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Int. J. Financial Stud. 2019, 7, 61; doi:10.3390/ijfs7040061 www.mdpi.com/journal/ijfs
Article
Financial Innovation and Financial Inclusion Nexus
in South Asian Countries: Evidence from Symmetric
and Asymmetric Panel Investigation
Md. Qamruzzaman
1,
* and Jianguo Wei
2
1
School of Business and Economics, United International University, Dhaka 1207, Bangladesh
2
School of Economics, Wuhan University of Technology, Wuhan 430070, China; weijg@whut.edu.cn
* Correspondence: zaman_wut16@yahoo.com
Received: 10 July 2019; Accepted: 27 September 201; Published: 15 October 2019
Abstract: This paper examines the nexus between financial inclusion and financial innovation while
incorporating financial development and remittance inflows in the case of six South Asian
countries—Bangladesh, India, Pakistan, Nepal, Bhutan, and Srilanka—by employing the panel
autoregressive distributed lagged model under a linear and nonlinear framework using monthly
data over the period 1990M1–2018M12. Further, a Granger-causality test with System GMM
specification was performed for assessing directional causality. The study findings from Panel
ARDL confirmed the positive association between financial innovation and financial inclusion,
which was observed both in the long run and short-run. Considering the nonlinearity in the
estimation, the standard Wald test confirms the existence of an asymmetric relationship both in the
short-run and in long run horizon regarding causality test results. The study findings support the
feedback hypothesis that the presence of bidirectional causality between the financial innovation
and financial inclusion is both in the short-run and long run. Since the study findings established a
critical relationship between financial innovation and financial inclusion, therefore effective policy
guidelines are suggested so that the contribution from financial inclusion and financial innovation
can assist in developing a vibrant financial sector.
Keywords: financial innovation; financial inclusion; symmetry; asymmetry; ganger-causality
JEL Classifications: G21; O16; O31
1. Introduction
A vibrant financial sector is characterized by diversified financial instruments, efficient financial
institutions, a wide range of financial services, and effective integration with economic activities.
More specifically, the well-functioned financial sector looks for continuous adaptation, evolvement,
and the diffusion of innovative financial assets, institution, and services along with easy access to
financial services, and readily available for the population. The role of financial innovation and
financial inclusion in the financial system by optimizing financial effectiveness and efficiency. The
role of financial innovation in the financial sector addressed in financial literature such as, assist in
improving banking performance (Chipeta and Muthinja 2018), financial efficacy, efficient financial
intermediation. On the other hand, the role of financial inclusion also addressed in finance literatures
such as the reduction of financing costs (Sarma and Pais 2008), the availability of formal credit, the
proliferation of savings (Calderón and Liu 2003; Demetriades and Luintel 1996; Ashraf et al. 2010),
quicken the capital formation (Babajide et al. 2015), the bank-based financial institutions development
(Swamy 2012) and financial stability. Evidently, the relationship between the financial inclusion and
financial innovation is implied and yet to test in empirical studies. Therefore, this study is an attempt
Int. J. Financial Stud. 2019, 7, 61 2 of 27
to unveil their existing association and explore their pattern of effect running from each other, that
is, symmetry or asymmetry.
Recently, financial issues pertinent to financial inclusion get immense attention among
researchers, policymakers, central banks, and financial institutions by admitting its critical role in
fostering the financial sector all over the world. Financial inclusion, according to World Bank
universal financial access by 2020, is one of the key aspects of poverty mitigation and inclusive
economic growth. It is because financial inclusion expedites economic growth through efficient
resource allocation, financial efficiency, the reduction of financing costs, lowering information costs
for credit approval, and institutional efficiency in managing funds (Sarma and Pais 2008). By
acknowledging the nexus between financial inclusion and economic growth, a number of finance
scholars including, Kim et al. (2018); Sharma (2016); Sanjaya and Nursechafia (2016); Kamboj (2014);
Adeola and Evans (2017) and Babajide et al. (2015) unveiled positive linkages.
Financial inclusion, according to Kumar and Mohanty (2011), is the provision of affordable,
accessible and relevant financial products to individual and firms that had previously not been able
to enjoy those benefits. Financially included individuals and firms enjoy certain benefits over
financially excluded pollution such as smooth income transaction, growing the business with
external financing, financial security through savings accumulation, and so forth. In particular,
financial inclusion enables the financial integration of the unbanked population into the formal
financial system by offering diversified financial services, assets and investment opportunities.
Hence, for attracting people in the financial system for enjoying financial services, it is indispensable
that financial institutions should expand their financial product and services through the adaptation
and diffusion of innovative financial instruments for investment, service for operational efficiency,
and the payment mode for intermediation efficiency. Thus, financial institutions persistently seek
innovative and improved financial services and assets so that large groups of the population can
attract and enable the satisfaction of their needs with innovative financial services and assets in the
form of financial innovation. Financial innovation, according to Tufano (2002), is the process of
emergence, diffusion, and popularization of new financial instruments, financial institutions,
financial technologies, and financial markets in the economy. The presence of financial innovation in
the financial system can be addressed in two different wings, such as the product innovation and
process innovation. The role of financial innovation in the financial system are multifold which are
observed in finance literatures such as, financial services diversification (Silve and Plekhanov 2014;
Bianchi et al. 2011), efficient financial intermediation (Johnson and Kwak 2012), technological
advancement (Michalopoulos et al. 2011), efficient resources allocation (Duasa 2014; Sood and Ranjan
2015), and institutional efficiency (Michael et al. 2015), thus eventually promotes financial sector
development.
A well developed and functioned financial sector is critically important for easy access to
financial information with minimal costs, transaction costs reduction, fair investment decision,
technological innovation, and growth stability. The technical innovation, according to Schumpeter
(1911), critically important for economic growth but the effects of fiscal and financial innovation on
economy receive little attention in empirical investigation. However, recent period financial
innovation and its potential impact has attracted immense interest among researchers and
encourages further investigation by considering the various aspect of the economy such as the
economic growth (Qamruzzaman and Wei 2017, 2018b, 2018c; Bara et al. 2016; Bara and Mudzingiri
2016), on firms performance (Muthinja and Chipeta 2018; Valverde et al. 2016), on money demand
(Dunne and Kasekende 2018; Kasekende 2016), on banking sector growth (Chipeta and Muthinja
2018; Kamau and Oluoch 2016), and many more. Financial innovation tends to accelerate the financial
development allowing investment diversifications and risk minimization and thus plays a decisive
role in economic growth (Bhatt and Mundial 1989). In addition, financial innovations augment the
capital accumulation process in the financial system by encouraging savings propensity among the
population with improved financial assists and intensify investment opportunity by offering
innovative and less risky financial instruments. Most prominently, financial innovations open a gate
Int. J. Financial Stud. 2019, 7, 61 3 of 27
for the undeserving population in the society to come under the umbrella of the formal financial
system and avail the benefit of finance.
In the finance literature, the contribution from financial innovation in the economy explained
with three key aspects was observed. First, financial innovation expands economic activities by
promoting financial inclusion, facilitating a financial transaction in international trade, enabling
remittance, and uplifting financial efficiency. Second, the innovation-growth hypothesis postulated
that financial innovation increases the quality of financial products and services (Schrieder and
Heidhues 1995; McGuire and Conroy 2013), expedites the financial development process (Ozcan
2008), improves capital accumulation and allocation processes (Allen 2011), and increases the level
of efficiency in financial institutions (Shaughnessy 2015). Third, financial innovation in the form of
institutional development in the financial system expedites the financial process with greater
accessibility to formal financial service, such as internet banking and mobile banking services
(Raffaelli and Glynn 2013; Hargrave and Van de Ven 2006), microfinance institutions, NGOs, and
hybrid organizational forms (Battilana and Dorado 2010). The institutional availability with offering
financial service improves the economy by including a greater number of people in the mainstream
economic development process (Epstein 1992; Siddiqui and Ahmed 2009; Glaeser et al. 2004).
On the other hand, financial inclusion, in definition, is the ease of financial service access,
availability, and the usage from formal financial institutions across the country. Innovative financial
services, products, and financial institutions entice society to becoming habituated in using financial
services from financial institutions, like the creation of accounts, borrowing funds, the use of ATMs,
amongst others. Nonetheless, financial inclusion is the ultimate output with the adaption and
diffusion of financial innovation. Therefore, the question can arise that do financial innovations
promote the speed of financial inclusion in the financial system or in another way, do financial
inclusion demands innovative financial instruments and services?
This study is novel in various aspects. First, with the study, for the first time, the financial
innovation index was developed as a proxy of financial innovation rather relying on a single
indicator. Even though the existing empirical literature had shown that a number of proxy indicators
were used to address financial innovation in the equation, no consensus indicators appear in this
regard. Therefore, this study tried to mitigate this gap by considering the financial innovation index
with three (03) proxies, which have been repetitively used in different studies. Second, though
empirical literature produces evidence regarding the financial inclusion index measuring the
financial inclusion effects no such study had been performed yet nevertheless. Third, so far, to the
best of the authors knowledge, this is the first ever-empirical investigation focusing on the nexus
between financial innovation and financial inclusion.
The remaining structures of the article are as follows. Detailed empirical literature allied to
present research in Section 2 is discussed. Section 3 deals with the research variables definition along
with the details of the different econometrical methodologies used in empirical investigation. The
mode estimation and its interpretation exhibited in Section 4, and finally, the summary findings and
policy implications are explained in Section 5.
2. Literature Reviews
The nexus between financial innovation and financial inclusion has yet to be tested, nonetheless,
a vast number of researchers have shown their keen interest in exploring the effects running from
financial inclusion and financial innovation to different aspects of the economy. With this connection,
pertinent study findings were summarized tagging with either financial innovation or/and financial
inclusion.
A. Financial Innovation and Its Role Understanding from the Empirical Literature
Financial innovation, in the Miller (1986) view, has been a critical and persistent ingredient for
economic progress because of the financial markets with financial innovation are able to produce a
multitude financial instruments, alternative risk transfer assets, and variants tax-deductible equity.
Although, the importance of financial innovation in the modern financial system is well
Int. J. Financial Stud. 2019, 7, 61 4 of 27
acknowledged and receives minimal attention from financial experts, researchers, policymakers, and
development agency.
However, a group of researchers put their considerable efforts of establishing the nexus financial
innovation-led economic growths and produced substantial evidence in favor of a positive
association between economic growth and financial innovation see, for example (Qamruzzaman and
Wei 2017, 2018a, 2018c; Laeven et al. 2014, 2015; Michalopoulos et al. 2009, 2011; Bara and Mudzingiri
2016; Bara et al. 2016). They argue that financial innovation expands economic activities through
capital accumulation, efficient financial intermediation, and financial institutional development.
Besides that, financial innovation is also dealing with financial instruments development, corporate
structure, financial reporting and techniques, and overall financial sector development.
Explaining the financial innovation-growth nexus, in accordance with existing empirical
findings, four types of the causal hypothesis available were observed. First, the supply leading
hypothesis that is, financial innovation promotes economic growth by allowing financing expansion,
trade efficiency, easy access to financial services, and efficiency in financial institutions of dealing
with a customer (Beck 2010; Shittu 2012). Second, the demand-leading hypothesis that is, economic
growth expands economic activities in both macro and micro level. Therefore, financial services
availability is imperative to maintain the normal speed of aggregated economic progression. Third,
the feedback hypothesis that is caused by both financial innovation and economic growth is also
known as bidirectional causality. The feedback hypothesis explained that the effect could be observed
from each other and empirical literatures have produced ample evidence in this regards see, (Bara
and Mudzingiri 2016; Bara et al. 2016; Qamruzzaman and Wei 2017, 2018a, 2018b, 2018c). Fourth, the
neutral hypothesis implies that no causality exists between financial innovation and economic
growth. In their respective studies, Lumpkin (2010) and Sekhar (2013) found evidence confirming no
causality between financial innovation and economic growth.
Financial innovation, according to Bhatt and Mundial (1989), reduces the risk and transaction
costs in the financial system through effective and efficient payment mechanisms, institutional
efficiency and thus accelerates capital market development. Financial innovation plays both objective
and subjective roles in financial development, such as increased savings propensity in the society by
offering innovative financial assets and the accumulation of capital for investment to increase output.
Financial innovation in the financial system leads to financial diversity by introducing diversified
financial instruments each of them possess unique the attributes and features. These diversifications
in financial assets and services encourage savings propensity in the society in the form of financial
assets and borrowing that ensure efficient allocation of economic resources in productive investment
projects. Further, the efficient allocation of savings into productive investment augments financial
activities and leads to ensure financial integration in the financial market, and thus allows financial
development, at large.
The effects of financial innovation also discussed on operational performance in light of the
efficiency of financial institutions, preferably bank-based financial institutions, such as Camelia and
Angela (2011) investigated financial innovation and operational efficiency of Romanian banks
spanning from 2002 to 2010. The data envelopment analysis was applied to reach conclusive
evidence. The study findings unveiled foreign banks operating in Romania are more efficient than
domestic banks. They postulated that foreign banks’ efficiency rely on financial products and service
diversifications and create customer-based operation. Further evidence relates to the financial
innovation-led financial performance observed in the Chipeta and Muthinja (2018) study. In that
study, they ascertain the positive association between financial innovation and operational
performance in Kenyan banks based financial institutions. Similar findings relating to Kenyan banks
performance with financial innovation was found in (Muthinja 2016; Makini 2010).
Financial innovation plays a critical role in the financial system in a two different way like
product innovations, referring to the emergence of new and innovative financial instruments in the
form of financial assets and process innovation, referring to the efficient dispatch of financial services
(Tufano 2003; Frame et al. 2004).
B. Financial Inclusion and Its Role Understanding from the Empirical Literature
Int. J. Financial Stud. 2019, 7, 61 5 of 27
Growing empirical literature identified the effects of financial inclusion in the economy are
versatile such as, augment consumption, productive investment, increase savings propensity,
manpower empowerment (Ashraf et al. 2010; Dupas and Robinson 2009). Furthermore, access to
financial service plays a critical role in reducing income inequality and poverty. A group of the
researcher including, Mookerjee and Kalipioni (2010), Banerjee et al. (2018), Galor and Zeira (1993),
and Beck et al. (2007) postulated in their respective study that a lack of access to financial services can
augment income inequality and poverty in the economy. With a similar note, Swamy (2012) argued
that the financial inclusion through the bank-based financial institutions accelerate access to finance
to poor and positively influence the reduction of income inequalities in the economy and the financial
intermediation boost inclusive economic growth.
A line of research findings available in finance literatures are those intended to explain the nexus
Financial inclusion-led economic growth, see for example (Adeola and Evans 2017). In a study,
Burgess and Pande (2005) documented the financial inclusion to foster economic growth through
poverty alleviation. Similar findings were also experienced by a number for researchers in their
studies including, Kim (2016) as observed in forty OECD countries, Babajide et al. (2015) as found in
Nigeria, Sharma (2016) as spotted in the emerging Indian economy, and Kim et al. (2018) as unveiled
for OIC countries. Financial inclusion extends the current consumption trend by allowing future
investment opportunities, implying that easy access to financial services creates ample scope for fund
accumulation by accepting financial assets, depositing money into the bank, availing credit facilities
for investment, and diversifying the investment risk.
Second thoughts prevail in the empirical literature pertinent to financial inclusion that is the
nexus between financial inclusion and financial development. Financial inclusion or banking sector
outreach in the economy is the process of availing required financial service at a fair price, at the right
place, and without any discrimination in the society. The prime target in financial inclusion should
be beneficial to poor and undeserving people who are not using formal financial services. It implies
that it thus brings the unbanked population into the formal financial system so that they are able to
avail financial services such as savings, deposits, credit facilities, and insurance. The inclusive
financial system entices savings propensity, capital accumulation, productive investment, and
entrepreneurial development that assist in improving the standard of living in society (Demirgüç-
Kunt and Klapper 2012). In addition, an inclusive financial system also reduces the possibility of
emerging informal credit sources in the economy. Thus, the all-inclusive financial system ensures
institutional efficiency, secure and safe savings and investments by facilitating all the range of
efficient financial services. Therefore, sustainable financial development can be observed in the
economy with effective and efficient implementation of financial inclusion.
Rasheed et al. (2016) investigated the role of financial inclusion on financial development
spanning 2004–2012 in a panel of 97 countries with system-GMM estimation. They unveiled a positive
association between financial inclusion and financial development. In a similar note, Allen et al.
(2014) claimed that, in Africa, innovation in financial services, like mobile banking, has a positive
effect on overcoming financial infrastructural limitations and allows the population to access
financial services. The inclusion of the depriving and geographically located population in the
mainstream of the financial system accelerates financial activities and simultaneously reduces the
market fraction. Further evidence is found in the Adeola and Evans (2017) study. They investigated
the relationship between financial inclusion, financial development and economic diversification in
Nigeria by applying the fully modified OLS. The study findings disclosed that a significant effect on
financial development from financial inclusion is proxy in terms of financial access and financial
usages, respectively.
The reverse effects, implying financial development accelerates financial inclusion, also
available in empirical studies. For example, Kumar (2013), explained in his study that banking
institution development allows greater access to formal financial services to the society, eventually
increasing financial inclusion as a whole.
Apart from leading financial inclusion-led economic growth and financial development,
observed a financial inclusion role was also observed in other economic aspects, such as, the
Int. J. Financial Stud. 2019, 7, 61 6 of 27
reduction of income inequality (Mookerjee and Kalipioni 2010), the positive effects on foreign capital
inflows (Qamruzzaman and Wei 2019), financial inclusion positively assisting in establishing
financial stability, and poverty reduction (Yunus 2011; Chibba 2009).
2.1. Motivation to Study Asymmetry Relationship
The nexus between financial innovation and financial inclusions is yet to be unleashed through
empirical investigation. Even though, empirical literature produced ample evidence focusing
financial innovation with other macroeconomic variables, such as financial innovation-led economic
growth, financial innovation-led financial development, and financial inclusion, such as financial
inclusion-led financial development, financial inclusion-led financial development, and financial
inclusion-led financial efficiency. Therefore, with the available nexus around financial innovation
and financial inclusion, it can be presumed that there is a relationship between financial innovation
and financial inclusion in the financial system.
The underlying motivation of investigating the asymmetric relationship between financial
innovation and financial inclusion is to address the impact of positive and negative changes in
financial innovation on financial inclusion and vice versa.
2.2. Research Questions and Proposed Hypotheses
The intended purpose of the study is not to unveil the key determinants for financial inclusion
but rather to drag-out fresh insights through exploring the nexus between financial inclusion and
financial innovation while incorporating two more variables namely, financial development and
remittance inflows by applying pooled group mean (PGM) panel ARDL and panel nonlinear ARDL
by following the proposed framework by Shin et al. (2014). Figure 1 depicts the summary of the
proposed hypotheses, describing the direction of possible causality among these aforementioned
variables. Pertinent to the current study, the following six (06) hypotheses were tested.
Figure 1. Conceptual framework of the possible pattern of causality between the variables.
𝐻
,
Financial innovation Granger-cause financial inclusion and vice-versa
𝐻
,
Financial innovation Granger-cause financial development and vice-versa
𝐻
,
Financial inclusion Granger-cause remittance inflows and vice-versa
𝐻
,
Financial development Granger-cause remittance inflows and vice-versa
𝐻
,
Financial development Granger-cause financial inclusion and vice-versa
𝐻
,
Financial innovation Granger-cause remittance inflows and vice-versa
Int. J. Financial Stud. 2019, 7, 61 7 of 27
3. Data and Methodology of the Study
This study explored the following: First, whether financial innovation positively induces the
speed of financial inclusion. Second, the type of causality that is running between financial inclusion
and financial innovation.
To do so, monthly cross-sectional data for six (06) countries representing the south Asian
economy for the period 1990M1 to 2018M12 were collected. All the data used in this study collected
from the central bank annual reports and online data archived of the respective countries see, Reserve
Bank of India (2019); State Bank of Pakistan (2019); Central Bank of Sri Lanka (2019); Nepal Rastra
Bank (2019); Royal Monetary Authority (2019) and Bangladesh Bank (2019).
For financial inclusion, empirical literature depicts the two lines of studies pertinent to financial
inclusion proxy. One group of researchers has shown their keen interest to rely on a single proxy for
financial inclusion representation in their empirical model. On the other hand, another group of
researchers devoted to constructing the financial inclusion index by taking a number of proxy
indicators with the construct procedure developed by Sarma (2008). This study followed the second
line of thought, that is, the construction of a financial inclusion index rather than relying on a single
proxy indicator. Therefore, a financial inclusion index was constructed with the application of the
indexing procedure initiated by Sarma (2008) (see Appendix A for more details discussion relating
to financial inclusion index construction).
For financial innovation, the selection of a single indicator for capturing the effect of financial
innovation in an empirical model was not wise because in the empirical literature, the authors
observed a number of proxy indicators that were used by researches in their studies. Therefore, for
the first time, we developed a financial innovation index with three (03) indicators was developed
for wide use in different studies. (For details of the variable definition and the index construction,
please see Appendix A). The principal component analysis techniques were applied for the financial
innovation index construction.
Apart from financial innovation and financial inclusion, two macroeconomic fundamentals were
also considered namely, financial development and remittance inflows in the economy as control
variables in the equation. From the motivation of incorporating those two variables that are in the
empirical literature, this study observed that both financial development and remittance play a
directive role in the financial system, therefore, acknowledging the possible effects of financial
development and remittance on financial innovation and financial inclusion also addressed in
empirical estimation. All variables are presented in logarithmic form. The descriptive statistics of
research variables are presented in Table 1.
Table 1. Summary of descriptive statistics.
Description Obs Mean Stdard
Depositors with commercial Banks 4032 35.9161 11.3339
ATMs per 100,000 adults 4032 92.1450 23.5900
Commercial bank branches per 100,000 adults 4032 8.0251 6.0510
Credit to the private sector 4032 69.2246 25.0111
The ratio of aggregate money supply to narrow money 4032 3.5131 0.3608
The ratio of Broad to narrow money 4032 4.0108 0.2058
The percentage change in domestic credit to the private sector 4032 0.0040 0.0443
Domestic credit to private sector (% of GDP) 4032 10.7436 3.6621
Per capita remittance received 4032 4.4871 2.6934
3.1. Modeling and Methodological Framework
The objective of this study is to explore new insights by explaining the nexus between financial
inclusion and financial innovation along with two control variables namely, financial development
and remittance inflows in South Asian countries. The generalized empirical model can be represented
in the following ways:
Int. J. Financial Stud. 2019, 7, 61 8 of 27
𝐼𝐹𝐼 =𝛽+𝛽𝐹𝐷 +𝛽𝑅𝐸 +𝛽𝐹𝐼𝑁 +𝜀 (1)
where IFI denotes financial inclusion, FD stands for financial development, RE represents remittance
inflows, and FIN denotes for financial innovation. 𝜀 for the residual term in the equation and
assumed to be normally distributed.
Cross Sectional Dependency Test
The cross-sectional dependency test is imperative in panel data empirical investigation, in
particularly, for representative countries containing similar economic attributes, like developing
countries, emerging economies, and transition countries. Due to trade internationalization, financial
integration, and globalization make a similar economy subject to experience the effect with any shock
in other countries. Therefore, investigating the presence of cross-sectional dependency would most
likely demand an empirical investigation with panel data. In the investigation, four tests have been
widely used. The Lagrange multiplier (LM) test was proposed by Breusch and Pagan (1980), which
is preferred in a situation when the cross-section (N) is smaller than time (T). Based on the following
equation, the LM test statistics can be constructed:
𝑦 =𝛼+𝛽𝑥 +𝑢 𝐼 = 1, … 𝑁,𝑡= 1, … 𝑇 (2)
where 𝑦 denotes dependent variable, 𝑥 are the independent variables and the subscript of t and
I represent for the cross-section and time period, respectively. The coefficients of 𝛼 and 𝛽
respectively represent the country-specific intercept and slope in the equation. In the contest of the
LM cross-section dependency test, the null hypothesis of cross-section independence— 𝐻 =
COV𝑢𝑢 = 0 for all t, and t ≠ j, against the alternative hypothesis of cross-sectional dependence—
−𝐻 = COV𝑢𝑢 ≠ 0 for at least t ≠ j. Moreover, the LM test statistics can compute the following
equation: 𝐿𝑀=𝑇∑∑ 𝜌
→()
(3)
where 𝜌 represents the pairwise correlation of the residuals.
The LM test is not suitable in a situation with a larger cross-section (N), therefore overcoming
this limitation, Pesaran (2004) suggest the following: The Lagrange multiplier (CDlm) that is the scaled
version of LM test: 𝐶𝐷 =
()∑∑ 𝑇𝜌 −1
(4)
Under a cross-sectional independence of the null hypothesis with t → ∞ and then N → ∞, CDlm
test statistics follow an asymptotic normal distribution (see (Nazlioglu et al. 2011; Menyah et al. 2014;
Wolde-Rufael 2014)). In the case of larger N relative to T, the CDlm estimation is subject to size
dissertation. Therefore, Pesaran (2006) proposed the following CD test, which is suitable in a situation
when N is larger than T: 𝐶𝐷 =
()∑∑ 𝜌
(5)
The CD test followed an asymptotically standard normal distribution for investigating the null
hypothesis of cross-sectional interdependency with t
→
∞ and then N
→
∞ in any order (Nazlioglu
et al. 2011). Furthermore, the CD test might produce distorted information in a situation where the
population average pairwise correlation is zero and the individual pairwise correlation is non zero.
Limiting the negative effect, Pesaran et al. (2008) proposed the bias-adjusted LM test. 𝐿𝑀 utilize
the exact mean and variance of the LM statisitcs in case of the large panel first t
→
∞ and then N
→
∞. The bias-adjusted LM statistics can be computed with the following equation:
𝐶𝐷 =
()∑∑ ()
𝑑
⃗
(𝑁,0) (6)
Int. J. Financial Stud. 2019, 7, 61 9 of 27
where k refers to the number of regresses, 𝑢 and 𝜐
specifies the mean and variance
of(𝑇−𝐾)𝜌
, respectively.
3.2. The Symmetric Panel ARDL
The investigation begins with an assumption of the symmetric relationship between financial
inclusion and financial innovation. Therefore, the framework study used is widely known as the
pooled group mean (PGM) or panel ARDL estimation initially proposed by Pesaran and Smith (1995).
Further development was performed by Pesaran et al. (1999) and a well-defined model was proposed
to investigate the long-run association of dynamic panel data having variables integration in mix
order, either I(0) or/and I(1).
Panel ARDL, according to Pesaran et al. (1999), possesses certain advantages concerning panel
dynamic estimation such as fixed effects, random effects, instrumental estimation or the generalized
method of moments (GMM) proposed by Anderson and Hsiao (1981), Arellano (1989), and Arellano
and Bover (1995). These methods can produce spurious results unless the coefficients are identical
across the countries (da Silva et al. 2018)
The basic assumptions of PGM are first, the error correction term is free from correlation biasness
and the normally distributed regressors. Second, there is a long run relationship between the
dependent and explanatory variable, and third, the long-run parameter remains the same across the
countries. Pesaran proposed the following generalized fo rm of Pan el ARDL a s an em piri cal stru cture:
𝑦 =∑𝛽𝑦,
+∑𝛾𝑥,
+𝜇+𝜖 (7)
This study estimated both the mean group (MG) proposed by Pesaran and Smith (1995) and the
pooled grouped mean (PGM) in order to ascertain the efficient estimator for empirical investigation.
Based on the standard Hausman test, the estimate failed to reject the null hypothesis that is there is
no difference between the mean group and pooled mean grouped estimation, implying that the
pooled grouped mean estimation is preferable. Therefore, this study performs an empirical model
estimation with pooled grouped mean proposed by Pesaran et al. (1999). The pooled grouped mean
can efficiently perform notwithstanding the variable order of integration either I (0) or/and I (0) see,
(Pesaran et al. 2001; Kim et al. 2010; Fang et al. 2015).
The generalized form of pooled group mean ARDL can be represented as follows
𝛥𝐼𝐹𝐼 =𝛽 +𝛽𝐼𝐹𝐼+𝛽𝐹𝐼𝑁 +𝛽𝐹𝐷 +𝛽𝑅𝐸+∑𝛾∆𝐼𝐹𝐼
+
∑𝛾∆𝐹𝐼𝑁
+∑𝛾∆𝐹𝐷
+∑𝛾∆𝑅𝐸
+𝜇+𝜀
𝑖 = 1, … ,N; 𝑡 = 1, …, T
(8)
where the subscript t is the number of periods and i is the sample unit. The long-run coefficient can
be found from 𝛽…𝛽 and the short-run coefficient from 𝛾…𝛾. The long-run coefficients as
computed −
; −
; 𝑎𝑛𝑑 −
since in the long-run, it is assumed that ∆𝐼𝐹𝐼, ∆𝐹𝐼𝑁,
∆𝐹𝐷, and ∆𝑅𝐸 is equal to zero(0).
Equation (10) can re-specified to include an error correction term in the following ways:
𝛥𝐼𝐹𝐼 =𝜕𝜌+𝛾∆𝐼𝐹𝐼
+𝛾∆𝐹𝐼𝑁
𝛾∆𝐹𝐷
+𝛾∆𝑅𝐸
+𝜀 (9)
where 𝜌, =𝐼𝐹𝐼 −𝜑 −𝜑𝐹𝐼𝑁−𝜑𝐹𝐷−𝜑𝑅𝐸 are the linear error correction term
of each unit and the coefficient of 𝜕 is the speed of adjustment towards long-run equilibrium. The
parameters 𝜑, 𝜑, 𝜑, and 𝜑 are computed as 𝜑 =−
, 𝜑 =−
, 𝜑 =−
and 𝜑 =
−
respectively. It is noticeable from both Equations (8) and (9) that there is a decomposition effect,
i.e., positive and negative change.
Int. J. Financial Stud. 2019, 7, 61 10 of 27
3.3. Asymmetric Panel ARDL
Unlike symmetric relationship, the asymmetric investigation requires two additional sets of data
representing positive shock and negative shocks in explanatory variables in the equation. This
version of the empirical model known as non-linear panel ARDL allows for an asymmetric response
from financial development, financial innovation, and remittance inflows to financial inclusion. In
other words, under this scenario, the positive and negative shock from financial innovation, financial
development, and remittance are not expected to have identical effects on financial inclusion. Thus,
the asymmetric version of Equation (8) is represented as follows:
𝛥𝐼𝐹𝐼 =𝛽 +𝛽𝐼𝐹𝐼+𝛽𝐹𝐼𝑁 +𝛽𝐹𝐷+𝛽𝑅𝐸
+𝛾∆𝐼𝐹𝐼
+𝛾
∆𝐹𝐼𝑁
+𝛾
∆𝐹𝐼𝑁
+𝛾∆𝐹𝐷
+𝛾∆𝑅𝐸
+𝜇+𝜀
(10)
𝛥𝐹𝐼𝑁 =𝛿 +𝛿𝐼𝐹𝐼+𝛿𝐹𝐼𝑁+𝛿𝐹𝐷+𝛿𝑅𝐸
+𝜇∆𝐼𝐹𝑁
+𝜇
∆𝐼𝐹𝐼
+𝜇
∆𝐼𝐹𝐼
+𝜇∆𝐹𝐷
+𝜇∆𝑅𝐸
+𝜋+𝜀
(11)
where 𝐹𝐼𝑁& 𝐹𝐼𝑁 stand for the positive and negative shock of financial innovation and 𝐼𝐹𝐼&
𝐼𝐹𝐼 represent the positive and negative shock of financial inclusions. The long run coefficients are
computed as 𝐹𝐼𝑁=
, 𝐹𝐼𝑁=
. These shocks are computed as the positive and negative
partial sum decomposition of financial innovation and financial inclusion in the following ways:
⎩
⎪
⎨
⎪
⎧
𝐹𝐼𝑁= ∆𝐹𝐼𝑁
= 𝑀𝐴𝑋(∆𝐹𝐼𝑁,0)
𝐹𝐼𝑁= ∆𝐹𝐼𝑁
= 𝑀𝐼𝑁(𝐹𝐼𝑁,0)
(12)
⎩
⎪
⎨
⎪
⎧
𝐼𝐹𝐼= ∆𝐼𝐹𝐼
= 𝑀𝐴𝑋(∆𝐼𝐹𝐼,0)
𝐼𝐹𝐼= ∆𝐼𝐹𝐼
= 𝑀𝐼𝑁(𝐼𝐹𝐼,0)
(13)
The error correction version of Equations (10) and (11) is as follows:
Δ𝐼𝐹𝐼 =𝜏𝜉 +𝛾∆𝐼𝐹𝐼
+𝛾
∆𝐹𝐼𝑁
+𝛾
∆𝐹𝐼𝑁
+𝛿∆𝐹𝐷
+𝛾∆𝑅𝐸
+𝜇+𝜀
(14)
The error correction term (𝜉) captures the speed of adjustment to long-run equilibrium in
panel asymmetric Equation (9). On the other hand, the associated coefficient explains how long it
requires to reach in the long run equilibrium in the presence of shocks in an explanatory variable in
the short run.
GMM-System Based Panel Granger-Causality Test
For specifying directional causality between financial inclusion, financial innovation, financial
development, and remittance inflows, we followed the panel error correction model causality test
discussed by Shabani and Shahnazi (2019) in their research work. Panel Granger—causality test with
the system-GMM application perform with the two-phase. In the first, the long run model estimation
with dynamic-OLS for retrieving the residuals. Second, the residual obtained from the DOLS
estimation used as an error correction term with first lagged, which specified the existence of long-
Int. J. Financial Stud. 2019, 7, 61 11 of 27
run causality in the model. The equations for the short run and long run causality estimation are
presented below:
𝛥𝐼𝐹𝐼 =𝛽 +𝛽𝐼𝐹𝐼
+𝛽𝐹𝐼𝑁
+𝛽𝐹𝐷
+𝛽𝑅𝐸 +𝜁𝐸𝐶𝑇+𝑒
(15)
𝛥𝐹𝐼𝑁 =𝛽 +𝛽𝐹𝐼𝑁
+𝛽𝐼𝐹𝐼
+𝛽𝐹𝐷
+𝛽𝑅𝐸 +𝜁𝐸𝐶𝑇+𝑒
(16)
𝛥𝐹𝐷 =𝛽 +𝛽𝐹𝐷
+𝛽𝐼𝐹𝐼
+𝛽𝐹𝐼𝑁
+𝛽𝑅𝐸 +𝜁𝐸𝐶𝑇+𝑒
(17)
𝛥𝑅𝐸 =𝛽
+𝛽𝑅𝐸
+𝛽𝐼𝐹𝐼
+𝛽𝐹𝐼𝑁
+𝛽𝐹𝐷+𝜁𝐸𝐶𝑇+𝑒
(18)
where p represents the optimal lag length, which is determined by using Akaike’s information
criterion (AIC), this study found optimal lag for the estimation is 2, ECT stands for error correction
term for assessing long-run causality, and 𝑒 for error term
The underlying principle of using the system-GMM in determining the causality test with the
panel error correction is consistent and unbiasedness in estimation. It is because the OLS based
estimation is biased and creates an endogeneity problem in estimation (Soto 2009; Combes and Ebeke
2011). Therefore, other econometric techniques are required.
The generalized method of moments (GMM) is a commonly used econometric methodology in
panel data estimation with endogenous regressors. In the empirical literature, there are two types of
GMM estimations used, the first difference GMM estimation proposed by Arellano and Bond (1991)
and the system GMM estimation proposed by Arellano and Bover (1995), and further developments
performed by Blundell and Bond (1998). The first difference GMM estimation suffers from week
instrument and a small sample size when endogenous variables are close to a random walk (Blundell
and Bond 1998). The emergences of system-GMM estimation overcome the weaknesses in first
difference GMM estimation (Arellano 2003; Baltagi 2008; Baum et al. 2007; Han et al. 2014). The
system-GMM preforms estimating in two system equations. First, the original levels equation with a
suitable lagged first difference as instruments and first difference equation with suitable lagged level
as instruments. The application of system-GMM reduces the finite sample biased and increased the
consistency in estimation (Blundell and Bond 1998). Therefore, system-GMM estimation was
performed by using prior developed Equations (15)–(18).
The short-run and long run causality, after system GMM estimation, is identified by applying a
standard Wald test. The null hypothesis of no causality is rejected if the coefficients of 𝛽 to 𝛽 =
0 and the coefficient of ECT is statistically significant to ascertain the existence of long run causality
in the equation.
4. Results and Discussion
4.1. Panel Unit Root. Cross-Section Dependency Test, and Cointegration Test
To test stationarity in the data set, several unit root tests were performed in accordance with
existing empirical literates. The panel unit root test includes namely, Levin, Lin and Chu t proposed
by Levin et al. (2002), the Breitung t-stat proposed by Breitung (2001), Im, Pesaran and Shin W-stat
proposed by Im et al. (2003), and ADF-Fisher Chi-square proposed by Maddala and Wu (1999) test,
having the null hypothesis that all the panels contain a unit root test and the Hadri Z-stat proposed
by Hadri (2000) with the null hypothesis that all the panels are stationary. Table 2 exhibits the results
of unit root tests. The study findings exposed that both variables were stationary after the 1st
difference I(1) in all tests except the Hadri-z test, which confirms the variables were stationary at a
level I(0).
Int. J. Financial Stud. 2019, 7, 61 12 of 27
Table 2. Panel unit root test.
Test
At Level After 1st Difference
Method IFI FI FD RE ∆IFI ∆FI ∆FD ∆RE
Null: unit root (assumes common unit root process)
LLC—t (Levin et al. 2002) −1.993 1.228 0.258 0.270 −4.715 *** −4.458 *** −1.944 ** −3.475 ***
Breitung t-stat (Breitung 2001) 1.132 0.448 1.963 1.159 −1.471 *** −3.083 *** −2.375 *** −3.730 ***
Null: Unit root (assumes individual unit root process)
IPS W-stat (Im et al. 2003) 0.903 1.995 1.692 0.891 −2.876 *** −2.959 *** −5.339 ** −1.877 ***
ADF—Fisher Chi-square (Maddala and Wu 1999) 5.535 1.307 1.555 4.153 22.839 *** 23.877 *** 18.156 ** 17.194 **
PP—Fisher Chi-square 10.187 0.8541 1.060 11.825 24.090 *** 20.996 *** 38.73 *** 53.313 ***
Null hypothesis: no unit root with the common unit root process
Hadri Z-stat (Hadri 2000) 4.489 4.372 *** 5.40
7
0.422 1.776
Note: ***, ** indicates level of significance at a 1% and 5%, respectively.
Table 3 exhibits the results of a cross-section dependency test. It was observed that the associated
p-value of all four models’ output is statistically significant at 1% of the level of significance. Thus,
this confirms the rejection of the null hypothesis and in another way, the presence of cross-section
dependence in the researcher variable can be assumed. Therefore, one can expect common
dynamisms available in financial inclusion, financial innovation, financial development, and
remittance inflows.
Table 3. Cross section dependency test.
Test IFI/FIN, FD, RE
LMBP (Breusch and Pagan 1980) 50.527 (0.000)
LMPS (Pesaran 2004) 12.854 (0.000)
CDPS (Pesaran 2006) 6.896 (0.000)
LMadj (Pesaran et al. 2008) 12.700 (0.000)
In the next, the model estimation involves assessing the possible existence of cointegration
between financial innovation and financial development by applying a panel cointegration test
suggested by Pedroni (1999, 2004); and Kao (1999). Table 4 reports the results of the panel
cointegration test. The panel cointegration test by model specification by Pedroni (1999, 2004)
produced 11 test statistics based on the within-dimension and between-dimension. It is apparent that
eight (08) out of eleven (11) test statistics are statistically significant at a 1% level of significance. These
findings conclusively rejected the null hypothesis no cointegration by confirming the long-run
association between financial innovation, financial inclusion, remittance, and financial development
in South Asian countries. Further, the long-run association between financial innovation, financial
inclusion, financial development, and remittance inflows was also established in Kao (1999) panel
cointegration model specification test.
Table 4. Panel cointegration test.
Alternative Hypothesis: Common AR Coefficients (within-Dimension)
Statistic Weighted Statistic
Panel v-Statistic 12.3317 *** 7.5106 ***
Panel rho-Statistic 12.4229 *** 13.4849 ***
Panel PP-Statistic 0.7521 0.5832
Panel ADF-Statistic −1.6157 *** −1.6267 ***
Alternative Hypothesis: Individual AR Coefficients (between-dimension)
Statistic
Group rho-Statistic 1.9559
Group PP-Statistic −3.8897 **
Group ADF-Statistic −1.4324 ***
Int. J. Financial Stud. 2019, 7, 61 13 of 27
Kao (1999): Cointegration test t-Statistic
ADF −0.5152 ***
Note: ***, ** indicates level of significance at a 1% and 5% respectively.
In the next step, this study moves further towards the cointegration test with Westerlund (2007),
because this test can be performed efficiently in either case that is existent and nonexistent of cross-
sectional dependence. Westerlund proposed cointegration test produces four test statistics, two for
Group and two for Panel(𝐺,𝐺 𝑎𝑛𝑑 𝑃,𝑃 ), of testing the null hypothesis that is no cointegration.
Table 5 reports the results of the Westerlund Panel cointegration test. Considering the test statistics
and associated p-value, it is convincing to reject the null hypothesis of no cointegration. That means,
in the long run, all the variables move together regardless of their common dynamic association.
Table 5. Westerlund Panel cointegration test.
Test Statistics Value p-Value
𝐺𝑟𝑜𝑢𝑝 4.719 0.000 ***
𝐺𝑟𝑜𝑢𝑝 2.939 0.009 ***
𝑃𝑎𝑛𝑒𝑙 9.055 0.014 **
𝑃𝑎𝑛𝑒𝑙 12.005 0.000 ***
Note: ***, ** indicates statistically significant at a 1% and 5% level of significance.
4.2. Empirical Model Estimation without Asymmetry
In the next step, the model estimation involves Panel ARDL (using Equations (10) and (11)) of
identifying the coefficients elasticity both in the long run and short run. Table 6 exhibits the results
of model estimation without asymmetry, where the results with financial inclusion as the dependent
variable reported in column [1] and column [2] depict the results with financial innovation as a
dependent variable in the equation, respectively.
For the long run, referring to the output reports in column [1] with financial inclusion as a
dependent variable, the study findings unveiled the long-term positive influence running from
financial innovation to financial inclusion that is the coefficient of financial innovation (a coefficient
of 0.771 ***) is positive and statistically significant at a 1% level of significance. In particular, a 10%
increase in financial innovation results in 7.71% development in financial inclusion. This finding
suggests that further development in financial innovation that is emergence, adaptation, and
diffusion of innovative financial assets, services, and instruments in the financial system can produce
a positive progress in financial inclusion. The possible development in financial inclusion can be
observed with financial innovation embraced in the financial system in South Asian countries. It is
because financial diversifications, the expansion of financial services coverage and offering improved
financial instruments in the financial system, which is the ultimate result from financial innovation
thus, assists in bringing financially deprived population into the formal financial system.
In regards to controlling variables that are financial development and remittance inflows, the
effect running to financial inclusion also observed positively linked. More precisely, the effect of
financial development (a coefficient of 0.010) being positive in sign and statistically significant at a
1% level of significance, is implying that future financial development in the south Asian economy
can boost the speed of the financial inclusion process in the economy. The underlying reason for
augmentation in financial inclusion is financial services availability, institutional effectiveness, and
services efficiency, which play a motivational role in encouraging people to enjoy existing financial
facilities, and eventually large segments of the population will be under the head of the financial
system. On the other hand, foreign remittance inflows induce (a coefficient of 0.032) positive progress
in financial inclusion. This study observed, in particular, 10% additional inflows of foreign remittance
in the economy can result in the acceleration in the speed of financial inclusion by 0.32%. This finding
suggests that excess money flows to households encourage them to transform their status from
unbanked to financial integration by availing financial instruments for savings with the intention of
Int. J. Financial Stud. 2019, 7, 61 14 of 27
future financial security and financial services for executing financial transactions, such as mobile
banking, internet banking, and so forth.
For the short-run, the coefficient of the lagged error correction term (a coefficient of −0.895) is
negative and statistically significant at a 1% level of significance, which is confirming the average
speed of correction towards the long run equilibrium from any prior year shocks is considerable.
Dealing with short-run elasticities running from financial innovation, financial development, and
remittance inflows, this study observed both the financial innovation (a coefficient of 0.254) and
financial development (a coefficient of 0.048) positively induced the process of financial inclusion. In
particular, south Asian countries can experience positive development in financial inclusion by 2.54%
and 0.48%, respectively with a 10% increase in financial innovation and financial development in the
financial system. Meanwhile, foreign remittance produces a statistically insignificant impact on
financial inclusion even though the elasticity of foreign remittance flow (a coefficient of −0.042) is
negative in sign. The Hausman test ultimately shows that it is impossible to reject the homogenous
constraint in long-term equilibrium at a 1% level of significance, meaning the PMG estimator is
suitable and effective for estimation of the pooling long-term coefficients.
Referring to the results reported with financial innovation as the dependent variable (see Table
7, column [2]). For the long run, study findings disclosed the effect running from financial inclusion
(a coefficient of 0.566), financial development (a coefficient of 0.776), and foreign remittance inflows
(a coefficient of 0.108) in the development process of financial innovation in the financial system is
positively linked and all the coefficients are statistically significant at a 1% level of significance. In
particular, dealing with the financial inclusion effect on financial innovation, a 10% increase in
financial inclusion was observed which can result in 5.66% development in financial innovation. The
plausible interpretation is that with the increase of access to financial services by the population
results in the higher financial demand in a diversified manner, implying that a financial system
expects innovative financial assists, services and instruments availability for satisfying the
continuous financial demand by the society. Thus, intensify financial innovation flourishment with
the help of financial inclusion in the economy. Further, dealing with the nexus between financial
development-led financial innovations, this study observed, in particular, 10% increases in financial
development can augment the financial innovation process by 7.76%. This finding depicts financial
development in the economy creates an ambiance favoring the embrace of new and innovative
financial assets, services, and instruments by financial institutions so as to serve the growing financial
demands in the economy. The study also divulges the positive association between foreign
remittances inflows and financial innovation that is the 10% additional inflow of remittance results
1.08% enhancement in financial innovation. The possible interpretation stands in explaining the
relationship that is households having an excess money supply, which induces savings propensity
with future investment. Therefore, financial system experienced investment diversification demand
from households and induced financial institutions to adopt innovative financial assets and services
for satisfying the persistent demand from potential investors.
For the short run, the error correction term (a coefficient of −0.582) is negative and statistically
significant at a 1% level of significance, implying the existence of the short-run association.
Considering the coefficients elasticity, it is obvious that financial inclusion (a coefficient of 0.095),
financial development (a coefficient of 0.027), and remittance inflows (a coefficient of 0.042) are
positively supplementing the process of financial innovation development. However, only the impact
running from financial inclusion and remittance inflows are statistically significant at a 1% level of
significance. The findings suggest that a growing trend in financial innovation in the short-run can
be observed with further improvement in financial inclusion and foreign remittance inflows in South
Asian countries. The Hausman test to specify model construction and validation, produces a statistic
of 0.92 with a p-value of 0.342, providing evidence that PGM is consistent and more efficient in
producing precise and reliable results with the pre-specified empirical model.
Int. J. Financial Stud. 2019, 7, 61 15 of 27
Table 6. Model estimation results without asymmetry.
Empirical Model Estimation
Financial Inclusion as Dependent Variable [1] Financial Inclusion as Dependent Variable [2]
Long-run elasticities
FIN 0.771 *** (0.306) -
IFI - 0.566 *** (0.167)
FD 0.010 *** (0.003) 0.776 * (0.017)
RE 0.032 *** (0.008) 0.108 ** (0.082)
Short-run elasticizes
ECT(−1) −0.859 *** (0.093) −0.582 ** (0.576)
∆FIN 0.254 *** (0.490)
∆IFI 0.095 ** (0.824)
∆FD 0.048 ** (0.010) 0.027 (0.055)
∆RE −0.043 (0.022) 0.042 **(0.108)
C 0.161 *** (0.351) −1.80 ** (1.651)
Hausman test 1.02 (0.627) 0.92 (0.342)
Log-likelihood 100.6903 83.81886
Note: ***, ** indicates level of significance at a 1% and 5% respectively.
Table 7. Empirical model estimation with asymmetry.
Model Estimation
Financial Inclusion as Dependent Variable [1] Financial Innovation as Dependent Variable [2]
Panel—A: Long-run model coefficients
FIV+ 0.260 *** (0.187)
FIV− 0.705 ** (0.548)
FIC+ 0.036 ** (0.098)
FIC- 0.443 *** (0.355)
FD 0.025 *** (0.078) 0.218 *** (0.109)
RE 0.031 ** (0.090) 0.115 *** (0.069)
Panel—B: Short—rum model coefficients
ECT(−1) −0.345 ** (0.329) −0.532 *** (0.217)
∆FIV+ 0.987 *** (0.4267)
∆FIV- 0.752 *** (0.443)
∆IFI+ −0.478 (0.201)
∆IFI- 0.478 *** (0.136)
∆FD 0.197 ** (0.157) 0.160 * (0.195)
∆RE 0.023 (0.019) 0.403 *** (0.128)
Panel—C: Test of symmetry
WLR 6.973 *** 15.220 ***
WSR 11.983 *** 15.342 ***
Hausman test 11.542 (0.416) 9.348 (0.4994)
Log-likelihood 128.394 273.983
Note: ***, ** indicates statistically significant at a 1% and 5% level of significance, respectively. Values
in () are standard error.
4.3. Empirical Model Estimation with Asymmetry
In this section, empirical model estimation involves the asymmetry that is the investigation of
positive and negative shocks of the independent variables on the dependent variable. The empirical
model estimation results exhibited in Table 7, column [1] contains model estimation with financial
inclusion as a dependent variable and column [2] contains model output with financial innovation as
a dependent variable, respectively.
Considering the results presented in column [1], assessing the long run and short-run
asymmetry effects of financial innovation, a standard Wald test was executed with the null
hypothesis that is “there are symmetric effects running from financial innovation to financial
inclusion. The Wald test F-statistics for the long run (a coefficient of WLR = 6.973) and for the short-run
Int. J. Financial Stud. 2019, 7, 61 16 of 27
(a coefficient of WSR = 11.983) clearly reject the null hypothesis in both situations. Alternatively, the
Wald test statistics confirm the asymmetry effect running from financial innovation to financial
inclusion. In regards to model consistency and precision in the empirical estimation, the Hausman
test produces statistics of 11.542 with a statistically insignificant p-value of 0.416, confirming that the
empirical model is consistent and more efficient in producing variables elasticities.
For the long run, it is ostensible that both positive shocks (a coefficient of 0.260) and negative
shocks (a coefficient of 0.750) in financial innovation are positively linked with financial inclusion.
These study findings imply that future changes experienced by financial innovation in either
direction that increases or decreass in both situations, financial inclusion is affected. In particular, a
10% increase in positive shocks in financial innovation can result in 2.6% growth in financial
inclusion, meanwhile, a 10% decrease in financial innovation can cause 7.05% declined trend in
financial inclusion. The magnitude of the negative shock is greater than the elasticity of the positive
shock of financial innovation on financial inclusion. On the other hand, for the short-run it is palpable
that, like long run model, the positive shocks (a coefficient of 0.987) and negative shocks (a coefficient
of 0.752) in financial innovation are positively linked to financial inclusion. More precisely, a 10%
growth in positive shocks in financial inclusion can result in a 9.87% improvement in financial
inclusion, meanwhile, a 10% decrease in financial inclusion can cause a 7.52% deterioration in
financial inclusion. The study findings suggest that the contraction financial policy might have
adverse causes in the process of financial innovation and the eventual outcome could be experienced
by the economy by obstructing the development process of financial inclusion with greater intensity.
Therefore, financial policy pertinent to financial stability and financial expansion is inevitable in
order to augment the existing financial inclusion trend in the economy with innovative financial
assets and services in the form of financial innovation.
For control variables, in the long run, the effect running from financial development (a
coefficient of 0.025) and remittance inflows (a coefficient of 0.031) are positive and statistically
significant at a 1% level of significance. Furthermore, in the short run, analogous to the long run, the
influence running from financial development (a coefficient of 0.197) and remittance inflows (a
coefficient of 0.023) also depicts positively with financial inclusion.
Referring to the results conveyed in column [2] with financial innovation as a dependent
variable, the presence of asymmetry effects of financial inclusion on financial innovation both in the
long run and in the short-run were investigated by applying the standard Wald test suggested with
the null hypothesis of symmetry. The Wald test F-statistics in the long run (a coefficient of 15.220)
and in the short-run (a coefficient of 15.342) are statistically significant at a 1% level of significance,
implying the rejection of the null hypothesis. The study findings suggest asymmetry effects running
from financial inclusion to financial innovation, which is applicable in both the long run and short
run. In regards to the positive and negative shocks in financial inclusion, this study observed in the
long-run, positive shocks (a coefficient of 0.036) and negative shocks (a coefficient of 0.443) were
positively linked with financial innovation. In particular, a 10% increase in financial inclusion can
result in a 0.36% increase in financial innovation. On the other hand, a 10% decrease in financial
inclusion can decrease the financial innovation evolvement in the financial system by 4.443%. It is
clearly manifested that negative shocks in financial inclusion produce significant magnitudes of
positive shocks in financial inclusion. Therefore, it is imperative to formulate financial policies in such
a way so as the existing process of financial inclusion moves with ease without facing any blockage,
because of the impediment in financial inclusion adversely deterring the normal process of financial
innovation.
For the short-run, the coefficient of the error correction term was observed to be negative and
statistically significant, implying the existence of a short-run convergence between financial
innovation and financial inclusion. Considering the short-run coefficients, the study findings divulge
a positive shock in financial inclusion is (a coefficient of −0.478) negatively linked to financial
innovation. However, the magnitude is statistically insignificant. Therefore, in the positive variation
of financial inclusion did not make any considerable impact on financial innovation. In contrast, the
negative shock (a coefficient of 0.478) in financial inclusion is also positively associated and the
Int. J. Financial Stud. 2019, 7, 61 17 of 27
coefficient is statistically significant at a 1% level of significance. These findings suggest that a 10%
decrease in financial inclusion results in a decrease trend in financial innovation by 4.78%. In line
with the study findings regarding a negative variation in financial inclusion is critical for
innovativeness in the financial system. That is why, the speed of financial inclusion might not be
hampered due to financial policy, if so happened, financial innovation also affected at large.
For control variables, namely, financial development and remittance inflows, it is apparent from
the finding that the coefficients elasticities, such as financial development (a coefficient of 0.443) and
remittance inflows (a coefficient of 0.218), exhibited a positive relationship. The study findings
suggest that future development in the financial sector could explore more opportunities in the
economy for the adaptation and diffusion of financial innovation, which eventually allows greater
financial diversity with innovative financial instruments, services, and institutions. Further, the
continual inflows of foreign remittance also intensifies the demand for financial products and services
that are the processes of financial innovation will receive a positive injection for further development.
4.4. Post Model Estimation with System-GMM Specification
In the next step, this study moved to investigate the robustness of the pre-specified empirical
model for explaining the nexus between financial inclusion and financial innovation. Table 8 reports
the results of the System-GMM empirical model. Panel-A indicates the short-run model estimation
with a symmetry test along with the coefficient of error correction term and Panel-B exhibits the long-
run model estimation with the symmetry test. From the model stability and validity diagnostic test
statistics that are the conventional AR (2) and Sargan test, it was observed that the null hypothesis
was not rejected at a 1% level of significance, implying that all regressors were valid instruments.
This conclusion is applicable for both models. For investigating the symmetric relation, a standard
Wald test was performed with the null hypothesis of symmetry in both the long run and short run.
For the short run, the findings from the Wald test with financial inclusion as the dependent variable
was (a coefficient of WSR = 12.705) and Wald test with financial innovation as the dependent variable
was (a coefficient of WSR = 18.873). This finding suggests that in the short-run, the relationship
between financial innovation and financial inclusion is asymmetric.
Finally, in the long-run symmetry, this study observed that the Wald test statistics (a coefficient
of WLR = 20.607, and WLR = 25.983) in both models were statistically significant at a 1% level of
significance. The results of the Wald test ascertain the existence of an asymmetric relationship
between financial innovation and financial inclusion. This conclusion is applicable for both empirical
models.
Table 8. Short- and long-run generalized method of moments (GMM) estimates and symmetry tests.
Model Estimation
Financial Inclusion as Dependent Variable [1] Financial Innovation as Dependent Variable [2]
Panel-A: short-run coefficients
IFI(−1) 0.537 *** [0.108]
FIN(−1) - 0.857 *** [0.060]
∆IFI+ - 0.029 ** [0.659]
∆IFI− - 0.815 ** [0.620]
∆
FIN+ 0.059 ** [0.064] -
∆FIN− 0.026 ** [0.036] -
Speed of adjustment 0.534 0.763
AR(2) 0.418 (0.675) −1.324 (0.553)
Sargon test 55.694 (0.156) 63.260 (0.723)
WSR 12.705 ** 18.873 (0.000)
Panel-B: long-run
IFI+ - 0.560 ** [0.252]
IFI− - 0.255 *** [1.318]
FIN+ 0.064 *** [0.035] -
FIN− 0.033 *** [0.088] -
WLR 21.607 *** 25.983 ***
Control variable
FD 0.016 *** [0.074] 1.118 *** [0.148]
RE 0.018 *** [0.237] 0.769 *** [0.102]
Int. J. Financial Stud. 2019, 7, 61 18 of 27
None: **, *** denotes level of significant at a 5% and 1% respectively. Values at parenthesis () indicates
standard error.
4.5. Panel Granger-Casualty with System-GMM Specification
This section moved to investigate the directional causality between financial inclusion, financial
innovation, financial development, and remittance inflows. To accomplish this, the process initiated
by Shabani and Shahnazi (2019) with the system-GMM specification was followed. The results of the
causality test are exhibited in Table 9. The presence of long-term causality in the empirical model can
be ascertained by observing the coefficients of ECT of each model. For long-run causality, the
coefficient of error correction term, in particular when financial inclusion, financial innovation, and
remittance inflows are considered as the dependent variable, are negative in sign and statistically
significant at a 1% level of significance. More precisely, the study findings divulge bidirectional
casualty between financial innovation and financial inclusion [IFI ⟷ FIN], implying that in south
Asian economy feedback hypothesis holds in explaining the causal relationship between financial
inclusion and financial development.
For short-run causality, the study findings unveiled a number of the casual relationship among
research variables. In particular, the feedback hypothesis holds for explaining the relationship
between financial inclusion and financial innovation [IFI ⟷ FIN], remittance inflows and financial
inclusion [IFI ⟷ RE]. These findings suggest that in the short run, any further development in either
variable namely, financial inclusion, financial innovation, and remittance inflows, show that the
ultimate effects can be observed in the associated variables. Furthermore, the study findings unveiled
unidirectional causality running from financial development to financial inclusion [FD → IFI] and
financial development to remittance inflows [FD → RE].
Table 9. Causality test with GMM specification.
Dependent Variable Short-Run Casualty Long-Run Causality
IFI FIN FD RE ECT(−1) Remarks
IFI - 8.347 *** 0.554 14.682 *** −0.0253 *** Presence
FIN 12.250 *** - 4.534 ** 1.411 −0.165 ** Presence
FD 0.155 13.092 *** - 1.854 0.135
RE 12.180 *** 1.774 9.184 *** −0.773 *** Presence
Note: ***, ** indicates statistically significant at a 1% and 5% level of significance, respectively.
5. Summary and Concluding Remarks
The relationship between financial innovation and financial inclusion is yet to undergo extensive
empirical investigation. This study, therefore, intended to mitigate the existing research gap by
unsheathing new insights pertinent to explain how financial inclusion and financial innovation
behave in the financial system due changes appeared in either variable. As a sample, we considered
six (06) South Asian countries covering a span of period 1990M1−2018M12. This study used monthly
data for empirical model estimation, which was exported from a central bank database of respective
countries. For the empirical investigation, a number of econometric methodologies were employed
including, PGM Panel ARDL proposed by Pesaran et al. (1999) and non-linear Panel ARDL by the
following model specifications proposed by Shin et al. (2014). Furthermore, by establishing the
directional causality between financial innovation, financial inclusion, remittance, and financial
development, the Granger-causality test with System-GMM specification following Shabani and
Shahnazi (2019) were applied. The key findings of this study are stated below:
First, referring to empirical model estimation with symmetry assumption (see, Table 7). Study
findings established a positive association between financial innovation and financial inclusion both
in the long run and short run. These findings suggesting that financial sector development either
encouraging adaption of improving financial services, instruments and institutions can progressively
encourage financial innovativeness in the financial system and vice-versa. Furthermore, for ensuring
financial efficiency or ensuring financial services easy access to the society and the pull unbanked
Int. J. Financial Stud. 2019, 7, 61 19 of 27
population into the formal financial system in both cases, the eventual effects can be observed in
financial inclusion and financial innovation. Hence, it can be assumed that a bidirectional relationship
prevails between them.
Second, the empirical model estimation with asymmetry assumption, study findings suggesting
that both financial innovation and financial inclusion will be experienced greater intensity in either
case of improvement in the financial system such as financial innovativeness or financial integration.
Considering the asymmetric response that is positive and negative shocks, we observed that in the
case of financial innovation both positive and negative shocks are positively linked with financial
inclusion. On the other hand, the asymmetric effects of financial inclusion that is positive and
negative shocks on financial innovation also positively associated. Therefore, it is important to
monitor and take necessary initiatives by financial regulatory authorizes concentrating financial
development so that the growth trend in financial innovation and financial inclusion remain stable,
especially in the long term.
Third, considering the output of the Granger causality test. A feedback hypothesis holds
explaining the causality between financial inclusion and financial innovation both in the long run
and short run. The study findings suggested that financial sector development with either variable
amplification that is, financial innovation or financial inclusion, the subsequent effect be observed in
another variable. On the other hand, any financial policies anticipated for limiting the financial
activities results, not only adverse consequences in financial inclusion, but also in financial
innovation.
Considering the results explain above, it is obvious that the intertwined relationship between
financial innovation and financial inclusion is critically important for vibrant financial sectors. This
is why government and policymakers should consider all aspects of financial innovation and
financial inclusion affects, not only each other, but also impacts on economic activities so that fiscal
policy can effectively guide further development in financial innovation and financial inclusions. In
addition, financial institutions should expand their financial activities by incorporating newly
emerged financial assets and services that are effectively work-out in other countries and allow
financial services to all with easy access.
Author Contributions: The concept and design of this article come from W.J. along with model formulation.
Data collection, empirical study review of conceptual development and drafting done by M.Q. In final editing
and overall development effort contribute by authors in the article, the ration of contribution equally likely.
Funding: This research received no external funding
Conflicts of Interest: The authors declare no conflicts of interest.
Appendix A. Details of Financial Innovation and Financial Inclusion Index Construction
Financial Innovation
Financial innovation, according to Tufano (2002), is the process of emergence, diffusion, and
popularization of new financial instruments, financial institutions, financial technologies, and
financial markets in the economy. The presence of financial innovation in the financial system can be
addressed in two different wings, such as product innovation and process innovation. Over the last
decade, a number of proxy indicators were used in the empirical study of addressing the effects of
financial innovation on various aspects. Aligning with existing literature in this study, we usd three
proxy indicators (see, Table A1) were used and moved to developed the financial innovation index
by applying principle component analysis (PCA), which is used as a proxy for financial innovation.
Table A1. Financial innovation proxy indicators.
Indicator Definition Reference
M3/M1 The ratio of Aggregate money
supply to Narrow money
Dunne and Kasekende (2018); Hye (2009); Mannah-Blankson and
Belnye (2004); Qamruzzaman and Wei (2017, 2018a, 2018b, 2018c)
M2/M1 The ratio of Broad to narrow
money Qamruzzaman and Wei (2017, 2018a, 2018b, 2018c)
Int. J. Financial Stud. 2019, 7, 61 20 of 27
Growth of DCP
The percentage change in
domestic credit to the private
sector
Ajide (2015); Michalopoulos et al. (2011)
Financial innovation composite index
Financial Inclusion Index
The importance of financial inclusion emerged due to nearly 3 billion of the population being
excluded from formal financial services known as financial exclusion (world bank). The concept of
financial inclusion is subject to country-specific financial exclusion and related macroeconomic
variables. Therefore, no agreeable and consensus definition is yet to appear in finance literature.
Financial inclusion, according to Gwalani and Parkhi (2014), is the way of availing and utilizing
formal financial services at a lower cost and affordable to reduce informal accounts. In other words,
financial inclusion means accessibility, availability, and the use of all formal financial services to all
(Kumar and Mohanty 2011). It is implying that the provision of access to financial services with the
minimum cost along with the efficient financial intermediation by the financial system.
Furthermore, addressing the effects of financial inclusion with single indicators is not so
straightforward. Since over the period, researchers in empirical studies used a number of proxy
indicators see, Table A2 and some of the study used the index by constructing more than one
indicator.
Table A2. Financial inclusion proxy indicators.
Dimension Definition Reference
Banking penetration Depositors with
commercial Banks
Adeola and Evans (2017); Evans (2015); Naceur et al.
(2015); Sarma (2008, 2012); Mbutor and Uba (2013
Access
ATMs per 100,000 adults Adeola and Evans (2017); Mookerjee and Kalipioni
(2010); Rasheed et al. (2016)
commercial bank branches
per 100,000 adults
Sarma (2008); Kumar (2013); Rasheed et al. (2016);
Gimet and Lagoarde-Segot (2012)
Usage Credit-credit to the private
sector Sarma (2008, 2012)
Before empirically investigating the nexus between financial innovation and financial inclusion
in south Asian countries, our own index of financial inclusion (IFI) was first constructed.
Constructing the index of financial inclusion, the authors closely followed the multidimensional
methodology proposed by Sarma (2008) with accessibility, availability, and usages of banking
services.
First, availability has been measured by two proxy indicators namely, automated teller machines
(ATM) per 100,000 adults and commercial bank branches per 100,000 adults following Adeola and
Evans (2017); Mookerjee and Kalipioni (2010); Rasheed et al. (2016); Sarma (2008); Kumar (2013);
Rasheed et al. (2016); Gimet and Lagoarde-Segot (2012). Second, accessibility has been measured by
the penetration of banking services proxied by the number of depositors with commercial banks per
1000 adults following Adeola and Evans (2017). Evans (2015); Naceur et al. (2015); Sarma (2008, 2012);
Mbutor and Uba (2013). Third, the proxy was used to measure the usage dimension by the total
deposits and credits relative to gross domestic product by following Sarma (2008, 2012). Table A2
represents the financial inclusion proxy indicators of south Asian countries in the year of 2017.
The study now proceeds to estimate the dimension index for each dimension by following the
Sarma (2008) specification using the following formula:
𝑑=𝐴−𝑚
𝑀−𝑚 (A1)
where 𝐴 is the actual value of dimension i, 𝑀 is the maximum value of dimension i, and 𝑚 is the
minimum value of dimension i, respectively, the output from formula ensure that 0<𝑑<1. The
higher the value 𝑑 indicates the higher the achievement in dimension i.
Int. J. Financial Stud. 2019, 7, 61 21 of 27
For availability dimension, first the dimension index was determined for each dimension, a
weighted average dimension index construct by allowing a two-third weight for a bank branch and
one-third for the ATM index for the availability dimension as suggested by Sarma (2008). The index
of financial inclusion then measured, with a given weight such as 1 for the index of bank penetration,
0.5 for Availability and 0.5 for usage, by the normalized inverse Euclidean distance of the point Di
from the ideal point I = (1, 1, 1, … 1). The exact formula is
𝐼𝐹𝐼=1−
(1−𝑃)+(0.5−𝐴)+(0.5−𝑈)
√
𝑛 (A2)
This study also observed in empirical literatures that financial inclusion is also affected by other
macroeconomic variables such as financial development, foreign remittance receipts, microfinance
institutions, foreign direct investment, trade openness see, e.g., (Aga and Peria 2014; Aggarwal et al.
2011; Anzoategui et al. 2011; Chowdhury 2011). Therefore, enhancing estimation robustness, two
more variables pertinent to the existing literature were included as the control variable namely,
financial development and remittance received. To capture the effect of financial development in the
model, the commonly used financial development indicator the ratio of broad money to GDP can be
considered see, (Calderón and Liu 2003; King and Levine 1993; Nyamongo et al. 2012), In addition,
remittance inflows proxied by remittance inflows to GDP (%).
Table A3. Variables Descriptions and Sources.
Variable Indicators Description Data Sources
Financial
inclusion
Banking
penetration Depositors with commercial Banks Reserve Bank of India (2019);
State bank of Pakistan (2019);
Central Bank of Sri Lanka (2019);
Nepal Rastra Bank (2019); Royal
Monetary Authority (2019) and
Bangladesh Bank (2019).
Access ATMs per 100,000 adults
commercial bank branches per
100,000 adults
Usage Credit-credit to the private sector
Financial
inclusion index
Authors calculation using multidimensional methodology proposed by
Sarma (2008)
Financial
Innovation
M3/M1 The ratio of Aggregate money
supply to Narrow money
Reserve Bank of India (2019);
State bank of Pakistan (2019);
Central Bank of Sri Lanka (2019);
Nepal Rastra Bank (2019); Royal
Monetary Authority (2019) and
Bangladesh Bank (2019).
M2/M1 The ratio of Broad to narrow
money
Growth of DCP The percentage change in domestic
credit to the private sector
Financial
innovation
index
Authors calculation using Principal component analysis (PCA)
Financial
development
Domestic credit
to the private
sector (% of
GDP)
Domestic credit to private sector
refers to financial resources
provided to the private sector by
financial corporations, such as
through loans, purchases of
nonequity securities, and trade.
International financial statistics
(IMF)
Remittance
Inflows
Per capital
remittance
received
Personal remittances comprise
personal transfers and
compensation of employees.
Personal transfers consist of all
current transfers in cash or in-kind
made or received by resident
International financial statistics
(IMF), World Development
Indicator (WB)
Int. J. Financial Stud. 2019, 7, 61 22 of 27
References
Adeola, Ogechi, and Olaniyi. Evans. 2017. Financial inclusion, financial development, and economic
diversification in Nigeria. The Journal of Developing Areas 51: 1–15.
Aga, Gemechu Ayana, and Maria Soledad Martinez Peria. 2014. International Remittances and Financial Inclusion
in Sub-Saharan Africa. Washington, DC: The World Bank.
Aggarwal, Reena, Asli Demirgüç-Kunt, and Maria Soledad Martinez Peria. 2011. Do remittances promote
financial development? Journal of Development Economics 96: 255–64.
Ajide, Folorunsho M. 2015. Financial Innovation and Sustainable Development in Selected Countries in West
Africa. Innovation in Finance 15: 85–112.
Allen, Franklin. 2011. Trends in Financial Innovation and Their Welfare Impact An Overview. Paper presented
at Welfare Effects of Financial Innovation, UK, London, November 10–11.
Allen, Franklin, Elena Carletti, Robert Cull, Jun ‘QJ’ Qian, Lemma Senbet, and Patricio Valenzuela. 2014. The
African financial development and financial inclusion gaps. Journal of African Economies 23: 614–42.
Anderson, Theodore Wilbur, and Cheng Hsiao. 1981. Estimation of dynamic models with error components.
Journal of the American Statistical Association 76: 598–606.
Anzoategui, Diego, Asli Demirgüç-Kunt, and María Soledad Martínez Pería. 2011. Remittances and Financial
Inclusion: Evidence from El Salvador. Washington, DC: The World Bank.
Arellano, Manuel. 1989. A note on the Anderson-Hsiao estimator for panel data. Economics Letters 31: 337–41.
Arellano, Manuel. 2003. Panel data Econometrics. Oxford: Oxford University Press.
Arellano, Manuel, and Stephen Bond. 1991. Some tests of specification for panel data: Monte Carlo evidence and
an application to employment equations. The Review of Economic Studies 58: 277–97.
Arellano, Manuel A., and Olympia Bover. 1995. Another look at the instrumental variable estimation of error-
components models. Journal of Econometrics 68: 29–51.
Ashraf, Nava, Diego Aycinena, Claudia Martinez, and Dean Yang. 2010. Remittances and the Problem of Self
Control: A Field Experiment among Migrants from El Salvador. Working Paper. Ann Arbor: Economics
Department, University of Michigan.
Babajide, Abiola A., Folasade B. Adegboye, and Alexander E. Omankhanlen. 2015. Financial inclusion and
economic growth in Nigeria. International Journal of Economics and Financial Issues 5: 629–37.
Baltagi, Badi. 2008. Econometric Analysis of Panel Data. Hoboken: John Wiley & Sons.
Banerjee, Prashanta K., Matiur Rahman, and JP Morgan Chase Bank. 2018. Financial Inclusion and Economic
Growth Empirics of Six South Asian Countries. Wealth: International Journal of Money, Banking & Finance 7:
4–9.
Bangladesh Bank. 2019. Online Data Archive. Available online: https://www.bb.org.bd/econdata/index.php
(accessed on 15 March 2019)
Bara, Alex, and Calvin Mudxingiri. 2016. Financial innovation and economic growth: Evidence from Zimbabwe.
Investment Management and Financial Innovations 13: 65–75.
Bara, Alex, Gift Mugano, and Pierre Le Roux. 2016. Financial Innovation and Economic Growth in the SADC.
Economic Research Southern Africa 1: 1–23.
Battilana, Julie, and Silvia Dorado. 2010. Building sustainable hybrid organizations: The case of commercial
microfinance organizations. The Academy of Management Journal 3: 1419–40.
Baum, Christopher F., Mark E. Schaffer, and Steven Stillman. 2007. Enhanced routines for instrumental
variables/generalized method of moments estimation and testing. The Stata Journal 7: 465–506.
Beck, Thorsten. 2010. Financial Development and Economic Growth: Stock Markets versus Banks? In Africa’s
Financial Markets: A Real Development Tool. Tilburg: Tilburg University.
Beck, Thorsten, Asli Demirgüç-Kunt, and Ross Levine. 2007. Finance, inequality and the poor. Journal of Economic
Growth 12: 27–49.
Bhatt, Vinayak Vijayshanker, and Banco Mundial. 1989. Financial Innovation and Credit Market Development.
Washington, DC: World Bank.
Bianchi, Javier, Emine Boz, and Enrique Mendoza. 2011. Macro-prudential Policy in a Fisherian Model of
Financial Innovation. Paper presented at 12th Jacques Polak Annual Research Conference, Washington,
DC, USA, November 10–11, pp. 1–15.
Blundell, Richard, and Stephen Bond. 1998. Initial conditions and moment restrictions in dynamic panel data
models. Journal of Econometrics 87: 115–43.
Int. J. Financial Stud. 2019, 7, 61 23 of 27
Breitung, Jörg. 2001. The local power of some unit root tests for panel data. Nonstationary Panels, Panel
Cointegration, and Dynamic Panels. Bingley: Emerald Group Publishing Limited, pp. 161–77.
Breusch, Trevor S., and Adrian R. Pagan. 1980. The Lagrange multiplier test and its applications to model
specification in econometrics. The Review of Economic Studies 47: 239–53.
Burgess, Robin, and Rohini Pande. 2005. Do rural banks matter? Evidence from the Indian social banking
experiment. American Economic Review 95: 780–95.
Calderón, César, and Lin Liu. 2003. The direction of causality between financial development and economic
growth. Journal of Development Economics 72: 321–34.
Camelia, Șargu Alina, and Roman Angela. 2011. Financial innovation and the Romanian banking sector
efficiency in the context of the financial crisis: Foreign versus domestic banks. Lessons Learned from the
Financial Crisis. Paper presented at 13th International Conference on Finance and Banking, Ostrava, Czech
Republic, October 12–13, pp. 548–65.
Central Bank of Sri Lanka. 2019. Online Data Archive, Available online:
https://www.cbsl.gov.lk/en/statistics/statistical-tables/financial-sector (accessed on 12 February 2019).
Chibba, Michael. 2009. Financial Inclusion, Poverty Reduction and the Millennium Development Goals. The
European Journal of Development Research 21: 213–30.
Chipeta, Chimwemwe, and Moses M. Muthinja. 2018. Financial innovations and bank performance in Kenya:
Evidence from branchless banking models. South African Journal of Economic and Management Sciences 21: 1–
11.
Chowdhury, Mamta B. 2011. Remittances flow and financial development in Bangladesh. Economic Modelling 28:
2600–8.
Combes, Jean-Louis, and Christian Ebeke. 2011. Remittances and household consumption instability in
developing countries. World Development 39: 1076–89.
da Silva, Patrícia Pereira, Pedro André Cerqueira, and Wojolomi Ogbe. 2018. Determinants of renewable energy
growth in Sub-Saharan Africa: Evidence from panel ARDL. Energy 156: 45–54.
Demetriades, Panicos O., and Kul B. Luintel. 1996. Financial Development, Economic Growth and Banking
Sector Controls: Evidence from India. The Economic Journal 106: 359–74.
Demirgüç-Kunt, Asli, and Leora Klapper. 2012. Financial Inclusion in Africa: An Overview. Washington, DC: The
World Bank.
Duasa, Jarita. 2014. Financial Development and Economic Growth: The Experiences of Selected OIC Countries.
International Journal of Economics and Management 8: 215–28.
Dunne, John Paul, and Elizabeth Kasekende. 2018. Financial Innovation and Money Demand: Evidence from
Sub-Saharan Africa. South African Journal of Economics 86: 428–48.
Dupas, R., and Jonathan Robinson. 2009. Savings Constraints and Microenterprise Deveiopment: Evidence from
a Field Experiment. NBER Working Paper Series. Cambridge: NBER.
Epstein, Stephan R. 1992. Regional Fairs, Institutional innovaton and Economic Growth in late Medieval Europe.
London: London School of Economics & Political Science, vol. 48.
Evans, Olaniyi. 2015. The Effects of Economic and Financial Development on Financial Inclusion in Africa.
Review of Economics and Development Studies 1: 17–25.
Fang, WenShwo, Stephen M. Miller, and Chih-Chuan Yeh. 2015. The effect of growth volatility on income
inequality. Economic Modelling 45: 212–22.
Frame, W. Scott, and Lynn Woosley. 2004. Credit scoring and the availability of small business credit in lowand
moderate-income areas. Financial Review 39: 35–54.
Galor, Oded, and Joseph Zeira. 1993. Income distribution and macroeconomics. The Review of Economic Studies
60: 35–52.
Gimet, Céline, and Thomas Lagoarde-Segot. 2012. Financial sector development and access to finance. Does size
say it all? Emerging Markets Review 13: 316–37.
Glaeser, Edward L., Rafael La Porta, Florencio Lopez-de-Silanes, and Andrei Shleifer. 2004. Do Institutions Cause
Growth? Cambridge: National Bureau of Economic Research.
Gwalani, Hema, and Shilpa Parkhi. 2014. Financial inclusion—Building a success model in the Indian context.
Procedia—Social and Behavioral Sciences 133: 372–78.
Hadri, Kaddour. 2000. Testing for stationarity in heterogeneous panel data. The Econometrics Journal 3: 148–61.
Han, Chirok, Peter CB Phillips, and Donggyu Sul. 2014. X-differencing and dynamic panel model estimation.
Econometric Theory 30: 201–51.
Int. J. Financial Stud. 2019, 7, 61 24 of 27
Hargrave, Timothy J., and Andrew H. Van de Ven. 2006. A collective action model of institutional Innovation.
Academy of Management Review 5: 864–88.
Hye, Qazi Muhammad Adnan. 2009. Financial innovation and demand for money in Pakistan. The Asian
Economic Review 51: 219–28.
Im, Kyung So, M. Hashem Pesaran, and Yongcheol Shin. 2003. Testing for unit roots in heterogeneous panels.
Journal of Econometrics 115: 53–74.
Johnson, Simon., and James Kwak. 2012. Is Financial Innovation Good For The Economy? In Innovation Policy
and the Economy. Edited by J. Lerner and Scott Stern. Chicago: University of Chicago Press, pp. 1–15.
Kamau, Daniel Mwangi, and Josphat Oluoch. 2016. Relationship between Financial Innovation and Commercial
Bank Performance in Kenya. International journal of Social Science and Information Technology 2: 1–19.
Kamboj, Sonia. 2014. Financial inclusion and growth of Indian economy: An empirical analysis. The International
Journal of Business & Management 2: 175.
Kao, Chihwa. 1999. Spurious regression and residual-based tests for cointegration in panel data. Journal of
Econometrics 90: 1–44.
Kasekende, Elizabeth. 2016. Financial Innovation and Money Demand in Sub-Saharan Africa. Ph.D. thesis,
University of Cape Town, Cape Town, South Africa.
Kim, Dai-Won, Jung-Suk Yu, and M. Kabir Hassan. 2010. Dynamic effects of trade openness on financial
development. Economic Modelling 27: 254–61.
Kim, Dong-Hyeon, Shu-Chin Lin, and Yu-Bo Suen. 2018. Financial inclusion and economic growth in OIC
countries. Research in International Business and Finance 43: 1–14.
Kim, Jong-Hee. 2016. A study on the effect of financial inclusion on the relationship between income inequality
and economic growth. Emerging Markets Finance and Trade 52: 498–512.
King, Robert G., and Ross Levine. 1993. Finance, entrepreneurship and growth. Journal of Monetary Economics 32:
513–42.
Kumar, Basant, and Brajaraj Mohanty. 2011. Financial Inclusion and Inclusive Develoment in SAARC Countries
with Special Reference to India. Vilakshan: The XIMB Journal of Management 8: 13–22.
Kumar, Nitin. 2013. Financial inclusion and its determinants: Evidence from India. Journal of Financial Economic
Policy 5: 4–19.
Laeven, Luc., Ross Levine, and Ross Levine. 2014. Financial Innovation and Endogenous Growth. Stanford: Stanford
University.
Laeven, Luc, Ross Levine, and Stelios Michalopoulos. 2015. Financial innovation and endogenous growth.
Journal of Financial Intermediation 24: 1–24.
Levin, Andrew, Chien-Fu Lin, and Chia-Shang James Chu. 2002. Unit root tests in panel data: Asymptotic and
finite-sample properties. Journal of Econometrics 108: 1–24.
Lumpkin, Stephen. 2010. Regulatory issues related to financial innovation. OECD Journal: Financial Market Trends
2: 1–31.
Maddala, Gangadharrao S., and Shaowen Wu. 1999. A comparative study of unit root tests with panel data and
a new simple test. Oxford Bulletin of Economics and Statistics 61: 631–52.
Makini, Samwel Omwenga. 2010. The Relationship between Financial Innovation and Financial Performance of
Commercial Banks in Kenya. Master’s thesis, University of Nairobi, Nairobi, Kenya.
Mannah-Blankson, Theresa, and Franklin Belnye. 2004. Financial Innovation and the Demand for Money in Ghana.
Accra: Bank of Ghana, pp. 1–23.
Mbutor, Mbutor O., and Ibrahim A. Uba. 2013. The impact of financial inclusion on monetary policy in Nigeria.
Journal of Economics and International Finance 5: 318–26.
McGuire, Paul, and John Conroy. 2013. Fostering financial innovation for the poor The policy and regulatory
environment. In Private Finance for Human Development. Edited by Pischke Awajdv. Washington, DC: The
Foundation for Development Cooperation.
Menyah, Kojo, Saban Nazlioglu, and Yemane Wolde-Rufael. 2014. Financial development, trade openness and
economic growth in African countries: New insights from a panel causality approach. Economic Modelling
37: 386–94.
Okere, Peter A., Ndugbu Michael, J. N. Ojiegbe, and Barr Lawrence Uzowuru. 2015. Bank and Non-Bank
Financial Institutions and The Development Of The Nigerian Economy. International Journal for Innovation
Education and Research 10: 23–36.
Int. J. Financial Stud. 2019, 7, 61 25 of 27
Michalopoulos, Stelios , Luc Laeven, and Ross Levine. 2009. Financial Innovation and Endogenous Growth.
Cambridge: National Bureau of Economic Research.
Michalopoulos, Stelios, Luc Laeven, and Ross Levine. 2011. Inancial Innovation and Endogenous Growth.
Cambridge: National Bureau of Economic Research, pp. 1–33.
Miller, Merton H. 1986. Financial innovation: The last twenty years and the next. Journal of Financial and
Quantitative Analysis 21: 459–71.
Mookerjee, Rajen, and Paul Kalipioni. 2010. Availability of financial services and income inequality: The
evidence from many countries. Emerging Markets Review 11: 404–8.
Muthinja, Moses Mwenda. 2016. Financial Innovations and Bank Performance in Kenya: Evidence from
Branchless Banking Models. Ph.D. thesis, University of the Witwatersrand, Johannesburg, South Africa.
Muthinja, Moses M., and Chimwemwe Chipeta. 2018. Financial innovation, firm performance and the speeds of
adjustment: New evidence from Kenya’s banking sector. Journal of Economic and Financial Sciences 11: 1–11.
Naceur, Sami Ben, Adolfo Barajas, and Alexander Massara. 2015. Can Islamic Banking Increase Financial Inclusion?
Washington, DC: International Monetary Fund.
Nazlioglu, Saban, Fuat Lebe, and Selim Kayhan. 2011. Nuclear energy consumption and economic growth in
OECD countries: Cross-sectionally dependent heterogeneous panel causality analysis. Energy Policy 39:
6615–21.
Nepal Rastra Bank. 2019. Online Data Archieve. Available online:
https://www.nrb.org.np/bfr/monthly_statistics.php?tp=Monthly_Statistics&&vw=15 (accessed on
15.03.2019).
Nyamongo, Esman Morekwa, Roseline N. Misati, Leonard Kipyegon, and Lydia Ndirangu. 2012. Remittances,
financial development and economic growth in Africa. Journal of Economics and Business 64: 240–60.
Ozcan, Yasar A. 2008. Health Care Benchmarking and Performance Evaluation “An Assessment Using Data
Envelopment Analysis (DEA). New York: Springer.
Pedroni, Peter. 1999. Critical values for cointegration tests in heterogeneous panels with multiple regressors.
Oxford Bulletin of Economics and Statistics 61: 653–70.
Pedroni, Peter. 2004. Panel cointegration: Asymptotic and finite sample properties of pooled time series tests
with an application to the PPP hypothesis. Econometric Theory 20: 597–625.
Pesaran, M. Hashem 2004. General Diagnostic Tests for Cross Section Dependence in Panel. CESifo Working Paper
Series 1229.
Pesaran, M. Hashem. 2006. Estimation and inference in large heterogeneous panels with a multifactor error
structure. Econometrica 74: 967–1012.
Pesaran, M. Hashem, Yongcheol Shin, and Ron P. Smith. 2001. Bounds testing approaches to the analysis of level
relationships. Journal of Applied Econometrics 16: 289–326.
Pesaran, M. Hashem, Yongcheol Shin, and Ron P. Smith. 1999. Pooled mean group estimation of dynamic
heterogeneous panels. Journal of the American Statistical Association 94: 621–34.
Pesaran, M. Hashem, and Ron Smith. 1995. Estimating long-run relationships from dynamic heterogeneous
panels. Journal of Econometrics 68: 79–113.
Pesaran, M. Hashem, Aman Ullah, and Takashi Yamagata. 2008. A bias-adjusted LM test of error cross-section
independence. The Econometrics Journal 11: 105–27.
Qamruzzaman, Md., and Wei. Jianguo. 2017. Financial innovation and economic growth in Bangladesh. Financial
Innovation 3: 19.
Qamruzzaman, Md., and Jianguo Wei. 2018a. Does Foreign Direct Investment, Financial Innovation, and Trade
Openness Coexist in the Development Process: Evidence from Selected Asian and African Countries?
British Journal of Economics, Finance and Management Sciences 16: 73–94.
Qamruzzaman, Md., and Jianguo Wei. 2018b. Nexus between financial innovation and economic growth in
South Asia: Evidence from ARDL and nonlinear ARDL approaches. Financial Innovation 4: 20.
Qamruzzaman, Md., and Jianguo Wei. 2018c. Financial Innovation, Stock Market Development, and Economic
Growth: An Application of ARDL Model. International Journal of Financial Studies 6: 69.
Qamruzzaman, Md., and Jianguo Wei. 2019. Do financial inclusion, stock market development attract foreign
capital flows in developing economy: A panel data investigation. Quantitative Finance and Economics 3: 88–
108.
Raffaelli, Ryan, and Mary Ann Glynn. 2013. Institutional Innovation: Novel, Useful, and Legitimate. In
Institutional Analysis Conference. Boston: Harvard Business School, pp. 1–35.
Int. J. Financial Stud. 2019, 7, 61 26 of 27
Rasheed, Balach, Siong-Hook Law, Lee Chin, and Muzafar Shah Habibullah. 2016. The role of financial inclusion
in financial development: International evidence. Abasyn University Journal of Social Sciences 9: 330–48.
Reserve Bank of India. 2019. Annual Data Archive. Available online:
https://www.rbi.org.in/Scripts/Publications.aspx?publication=Annual (accessed on 15 February 2019).
Royal Monetary Authority. 2019. Central Bank of Bhutan. Available online:
https://www.rma.org.bt/publication.jsp?id=5 (accessed on 15 March 2019).
Sanjaya, I. Made, and Nursechafia Nursechafia. 2016. Financial Inclusion and Inclusive Growth: A Cross-
Province Analysis In Indonesia. Buletin Ekonomi Moneter dan Perbankan 18: 281–306.
Sarma, Mandira. 2008. Index of Financial Inclusion. Working Paper. Delhi: Indian Council for Research on
International Economic Relations.
Sarma, Mandira. 2012. Index of Financial Inclusion—A Measure of Financial Sector Inclusiveness. Working Paper.
Berlin: Hochschule für Technik und Wirschaft.
Sarma, Mandira, and Jesim Pais. 2008. Financial Inclusion and Development: A Cross Country Analysis. New Delhi:
Madras Schools of Economics.
Schrieder, Gertrud, and Franz Heidhues. 1995. Reaching the Poor through Financial Innovations. Quarterly
Journal of International Agriculture 2: 132–48.
Schumpeter, Joseph. 1911. The Theory of Economic Development. Cambridge: Harvard University Press.
Sekhar, Satya, and V. Gudimetla. 2013. Theorems and Theories of Financial Innovation: Models and Mechanism
Perspective. Financial and Quantitative Analysis 1: 26.
Shabani, Zahra Dehghan, and Rouhollah Shahnazi. 2019. Energy consumption, carbon dioxide emissions,
information and communications technology, and gross domestic product in Iranian economic sectors: A
panel causality analysis. Energy 169: 1064–78.
Sharma, Dipasha. 2016. Nexus between financial inclusion and economic growth: Evidence from the emerging
Indian economy. Journal of Financial Economic Policy 8: 13–36.
Shaughnessy, Haydn. 2015. Innovation in Financial Services: The Elastic Innovation Index Report. La Hulpe:
Innotribe, vol. 21.
Shin, Yongcheol, Byungchul Yu, and Matthew Greenwood-Nimmo. 2014. Modelling asymmetric cointegration
and dynamic multipliers in a nonlinear ARDL framework. Festschrift in Honor of Peter Schmidt. Berlin:
Springer, pp. 281–314.
Shittu, Ayodele Ibrahim. 2012. Financial Intermediation and Economic Growth in Nigeria. British Journal of Arts
and Social Sciences 4: 164–79.
Siddiqui, Danish Ahmed, and Qazi Masood Ahmed. 2009. The Causal Relationship between Institutions and
Economic Growth: An Empirical Investigation for Pakistan Economy. International Economics and Business
5: 1–19.
Silve, Florent, and Alexander Plekhanov. 2014. Institutions, Innovation and Growth: Cross-Country Evidence.
London: European Bank for Reconstruction and Development, vol. 28.
Sood, Vishal, and Poorva Ranjan. 2015. Financial Innovation in India: An Empirical Study. Journal of Economic
and Business Review 10: 1–20.
Soto, Marcelo. 2009. System GMM Estimation with a Small Sample. Barcelona: Barcelona Graduate School of
Economics.
State Bank of Pakistan. 2019. Online Data Archive. Available online: http://www.sbp.org.pk/ecodata/index2.asp
(accessed on 19 March 2019).
Swamy, Vighneswara. 2012. Bank-Based Financial Intermediation for Financial Inclusion and Inclusive Growth.
Rochester: SSRN.
Tufano, Peter. 2002. Financial Innovation. In The Handbook of Economics of Finance. Amsterdam: Elsevier.
Tufano, Peter. 2003. Financial Innovation. In The Handbook of Economics of Finance. Amsterdam: Elsevier, vol. 1,
pp. 307–35.
Carbó Valverde, Santiago, Rafael López del Paso, and Francisco Rodríguez Fernández. 2016. Financial
innovation in Banking: Impact on Regional Growth. Journal of Development Economics 10: 1–40.
Westerlund, Joakim. 2007. Testing for error correction in panel data. Oxford Bulletin of Economics and Statistics 69:
709–48.
Int. J. Financial Stud. 2019, 7, 61 27 of 27
Wolde-Rufael, Yemane. 2014. Electricity consumption and economic growth in transition countries: A revisit
using bootstrap panel Granger causality analysis. Energy Economics 44: 325–30.
Yunus, Muhammad. 2011. Opening remarks: Financial inclusion and the regulation of microfinance. Bank for
International Settlements Papers 56: 7–9.
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