthermore, there is a need to improve the quality of loan assets.

Keywords

Determinants efficiency growth shadow bank

Introduction

Efficiency is an essential attribute because financial resources are scarce in any economy, particularly in developing countries, and therefore, efficient intermediation is imperative for development. Banks primarily do this intermediation in developing economies like India, and the financial resources are supplied mainly by banks; therefore, bank efficiency matters (Sathye, 2003, 2005). Their efficiency determines the level of economic development and, subsequently, financial stability (Jreisat et al., 2018). Over the years, non-banking entities, popularly known as non-banking finance companies (NBFCs), have also come to be known as shadow banks (SBs) (FSB, 2011; Pozsar, 2008)—used interchangeably in this article. These SBs play a complementary role in economic development (Acharya et al., 2013), which have flourished during the post-global financial crisis (GFC) phase. They play a range of functions through liquidity, enhance the spectrum of risk availability to investors, improve the mobilization of savings and investment (Sufian, 2007a) and act as catalysts to broaden the economy’s financial landscape (Neelima & Kumar, 2017). Financial stability and economic development are the twofold outcomes of efficient intermediation by both banks and SBs.

The existence of SBs along with traditional banks enhances the growth and stability of an economy. However, the post-GFC suggests that SBs play a vital role in an economy’s financial strength. Globally, the rise of non-banking activities is evident (FSB, 2020). The Financial Stability Board identified those non-banking financial intermediaries (NBFIs) that can give rise to systemic risk under the financial stability perspective. It includes firms that are susceptible to runs, lending them dependent on short-term funding, market intermediation dependent on short-term funding of client assets, facilitating credit creation and securitization-based intermediation (FSB, 2015). SB activities outside the traditional banking business are not subject to tight regulations, unlike conventional banks. Their actions enhance liquidity and credit risk (CTR) as their activities lack transparency, making it challenging to decide. They induce higher costs as compared to traditional banks, resulting in cost inefficiencies, leading to profit inefficiencies entailing risk. The risk increases in SBs are inevitable and can threaten the entire economy, as they have a contagious effect, exposing the economy to vulnerability (Ding et al., 2019). Therefore, their efficiency equally important as are the banks.

The literature on bank efficiency is abundant, whereas studies on SBs are scarce (Dutta et al., 2020; Sufian, 2007b). Notably, in developing economies, data availability on SBs places an inordinate constraint for pursuing such studies. Slowly, SBs tap the capital market through equity issues, bringing many small investors into their fold. This capital market entry of SBs further amplifies the requirement of efficiency studies on such market-listed SBs, as their failure would trigger the same amount of impact as the failure of banks (Ahmad et al., 2019; FSB, 2011; Pozsar, 2008; Pozsar et al., 2013; Sedunov, 2016; Sinha, 2013). Hence, a study on listed SBs is a priori needed to discover the various determinants that affect their efficiency so that regulators can take corrective action at the earliest and enable the investors to make an informed decision about entry and exit.

The scheme of the article is as follows: the next section highlights the status of the Indian SB sector, followed by a literature review that provides insights into efficiency analysis. The fourth section outlines the objective and the rationale of the article, and the fifth section describes the detailed methodology, followed by empirical findings with discussion. The final section offers a conclusion along with the managerial implications and limitations of the study.

Indian Shadow Banking Industry

SBs are known as NBFCs in India (Sinha, 2013), which include finance companies registered under the Reserve Bank of India (RBI) and others such as housing finance companies, merchant banking companies, stock exchanges, companies engaged in the business of stock-broking/sub-broking, venture capital fund companies, Nidhi companies, insurance companies and chit fund companies registered under other regulators (Gandhi, 2014). Based on the liability structure of NBFC, the RBI identified systemically important NBFCs that fall under RBI registrations and are too big to fail under the perspective of financial stability. India (13%) holds the third largest share of global finance company assets after the USA (34%) and Japan (16%), and also registered the highest growth (10%) among the eight emerging economies (FSB, 2020).

SBs in India have been around since the 1950s but have received more prominence since the 1970s. Since then, their growth rate has accelerated along with the size that emphasizes their growing importance in an economy (Sherpa, 2013). Their ubiquitous presence across the spectrum of the economy has enhanced the status of the Indian financial system at the global level. They complement and supplement the banking industry by extending the credit where banks are unable to reach. Two decades back, the trend of establishing SBs was very much at its peak, which brought unregulated financial entities in plenty (Pozsar, 2008). The exploitation of depositors and fraudulent activities by many forced the country’s central banker, that is, the RBI, to regulate, and of late, their numbers drastically reduced. At present, there are about 10,000 RBI-regulated SBs/NBFCs operating in India, out of which listed SBs in the stock market are very few.

In India, it is the third largest segment of the financial sector after commercial banks and insurance (RBI, 2020), which, in terms of liability structure, divides into deposit-taking NBFC (NBFC-D) and non-deposit-taking NBFC (ND-NBFC). The ND-NBFC, whose asset size is around US$70 million or above, is systemically important (SI-ND-NBFC) and constitutes 85.7% of the total NBFC assets. NBFCs as a whole are the largest borrowers of funds in the financial system with gross payable of US$13.38 trillion and gross receivables of US$1.28 million as of end of September 2020 (RBI, 2021), which reflects the interconnectedness between the banks and NBFCs, visibly more pronounced in the past few years (RBI, 2021). As estimated, the NBFC’s failure can result in a huge loss of the banking system that can impact 2.26% of the bank’s Tier 1 Capital (RBI, 2021).

Since the GFC of 2007–2008, there has been a steady rise in Systemically Important Non Deposit taking Non Bank Financial Companies (SI ND NBFC)’s assets, borrowings and advances until 2017, after which all of them show an accentuated growth (Figure 1). After the Infrastructure Leasing and Financial Services (IL&FS)¹ crisis, the asset growth of the NBFC sector registered a decline due to liquidity stress in the economy, which is more pronounced in systemically important constituents (Ahmad et al., 2019). The overall efficiency, measured through the return on assets, has declined over the years, and the quality of assets, measured through non-performing loan (NPL) assets, has also drastically deteriorated (Figure 2).

Literature Review

Approaches to Efficiency Measurement

There are various approaches to the measurement of efficiency. Financial ratios are one such technique, to evaluate institutions’ efficiency, representing the short-term operating performance measures (Oberholzer & Westhuizen, 2004). But it has various shortcomings as the approach fails to take into account the multidimensional perspective following biased or misleading results, providing an incomplete view of organizations’ efficiency (Oberholzer & Westhuizen, 2004; Quaranta et al., 2018; Ray & Chen, 2010). This efficiency measure relies on a single indicator, but it fails to consider the multiple inputs that produce outputs (Avkiran, 2011). The accounting rules and regulations measured by financial ratios assume a constant return of scale that is not prevalent in financial institutions (Quaranta et al., 2018). Therefore, to get a holistic view of financial institutions’ efficiency, the financial ratio–based measure is not appropriate.

Two other techniques to evaluate efficiency that can work with multiple inputs and outputs are the parametric approach, based on econometrics, known as the stochastic frontier approach (SFA), and the non-parametric approach based on linear programming known as data envelopment analysis (DEA) (Quaranta et al., 2018; Ray & Chen, 2010). The SFA is a widely used measurement tool. But higher complexity is associated with this technique. Further, this approach looks into the overall efficiency; decomposition into the various components is not possible. The result’s usefulness depends on how appropriately the functional set forms (Quaranta et al., 2018). Understandably, in the context of efficiency, SFA is not an appropriate measure.

DEA is the most frequently used in efficiency studies, developed by Charnes et al. (CCR) (1978) and refined further by Banker et al. (1984). As a measurement tool, DEA is widely used for analysing banks’ efficiency and deals with multiple inputs and outputs without specifying the production/cost function (Quaranta et al., 2018). It involves examining the various decision-making units (DMUs) for the given input and output specifications (Holod & Lewis, 2011). It is a technique that measures the efficiency of DMU relative to the frontier, which is a widely used measure in the banking sector (Liu et al., 2013).

Global and Indian Efficiency Studies

The operational and regulatory environment changes are significant concerns for optimizing cost- and revenue-driving efficiency (Chortareas et al., 2013). Since efficiency entails employing the lowest possible level of one or more inputs to generate the highest possible output level (Quaranta et al., 2018), researchers find it quite feasible to fit the input–output model through the DEA in the banking industry. Early shreds of evidence of the application of DEA in banking can be traced back to the pre-GFC period, where Favero and Papi (1995) and Girardone et al. (2004) identified technical and allocative inefficiency in bank’s functioning. In recent literature, this technique is quite popular globally (Mansour & El Moussawi, 2020; Minviel & Bouheni, 2021; Partovi & Matousek, 2019; Saeed et al., 2020; Yin, 2021). Most of these studies analyses different dimensions of efficiency, for example, identification of efficient banks (Bhatia & Mahendru, 2016; Bikker, 2010; Hamid et al., 2017; Partovi & Matousek, 2019), drivers of efficiency (Eyceyurt et al., 2017; Saeed et al., 2020; Sufian, 2009a) and reasons for inefficiency–pure technical efficiency—PTE (Bhatia & Mahendru, 2016; Fujii et al., 2014; Tandon et al., 2014); higher CTR lowers the cost-efficiency (Sarmiento & Galán, 2017) or efficiency changes over time (Mansour & El Moussawi, 2020; Soltane Bassem, 2014).

Studies on the efficiency of Indian banks with the application of DEA are quite a few. Studies on Indian banks’ ownership found that managerial inefficiency is one of the major contributing factors to a bank’s inefficiency (Ataullah et al., 2004; Fujii et al., 2014). A similar study with contrasting results reveals that most of the banks are pure technically efficient but scale inefficient (SI), warranting expansion of operational abilities by the management (Tandon et al., 2014). A recent study also reveals that Indian banks are pure technical inefficient (PTI), and foreign banks tend to outperform Indian banks in terms of efficiency (Arora et al., 2018). A comparative study of efficiency (Goyal et al., 2019) and quality of assets’ linkage with efficiency (Hafsal et al., 2020) are some of the latest research studies on the application of DEA in India.

As evident from the extant global literature, plenty of studies on banks’ efficiency are available, whereas efficiency studies on SBs are less. Few researchers have applied DEA to the entire NBFC sector (Dutta et al., 2020; Fadzlan, 2008; Sufian, 2007a, 2008, 2009b). A recent study on SB activities of 272 Chinese banks indicates that their activities are positively correlated with high risk, increasing cost and profit inefficiency (Ding et al., 2019). In the literature, we find studies on the different segments of NBFC like development financial institutions (Yadav & Katib, 2015), on investment institutions (Radi et al., 2012) and on microfinance institutions—MFIs (Abdelkader et al., 2012; Haq et al., 2010; Pal & Mitra, 2018; Servin et al., 2012; Tahir & Che Tahrim, 2013; Wijesiri et al., 2015), where they have identified the level of technical, pure and scale efficiency (SE).

In the Indian context, only one study is available on the efficiency of entire NBFCs (Dutta et al., 2020) using the super-efficiency DEA model. This study reveals that NBFCs are SI and hence need to operate at an optimal scale to achieve economies of scale to reach the efficient frontier. Few studies were also visible in MFIs (Chanu & Das, 2014; Dash, 2016; Khan et al., 2021; Khan & Gulati, 2019; Sinha & Pandey, 2019) in the same lines as the global studies; they have also identified levels of the different components of efficiency.

Efficiency Determinants

Several studies have identified the drivers of efficiency in banks (Eyceyurt et al., 2017; Partovi & Matousek, 2019), where bank-specific variables have prominently surfaced. Based on theoretical literature, size is the most critical efficiency determinant. It reveals that large banks tend to outperform the smaller banks (Adeabah et al., 2019; Ataullah et al., 2004; Fang et al., 2019; Thi My Phan et al., 2016). Large-sized banks are more prepared to operate under adverse scenarios because of the regulators’ continuous and rigorous monitoring of their risk accumulation (Sarmiento & Galán, 2017). But Indian banks with an increase in size become inefficient, implying that size and efficiency are inversely related (Moudud-Ul-Huq, 2019). Similar results are also evident in NBFC sectors (Fadzlan, 2008; Sufian, 2009b). Correlating with other studies, a study examining G7 country’s investment banks found that size plays a vital role in profit and cost-efficiency (Radi et al., 2012).

Lower efficient banks concerning cost and profit enhance risk (Fiordelisi et al., 2011; Thi My Phan et al., 2016). On similar lines, Sarmiento and Galán (2017) examined 31 commercial banks in Columbia and found that higher CTR lowers the cost-efficiency. CTR exposure is primarily evident among smaller banks during the financial crisis, resulting in the decrease of capitalization, impacting efficiency. Bank efficiency and CTR correlate positively, and NPL is one of such risk measures (Pati, 2017; Sarmiento & Galán, 2017; Thi My Phan et al., 2016). Several other global studies have also identified NPL as one of the drivers of efficiency (Ataullah et al., 2004; Fujii et al., 2014; Mamatzakis et al., 2016; Sun & Chang, 2011). Considering the lagged variables of NPL to understand the successive impact on efficiency, studies reveal that NPL affects the firm in the current year and the succeeding year (Berger & DeYoung, 1997; Kasman & Carvallo, 2014; Trujillo-Ponce, 2013). A high interest rate and a lower economic growth rate in India reduce borrowers’ repayment capacity, enhancing the NPL and inefficiency (Thi My Phan et al., 2016), and the NPL also causes substantial efficiency losses (Hafsal et al., 2020) and also the profitability (Pati, 2017)

The banks’ inefficiency is further enhanced by return on asset (ROA) volatility (Sun & Chang, 2011). Abidin et al. (2021) examined the rural and non-conventional banks in Indonesia, where they found that an increase of ROA by 1% enhances the efficiency by 2.5%. Net interest margin is also a positive contributor to efficiency (Assibey & Asenso, 2015; Mamatzakis et al., 2016). Investment by total asset represents the banks’ earning capacity and is a significant indicator of unearthing bank efficiency (Ataullah & Le, 2006). Price of capital (PC), price of funds (PF) and operating cost are some other commonly found correlates of efficiency (Partovi & Matousek, 2019; Moudud-Ul-Huq, 2019; Sun & Chang, 2011). Liquidity aspects also drive efficiency, and researchers have captured it through the current ratio (Fang et al., 2019) and short-term fund by total assets ratio (Fang et al., 2011).

Though the above-mentioned literature review is prominently tilted towards banks, the similarity of both banks and SBs, based on their loan-giving activities, helps us formulate the following hypothesis to be tested empirically in the latter case.

Hypothesis: SB-specific variables significantly drive efficiency.

Objective of the Study

The growing importance of the SBs in an economy and the vacuum in the existing literature warrant the article to examine their efficiency in the Indian context. In the Indian context, there are ample studies on the various dimensions of conventional banking and MFIs. Apart from few efficiency studies on MFIs, which are also a part of the total NBFC sector in India, efficiency studies on other NBFCs are rare in India. Therefore, the primary objective of our study is to fill up the existing gap in the literature on SBs in India and supplement the global studies. Second, the article evaluates their efficiency over 10 years and their changes thereof. Third, the objective is also to identify the various determinants responsible for the efficiency of SBs in India.

The Rationale of the Study

The past decade witnessed tremendous growth of SB activities in the emerging economies, including India. In these economies, the importance of SBs have increased since they often provide credit to various sectors of the economy where conventional banks cannot reach (Acharya et al., 2013; Gandhi, 2014). They supplement and complement the traditional banking system (Sinha, 2012). In addition, their appearance in the capital market has emerged as a prominent sector for investors, owing to substantial growth (Moudud-Ul-Huq, 2019). The recent crisis developed in the Indian economy revealed their interconnectedness with the overall financial system, which warrants close monitoring (Ahmad et al., 2019). Therefore, the detailed efficiency analysis and their determinants will entail the regulators to take corrective action at the earliest and enable the investors to make an informed decision about entry and exit. Furthermore, it is crucial because of their growing interconnectedness with conventional banks and the possible cascading impact on the overall economy in case of their failure.

Methodology

Data Source and Sample Frame

The article examines the efficiency of the listed non-deposit-taking systemically important SBs and their efficiency drivers for the study frame of 10 years. Out of 40 SBs, 5 are excluded due to the non-availability of the information. The other six are excluded because of all-equity firms; inclusion of those would give a biased result. Based on the available data of 29 SBs and their asset size, we have segregated our sample into two sets of large and small SBs. The efficiency of SBs has been analysed for the time frame of 10 years, that is, from 2010–2011 to 2019–2020. The study is based on secondary data and the data collected from their respective annual report. For calculating the efficiency, the study uses DEAP 2.1 software developed by Coelli (1996).

Empirical Framework

There are various approaches evident to measure efficiency. It is evaluated by employing both parametric and non-parametric techniques. However, the parametric methods are complex in nature, and the result’s appropriateness depends on the functional set formed (Quaranta et al., 2018). But the non-parametric approach, that is, the DEA method, is less restrictive and easy to apply and allows the various diagnostic for the cause of efficiency. Therefore, the DEA model is employed in our study to measure multiple DMUs applying a linear programming model. A firm having a DMU that lies on the frontier with an efficiency score of 1 is considered the best practising firm.

DEA models are either constant or variable returns on the scale. Initially, Charnes et al. (1978) developed the model based on a constant return to scale when all the DMUs operate at an optimal scale. Imperfect competition, financial constraints and others can result in DMU not running at an optimal scale. In these scenarios, using the Charnes, Cooper and Rhodes (CCR) model will not be appropriate for financial institutions. The CCR model was expanded by Banker et al. (1984) to enfold the Variable Return to Scale (VRS) model (Banker Charnes and Cooper [BCC] model). This model further decomposes the technical efficiency into PTE and SE (Goyal et al., 2019).

It is possible to estimate the input- or output-oriented model under CCR and BCC models. In the input-oriented model, DEA looks into the minimization of inputs, keeping the output constant. In contrast, the output-oriented DEA identifies DMU with output maximization as efficient DMU (Holod & Lewis, 2011).

To determine the efficiency, we have used an output-oriented VRS model, using the actual input level utilized by DMUs, to produce the required output, following the intermediation approach (Holod & Lewis, 2011). Mathematically, the corresponding VRS model, based on an output-oriented approach, can be written as follows:

\begin{matrix} \max δ \\ s . t \sum_{j = 1}^{N} λ_{j} y^{j} \geq δ y^{0}; \\ \sum_{j = 1}^{N} λ_{j} y^{j} \leq \frac{1}{δ} x^{0}; \\ \sum_{j = 1}^{N} λ_{j} = 1; \\ λ_{j} \geq 0 (j = 1, 2, ..., N); δ u n r e s t r i c t e d . \end{matrix}

(Suppose that x is the n-element input vector of firm j, and y is its m-element output vector.)

The approach followed in the study is the intermediation approach, owing to the SBs functioning on input–output relationship. Since SBs are involved in a long financial intermediation activity chain, including credit intermediation, maturity and liquidity transformation, the intermediation approach is used in the DEA model for assessing their efficiency, which is superior to other approaches (Eyceyurt et al., 2017; Minviel & Bouheni, 2021; Tandon et al., 2014). Following this approach, capital, interest expenses and operating expenses are considered as inputs to produce loans and advances, interest and non-interest income as outputs (Table 1).

Table 1.

Input and Output Specifications for DEA.

Inputs/Outputs	Definitions
Capital (x1)	Total equity + reserve and surplus
Operating expenses (x2)	Payments to and provisions for employees + rent, taxes, and lighting + printing and stationery + advertisement and publicity + depreciation on fixed assets (other than assets given on lease + directors’ fees, allowances and expenses + auditors’ fees and expenses + law charges + postage, telegrams, telephones, etc. + repairs and maintenance + insurance
Interest expenses (x3)	Interest on public deposit + interest on inter-corporate deposit + interest on credits from banks + interest on credits from financial institutions + other financing charges
Loans and advances (y1)	Term loans + loans repayable on demand + inter-corporate deposit
Interest income (y2)	Interest on intercorporate deposit + interests on other loans and advances
Non-interest income (y3)	Guarantee fee + underwriting commission + dividend + misc income

Source: The authors.

In the second stage, the study tries to measure the productivity change in the Indian SBs. The Malmquist total factor productivity is the most commonly applied analytical change to measure the productivity change over the two time periods. It has many attractive features, and one of the most important features of these indices decomposes the total factor productivity (TFP), that is, Malmquist Index (MI) into technological change and efficiency change (Fujii et al., 2014). The technological change measures the shift in the frontier between time t to t + 1; hence, it can be termed as frontier shift. Efficiency change measures the degree of catching up or the changes in the best practices for each observation between time t to t + 1.

The (output-oriented) DEA-MI, which measures the productivity change of a DMUo at times t + 1 and t, can be expressed as follows:

M I_{o} = \frac{D_{o}^{t + 1} (x_{o}^{t + 1}, y_{o}^{t + 1})}{D_{o}^{t} (x_{o}^{t}, y_{o}^{t})} {[\frac{D_{o}^{t} (x_{o}^{t + 1}, y_{o}^{t + 1})}{D_{o}^{t + 1} (x_{o}^{t + 1}, y_{0}^{t + 1})} \frac{D_{o}^{t} (x_{o}^{t}, y_{o}^{t})}{D_{o}^{t + 1} (x_{o}^{t}, y_{o}^{t})}]}^{1 / 2}

MI₀^t,t+1 = technical efficiency or efficiency change × technological change

MI₀ > 1 indicates progress in the TFP of the DMUo from the period t to t + 1, while MI₀ = 1 and MI₀ < 1 means, respectively, the status quo and decay in productivity.

T F P^{t + 1} = E F F C H_{t}^{t + 1} \times T E C H C H_{t}^{t + 1}

Efficiency change or technical efficiency is further decomposed into allocative efficiency, SE and PTE (Fujii et al., 2014). Allocative efficiency is the ability to select the less costly resource able to manage a defined activity. Furthermore, the efficiency is related to scale (SE) or management practices (PTE). These measurements allow knowing the most efficient DMUs over the selected sample and their transitions over time.

The next step of our study includes the determination of the relationship between bank-specific characteristics and efficiency. The level of efficiency measured through DEA is censored, that is, data are restricted, and the efficiency score ranges between 0 and 1. Since most of the regression gives a biased result (Sufian, 2009a), researchers use Tobit regression (Defung et al., 2016, Eyceyurt et al., 2017) to achieve this objective. Therefore, to find the correlation between the bank-specific characteristics and efficiency, the Tobit regression method is applied. The basic model is as follows:

y_{i}^{*} = β^{'} z_{i} + ε_{i}; y_{i} = y_{i}^{*} i f y_{i} \geq 0

_and y_{i = 0} otherwise, where z_i and β are the vectors of explanatory variables and their coefficients, respectively; β is the set of parameters to be estimated, whereas y_i and y_i* are the observed DEA efficiency score and the vector of a latent variable.

Here, the dependent variable is the DEA score that ranges between 0 and 1. The choice of independent variable is based on the existing literature of banking and MFIs that have extended to the SB domain.

We use the following working model according to the basic regression equation:

Y_{i} = β_{1} B S + β_{2} N I M + β_{3} R O A + β_{4} P C + β_{5} P F + β_{6} N P L + β_{7} C T R + β_{8} C R + β_{9} I T A + β_{1} S F T A + β_{11} O E R + e_{i j}

The selected variables β₁ to β₁₁ are the coefficient of the independent variable (Table 2) and are the intrinsic factors that can influence the efficiency of the SBs, and ε_i is the disturbance term.

Table 2.

Variables and Definitions.

Variables	Definitions
BS—Bank size	Log of total assets
ROA—Return on asset	Total income/total assets
NIM—Net interest margin	(Interest income–interest expenses)/total earning assets
CTR—Credit risk	Total loans/total assets
PC—Price of capital	Non-interest expenses/total fixed asset
PF—Price of funds	Interest expenses/total deposits
CR—Current ratio	Current assets/current liability
LR—Liquidity risk	Short-term funding/total assets
OER—Operating expenses ratio	Operating expenses/total income
ITA—Investment to total assets	Investment/total assets
SFTA—Short-term fund by total asset	Short-term fund/total assets

Source: The authors.

Empirical Analysis and Discussion

The efficiency analysis of the individual SBs is undertaken by employing the DEA BCC model where the efficiency score ranges between 0 and 1. The DMUs with the score of 1 imply that the firm is in the efficient frontier, and the score below 1 implies deviation from the frontier, and there is a further scope of improvement. In 2010–2011, barring few, most of the SBs could reach the frontier (Table 3). The efficiency score ranges between 81% and 86% for the inefficient firms, suggesting an improvement scope by 14–19%. A decline in the number of fully efficient banks is observed during the study period. The decline in efficiency could be due to the deteriorating asset quality as well as the IL&FS crisis,¹ which led to the loss of other SBs. Among the SBs that could not reach the frontier in 2010–2011, their efficiency score declined further during the study period. But, Paisallo Digital and SREI Infrastructure Ltd improved their performance and reached the frontier.

Table 3.

Efficiency Scores (VRSTE) of the Large Firms.

DMU (Firm) ID	Name of the Firm	2010–2011	2013–2014	2016–2017	2019–2020	10 Years Avg.	Overall Rank
1	Cholamandalam Investment and Finance Ltd	1	1	1	1	1	1
2	IDFC LIMITED	1	1	1	1	1	1
3	Spandana Sphoorty Financial Limited	1	1	1	1	1	1
4	L & T Finance Ltd	1	1	1	1	1	1
5	Magma FinCorp	1	1	1	1	1	1
6	SREI infrastructure ltd	0.81	0.66	1	1	0.867	7
7	Capri Global Financial Ltd	1	1	1	1	1	1
8	India Infoline Finance ltd	1	0.854	0.819	0.759	0.858	8
9	Indostar Capital Finance Ltd	1	0.876	1	0.742	0.904	5
10	IFCI ltd	1	1	0.754	1	0.938	3
11	Indian Renewable Energy Development Agency	1	1	1	0.968	0.992	2
12	Paisallo Digital Limited	0.826	0.86	0.874	1	0.89	6
13	PNB Gilts Ltd	1	1	1	1	1	1
14	Power Finance corporation	1	1	1	1	1	1
15	PTC India Financial Service	1	1	1	0.676	0.919	4
16	REC Limited	1	1	1	1	1	1
17	Religare Finvest LTD	0.863	1	0.666	0.269	0.699	10
18	Satincare Credit Care Network Limited	1	1	1	1	1	1
19	Tourism Finance Corporation	1	1	1	1	1	1
20	Manappuram Finance Ltd	1	1	1	1	1	1
21	Muthoot Finance	1	1	1	1	1	1
22	Credit Access Gramin Limited	1	0.54	0.859	1	0.849	9
23	MAS Financial Service	1	1	1	1	1	1

Source: The authors.

By segregating the mean efficiency score into overall technical efficiency (OTE), PTE, and SE (Table 4), we get a detailed picture. The mean efficiency that declined in the initial years has improved in the second half of the study period. However, inconsistency is observed in the OTE score. The decomposition of OTE reveals that in the recent years, the managerial efficiency (PTE) has declined and SE has improved, which was otherwise observed in the beginning years.

The analysis of inefficiency (Table 5) suggests that the number of SI firms is more than the pure inefficient firms over the years, implying that majority of the SBs are not operating at an optimal scale. Less number of PTI firms over the years suggests that managerial practices followed in the firms are better than their achievement of economies of scale. But over the years, improvement of SE reduced the number of SI firms substantially, which is corroborated through the improvement of SE scores presented in Table 4.

Table 4.

Mean Efficiency Scores of the Firm.

Year	2010–2011	2011–2012	2012–2013	2013–2014	2014–2015	2015–2016	2016–2017	2017–2018	2018–2019	2019–2020	Std. Dev.
OTE	0.942	0.918	0.811	0.881	0.869	0.871	0.92	0.919	0.934	0.913	0.039
PTE	0.978	0.976	0.900	0.947	0.949	0.939	0.955	0.961	0.941	0.931	0.023
SE	0.962	0.941	0.902	0.928	0.913	0.926	0.962	0.953	0.992	0.980	0.029

Source: The authors.

Table 5.

Number of Pure and Scale-inefficient Firms.

Year	2010–2011	2011–2012	2012–2013	2013–2014	2014–2015	2015–2016	2016–2017	2017–2018	2018–2019	2019–2020
PTI	3	3	8	5	6	7	5	5	4	5
SI	9	8	15	14	14	14	9	8	6	10

Source: The authors.

The yearly change in efficiency and a significant deviation therein show strong evidence that any individual approach for measuring bank efficiency may provide partial evidence and may mislead potential benchmarking. Therefore, Malmquist Productivity Index (MPI) is applied to understand the source of overall productivity change with the further decomposition of the efficiency result. MPI requires fulfilling the condition of homogeneity. Since our sample of 29 firms does not conform to this requirement, it is segregated into small firms (6) and large firms (23). For all small firms, the efficiency score is 1, indicating that they are operating at an optimal scale, and thus not analysed under MPI. The productivity index greater than 1 suggests the amelioration in the productivity for the study period.

The productivity change (tfpch) of the 23 large firms (Table 6) reveals an upsurge, except for 9 firms whose productivity is less than 1, and 14 firms whose productivity score is 1 or more than 1; for 6 firms, the growth is attributed to only technological change (techch); for 7 firms, the growth is attributed to both efficiency change (effch) and techch; and for one firm, there is a decline in effch but improvement in techch. For the other nine firms, in case of seven firms, less productivity is mainly attributed to decline in effch, and for two firms, it is because of decrease in techch.

Table 6.

Malmquist Index Summary of Large Firms.

DMU (Firm) ID	effch	pech	sech	techch	tfpch
1	1	1	1	1.037	1.037
2	1	1	1	1.116	1.116
3	1	1	1	1.025	1.025
4	1	1	1	0.963	0.963
5	1.001	1	1.001	1.021	1.023
6	1.018	1.024	0.995	1.001	1.019
7	0.999	1	0.999	0.884	0.883
8	0.965	0.97	0.996	1.024	0.988
9	0.967	0.967	1	1.446	1.399
10	0.987	1	0.987	1	0.987
11	0.996	0.996	1	0.979	0.975
12	1.016	1.022	0.994	1.022	1.038
13	1.008	1	1.008	1.017	1.025
14	1	1	1	1.001	1.001
15	0.957	0.957	1	1.013	0.97
16	1	1	1	1.027	1.027
17	0.874	0.879	0.994	1.012	0.884
18	1.04	1	1.04	1.126	1.171
19	0.998	1	0.998	0.992	0.99
20	1	1	1	1.004	1.004
21	1.01	1	1.01	1.031	1.041
22	1.032	1	1.032	1.038	1.071
23	1	1	1	0.997	0.997
Mean	0.994	0.992	1.002	1.03	1.023

Source: The authors.

Further, decomposition of effch into pure efficiency change (pech) and SE change (sech) (Table 6) helps us to analyse efficiency in greater detail. An improvement in the sech and a decline in pech are observed through the mean scores for all the large firms. The latter indicates that there is an overall decline in the managerial efficiency. Firm-wise analysis reveals that only two firms show an improvement in pech, whereas five firms show a decline and for other it remains constant, indicating the inability of managerial decision-making to allocate resources effectively. Similarly, five firms show an improvement in sech, and seven firms show a deterioration, suggesting that resource utilization is not optimal, failing to attain economies of scale.

Analysis of the productivity change over the years (Table 7) reveals an increase in TFP (tfpch) by 2.3%. The yearly change of tfpch, which was highest during the 2011–2012 period, shows a clear decline over the years. This is mainly due to decline in effch. The grand mean of large firms’ effch based on VRS was 0.994, indicating that all the firms are operating at a very high-efficiency level (Table 7). A detailed analysis of year-wise change reveals the reason behind the change in efficiency (Table 4), which is primarily due to the improvement in the SE rather than their PTE.

The efficiency analysis of the Indian SBs highlights some of the important key points. Initially, the number of inefficient SBs was only three, whereas the number of inefficient SBs have expanded and also their level of inefficiency. From 2016 to 2017, the level of inefficiency deteriorated; further, that can also be attributed to the IL&FS crisis, where the other SBs have also faced huge losses and the rise of Non Performing Asset (NPA) that indicates banks’ asset quality has expanded at large. By further decomposing the efficiency level over the study period, the result confirms that the firm’s performance has significantly deteriorated. It also sheds light on the growing managerial inability that restrains the firm from reaching the efficient frontier. Even though the overall SE of the firm is above unity, little negligence can hinder the firm from operating at an optimal scale. The pure technical inefficiency indicates that the SBs gear up their operation by expanding the size and asset quality to achieve operational synergy.

Table 7.

Malmquist Index Summary of Annual Means.

Year	effch	pech	sech	techch	tfpch
(2011–2012)	0.969	0.996	0.973	1.266	1.227
(2012–2013)	0.845	0.893	0.946	1.375	1.161
(2013–2014)	1.129	1.08	1.045	0.791	0.893
(2014–2015)	0.988	1.004	0.984	1.063	1.051
(2015–2016)	1.005	0.99	1.015	1.056	1.061
(2016–2017)	1.06	1.019	1.04	0.893	0.947
(2017–2018)	0.995	1.007	0.987	0.961	0.956
(2018–2019)	1.011	0.965	1.048	1.101	1.113
(2019–2020)	0.967	0.979	0.987	0.891	0.861
Grand mean	0.994	0.992	1.002	1.03	1.023

Source: The authors.

Tobit Regression

Regression analysis is employed to explain the efficiency scores obtained by the SBs. The various literature highlighted the intrinsic variables that determine the efficiency of financial institutions. The descriptive statistics (Table 8) of the selected variables provide some insight about their nature.

Table 8.

Descriptive Statistics of the Intrinsic Variables.

Variable	Mean	Std. Dev.	Min	Max
Size	3.946652	0.690049	2.46	5.56
ROA	0.033377	0.065234	−0.4218	0.3913
ITA	0.132622	0.240086	4.34E-06	0.995079
OER	0.387013	2.187975	0.005263	32.21739
CTR	0.748328	0.647996	2.14E-06	8.770361
NPAR	0.053474	0.107276	0	0.603324
PF	0.101685	0.059293	0.00015	0.569235
SFTA	0.275494	0.27927	8.77E-07	0.923082
CR	4.821222	17.83183	0.18	180.6486
NIM	0.097772	0.159482	−0.25195	1.088936
Eff_scr	0.944204	0.123106	0.269	1

Source: The authors.

A lot of heterogeneity is observed in their characteristics. Size shows a high variation. In the category of large-sized banks, few government-owned banks approximately hold one-third of total NBFC assets. In addition, after the IL&FS crisis, few government-owned enterprises and relatively small firms showed a decline in profitability, and many firms registered losses. Therefore, the huge variations in profitability are evident. From 2010–2011 to 2015–2016, the NPA ratio was very low. However, a sudden spike was apparent in the later years of the study. Similarly, in the initial years, most liquid assets were lying idle, which was evident through the high current ratio; however, firms started disbursing more loans in the later years. Hence, the investment and net interest margin (NIM) of the firms increased.

The Tobit regression model (Table 9) is applied to relate efficiency with firm’s specific characteristics. The dependent variable, that is, the efficiency score, which is derived by applying the BCC model, ranges between 0 and 1. The regression model highlights the influential variable that impacts the efficiency at the firm level.

Table 9.

Tobit Regression Model on Bank Variables.

Eff_scr	Coef.	Std. Err.	z	P >\|z\|	[95% Conf. Interval]
Size	0.19422	0.01017	19.1	0.000	0.1743	0.21415
ROA	0.26837	0.1903	1.41	0.158	−0.1046	0.64135
ITA	0.12519	0.0515	2.43	0.015	0.02426	0.22612
OER	0.00051	0.00489	0.1	0.917	−0.0091	0.0101
CTR	0.04027	0.01538	2.62	0.009	0.01014	0.07041
NPAR	−0.2367	0.10238	−2.31	0.021	−0.4373	−0.036
PF	0.41909	0.16616	2.52	0.012	0.09342	0.74476
SFTA	−0.0035	0.05462	−0.06	0.949	−0.1105	0.10355
CR	0.00201	0.00061	3.31	0.001	0.00082	0.00319
NIM	0.15692	0.08358	1.88	0.06	−0.0069	0.32074
/sigma_u	0.14389	0.02749	5.23	0.000	0.09001	0.19778
/sigma_e	0.11156	0.00563	19.82	0.000	0.10052	0.12259
rho	0.6246	0.09594			0.42931	0.79197

Source: The authors.

Note: LR test of sigma_u = 0: chibar²(01) = 117.81

Prob > = chibar² = 0.00

To begin with, the academic literature supports the fact that size positively influences efficiency, and in an adverse scenario, the larger firms are more prepared than smaller firms. However, few studies contradicted that size does not influence efficiency. Hence, the precise impact is not certain and is left to be decided. Our regression result indicates that size is the most influential variable impacting efficiency (Table 9).

The profitability and earning capacity of the firms are captured through the indicators such as ROA, NIM and ITA. ROA is the indicator of measuring managerial efficiency. The ratio shows how a bank can convert its asset into net earnings. A higher ratio indicates a better performance; hence, a positive influence on efficiency is expected. However, our result does not reveal its significant impact on efficiency; therefore, it contradicts the previous studies (Fadzlan, 2008; Sufian, 2009b). The positive relationship with NIM and ITA in the model suggests that a better investment decision enhances the firm’s earning capacity. CR measures the liquidity of the firm that determines the ability of the firm to meet its short-term obligations that shows a positive influence on efficiency.

Total loans disbursed to total assets is one way to measure the CTR. This signifies the ability of the firm to generate more loans from the asset, which is likely to increase profitability, and, hence, considered an efficiency enhancer. However, building up more loans to lure more earnings can deteriorate asset quality, which is captured through NPAR. The high level of NPAR can adversely impact efficiency. This is employed with its 1-year lag value to reveal its subsequent impact on the efficiency of the next year. The results portray that CTR positively influences efficiency, that is, more loans disbursed drives the efficiency upward. Simultaneously, it also reveals that poor quality of assets have a negative impact on efficiency.

Overall, the study suggests that the size, earning capacity and liquidity positively influence efficiency, and poor quality of loans negatively influence efficiency. This also corroborates with our research findings about efficiency that SBs need to be careful while disbursing the loans and try strategies to enhance the firm’s earning capacity. Size remains as the dominant influencing variable of efficiency. The findings are in line with previous research work, emphasizing that size plays an important role in efficiency, signifying that larger firms are more efficient than smaller firms (Sufian, 2009a). This observation is important from the Indian point of view as, over the years, SBs are in pursuit of achieving higher scale through mergers and acquisitions. Since a positive relationship exists, expansion of scale is going to be instrumental in enhancing the overall efficiency of this sector as a whole. This is possible by increasing their customer base and improving the service to achieve economies of scale. However, they also need to be cautious about the deteriorating asset quality, which can hamper the activities and change the individual institutions’ financial landscape. Therefore, the enhancement of the operational and managerial efficiency scale will enable the SBs to be on the efficient frontier.

Conclusion

We hold the view that to improve efficiency, the firm needs to focus on improving the operating efficiency to achieve economies of scale and that can be easily achieved by an extra focus on improving managerial practices. Pure technical inefficiency and scale inefficiency are the major reasons for inefficiency of SBs in India. Managerial inability and scale inefficiency are evident among the DMUs that refrain them from being on the efficient frontier. Since size positively impacts efficiency, small firms need to scale up their operations to achieve the adequate threshold level. Containment of poor quality of assets is imperative. We agree with the previous studies (Ataullah et al., 2004; Fujii et al., 2014; Sun & Chang, 2011) that a proper and careful selection needs to be undertaken for advancing the loan. Otherwise, unmindful disbursement of loan without assessing the quality can drag the efficiency further.

In listed SBs, many small investors direct their resources towards investment to get high returns. This study on efficiency will enable the investors to make better decisions. The efficiency analysis determines how effectively the firm utilizes the resources and manages their asset and liabilities. It also reveals the further scope of improvement and how the management focuses on it. The main difference between efficient and inefficient firms is largely due to the deterioration of asset quality. Risk and asset quality factors also appear to ‘matter’ in efficiency. Therefore, the proper analysis of the factors that influence efficiency can help investors make informed decisions. Efficiency leads to stability, so investors can be vigilant in making better decisions.

Managerial Implications

This article gives rise to various policy implications. First, the decomposition of efficiency score obtained reveal that most of the firm’s TFP has been enhanced, which is attributed to the technological progress rather than the efficiency change. The managers need to find out the various ways to improve technical efficiency so that the corresponding benefits can be best harnessed. Second, the study reveals that large-sized banks are more efficient than smaller ones, implying that the efficiency can be improved by restructuring the firm’s operations to reduce the smaller or less efficient ones. Third, the relation between NPA and efficiency is negative. Thus, SBs should engage in various strategies so as to reduce the deteriorating asset quality that increases operational cost with harmful consequences on banking efficiency and stability. The results obtained allow the managers to understand the complexity of a bank’s performance by focusing on the organizational aspects and their interactions with the bank efficiency.

Limitations and Future Scope

The finding of this study needs to be factored with the fact that the data set is only confined to the listed firms, which could be the limitation of this research work. Expanding the data set to all the systemically important firms would provide a better insight into the efficiency drivers. A comparative study of listed and non-listed SBs would be a fruitful exercise for the whole economic system as an indication to future researchers. Furthermore, as a technological improvement, other advanced versions of the DEA technique could also be employed to arrive at the efficiency score.

Footnotes

Acknowledgement

The authors are grateful to the anonymous referees of the journal for their extremely useful suggestions to improve the quality of the article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Notes

ORCID iDs

Nandita Bhattacharjee

Ambika Prasad Pati

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