Impact of Fintech Adoption on Select Macroeconomic Variables in India: An ARDL Approach

Abstract

Fintech adoption (technology-enabled finance) has not only supplemented the existing physical financial setups for India but has also stretched its wings towards providing the financial services to those who had limited or no access to physical banking (and other financial institutions). Moreover, COVID-19-based restrictions have further strengthened the Fintech adoption among suppliers, regulators and consumers of financial services which in turn has benefited the economy at large. The present study is an attempt to empirically evaluate the impact of Fintech adoption on India’s select macro-economic variables. The macro-economic variables, namely economic growth; income per individual; official exchange rate as a proxy of financial stability vis-à-vis world at large; and labour participation were examined. An index was formed via principal component analysis to capture Fintech adoption over the period 2001–2020 and ARDL framework was employed to examine the results. The results indicate that Fintech adoption has supported India’s economic growth, income per capita and also official exchange rate. Intense regulatory measures towards technical-efficient financial structures might have contributed towards such results. However, the results indicate that the growth in digital financial medium is leading to a reduction in jobs in India.

JEL Classification: E51, O40, F31, J21

Keywords

Fintech economic growth exchange rate labour participation

Introduction

Fintech (or finance technology) refers to infusing technology into financial service and financial delivery structures/designs. Emergence of Fintech sector has improved delivery systems and procedure, and hence has facilitated the financial structure with strong technology networks. Moreover, COVID-19-based restrictions have further strengthened the Fintech adoption (Fu & Mishra, 2020; Imam et al., 2022) among suppliers, regulators and consumers of financial services which in turn has benefitted the economy at large (Shofawati, 2019). The major motive is to improve consumer outcomes, and to provide ease in finance and investment-related opportunities (Frost, 2020). As per the RBI bulletin November 2020, Fintech sector has reduced transaction costs for customer, supported modern banking and financial institutions, optimised costs, reduced credit risk and assisted financial inclusion.

We can briefly discuss, three integrated pillars of Fintech sector that provided synergy to the financial system. Bank for International settlement (2020) studied them as key pillars that are significant for regulators across the world and hence provide fintech leverage to financial system of an economy (Ehrentraud et al., 2020; Saroy et al., 2020). The first pillar is policy empowerment pillar that covers open banking; digital ID; data protection; cyber security; and innovation facilitators. The policy initiative pillar provides crux to the Fintech-enabled regimes. Second important wing covers technology-enabled aspects, cloud computing; application programming interface; biometric; distributed ledger technology; and other technology supports. They work as building blocks for Fintech sector. Third and the last pillar captures End-user Fintech activities such as deposit and lending (digital balance sheet lending; internet banking; loan-crowd funding), capital raising (crowd-equity funding), payment and clearing settlement (e-money; digital payment services), insurance and technology-enabled business models, and other financial activities related to crypto assets (see Figure 1). These three pillars provide a strong structure to Fintech sector and enable economies (and world at large) to move towards digitalisation and cashless systems.

Figure 1.

Source: Based on information collected from Financial Stability Institute, BIS (2020) and RBI bulletin, November 2020.

Fintech and India

Digitalisation measures and legislative initiatives taken by the financial institutions and government of India gave way to Fintech adoption in India. Digitalisation of financial services in India started around 1990 with introduction of internet banking. ICICI bank was pioneer in introducing e-banking in India though with limited services and by the end of 2000, many leading banks in India incorporated non-branch services via internet banking. Furthermore, the Government of India and RBI introduced various norms and provisions to strengthen and validate online transactions. IT Act, 2000 (effective from 17th October 2000) was presented to provide legal strength to electronic transactions and other regimes of electronic commerce (Lal & Saluja, 2012). Introduction of ATMs and credit cards further strengthened the digital world for financial and banking sectors. In 1994, Stanford Federal Credit Union provided its customers with to access banking functions via World Wide Web. However, it was Global Financial Crisis 2008 that supported a new paradigm for Fintech sector (Arner & Barberis, 2015; Buckley et al., 2016). Post 2008, various digital wallets and apps reshaped Fintech for India.

Being one of the most dynamic and adaptive economies of the world, India performed well towards digital adoption and is likely to perform better in the years to come. India is likely to become the largest digital market in the world having one of the highest Fintech adoption rates of 87% (in 2019) as compared to the world average Fintech adopted rate of 64%,¹ as per EY Fintech adoption index report, 2019. Moreover, digital India was an iconic initiative among the various paperless and cashless measures taken by the Government of India. Digital payment division, digital payment dashboard, BHIM cashback schemes for merchants, BHIM Aadhaar merchant incentive schemes, BHIM referral bonus schemes for individuals, various promotional and awareness initiatives for banks² were few significant measures to strengthen digital economy. Not only government, even private corporates; customer-oriented markets; and various financial institutions also worked towards strengthening India’s digital mechanisms. With this background, India’s Fintech adoption is likely to contribute towards financial inclusion and financial stability in India.

Fintech and Financial Inclusion

Fintech adoption has tremendously contributed towards financial inclusion especially in developing economies like India where a large section of population has no or limited access to branch banking and to other financial institutions. For India, Fintech adoption and digital mechanisms are emerging as the fourth significant arm of financial system next to large banks, mid-sized banks, small finance banks, and regional rural and cooperative banks (Das, 2020; RBI Bulletin November 2020). Fintech largely includes internet banking, mobile payment, crowdfunding, online money leading, open banking and other technology-enabling financial service models. Technology has provided strong democratic digital platforms for financial services. These platforms have not only led to reduction in transaction costs but have also eased financial supports especially for those residing in the interiors of India. Fintech adoption can be associated with financial penetration hence likely to contribute towards strengthening the economic and financial parameters of an economy. Therefore, a study of Fintech adoption can help us to capture its impact on financial inclusion and macroeconomic parameters.

Literature Review

Empirical literature for Fintech adoption is restricted but number of studies have examined the association between economic growth and Fintech supportive (technology-enabled) variables such as telecommunication, internet innovation, ATM and IT infrastructure. Therefore, we have divided our literature into few sub-sections.

The first sub-section covers the studies that have captured the impact of Fintech sector on various macro-economic variables. Appiah-Otoo and Song (2021) captured the impact of Fintech adoption on poverty by employing Instrumental variables estimator using the Generalised Method of Moments for a panel of 31 provinces of China for the period 2011–2017. The study suggested that fintech, third-party payment, and credit are reducing poverty in China, and also Fintech adoption is supporting economic growth and financial development in China. Tochukwu (2020) studied the relationship between fintech and bank credit risk for the period 1995–2018 by employing ARDL pooled mean group, mean group and GMM estimation. The study formed an index for capturing the impact of Fintech sector by employing principal component analysis (PCA). The results indicated that bank credit risk increases with adoption of Fintech and also predicted a U-shaped relation between Fintech and credit risk. Chinoda and Mashamba (2021) employed SEM to analyse relationship between financial technology, financial inclusion and income inequality for a panel of 25 African countries for the periods 2011, 2014 and 2017. The results indicated that financial inclusion is compatible with the fintech-income inequality relationship and hence reduces income inequality in Africa. Narayan (2019) employed regression analysis to capture the impact of fintech on economic development for the period 1998–2017. The results indicated a positive and significant impact of fintech sector on economic growth, trade, FDI and financial markets. Similarly, Najwa and Daud (2018) examined the relationship between Fintech and economic growth for a panel data of 19 countries for the period 1988–2015 by employing panel ARDL and POLS techniques. The study employed GDP as proxy of economic growth and mobile subscription as proxy of Fintech. The long-run results suggested Fintech relationship with economic growth. Kammoun et al. (2020) also estimated the impact of Fintech on economic performance but the study captured 10 MENA countries for a timeseries of 2011, 2014 and 2017. The study suggested positive impact on economic performance. Shaikh et al. (2022) examined the impact of Fintech performance on Bahrain’s economic development by employing new digital system as a proxy of Fintech performance. A review of studies associated with Fintech sector and macro-economic variables suggests that Fintech adoption has significantly contributed towards poverty reduction (Appiah-Otoo & Song, 2021), reduction in income inequality (Chinoda & Mashamba, 2021) and various economic parameters (Najwa & Daud, 2018; Narayan, 2019). In this sub-section of our literature review, we have captured the studies that have empirically examined the technology-enabled indicators of Fintech adoption. Starting with telecommunication and economic growth, Ghosh (2016) studied the impact of telecommunication policies on Indian states for the period 2001–2012 by employing panel regression techniques. The results indicated a positive and statistically significant impact of mobile usage on Indian states; the impact is estimated to have positive and significant influence on financial inclusion. Hossine Sharif (2016) estimated the impact of telecommunication on economic growth of Bangladesh by employing OLS regression frameset. GDP per capita was taken as proxy of economic development. Internet users, telecommunication density and telecommunication revenue were taken as indicators of telecommunication industry. The results suggested that telecommunication industry had a significant and positive impact on the economic development of Bangladesh. Similarly, Darma and Ali (2016) empirically suggested the impact of telecommunication investment on economic growth in West Africa for the period 2001–2013 by employing panel data. For testing the results GMM and granger causality were employed. Results suggested that FDI and telecommunication had a positive but insignificant impact on economic growth. Zahra et al. (2008) also studied association between telecommunication infrastructure and economic growth for 18 years for the period 1985–2003 by employing dynamic fixed and random effects The study gathered data for twenty-four low-income, middle-income and high-income countries. The results indicated a positive and significant impact of telecommunication on the real GDP per capita of the countries. Oladipo Olalekan (2019) examined the causal-effect relationship between telecommunication infrastructures, economic growth and development for 46 African countries for the period 2000–2015. GDP was taken as proxy of economic growth and for telecommunication an index was formed using PCA. Indicators of telecommunication employed were fix line, mobile line and internet access. The results indicate that telecommunication infrastructures supported economic growth in Africa. Katz and Emara (2022) examined the impact of telecommunication on economic growth of Egypt over the period 2000–2019 using structural equations. Two indicators of telecommunication were incorporated in the study, namely mobile subscribers and mobile broadband. Results indicated that telecommunication indicators significantly contributed towards Egyptian GDP growth. Studies associated with telecommunication (with varied sample size) suggested that growth in telecommunication and technology has also contributed towards economic parameters, FDI and financial inclusion.

The third sub-section summarised studies capturing empirical association between internet usage/innovation and economic growth. Bakari (2019) examined the relationship between innovation and economic growth. The study incorporated panel data from 76 developed and developing countries over the period 1995–2016 and employed panel ARDL estimation. The study found that there exists unidirectional long-run relationship between innovation and economic growth. Rangkakulnuwat and Dunyo (2018) studied the association between internet and economic growth in the African region. The study employed FE-IGLS estimation for a panel data of 19 countries over a period of 2003–2014. The study indicated that internet has an impact on economic growth when it is complementary to physical capital and technology. Adeoye and Alenoghena (2019) also examined the association between internet usage, financial inclusion and economic growth for the period 1999–2016 in Nigeria. By adopting FMOLS approach, the study estimated a positive and significant effect of internet and broad money on financial inclusion. Bakari and Tiba (2020) captured the impact of the internet on economic growth for four North African economies over the period of 1995–2017 via various econometric techniques such as the ARDL bounds testing approach, panel ARDL Model, OLS Fixed Effect, OLS Random Effect, FMOLS, 2 SLS, RLS, GLM and GMM. The results for time series indicated a negative impact of internet on economic growth for each country individually whereas panel analysis suggested negative and significant impact on economic growth for all countries collectively. Jin and Jin (2014) captured the relationship between Internet education and economic growth using a cross-section study of 36 high-income countries. Internet usage rates were taken as a proxy for Internet education across countries. The regression results indicated a positive and significant impact of Internet usage on economic growth. Studies examining relationship between internet usage and economic growth also depicted encouraging results by employing varied samples and research tools.

Last section of our review captures the studies that have examined the relationship between financial inclusion and economic development. Dudhe (2021) examined the relationship between financial inclusion and economic growth over the period of 2008–2017 for India by employing regression analysis. Indicator of Financial inclusion employed was growth in ATM and the variable was found to be statistically significant for India’s economic growth. Maune et al. (2020) examined the impact of financial inclusion on Zimbabwe’s economic growth for the period 2011–2017 using simple regression. The indicators of financial inclusion captured were information and communication technology, and mobile network. The results indicated a positive relationship between financial inclusion and economic growth in Zimbabwe. Wokabi and Fatoki (2019) examined the determinants of financial inclusion for East African countries by employing fixed effects estimation for the tenure of 17 years starting from 2000 to 2016. Domestic credit to private sector by banks was employed as an indicator of financial inclusion. The study found that income (GDP) is positive and significant determinant of financial inclusion. Ali (2019) examined the impact of financial inclusion on economic growth of six Asian countries for the years 1997–2016 by employing panel regression. Credit to private banks was employed as an indicator of financial inclusion. The study depicted a positive and significant association between financial inclusion and economic growth.

Fintech adoption and technology employed in order to strengthen fintech adoption have directly and indirectly supported financial inclusion both in developed as well as developing economies and hence, have contributed significantly towards various macro-economic variables. A review of studies depicted that the empirical literature covering the impact of Fintech, Fintech adoption indicators (telecommunication, internet and innovation) and financial inclusion can help us to shape our study and also assist us to identify various Fintech adoption indicators that can be employed to empirically capture the impact of Fintech sector on India’s macroeconomic variables.

Research Gap

Number of studies have discussed the impact of Fintech adoption on Indian market and banking systems (Shukla & Dubey, 2022; Singh et al., 2021; Thomas & Tiwari, 2021). We were also able to find few studies that have captured impact of Fintech adoption on economic growth, official exchange rate and other macro-economic variables (Najwa & Daud, 2018; Narayan, 2019; Narayan & Sahminan, 2018) but none of them have examined these variables for India. Therefore, the present study empirically examines the impact of Fintech adoption on India’s macroeconomic variables, namely economic growth; output per worker; official exchange rate; and labour participation/employment. Incorporating four different macroeconomic parameters (as separate dependent variables) will help us to examine the impact of Fintech adoption more extensively. Moreover, a composite study with four different dimensions (of macro-economic development) will help us to understand the impact of Fintech adoption on India’s domestic appraisal parameters as well as international yardsticks. Along with Fintech adoption, the present study also encompasses explanatory variables such as political stability, economic freedom (as a proxy of globalisation) and labour participation over the period 2001–2020 in order to address India’s digital and globalisation era.

Hypothesis Development

Based on the literature review we were able to ground the gaps in the research and state our hypothesis for the present study:

H₁: Fintech adoption has positive impact on economic growth (GDP) of India.

H₂: Fintech adoption has positive impact on per capita income (GDP per capita) of India.

H₃: Fintech adoption has positive impact on output per worker in India.

H₄: Fintech adoption has positive impact on official exchange of India.

Data Source and Methodology

In order to study the impact of Fintech adoption on India’s economic parameters, initially few fintech indicators were identified based on the existing literature and then an index for Fintech was formed via PCA. Subsequently, various explanatory variables (including the Fintech index) were examined by employing ARDL framework.

Fintech Indicators

Studies capturing Fintech adoption have either employed Fintech technology-related variables as indicators or proxy of Fintech adoption (Hornuf & Haddad, 2016; Okoli, 2020) or financial service providers (or Fintech startups) as reflectors of fintech adoption/growth (Narayan, 2019; Othman et al., 2021). Data relating to financial service providers over the period 2001–2020 for India was not available therefore the present study employed technology-enabled financial variables as proxy of Fintech adoption. In order to capture technology-enable financial services domain, three variables facilitating Fintech adoption were used to form an index via PCA, namely Automated teller machines (ATMs) (per 100,000 adults); Mobile cellular subscriptions; and Fixed board band. These variables were employed by few studies (Campos & Kinoshita, 2010; Samargandi et al., 2015; Tochukwu, 2020) in order to explain Fintech sector. A pictorial representation (see Figure 2) of these variables indicates an upsurge in last two decades.

Figure 2.

Source: Authors’ own based on the data collected from the World Bank database.

There has been a massive penetration of mobile phone connections in India. As per digital India 2020, mobile connections 2020 accounted for 78% of India’s population.³ Similarly, the number of internet users in India has reached 687.6 million with an increase of 23% in 2019–2020. Number of ATMs⁴ has increased to more than 2.13 lakhs by end of September 2021 and 47% of them are localised in rural and semi-urban regions. The trends indicate that these variables are well-captive of Fintech adoption in India and can be gauged to study the impact of Fintech adoption on India’s economic parameters.

Data Source

Table 1 depicts the sources of data collection and description for various explanatory and dependent variables (GDP, GDP per capita, official exchange rate and labour participation) employed in the study to explain the impact of Fintech adoption on economic development parameters of India.

Table 1.

Source and Definition of Variables.

Variables (with Measurement)	Source	Variable Description	Expected Sign	Nature
Fintech variables 1. ATM per 1,00,000 adults 2. Mobile cellular subscription 3. Fixed board band FINTECH Index was formed via PCA by employing these three variables	World Bank database	Fintech adoption indicators. Growth in fintech adoption will stimulate financial inclusion and penetration for India hence likely to be positively associated with India’s development parameters. Campos and Kinoshita (2010), Samargandi et al. (2015) and Tochukwu (2020) captured similar variables to form an index for Fintech sector.	Positive	Explanatory variables
Inflation, consumer prices annual (INFLA)	World Bank database	The variable is associated with price rise and hence leading to reduction in consumption and GDP. Indicative of economic instability. Likely to have a negative relationship with economic parameters. An increase in inflation will lead to more instability in the economy	Negative
Economic freedom (Ecofre) based on 12 factors classified in four different categories, namely 1. Rule of law (property rights, government integrity, judicial effectiveness), 2. Government size (government spending, tax burden, fiscal health), 3. Regulatory efficiency (business freedom, labour freedom, monetary freedom), 4. Open markets (trade freedom, investment freedom, financial freedom)	Heritage Foundation	The variable is captive of trade openness, financial openness, liberal policies and global compatibility. The study incorporates economic freedom index to examine the impact of liberal trade and investment policies (more towards liberalisation and globalisation) and other administrative measures of government on various economic parameters. The variable is expected to have a positive association with economic growth, income per worker, official exchange rate and labour participation.	Positive
Political stability and no violence	World Bank database	The variable is included to study the impact of political stable environment on various economic parameters, namely GDP; GDP per capita and labour participation.	Positive
Gross domestic product (GDP)	World Bank database	GDP as one dependent variable is incorporated to study the impact of fintech adoption on economic growth of India. Narayan (2019) examined association between Fintech and Indonesia’s economic growth.		Dependent variables
GDP per capita	World Bank database	GDP per capita as another dependent variable (as separate specification) is included to examine the impact of fintech on economic output per person/worker in India. Zahra et al. (2008) studied relationship between telecommunication and GDP per capita.
Official exchange rate (local currency units (rupee) per US$ average during the given year)	World Bank database	Official exchange rate refers to strength of domestic currency vis-à-vis international currency market/s. The variable was examined as dependent variable to study the impact of fintech adoption on India’s official exchange rate. Narayan and Sahminan (2018) examined relationship between Fintech and Indonesia’s official exchange rate.
Labour participation, total	World Bank database	Employment/human capital. The variable was captured as a dependent variable to examine the impact of fintech on labour participation in India.		Explanatory variable (for Models 1, 2 and 3)/Dependent variable (for Model 4)

Source: The authors.

Research Methodology

We have divided our methodology into two sections. The first part discusses the procedure adopted to calculate Fintech index and the second section focuses on the functional model and ARDL framework employed to capture the impact of Fintech adoption on India’s macro-economic parameters.

Principal Component Analysis: Fintech Index

As discussed in section 4.1, the present study incorporates ATMs (per 100,000 adults); Mobile cellular subscriptions; and Fixed board band as Fintech indicators/variables. These three variables of Fintech adoption are bound to be correlated. We can either drop the correlated variables or transform the set of variables into a statistically independent explanatory variable/s by employing PCA tool (Kurul, 2017; Murthy & Bhasin, 2015). The present study opts for forming an index for Fintech adoption by employing PCA. PCA reduces dimensionality of the variables by bringing out fewer components for the set data that are captive of maximum variance (principal components).⁵ In order to understand the explanatory powers of the principal components, PCA generates total variance explained and cumulative percentage of explained variance for each component.

Moreover, to examine the suitability for PCA, Kaiser–Meyer–Olkin (KMO) and Bartlett’s Test of sphericity were employed. KMO is conducted to measure data adequacy and Bartlett’s test is conducted for suitability of data (Othman et al., 2019). The results for Bartlett’s test for sphericity rejected the null hypothesis (identity matrix for correlation matrix) as the p value was found to be less than 0.05 (see Annexure 1) and the KMO measure was found to be more than 0.75 hence the degree of information among the variables in the data set are highly overlapping. Hence, Fintech index formed by employing PCA is reflective of original data.

Functional Form and ARDL Framework

The functional form of our model to study the impact of fintech adoption can be stated as follows:

Economic parameter = f(Inflation, fintech index, economic freedom, political stability, Labour participation)

Hence, the econometric model for the study takes the following shape:

{EcoParameters}_{t} = {INFL}_{t} {+FINTECH}_{t} {+EcoFre}_{t} {+PolStab}_{t} {+Labr}_{t} + ε

(1)

Where INFL_t indicates inflation in the year t, FINTECH_t captures the fintech adoption index for the year t, EcoFre_t refers to the economic freedom index, PolStab_t represents political stability for the year t, Labr_t captures the labour participation for the year t and ε represents the error term. Equation (1) is captured for four different models where Models 1, 2, 3 and 4 represent GDP (economic growth), GDP per capita (output per inhabitant/worker), official exchange rate/OffExc_t (strength of domestic currency vis-à-vis international currency markets) and labour participation/Labr_t (human capital/employment) representatively as the economic parameters (EcoParameters_t). Although, Models 1 through 3 incorporate labour participation as an explanatory variable whereas for Model 4, the variable is examined as a dependent variable to study the impact of Fintech on human capital/employment. Four model specifications are incorporated to expand the spectrum and dimensions of our study wherein domestic parameters such as economic growth; output per worker; and human capital and international yardstick, namely official exchange rate are captured. A study of four different aspects will help us to empirically provide a larger picture of India’s growth potential and financial environment. Hence, we can empirically ascertain whether or not a digital financial platform (lending, payments, investment and finances) is supporting India towards reducing cost of financing in the domestic market and supporting financially stable environment vis-à-vis world at large.

In order to capture the explanatory variables for all four models, the present study adopts autoregressive distributed lag (ARDL) frameset in order to address the endogeneity issues (Rahman & Kashem, 2017) associated with the time series data. ARDL framework examines the explanatory variables with correct lag structure both for endogenous and exogenous variable. Moreover, ARDL carries a significant attribute in analysing and explaining economic variable in a better way (Chetty, 2018). Changes in the economic variables are likely to bring changes in other economic aspects over the period of time. Thus, both long- and short-run behaviour towards economic variables is always interesting to study via ARDL specification that incorporates the significance of distributed lag framework. ARDL was proposed by Pesaran et al. (2001). The model is said to be more captive for studies with smaller and finite sample size (Ghatak & Siddiki, 2001; Muhammad & Abdullahi, 2020). Moreover, it generates robust results even when the variables are stationary either at levels or for first difference [but ARDL is not recommended in case the variables are stationary at I (2)]. ARDL is also equipped to generate results both for long run and short run. Our models with ARDL framework can be stated as follows:

Model 1: Fintech and economic development

\begin{array}{l} D ​ G D P_{t} = β_{0} + \sum_{i = 1}^{n} a_{1} D ​ G D P_{t - i} + \sum_{i = 1}^{n} a_{2} D ​ {INFL}_{t - i} + \sum_{i = 1}^{n} a_{3} ​ Eco ​ {Fre}_{t - i} + \\ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ \sum_{i = 1}^{n} a_{4} ​ {PolStab}_{t - i} + \sum_{i = 1}^{n} a_{5} D ​ {Labr}_{t - i} + \sum_{i = 1}^{n} a_{6} D ​ {FINTECH}_{t - i} + \\ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ φ_{1} ​ {GDP}_{t - 1} + φ_{2} ​ {INFL}_{t - 1} + φ_{3} ​ Eco ​ {Fre}_{t - 1} + φ_{4} ​ {PolStab}_{t - 1} + φ_{5} ​ {Labr}_{t - 1} + \\ φ_{6} ​ {FINTECH}_{t - 1} + μ_{i} \end{array}

(2)

Model 2: Fintech and output per worker

\begin{array}{l} D ​ G D P ​ {pc}_{t} = β_{0} + \sum_{i = 1}^{n} a_{1} D ​ G D P p c_{t - i} + \sum_{i = 1}^{n} a_{2} D ​ {INFL}_{t - i} + \sum_{i = 1}^{n} a_{3} ​ Eco ​ {Fre}_{t - i} + \\ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ \sum_{i = 1}^{n} a_{4} ​ {PolStab}_{t - i} + \sum_{i = 1}^{n} a_{5} D ​ {Labr}_{t - i} + \sum_{i = 1}^{n} a_{6} D ​ {FINTECH}_{t - i} + \\ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ φ_{1} ​ GDP ​ {pc}_{t - 1} + φ_{2} ​ {INFL}_{t - 1} + φ_{3} ​ Eco ​ {Fre}_{t - 1} + φ_{4} ​ {PolStab}_{t - 1} + φ_{5} ​ {Labr}_{t - 1} + \\ φ_{6} ​ {FINTECH}_{t - 1} + μ_{i} \end{array}

(3)

Model 3: Fintech and official exchange rate

\begin{array}{l} D ​ {OffEx}_{t} = β_{0} + \sum_{i = 1}^{n} a_{1} D ​ O f f E x_{t - i} + \sum_{i = 1}^{n} a_{2} D ​ {INFL}_{t - i} + \sum_{i = 1}^{n} a_{3} ​ Eco ​ {Fre}_{t - i} + \\ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ \sum_{i = 1}^{n} a_{4} ​ {PolStab}_{t - i} + \sum_{i = 1}^{n} a_{5} D ​ {Labr}_{t - i} + \sum_{i = 1}^{n} a_{6} D ​ {FINTECH}_{t - i} + \\ ​ \sum_{i = 1}^{n} a_{7} D ​ {GDP}_{t - i} + φ_{1} ​ {OffEx}_{t - 1} + φ_{2} ​ {INFL}_{t - 1} + φ_{3} ​ Eco ​ {Fre}_{t - 1} + \\ ​ φ_{4} ​ {PolStab}_{t - 1} + φ_{5} ​ {Labr}_{t - 1} + φ_{6} ​ {FINTECH}_{t - 1} + φ_{6} ​ {GDP}_{t - 1} + μ_{i} \end{array}

(4)

Model 4: Fintech and labour participation

\begin{array}{l} D ​ L a b r_{t} = β_{0} + \sum_{i = 1}^{n} a_{1} D ​ L a b r_{t - i} + \sum_{i = 1}^{n} a_{2} D ​ {INFL}_{t - i} + \sum_{i = 1}^{n} a_{3} ​ Eco ​ {Fre}_{t - i} + \\ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ \sum_{i = 1}^{n} a_{4} ​ {PolStab}_{t - i} + \sum_{i = 1}^{n} a_{5} D ​ {GDP}_{t - i} + \sum_{i = 1}^{n} a_{6} D ​ {FINTECH}_{t - i} + \\ ​ ​ φ_{1} ​ {Labr}_{t - 1} + φ_{2} ​ {INFL}_{t - 1} + φ_{3} ​ {EcoFre}_{t - 1} + φ_{4} ​ {PolStab}_{t - 1} + φ_{5} ​ {GDP}_{t - 1} + \\ ​ φ_{6} ​ {FINTECH}_{t - 1} + μ_{i} \end{array}

(5)

Where Δ denotes the first difference operator; n represents the optimal lag length; β₀ represents the intercept; α₁, α_2, α_3, α₄, α₅, … denotes short-run coefficients; and φ_1, φ_2, φ_3, φ_4, … are coefficient for long run.

It is suggested to test the stationarity of the data before employing ARDL framework, the present study uses Augmented Dickey–Fuller (ADF) and Phillips Perron (PP) unit root tests for examining the stationarity issues of our data. Moreover, in order to validate cointegration test, bound test for cointegration was conducted separately for all four models’ understudy. Null hypothesis for bound testing suggested no cointegration among the variables and F-statistics need to be greater than upper bound critical value in order to reject the null hypothesis.

Bound Test Results Interpretation F-statistics > Upper bound critical value Cointegration F-statistics < Lower bound critical value No cointegration F-statistics between upper and lower bound value Inconclusive

Once the bound test supports the presence of cointegration, ARDL specification can be employed to establish long- and short-run association among the variables. Furthermore, lag length for Equations (2), (3), (4) and (5) are facilitated via AIC/Akaike Information Criterion. Liew (2004) recommended use of AIC lag length in case the time series data is less than 60. Furthermore, in order to study the short-run dynamics, the relationship between the dependent and independent variables with ECM/error correction model can also be stated as follows:

E c o P a r_{t} = α_{0} + \sum_{i = 1}^{n} φ_{1} Δ E c o P a r_{t - i} + \sum_{j = 1}^{m} \sum_{i = 0}^{q} ω_{j} Δ ​ V_{j t - 1} + θ E C T_{t - 1} + μ_{i}

φ₁ and ω_j indicate short-term coefficients; θ represents the speeding of turning parameter; ECT_t_–1 represents the error correction term that results from the error cointegration model. The value of θ indicative of long-run convergence has to necessarily lie between –1 and 0. In other words, ECM states whether or not dependent and the independent variable are consistent in the short run and are assisting a long-run cointegrating relationship.

Moreover, the study examines time series data therefore few diagnostic tests associated with heteroscedasticity, serial correlation and fitness of model were also conducted.

Results and Analysis

The result section covers step-wise outcome of various statistical tools employed.

Result for Unit Root Test

Result for most of the variables indicate stationarity in first difference by employing both ADF and PP unit root testing (see Table 2). However, labour participation variable was found to be stationary at levels both for ADF and PP unit root testing (without trends). Similarly, inflation and political stability variables were found to be stationary at levels (without trends) with ADF testing. Lack of uniformity associated with stationarity results appreciated use of ARDL specification.

Table 2.

Unit Root Testing.

Variables	ADF				PP
	Without Trends		With Trends		Without Trends		With Trends
	At Level	Difference	At Level	Difference	At Level	Difference	At Level	Difference
GDPpc	–0.933 (0.755)	–3.865** (0.01)	–2.602 (0.283)	–3.721** (0.047)	–0.975 (0.74)	–3.679** (0.014)	–2.566 (0.297)	–3.462*** (0.075)
GDP	–0.587 (0.852)	–3.872** (0.01)	–3.152 (0.123)	–3.576*** (0.061)	–0.575 (0.855)	–3.679** (0.014)	–2.856 (0.197)	–3.302*** (0.098)
EcoFre	–2.024 (0.275)	–8.011* (0.000)	–4.085** (0.023)	–7.867* (0.000)	–2.883*** (0.066)	–8.301* (0.000)	–4.121** (0.022)	–9.187* (0.000)
PolStab	–2.779*** (0.08)	–4.523* (0.003)	–1.263 (0.866)	–5.341* (0.002)	–2.841 (0.071)	–4.493* (0.003)	–1.188 (0.884)	–5.341* (0.002)
OffExc	0.837 (0.992)	–3.424** (0.024)	–1.647 (0.735)	–4.197** (0.02)	0.871 (0.993)	–3.394** (0.025)	–1.573 (0.765)	–4.203** (0.02)
Labr	–3.908* (0.009)	–1.917 (0.318)	–1.519 (0.786)	–3.354*** (0.09)	–3.908* (0.009)	–1.917 (0.318)	–1.007 (0.919)	–3.474*** (0.073)
Infla	–3.385** (0.029)	–3.447** (0.023)	–1.507 (0.79)	–3.305*** (0.097)	–1.58 (0.473)	–3.409** (0.025)	–1.507 (0.79)	–3.238*** (0.108)
ATM	–0.659 (0.833)	–3.857** (0.039)	–2.1 (0.495)	–1.974*** (0.076)	–0.056 (0.941)	–2.055*** (0.063)	–1.957 (0.586)	–1.887*** (0.062)
MobSub	–1.407 (0.556)	–2.588** (0.015)	–2.215 (0.454)	–2.603*** (0.083)	–0.757 (0.808)	–2.147** (0.03)	–1.471 (0.803)	–2.083** (0.052)
FixBrd	0.325 (0.973)	–2.235** (0.024)	–4.286** (0.021)	–2.254* (0.428)	0.096 (0.957)	–2.07*** (0.058)	–2.057 (0.535)	–2.019*** (0.053)

Source: The authors.

Note: *, ** and *** indicates the significance level of 1%.5% and 10%, respectively

Bound Testing for Cointegration Results

In order to identify long-run cointegration between the dependent and independent variable (for all four model specifications), F-statistics bound testing was employed. F-statistics for all models (see Table 3) were found to be more than upper bound critical value suggesting rejection of null hypothesis. The results indicated long-run cointegration among the variables.

Table 3.

F-statistics.

(Null Hypothesis: No Levels Relationship)
Test Statistic	Value	Significance	I(0)	I(1)
Model 1 GDP and Fintech adoption
F-statistic	7.887239	10%	2.08	3
k	5	5%	2.39	3.38
		2.50%	2.7	3.73
		1%	3.06	4.15
Model 2 GDP per capita and Fintech adoption
F-statistic	7.292817	10%	2.08	3
k	5	5%	2.39	3.38
		2.50%	2.7	3.73
		1%	3.06	4.15
Model 3 Official exchange and Fintech adoption
F-statistic	6.207252	10%	2.2	3.09
k	4	5%	2.56	3.49
		2.50%	2.88	3.87
		1%	3.29	4.37
Model 4 Labour and Fintech adoption
F-statistic	4.746394	10%	2.08	3
k	5	5%	2.39	3.38
		2.50%	2.7	3.73
		1%	3.06	4.15

Source: The authors.

ARDL: Long-run and Short-run Coefficients

Result for all four model specifications using ARDL has been captured in this section model-wise.

Model 1: GDP and Fintech and Model 2: GDP per Capita and Fintech Results

The results for Model 1 are stated in Table 4. The optimal lag length suggested for Model 1 is ARDL (1,1,0,1,1,0). R-square, representing the fitness of goodness is more than 90%. Error correction term is found to be negative and significant indicating an association with the long-run coefficients. Moreover, the results for long-run and short-run coefficients are more or less same except for inflation. The results for GDP and Fintech/Model 1 suggest positive and significant coefficients for economic freedom indicating that India is experiencing a positive association between economic growth and economic freedom policies in terms of trade openness, financial openness and other liberal measures both in long and short run. Similarly, results for political stability and no violence indicated that India is able is contribute more towards economic growth with a politically stable environment. Result for labour participation is also encouraging for Model 1 suggesting an upsurge in the economic growth (GDP) with a rise in labour participation. However, the result for inflation indicated that with rise in level of inflation (economic instability), GDP for India falls significantly in the long run whereas inflation coefficient was negative but insignificant in the short run. The result for Fintech index is found to be highly significant and positive (both in the short and long run) suggesting a rise in India’s economic growth as a result of jump in Fintech-related technology supports in India. Appiah-Otoo (2021) also suggested complementary relationship between China’s economic growth and Fintech. Narayan (2019) also found positive association between Indonesia’s fintech industry and GDP.

Table 4.

Long-run and Short-run Coefficient for Models 1 and 2.

	Model 1 GDP and Fintech			Model 2 GDP per Capita and Fintech
	Long-run Coeff	Short-run Coeff	p Value	Long-run Coeff	Short-run Coeff	p Value
Dep Variable	GDP			GDP per Capita
lnINFLA	–0.314*** (0.0479)		.000	–0.289*** (0.0449)		.000
lnLABR	11.20*** (0.946)		.000	10.18*** (0.889)		.000
lnEcoFre	5.642*** (1.385)		.003	5.362*** (1.299)		.003
lnPolStab	0.564* (0.256)		.055	0.466* (0.241)		.085
lnFINTECH	0.114*** (0.0158)		.000	0.094*** (0.0148)		.000
ECT		–0.813*** (0.14)	.000		–0.851*** (0.144)	.000
D.lnINFLA		–0.017 (0.0693)	.812		–0.009 (0.0673)	.896
D.lnLABR		2.559** (0.948)	.024		2.451** (0.924)	.026
D.lnEcoFre		1.997** (0.661)	.014		1.992** (0.648)	.014
D.lnPolStab		0.459* (0.224)	.071		0.396 (0.216)	.100
D.lnFINTECH		0.093*** (0.0252)	.0000		0.079*** (0.022)	.0000
Constant		–60.88*** (9.438)	.000		–64.44*** (9.746)	.000
Lag length	ARDL (1,1,1,1,0,0)			ARDL (1,1,1,1,0,0)
Observations	19				19
R-square	90.100%				90.20%
Adj R-square	80.180%				80.33%
Log likelihood	58.4713				58.8134

Notes: Standard errors in parentheses.

***, ** and * indicates p < .01, p < .05 and p < .1, respectively.

Results for Model 1 suggested that economic freedom, labour participation and political stability are positively contributing towards India’s economic growth. Moreover, Fintech adoption is leading to financial inclusion and hence contributing towards India’s economic growth.

Model 2: GDP per Capita and Fintech

Results for Model 2 (see Table 4) are more or less similar to Model 1. ECT for Model 2 is also found to be negative and highly significant indicating long-run relationship among the variables. The results indicate economic freedom, labour participation and political stability is positive factor that is also suggestive towards income per capita. Similarly, inflation depicted a negative and significant impact on per capita income for India but only for the long-run dynamics. Also, the coefficient for Fintech adoption for Model 2 indicated a positive association with GDP per capita. Fintech adoption is easing the delivery of financial services in terms of lending, financing and investing privileges to Indian consumers and hence assisting income level of Indian consumers. Better adoption of Fintech is definitely leading to reduction in transaction costs (of financial services) and improving access to financial services.

Model 3: Official Exchange Rate and Fintech

ARDL ((1,0,1,0,0,1,1) lag length was suggested for Model 3 (see Table 5) and R-square was found to be 89.7% (see Table 5). The error correction term (suggesting long-run association among variables) for Model 3 was also found to be negative and highly significant. The results for Model 3 indicated negative and significant coefficient for inflation, and positive and signification relationship with economic growth. Outcome for political stability, economic freedom index and labour participation was found to be positive but insignificant.

Results for Fintech adoption were found to be positive and significant but only for long-run coefficients. With passage of time, Indian economy is likely to reduce cost associated with financial services via Fintech adoption and hence strengthen financial service delivery channels in the international markets leading to a stable and positive association with India’s official exchange rate in long run. Narayan & Sahminan (2018) estimated similar results for exchange rate and Fintech for Indonesia.

Model 4: Labour Participation and Fintech

Model 4 (see Table 5) discusses the relationship between labour participation and Fintech adoption in India. Results were captured via ARDL (1,0,0,1,0,1) with R-square as 84.6%. Most of the coefficients examined under Model 4 generated outcome similar to results captured via Models 1 and 2 but the results for Fintech index was found to be negative and significant for Model 4. The results indicate that with an increase in Fintech adoption, labour participation in India is reducing; might be Indian labour is not trained enough to find employment in the sectors where both financial services and technology specialisation is required simultaneously or due to an increase in digital platforms need for labour participation is reducing.

Table 5.

Long-run and Short-run Results for Models 3 and 4.

Variables	Model 3 Off-exchange and Fintech			Model 4: Labour and Fintech
Variables	Long-run Coeff	Short-run Coeff	p Value	Long-run Coeff	Short-run Coeff	p Value
Dep Variable	Official Exchange			Labour
lnINFLA	–0.251* (0.126)		.083	–0.022** (0.0083)		.024
lnGDP	0.548* (0.238)		.05	0.074*** (0.0132)		.0000
lnLABR	3.207 (2.011)		.15	–		–
lnEcoFre	0.181 (0.765)		.819	0.512*** (0.154)		.008
lnPolStab	0.499 (0.371)		.216	0.107** (0.0431)		.032
lnFINTECH	0.195*** (0.040)		.001	–0.011*** (0.002)		.002
ECT	–0.628* (0.283)		.057	–1.445*** (0.332)
D.lnINFLA		–0.157** (0.0503)	.014		–0.031** (0.011)	.018
D.lnGDP		0.554*** (0.151)	.006		0.107*** (0.028)	.004
D.lnLABR		2.015* (0.903)	.056		–	–
D.lnEcoFre		0.114 (0.464)	.812		0.378* (0.181)	.063
D.lnPolStab		0.095 (0.284)	.747		0.155** (0.0611)	.03
D.lnFINTECH		0.044 (0.0538)	.432		–0.043** (0.0155)	.019
Constant		–11.88 (6.645)	.112		9.854*** (2.183)	.001
Lag length	ARDL (1,0,1,0,0,1,1)			ARDL (1,0,0,1,0,1)
Observations	19			19
R-square	89.70%			84.60%
Adj R-square	76.85%			72.31%
Log likelihood		65.1644		84.778

Source: The authors.

Notes: Standard errors in parentheses.

***, ** and * indicates p < .01, p < .05 and p < .1, respectively.

Results for Diagnostic Tests

As discussed earlier, in order to validate the equations understudy, few diagnostic tests were performed for each model separately. Being time series data, ARCH heteroskedasticity test was employed to check the presence of time-varying volatility; examining whether or not error term for the current year t is function of previous years’ (t–1) error term. The results rejected the presence of heteroskedasticity for all four models understudy (see Table 6). Similarly, to detect the presence of serial correlation (where variable (t) and lagged variable(t–1) are observed to be correlated over time), Breusch–Pagan LM test was performed and the result indicated rejection of null hypothesis (H₀: correlated over time). Furthermore, Ramsey REST test was performed to test general specification of models and the results supported the functional form (see Table 6).

Table 6.

Results for Diagnostic Test Model-wise.

Test	Statistic	p Value	Statistic	p Value	Statistic	p Value	Statistic	p Value
Model	GDP		GDP per Capita		Official Exchange		Labour Participation
ARCH Heteroskedasticity test	Prob. F (1,16)	.1724	Prob. F (1,16)	.1975	Prob. F(1,16)	.6858	Prob. F(1,16)	.5483
Breusch–Pagan LM test for serial correlation	Prob. F (2,7)	.2888	Prob. F (1,16)	.1975	Prob. F(2,9)	.7453	Prob. F(2,7)	.1327
Ramsey RESET test	(1, 8)	.2944	(1,8)	.4733	(1, 10)	.1376	(1, 8)	.594

Source: The authors.

Conclusion

Fintech has emerged as financial facilitator for various financial services. Fintech adoption has not only acted as a game changer for financial sector but has also extensively contributed towards financial inclusion in developing economies. Hassle-free delivery mechanisms, cost-effective financial services for all segments of society, strong and diversified financial channels and strong technology support towards lending; payments; investments; and other financial services are major slices of Fintech that have contributed towards promoting economic growth and financial stability. Hence, the present study was an attempt to empirically evaluate the impact of Fintech adoption on India’s select macro-economic parameters, namely GDP; GDP per capita; official exchange rate; and labour participation. Four discrete regression equations were employed to examine the impact of Fintech adoption on each macro-economic parameter individually. Presence of mix stationarity at levels and first difference suggested use of ARDL framework over the period of 2001–2020. The results suggested positive and significant impact of Fintech adoption on economic growth (GDP) and income per capita (GDP per capita) both in the long and short run. The results for official exchange rate were positive and significant only for long-run dynamics suggesting a financially strong and stable environment via Fintech will enhance India’s currency exchange potentials in the long run. However, the outcome for labour participation (Model 4) was negative and significant (both in long and short run), indicating a need for a better-trained workforce that can adapt both towards financial and technical skills.

In nutshell, we can state that Fintech adoption has significantly contributed towards economic growth. Moreover, the results for income per capita indicate a positive and significant penetration impact of Fintech adoption on the masses of the economy. In the long run, Fintech adoption is also likely to contribute positively towards currency exchange logistics of India and provide a stable financial environment vis-à-vis world at large. Hence, Fintech adoption has definitely supported India’s economic growth, financial inclusion requisitions and financial stability but India still needs to work towards developing Fintech-equipped labour force.

Policy Implications

Fintech adoption has given better avenues to financial services sectors in India. Banking, insurance and investment sectors have elaborated as a result of digital infrastructural boom which in turn is working towards providing progressive and improved financial services to Indian consumers and society at large. In order to strengthen and support Fintech ecosystem, the Government of India has undert-aken numerous measures such as Jan Dhan Yojana; authentical digital solutions via digital KYC, digital signatures, various digital identification procedures and norms; Aadhar-enabled payment system; digital/e-wallet applications; and various other digital distribution structures. Moreover, GoI has strengthened various laws and legislations to further support Fintech adoption in India.

In the given scenario, Fintech sector is likely to grow magnificently in the years to come hence policy measures towards infusing investment in Fintech enabling technology, technology-enabled talent and workforce, and innovative infrastructure is the need of the hour. Furthermore, Fintech-enabled technology should be customised as per the need of the financial service demanded and offered both in the urban as well as rural regions of India. Measures and regulations towards reducing (and eliminating) technology-associated crimes and frauds should also be more intensive. Strengthening and promoting Fintech start-ups that may assist towards addressing disturbances associated with adoption of new technology that eases financial services to rural population of India. Lastly, the policymakers need to work along with international bodies in order to develop cross-country technology-enabled schemes and applications, and identify and combat global challenges associated with Fintech adoption.

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

Appendix

Annexure 1.

Results for Principal Component Analysis, KMO and Bartlett’s Test.

Principal Components/Correlation
Component	Eigenvalue	Difference	Proportion	Cumulative
Comp1	2.88911	2.80775	0.963	0.963
Comp2	0.0813612	0.051831	0.0271	0.9902
Comp3	0.0295302	.	0.0098	1
Principal Components (Eigenvectors)
Variables	Comp1	Comp2	Comp3	Unexplained
Mobsub	0.5801	–0.3953	–0.7122	0
ATM	0.572	0.8202	0.0107	0
Fixbroad	0.5799	–0.4136	0.7019	0
Bartlett Test of Sphericity
Chi-square		60.471
Degrees of freedom		3
p Value		.000
H₀: Variables are not intercorrelated
Kaiser–Meyer–Olkin Measure of Sampling Adequacy
KMO = 0.770

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