Purpose — The following research will look into the empirical relationship of introduction of Central Bank Digital Currency (CBDC) to two important banking variables, deposit growth and credit growth in the Indian commercial banking system. The rationale behind the study is the rising policy concern over the world about CBDC and the critical poverty of bank level empirical data on its impacts especially in the case of large emerging economies where the financial structure, regulatory framework, and digital infrastructure system vary significantly with the advanced economies where prevailing theoretical frameworks are optimized Design/Methodology/Approach — A quantitative, non-experimental research design is implemented. The results of the analysis of panel data on 19 Indian commercial banks in the period between the fiscal years 2018 and 2025 provide 151 bank-year observations. Two Ordinary Least Squares (OLS) regression equations are estimated in which the deposit growth is the dependent variable and the credit growth is the dependent variable. The CBDC variable is operationalised as a binary dummy variable, which will be set to 1 between 2022 to 2025 Indias Digital Rupee ( e- INR) pilot programme and to 0 between 20182021 which is the pre-pilot period. This includes GDP growth rate and RBI repo rate that are the control variables in the macroeconomy and the current monetary policy condition and environment respectively. Findings — The results indicate that there is an asymmetric pattern of association. There is no statistically significant difference in the growth of deposits between the two periods of CBDC introduction (b3 = ?0.0148, p = 0.605), which means that there is no statistically significant deposit displacement throughout the pilot period. GDP growth alone (b1 = 0.0301, p < 0.001) and the repo rate (b2 = ?0.0338, p = 0.025) have the largest negative effect on deposit behaviour, which is secondary. The deposit growth model has a good level of explanatory power (R2 = 0.453). Conversely, the CBDC dummy variable relates positively and significantly to credit growth (b3 = 0.0698, p = 0.008), which is an unexpected result that contradicts the theoretical account of disintermediation of the bank, but is in line with complementarity arguments and the institutional aspects of the pilot phase. There is less explanatory power of the credit growth model (R2 = 0.099). Research Limitations and Implications — The CBDC dummy records the time of introduction and not the actual intensity of adoption, allowing the CBDC coefficient susceptible to omitted variable bias in simultaneous structural changes like the post-COVID credit recovery of India. The lack of bank-level control variables and fixed effects is a known weakness. Despite this, the research offers empirical data that the conservatively designed e-INR pilot in India has not upset deposit mobilisation, and the positive credit association can be credited to the digital infrastructure complementarities, and not direct CBDC impacts. The results have a direct implication on the design calibration of the CBDC and a gradual scaling of the e-INR programme. Originality/Value — The research may be described as one of the first attempts to make use of bank-level OLS panel regression to empirically measure the relationship between the introduction of CBDC and the banking performance in the Indian environment. Available literature in this field has been largely theoretical, simulation oriented or biased on developed economies. The study adds a new empirical layer, previously lacking the country-specific, institution-level, quantitative evidence of the current policies, to a literature that has so far remained devoid of CBDC policy dummy and macroeconomic control.
Paul et al. (Thu,) studied this question.