Can Mobile Financial Services Influence Financial Behaviour in India?

I. Introduction

There exists a large gap in bank account ownership in developed and developing economies (Demirgüç-Kunt et al., 2018). Further, almost half of the world’s unbanked population resides in Bangladesh, China, India, Mexico, Kenya and Nigeria. The extant literature indicates that financial inclusion ¹ is related to an increase in welfare. At the macroeconomic level, financial inclusion fosters economic growth (Sahay et al., 2015), reduces poverty in developing economies (Burgess et al., 2005), reduces inequality (Demirgüç-Kunt & Levine, 2009; Neaime & Gaysset, 2018) as well as reduces carbon emissions (Renzhi & Baek, 2020). Among microeconomic outcomes, most striking are the positive effects of financial inclusion on women’s health outcomes (Prina, 2015), women’s position in the household (Ashraf et al., 2010) and better children’s outcomes (Duflo, 2003).

Given empirical evidence on the multitude of positive effects of financial inclusion on economic outcomes, studies have examined what interventions can foster faster financial inclusion. Literature suggests that stronger legal rights, proximity to financial institutions and a stable political environment (Allen et al., 2016) are conducive for the use of bank accounts. Financial literacy is another channel that can improve financial inclusion (Grohman et al., 2018). Social trust improves the use of basic financial services (Xu, 2020). In recent times, policymakers are of the view that digital financial services (DFS) and financial technology (fintech) have the potential to fasten the pace of financial inclusion in developing economies as they reduce the cost of transactions, improve trust and increase the speed of transaction (World Bank, 2020). Ghosh (2017) found that individuals with mobile phones are more likely to own bank accounts than non-mobile phone users. Mobile financial services (MFS) increase the likelihood of savings and the amount saved by African individuals (Loaba, 2021; Ouma et al., 2017). Fanta and Makina (2019) find that internet access and usage of mobile phones increase the usage of financial services like ATMs in African countries. The evidence related to the use of mobile for financial transactions and their effect on financial outcomes is still scarce, especially outside Africa. This study intends to fill this gap in the literature and address the question in the context of a fast-developing economy like India, which is home to a large proportion of financially excluded population in the world.

The question assumes importance in the Indian context as the teledensity in India is quite high at over 86 per cent and wireless teledensity is very high at 84.17 per cent (TRAI Annual Report, 2022). Even among internet users, approximately 96.5 per cent use internet on their mobile device (TRAI Annual Report, 2022), emphasizing the potential of mobile telephony to affect outcomes. Further, the coronavirus pandemic has highlighted the importance of DFS as, owing to the pandemic, individuals were forced to transact remotely given mobility constraints. In fact, studies have documented that the pandemic has accelerated the use of DFS (Arner et al., 2021). Hence, to what extent use of MFS can improve financial behaviour becomes important, especially in developing economies like India.

The Pradhan Mantri Jan Dhan Yojana (PMJDY), introduced in 2014, is the flagship financial inclusion programme in India with an objective that each household can access low-cost saving bank account. PMJDY also improved financial literacy, access to formal credit, insurance and pension and has over 424 million beneficiaries. ² However, PMJDY followed a bank-driven financial inclusion model. The Unified Payments Interface (UPI), launched in 2016, was one of the early policy interventions that pushed the growth of DFS in India. The fintech companies in India like PhonePe, Paytm, along with payment banks, have led the DFS growth. This was further facilitated by reduced internet cost and the availability of low-cost smartphones in the last few years in the country. Given this transition, albeit a slow one from brick and mortar bank-based model to the growing importance of DFS in the country, taking stock of whether the use of MFS improves financial behaviour becomes an important question.

This study finds that the use of MFS improves the likelihood of having insurance along with increasing the probability of investment, savings and borrowing from formal institutions in India. Further, the relationship is stronger for females and younger individuals. The findings of the article underscore the importance of accelerating DFS to fasten the pace of financial inclusion in the country.

The study contributes to the growing literature on the role of DFS on outcomes. Jack and Suri (2014) document that the use of mobile money shields household consumption from negative shocks. Konte and Tetteh (2022) find that use of mobile money is related to improved firm-level productivity in African economies. On the other hand, Gosavi (2018) finds that mobile money improves access to credit for firms. At the same time, Ouma et al. (2017) and Loaba (2021) find that mobile money is related to higher household savings. Our study finds that access to MFS is related to improved financial behaviour in developing economies. Additionally, methodologically, our article also addresses the possible endogeneity related to the MFS variable. Loaba (2021) addresses endogeneity related to the use of MFS using an instrumental variable approach. We employ a propensity score matching (PSM) method as well as the Lewbel’s method that relies on an internally generated instrument to account for potential endogeneity.

The rest of the article is organised as follows. Section II presents the conceptual framework, whereas Section III gives the data and the variables of the study. Section IV discusses the methodology employed, and Section V elaborates on the results. Finally, Section VI summarises the findings and concludes.

II. Conceptual Framework

MFS provides consumers with more real-time control over finance (Brainard, 2016). There are several possible pathways through which MFS can influence financial behaviour of individuals. First, mobile money reduces transaction costs (Jack & Suri, 2014), which can be beneficial for economic outcomes. Specifically, digital payment platform makes financial transactions a smooth experience. In other words, MFS enables individuals to buy and sell financial products like shares, bonds, etc., as well as to purchase insurance products online, pay regular premium and make a claim at the time of need in a hassle-free manner. Hence, using MFS can improve individuals’ financial behaviour by simplifying financial transactions. In fact, MFS improves the likelihood of savings due to convenience of frequent and smaller transactions (Loaba, 2021; Ouma et al., 2017). Further, buying financial products through conventional mode requires individuals to incur time as one has to travel to the workplace of the financial service provider. Such time costs may deter individuals from saving, investing, borrowing from formal sources or purchasing insurance. The use of MFS may help in saving the cost of travelling, and time-constrained individuals may start exhibiting superior financial behaviour as most of the transactions can be done remotely by devoting a few minutes. Next, several banks provide consumer credit through their mobile applications (Yang & Zhang, 2022), which can increase formal borrowing. Gosavi (2018) also documents that mobile money services improve access to formal credit for firms in Africa.

Further, online payment facilities may improve the financial behaviour of individuals by increasing access to fintech and insuretch service providers. Several insuretech companies provide micro-insurance products, making it easier for poor people to own insurance. This improved access to affordable financial products can, in turn, improve financial behaviour of individuals. Next, MFS allows individuals to maintain their bank accounts and perform banking-related work very easily and quickly without the need for any direct communication with bank employees. Often, the lack of proper communication skills of financial service providers lead to poor financial behaviour like lack of investment, not purchasing insurance, or taking the informal credit route.

III. Data and Variables

The study utilises the data provided by the Financial Inclusion Insights (FII) programme by Kantar, which is supported by the Bill and Melinda Gates Foundation. The FII program conducts a nationally representative survey in eight Asian and African countries. Our analysis is based on the most recent sixth wave of the FII programme in India. The survey was conducted between September and December 2018, covering 48,027 individuals aged 15 years and above. ³

The study considers three financial outcomes to reflect the investment, savings, credit behaviour and risk management practices of individuals. The first outcome is whether the individual is investing in financial products. The investment variable is a binary outcome that takes the value one if the individual invested in either local shares, foreign shares, bonds, chit funds, land, gems or jewellery. The second outcome variable is savings, which takes the value one if the individual saves and zero otherwise. The third outcome variable is insurance, which takes the value one if the individual has life insurance and zero otherwise. The next outcome is related to the borrowing behaviour of individuals. The borrowing dummy takes the value one if the individual borrowed from formal financial institutions like any bank, post-office, using a card, self-help group or microfinance institutions and zero for borrowing from moneylenders or friends and relatives. These outcome variables have been considered by other studies like Gao and Fok (2015) in the Chinese context.

The interest variable is the use of MFS variable, a dummy that takes the value one if the individual used mobile for paying or receiving money and zero otherwise. Based on literature, we control for various socio-economic characteristics, including age, level of education, having a bank account, marital status, gender, economic status of the household, religion, area of residence and state. Table 1 provides the definition of the variables used in the analysis. Individuals with higher education, age of the household head, those residing in urban areas, belonging to a dominant religious group, economically better off and working are more likely to exhibit superior financial behaviour (Gao & Fok, 2015). Married individuals are expected to exhibit better financial behaviour owing to an increase in household responsibility (Ouma et al., 2017). Individuals staying in urban areas are likely to save and invest more because of better access to financial products (Ouma et al., 2017). Having a bank account improves access and understanding of financial products and is likely to have a positive relationship with financial behaviour.

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Table 1.

Variable Definitions.

Variable	Description
Dependent variable
Investment	Dummy variable = 1 if individual invests and 0 for non-investors
Savings	Dummy variable = 1 if individual saves and 0 for others
Insurance	Dummy variable = 1 if individual has a life insurance and 0 for others
Borrowing	Dummy variable = 1 if individual borrows from banks, non-banking financial institutions, using credit card, self-help groups and from microfinance institutions and 0 for borrowing from friends, relatives and moneylenders
Interest variable
Mobile financial services (MFS)	Dummy variable = 1 if the individual uses mobile for financial transactions, including sending or receiving money or making a payment or bank transaction and 0 for non-users
Controls
Age	Age of the individual in years
Married	Marital status, 1 for married and 0 for unmarried
Urban	Dummy variable = 1 if the individual resides in urban areas and 0 if the individual resides in rural area
Bank account	Dummy variable = 1 if the individual has a bank account and 0 otherwise
Female	Dummy variable = 1 if the individual is a female and 0 for males
Poor	Dummy variable = 1 for poor and 0 for non-poor
Religion	Religious identity of the individual—Hindu, Muslims or others
Education	Educational attainment of individuals—Illiterate, literate with no formal education, <5 years of schooling, primary completed (5 years of schooling), middle school (6–8 years), secondary completed (10 years), higher secondary completed (12 years), non-technical diploma, technical diploma, graduate or post graduate
Occupation	Occupational status of the individuals: Not working, agriculture, part-time, non-farm business or salaried

Table 2 provides the summary statistics of the outcome variables, MFS and the other controls for the full sample (column 1) and separately for the MFS adopters (column 2) and MFS non-users (column 3). Less than 10 per cent use mobile for financial transactions and the share of individuals who invest is also quite low. The insurance penetration is also low and among the borrowers around 17 per cent borrow from formal sources. The descriptive statistics indicate that there is significant scope to improve the financial behaviour of individuals. Interestingly, we observe that the share of individuals with favourable outcomes is higher among the sample of MFS users compared to non-users. The univariate statistics suggest that MFS can possibly improve the financial outcomes in developing economies and need further exploration.

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Table 2.

Summary Statistics.

	Full	MFS	No MFS
Dependent variable
Investment (%)	6.15	10.71	5.67
Savings (%)	48.04	50.72	47.76
Insurance (%)	9.83	18.76	8.89
Borrowing (%)	17.86	25.95	17.07
Interest variable
MFS (%)	9.58
Controls
Age (in years)	37.69	31.82	38.32
Married (%)	80.95	65.32	82.61
Urban (%)	31.17	49.23	29.26
Bank account (%)	77.22	79.30	77.10
Female (%)	47.57	66.23	45.60
Poor (%)	68.15	41.66	70.95
Religion (share in %)
Hindu	86.94	87.40	87.06
Muslim	10.07	7.67	10.35
Others	2.98	4.93	2.59
Education categories (share in %)
No formal education	28.48	6.68	30.79
Literate—no formal education	4.04	2.88	4.17
<5 years	6.26	4.19	6.48
Primary completed	8.14	5.35	8.44
Middle school	18.32	13.18	18.86
Secondary completed	15.62	17.31	15.44
Higher secondary completed	10.92	20.48	9.91
Non-technical diploma	1.33	5.02	0.94
Technical diploma	0.69	3.17	0.43
Graduate	4.95	16.57	3.72
Post-graduate	1.24	5.17	0.82
Occupation (share in %)
Not working	48.79	32.79	50.50
Agriculture	15.14	22.34	14.37
Irregular/part-time work	9.18	7.27	9.34
Non-farm business	9.80	11.32	9.63
Salaried	17.12	26.28	16.15

Note: MFS, mobile financial services.

Given that the financial outcomes variables considered in our analysis are binary, we employ probit specification for analysing the relationship between the MFS variable and various financial outcomes. The following equation is estimated to analyse the relationship:

Where Y i refers to the financial outcomes and β 1 captures the relationship between MFS and financial outcomes. X i k are the set of control variables that may affect financial outcomes. The state dummies capture factors like specific local policies that act as push factors for improving financial outcomes of individuals or the number of financial firms in the administrative area. The errors are clustered at the neighbourhood level in all the specifications.

However, if the MFS variable is endogenous owing to omitted variables or reverse causality, the probit estimator can be biased. One may argue that individuals who are aware are the MFS adopters as well as borrow from formal financial institutions or buy insurance. To address the possible endogeneity related to the MFS variable, we adopt two alternate estimation techniques. First, we use Lewbel’s method (Lewbel, 2012), which utilises heteroscedasticity-based internal instrument in the absence of a valid exogenous instrument. Next, we also use a PSM method that allows us to draw causal inference using observational data (Rosenbaum & Rubin, 1983).

The study finds that the use of MFS improves the financial behaviour of individuals in the form of a higher likelihood of investment, savings, borrowing from formal financial institutions and having insurance. The results remain consistent if the interest variable is endogenous and we employ alternate estimation techniques like Lewbel’s method and the propensity score matching method. Further, we find that the results are more prominent for females and younger cohorts.

The results assume importance from a policy viewpoint. First, the main finding underscores the need to fasten the pace of MFS in developing countries as it has the potential to improve the financial behaviour of individuals. Second, the finding also suggests the need for complementary policies like awareness campaigns to ensure that individuals have confidence and trust while using mobile for financial transactions and do not become victims of cybercrime. Third, there is a scope for financial institutions to partner with mobile service companies to provide financial products and leverage the benefits of mobile phone services in influencing financial behaviour. This aspect has also been highlighted by Loaba (2021).

In spite of the policy implications, the study has a few limitations. In the absence of longitudinal data, the article is unable to comment on whether overtime improvements in access to MFS have a differential marginal effect on financial behaviour. Second, accounting for other supply-side factors, including internet speed, cost and access to electricity, remains another important dimension to identify the additional vulnerabilities and devise targeted policies for attaining the twin objectives of greater outreach of DFS and making financial inclusion pro-poor.