Demonetization and digital payments in India: perception and reality

ABSTRACT

In the context of the demonetization experiment of November 2016 in the Indian economy, this paper aims at looking into its impact on digital payments. Using the major digital payment modes and following the methodology of time-series outlier detection proposed by Chen and Liu (1993), the impacts are found to be mostly transitory in nature.

KEYWORDS:

• Demonetization
• digital payment system
• Arima model
• outlier detection

JEL CLASSIFICATION:

• E 51
• E 52
• E 58
• C22

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ Given the objective of our study, we consider customer-based transaction volumes and values only, not interbank transaction data.

² Also note, AO and LS are two boundary cases of a TC, where δ = 0 and δ = 1.

³ See https://dbie.rbi.org.in/DBIE/dbie.rbi?site=home for data (accessed in September 2019).

⁴ The best-fitted model $Y_t$was selected based on several information criterion and in sample RMSE (Hyndman and Khandakar 2008). We use R routine ‘tsoutlier’ developed by López-de Lacalle (2016) to detect the type of outlier in ${Y_t}^{\ast }$ series along with coefficient value of $\omega$ with t-statistics.

⁵ Running a basic t-test for equality of means before and after the date of demonetization confirms the existence of a break in all the three series. These are broadly in line with Maiti (2017). However, because of the inherent time series properties of the series (such as seasonality, stationarity and time dependence structure), the assumption of a t-distribution might be premature.

⁶ While in terms of volume, mobile transactions constitute 21% among these three major digitally based transactions, in terms of value, this is even less than 1%.