Financial Inclusion—A Study of Informal Workers from Delhi NCR

Abstract

Financial inclusion is a process of providing financial services to everyone at an affordable cost, particularly to weaker sections and low-income groups. The study analyses the level of awareness, usage of banking services and other financial benefits and explores the hindrances to financial inclusion faced by the informal workers in Delhi National Capital Region (NCR). An empirical survey is done, and 760 responses are evaluated to understand the level of financial inclusion using correlation, logit regression analysis, etc. Findings of the study indicate that level of financial awareness and inclusion increase with age, education and income for fixed salary workers. Major hindrances to financial inclusion are lack of savings for using financial services, lack of necessary documentation and lack of trust in the formal banking system. Therefore, the government needs to make more efforts to reach out to the urban poor and informal workers for all-around inclusiveness.

JEL Codes: D14, E26, G2, J46, O1

Keywords

Financial awareness financial inclusion financial institutions financial services India logit regression Pradhan Mantri Jan Dhan Yojana

I. Introduction

The world continues to urbanise. Though urbanisation around the globe is accelerating at an unparalleled degree, urban poverty is also on the rise (Claessens, 2006; United Nations, 2019). Urban poverty in most of the developing countries is considered a spill-over of rural poverty. The governments in these countries primarily focus upon introducing public and economic welfare policies for the underprivileged sections of the society (Beck et al., 2006; Varghese & Roosevelt, 2018). Urban poverty is visible in megacities due to influx of migrant labour searching for better livelihood opportunities and often settling down in slums, earning less due to performing low-skilled jobs. Majority of the informal workers are migrants with low literacy rate, ending up doing low-skilled or unskilled jobs like daily labour, domestic helpers, security guards, gardeners, sweepers, etc., thus earning very low wages. Hence, informal sector workers refrain themselves from associating with the formal banking system due to lack of knowledge, perceived bottlenecks and easy access to informal financial services.

Many countries have taken significant initiatives for financial inclusion, such as promoting opening of bank accounts, encouraging investment in insurance schemes, retirement funds and facilitating credit for better health facilities, education and business (Demirgüç-Kunt et al., 2017; Demirgüç-Kunt & Klapper, 2013; Singh et al., 2014). Financial inclusion refers to ‘universal access to a wide range of financial services at a reasonable cost. These include not only banking products but also other financial services such as insurance and equity products’ (The Committee on Financial Sector Reforms with Chairman Dr Raghuram G. Rajan) (Chakravarty & Pal, 2013; Financial Inclusion Advisory Committee, 2020; Kumar, 2013). Financial inclusion has gained momentum in the recent past globally. As per the Global Findex Database (GFD) 2017, 1.2 billion adults have enrolled themselves in the formal banking system since 2011. India has witnessed similar growth trends in the number of bank accounts between the years 2014 and 2017 (almost 83% had a bank account as per GFD), which was achieved with the initiatives of the Union Government through the Pradhan Mantri Jan Dhan Yojana (PMJDY) in 2014. This was followed by Pradhan Mantri Jan Suraksha scheme in 2015, which provided access to various financial services like savings bank accounts, credit, insurance and pension facilities.

World Bank in the report 2017 observed that India, despite having relatively high account ownership, still has a large share of the global unbanked population because of its sheer size (50% had inactive accounts). The reason for this might be lack of implementation of the PMJDY scheme, which had led to increase in account ownership.

Present study is an attempt to examine financial inclusion among the informal workers in Delhi National Capital Region (NCR) and explore the level of awareness and usage of banking services and other financial benefits like insurance, pension and direct benefit transfer (DBT) schemes, as well as hindrances in adopting formal financial services. Study is organised in six sections including introduction, literature review, research methodology, findings, comparison with GFD 2017 and conclusions and limitations.

II. Literature Review

Financial inclusion is not a new concept; however, it has now become a subject of intense research. A strand of literature has taken into consideration the significant role of financial inclusion. Inclusive financing is referred to as element of inclusive economic growth and development, through promotion of saving bank or utility financial institutions (Peachey & Roe, 2006). An inclusive, well-operational financial system boosts economic development and financial growth by reducing the income gap and poverty (Beck et al., 2006), which enhances economic growth and progress of financial markets. Expansion of the banking sector and inclusive financial system are key to economic growth and reduction of poverty (Bergeijk et al., 2019; Chen & Yuan, 2021). To encourage the social and economic well-being of the underprivileged, governments in developing economies endorse affordable finance (Neaime & Gaysset, 2018; Sarma & Pais, 2008).

Studies on Financial Access, Awareness, Usage and Availability

With more and better access to financial services, both households and individuals in the developing nations can manage the cost of credit, minimise risk, support future investment and expenditure (Loukoianova et al., 2018). Rural and urban poor can have admittance to the healthcare and education sector through better financial access (Allen et al., 2016).

The major thrust for financial inclusion in India can be observed in 2005 when the Reserve Bank of India (RBI) highlighted the need for banks to reach out to the masses and adopted a planned and structured approach by implementing policies to promote financial inclusion (Bapat & Bhattacharyay, 2016). Empirically, with the increase in financial access, the poor household will have better savings, payments and credit payments (Caskey et al., 2006; Dupas & Robinson, 2013).

Eased access to formal finance and credit services increases employment, education and credit to households, ultimately smoothing consumption, saving and investment among the poor (Caire & Becker, 1967). The enhanced financial service access boosts economic growth and development through an increase in savings, penetration of credit (Beck et al., 2006; Gupte et al., 2012; Kim & Kim, 2016), smooth flow of credit and investment (Dabla-Norris et al., 2015).

Based on a plethora of research in this area, it can be concluded that studies on financial inclusion broadly cover three factors: Financial access, financial awareness and usage of financial products. The three components are associated with the supply-side and demand-side factors. The penetration of the banking and allied activities in far distance geographic areas, access to financial products and services are influenced by supply-side factors (Allen et al., 2016; Han & Melecky, 2013; Hannig & Jansen, 2011). Financial awareness or financial literacy, when seen through the demand perspective, means the ability of an individual to make sound financial decisions (Atkiinson & Messy, 2013). Whereas financial usage confers to both demand and supply-side factors, reflecting the financial reachability and efficiency of the financial system (Fungacova & Weill, 2014).

Studies on Financial Inclusion in India

The Government initiatives like ‘Jan Dhan Yojana’, ‘Aadhaar’ and ‘Mobile phone connectivity’ referred to as JAM, helped regulate the transactions and deliver benefits to the poor (Financial Inclusion Advisory Committee, 2020; Paramasivan & Arunkumar, 2018; Paul & Silambarasan, 2020). The implementation and increasing awareness over time regarding the various schemes provided under PMJDY can impact the financial tripod (Joshi & Rajpurohit, 2016). Initiatives like PMJDY and increase in the number of bank branches in rural areas and credit deposit ratio have a significant and positive impact on the gross domestic product (GDP) of an economy, indicating that better access and usage to financial services promotes the development of an economy (Iqbal & Sami, 2017; Kumar & Chauhan, 2015).

The reach and usage of PMJDY accounts and RuPay card are more popular among rural and poor women despite not having formal education (Singh & Naik, 2018). Vernacular media has helped in achieving a higher level of financial literacy, whereas technological upgradations like voice notes in local languages help rural poor to utilise the ATMs to withdraw and deposit money (Bhatia & Chatterjee, 2010; Gupta, 2011; Nair & Tankha, 2015; Paramasivan & Arunkumar, 2018).

Studies on Barriers to Financial Inclusion

The poor and the disadvantaged population face many demand and supply-side barriers in accessing formal financial services and products (Stiglitz & Weiss, 1981). Beck et al. (2006) analysed various indicators capturing barriers to usage of banking services on reachability and affordability related to deposits, payments and fund disbursements. Claessens (2006) in his study states that developing nations have a high risk of missing ‘targeted groups’, that is, lack of financial inclusion due to poor institutional infrastructure, lack of competition, controlled markets and lack of technological development. Boateng (2018) listed hindrances to financial inclusion as ‘distance to bank’, restrictive regulations, lack of financial infrastructure and governance failure. Various programmes launched by the government like Digital India, Priority Sector Lending, mobile banking, etc., are monitored through continuous training programmes and financial awareness drives conducted by banks and other financial institutions (Ambarkhane et al., 2016; Shekhar, 2018).

Taking the directions from these studies, the objectives for the present study have been formulated in the following section.

III. Objectives and Methodology

Research Objectives

To assess awareness and actual usage of banking, insurance and pension facilities.

To study the relationship of socio-demographic attributes with respect to financial inclusion and financial awareness, respectively.

To study the impact of awareness about government schemes on financial inclusion.

To find out major hurdles to financial inclusion.

Sample Selection and Data Collection

As per the Census Report 2011, Ministry of Home Affairs, GOI, (Ministry of Labour & Employment, n.d.), more than 20 lakh people have migrated to NCT of Delhi for the purpose of work, employment and business. In the coming decade, 40% of the Indian population will reside in urban settlements, and 66% of them will come under the category of urban poor (Masika et al., 2015). Urban poor occupy less than one-fifth of the total land taken up by residential development in urban areas of Delhi, which include the slum settlements, resettlement colonies, etc. Delhi, being the capital city, attracts a huge inflow of migrants who come in search of better livelihood. Hence, there is a need to look at the extent of financial inclusion of urban poor, which will help to understand their needs and design appropriate schemes for them. Among informal workers, different groups have different needs in terms of financial services depending upon their employment. Therefore, in the present study, the sample of informal workers¹ has been selected from Delhi NCR, divided into six categories: (a) Daily wagers and rickshaw pullers, (b) drivers and delivery boys, (c) domestic helpers, (d) security guards and gardeners, (e) sweepers and (vi) street vendors (vegetable and fruit vendors). Convenience sampling has been used to collect the data. Eight hundred respondents were approached across Delhi NCR out of which 760 responses were received.

Research Methodology

The study evaluates the level of awareness and usage of financial products and services, followed by investigating the influence of socio-demographic traits of the individuals on financial inclusion, using simple statistical tools like percentage analysis, ‘Pearson correlation’ and ‘logit regression’.

Logit regression is used to analyse the influence of socio-demographic characteristics on financial inclusion and financial awareness. Binary dependent variables are regressed upon independent categorical or dummy variables like age, income, occupation and education level. Financial inclusion means whether the individual holds the bank account or not, where, 1 indicates having a bank account and 0 indicates not having a bank account (Sarma, 2012, 2015). Financial awareness variable is the composite variable, derived from the level of awareness about the different schemes² associated with the zero balance, no frill or PMJDY accounts, where 1 represents awareness about at least one scheme and 0 indicates unawareness about any scheme.

The logit model is based on Bernoulli distribution to model ‘dichotomous variable or binary variable’, and the transformation is defined in Eq. (1) as:

F (x^{'} β) = Δ (x^{'} β) [1 - Δ (x^{'} β)] β_{j} = \frac{e^{x^{'} β}}{{(1 + e^{x^{'} β})}^{2}} β_{j}

(1)

The logit function, F( $x^{'} β$ ), is the cumulative distribution function of the logistic distribution, and j refers to the jth independent variable. The following equations were analysed to see the relationship between socio-demographic attributes, financial inclusion and financial awareness.

{FA}_{i} = β_{i} + β_{1 i} (A_{i}) + β_{2 i} (E_{i}) + β_{3 i} ({MSE}_{i}) + β_{4 i} (I_{i}) + β_{5 i} ({EM}_{i}) + β_{7 i} ({FI}_{i}) + ξ_{i}

(2)

{FI}_{i} = β_{i} + β_{1 i} (A_{i}) + β_{2 i} (E_{i}) + β_{3 i} ({MSE}_{i}) + β_{4 i} (I_{i}) + β_{5 i} ({EM}_{i}) + β_{6 i} ({FA}_{i}) + β_{8 i} ({DBT}_{i}) + β_{9 i} ({IS}_{i}) + β_{10 i} ({PB}_{i}) + β_{11 i} ({PMJDY}_{i}) + ξ_{i}

(3)

Where in Eqs. (2) and (3), ${FI}_{i}$ represents financial inclusion, ${FA}_{i}$ represents financial awareness among the respondent i $, β_{i}$ , $β_{1 i}$ , $β_{2 i}$ , $β_{3 i}$ , $β_{4 i}$ , $β_{5 i}$ , $β_{6 i}$ , $β_{7 i}$ , $β_{8 i}$ , $β_{9 i}$ , $β_{10 i}$ and $β_{11 i}$ represent the constant coefficient, coefficient for age, education, monthly wage/salary earners, income, number of earning members in the family, financial awareness, financial inclusion, awareness about PMJDY, awareness about insurance schemes, awareness about pension benefit and awareness about DBT respectively. $ξ_{i}$ represents the error component in regression equation.

The interpretation of coefficients is done using the odds ratio, that is, ratio of the success over failure. Odds ratio is used to predict the probability of an event occurring based on a one-unit change in an independent variable when all other independent variables are kept constant. Further, goodness-of-fit is measured using Hosmer–Lemeshow (H&L) statistics. The significance value more than 0.05 for the H&L statistic indicates good fit, which means that the model sufficiently depicts the data.

IV. Findings and Analysis

Socio-demographic Analysis

The analysis of socio-demographics revealed some important observations for usage of financial products. First, our sample consists of 73.2% males and 26.8% females. Second, 34.5% of the respondents are aged between 25 and 34 years, 28.9% are between 35 and 44 years and 15.5% are between 45 and 54 years, representing millennials in majority. Third, with respect to education, 34.7% respondents have a primary degree, followed by secondary degree (19.7%), senior secondary degree (15.3%) and 25.8% respondents with no formal education. Fourth, 20.5% of participants are house helpers followed by daily wagers/rickshaw pullers (20.0%), vendors (18.9%) and the remaining 40.6% represent drivers/delivery boys, sweepers and security guards/gardeners.³ Fifth, 55.8% and 25% of respondents fall under the income group of ₹50,000–120,000 and 120,000–250,000 respectively. Sixth, the majority of the respondents have 4–6 members in the family (57.9%), 1–2 earning members (79%) and 1–2 school going children (60.3%).

Assessing Financial Awareness and Actual Usage of Banking, Insurance and Pension Facilities

This objective primarily focuses on the analysis of the users and beneficiaries of banking, insurance and pension schemes associated with basic savings bank deposit accounts. Out of 760 respondents, 73.9% have a bank account. Seventy-five per cent of people having bank account use their account on regular basis, and 52% use on monthly basis, with 47% of them have RuPay debit cards. Only 16.58% opened accounts under the PMJDY scheme, out of which 59% have RuPay/debit card. Among the RuPay card holders, 45.71% use their card, and 45.31% of them use it on a monthly basis. Among the 562 account holders, only 9.3% have taken a loan from a bank for personal needs. Fifty-five percent save for the future needs, out of which 69.86% save in formal financial services or banking instruments. Only 12.18% respondents make their bill payments through a bank account. It is further observed that 63.68% respondents are aware about PMJDY, 53.95% about DBT, whereas only 26.1% and 18.9% of respondents are aware about insurance policies [Pradhan Mantri Jeewan Jyoti Bima Yojana (PMJJBY), Pradhan Mantri Suraksha Bima Yojana (PMSBY)] and pension benefit [Atal Pension Yojana (APY)] respectively.

Further, there is a need to understand if the 562 respondents having bank accounts are the actual beneficiaries of the schemes launched by the government in relation to DBTs, insurance schemes and pension policies. However, there are 198 unbanked respondents who may be aware but have not received any benefit.

Results reported in Table 1 indicate that 50% of respondents received financial assistance through DBT. Almost 50% of the respondents have received under two schemes Sarva Shiksha Abhiyaan and Pradhan Mantri Ujjwala Yojana. But looking towards pension and insurance schemes linked to these accounts, only 10.68% and 17.43% have been actual beneficiaries of these schemes respectively. There is a huge gap in this area which the policymakers need to focus upon, so that the underprivileged segment gets all kinds of financial benefits.

Table 1.

Actual Benefits Received by the Respondents Having the Bank Accounts.

What Benefits Have You Received?	Response	Percentage
Direct benefit transfer	Yes	68.68%
Direct benefit transfer	No	31.32%
Pension benefit	Yes	10.68%
Pension benefit	No	89.32%
Insurance benefit	Yes	17.43%
Insurance benefit	No	82.57%

Source: ‘Field Survey Data by Authors’.

Evaluating the Relationship Between Socio-demographic Attributes, Financial Inclusion and Financial Awareness

The relationship between socio-demographic attributes such as age, education, occupation and income and opening of bank accounts shows a significant positive correlation with p value < .001 (Table 2). The finding indicates that with increase of age, education, occupation and income, opening of bank accounts also increases (Bapat & Bhattacharyay, 2016). Also, positive and significant correlation is observed between regular usage of bank accounts and education of the respondents. However, a significant negative correlation is observed with occupation. No significant relationship is observed between debit card use and demographic variables like age, occupation and income.

Table 2.

Assessing Correlation Between Socio-demographic Attributes with Respect to Actual Usage and Awareness of Financial Services.

Variables and Correlation		Age	Education	Occupation	Income
Do you have a bank account?	Pearson correlation	0.145^**	0.232^**	0.101^**	0.240^**
Do you have a bank account?	Sig. (2-tailed)	0.000	0.000	0.005	0.000
Do you use bank account on a regular basis?	Pearson correlation	0.004	0.140^**	–0.112^**	0.009
Do you use bank account on a regular basis?	Sig. (2-tailed)	0.923	0.001	0.008	0.836
Have you used your ATM/debit card or mobile phone for payment during any online purchases?	Pearson correlation	0.034	0.180^**	–0.071	0.039
	Sig. (2-tailed)	0.342	0.000	0.051	0.278
Are you aware about DBT?	Pearson correlation	0.190^**	0.071	–0.014	–0.089^*
Are you aware about DBT?	Sig. (2-tailed)	0.000	0.050	0.710	0.014
Are you aware about Pradhan Mantri Jeevan Jyoti Bima Yojana (PMJJBY)/Pradhan Mantri Suraksha Bima Yojana (PMSBY)?	Pearson correlation	0.140^**	0.141^**	0.021	–0.021
	Sig. (2-tailed)	0.000	0.000	0.556	0.561
Are you aware about APY?	Pearson correlation	0.042	0.287^**	–0.168^**	0.094^**
Are you aware about APY?	Sig. (2-tailed)	0.249	0.000	0.000	0.010

Source: ‘Field Survey Data by Authors’.

Notes: *Correlation is significant at the 0.05 level (2-tailed); **correlation is significant at the 0.01 level (2-tailed). APY, Atal Pension Yojana; DBT, direct benefit transfer.

Table 2 also summarises the correlation between financial awareness and demographic characteristics. Age has significant positive correlation with awareness of benefits under DBT (r = 0.190, p value < .001) and awareness of insurance benefit (r = 0.140, p value < .001). Qualification has a significant impact on awareness of insurance benefit (r = 0.141, p value < .001) and awareness of pension benefit (p value < .001), which signify that with the increase in the level of qualification, awareness of insurance and pension benefits also increases (Allen et al., 2016; Fungacova & Weill, 2014; Zins & Weill, 2016). There is a low but significant correlation observed between income and awareness of APY (r = 0.094, p value = .01). Similarly, a significant relationship between occupation and financial awareness can be seen. As the respondents in the study are informal workers and usually paid in cash, this could be the reason for the lack of awareness and inclusion.

It is very important to assess the impact of socio-demographic variables on financial inclusion and awareness. To evaluate the same, logit regression is used to find out which socio-demographic attributes increases the likelihood of being more aware and inclusive in the formal financial system. In the study, 80% of the respondents are aware of at least one of the government’s financial schemes; however, 20% of the respondents are completely unaware about any of the government schemes.

Model 1 in Table 3 shows positive and significant impact of age, income and education on financial awareness, but the H&L chi² goodness-of-fit test fails for this model. Therefore, in Model 2, an additional variable of financial inclusion is added. The results indicate that with the increase in age and education, the likelihood of being financially aware increases by 1.35 times and 2.05 times respectively. The respondents who have a bank account are five times more financially aware than those who do not have a bank account. Respondents with fixed monthly wage/salary are more aware about schemes like PMJDY, DBT, pension and insurance schemes. Also, with the increase in the number of earning members in the family, financial awareness increases.

Table 3.

Assessing the Impact of Socio-demographic Attributes on Financial Awareness.

Financial Awareness	Model 1		Model 2
Financial Awareness	Odds Ratio	SE	Odds Ratio	SE
Age	1.459***	0.130	1.349***	0.123
Income	1.297***	0.193	0.794	0.119
Education	2.305***	0.473	2.054***	0.451
Monthly wage/salary earners	0.869	0.50	1.427*	0.301
Number of earning members in the family	0.774	0.128	0.736*	0.128
Financial inclusion	–	–	5.801***	1.245
Pseudo R²	0.06		0.156
Hosmer–Lemeshow chi²	29.21**		11.28
Number of observations			760

Source: ‘Field Survey Data by Authors’. *Variable is significant at the 0.10 level; **variable is significant at the 0.05 level; ***variable is significant at the 0.01 level. SE, standard error.

In Table 4, Model 3 indicates that age, income and education (p value < .001) add significantly to the model/prediction, suggesting that the three variables have significant influence on financial inclusion, same has been observed by Bapat and Bhattacharya (2016). The odds ratio signifies that with the increase in age, the likelihood of opening a bank account increases by 1.48 times. Similarly, with the increase in income and education, likelihood of bank account opening increases by 2.17 times and 2.92 times respectively.

Table 4.

Assessing the Relationship Between Financial Inclusion, Socio-demographic Attributes and Financial Awareness About Different Government Schemes.

Financial Inclusion		Model 3		Model 4		Model 5
Financial Inclusion		Odds Ratio	SE	Odds Ratio	SE	Odds Ratio	SE
Socio-demographic variables	Age	1.488***	0.128	1.377***	0.123	1.297***	0.122
	Income	2.174***	0.318	2.211***	0.346	2.423***	0.408
	Education	2.916***	0.577	2.386***	0.511	2.064***	0.467
	Monthly wage/salary earners	1.076	0.058	1.153***	0.068	1.189***	0.077
	Number of earning members in the family	1.101	0.194	1.17	0.209	1.364*	0.262
Financial awareness	Awareness about PMJDY	–	–	–	–	3.928***	0.818
	Awareness about insurance schemes	–	–	–	–	3.018***	0.533
	Awareness about pension schemes	–	–	–	–	4.038***	0.575
	Awareness about DBT	–	–	–	–	2.381***	0.507
	Financial awareness	–	–	6.206***	1.342	–	–
Pseudo R²		0.11		0.19		0.27
Goodness-of-fit test	Hosmer–Lemeshow chi²	13.34		9.23		12.04 (0.17)
Number of observations		760		760		760

Source: ‘Field Survey Data by Authors’. *Variable is significant at the 0.10 level; ***variable is significant at the 0.01 level. DBT, direct benefit transfer; PMJDY, Pradhan Mantri Jan Dhan Yojana; SE, standard error.

Model 4 captures the overall impact of financial awareness; another variable is used along with socio-demographic attributes. It is noticed that financial awareness and fixed monthly wages and salaries, along with age, education and income are highly significant. This implies that if people are aware about how to save, invest and manage finance in different financial products and services, the likelihood of them being part of a formal financial system increases by six times. Similarly, a positive and a significant impact of increase in age, education and income can be noticed on financial inclusion. A study for Africa by Zins and Weill (2016) also found that financial awareness/literacy increases financial inclusion.

Model 5 examines the influence of socio-demographic attributes and awareness about PMJDY DBT, insurance and pension schemes on likelihood of having the bank account. The odds ratio reported in Table 4 signifies that with the increase in awareness of DBT, insurance schemes and pension benefits, the likelihood of opening a bank account increases by 2.48 times, 3 times and 4 times respectively. Further, awareness about PMJDY significantly increases the likelihood of opening a bank account by 3.93 times. Also, in comparison to variable wage earners, the respondents who have fixed monthly wage or salary are 1.2 times more likely to open a bank account. The other socio-demographic attributes are also having a positive and significant influence on financial inclusion.

Assessing Hindrances to Financial Inclusion

In Table 5, it is observed that the majority of the respondents do not have savings to use financial services (47.47%), as their earnings are bare minimum to meet basic needs. Other reasons for financial exclusion are lack of necessary documentation, lack of trust in financial institutions, family members already having a bank account, etc.

Table 5.

Hindrances to Financial Inclusion.

Reasons for not Having Bank AC	Frequency (N)	Percentage (%)
Financial institutions are too far away	4	2.02
Financial services are too expensive	6	3.03
Lack necessary documentation (identity card, wage slip, etc.)	35	17.68
A family member already having a bank account	20	10.10
Do not require formal financial services	9	4.55
No savings to use financial services	94	47.47
Religious reasons	0	0.00
Lack of trust in financial institutions	30	15.15
Total	198	100.0

Source: ‘Field Survey Data by Authors’.

V. Discussion

GFD accounts for financial inclusion globally and measures how the adults save, borrow, pay and manage risk across different countries. The database is updated every 3 years and as per the previous Findex Report in 2017, there is a great rise in the number of adults who have an account with banks and other financial institutions or have registered through mobile applications for financial transactions. Also, digitisation is helping the economies to achieve financial inclusion targets. The GFD has been compared to study results in Table 6.

Table 6.

Comparative Statement of Global Findex Results vis-a-vis Study Results.

Variables	2011	2014	2017	Study Results
Having a bank account
Account (% age 15+)	35%	53%	80%	74%
Account, young adults (% ages 15–24)	27%	43%	71%	53%
Account, older adults (% ages 25+)	38%	57%	83%	77%
Saved any money in the past year (% age 15+)		38%	34%	55%
Saved at a financial institution (% age 15+)	12%	14%	20%	70%
Saved at a financial institution, young adults (% age 15–24)	8%	10%	13%	68%
Saved at a financial institution, older adults (% age 25+)	13%	16%	22%	70%
Debit card ownership (% age 15+)	8%	22%	33%	17%
Borrowed from a financial institution (% age 15+)	8%	6%	7%	6%
Borrowed from a financial institution, young adults (% age 15–24)	4%	4%	5%	17%
Borrowed from a financial institution, older adults (% age 25+)	9%	7%	7%	5%
Borrowed from family or friends (% age 15+)	20%	32%	33%	65%
Borrowed from family or friends, young adults (% age 15–24)	17%	27%	28%	7%
Borrowed from family or friends, older adults (% age 25+)	21%	34%	34%	60%
Paid utility bills: Using an account (% age 15+)		3%	7%	9%
Paid utility bills: Using a mobile phone (% age 15+)		0%	1%	7%
Paid utility bills: Using cash only (% age 15+)		36%	28%	56%
Received government transfers in the past year (% age 15+)		10%	8%	54%
Received government transfers in the past year, young adults (% age 15–24)		11%	7%	9%
Received government transfers in the past year, older adults (% age 25+)		9%	9%	56%
Received government transfers in the past year, primary education or less (% age 15+)		11%	9%	58%
Received government transfers in the past year, secondary education or more (% age 15+)		9%	7%	42%
Used a mobile phone or the internet to check account balance in the past year (% age 15+)			7%	46%
Used the internet to pay bills in the past year (% age 15+)			3%	9%
No. account because financial institutions are too far away (% age 15+)			5%	2%
No. account because financial services are too expensive (% age 15+)			6%	4%
No. account because of lack of necessary documentation (% age 15+)			5%	16%
No. account because of lack of trust in financial institutions (% age 15+)			4%	16%
No account because of religious reasons (% age 15+)			1%	0%
No. account because of insufficient funds (% age 15+)			11%	41%
No. account because someone in the family has an account (% age 15+)			11%	11%
No. account because of no need for financial services only (% age 15+)			0%	9%

Source: ‘Global Findex 2017 and Authors’ Compilation’.

Our results show that 77% out of the total 760 respondents have a bank account, whereas GFD reports 83% with bank account. Despite having a bank account, the respondents do not borrow money through formalised channels. Only 5% as per study estimates and 7% as per GFD borrow annually, but they prefer borrowing from their friends and family. As per the study, 65% of the of the sample and as per GFD 33% of them borrow through informal channels. To pay the utility bills, 56% of the respondents prefer cash than bank transfers. Almost 70% of the respondents who have a bank account keep savings in these accounts. As per GFD, 66% of the Indian population who have inactive bank accounts use mobile phones to make payments and check balances online but the study estimates this figure to be at 46%.

VI. Conclusions and Recommendations

Financial inclusion, a global initiative for providing universal access to appropriate financial products and services for the underprivileged, has recaptured attention of many researchers and policymakers worldwide. The present study is an attempt to explore and understand the level of awareness and usage of bank accounts and other financial products (particularly insurance, pension and direct benefits transfers) associated with PMJDY accounts, the zero balance accounts and no-frill accounts. The findings, based on the sample of 760 informal workers taken from Delhi NCR, suggest that an economically active population of age group 25–55 years are the major users of the formal banking system and hold bank accounts. Nearly half of the respondents use their bank accounts monthly. Majority of the respondents are aware about PMJDY and DBT, but there is a noticeable gap in the awareness for insurance and pension schemes. It can be concluded that increase in age, income, education along with fixed monthly wage/salary leads to increase in financial awareness and financial inclusion. Also, awareness and knowledge about different financial products and services increases financial inclusion. The logit model results also indicate that with the increase in knowledge and awareness about different financial schemes and products, the likelihood of opening a bank account increases significantly. Further, comparison of study results with GFD report are somewhat similar.

The study concludes that some of the deterrents for financial exclusion are lack of funds, lack of necessary documentation, general distrust of banks, etc. There is a huge gap between awareness and usage of banking facilities and government schemes. Despite many initiatives taken by GOI, a lot needs to be achieved in terms of bringing unbanked and unserved people into the formal banking system. Government needs to make more efforts to reach out to urban poor for all-round inclusiveness. Also, financial institutions need to make an effort to promote financial literacy for insurance schemes, pension schemes and DBTs among the account holders. Documentation and paperwork should be reduced for the urban poor so that they can easily adapt to formal channels of savings and investment. Using digital technology, mobile banks and digitisation of payment systems can help reach the masses. A target-based approach can be adopted for the same.

Limitations and Future Scope of the Study

Due to limited time and resources, the data have been collected through convenience sampling. The study is based on the sample collected from Delhi NCR only, though this can be a good representative of the informal workers as the migration rate of labourers is 43%.⁴ However, for more comprehensive results, this study can be extended to other metropolitan cities also.

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

Jappanjyot Kaur Kalra

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