Did the global financial crisis alter the competitive conditions in the Indian banking industry?

ABSTRACT

This article addresses a pertinent research question: Did the global financial crisis alter the competitive conditions in the Indian banking industry? In order to find the answer of this research question, we applied a dynamic version of the non-structural Panzar-Rosse model on a unique unbalanced panel dataset of Indian banks spanning over the period from 1998/99 to 2015/16. The robust estimates of H-statistic computed on the basis of the generalized method of moments estimates of the elasticities of input prices show that (i) Indian banks earned their interest and total revenue under monopolistic competition throughout the whole of the sample period and (ii) the global financial crisis altered the competitive conditions in the banking industry, and market moved closer to perfect competition following the financial crisis, especially when interest-bearing activities were in focus.

KEYWORDS:

• Bank competition
• dynamic Panzar-Rosse model
• Indian banks
• global financial crisis

JEL CLASSIFICATION:

• G21
• L10
• C23

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ To compute cost-efficiency scores, we employ Tone’s (2002) new cost DEA model. This model allows the existence of imperfect competition in the market and attempts to optimise the bank’s input cost rather than input quantities. In the present study, the input vector contains the loanable funds, fixed assets, staff as variable inputs and the equity as a non-cost quasi-fixed input. The output vector consists of advances, investments and non-interest income.

² Note that we have taken an equity to total assets as a control variable because we consider no price is associated with the equity capital (Shaffer 1982).

³ The bank concentration variable HHI is measured as a sum of squared market shares of a bank advances multiplied by 10,000.

⁴ Gelos and Roldos (2004) suggest the use of a panel estimation approach by interacting the input price with dummy variable in the second sub-period instead of estimating the equation separately for sub-periods. This procedure allows us to retain complete information and obtain reliable estimates by observing the behaviour of banks over time.

⁵ For detailed discussion of Panzar-Rosse H-statistic, interested readers may refer to Panzar and Rosse (1987).

⁶ The overall validity of the instruments is tested by using the Difference-in-Hansen test of exogeneity of instruments, and the assumption of serially uncorrelated errors is tested using Arellano–Bond AR(1) and AR(2) tests. Further, we ensure that the number of instruments does not exceed the number of cross-sectional units in the panel (Roodman 2009b).

⁷ It is worth noting here that the nature of competition identified remains robust to different panel data estimators (fixed-effects and difference GMM); revenue and profit model specifications (with trend and yearly dummies); and separately across two sub-periods (1998/99–2006/07 and 2007/08–2015/16). For the sake of brevity here, we are not reporting the detailed results. However, the results may be available upon request from the authors.