Scale Economies in Indian Commercial Banking Sector

Abstract

Two alternative estimation models, i.e., a translog cost function and data envelopment analysis (DEA) based on a cost model are compared and contrasted in revealing scale economies in the Indian commercial banking sector. The empirical results indicate that while the translog cost model exhibits increasing returns to scale for all the ownership groups, the DEA model reveals economies of scale only for foreign banks, diseconomies of scale for nationalized banks, and both economies and diseconomies of scale for private banks. The divergence of the results obtained from these two estimation models should concern model builders. From an empirical perspective the definition of scale economies through a constant input mix is very restrictive. The DEA cost model is much more flexible in this respect: It neither requires the restrictive assumptions that the unit factor prices are always available with certainty, nor that these prices are exogenous to the firms. However, the very volatile nature of the banking industry might question the validity of the empirical estimates in this deterministic setting. Therefore, further research is required to examine the bank performance behavior using both SFA and chance constrained DEA for the comparison in a stochastic setting.

Economies of Scale of Sugarcane Cooperatives in East Java Province and Their Influencing Factors

“…Furthermore, the TCF can detect a U-shaped average cost curve if one exists in the data, which is the restrictive property of production function like the Cobb-Douglas (Brown and O'Connor, 1995). The TCF has been used in many studies on economies of scale in broad type of industries, such as in credit union (Brown and O'Connor, 1995), cooperative (Kebede and Schreiner, 1996;Liu and Bailey, 2012), banking (Deelchand and Padgett, 2009;Sahoo and Gstach, 2011;Fu and Sio, 2011), payment processing (Beijnen and Bolt, 2009), electricity (Tuthill, 2008), fifteen major sectors of the economy (Haouas and Heshmati, 2013), water utilities (Horn and Saito, 2011), airport (Martin and Voltes-Dorta, 2008), and tourism (Shi and Smyth, 2012) Following Liu and Bailey (2012), this study used producer approach in which cooperatives were treated as firms that provide services to consumers. With this approach, only labor and physical capital were considered as inputs necessary to conduct transactions (Margono et al, 2010;Deelchand and Padgett, 2009).…”

Data envelopment analysis for scale elasticity measurement in the stochastic case: with an application to Indian banking

“…However, DEA 2 has distinct advantages over stochastic frontier estimation (Sahoo and Tone 2022 ). First, DEA avoids the choice of specific functional forms and the choice of the stochastic structure, which the stochastic frontier approach suffers from due to which it can confound the effects of misspecification of functional form with scale economies (Fusco et al 2018 ; Sahoo and Acharya 2010 ; Sahoo et al 2017 ; Sahoo and Gstach 2011 ). Second, contrary to the general belief, DEA is a full-fledged statistical methodology based on the characterization of firm efficiency as a stochastic variable.…”

Evaluating the potential efficiency gains from optimal industry configuration: A case of life insurance industry of India

“…DEA has some distinct advantages over stochastic frontier estimation. First, DEA avoids the choice of specific functional forms and the choice of the stochastic structure, which the stochastic frontier approach suffers from (Fusco et al, 2018; Sahoo & Acharya, 2010; Sahoo et al, 2017; Sahoo & Gstach, 2011). Second, contrary to the general belief, DEA is a full‐fledged statistical methodology, based on the characterization of firm efficiency as a stochastic variable.…”