INTRODUCTION:

A transaction that involves the transfer of value from one account to another using digital means is referred to as electronic or digital payment. Electronic payments are not just convenient but also play a significant role in stimulating economic growth. In a study by Moody Analytics on the economic significance of digital payments, it was found that for every one percentage increase in card usage, increases developed economies GDP by 0.04% and emerging economies by 0.02%, assuming all other factors remain the same⁹. Seamless fund transfers with minimum transaction cost through digital modes helped in achieving mass adoption in less time.

Consumption by households is one of the principal determinants of Gross Domestic Product (GDP). In India, about three-fifths of the GDP constitutes consumption. There exists a strong and direct positive correlation between household consumption and GDP. The more households consume, the more demand for products and services translates into increased production, more jobs, higher incomes, and strong economic growth¹¹. Smartphone evolution, penetration, and growing use of mobile internet enabled new payment capabilities for businesses and individuals. The facility to make payments online has brought significant change in the buying behaviour of consumers. Payment by digital modes drives consumption by way of providing customer transaction data access to marketing agencies and companies. Analysis of customer transaction data helps companies understand customer consumption preferences and buying behaviour and also guides in decisions relating to designing sales promotion schemes to increase consumption. Digital payment statistics enable businesses to offer personalised services and unlock more revenue. Today with increasing smartphone penetration and greater internet connectivity, households have changed the way they make payments and are increasingly showing a preference for making payments online. Digital payments in one way promotes financial inclusion and helps in boosting finance health status of the economy¹². According to NPCI-PRICE REPORT, 2020, 32% of Indian households are using digital means for making payments¹⁹. The chart below shows a strong and positive relationship between digital payments and GDP.

Figure 1. Relationship between GDP and Retail Digital Payments

Source: Author’s Computation based on data from RBI Database

The digital payment system in India has been showing tremendous growth in the last few years. The introduction of innovative and faster payment modes such as Bharat Bill Pay Service (BBPS), Unified Payments Interface (UPI) Prepaid Payment Instruments (PPIs), Immediate Payment Service (IMPS), Aadhar-enabled Payment Service (AePS) etc., have been significantly contributing to the growth in the volume of digital transactions. At present banks and other financial institutions are offering digital payment facilities as a one-stop solution to increase their customers’ experience and it has become a key lever for customer acquisition.

Figure 2. Payment Transaction Volume Analysis

Source: Author’s Computation based on data from RBI Database

Figure 2 shows that the proportion of the volume of retail digital payment transactions in total retail transactions increased from 92.70% in 2015-16 to 98.5% in 2020-21, whereas the share of paper-based transactions fell from 7.3% in 2015-16 to 1.5% in n 2020-21.

Figure 3. Payment Transaction Value Analysis

Source: Author’s Computation based on data from RBI Database

The analysis from the above graph shows that the proportion of value of retail digital payment transactions in total retail transactions increased from approximately 60% in 2015-16 to 86.5% in 2020-21, whereas the share of paper-based transactions fell from 40.3% in 2015-16 to 13.5% in 2020-21.

Paper-based payments have decreased drastically whereas digital payments recorded exponential growth. Smartphone penetration, internet connectivity, a greater number of financial institutions offering the facility to pay online, and a push from the Government for the adoption of digital payments propelled the growth trajectory of digital payments in India. The shift in payment preference in the last 10 years is evidenced by the fact that the volume of paper clearing, which comprised 60% of total retail payments in the financial year (FY) 2010-11, shrunk to 3% in FY 2019-20²⁰. Events such as demonetization and covid outbreak fuelled the growth of online banking transactions even more in India.

Based on the payment statistics in 2020, the share of instant payments and other electronic payments accounted for nearly two-fifth of all payments and it is estimated to grow to 71.7% by the end of 2025¹⁵. It is estimated that the volume and value of digital transactions in India will reach 167 billion and INR 238 trillion respectively by 2025⁷.

India stands top in digital payments segment with 89.5 million transactions recorded in 2022. Nearly one in two global real time payments done by India. Brazil, China, Thailand and South Korea were among the top five countries leading in digital payment landscape²⁸.

However, the degree of usage of digital payment modes depends on the trust and experience with online frauds²⁵. Lack of security and the possibility of fraud were the two main inhibitions to the usage of electronic channels offered by banks²³.

The majority of the population in India is under the low-income category and are having low financial and digital literacy. How the transition to cashless payments impact India’s economic growth is what forms the core objective of the study.

REVIEW OF LITERATURE:

Adopting the digital mode for making payments also benefits the Government by way of bringing in many unorganised businesses into the formal banking system. A Higher rate of financial inclusion leads to increased tax revenues⁵. Retailers who evaded tax earlier by not reporting full revenues will now come under the Government’s supervision and make tax payments promptly. Online payments also help the Government in reducing the printing cost of currency. Besides extending the benefits to economy and customers, higher adoption of digital modes for financial transactions helps small and medium enterprises to exploit more opportunities by way of wide market penetration¹⁸. In addition to cost-effective payments, digital payments encourage consumers to spend more and bring in more consumer demand to firms in emerging markets³. Owing to lower salience compared to cash, payment through digital modes encourages overspending¹. On the other hand, the digital payment apps also play a major role in personal finance management. These apps keep track of payments and receipts and help individuals by providing greater control over spending habits.

This section brought into light the past studies that were studied on exploring the factors influencing digital payment adoption and its impact on economic growth.

Factors affecting Digital Payments:

M. Narayanan, S. and Chandrasekaran (2023) analysed the use of internet banking and found that among the green banking initiatives, internet banking was found most used followed by debit card and credit card⁸.

Niyati Chaudhary and Sakshi Anand (2022) pursued research with the objective of finding the effectiveness of digital payment during the pandemic COVID-19. The findings of the study were, middle aged respondents mostly use digital services and the respondents reported they were aware of digital services and perceived much better than the traditional methods of payments¹⁴.

Prashant Debnath and P. Chellasam (2022) examined the satisfaction of bank customers at Coimbatore towards digital banking services such as Digital Wallet, Digital Insurance, Digital Chatbot and Digital Card management and found customers were satisfied with the digital services¹⁶.

Vinod N, Sambrani and Jayadatta S (2020) reported that digital wallets brough in a new paradigm shift in the financial sector. Companies such as American Express, Visa Inc, PayPal Holdings, NXT-ID Inc, Google Inc are continuing as top players in the digital payment landscape. The authors opined that the digital wallets bring change in consumer lifestyle²².

Shamsher Singh and Ravish Rana (2019) undertook research with the objective of studying the perception on digital banking services. The study found that SBI followed by Axis bank and Paytm were the most used among the digital service providers as the respondents felt them as trustworthy and reliable²⁴.

Tanuja Puri (2019) conducted an interesting study on use of E-wallets by working women in Delhi. The author through her study exposed that working women prefer Paytm more than any other digital service provider due to its faster service. Further, the study also found that cashbacks and discounts were the most influencing factors among working women in Delhi for the adoption of digital payments²⁷.

Kanchan Kathial (2018) investigated the impact of demonetization and found that digital payments increased by 250 percent after demonetization and the growth rate has started declining post cash crunch⁶.

Debashree Chakraborty (2015) in his conceptual study highlighted the challenges of E banking services. The author stressed upon the need for separate and more stringent law for cyber frauds to promote the adoption of online banking⁴.

R. Vijayakumar, R. Radhakrishan and R. Anitha (2011) undertook research with the objective of examining the influence of demographic variables on E-banking services. The study found that among the selected demographic variables, banking customers’ occupation and income level significantly impact the satisfaction level towards e-banking services¹⁷.

Digital Payments and Economic Growth

Nenavath Sreenu (2020) investigated the effect of cashless payment policy on Indian economic growth during 2010-2018. Panel VEC model and co-integration techniques were adopted to analyse the data. The findings of the study were digital payments impact negatively in short run. However, in long run digital payments impact economy positively but indirect¹³.

Slozko, O. and Pelo, A. (2014) concluded through their study that e-payments increase consumption which leads to increase in demand and financial status of the nation. Further, the study also revealed positive correlation between e-payments and GDP growth²⁶.

OBJECTIVE AND METHODOLOGY:

This paper attempts to investigate the co-integration relationship between digital payments and economic growth in India. GDP has been considered as the measure of economic growth. The study is based on secondary data. GDP and digital payments data has been gathered from the Reserve Bank of India (RBI) website. Digital payment data includes retail electronic payments such as NEFT, IMPS, NACH, UPI payments, direct debits, card payments and, prepaid payment instruments. Quarterly observations during the period FY 2015-16 to FY 2020-21 have been considered for analysis.

Tools and Techniques:

Due to less friction and more efficiency, digital payment has got wider acceptance in less duration. The present study is an effort to analyse the following questions:

a. What is the impact of retail digital payments on India’s economic growth (GDP)?

b. Is GDP causing digital payments to grow or is digital payments causing GDP to grow?

Analytical research design has been adopted to measure the effect or relationship between dependent variable (GDP) and explanatory variable (Retail digital payments) using time-series secondary data. To conduct this analysis, the Augmented-Dickey Fuller (ADF) test, Johansen co-integration test and Granger causality tests have been applied. Analysis has been performed using E-Views software. The study regarded quarterly observations from FY 2015-16 to FY 2020-21. GDP and digital payments data have been gathered from the Reserve Bank of India (RBI) website. Digital payment data includes retail electronic payments such as NEFT, IMPS, NACH, UPI payments, direct debits, card payments, and prepaid payment instruments. One of the major limitations of the present study is its generalisability due to the limited time frame. The results of the study are subject to the macro-economic conditions prevailed during the period of study.

Specification of Model:

GDP = a₀+ a₁* DIGIPAY + E_t

E_t = Error term

Table 1(a): Results of Augmented-Dickey Fuller Test (ADF) - GDP Variable

Variable	@ Level	t-Statistic	p- Value^***	@First Difference	t-Statistic	p- Value^***
GDP	Intercept	-1.44432	0.5429	Intercept	-5.54238	0.0002^*
	Trend and Intercept	-3.24314	0.101	Trend and Intercept	-4.12308	0.0208^**
	None	1.356464	0.9513	None	-5.18502	0.00^*

Source: Author’s computation

*Significant @ 1% level

**Significant @ 5% level

***Mackinnon (1996) one-sided p-values

Table 1(b): Results of Augmented-Dickey Fuller Test (ADF) – Retail Digital Payments Variable

Variable	@ Level	t-Statistic	p- Value^***	@First Difference	t-Statistic	p- Value^***
DIGIPAY	Intercept	-1.224	0.6458	Intercept	-4.9932	0.0006^*
	Trend and Intercept	-1.9223	0.6106	Trend and Intercept	-4.9836	0.0032^*
	None	2.92658	0.9983	None	-3.7542	0.0007^*

Source: Author’s computation

*Significant @ 1% level

***Mackinnon (1996) one-sided p-values

To have a robust regression model, the variables under the study need to be stationary and to test the stationarity, Augmented-Dickey Fuller (ADF) test has been applied. The results of unit root analysis indicate that both the series, GDP, and digital payments are non-stationary at the level and accepted the null hypothesis (p>0.05). After the first difference, GDP and DIGIPAY have become stationary and are integrated of order one i.e., I(1) at 5% and 1% level of significance respectively.

The next step in the analysis is to examine the relationship between GDP and DIGIPAY using the Johansen cointegration test. To perform co-integration analysis, appropriate lag order should be selected. Using VAR lag order selection criteria, lag order 1 has been selected as it was indicated by all the criteria (Akaike Information Criterion, Schwarz Information Criterion (SIC) and Hannan-Quinn Information Criterion (HQ)).

Results of Johansen Co-integration Test:

Table 2: Unrestricted Co-integration Rank Test (Trace)

Hypothesized No of Co-integrating Equations	Eigen Value	Trace Statistic	Critical Value @ 5% Level	p-Value^**
None	0.628244	24.41749	15.49471	0.0018^*
At most 1	0.113406	2.648108	3.841466	0.1037

^*Significant at 5% level

^**MacKinnon-Haugg-Michells (1999) p-Values

Source: Author’s computation

Table 3: Unrestricted Co-integration Rank Test (Max-Eigen)

Hypothesized No of Co-integrating Equations	Eigen Value	Max-Eigen Statistic	Critical Value @ 5% Level	p-Value^**
None	0.628244	21.76938	14.26460	0.0027^*
At most 1	0.113406	2.648108	3.841466	0.1037

^*Significant at 5% level

^**MacKinnon-Haugg-Michells (1999) p-Values

Source: Author’s computation

The results showed that the null hypothesis which states there exists no co-integrating relationship between the study variables is rejected at a 5% level of significance. The test statistics of trace and Max-Eigen are more than their respective critical values indicating one co-integrating equation exists between real GDP and DIGIPAY. The result of the Johansen test signifies the presence of a long-run association between the variables under the study. Further, to understand the behaviour of study variables in the short-run consistent with long-run relation and to find the speed at which the dependent variable adjusts to equilibrium, the Vector Error Correction Model (VECM) has been performed.

D(QGDP) = C(1)*( QGDP(-1) - 0.336817863774*QDIGIPAY(-1) - 10.03539754 ) + C(2)*D(QGDP(-1)) + C(3)*D(QDIGIPAY(-1)) + C(4)

Table 4: Results of Vector Error Correction Model (VECM)

	Coefficient	Std. Error	t-Statistic	p-Value
ECT	-2.102530	0.385586	-5.452811	0.0000
C(2)	1.086893	0.359627	3.022278	0.0073
C(3)	-0.660444	0.263957	-2.502092	0.0222
C(4)	0.038026	0.015321	2.481896	0.0232

Source: Author’s computation

Table 5: Regression Model

R²	Adjusted R²	F-statistic	p-Value
0.639967	0.579961	10.66512	0.000294^*

Dependent variable: D(GDP)

^*significant @ 1% level

Source: Author’s computation

In the above equation, C(1) represents the coefficient of the cointegrating equation (error correction term) and if the coefficient is negative and significant, there exists a long-run association between DIGIPAY and GDP. It is evident from the analysis that the coefficient of C (1) in the output shows a negative significance that indicates long-run causality running from DIGIPAY to GDP. The model also showed significant explanatory power with 58%. In addition, to estimate the presence of short-run causality, the Wald test was performed.

Table 6: Results of the Wald test

Test Statistic	Value	p-Value
F-Statistic	6.260466	0.0222*
Chi-square	6.260466	0.0123*

*Significant at 5% level

Source: Author’s computation

The null hypothesis of the Wald test indicates that the coefficients of lagged values of an independent variable are zero i.e., no short-run association exists between the variables. The result of the Wald test indicates the presence of a short-run association running from DIGIPAY to GDP at a 5% level of significance. The findings of the study are intact with the previous studies explaining the long run association between digital payments and economic growth.

Finally, this paper attempted to find out the direction of causality, whether there exists uni-directional causality or bi-directional causality between the variables under study. To determine the pattern of such a relationship, Granger (1969) has developed the causality test method. If current and lagged values of an independent variable improve the prediction of the future value of a dependent variable, then we can say that the independent variable granger causes the dependent variable.

Table 7: Results of the Granger Causality Test

Null Hypothesis	F-Statistic	p-Value
DIGIPAY does not Granger Cause GDP	0.00865	0.9269
GDP does not Granger Cause DIGIPAY	0.67194	0.4225

Source: Author’s computation

The analysis presented above shows the acceptance of the null hypothesis that indicates there exists no causality between the GDP and retail digital payments at a 5% level of significance. The results of Granger Causality do not imply direct causation but helps in identifying the usefulness of one variable in predicting another variable.

CONCLUSION:

From barter system to digital payment, the payment system landscape in India has undergone significant change. During 2010-11, paper-based transactions constituted 60% of total retail payments volume and 89% of total retail transaction value whereas in 2019-20, the share of paper-based transactions in total retail volume fell to a meagre 3% and in terms of retail transaction value, recorded just at 20%²⁰. Increased use of digital payments not only makes the economy more efficient but also boosts consumption and GDP. A sustainable payments system is not just about technology, but about driving incentives for others to participate in it and derive value from doing so. The growing adoption of digital modes for making payments helps the Government in reducing the size of the grey economy and build a fair and more transparent payment ecosystem for all. Hence Government should expand the necessary infrastructure to promote digital payment and also institute strict laws and technology in place to prohibit cyber frauds.

CONFLICT OF INTEREST:

The author has no conflicts of interest regarding this investigation.

DECLARATION:

The Paper is the original work of the author and that the paper has not been submitted for publication elsewhere.

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Received on 31.07.2023 Modified on 20.08.2023

Asian Journal of Management. 2023;14(4):303-308.

DOI: 10.52711/2321-5763.2023.00049