Banking or Under-banking: Spatial Role of Financial Inclusion and Exclusion

Abstract

Access to bank account is only a part of the problem when we talk of financial inclusion because several people with a bank account are not necessarily using them to deposit their savings or carry out transactions. This article makes an attempt to examine the reasons for low utilisation of banking facilities. It employs financial inclusion insights (FII) data for Indian population to find out an outcome of financial inclusion (and thus social inclusion as well) based on the usage of banking services with covariates like financial literacy, the probability that any financial service is accessible to the respondent in terms distance, type of mobile phone and spatial density. We use truncated probit model to measure the incidence of under-banking. Our findings show that there is a negative association between supply-side constraints and usage of banking services, implying that low access to financial services in time and space stands as a hindrance to financial inclusion. Further, we find from the financial inclusion and exclusion map at the district level that even though economic agents intend to participate in the space in which he/she is living is not much inclusive.

Keywords

Financial inclusion spatial exclusion spatial density dormant account financial literacy

Introduction

Individuals can use financial services to smooth their consumption, absorb unforeseen shocks and make household investments (Collins et al. 2009). While much received work on financial inclusion focuses on its benefits for individuals, it is also useful to the economy at large. Greater access to accounts can result in a larger deposit base for banks or one that is more resilient in times of financial stress (Han and Melecky 2013). Globally, about 2.5 billion people are unbanked (Allen et al. 2016), though the unbanked number has come down to 1.3 billion in 2017. Supply-side factors point to insufficient progress in branching or outreach (Beck et al. 2007), while demand-side factors emphasise distrust of financial institutions, varying financial behaviour and insufficient financial literacy (Bertrand et al. 2004; Cole et al. 2011). The financial market in the emerging countries has remained underutilised due to the information gap between consumers’ financial service providers arising out of financial illiteracy (OECD 2005). To bridge the gap in information asymmetry, the Reserve Bank of India (RBI) has launched an initiative in 2007 to establish Financial Literacy and Credit Counselling Centres throughout the country. But how does higher financial literacy translate into greater wealth for an individual? Financial literacy has been proven to affect both saving and investment behaviour, and debt management and borrowing practices and also higher financial literacy are more likely to lead to planning for retirement, probably because they are more likely to appreciate the power of interest compounding and are better able to do calculations (Lusardi and Mitchell 2011). In the context of India, financial literacy varies state- and district-wise. The level of financial literacy can be inspected using financial knowledge, financial behaviour and financial attitude (OECD 2011).

Our article contributes to literature by providing in-depth analysis on the order choice of agents for financial inclusion process and evolution of the financial inclusion in India. It also contributes to the existing literature on macro-behaviour arising out of agent’s micro-motives in a distinct way. We see the impact of technology adoption and spatial inclusion explained in methodology section in detail. We find a negative relationship between supply-side constraints and usage of banking services, suggesting low access to financial services in time and space, which stands as a hindrance in financial inclusion. Financial inclusion is not evenly distributed throughout the country, and hence, it is subject to considerable spatial inequalities. Cartographic representations and symbol maps enable us to visualise the extremely heterogeneous situation of financial inclusion.

Rest of the article is organised as follows: the second section presents some facts about financial inclusion in India. The third section discusses literature review. The fourth section provides methodology used in the study. The fifth section deals with results and discussion. The sixth section concludes the article.

Stylised Data on Financial Inclusion in India

Financial inclusion in India is defined ‘as the process of ensuring access to financial services and timely and adequate credit where needed by vulnerable groups such as the weaker sections and low income groups at an affordable cost’ (Rangarajan Committee 2008). In 2014, the country was host to the world’s largest financial inclusion intervention called the Pradhan Mantri Jan Dhan Yojana (PMJDY) scheme, which was the latest, and undoubtedly largest, intervention in a long list of reforms aimed at increasing financial inclusion. Financial inclusion in India is now mandated, that opens up new challenges for getting bank accounts to the poor. Having access to a banking account generally qualifies a household or individual to be designated as financially included. Individuals with bank account ownership has witnessed a huge structural change in India, starting at 35% bank account ownership in 2011 to 80% ownership in 2017 (World Bank 2017). Demand for bank account that was nudged by a policy of financial inclusion—PMJDY—increased the number from 53% in 2014 to 80% in 2017. For example, dormant accounts represent those bank accounts of individuals that do not have any kind of banking activities to show in the account. Accounts are kept silent. But dormant account does not mean dormant financial lives. The majority of account enrolment takes place without any knowledge transfer, and customers are unsure of their account features and how to access their benefits through them. Out of the 80% bank account ownership in 2017, only 41% were in use, and the remaining 39% of the bank accounts were not in use in terms of the type of deposit or withdrawal from these bank accounts.

Table 1 presents the number of account ownerships in the bank, financial institutions and mobile money in India. Mobile money account ownership does not reflect any significant progress. India had only 2% of existing mobile money account users in 2017. Demand for financial services/products from those who belong to the poorer section of the society is 77%. When individuals do not have a bank account or a mobile money account, they tend to keep themselves out of the ambit of formal banking financial network, and this leads to social exclusion (Leyshon and Thrift 1995).

Table 1.

Description of Account Ownership in India (%) for age 15+.

Indicator	Percentage
Account	80
Financial Institution account	80
Mobile money account	2
Female	77
Out of labour force	75
Poorest 40%	77
Rural	79

Source: Global Financial inclusion Dataset, 2017.

As per Table 2, out of the large chunk of population that holds a bank account, 39% of the accounts are dormant accounts that have had no financial activity for a long period of time, except for the posting of interest. Usage of debit card and digital payment channel for digital payments is not in much demand. For poor households, credit is not the only principal financial service they need: good savings, payment services and insurance may rank higher. When people participate in the financial system, they are better able to manage risk and absorb financial shocks, invest in a business and fund large expenditures like education or a home improvement (Lusardi and Mitchell 2011).

Table 2.

Description of Usages of Various Financial Products by People Who Hold Bank Accounts.

Indicators	Percentage
Account holders	80
Dormant accounts	39
Used the debit card to make a payment in the past year	12
Digital payments in past year	20
Paid utility bills in past year	42
Received private sector wages in the past year	19
Received government payments in the past year	14
Received domestic remittances: using an account	5
Received domestic remittances: using OTC service	3
Send domestic remittances: using an account	6
Send domestic remittances: using OTC service	0

Source: Global Financial inclusion Dataset, 2017.

Therefore, an inclusive financial system allows both access to bank account and usage of formal financial services that are likely to benefit the poor and underprivileged section of the society by identifying opportunities and removing supply-side barriers for not raising the demand from the population. Hence, measuring financial inclusion is essential to understand an individual’s growth and economic development.

Literature Review

Financial inclusion (or, alternatively, financial exclusion) has been defined in the literature in the context of a larger issue of social inclusion (or exclusion) in a society. One of early definitions by Leyshon and Thrift (1995) defines financial exclusion as referring to those processes that serve to prevent certain social groups and individuals from gaining access to the formal financial system. Financial exclusion is defined as the inability to access necessary financial services in an appropriate form (Sinclair 2001). Exclusion can come about as a result of problems with access, conditions, prices, marketing or self-exclusion in response to negative experiences or perceptions. It is broadly the inability (however occasioned) of some societal groups to access the financial system (Carbo et al. 2005). Financial Inclusion is not bounded just to the access of credit or access to savings account, but it is a multidimensional phenomenon, which captures the level of financial inclusion heterogeneity in a geography (Satyasai and Kumar 2020). There is an increase in credit by households whenever there is the prospect of an increase in income (Mutezo 2014). Household consumption is identified with household income in the long haul and interest rates (Kim et al. 2014). There is a significant and positive trend between wealth and debt behaviour (Herispon 2019), and it is hard for somebody to stay away from debt, who has an obsession with his/her current lifestyle, and debt or credit from financial institution acts as an alternative income (Baker 2014). So, wealth dimension is very important from a consumer’s perspective.

Quasi-experimental evidence from India’s social banking suggests that increasing financial accessibility is an instrument in enhancing the capability of individuals, and it, subsequently, enables them to get rid of poverty (Burgess and Pande 2005). We need to consider the complexity of the Indian society and look for financial inclusion at a disaggregated level as the outreach of the banking sector varies across countries (Beck et al. 2007), and when it comes to monetary transactions, the villagers prefer channels that they trust (Donner and Tellez 2008; Gu et al. 2009; Luarn and Lin 2005).

According to Chakrabarty (2012), providers of financial services generally target the economically active group, namely the middle-aged population. Schemes for the old and young are scarce, as the banks do not expect any business or profit from them. Age group is a crucial factor for the determination of financial inclusion (Uddin et al. 2017).

While it is important to understand how financially literate people are, in practice, it is difficult to explore how people process economic information and make informed decisions about household finances (Lusardi and Mitchell 2011). Financial literacy is defined as a combination of awareness, knowledge, skills, attitude and behaviour necessary to make sound financial decisions and ultimately achieving individual financial well-being (OECD 2011). Individuals achieve financial literacy through the process of financial education (Kumar and Saini 2020).

People are part of ‘communities without propinquity’, that is, ‘footloose’ relations, associations and institutions (Webber 1963). The basic idea is that the expansion of social relations that transcends place causes a decline of local solidarity, the ‘eclipse of community’ (Stein 1964). The ‘neighbourhood’ loses its relevance as a meaningful ‘framework of integration’ (Van Doom 1955). Accordingly, the district and the neighbourhood have an impact on the lives of individuals. From this perspective, the neighbourhood exerts influence on the degree of social exclusion and social cohesion (Bolt et al. 2016). Policymakers need to look at how spatial interactions may help to provide convergence between the financial inclusion levels of countries because the studies about this issue are still insufficient (Bozkurt et al. 2018).

Demand for and usage of financial services or products require some kind of capability to make a choice (Sen 1989). A gap exists in the usage of financial services in terms of physical banking infrastructure in parts of rural and remote areas. Poor connectivity of Internet plays a key role in creating the gap between the last-mile customer and the financial service provider, and hence, digital divide is established; the innovation and design of banking services or products do not provide flexibility to the customer who is employed in an informal occupation; local culture and norms create sociocultural barriers, which lead to the restricted freedom for the women to use the banking services (Nadan et al. 2021), particularly in emerging market economies like India. Under this backdrop, we, in this article, look at the role of socio-economic variables like education, wealth, demographic factors like age groups, demand-side barriers like financial literacy and technology adoption along with supply-side constraints as financial accessibility and spatial spread of financial inclusion.¹

Methodology

Theoretical Framework

According to the Centre for Financial Regulation and Inclusion, up-taking of financial service does not necessarily translate into usage. According to Carrington et al. (2010), any individual interacts with all the contextual factors when taking financial service usage decision. For example, someone decides to transfer the money using Internet banking but when he starts to use the service, Internet is down. The provider of the financial service is important, but it is the decision-making framework of consumers, which leads to usage of financial service, and there are various factors associated with consumer decision, and one very crucial factor is the space where he or she is living. This understanding from a consumer’s point of view is very important to find out the drivers of usage (Bester et al. 2016). And, thus, intent or decision to use financial service can be differentiated or distinguished from an individual’s actual action. This article specifies two models. In both the models, we have generated a dependent variable, which shows the extent of financial service. In the first model, the dependent variable is ordinal or hierarchical in nature, that is, bank use, with three indications, where 0 signifies not having a bank account, entry of 1 indicates having a bank account and 2 indicates usage of financial services. The first model is employed to find out the significant strength of the various factors influencing the responsiveness of the agent towards her/his demand for financial services, which ultimately leads to financial inclusion. In order to analyse both ends of demand separately, we have incorporated a separate econometric technique—ordered probit model for model I.

For a sustained use of financial services, a specific event or favourable circumstance is required to trigger the event. Sometimes there is a compulsion or auto-enrolment to take up the financial device due to some scheme, but there is a breakdown or discontinuation in usage, leading to dormancy as a result. Such accounts are owned but not used, which limit the benefits to consumers that can be extracted from the financial services sector (FinMark Trust 2018). The proportion of dormant account has increased at a faster rate in some of the countries. According to Global Findex survey 2017, the percentage of adults having a dormant account is 39% in India. And, thus, analysing the usage of financial service becomes a sweet spot in financial inclusion, and finding this sweet spot is very challenging. So the effect of making choice 1, that is, having a bank account, will affect the agent to go for choice 2, that is, whether he or she uses financial services frequently, and this is the motivation of our research. And so model II only deals with a sample of individuals who own a bank account to see if they are active users of financial services and the extent of their inclusive behaviour.

Data and Variables

This article employs the Financial Inclusion Insight survey, conducted by Inter media, a not-for-profit global consultancy, specialising in strategic research and evaluation. The Data for our analysis are based on the fifth wave of the survey, comprising 47,000 individuals, which was conducted during December 2017. The samples of the FII surveys cover the adult populations of the countries, not the entire populations. In most cases, this means the samples cover individuals aged 15 years and over.

The variables used in the models are specified as follows:

Bank use: It is a dummy dependent variable with three order-preserving categories, that is, dummy 0, if an individual does not have a bank account; dummy 1, if an individual has a bank account; dummy 2, if an individual uses advanced banking services such as mobile payment, school fee, sent or received money or grants from the government.

Wealth: This is generated by using principal component analysis predictions on basic and necessity goods such as stove/gas burner, pressure cooker, refrigerator, etc.

Perception on wealth: It is a dummy variable with 1 if an individual has enough money to buy basic amenities and can afford expensive items, else 0 if he/she does not have enough money to buy basic amenities.

Education level: Level of education is divided into four categories—no education, primary, secondary and higher education.

Behaviour: Financial behaviour is determined by examining spending behaviour of the individual.

Attitude: Financial attitude is determined by looking at survey questions—how involved or uninvolved are they in deciding? How is your mobile phone used? How is your mobile account being used? How is your bank account maintained?

Knowledge: Financial knowledge is determined on the basis of their response on basic mathematical questions like addition and subtraction.

Mobile ownership: It is a dummy variable with four different types of mobile phone users, that is, basic feature, smartphone and no phone.

Age group: Adults (15+) are divided into different cohorts—16–25, 26–35, 36–45, 46–55 and 55+—so that each cohort’s economic motive can be analysed.

Financial accessibility: It is a dummy variable with accessibility to any financial service available within 10 km of an individual’s residence, otherwise 0.

Spatial inclusion: It is a probability that any financial service is accessible within 0.5 km of an individual’s residence clustered by district.

Spatial exclusion: It is a probability that any financial service is accessible at a distance more than 5 km of an individual’s residence clustered by district.

Spatial density: An individual’s residence is categorised into seven dummies, that is, from village—class three, to town—class one.

Model I

An ordered probit model is adopted to estimate relationships between an ordinal dependent variable and a set of independent variables. An ordinal variable is a variable that is categorical and ordered, which entails the rank ordering of cases into three or more categories, based on the degree to which a given phenomenon is present (Mahoney 1999) (for instance, no bank account, having a bank account, using banking financial services). An agent’s demand for financial inclusion requires putting the financial literacy attributes with socio-demographic characters and other proximate variables in increasing financial inclusion opportunities and accessibility to financial services or products. The structural model of a usage of banking financial services, that is:

y_{i}^{*} = x_{i} β + ϵ_{i}, where ϵ_{i} \sim N (0, σ 2) . x s are observed for all cases .

where,

y* = latent variable;

β = column vector of structural coefficients; and

x = row vector with intercept and the ith observation of x.

We assume that a latent variable ‘Bank use’ is a continuous variable, which is mapped to an observed value y. The variable y is thought of as providing incomplete information about an underlying y* according to the measurement equation, that is:

Y_{i} = m if τ_{m - 1} \leq y_{i}^{*} < τ_{m} for m = 1 to j

The observed y is related to y* according to the measurement model, that is:

\begin{array}{l} Y_{i} = \{0 = no bank use, if τ_{0} = - \infty \leq y_{i}^{} < τ_{1} \\ 1 = bank use, if τ_{1} \leq y_{i}^{*} < τ_{2} \\ 2 = advanced bank use if τ_{2} \leq y_{i}^{} < τ_{3} = \infty\} \end{array}

where

Y = observed variable;

y* = unobserved/latent variable; and

τ = threshold/cut points.

Model II

A truncated probit regression model is specified. Truncation limits the data more severely by excluding observations based on characteristics of the dependent variable. In our case, we sample only individuals who have bank account and who also use banking financial services because individuals who do not have a bank account are less likely to use banking services. The truncated regression model is used to analyse these types of data.

y_{i}^{*} = x_{i} β + ϵ_{i}, where ϵ_{i} \sim N (0, σ 2) . x s are observed for all cases .

where

y* = latent variable;

β = column vector shows structural coefficients; and

x = row vector with intercept and the ith observation of X.

The observed y is related to y* according to the measurement model, that is:

\begin{matrix} Y_{i} = \{y_{i}^{*} if y_{i}^{*} > τ, \\ τ_{y} if y_{i}^{*} \leq τ\} \end{matrix}

Combining the aforementioned two equations:

\begin{array}{r} Y_{i} = \{y_{i}^{*} = x_{i} β + ϵ_{i} if y_{i}^{*} > τ \\ τ_{v}, if y_{i}^{*} \leq τ\} \end{array}

where

Y = censored variable;

y* = latent variable; and

τ = value assigned to y if y* is censored.

Results and Discussion

Table 3 presents the regression results from probit models. In the output of model 1, at iteration 0, a null model is fitted, that is, the intercept-only model. It then moves on to fit the full model and stops the iteration process once the difference in log likelihood between successive iterations become sufficiently small. The likelihood ratio indicates that our model as a whole is statistically significant, as compared to the null model with no predictors. The regression results show that individual characteristics play a significant role in measuring financial inclusion. Wealth in terms of living standards, education, food and clothing can be treated as proxy to see the ability of an individual to use banking financial services. In our case, an individual’s wealth shows significant relation as more the wealth of an individual, the more is the usage of services provided by banks. But after controlling the wealth effect, the role of an individual’s financial literacy is a major covariate for the usage of formal banking services.

Table 3.

Regression Results.

Indicator	Model I (ordered probit, dependent variable is bank use)	Model II (probit truncated, dependent variable is bank use)
Number of obs.	46813	41673
LR χ² (40)	4180.7	4020
Prob. > χ²	0.000	0.000
Pseudo R²	0.0613	0.113
Log likelihood	–31985.1	–15863
Y = bank use	Coef.	Coef.
Wealth	0.021 (5.48*)	0.010 (1.94)
Perception of wealth (ref.: hh is not having enough money)
Having enough money to buy basic goods	0.129 (7.13*)	0.002 (0.06)
Can buy basic goods	0.152 (6.68)*	–0.069 (–2.16*)
DK (do not know)	0.164 (2.37*)	–0.192 (–1.84)
Education level (ref.: no education)
Primary	0.005 (0.26)	–0.042 (–1.61)
Secondary	0.091 (5.5*)	0.103 (4.39*)
Senior secondary	0.143 (6.3*)	0.209 (6.72*)
Graduate	0.18 (6.77*)	0.306 (8.7*)
Financial literacy
Behaviour	0.014 (0.82)	–0.001 (–0.03)
Attitude	0.014 (0.26)	0.017 (0.24)
Knowledge	–0.067 (–1.25)	–0.036 (–0.5)
No asset	0.006 (0.27)	–0.016 (–0.51)
mobile ownership (ref.: none)
Basic	0.045 (3.13*)	0.208 (10.16) *
Feature	0.024 (1.18)	0.145 (5.01*)
Smartphone	0.196 (9.06*)	0.414 (14.35*)
Age group (ref.: 16–25)
26–35	0.061 (3.5*)	0.101 (4.02*)
36–45	0.099 (5.27*)	0.200 (7.52*)
46–55	0.108 (5.16*)	0.265 (8.99*)
55+	0.173 (8.36*)	0.422 (14.51*)
Gender (ref.: female)
Male	0.035 (2.76*)	–0.054 (–3.07*)
Financial accessibility	–0.032 (–2.31*)	–0.032 (–1.71)
Occupation: unemployed	0.118 (3.51*)	0.231 (5.18*)
Saving type
Formal savings	0.51 (38.27*)	0.386 (21.35*)
Informal savings	0.787 (40.88*)	0.848 (36.71*)
Borrower	0.053 (2.81*)	0.110 (4.37*)
Spatial inclusion	–0.046 (–3.72*)	–0.018 (–1.04)
Spatial exclusion	–0.045 (–5.58*)	0.016 (1.37)
Zone (ref.: North)
South	0.268 (14.11*)	0.748 (27.82*)
East	0.166 (9.76*)	0.368 (14.52*)
West	0.14 (7.82*)	0.391 (14.6*)
B_dist	0.22 (1.19)	–0.066 (–0.26)
A_dist	–0.093 (–0.17)	–1.718 (–2.06*)
K_dist	2.668 (5.02*)	5.793 (7.98*)
Spatial density (ref.: village class 3)
Village class 2	0.1 (5.08*)	0.057 (1.99*)
Village class 1	0.14 (6.89*)	0.135 (4.64*)
Town class 5	0.035 (1.34)	–0.081 (–2.16*)
Town class 4	0.196 (5.63*)	0.132 (2.79*)
Town class 3	0.156 (5.98*)	0.164 (4.55*)
Town class 2	0.059 (1.83)	0.099 (2.16*)
Town class 1	0.108 (3.51*)	0.032 (0.78)
/cut1	–0.496
/cut2	1.988
_constant		2.21734(–35.5*)

Source: The authors.

Note: *Denotes that the p-value of the variable is significant at 95% confidence interval.

Values in parenthesis represent Z-statistic value.

Table 4.

Variance–Covariance Matrix (VCE) Corresponding to the Parameter Estimates.

Pr(bank use = 0), predict()
Spatial density	Margin	Z-statistic
Village class 3	0.128	37.73*
Village class 2	0.110	48.15*
Village class 1	0.103	48.03*
Town class 5	0.122	32.43*
Town class 4	0.094	19.5*
Town class 3	0.100	29.84*
Town class 2	0.117	23.08*
Town class 1	0.108	24.07*

Source: The authors.

Note: *Denotes that the p-value of the variable is significant at 95% confidence interval.

Model 1

Education of an individual has a significant role in financial inclusion. As scholastic attainment increases from primary education to graduate, there is an 18% chance of increased financial inclusion in comparison to no education. Individuals belonging to the old-age cohort show a high significant impact and a 17% chance of being involved in the financial inclusion process. Male individuals are significant with 3.5% of a more likely chance of using financial services with a positive coefficient with as compared to females. Structural variable like unemployment is significant and around 12% of the unemployed are more likely to use financial services after having a bank account. Savings in formal and informal types of institutions lead to an individual’s financial inclusion, and credit is indispensable to those who find a negative mismatch between their income and expenditure, protecting the households from unexpected income shocks. Analysis reveals that individuals who have informal savings are 79% more likely to use banking services, and individuals having formal savings enhance their involvement in using financial activities by 51%, while individuals who borrow money also show significant involvement in using banks’ financial services. Region has a very important role to play. All the regions (South, East and West) with respect to North has a very significant role. With respect to the north region, the southern region is financially included with 27% more chances of using financial services.

Model 2

A measure of financial inclusion based on proportion of ‘banked’ adults, is a very important aspect of financial inclusion. Education, here too, plays a significant role, and higher education has a role in eradicating incidences of under-banking among people, as with higher education, people are 31% more likely to use their account actively in comparison to no education. People with smartphones are 41.4% more likely to use the financial services as compared to people with no mobile phones. People belonging to the higher age group (55+) are significant with 42.2% more likely to use banking services in comparison to the younger age group (16–25) and the reason for this can be saving at older age either to cater to health issues or for pension benefits. A total of 5.4% of male individuals are not likely to significantly use financial service as compared to females as a reference category. Saving, whether formal or Informal, is the reason for people to be more active users of financial services, with 39% and 85%, respectively. With reference to the northern region, the southern region has more active users of financial services, with 75% of individuals showing active behaviour towards using banking services—one reason can be financial literacy due to technological advancements, improvements in infrastructure and higher levels of education, with major changes occurring in the way of banking facilities being made available to users.

Demand for and usage of banking financial services requires some kind of capability to go for choice modelling. In this context, a rational agent’s micro-motives need to be looked at in a discreet manner. So, demand-side barriers such as an individual’s attitude, behaviour and knowledge as a separate component of financial literacy is not significant in both our models to explain the financial inclusion process, but the knowledge variable shows a negative coefficient, implying the failure of institutions by not pushing agents to use banking financial services. Agent’s attitude and knowledge are significant in our model with negative and positive coefficients, respectively, when we cluster both the variables at the district level.

Figure 1 shows state-wise score of financial literacy, which is based on attitude, behaviour and knowledge. The attitude score is based on the mean calculated from a question on how likely an individual would involve himself in maintaining a bank account, mobile phone or mobile money account. It ranges between 0 and 3. The average of behaviour score is computed as a count of the number of ‘financially savvy’ behaviours relating to budgeting, active saving, avoiding borrowing to make ends meet, choosing products, keeping watch on financial affairs, striving to achieve goals, making considered purchases or paying bills on time. It ranges between 0 and 11. The mean score of knowledge is computed as the number of correct responses of the seven questions asked in the FII questionnaire. It ranges between 0 and 8.

Figure 1.

Source: The authors.

Figure 2 reveals that Kerala has the highest number of smartphones, with women in rural areas, which implies that Kerala has an economic motive for inclusive growth. But access to mobile phones does not show any type of inclusive process till the causal effect of mobile telephony on economic outcomes is easily understood. In Table 3, to understand the role of diffusion of information and communication technologies in driving economic growth, we employ mobile phones as an explanatory variable with basic feature and smartphone users relative to users who do not use mobile phones. Basic and smartphone users have a significant impact on participating in financial inclusion process, that is, nearly 4% and 19%, respectively, in Model 1. However, the feature mobile phone–type holders do not show a significant usage of financial services.

Figure 2.

Source: The authors.

A Disaggregated Look

Spatial inclusion is a probability of financial services accessibility within 0.5 km of an individual’s residence along with agents’ demand for financial services that is clustered at district level. Spatial exclusion is a probability of lack of financial services accessibility or accessibility of financial services, which is more than 5 kms from an individual’s residence along with agents not demanding financial services clustered at the district level. Both spatial inclusion and spatial exclusion are supply-side factors. The coefficients of these two are negative and statistically significant. This means that supply-side constraint with lack of infrastructure is a factor associated with not using banking financial services. Spatial exclusion is a space where accessibility for usage of banking financial services is not available, whereas spatial inclusion is where institutional facilities are easily accessible. By incorporating spatial density variable with banking infrastructure space, we try to capture the behaviour of socially excluded population in the quest for usage of banking financial services. This is presented in Figure 3. Spatial density is a variable with dummies of different village class and tier class to visualise the movement of individuals from village class to towns (Tier 1) for using the banking services. Table 4 represents the Co-variance between bank use and spatial density. And all village class and tier class is highly significant. With the movement in spatial density, more of the socially excluded population would want to use banking services, but less accessibility to banking financial services in time and space are a hindrance in financial inclusion. Movement of individuals towards tier class 1 leads to more inclusion.

Figure 3.

Source: The authors.

When we look at the availability of financial services within 0.5 km at the district level, we observe that space has a lot to do with making the financial resource available to people. It is observed from Figure 4 that the southern zone is better at financial inclusion, which is consistent with findings from our regression analysis. It is evident in Figure 5 that financial exclusion prevails more in central parts of India, which is also justified from Figure 3, that is, probability of exclusion is more in villages and states in central parts of India. Policymakers should work towards increasing banking infrastructure, technology adoption and financial literacy. Micro-motives of agents for availing formal banking services can lead to macroeconomic goals (financial inclusion).

Figure 4.

Source: The authors.

Figure 5.

Source: The authors.

Concluding Remarks

This article confirms that the usage of formal finance does not appear to have adequately embodied in vast segments of the society due to the lack of financial literacy, micro-incentives and supply-side constraints to economic agents. Spatial dimensions of financial inclusion/exclusion process conclude that financial inclusion is subject to considerable territorial inequalities at the district level, which implies that financial inclusion is not distributed homogeneously throughout the country. It is observed that both spatial inclusion and spatial exclusion are huge and impact an agent’s decision to use banking services. On the other side, demand-side constraints like person’s attitude, behaviour and knowledge for dealing with their funds are not noteworthy enough to clarify the variety in person’s monetary consideration measure and yet the attitude coefficient sign is positive. However, knowledge variable shows negative coefficient, which infers the disappointment of establishments in boosting the demand for utilising the banking facilities or services. Technology innovation in terms of access to mobile phones and mobile money banking is very helpful in motivating individual’s demand for use of banking services, while the dispersion of innovation is not spread to the weak area of the general public, and therefore, they are financially excluded. Bank account ownership is necessary but not sufficient for usage for banking services, and hence, financial inclusion is not fully achievable. The financial inclusion is additionally determined by various components of human development like the level of education and endowment of wealth. Individuals with high education levels and wealth are ready to contribute towards financial institutions by maintaining their savings and borrowings from formal institutions, leading an individual into the mould of financial inclusion. From our viewpoint, it appears that financial exclusion is a confluence of multiple barriers: lack of access, lack of physical and social infrastructure, lack of support and lack of technology adoption, among many others. Therefore, legislative attention as an institution should work effectively by incentivising the economic agent’s micro-motives for use of formal financial services to achieve the macroeconomic goals like financial inclusion.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

Notes

ORCID iD

Rajat Singh Yadav

References

Allen

, Demirguc-Kunt

, Klapper

, and Peria

M. S. M.

2016. ‘The foundations of financial inclusion: Understanding ownership and use of formal accounts’. Journal of Financial Intermediation, 27: 1–30.

Baker

S. R.

2014, April. Debt and the Consumption Response to Household Income Shocks . Stanford, California: Economics Department Stanford University, pp. 1–46.

Beck

, Demirguc-Kunt

, Martinez

, and Peria

. 2007a. ‘Reaching Out: Access to and Use of Banking Services Across Countries’. Journal of Financial Economics 85(1): 234–66.

Beck

, Demirgüç-Kunt

, and Levine

2007b. ‘Finance, Inequality and The Poor’. Journal of Economic Growth 12: 27–49.

Bertrand

Marianne

, Mullainathan

Sendhil

, and Eldar

Shafir.

2004. ‘A Behavioural-Economics View of Poverty’. American Economic Review, American Economic Association 94(2): 419–23.

Bester

, Hougaard

, Gray

, and Saunders

2016. ‘Homefield Advantage: Learning from the Popularity of Local Financial Service Providers’. http://cenfri.org/documents/MAP/2016/Insight%20Note%203%20-%20Homefield%20advantage.pdf

Bozkurt

, KarakuŞ

, and Yildiz

2018. ‘Spatial Determinants of Financial Inclusion over Time’. Journal of International Development 30 (8): 1474–504.

Burgess

, and Pande

2005. ‘Do Rural Banks Matter? Evidence from the Indian Social Banking Experiment’. The American Economic Review 95(3): 780–95.

Carrington

M. J.

, Neville

B. A.

, and Whitwell

G. J.

2010. ‘Why Ethical Consumers Don’t Walk Their Talk: Towards a Framework for Understanding the Gap Between the Ethical Purchase Intentions and Actual Buying Behaviour of Ethically Minded Consumers’. Journal of Business Ethics 97 (1): 139–58.

10.

Carbo

, Gardener

E. P.

, and Molyneux

2005. Financial Exclusion in Europe . UK: Palgrave MacMillan.

11.

Collins

, Morduch

, Rutherford

, and Ruthven

2009. Portfolios of the Poor: How the World’s Poor live on $2 a Day . Princeton, New Jersey: Princeton University Press.

12.

Chakrabarty

K. C.

2012, November. ‘Financial Inclusion: Issues in Measurement and Analysis’ (Irving Fisher Committee Workshop on Financial Inclusion Indicators, Malaysia).

13.

Cole

, Sampson

T. A.

, and Zia

B. H.

2011. ‘Prices or Knowledge? What Drives Demand for Financial Services in Emerging Markets?’ Journal of Finance 66 (6): 1933–67.

14.

Donner

, and Tellez

A. C.

2008. ‘Mobile Banking and Economic Development: Linking Adoption, Impact, and Use’. Asian Journal of Communication 18: 318–22.

15.

FinMark Trust. 2018. Impact evaluation of savings groups and Stokvels in South Africa: The economic and social value of group-based financial inclusion. FinMark Trust Report, pp. 1–103.

16.

J. C.

, Lee

S. C.

, and Suh

Y. H.

2009. ‘Determinants of Behavioral Intention to Mobile Banking’. Expert Systems with Applications, 36(9): 11605–11616.

17.

Han

, and Melecký

‘Financial Inclusion for Financial Stability: Access to Bank Deposits and the Growth of Deposits in the Global Financial Crisis’. Policy Research Working Paper no. WPS 6577, Washington, D.C., World Bank Group, 2013.

18.

Herispon

2019. ‘The Effect of Financial Inclusion and Banking Behavior on Household Debt Behavior’. JEBI (Jurnal Ekonomi dan Bisnis Islam) 4 (1): 51–64.

19.

Kim

Y. K.

, Setterfield

, and Mei

2014. ‘A Theory of Aggregate Consumption’. European Journal of Economics and Economic Policies: Intervention 11 (1): 31.

20.

Kumar

, and Saini

V. K.

2020. ‘National Strategy for Financial Education in India’. Himalayan Journal of Social Sciences and Humanities 15: 31–36.

21.

Leyshon

, and Nigel

Thrift

1995. ‘Geographies of Financial Exclusion: Financial Abandonment in Britain and the United States’. Transactions of the Institute of British Geographers 20 (3): 312–41.

22.

Lusardi

, and Mitchell

O. S.

2011. ‘Financial Literacy and Planning: Implications for Retirement Well-being’. In Financial Literacy: Implications for Retirement Security and the Financial Marketplace , edited by Mitchell

O. M.

, and Lusardi

, 17–39, Oxford and New York: Oxford University Press.

23.

Luarn

, and Lin

H. H.

2005. ‘Toward an Understanding of the Behavioural Intention to Use Mobile Banking’. Computers in Human Behaviour 21: 873–91.

24.

Mahoney

1999. ‘Nominal, Ordinal, and Narrative Appraisal in Macro causal Analysis’. American Journal of Sociology 104 (4): 1154–96.

25.

Mutezo

2014. ‘Household Debt and Consumption Spending in South Africa: An ARDL-Bounds Testing Approach’. Banks & bank systems , 9 ( 4 ), 73–81.

26.

OECD. 2011. Improving financial literacy: Analysis of issues and policies. OECD Publishing. https://doi.org/10.1787/9789264012578-en.

27.

OECD. 2005. Bank of Italy symposium on financial literacy: Improving financial education efficiencys. OECD Publishing.

28.

Rangarajan Committee. 2008. Report of the Committee on Financial Inclusion . Government of India.

29.

Satyasai

K. J. S.

, and Kumar

2020. NAFINDEX: Measure of Financial Inclusion based on NABARD All India Rural Financial Inclusion Survey (NAFIS) Data . Rural Pulse.

30.

Sen

Amartya.

1989. ‘Development as Capability Expansion’. Journal of Development Planning 19: 41–58.

31.

Sinclair

S. P.

2001. ‘Financial Exclusion: An Introductory Survey’. Report of Centre for Research in Socially Inclusive Services, Heriot-Watt University, Edinburgh.

32.

Stein

M. R.

1964. ‘The Eclipse of Community: Some Glances at the Education of a Sociologist’. In Reflections on Community Studies , edited by Vidich

Arthur J.

, Bensman

Joseph

, and Stein

Maurice R.

. New York: John Wiley.

33.

Van Doom

J. A. A.

1955. ‘Wijk en stad; reële integratiekaders?’ (Thorn, J. V. (1955, December). Neighborhood and city: Real integration frameworks. In Prae Opinions for the congress on social cohesion in new urban neighborhoods (Vol. 17). Amsterdam: ISON

34.

Webber

M. M.

1963. ‘Order in Diversity: Communities Without Propinquity’. In Cities and Space , edited by Wingo

Jr. , 23–54. Baltimore: John Hopkins Press.

35.

Uddin

A. A.

, Chowdhury

M. A. F.

, and Islam

2017. ‘Determinants of Financial Inclusion in Bangladesh: Dynamic GMM and Quantile Regression Approach’. Journal of Developing Areas 51 (2): 221–37.

36.

World Bank. 2017. Global Findex Database . Washington DC: The World Bank.

37.

Zwiers

, Bolt

, Van Ham

, and Van Kempen

2016. ‘The global financial crisis and neighborhood decline’. Urban Geography, 37(5): 664–684.