Background of the Study

Financial inclusion is increasingly being recognized as one of the decisive factors towards economic growth and an important tool to address the issue of equitable growth and helpful in minimizing the problem of poverty (RBI, 2019; Thomas et al., 2017). Financial inclusion is considered as one of the visionary strategies to include more people under the financial literacy umbrella which resultant in fostering economic growth and expansion of the country. It also improves the employment rate of the developing countries which contributes to the overall social and economic development of the countries. On the other hand, financial crisis is a big threat for any economy and is largely caused because of financial instability. The financial institution, credit norms and standard relaxation are the main barriers behind financial stability. In developing countries, innovative strategies for financial inclusion basically include poverty alleviation and making financial products and services accessible for the unbanked population. The widespread availability of various policies is implemented in tandem towards the path of economic growth. A financially inclusive system will be more effective in discouraging the growth of moneylenders who usually charge exorbitant finance charges and in contrast, a financially inclusive economy will promote the delivery of financial services to the people at an affordable price. In India, the initiative of financial inclusion started in the year 1956 with the nationalization of life insurance companies followed by banking sector nationalization in 1969. Since then, numerous steps have been taken up to endorse financial and banking services that primarily include opening up bank branches in the remote areas, extending finance facilities to the socially excluded section and adjusting of finance cost of loans for low income-earners (Lenka & Barik, 2018; RBI, 2019). Income, as measured by per capita GDP, is considered a significant factor explaining an economy’s progress of financial inclusion (Sharma & Pais, 2008). The banking sector is the dominant player in the Indian financial system and in the process of funds intermediation. Thus, access to formal banking services, that is, financial inclusion by the unbanked/underprivileged population is indispensable to achieve all-around economic development of a nation. To inculcate and promote the habit of savings and thrift among the rural population, the Government of India, RBI and the banking sector in a comprehensive manner have been taking up several measures to minimize the unbanked population. It is marked from the reports and literature that India has shown impressive expansion in financial inclusion initiatives. And the extent of financial inclusion has shifted from the opening of bank accounts of households to access their accounts for mobilizing deposits and delivering loans has gradually appeared as the prime objective of financial inclusion. At this backdrop, this study is an attempt to examine financial inclusion dimensions of the Indian banking sector in terms of penetration, accessibility, and usage and their impact on economic growth. The study also conducts causality analysis to examine the finance growth linkage in association with financial accessibility and economic growth.

The article is structured as per the following sections: The second section discusses the review of past studies. The third section highlights the recent policy initiatives and progress under financial inclusion. The fourth section describes the data sources and methodology adopted. The fifth section indicates the econometric model used in the study. The sixth section exhibits empirical results and discussions, the seventh section explains the implication of the study. And the eigth section indicates the concluding remarks of the study.

Review of Past Studies

Financial inclusion is a widespread phenomenon and its literature is very intense and application of financial inclusion practices is also more or less universal regardless of the development of economies. In this context, Sharma and Pais (2008) conducted a cross-country empirical analysis to examine the correlation between financial inclusion and economic development. The study focused to find out the factors such as physical infrastructure, NPAs, CAR, literacy, urbanization, and inequality that are substantially related to financial inclusion. The findings of the study reveal that per capita GDP, income inequality, adult literacy, and urbanization are some of the significant factors that explain the progress of the financial inclusion of a nation. In their research, Shafi and Reddy (2016) investigated the progress of financial inclusion in India concerning PMJDY and its impact on banking performance. The study indicated that excessive opening of zero balance account was a constraint against this scheme. The findings of the study revealed that the impact of PMJDY is high on banking performance. In her study (Sharma, 2016), attempted to find out the relationship between financial inclusion and economic growth. The author applied VAR analysis and Granger causality test, found a positive and significant relationship between the growth in GDP per capita income with three important factors of financial inclusion, namely, banking penetration, delivery of banking services and usage of banking services. The findings of the study reveal that geographic expansion and economic growth causality is bi-directional and between the number of deposits and GDP causality is unidirectional. Finally, the study concluded that the role of financial inclusion is vital for the economic growth of a nation. In a similar type of study conducted in the year 2017, Thomas et al. (2017) assessed the associations between the accessibility of financial services and economic development in SAARC countries. The study statistically found that the higher the financial access, the higher will be the income for economies. The findings of the study conclude that increased financial accessibility can lead to economic development in a country.

Later, another study (Parida, 2018) found that Jan Suraksha Schemes has emphasized the demand for insurance products as well as accelerated insurance consumption and penetration which made the scheme a vital driver of financial inclusion. The survey spreaded awareness among the respondents about the various types of insurance products available in the market. The conclusion drawn from the survey states that incomes, savings and return on investment are the factors that pushed the respondents to buy an insurance policy. However, the launching of Jan Suraksha schemes proved a successful attempt to raise the demand for Jan Suraksha policies. Another research (Lenka & Barik, 2018) was undertaken to measure financial services accessibility, availability and usage both in urban and rural India. The period selected for the study is 1991–2014. The article found that the surge in financial inclusion in urban masses is more than the rural masses of India. In another study, Lenka and Barik (2018) also investigated whether an expansion of mobile and internet services fostered accessibility of financial services or not. The results of the study reported that there is a positive association between increased mobile and internet usage and the accessibility of financial services. The study also found that the factors like income and educational levels have a positive association with financial inclusion. While factors like the size of the rural population and unemployment have a negative association with financial inclusion. In a study, Kim et al. (2018) examined the effect of financial inclusion on the economic development of Islamic countries (OIC) and found the financial inclusion positively influences the economic growth and the empirical results has shown the existence of mutual causality between each other. Recently, Barik and Sharma (2019) in their study analysed the status of financial inclusion between different groups of people in India. The study confirms that there is high growth in the penetration of financial inclusion and access to digital payment. Analysing the status of financial inclusion among different groups, the study found that the use of digital banking among the rural people, women, elderly people, and less educated people is comparatively less. On the other hand, Le et al. (2019) have examined the trends of financial inclusion, financial efficiency and sustainability of Asian countries. The results of the study reported that negative association exists between financial inclusion and financial efficiency and positive association is found between financial efficiency and financial sustainability. This result is attributed on the ground that increase in financial inclusion may lead to information asymmetries contributing to lowering financial efficiency. In their study Erlando et al. (2020) analysed the role of financial inclusion towards economic growth, alleviating poverty and reducing income inequalities in Eastern Indonesia using VAR bi-variate causality and PVAR and the resuts of the study empirically found that the relationship between financial inclusion and economic growth is positive, while negative relationship is found with poverty alleviation and income inequality. Thathsarani et al. (2021) undertook a study to examine how FI impacts the economic growth and the human development of Asian countries. The results of the study proves a short run impact of FI on economic growth and long run negative impact of FI on economic growth largely because of speed of FI are hampered by the country specific financial policies. Singh and Stakic (2020) assessed the financial inclusion status of SAARC countries’ economic growth which has shown a very low growth due to adverse position of financial inclusion in Afganistan and Pakistan. Ghosh (2020) studied the population covered below proverty line and above poverty line and found that they are facing the problem of physical visit to bank branches due to far-flung constraint. According to Dahiya and Kumar (2020), financial service acessibility and penetration are the non-influencing tools for economic development, while they suggested that converting savings to investments will foster the economic growth.

Over the past two decades, financial inclusion has been playing a significant impact in the financial sector, and thus this topic is gaining enormous research interests globally to measure the influence of financial accessibility in accelerating economic growth. However, only a few Indian studies (Sharma & Pais, 2008; Sharma, 2016) as well as foreign studies (Erlando et al., 2020; Kim et al., 2018; Le et al., 2019; Thomas et al., 2017) have been conducted to examine the linkage between financial inclusions and different aspects of economic growth and these studies suggested that the reach of financial accessibility paves the way for economic growth. On the contrary, Adedokun and Aga (2021) opined that the economic growth will accelerate financial inclusion. In that context, the purpose of this study is to analyse synergies between financial accessibility and its influence on growth of the economy. Therefore, this study contributes to the existing body of literature on this subject by fulfilling the following objectives:

• To investigate the effect of financial products and services penetration on GDP of India,

• To measure the impact of accessibility of financial products and services on GDP of India,

• To study the impact of usage of financial products and services on GDP of India.

Recent Policy Initiatives and Status of Financial Inclusion

Various innovative financial inclusion schemes and policies have gained great attention globally as its major concern is to bring maximum people under financial systems that are not accessing the banking/financial products and services. To extend banking facilities at low cost to the people of excluded sections, RBI has floated the financial inclusion policy in the year 2005–2006. In addition to RBI, other policy-making institutions, namely, NABARD, SIDBI, IRDAI and PFRDA, have also made efforts to devise suitable regulations and guidelines for strengthening financial inclusion and to deliver financial services to the unbanked sections (Parida, 2018). Some of the important measures undertaken for encouraging financial inclusion are discussed below.

Progress Under Pradhan Mantri Jan Dhan Yojana

To realize the objective of financial inclusion nation-wide, on 28 August 2014, the Prime Minister launched an ambitious scheme in the name of Pradhan Mantri Jan Dhan Yojana (PMJDY) with a target to open at least one account by the estimated 750 million unbanked households in the country under this scheme by 26 January 2015 (Parida, 2018). The policy PMJDY has developed as one of the effective and widespread initiatives to meet the target of financial inclusion by delivering banking facilities and services to all households in India. This PMJDY received ample response even from the underprivileged sections. The growth in PMJDY is presented as under.

The achievement under PMJDY is indicated in Table 1. It is evident from Table 1 that as of 19 May 2021, the number of total beneficiaries brought under the umbrella of PMJDY is 4240 million and in this regard, public sector banks are assuming a significant responsibility in pursuing the national vision of inclusive growth. As a result, these beneficiary accounts are contributing to deposit mobilization to the tune of  146,6588.8 million.

Table 1. Progress Under Pradhan Mantri Jan Dhan Yojana as on 19/05/2021.

Source: Prepared from the website of Jan Dhan Yojana (https://pmjdy.gov.in/account).

Progress Under Jan Suraksha

Besides PMJDY, on 9 May 2015, another three ambitious social security schemes, namely, Pradhan Mantri Jeevan Jyoti Bima Yojana (PMJJBY), Pradhan Mantri Suraksha Bima Yojana (PMSBY), and Atal Pension Yojana (APY) have been launched by the Prime Minister under the Jan Suraksha initiative. These Jan Suraksha schemes have been introduced with a broad objective to bring mass people, especially the poor and underprivileged section, under the umbrella of insurance services at a nominal price (Parida, 2018). The achievement of these Jan Suraksha schemes is shown as follows:

The data in Table 2 reports that within the period 4 years of launching these schemes, a good number of policies have been opened, that is, under PMJJBY, PMSBY and APY 53.2 million, 134.7 million and 58 million policies opened respectively. And total premium collected through these schemes jointly is 20,720 million.

Table 2. Performance of Jan Suraksha Scheme as of 26 March 2018.

Source: Parida (2018).

Note: *Premium amount under APY differs according to age and the amount of pension the individual chooses.

Progress Under Pradhan Mantri Mudra Yojana

Providing safe, adequate, and affordable credit facilities to the underprivileged population are some fundamental requirements for fostering financial sector growth, reducing income inequalities, and eradicating poverty. In this line, Pradhan Mantri Mudra Yojana (PMMY) is yet another flagship scheme of the Government of India to fuel up the agenda of ‘funding the unfunded’, that is, delivering credit at an affordable cost. This scheme is an attempt to bring micro-enterprises within the formal financial system and to provide affordable loans in the manner of ‘Sishu’, ‘Kishore’ and ‘Tarun’ channelized through banking institutions, non-banking institutions, and micro-finance institutions. In the financial year 2019–2020, the total sanction of loans accounted 3.37 trillion which exceeded the set target of 3.25 trillion (Micro Units Development and Refinance Agency Limited, 2020). Table 3 shows the disbursement status of the PMMY loan.

Table 3. Status of Loan Disbursement Under Pradhan Mantri Mudra Yojana.

Source: Prepared from Gupta (2021).

Under Mudra Yojana, more than 550 million new entrepreneurs have availed loans and total credit of more than 10 trillion has been sanctioned (Gupta, 2021). The category-wise performance of loan sanctioning for the financial year 2019–2020 about target vis-à-vis achievement of lending institutions is exhibited in Figure 1.

Figure 1. Target vs Achievement Performance of Pradhan Mantri Mudra Loan for the FY 2019–2020 ( in Crore).

Source: Micro Units Development and Refinance Agency Limited (2020).

The loan sanctioning target of 3.25 trillion set by the Government of India for the financial year 2019–2020 was channelized by different lending institutions, namely, banking, non-banking and micro-finance institutions according to their outreach. Considering the data from Table 3, it can be understood that though public sector banks made the highest loan sanctioning their achievement is less than the target among all other lending institutions. While if we compare the target vs. achievement, it is the private sector banks that are doing outstanding performance and show their better ability in higher credit delivery. Looking at the performance of lending institutions, namely, small finance banks, microfinance institutions, and non-banking finance companies, it can be said that all of these intuitions have performed well and their achievement is higher than the target.

Progress Under Banking Sector

The national strategy for financial inclusion is one of the broad convergence of action taken by RBI to reduce the unbanked population at the national level. Under the aegis of Financial Inclusion Advisory committee develops models as well as reviews the policies implemented to spread financial literacy amongst the priority sector. RBI has mandated a specific target of financing to thrust weaker sectors of society whose livelihood lies mostly on agriculture and micro, small and medium enterprises. RBI has set a self-target for every scheduled commercial bank, registered NBFCs, rural co-operative banks, and regional rural banks in many outlets, basic savings bank deposit accounts, the opening of bank branches, building business correspondents, issue Kisan credit cards, and so on, to make India’s vulnerable group of population accessible to financial goods and services.

From Table 4, it is observed that the business correspondents ratio compared to 2010 has increased in 2019 and 2020. On the other hand, commercial bank branches were reduced in the years 2019 and 2020. Therefore from the data, it is perceived that the central bank is more focused on building the business correspondents model to reach the untapped area in every state of India.

Table 4. Status of Financial Institutions Under Banking Sector Using Select Indicator (End-March).

Source: Prepared from RBI Annual Reports (https://m.rbi.org.in/Scripts/AnnualReportPublications.aspxId=1288).

Data Source and Research Methodology

This study extracted relevant data published by the World Bank to evaluate the development and growth status of financial inclusion based on various constraints. To examine the interconnection of financial inclusion on the economic growth of India, the study emphasized different measures and policies undertaken by banking institutions. The Indian government has floated several financial development programmes such as Pradhan Mantri Jan Dhan Yojana, Mudra Loans for small and medium enterprises, insurance facilities through Pradhan Mantri Surakhsha Bima Yojana, and pension facilities through Atal pension Scheme, issuance of RuPay card & Kisan credit cards, direct benefit transfer schemes, Aadhaar enabled schemes and unified payment interface. Therefore this research article will find the impact of these schemes in accelerating financial inclusion in India.

To measure the impact of these schemes three dimensions of financial inclusions are to be examined.

1.     Penetration of banking institutions;

2.     Accessibility of financial services and products; and

3.     Usage of the financial services and products.

To examine the penetration of the banking institution variables selected are:

• Number of deposit account per 1,000 adults

• Number of loan accounts per 1,000 adults

The number of accounts is a key factor to measure the increased awareness of the banking services among the population by considering the numbers of individuals having at least one financial product accessibility. In line with the findings of Sharma (2016), this indicator well explains the penetration of the banking services to the unbanked population. To understand the accessibility of the financial services, the variables selected are : Number of ATMs per 1,000 km; Number of ATMs per 0.1 million adults; Number of bank branches per 1,000 km; Number of bank branches per 0.1 million adults; Number of the Insurance corporation; and Number of insurance corporation per 0.1 million adults. These variables are selected in line with the findings of (Ghosh, 2012), which exhibits the geographical and demographic outreach of the financial services in the different states of India. Ghosh (2011) measured the extent of financial services within different states of India with the help of the above indicators.

Second dimension considered in the study is accessibility of financial products and services which further enhances financial development and leads to sound economic growth of the country. Opening of new bank brances, Automated Teller Machines, Point of Sale devices are some of the technics use to increase the active user and banking transactions. New bank branches is an expensive toil rather than working for digital financial services like POS, micro ATM, white label ATMs and business correpondents. POS devises with the help of business correspondents creating the new model in the Indian banking system. It is one of the most cost-efficient models in the present banking system to include the maximum number of population under the financial inclusion umbrella. Uzma and Pratihari (2019) exhibits that due to business correspondents, range of financial products increased and induced more coverage with improved quality of financial services.

Finally, the last dimension usage is measured with the help of outstanding deposits as a percentage of GDP, and outstanding loan amount as a percentage of GDP. These dimensions of usage were also employed by Noelia Cámara (2017) where they demonstrated savings and loans are formal financial products and services adaptability by an individual. The savings and investments are tools used to measure the financial stability in the economy. Imbalance in fiancial stability creates barrier in economic progess of the country.

In our study, GDP per capita is considered as a dependent variable to measure the impact of financial inclusion on the economic growth of the country. The time period chosen for the study is from 2006 to 2019 with an aim to examine the accerelation in financial inclusion on the economic growth of India since inception, that is, from year 2005, while periods after 2019 are assumed as abnormal year due to the outbreak of pandemic COVID-19 and thus excluded from the study. The data was extracted from the World Bank and IMF Financial Access Survey database. The collected data of the study has been analysed using econometric software E-views version 11.

Econometric Model

To understand the impact of financial inclusion on the economic escalation of a country, the unidirectional or bi-directional causality between the two is applied. The unrestricted vector autoregression model (VAR) is used to test the existence of interconnection between financial inclusion and economic growth. Unrestricted VAR model put forwarded by Sims (1980) is used where all the variables are considered as endogenous and also measures the lag effect on the data series. The model exhibits that the variables are prejudiced by the previous year’s value which means there is lag effect in the data series. Thus, to examine whether the supply of financial products has a significant impact on economic growth or whether the country’s economic growth influences to raise the demand of the financial products and services, we applied this model. The final desired outcome has been validated by using the VAR Granger causality/Block homogeneity Wald test (Lucas, 1988).

For the purpose of examining individual financial inclusion variables, causality relationship with GDP per capita and overall growth index is constructed to measure the level of penetration, the magnitude of access, and the usage that scale up the economic growth.

Table 5 explains the different exogenous variables considered in the study. To apply the unrestricted VAR model in the time series data, we converted time series data into stationary data series, as the time series data suffers from non-stationarity. The Augmented Dickey-Fuller test and Phillips and Perron tests are adopted to test the stationary and to obtain the optimum lag on the basis of lowest Akaike information criterion (AIC).

Table 5. Exogenous Variables.

The VAR model is constructed on past lag of itself and past lag of other variables with the error term in a linear function. The linear equation to find the influence of explanatory factors of financial inclusion of GDP per capita with two lag is:

GDP per capita t = β0 + β1GDP per capita (t–1) + β2 GDP per capita (t–2) + β3 Bank Penetration (t–1) + β4 Bank Penetration (t–2) + error term

The linear equation to find the influence of GDP per capita on the explanatory factor with two lag is:

Bank Penetration t = β0 + β1Bank Penetration (t–1) +β2 Bank Penetration (t–2) +β3GDP per capita (t–1) + β4 GDP per capita (t–2) + error term

 In this equation, t indicates the time period 1 and t–1 and t–2 represent lag1 and lag 2 relationships.

The above will be the same for the other individual variables selected to measure usage and accessibility of financial products and also to explore the causality relationship within the individual parameters.

Empirical Results and Discussion

The main objective of this article is to measure the impact of financial inclusion on the economic growth of the country. This section describes the implication of individual variables of financial inclusion and economic growth. For measuring the degree of association among the variables, descriptive statistics are computed and is indicated in Table 6.

Table 6. Descriptive Statistics.

 

Overall penetration, access, and usage covers the composite overall growth that occurred with the help of the selected variables. Unrestricted VAR model is applicable on stationary time series data and to substantiate this, unit root test with the help of ADF and PP is applied. The time-series data after the unit root test is given in Table 7. All the individual variables are tested at different level to reject the null hypothesis and to make the data series acceptable at 5% significance level. The linear equation is formed by taking GDP per capita as a dependent variable. Therefore the study will examine all the significant models assuming GDP as a dependent variable.

Table 7. Results of the Unit Test Root.

Note: Data series tested at 5% level to find the presence of unit root in the data series.

Table 8 represents the outcomes of the VAR model, regressors are the bank’s overall penetration and its dimension on the dependent variables that is GDP per capita. Model 1 demonstrates the overall bank penetration with lag 1 provides positive impact but lag 2 deliver negative appurtenances on economic growth. In the same manner number of deposit accounts per 1,000 adults in lag 1 explores positive and lag 2 deliver negative impressions on GDP. The model is fit as R-square is 0.95 and Adjusted R-square is 0.86 states that explanatory variables accurately explain the linear association between the variables. It means that increase in number of deposit account enhances the process of saving which brings financial stability. It is also an indication that large amount of liquidity in the hand of individual due to greater option of income generation. Once the process of saving increases then it will encourage better standard of living, stimulate investment, prosperity of an individual and accelerates GDP.

Table 8. Vector Auto-regression Model Between Gross Domestic Product and Penetration.

Notes: Value of t represented in parentheses.

*** and ** represent 1% and 5% significance level, respectively.

Model 2 is created on overall bank penetration based on various parameters such as the number of deposit accounts per 1,000 adults and the number of loan accounts per 1,000 adults to scrutinize the individual connectivity with the banking system. It is found that number of loan account per 1,000 adults with both lags effect have insignificant relation with GDP. That means people still rely on informal financial alternatives like shahukar, money lenders, and like, which are very expensive, but they find it more flexible and hassle free. Therefore, the existence of informal financial system is disturbing the financial stability which ultimately hampers the economic growth of the country.

Table 9 exhibits the overall accessibility of the banking products which is demonstrated by the availability of bank branches, ATMs, and Insurance corporations to each geographic unbanked area of the country concerning GDP. Model 3 shows overall accessibility of lag 2 has a negative effect on the GDP. Iqbal and Sami (2017) exhibited the same result where growth in the number of ATMs has a negative impact on the GDP of India. Model 4 shows the same results as ATMs per 0.1 million adults is an insignificant effect on GDP. Barajas et al. (2020) stated that financial literacy rate in India is around 10% to 30% of total population and also found number of ATMs have positive correlation with income. Improvement in financial accessibility with opening of new bank branches and ATMs is one of the aim of financial inclusion. However, Kapur and Reddy (2020) mentioned that banks started reducing ATMs counters due ‘cash in cash out’ model. Through micro ATMs which is operational with the help of SIM card which can save heavy investment in ATMs infrastructure. All business correspondents are carrying micro ATMs to deal with banking transaction conveniently. Moreover, ‘cash in cash out’ model is inter functionable with all the banks.

Table 9. Vector Auto-regression Model Between Gross Domestic Product and Access.

Notes: Value of t represented in parentheses.

***, ** and * represent 1%, 5% and 10% significance level, respectively.

Model 5 reveals that bank branches support the growth rate of GDP. The results explore the number of bank branches per 0.1 million adults lag 1 and lag 2 both stand significant and have a positive influence on the improvement of GDP. From the result, it is found that traditional brick and mortar with the adoption of ICT infrastructure build the trust and reliability among the individuals which help to foster economic growth as well. Through this, government is giving effort to encourage underprivileged people to enter in formal banking arena which improves the living standard of people and brings prosperity in their life.

Model 7 and model 8 focus on the insurance facilities availability among the unbanked areas and found that there is an insignificant influence on the growth of GDP. The supply of financial products like insurance is still an inconvenience for the people residing in remote areas because of low penetration and high protection gap (D’souza, 2018). It has been reported that in India, more than 170.3 million of people across the states have enrolled in the Pradhan Mantri Suraksha Bima Yojana in 2019–2020 (Madia, 2019). Ray et al. (2020) stated that insurance penetration is very low in India. Low premium insurance schemes brought by Indian government to increase penetration of insurance products but due to lack of awareness and low financial literacy, government is unable to build trust among investots to push the insurance products. Insurance products are capital consuming financial products where the main challenge is lack of capital in the hand of insurer. It is found by Ray et al. (2020) that insurers are struggling to meet essential requirement of living instead of covering health under insurance schemes.

Table 9 displays the association of usage of financial products with economic growth. Table 10 exhibits the overall usage of financial products in lag 2 is positive and has a significant effect on GDP. Outstanding deposits as a percentage of GDP in lag 2 were found to be significant but provide a downbeat result on economic growth. Deceleration of income growth due to the slowdown in deposit mobilization in the years 2016 and 2018 by 3% and 4% is the barrier behind the economic growth of the country. With the help of Pradhan Mantri Jan Dhan Yojana government so far has opened 373.4 million account in 2019 with a deposits of 1 trillion was a miniature part of the total deposits. On the other hand, the government added more benefits like RuPay card and accidental insurance to make it more attractive so to raise maintenance costs (Adhikari, 2019). Therefore, slowdown in deposit mobilization and an increased in maintenance cost affected the economic growth rate.

Table 10. Vector Auto-regression Table Between Gross Domestic Product and Usage.

Notes: Value of t represented in parentheses.

** and * represent 5% and 10% significance level, respectively.

Model 11 describes the outstanding loan as not holding significant relation with the growth rate of GDP. PTI (2021) elucidates that high percentage of outstanding loan to GDP signifies aggressive participation of banking sector in real economy whereas lower percentage shows the need of more formal credit. The study reveals insignificant impact of outstanding loan on GDP which means vulnerable section of the India’s population are availing informal mode of credit still rampant in practice to meet their credit requirement. Thus informal system of money lending has become a big challenge towards financial development.

Table 11 is the result of the VAR Granger causality test with an optimum lag by using the lowest AIC value which is evident in lag 2. There is no unidirectional and bi-directional relation that exists between overall bank penetrations with GDP. The existence of unidirectional relation between numbers of deposits accounts for GDP at a 10% significance level. Number of loan account per 1,000 adults demonstrates unidirectional causality with GDP. Credit development is the toolkit of monetary policy. Accessibility of the banking products exhibits a unidirectional relation with economic growth at a 5% significance level of. Bank branches with traditional models in various geographical outreach areas reveal that it is one of the most important dimensions which enriches economic growth. Several Insurance Corporations unveil unidirectional relation with GDP. Lastly, the usage of financial products demonstrates unidirectional relation with GDP at a 10% significance level. The economic growth of the country creates the demand for deposits as well as loans to support the household consumption of the individuals.

Table 11. VAR Granger Causality/Block Exogeneity Wald Tests (Number of Lags = 2).

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

Implication of the study

From the analysis of study, it can be evident that government schemes and initiatives have greatly helped to penetrate the banking products among the unbanked population. PMJDY is one of the landmark initiatives that has pushed the people to enter under formal financial system. Innovative financial products are largely encouraging to save earnings of unbanked people now a days which help to accelerate financial inclusion. Another dimension found is number of bank branches which ensures financial accessibility than ATM as per the study. Bank branches and ATMs are expensive endeavours for banks that is the reason digital banking are more stimulated in remote areas for curtailing cost. Bansal (2014) demonstrates the requirement of proper financial inclusion model and products available to cover the gap created between the people residing in urban and rural part of India. Digital banking like mini ATM, White ATM are advance ‘cash in cash out’ model that help banks to deal with inaccessible areas. To increase active banking transactions of the people, adequate financial infrastructure needs to be more emphasized. Lastly, penetration and accessibility encourage people to develop effective usage of the financial products. It is found from the study that outstanding amount in the deposits account has significant impact on GDP. As government of India schemes directed towards ‘cashless economy’, where every individual must channelizes their funds with formal financial system. Therefore, policy maker must give more emphasis on account opening scheme with added loan benefit so that the correlation of savings and investment with income of the unbanked population will get a formal linkage with the help of financial system. The present research demonstrates that loan account and outstanding loan amount are insignificant impact on GDP. Informal money lending system is the biggest barrier in the development of financial system. It is important to build confidence among the investors with the help of financial awareness programmes, along with that more tailormade products to match the financial needs.

Concluding Remarks

The study is undertaken to investigate the impact of government schemes on the economic growth of the Indian economy. Time-series data from 2006 to 2019 are taken to find the effect of financial products and services on the GDP per capita of the economy. The test applied is the VAR model and Granger causality to test the impact of government schemes considering various financial inclusion dimensions on the economic growth of the country. VAR results demonstrated the level of significance of penetration, accessibility, and usage on GDP. Granger causality test displayed that all the selected dimensions for financial inclusion are unidirectional to GDP. However, the test displays the unidirectional relation of GDP on the outstanding loans and deposits. Therefore, escalation in the growth rate of GDP generates the demand for financial products among individuals. The study found neither unidirectional nor bi-directional relation that exists between overall bank penetrations with GDP. On the other hand, the accessibility of the banking products and the usage of financial products exhibited a statistically significant unidirectional relation with economic growth.

This research article will provide information to the policymaker about the existence of association of various government schemes outreach in promoting financial inclusion and simultaneously contribute to the economic growth of the country. Availability of credit to household sector as a financial products is not drawing benefit towards the economic development. Even the schemes like Pradhan Mantri Jeevan Jyoti Bima Yojana (PMJJBY), Pradhan Mantri Suraksha Bima Yojana (PMSBY) have low penetration. Minimum access of these financial products means either people are unaware about the products or feels it is cost ineffective, or do not know the actual use of it (Beck et al., 2007). Maity and Sahu (2020) mentioned the factors responsible behind minimum access and usage of the financial products and services are non avaialbility of financial services, income inequality, cultural, demographic, financial and geographical.To eradicate poverty, foremost tool is to save more and more earnings. Maximum saving accounts with active transactions on it, is the weapon of financial inclusion. In a developing country like India, where there is inequality of income most of the people rely on informal financial products to fulfil their requirement. Participation of bank with digital infrastructure along with promotion of more innovative schemes will improve the living status of unbanked people. Ghosh (2009) said techonological advancement in banking sector has the ability to reach large common people. Chakravarty and Pal (2013) indicated two policy target regarding credit availability and banking penetration to increase financial inclusion. Therefore, the study suggests the policy reformer to bring more active schemes associated to opening of saving accounts, which may be the only tool to reduce informal credit, with additional benefit to escalate awareness on new banking products and its uses.

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.

ORCID iD

Mili Kar  https://orcid.org/0000-0001-5592-273X