ArticlePDF Available

Financial infrastructure—total factor productivity (TFP) nexus within the purview of FDI outflow, trade openness, innovation, human capital and institutional quality: Evidence from BRICS economies

Applied Economics

July 2022
55(5):1-19

DOI:10.1080/00036846.2022.2094333

Authors:

Faheem Ur Rehman

Ural Federal University

Md. Monirul Islam

Bangladesh Institute of Governance and Management (BIGM), University of Dhaka Affilt., Dhaka-1207, Bangladesh

BRICS countries’ contribution to the global economy has received wider attention. The critical factor behind their role is financial market reform that stimulates these economies’ productivity growth. This research contributes to constructing a comprehensive index of financial infrastructure and measuring its relationship with BRICS economies’ total factor productivity (TFP) within the purview of outward FDI, trade openness, human capital, innovation and institutional quality during 1990–2019 using the CS-ARDL technique. The findings divulge a significant and positive role of financial infrastructure in TFP both in the long and short runs, while outward FDI, trade openness, human capital, and innovation walk on the same footing in BRICS countries. Moreover, the CS-ARDL-based investigated findings remain the same across the two-way fixed effect with Driscoll and Kraay Standard Error technique. Therefore, BRICS countries’ more promotion of financial dynamics and other ancillary economic, demographic, and technological factors is critical to stepping towards the spectacular growth trajectory.

Content uploaded by Faheem Ur Rehman

Content may be subject to copyright.

Full Terms & Conditions of access and use can be found at

https://www.tandfonline.com/action/journalInformation?journalCode=raec20

Applied Economics

ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/raec20

Financial infrastructure—total factor productivity

(TFP) nexus within the purview of FDI outflow,

trade openness, innovation, human capital

and institutional quality: Evidence from BRICS

economies

Faheem Ur Rehman & Md Monirul Islam

To cite this article: Faheem Ur Rehman & Md Monirul Islam (2022): Financial infrastructure—total

factor productivity (TFP) nexus within the purview of FDI outflow, trade openness, innovation,

human capital and institutional quality: Evidence from BRICS economies, Applied Economics, DOI:

10.1080/00036846.2022.2094333

To link to this article: https://doi.org/10.1080/00036846.2022.2094333

Published online: 11 Jul 2022.

Submit your article to this journal

View related articles

View Crossmark data

Financial infrastructure—total factor productivity (TFP) nexus within the purview

of FDI outow, trade openness, innovation, human capital and institutional

quality: Evidence from BRICS economies

Faheem Ur Rehman

a,b

and Md Monirul Islam

Graduate School of Economics and Management, Ural Federal University, Yekaterinburg, Russia;

Business School, Ningbo Tech University,

Ningbo, Zhejiang, China

ABSTRACT

BRICS countries’ contribution to the global economy has received wider attention. The critical

factor behind their role is nancial market reform that stimulates these economies’ productivity

growth. This research contributes to constructing a comprehensive index of nancial infrastructure

and measuring its relationship with BRICS economies’ total factor productivity (TFP) within the

purview of outward FDI, trade openness, human capital, innovation and institutional quality during

1990–2019 using the CS-ARDL technique. The ndings divulge a signicant and positive role of

nancial infrastructure in TFP both in the long and short runs, while outward FDI, trade openness,

human capital, and innovation walk on the same footing in BRICS countries. Moreover, the CS-

ARDL-based investigated ndings remain the same across the two-way xed eect with Driscoll

and Kraay Standard Error technique. Therefore, BRICS countries’ more promotion of nancial

dynamics and other ancillary economic, demographic, and technological factors is critical to

stepping towards the spectacular growth trajectory.

KEYWORDS

Financial infrastructure;

ﬁnancial stability, Total

factor productivity; CS-ARDL,

BRICS

JEL CLASSIFICATION

G3; G10; G15; G18; G24; C23

I. Introduction

Economic theories generally reveal that invest-

ment or capital ﬂows expedite economic growth

(Zaman et al. 2021; Rehman et al. 2019).

However, the investment ﬂows depend on the

ﬁnancial system as this mechanism (ﬁnancial

system) transmits investments into capital,

creating values for numerous usages in the pro-

duction process. That is why a well-functioning

ﬁnancial system must utilize resources to accel-

erate industrial productivity. Generally, ﬁnancial

systems consist of ﬁnancial markets that stimu-

late income growth by lowering transaction

costs and thus making resource distribution

more manageable. Therefore, ﬁnancial develop-

ment-laden economic growth is assumed in two

ways: the ﬁrst is roughly concerned with capital

accumulation, and the second path is linked to

the total factor productivity (Imbruno et al.

2022; Liu et al. 2021; Rehman and Ding 2019).

Capital accumulation steers economic growth

through its (capital) substantial utilization in

an economy’s development projects.

Furthermore, the total factor productivity

(TFP)-laden economic growth is a result of the

eﬃcient allocation of resources where there is

a deﬁciency of information asymmetry in the

exploitation of ﬁnancial technology innovation.

Financial development accelerates productivity

growth through proper allocation of resources.

However, an ineﬃcient ﬁnancial process instigates

economic distortion by increasing transaction costs

in business operations (Cho and Chen 2021; Liu

et al. 2021). By referencing BRICS economies like

India and Russia, Hsieh, and Klenow (2009) illu-

strated that illustrated that lower TFP occurs due to

the improper distribution of resources across ﬁrms.

For example, their estimated TFP in the manufac-

turing sector was 50–60% and 25–40% for Russia

and India, respectively, when labour and capital are

reallocated hypothetically to equalize marginal

product to the US level. Figure 1 visualizes the

trends of TFP in BRICS countries, representing

a progressive trend since the last decades. China

is leading in TFP growth, while other BRICS coun-

tries are converging at the same point.

CONTACT Faheem Ur Rehman rehman@urfu.ru Graduate School of Economics and Management, Ural Federal University, Mira 19, Yekaterinburg

620002, Russia

APPLIED ECONOMICS

https://doi.org/10.1080/00036846.2022.2094333

Financial development boosts productivity

through technological growth and innovation.

Schumpeter and Backhaus (2003) pointed out that

a developed ﬁnancial system provides credit facil-

ities to innovative manufacturers for productivity

enhancement. Greenwood and Smith (1997) sug-

gested that a developed ﬁnancial system reduces

transaction costs for increasing transactions,

which helps achieve productivity levels. This

hypothesis is tested by Rajan and Zingales (1998),

concluding that for ﬁrms that receive more external

ﬁnance, their growth rate becomes high due to the

developed ﬁnancial market. Guillaumont

Jeanneney, Hua, and Liang (2006) proved that eco-

nomic development enhances eﬃciency that

improves TFP. Our computed ﬁnancial infrastruc-

ture index in Figure 2 visualizes the progressive

trend of this index in the context of BRICS coun-

tries over the last decade. Speciﬁcally, India is the

leading economy in the overall ﬁnancial infrastruc-

ture index, followed by China, South Africa, Brazil,

and Russia.

However, a country’s total productivity is not

spurred only by the ﬁnancial infrastructure, but

other macroeconomic elements like outward FDI,

trade openness, innovation, human capital and

institutional quality matter. Speciﬁcally, outward

FDI becomes instrumental to ﬂuctuating produc-

tivity in economies. The relationship between

external and local activities becomes operational

within the ambit of ﬁrms’ ﬁnancial portfolios in

a situation where static investments in diverse loca-

tions contest for funds caused by costly foreign

ﬁnancing (Liaqat et al. 2022) Given this, the invest-

ment decision of scarce resources abroad inexor-

ably decreases the chances of the home country’s

concomitant investments. It implies that each dol-

lar of outward FDI misplaces a dollar of local

investment (Desai, Foley, and Hines 2005;

Feldstein 1995; Herzer and Schrooten 2008). In

addition, when the investments in foreign coun-

tries come without regard for investments needed

to sustain the home economy’s productivity, out-

ward FDI may reduce the domestic productivity of

the investment ﬁrm in the long run. However, out-

ward FDI potentials help increase any economy’s

productivity if these funds are used to import

goods and services from external countries, using

the calibre of external workers, machinery and

research and development (R&D) (Herzer 2011).

Apart from the outward FDI-total productivity

nexus, the trade openness-economic growth rela-

tionship occurs due to an economy’s augmented

eﬃciency in distributing resources and economies

0.5

1.5

2.5

1990 1995 2000 2005 2010 2015 2020

Brazil Russia India China South Africa

Figure 1. Trends of TFP Growth in BRICS Countries. Source: Penn World Table” version 9.1.

1995 2000 2005 2010 2015 2020

Financial infrastructure

Index

Brazil Russia India China South Africa

Figure 2. Movement of ﬁnancial infrastructure index in BRICS Countries. Source: Authors’ Own Calculation

2F. U. REHMAN AND M. M. ISLAM

of scale (Grossman and Helpman, 1991; Obstfeld

and Rogoﬀ 1996). For example, in the case of total

factor productivity, trade theory reveals that trade

openness contributes to improving productivity,

including labour and total productivity (TFP) ema-

nating from economies of scale (Krugman 1981).

Moreover, trade openness leads to augmenting

TFP through increasing labour productivity (Cho

and Chen 2021).

The role of human capital in the production

method is not traced as an input, but its eﬀect

spurs the total productivity of an economy

(Benhabib and Spiegel et al. 1994). Besides, knowl-

edge spillovers as part of human capital are the

engine to expand economic growth. Knowledge

usually prevents diminishing returns in economies

by developing human capital skills via technologi-

cal learning. Moreover, knowledge spillovers

through developing human capital help increase

production returns (Xu, Lai, and Qi 2008). The

endogenous growth model is critical to under-

standing the nexus between innovation and TFP

growth. An abundance of skilled labour and R&D

subsidies downsize the marginal costs of R&D

activities and enhance the level of innovation and

the growth rate of TFP. Besides, market size incre-

ment contributes to boosting innovators’ revenues

for implementing more innovative projects, and it

helps intensify higher total factor productivity

(TFP) growth (Comin 2010). Moreover, innova-

tion becomes more dynamic if the economy utilizes

physical technology (Comin, Hobijn, and Rovito

2006; Hall and Jones 1999; Solow 1956). Besides,

variation in the use of physical technology yields

cross-country diﬀerences in the TFP rate.

Therefore, recently, innovation means technologi-

cal innovation, as argued by many economists.

Institutional quality inﬂuences aggregate pro-

ductivity by allocating resources eﬃciently.

Speciﬁcally, institutions play a substantial role in

boosting TFP by utilizing the latest technology in

investment, maintaining transaction costs, mana-

ging incentive mechanisms, and market-oriented

policies and the environment (Rehman and Sohag

2022; Quijada 2007). According to Khan et al.

(2019), good quality of the institutions bring out

expected outcomes in two ways: a) institutions

protect rights by ensuring people’s access to eco-

nomic opportunities equally and provide

remuneration for those who contribute to the pro-

duction process. On the other hand, bad institu-

tions signiﬁcantly constrain productivity by

encouraging resource misallocation. Moreover,

quality institutions mobilize resources properly to

contribute to the entire production process (Islam

et al. 2022a, 2022b).

We contribute to existing ﬁnancial literature

in several ways. First, we devise a novel com-

prehensive index of ﬁnancial infrastructure fol-

lowing the index constructing technique used by

Donaubauer, Meyer, and Nunnenkamp (2016)

and Kaufmann, Kraay, and Mastruzzi (2011).

However, our ﬁnancial index is diﬀerent from

the index constructed by Donaubauer, Meyer,

and Nunnenkamp (2016) and Kaufmann,

Kraay, and Mastruzzi (2011) due to the utiliza-

tion of recent data properties with diﬀerent

dimensions, especially in the case of BRICS

countries. Besides, Donaubauer, Meyer, and

Nunnenkamp (2016) devised the ﬁnancial infra-

structure index in which they employed tradi-

tional aggregated data in only their quantitative

forms and used it in the case of trade. However,

in this study, we use a ﬁnancial infrastructure

index consisting of 8 quantitative and qualitative

variables by employing the unobserved compo-

nent model (UCM) technique during 1990–

2019. Furthermore, this study normalizes the

dataset by population using the UCM para-

meter. This technique also captures the quanti-

tative and qualitative variables (see section III),

making regression analysis misspeciﬁcation-free.

Besides, we claim that our novel formulated

index of ﬁnancial infrastructure contains

a mixed order of stationarity status. This situa-

tion allows us to employ a rigorous econometric

analysis, namely the CS-ARDL technique. The

study ﬁndings’ robustness is extraordinarily

checked using alternative computation para-

meters, e.g. Two-way Fixed Eﬀect with Driscoll

and Kraay Standard Error technique that is

barely common to the existing kinds of litera-

ture. Besides, previous studies hardly utilized

outward FDI, trade openness, human capital,

innovation and institutional quality variables in

the case of the nexus between ﬁnancial infra-

structure and total factor productivity (TFP) in

the context of BRICS economies. Moreover, the

APPLIED ECONOMICS 3

study ﬁnding proves an expediting role of ﬁnan-

cial development in TFP in BRICS countries

within the purview of trade, outward FDI, inno-

vation, human capital and institutional quality

that has signiﬁcant policy implications. Based on

this ﬁnding, the policymakers would solely con-

centrate on developing the array of ﬁnancial

development for BRICS countries’ overall pro-

duction systems.

II. Review of literature

This literature review is of two strands: ﬁnancial

infrastructure-induced total factor productivity

(TFP) and outward FDI, trade openness, innova-

tion, human capital and institutional quality-

induced total productivity factor (TFP).

Financial infrastructure-induced total factor

productivity (TFP)

Studies by Schumpeter and Nichol (1934),

Goldsmith (1969), Shaw (1973) and McKinnon

(1973) investigated the importance of the ﬁnancial

system to economic growth from the diﬀerent

angles of empirical inquiries. Goldsmith (1969)

ﬁrstly sought out the impacts of economic growth

on the ﬁnancial market; secondly, the ﬁnancial

development’s impact on economic growth;

thirdly, the inﬂuence of the ﬁnancial system on

a country’s growth rate. He ﬁrst established that

economic development spurs the ﬁnancial system.

His second proposition assumed that banking sec-

tor growth as a part of ﬁnancial development inﬂu-

ences higher economic development than the

national output level. He failed to address

the second point, i.e. the relationship between

ﬁnancial development level and economic growth.

However, his study established a positive correla-

tion between ﬁnancial development and economic

activity level. Thus, he did not arrive at any con-

crete conclusion due to data limitations in a cross-

country analysis. He established his third assump-

tion relating to the eﬀect of ﬁnancial development

on a country’s growth within the empirical

procedure.

After Goldsmith (1969), the 1960s and 1970

witnessed much progress in investigating the

relationship between poverty, ﬁnancial

development, and economic growth. To this

end, two groups appeared in economic studies:

structuralist and repressionist. Structuralist

research emphasizes ﬁnancial variables’ structure

and qualitative composition that are instrumen-

tal in enhancing economic development. Mainly,

the ﬁnancial product status helps mobilize sav-

ings in increasing capital formations and hence

economic growth. This level of income growth

reduces the poverty of an economy (Chaﬀai

2021; Esfahani 1994; Guha-Khasnobis 2008).

On the other hand, ﬁnancial repressionist

research is carried out by Shaw (1973) and

McKinnon (1973), known as the hypothesis of

McKinnon-Shaw. This theory suggests an appro-

priate return rate on real cash balances of ﬁnan-

cial liberalizations as an economic growth

driver. This premise is based on the principle

that negative or meagre real interest rates damp

saving, by which loanable funds supply shrinks

and growth rate decreases. Therefore, the

McKinnon-Shaw model emphasized ﬁnancial

liberalization that will create competition.

A rise in interest rate will boost saving and

capital formation, enhancing investment and

economic growth.

Many empirical studies have mixed conclu-

sions amid a lengthy debate on the ﬁnancial

development-economic growth nexus. Some stu-

dies investigated the causal relationship between

these two variables. Moreover, empirical ﬁndings

hardly developed any speciﬁc prediction in the

nexus between these variables. For example, King

and Levine (1993) found a signiﬁcant positive

relationship between ﬁnancial development and

total productivity in the presence of per capita

GDP growth rate and per capita stock. Using the

averaged non-overlapping and generalized move-

ment method, Beck, Demirguc-Kunt, and Levine

(2004) found that the development of the bank-

ing sector and stock markets positively aﬀect

economic growth. Dogan, Madaleno, and

Altinoz (2020) applied development indicators

of the stock market and banking industry and

concluded that the development indicator of the

stock market has signiﬁcant positive eﬀects on

economic growth. However, economic growth is

negatively aﬀected by the development of the

banking sector within the purview of the

4F. U. REHMAN AND M. M. ISLAM

development indicator of the stock market.

A positive correlation between economic growth

and ﬁnancial development was found by Leitao

(2010) for 27 EU nations and ﬁve countries of

BRICS in the case of the data from 1980 to 2006.

Adusei (2013) undertook the dynamic GMM

method for 24 countries selected from Africa

from 1981 to 2010 and found a positive relation-

ship between economic growth and ﬁnancial

development. This research also proved that the

Granger causality test found bidirectional causal-

ity between economic growth and ﬁnancial devel-

opment. established a similar outcome, i.e.

bidirectional causality between ﬁnancial develop-

ment and income growth in Nigeria. Other stu-

dies, such as Luintel and Khan (1999) and Li and

Marinč (2018), remain on the same footing

regarding the causal relationship between eco-

nomic growth and ﬁnancial development.

Abdelbary and Benhin (2019) suggested that

economic growth does not cause ﬁnancial develop-

ment; instead, ﬁnancial openness causes economic

growth. The study ﬁndings stated that economic

growth Grange causes ﬁnancial development.

Menyah, Nazlioglu, and Wolde-Rufael (2014)

found a weak causality between ﬁnancial develop-

ment and economic growth. They concluded that

economic growth depends upon ﬁnancial develop-

ment, and the pattern of causation varies from one

country to another. Mlachila and Ouedraogo

(2020) pointed out that banking sector credit pro-

vision to private businesses is helpful to GDP

growth, stock market return, and dividend, which

are indicators of capital accumulation, productiv-

ity, and economic development. However, there is

no long-run relationship between bank branch

numbers and GDP for the Indian economy, as

investigated by Bhanumurthy and Singh (2013).

Finally, the study by Menyah, Nazlioglu, and

Wolde-Rufael (2014) did not conclude any causal

relation between economic growth with trade and

ﬁnancial development. For searching the future

direction of the ﬁnancial research, the study of

Patel et al. (2022) undertook a meta-literature

review covering the topic ‘ﬁnancial market integra-

tion (FMI)’ by including 260 scientiﬁc articles from

1981 to 2021. This research explored some dimen-

sions of FMI that help productivity augmentation

for the economy.

Outward FDI, trade openness, innovation, human

capital and institutional quality-induced total

productivity factor

Apart from ﬁnancial infrastructure, other macro-

economic, demographic, technological and gov-

ernance dynamics inﬂuence the TFP in any

economy. Studies on outward FDI-driven total

productivity factor (TFP) are mainly concerned

with the economic growth of diﬀerent economies.

Despite this, Damijan, Polanec, and Prašnikar

(2007) examined the nexus between outward FDI

and productivity in the case of Slovenia using

microlevel data. It proved a positive relationship

between diﬀerent ﬁrms’ external connectivity that

spurs the total productivity in Slovenia. Using the

panel cointegration technique, Herzer (2010) con-

sidered 33 developing countries’ contexts concern-

ing the relationship between outward FDI and TFP

during 1980–2005. This study explored a strong

positive inﬂuence of outward FDI on TFP in the

long run for these economies. Another study by

Herzer (2012) investigated a similar relationship

between outward FDI and TFP. This study used

both the TFP and economic growth, considering

them as the dependent variables of this study in the

context of Germany. His ﬁndings showed

a bidirectional causal association between outward

FDI, TFP and economic growth. It implies that

both output and productivity of Germany’s econ-

omy are the cause and consequence of outward

FDI, as proven in this study outcome. Finally, the

very recent studies such as Wu et al. (2020), Xie

and Zhang (2021) and Song et al. (2021) delved

into the nexus between outward FDI and the total

green productivity in the contexts of diﬀerent

economies.

TFP is caused by trade openness due to econo-

mies of scale. The study of Miller and Upadhyay

(2000) utilized the factors – trade, openness expo-

sure and human capital on TFP in the case of

developed and developing countries. They found

that trade orientation mainly inﬂuences outward-

oriented economies’ productivity. Abizadeh and

Pandey (2009) scrutinized the inﬂuence of trade

openness and structural change on TFP from the

perspective of 20 OECD member economies dur-

ing 1980–2000. Their ﬁndings discovered an inﬂu-

ential proﬁle of trade openness on TFP as trade

APPLIED ECONOMICS 5

leads to boosting the service sector of these coun-

tries. However, the recent investigations are largely

conﬁned to the study of the nexus between trade

openness and green total factor productivity (Ding

et al. 2021; Huang and Liu 2021).

The knowledge spillovers eﬀect is a core com-

ponent of human capital that stimulates the TFP

of an economy. Miller and Upadhyay (2000)

calculated the parsimonious aggregate produc-

tion function within the purview of openness,

trade, and human in both developed and devel-

oping countries and found a positive role of

human capital in the total process of TFP.

Considering the 1985-to 2004, Wei and Hao

(2011) proved a positive inﬂuence of human

capital on TFP in Chinese provinces within the

stochastic frontier approach. The author con-

ﬁrmed these ﬁndings while dividing human

capital into qualitative and quantitative forms

in estimation. Finally, Fassio, Kalantaryan, and

Venturini (2020) studied the role of foreign

human capital in TFP from three EU countries,

including the UK, Germany and France, cover-

ing the period 1994–2007. Migrants as the com-

ponent of human capital lead to enlarging

productivity where they are employed. Besides,

highly educated migrants positively aﬀect the

production method of the high-tech sector and

have a less positive impact on the service-

oriented sectors of these EU economies.

Physical technology-relevant innovation contri-

butes to higher TFP in any economy. By including

48,794 ﬁrms from 23 industries in Taiwan, Chang

and Robin (2008) attempted to measure the role of

innovation in TFP augmentation within the pro-

pensity score matching (PSM) approach. The ﬁnd-

ings extraordinarily expressed an adverse eﬀect of

innovation on TFP in most of Taiwan’s industries.

Catching-up strategies the study observes in the

case of innovation’s downbeat move towards the

TFP growth in Taiwan. The research done by

Karaﬁllis and Papanagiotou (2011) delved into the

eﬀect of innovations on the TFP of organic olive

cultivators in the context of the Greek region. This

study ﬁnding reveals technology variance that sti-

mulates the formation of TFP variations in this

region. The mediating role of development in the

innovation-TFP nexus was also explored by

Demmel et al. (2017) while studying the context

of four Latin American countries – Argentina,

Colombia, Peru and Mexico from 2006 to 2010.

Finally, Lee and Xuan (2019) investigated the

impact of innovation management and technology

on TFP and economic growth in China during

1977–2016. Manufacturing technology and pro-

duction-related innovation positively increase

TFP in the Chinese ﬁrm-level production process.

Through resource mobilization, institutional

quality impacts the total factor productivity

(TFP). As such, Quijada (2007) studied to inspect

how institutional quality aﬀects TFP from the per-

spectives of Latin American countries. The study

ﬁndings divulged that the signiﬁcant indicator of

institutional quality – economic liberty positively

impacts the TFP in the short run in these countries.

The 24 European countries’ contexts were investi-

gated by Kaasa (2016) concerning the role of insti-

tutional quality in enhancing social capital and

labour productivity. The empirical ﬁndings discov-

ered that trust towards institutions and civic parti-

cipation are discussed in the literature to be the

signiﬁcant indicators of productivity in this region.

How do local institutions impact the total factor

productivity (TFP) studied by Lasagni, Nifo, and

Vecchione (2015) in the context of 4000 ﬁrms in

Italy. The ﬁndings found a better performance of

local institutions in expediting the ﬁrm-level TFP

in this country.

The critical assumption drawn from the above

literature review includes that majority of the

researchers investigated the connection between

ﬁnancial development and income growth or eco-

nomic development from the perspectives of dif-

ferent economies. The study on the nexus between

ﬁnancial development and total productivity factor

(TFP) along with outward FDI, trade openness,

human quality, innovation and institutional quality

is almost scarce in the existing kinds of literature.

Besides, the previous literature hardly considered

the case of BRICS economies to quest for the same

nexus. From this viewpoint, our study would add

value to economic literature in examining the

ﬁnancial development-total productivity (TFP)

nexus in the context of BRICS countries.

6F. U. REHMAN AND M. M. ISLAM

III. Methods and materials

Model

This research investigates the nexus between ﬁnan-

cial development and total factor productivity

(TFP) within the purview of outward FDI, trade

openness, innovation, human capital and institu-

tional quality in BRICS countries, including Brazil,

Russia, India, China and South Africa. Based on the

research purpose, we can draw the following asso-

ciation among the variables within a panel frame-

work as follows:

lnTFPit ¼αit þβlnFINit þ�lnOFDIit þγlnTOit

þθlnINOit þλlnHCit þϑlnIQit þεit

(1)

In Eq.(1), i depicts the i

sample country in the

panel; t denotes the time 1990–2019; α expresses

the constant; β , ,�γ , θ , λ and ϑ denote the

coeﬃcients of ﬁnancial infrastructure (lnFIN), out-

ward FDI (lnOFDI), trade openness (lnTO), inno-

vation (lnINO), human capital (lnHC) and

institutional quality (lnIQ), respectively, and εit

demonstrates the error term.

Data construction and collection

This study constructs an index of ﬁnancial infra-

structure using an Unobserved Component Model

(UCM) approach. This technique can eﬃciently

pick up several features of the respective variables

from various data sources, making an index, as

Kaufmann, Kraay, and Mastruzzi (2011) proposed.

In our index formulating process, the UCM con-

siders unobserved ﬁnancial infrastructure aspects

as random components and makes them observed

as components. This method has two signiﬁcant

advantages. First, the created index is more likely to

be particular and valuable in producing quality

data according to Kaufmann, Kraay, and

Mastruzzi (2011). The UCM-based index becomes

more potent than a single indicator implemented

in regression estimation. Second, rather than focus-

ing on a single characteristic, the developed index

is eligible for the case of a vast number of countries.

The UCM technique allows data extension to com-

pare the index’s quality and quantity across

countries.

The key feature of the UCM approach used in

this study closely resembles the framework of

Krarti, Dubey, and Howarth (2019), who created

a governance index considering several governance

indicators. As previously stated, this research aims

to provide summary characteristics of the ﬁnancial

index for each country c included in the dataset by

focusing on the observed ﬁnancial j indicators. The

sole idea is that observable ﬁnancial indicators

usually include an unobservable variable to emu-

late a country’s core ﬁnance c. However, we calcu-

late the predicted value of an unobserved common

component of the ﬁnancial indicators, which are

dependent on observed data indicators j for coun-

try c.

FIN InfcjSC1:. . . ;Scj

 ¼XJ

j¼1δcj

Scj γj

θj

(2)

In the Equation (1), Scj denotes the country c for

a each of the variables, which is expressed by j.

Similarly, δcj denote the particular weights, while,

γj and θj are the parameters. So, the acquired index

is then the assessed by the summation of all

j variables, which is additional weighted accord-

ingly to their correctness.

Scj ¼γjþθjInfcþerrorcj

  (3)

Equation (2) states the observed scores Scj for

a country c as linearly linked with the unobserved

component of Infc ﬁnancial infrastructure. The

preceding equation’s parameters γjandθj represent

unobserved expressions in observed data while

accounting for varied measurement units and

data sources. The error term represents each indi-

cator’s sample variation and perceived error errorcj.

It is also considered to be equally and indepen-

dently distributed, with a mean EðerrorcjÞ ¼ 0 and

a cross-country constant variance, although it var-

ies across the indicators. It is additionally consid-

ered that the error is independent across the

sources i.e Eðerrorci;errorcj Þ ¼ 0fori�j. This allows

the precise unobserved elements of each data

resource that is incorporated into the aggregated

ﬁnancial index to be identiﬁed. As a result, the

correlational link of one data with another can be

attributed to the same unobserved underlying Infc

infrastructure. For attaining the γj;θj and varj

APPLIED ECONOMICS 7

estimates, the essential Log Likelihood (LL) func-

tion is optimized subject to the constraint thatvarj

θjandγj;are random indicators having normal dis-

tribution (mean and standard deviation equal to

zero and one, respectively). UCM speciﬁes the

weights δcj as inversely related to the variance of

the jth indication and positively connected to the

variances in overall indicators to identify the

weights for each indicator.

δcj ¼var2

1þPJ

j¼1var2

(4)

The lower variance value for the jth indication

could imply good accuracy and the related weigh-

tage connected with the indicator. We also need to

rescale the index to guarantee that it is comparable

between years and economies, as the sample size in

our study ﬂuctuates each year due to diﬀerences in

data availability in the case of several underlying

ﬁnancial variables. Rescaling would prevent the

index from being distorted for economies with

poor performance in previous periods. For this,

we used 2015 as the baseline year, with a mean

and standard deviation of zero and one, respec-

tively. For periods other than 2015, we regulated

the score such that we had the same countries’

sample as in the benchmark year. Therefore, we

rescaled the index in periods other than the bench-

mark period such that it has a standard deviation

and mean of zero and one, respectively. After

adjustment, the index is then

Infc;t;Adj ¼Infc;tInf c;tþ1;Add

ntþ1nt

Infc;t;miss

ntntþ1

(5)

where Infc;t;Adj and Infc;t;miss correspond to the

mean value of the indicator for sample countries in

the following years and missing the next years,

respectively. The total number of economies in

the sample in the respective year are represented

by n. The larger the number of economies included

in the more recent years sample, the lower the

mean value for previous years. Likewise, the esti-

mated index’s standard deviation needs to be

rescaled by the following factors:

ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

ntþ1

ntntþ1nt

var Infc;tþ1;add

 þInf2

c;tþ1;add

h i

ntntþ1

var Infc;t;miss

 þInf2

c;t;miss

h i (6)

where var Infc;tþ1;add

  and Infc;t;miss show the

variance or dispersion for the countries in the

pattern explained previously in case of mean indi-

cators. The greater dispersion of scores in the add-

ing countries and the lower the dispersion of scores

in the missing countries in the subsequent year, the

more countries in the data are impacted. As

a result, our rescaled index is now comparable

across the sample countries and time. We normal-

ize the metrics by geographic area and population

density to account for the diﬀerences between

countries.

Table 1. Description of variables and data sources.

Variables Notation Descriptions Sources

Dependent Variables

Total Factor

Productivity

TFP Total output

weighted average of the inputs

TFP is measured at constant national prices (2011 percent 1)

Penn World Table” version 9.1.

Independent Variables

Financial

infrastructure

index

FIN See Section III and Appendix-A World Bank (Global Financial

Development Database)

Outward Foreign

Direct Investment

OFDI Foreign direct investment outﬂow (in Million USD) UNCTAD

Trade openness TO Export þImport

GDP WDI

Innovations INO Numbers of patent applications submitted by the resident and non-resident

people from inner and outer parts of diﬀerent countries.

WDI

Human Capital

Institutional

Quality

Secondary School enrollment (net %)

Six country risk indicators, including corruption, law and order, democratic

accountability, government stability, bureaucratic quality and investment

proﬁle are taken using Principal Component Analysis (PCA).

WDI

International Country Risk

Guide

8F. U. REHMAN AND M. M. ISLAM

The total factor productivity (TFP) is chosen as

a dependent variable for his study, and Penn World

data is gathered for this variable. TFP is calculated

by aggregating PF and dividing it by the average

capital and labour input weightages. It ranges from

0 to 1, with 1 indicating the maximum eﬃciency

level on the edge and 0 indicating the maximum

distance from the edge. Section 3.2 provides more

information about the creation of ﬁnancial infra-

structure, and Appendix-A contains the deﬁnitions

of variables utilized in constructing this novel

ﬁnancial infrastructure index. The rest of the vari-

ables, data source, and description of our selected

variables are presented in Table 1.

Econometric methodology

Cross-Sectional dependence test

The selection of appropriate econometric techniques

for panel data models depends on the cross-sectional

dependence (CD) test results. Therefore, the study

utilizes the cross-sectional dependence (CD) test

proposed by Pesaran and Yamagata (2008), which

allows us to compare the null – hypothesis (H

) of

cross-sectional independence against cross-sectional

dependence among the variables. The CD test is

based on the following statistics:

CD ¼ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

N N 1ð Þ

N1

i¼1X

j¼iþ1

Fdð Þ^

ρ2

ij E½Fdð Þ^

ρ2

var F dð Þ^

ρ2

h i(7)

where ^

ρ2

ij denotes the correlation between each

pair of the residuals obtained from ordinary least

squares (OLS) estimation, the CD test is also eligi-

ble for small cross-sectional panel data with

a small-time range (Le and Sarkodie 2020).

Panel unit root test

The ﬁrst-generation panel unit root tests assume

that there are no unit roots in the panel data and

are cross-sectionally uncorrelated. To overcome

this drawback, Pesaran (2007) developed

the second generation panel unit root tests, namely

CIPS, for balanced panel data to test the null

hypothesis of non-stationarity. Chudik and

Pesaran (2015) suggested the CIPS statistic as:

CIPS N;Tð Þ ¼ 1

i¼1

tiN;Tð Þ (8)

where t

(N, T) indicates the t statistic of θi.

Cross-Sectional Autoregressive Distributed Lags (CS –

ARDL) approach

The Cross-Sectional Autoregressive Distributed

Lags (CS-ARDL) approach is generally used for

analysing cointegrating relationship among the

variables. This technique, i.e. the CS–ARDL, has

various advantages over other econometric models

utilized for regression analysis. More speciﬁcally,

the CS-ARDL framework considers the one-year

lag of the predicted variable as weakly exogenous

predictors within the framework of error correc-

tion (EC) mechanism. The most signiﬁcant advan-

tage of the CS-ARDL procedure is that it permits to

control an unobservable issue to measure the long-

term eﬀects in the regression model. Moreover, it

enables managing cross-sectional dependence

(CD) in long and short runs.

The current study employs the pooled mean

group (PMG) estimation-based CS-ARDL proce-

dure upholding the idea that assessments are asymp-

totically unbiased with “‘N→∞’ for both ﬁxed ‘T’

and ‘N→∞’. However, the current study demon-

strates the common-correlation bias because of

unobserved and observed issues in long and short-

run dynamics. The experimental model for panel

data under the CS-ARDL framework is as follows:

ΔTFPit ¼μiþ ;iTFPit1βð

iXit1ϑ1iTFPt1ϑ2i �

Xt1Þ þ X

p1

j¼1

πijΔTFPitj

þX

q1

j¼0

ωijΔXitjþρ1iΔTFPtþρ2iΔXtþεit

(9)

Here, TFPit Xit denotes dependent variable;

Xitshows the regressors vectors – outward FDI,

trade openness, innovations, human capital and

institutional quality; TFPt1 indicates the long-

run estimated values of the dependent variables;

�

Xt1displays the long-run anticipated values of

the predictor variables. Besides, short run expected

values of the dependent variable and regressors

APPLIED ECONOMICS 9

shown by TFPitjand Xitj, respectively, and

εitdenotes error term. In addition, Index J¼

1. . . :J displays cross-sectional components;

t¼1. . . . . . Tshows the time span; πij and ωij

notice the short-term coeﬃcients of the dependent

and independent variables, respectively; and ρ1i

and ρ2i are the mean values of dependent and

independent variables, respectively.

Two-Way ﬁxed eﬀect with Driscoll Kraay standard

errors technique

This study utilizes the two-way ﬁxed eﬀect with

Driscoll Kraay standard errors technique. It is sui-

table robustness checking procedure due to its

power to control disturbance terms in diﬀerent

panels associated with cross-sectional (spatial)

dependency. Cross-sectional dependence is espe-

cially evident when i and j are divergent panels.

This problem frequently occurs when a series of

macroeconomic data with somewhat long periods

are used (Torres-Reyna 2007). First of all, the

Driscoll Kraay standard errors procedure checks

the issues of heteroscedasticity, cross-sectional

dependency and autocorrelation issues. Secondly,

this technique considers average values in the

regressors with their errors appearing. Then, it

utilizes these values in a weighted HAC estimator

to construct standard errors that become cross-

sectionally robust. The ﬁxed eﬀects estimator

requires two steps to be implemented. All variables

zit 2yit;xit

f g in the model for the ﬁrst step are

within-transmuted as follows:

zit ¼zit �

ziþz

¼(10)

Where

�

zi¼T1

iPTi

t¼ti1zit and z

¼¼PTi

ð Þ  1PiPtzit

It is mentioning that within-estimator relates to

OLS estimator of ~

yit ¼~

it ; þ ~

εit is the second step.

It measures the transmuted regression model in

Equation (10) by using pooled OLS estimation.

IV. Results and discussions

Table 2 summarizes the descriptive statistics of the

study variables. The TFP mean value is 0.694, and

the standard deviation (SD) is 0.424, indicating that

the degree of eﬀectiveness is high, with slight var-

iation across the sample economies during the

chosen years. Financial infrastructure has a mean

of 2.595 and a standard deviation of 1.091, indicat-

ing less erraticism in the sample group during the

concern years. The average and standard deviation

of institutional quality are 3.334 and 0.123, respec-

tively, showing signiﬁcant variation across the sam-

ple countries. Human capital has a mean value of

4.306 and a standard deviation of 0.294, indicating

more diversity in HC across the countries and years

studied. Outward foreign direct investment has

a mean and standard deviation of 9.259 and

1.535, respectively, indicating that OFDI is highly

moveable throughout the years and countries stu-

died. The trade openness’ mean value is 3.587, with

a standard deviation of 0.360, indicating a lower

level of variability across the selected years and

among the sample economies. Finally, the mean

value of innovation is 9.595, with a standard devia-

tion of 1.026, indicating more signiﬁcant variability

over the selected years and among the sample

economies. Overall, the summary statistics show

minor standard deviations among the time-series

variables, indicating normal distribution.

Therefore, this dataset will be eligible for exploita-

tion in the regression model.

Table 3 shows the empirical results of the cor-

relation matrix with levels of signiﬁcance for the

concerned indicators. TFP has a considerable posi-

tive link with ﬁnancial infrastructure, human capi-

tal, outward foreign direct investment, innovation,

trade openness and institutional quality, according

to the empirical ﬁndings. A systemic order is used

to classify appropriate econometric techniques for

the concerned model estimate.

This research principally uses the CD test to inves-

tigate the common correlation among concerned

Table 2. Descriptive statistics.

Variables Mean SD Min. Max.

LNTFPit 0.694 0.424 0.267 1.934

LNFINi;t2.595 1.091 0.000 4.655

LNIQi;t3.334 0.123 2.917 3.566

LNHCi;t4.306 0.294 3.607 4.700

LNOFDIi;t9.259 1.535 5.531 11.561

LNTOi;t3.587 0.360 2.763 4.158

LNINOi;t9.595 1.026 8.051 12.87

Observations 95 95 95 95

10 F. U. REHMAN AND M. M. ISLAM

economies across the researched period. The CD

test – statistics assess the common – correlation

over the cross-sections emanating from the simple

OLS regression. Furthermore, the second generation

panel unit-root test, namely CIPS, clearly ﬁnds the

integration order of all selected study indicators.

Finally, the mixed order of integration of our vari-

ables of interest at I(0) or I(1) and the presence of CD

are adequate for assessing the empirical study model

using the CS-ARDL approach. (Tugcu and Tiwari

2016).

Table 4 shows the CD test ﬁndings, e.g. the

common correlation coeﬃcients for the complete

set of variables. The presence of CD in the selected

variables is apparent in the second column. The

third and fourth columns show the results of each

indicator’s integrated order. The unit-root inter-

cept assessment approach is used in this investiga-

tion. The CIPS test measures total productivity

factor (TFP), ﬁnancial infrastructure (FIN),

human capital (HC) and trade openness (TO) to

be stationary at levels I(0) and the ﬁrst diﬀerence I

(1). On the other, innovation (INO), outward FDI

(OFDI) and institutional quality (IQ) are stationary

at the level I(0). This stationarity status indicates

a mixed order of integration among the variables.

This composite integrating order among the vari-

ables allows the CS-ARDL technique for empirical

estimation.

Table 5 shows how the CS-ARDL estimation

technique tackles the CD problem under three

cases. First, this study resolves the CD issue in the

short estimator in Model − 1. Model − 2 removes

the CD dilemma in the long run. Finally, the cur-

rent study eliminates the CD issue for long and

short-run estimation in Model − 3. For interpreta-

tion of assessment parameters, we accept Model − 3

results as a common correlation inﬂuence in the

presence of each indicator throughout the con-

cerned sample countries over the study period. In

our Model-3, the error-correction term (ECT-1)

coeﬃcient appears to be negatively signiﬁcant to

conﬁrm a long-run relationship between the vari-

ables. The negative coeﬃcient shows the speed of

adjustment from short-term disturbance to the

long-term viability of the Model’s regressors. The

adjustment speed to the equilibrium point in the

long-run relationship is 24% for this study.

According to our central hypothesis, ﬁnancial

infrastructure signiﬁcantly fosters the total produc-

tivity factor (TFP) in both the long run and short

run. This scenario indicates that the assumption of

ﬁnancial infrastructure-induced TFP prevails in

the case of BRICS economies (Table 5 and

Figure 3). This study suggests that improving the

quality and quantity of ﬁnancial infrastructure

boosts up total factor productivity in both the

long and short terms in the sampled economies

across the period investigated. The results reported

in Table 5 align with the idea that a well-developed

ﬁnancial system allows households to switch from

tangible unproductive assets to productive ﬁnan-

cial assets, increasing credit supply. As a result, the

economy’s investment level rises in tandem with

the increase in real credit supply, resulting in a high

level of productivity. Besides, the nexus between

ﬁnancial and entrepreneurial dynamics could con-

siderably proliferate the possibility of growth-laden

strategy and productivity (Dogan, Madaleno, and

Table 3. Correlation matrix.

Variables LNTFPit LNFINi;tLNIQi;tLNHCi;tLNOFDIi;tLNTOi;tLNINOi;t

LNTFPit 1.00

LNFINi;t0.194 1.00

LNIQi;t0.133 0.565 1.00

LNHCi;t0.191 0.214 0.149 1.00

LNOFDIi;t0.332 0.192 0.124 0.026 1.00

LNTOi;t0.339 0.026 0.016 0.447 0.078 1.00

LNINOi;t0.372 0.103 0.037 0.092 0.805 0.205 1.00

Table 4. Tests for cross-sectional dependency and integration

order.

Variables CD-test CIPS

Level First Diﬀerence

LNTFPit 1.328* -1.601* -7.842***

LNFINi;t2.53*** -2.798** -9.604***

LNIQi;t2.44*** -1.349 5.342***

LNHCi;t2.28*** -1.645* -8.131***

LNOFDIi;t3.82*** -1.583 -7.822***

LNTOi;t3.36*** -2.432** 9-.822***

LNINOi;t2.41*** -1.357 -7.105***

***, ** and * denotes signiﬁcance level at 1%, 5% and 10% respectively.

APPLIED ECONOMICS 11

Altinoz 2020). This situation helps an economy to

be more productive and eﬃcient in performance.

This ﬁnding relating to ﬁnancial infrastructure-

stimulated TFP in the context of BRICS countries

is supported by Sanﬁlippo (2015).

Table 5 and Figure 4 suggests that the eﬀect of

outward foreign direct investment on TFP is posi-

tive and signiﬁcant. External foreign direct invest-

ment theoretically boosts the host country’s total

factor productivity through the following mechan-

isms: First, the reverse technological spillover eﬀect

of this investment will enhance productivity in the

home country, enhancing its ability to produce

more sophisticated goods (Sethi et al. 2021).

Second, industries in the host nation can lower

their production costs through OFDI and reduce

the price of more sophisticated products while free-

ing up more resources for R&D, boosting produc-

tivity. Third, enterprises in the home country will

gain more information from overseas markets due

to OFDI, lowering the cost of communication and

increasing the probability of producing more com-

plex products. The positive relationship between

OFDI and TFP is supported by the earlier studies

including Damijan, Polanec, and Prašnikar (2007),

Herzer (2011) and Herzer (2012).

Table 5 and Figure 5 show that innovations

boost TFP, conﬁrming the current study’s hypoth-

esis as proved by the previous empirical studies

done by Supekar et al. (2019), Benhabib, Perla,

and Tonetti (2021) and Wang and Dass (2017).

Innovations aﬀect TFP through the direct channel

of research and development (R&D) activities in

adopting advanced technologies, such as computer

.2 .4 .6 . 8 1 1 .2

Total Fa cto r P rod uc tiv ity

0 1 2 3 4 5

Financial Infrastructure Index

95% CI Fitted values

Figure 3. TFP and FIN.

-.5 0 .5 1

Tota l fac tor Pro duc tivity

2 4 6 8 10 12

FDI outflow

95% CI Fitted values

Figure 4. TFP and OFDI.

Table 5. CS-ARDL estimation results for ﬁnancial infrastructure-

TFP nexus.

Variables

CD-SR(Model-

CD-LR(Model-

CD-SR and LR

(Model-3)

Long Run

Results

LNTFPit 1.983*** 1.143*** 1.074***

(0.466) (0.278) (0.208)

LNFINi;t2.971*** 1.943** 1.514***

(0.259) (0.153) (0.039)

LNIQi;t0.380*** 0.216** 0.170***

(0.018) (0.016) (0.019)

LNHCi;t1.947*** 1.268*** 0.953***

(0.861) (0.126) (0.065)

LNOFDIi;t1.634*** 1.009*** 0.986**

(0.006) (0.000) (0.000)

LNTOi;t0.826 0.725 0.423**

(0.726) (0.528) (0.187)

LNINOi;t1.862*** 1.569*** 1.097***

(0.526) (0.238) (0.176)

Short Run

Results

LNTFPit 0.536** 0.469** 0.826**

(0.167) (0.170) (0.071)

LNFINi;t0.341** 0.736* 0.629**

(0.160) (0.163) (0.156)

LNIQi;t0.581* 0.254* 0.165**

(0.340) (0.133) (0.060)

LNHCi;t0.604 0.477 0.217

(0.880) (0.578) (0.348)

LNOFDIi;t0.105 0.126 0.079

(0.141) (0.178) (0.089)

LNTOi;t0.432 0.762 0.423

(0.372) (0.563) (0.329)

LNINOi;t0.976** 0.653* 0.592**

(0.168) (0.164) (0.015)

Cons:4.7623*** 4.301*** 3.634***

(0.516) (0.594) (0.187)

ECT 1ð Þ −0.594*** −0.417*** −0.348***

(0.096) (0.013) (0.021)

Obs:95 95 95

N 05 05 05

***, ** and * denotes signiﬁcance level at 1%, 5% and 10% respectively.

12 F. U. REHMAN AND M. M. ISLAM

hardware, advanced machinery etc. The innovation

approach is also concerned with some non-

research and development activities, including

acquiring patents, software, licences, and training

related to modern products and procedures, mar-

ket – studies, the viability of launching new pro-

jects, and speciﬁc other mechanisms relating to

production engineering and design. Furthermore,

R&D and non-R&D activities promote TFP by

decreasing production costs with more eﬃcient

techniques and diversifying products to achieve

economies of scale (Pradhan 2019; Pradhan et al.

2021).

The long-run results depicted in model 3

reveal that the trade openness (TO) coeﬃcient

is signiﬁcant and positive, indicating that TO

enhances TFP (See Table 5 and Figure 6). This

ﬁnding of the study is in line with the pioneer-

ing research by Grossman and Krueger (1991),

arguing that TO encourages the essential ‘sup-

ply-side impacts’. It means that the TO upgrades

the economic and technological eﬃciencies on

a microeconomic level, stimulating TFP.

Additionally, modern technology becomes read-

ily available through the TO in home countries,

resulting in increasing TFP. Finally, enough

empirical evidence suggests that importing mod-

ern technologies from international ‘technology

leader’ economies boosts TFP in many host

economies (Li and Marinč 2018). Besides, trade

openness accelerates bank growth by decreasing

the charge and risk and increasing the volume

of bank credit – the core component of ﬁnancial

development .

Both Table and Figure 7 show that human capi-

tal increases total factor productivity (i.e. the coef-

ﬁcient is signiﬁcantly positive), conﬁrming our

claim that HC is critical for developing and adopt-

ing cutting-edge technology, which boosts produc-

tivity. Higher-skilled human labour allows for

more eﬃcient inventions and production and

more accessible expansion and distribution at the

technological frontier. Besides, ﬁnancial market

development considerably leads to the eﬃciency

of total R&D investment, and this investment port-

folio helps increase the TFP in an economy

(Chowdhury and Maung 2012). TThe study out-

come concerning the positive inﬂuence of human

.6 .7 .8 .9 1 1.1

Total Factor Productivity

8 10 12 14

Innovations

95% CI Fitted values

Figure 5. TFP and INO.

.2 .4 .6 .8 1 1.2

Total Factor Productivity

2 2.5 3 3.5 4

Trade openess

95% CI Fitted values

Figure 6. TFP and to.

.2 .4 .6 .8 1 1.2

Total Factor Productivity

0 1 2 3 4 5

Human Capital

95% CI Fitted values

Figure 7. TFP and HC.

APPLIED ECONOMICS 13

capital on spurring TFPT is consistent with pre-

vious studies, such as Khanna and Sharma (2021)

and Liu et al. (2021).

Institutional quality is positive and signiﬁcant in

both the long and short run in the case of BRICS

economies, as revealed in Table 5 and Figure 8. The

quality of institutions mainly helps augment an

economy’s production to mobilize resources by

applying proper rules and regulations. The institu-

tional potentials also inﬂuence the policy process of

the country The concerned institutions of BRICS’

economies utilize their quality mechanism and

procedure to encourage public and private

entrepreneurs, external investors and stakeholders

to accelerate production systems. Applying regula-

tory quality, these institutions make a business-

friendly climate for the investors, which stimulates

the whole production process of the BRICS econo-

mies. The studies by Quijada (2007), Balcerzak and

Pietrzak (2016), Ngo and Nguyen (2020) and

Nasreen et al. (2020) corroborate this study’s ﬁnd-

ings on the ground that BRICS countries’ institu-

tional quality is primarily associated with the

successful mobilization of resources.

Robustness check

Our empirical ﬁnding are strongly robust across the

speciﬁcation of an alternate estimator namely, two-

way ﬁxed eﬀect with Driscoll and Kraay Standard

Error technique.

The ﬁndings indicate that ﬁnancial infrastruc-

ture promotes total factor productivity. In addi-

tion, other control variables such as outward FDI,

trade openness, innovation, human capital and

institutional quality also aﬀect TFP positively and

signiﬁcantly (See Table 6). These ﬁndings are in

line with the CS-ARDL approach, though there

exists deviation in coeﬃcient values found from

this robustness check method. More importantly,

signs and signiﬁcance levels obtained from this

robustness check procedure are similar to the ﬁnd-

ings emerging from the CS-ARDL method.

V. Conclusion and policy implications

The view of this study is solely concerned with the

ﬁnancial infrastructure-induced augmentation of

total factor productivity (TFP). The nexus between

these two core indicators in BRICS economies is

established by the current study covering 1990–

2019. Therefore, this study’s contribution includes

constructing a ﬁnancial infrastructure index within

the unobserved component model (UCM) frame-

work. Besides, using a cointegration analysis tech-

nique, namely the CS-ARDL and a robustness

estimation method, i.e. two-way ﬁxed eﬀect with

Driscoll and Kraay standard error technique, talks

about this study’s potential in the value addition

realm of ﬁnancial study literature.

The ﬁnding emerging from the CS-ARDL

procedure supports that the existing ﬁnancial

0 .5 1 1.5 2

Total Factor Productivity

8 10 12 14

Institusional Quality

95% CI Fitted values

Figure 8. TFP and IQ.

Table 6. Results of two-way ﬁxed eﬀect with Driscoll and Kraay

Standard Error.

Variables

Two-way Fixed Eﬀect with Driscoll and Kraay

Standard Error

LNTFPit 0.998***

(0.138)

LNFINi;t1.552***

(0.383)

LNIQi;t2.208***

(1.996)

LNHCi;t1.699***

(0.235)

LNOFDIi;t0.315**

(0.017)

LNTOi;t1.239***

(0.160)

LNINOi;t1.827***

(0.483)

Cons:4.724

(1.624)

TimeEffect NO

RegionalFixedEffect NO

Obs:95

Rsquared 0.457

N 05

***, ** and * denotes signiﬁcance level at 1%, 5% and 10% respectively.

14 F. U. REHMAN AND M. M. ISLAM

infrastructure in BRICS countries boosts total

factor productivity (TFP) in both the short and

long runs. The selected control variable (i.e.

human capital) is one of the critical variables

used in this study is helpful to promote TFP in

these economies in the long run, though, in the

short run, it plays an insigniﬁcant role in pro-

moting TFP. In addition, the innovation factor,

trade openness, and outward FDI contribute to

stimulating TFP in the short and long run in

BRICS economies. The present empirical study

has signiﬁcant policy implications in BRICS

countries and similar progressive countries glob-

ally. The policymakers of these economies

should have a keen eye for the augmentation

of ﬁnancial infrastructure’s qualitative and quan-

titative formation. So, to uphold the contribu-

tion of the ﬁnancial infrastructure dynamic, the

policymakers should increase economic eﬃ-

ciency by reducing the pressure on the balance

of payments. Consequently, the potential of

ﬁnancial infrastructure would create a market

for employment opportunities in the manufac-

turing, maintenance, and other industrial sec-

tors. The policymakers of BRICS countries

should adopt the best policy option to mitigate

potential volatility in ﬁnancial infrastructure

triggered by so-called corporatism.

This study ﬁnding also points out exploiting the

potentials of diﬀerent macroeconomic determinants

such as outward FDI, trade openness, innovation and

human capital. The development of the demographic

factor such as human capital augmentation is a key to

augmenting the TFP. In this regard, governments of

these economies should provide technical education,

training, and skills development programs consider-

ing the goal of “Industrial Revolution 4.0”. Besides,

encouraging innovation-related works and expanding

expenditure for R&D potential are required to accel-

erate these economies’ productivity. Furthermore, the

policymakers should prioritize the outward FDI-

driven business opportunities and outcomes. The

trade openness-led TFP stimulation process suggests

making the local markets more competitive by remov-

ing all trade barriers and encouraging technological

diﬀusion to expedite total productivity momentum.

Moreover, resource mobilization is critical to reaching

a viable spectrum of real productivity. Only improv-

ing institutional quality can help mobilize resources to

attain this desired goal of impressive productivity in

the BRICS countries.

Disclosure statement

No potential conﬂict of interest was reported by the author(s).

ORCID

Md Monirul Islam http://orcid.org/0000-0002-9818-1676

References

Abdelbary, I., and J. Benhin. 2019. “Governance, Capital and

Economic Growth in the Arab Region.” The Quarterly

Review of Economics and Finance 73: 184–191.

doi:10.1016/j.qref.2018.04.007.

Abizadeh, S., and M. Pandey. 2009. “Trade Openness,

Structural Change and Total Factor Productivity.”

International Economic Journal 23 (4): 545–559.

doi:10.1080/10168730903372273.

Adusei, M. 2013. “Financial Development and Economic

Growth: Evidence from Ghana.” The International

Journal of Business and Finance Research 7 (5): 61–76.

Balcerzak, A. P., and M. B. Pietrzak. 2016. “Quality of

Institutions and Total Factor Productivity in the

European Union.” Statistics in Transition New Series

17 (3): 497–514. doi:10.21307/stattrans-2016-034.

Beck, T., A. Demirguc-Kunt, and R. Levine. 2004. Finance,

Inequality, and Poverty: Cross-Country Evidence. Mass.,

USA: National Bureau of Economic Research Cambridge.

Benhabib, J., and M. M. Spiegel. 1994. “The Role of

Human Capital in Economic Development Evidence

from Aggregate Cross-Country Data.” Journal of

Monetary Economics 34 (2): 143–173.

Benhabib, J., J. Perla, and C. Tonetti. 2021. “Reconciling Models

of Diﬀusion and Innovation: A Theory of the Productivity

Distribution and Technology Frontier.” Econometrica 89 (5):

2261–2301.evidence. doi:10.3982/ECTA15020.

Bhanumurthy, N. R., and P. Singh. 2013. “Financial Sector

Development and Economic Growth in Indian States.”

International Journal of Economic Policy in Emerging

Economies 6 (1): 47–63. doi:10.1504/IJEPEE.2013.054472.

Chaﬀai, M. 2021. “Discernible Diﬀerences in the Building

Façades, but Not in the Productivity Numbers:

A Comparison Between Domestic and Foreign Banks in

North Africa.” The Quarterly Review of Economics and

Finance 80: 344–357. doi:10.1016/j.qref.2021.03.005.

Chang, H. H., and S. Robin. 2008. “The Impact of Online

Store Environment Cues on Purchase Intention: Trust and

Perceived Risk as a Mediator”.” Online Information Review

32 (6): 818–841. doi:10.1108/14684520810923953.

Cho, T.-Y., and Y.-S. Chen. 2021. “The Impact of Financial

Technology on China’s Banking Industry: An Application

APPLIED ECONOMICS 15

of the Metafrontier Cost Malmquist Productivity Index.”

The North American Journal of Economics and Finance 57:

101414. doi:10.1016/j.najef.2021.101414.

Chowdhury, R. H., and M. Maung. 2012. “Financial Market

Development and the Eﬀectiveness of R&D Investment:

Evidence from Developed and Emerging Countries.”

Research in International Business and Finance 26 (2):

258–272. doi:10.1016/j.ribaf.2011.12.003.

Chudik, A., and M. H. Pesaran. 2015. ”Large Panel Data

Models with Cross-Sectional Dependence: A Survey.” In

The Oxford Handbook of Panel Data, edited by

B. H. Baltagi, 3–45. New York: Oxford University Press.

Comin, D. A., B. Hobijn, and E. Rovito. 2006. Five Facts You

Need to Know About Technology Diusion. Mass., USA:

National Bureau of Economic Research Cambridge.

Comin, D.(2010). total factor productivity. In: Durlauf, S.N.,

Blume, L.E. (eds) Economic Growth. The New Palgrave

Economics Collection. Palgrave Macmillan, London. doi:

https://doi.org/10.1057/9780230280823_32.

Damijan, J. P., S. Polanec, and J. Prašnikar. 2007. “Outward FDI

and Productivity: Micro-evidence from Slovenia.” World

Economy 30 (1): 135–155. doi:10.1111/j.1467-

9701.2007.00876.x.

Demmel, M. C., J. A. Máñez, M. E. Rochina-barrachina, and

J. A. Sanchis-llopis. 2017. “Product and Process Innovation

and Total Factor Productivity: Evidence for Manufacturing

in Four Latin American Countries.” Review of Development

Economics 21 (4): 1341–1363. doi:10.1111/rode.12323.

Desai, M. A., C. F. Foley, and J. R. Hines. 2005. “Foreign Direct

Investment and the Domestic Capital Stock.” The American

Economic Review 95 (2): 33–38. doi:10.1257/

000282805774670185.

Ding, L., M. Wu, Z. Jiao, and Y. Nie. 2021. “The Positive Role of

Trade Openness in Industrial Green Total Factor Productivity

—provincial Evidence from China.” Environmental Science

and Pollution Research 1–14. doi:10.1007/s11356-020-11060-z.

Dogan, E., M. Madaleno, and B. Altinoz. 2020. “Revisiting the

Nexus of Financialization and Natural Resource Abundance in

Resource-Rich Countries: New Empirical Evidence from Nine

Indices of Financial Development.” Resources Policy 69:

101839. doi:10.1016/j.resourpol.2020.101839.

Donaubauer, J., B. E. Meyer, and P. Nunnenkamp. 2016. “A New

Global Index of Infrastructure: Construction, Rankings and

Applications.” The World Economy 39 (2): 236–259.

doi:10.1111/twec.12290.

Esfahani, H. S. 1994. Regulations, Institutions, and Economic

Performance: The Political Economy of the Philippines’

Telecommunications Sector. Vol. 1294. Policy Research

Working Paper Series 1294, World Bank Publications.

Fassio, C., S. Kalantaryan, and A. Venturini. 2020. “Foreign

Human Capital and Total Factor Productivity: A Sectoral

Approach.” Review of Income and Wealth 66 (3): 613–646.

doi:10.1111/roiw.12426.

Feldstein, M.,1995. "The Eﬀect of Marginal Tax Rates on

Taxable Income: A Panel Study of the 1986 Tax Reform

Act," Journal of Political Economy, University of Chicago

Press, vol. 103(3), pages 551-572, June.

Goldsmith, R. W. 1969. Financial Structure and Development.

New Haven: Yale University Press.

Greenwood, J., and B. D. Smith. 1997. “Financial Markets in

Development, and the Development of Financial Markets.”

Journal of Economic Dynamics & Control 21 (1): 145–181.

doi:10.1016/0165-1889(95)00928-0.

Grossman, G. M., and A. B. Krueger. 1991. Environmental

Impacts of a North American Free Trade Agreement. Mass.,

USA: National Bureau of economic research Cambridge.

Guha-Khasnobis, B. 2008. Financial Development,

Institutions, Growth andPoverty Reduction. Basudeb

Guha-Khasnobis (senior research fellow): Springer.

Guillaumont Jeanneney, S., P. Hua, and Z. Liang. 2006. “Financial

Development, Economic Eﬃciency, and Productivity Growth:

Evidence from China.” The Developing Economies 44 (1):

27–52. doi:10.1111/j.1746-1049.2006.00002.x.

Hall, R. E., and C. I. Jones. 1999. “Why Do Some Countries

Produce so Much More Output per Worker Than Others?”

The Quarterly Journal of Economics 114 (1): 83–116.

doi:10.1162/003355399555954.

Herzer, D., and M. Schrooten. 2008. “Outward FDI and Domestic

Investment in Two Industrialized Countries.” Economics

Letters 99 (1): 139–143. doi:10.1016/j.econlet.2007.06.014.

Herzer, D. 2010. “Outward FDI and Economic Growth.” Journal

of Economic Studies 37 (5): 476–494. doi:10.1108/

01443581011075424.

Herzer, D. 2011. “The Long-Run Relationship Between

Outward Foreign Direct Investment and Total Factor

Productivity: Evidence for Developing Countries.” The

Journal of Development Studies 47 (5): 767–785.

doi:10.1080/00220388.2010.509790.

Herzer, D. 2012. “Outward FDI, Total Factor Productivity and

Domestic Output: Evidence from Germany.” International

Economic Journal 26 (1): 155–174. doi:10.1080/

10168737.2010.538430.

Hsieh, C. T., and P. J. Klenow. 2009. “Misallocation and

Manufacturing TFP in China and India.” The Quarterly

Journal of Economics 124: 1403–1448. doi:http://dx.doi.org/

10.1162/qjec.2009.124.4.1403.

Huang, Q., and M. Liu. 2021. “Trade Openness and Green

Total Factor Productivity: Testing the Role of

Environment Regulation Based on Dynamic Panel

Threshold Model.” Environment, Development and

Sustainability 24: 1–26.

Imbruno, M., R. Kneller, R. Pittiglio, and F. Reganati. 2022.

“Outward FDI Spillovers from Inward FDI: Evidence from

Italian Firms.” Structural Change and Economic Dynamics

61: 434–443. doi:10.1016/j.strueco.2022.02.016.

Islam, M., M. Ali, B. Ceh, S. Singh, M. K. Khan, and

V. Dagar. 2022a. “Renewable and Non-Renewable

Energy Consumption Driven Sustainable Development

in ASEAN Countries: Do Financial Development and

Institutional Quality Matter?” Environmental Science

and Pollution Research 1–17. doi:10.1007/s11356-021-

17416-3.

Islam, M. M., M. Irfan, M. Shahbaz, and X. V. Vo. 2022b.

“Renewable and Non-Renewable Energy Consumption in

16 F. U. REHMAN AND M. M. ISLAM

Bangladesh: The Relative Inﬂuencing Proﬁles of Economic

Factors, Urbanization, Physical Infrastructure and

Institutional Quality.” Renewable Energy 184: 1130–1149.

Kaasa, A. 2016. “Social Capital, Institutional Quality and

Productivity: Evidence from European Regions.” Economics

& Sociology 9 (4): 11. doi:10.14254/2071-789X.2016/9-4/1.

Karaﬁllis, C., and E. Papanagiotou. 2011. “Innovation and

Total Factor Productivity in Organic Farming.” Applied

Economics 43 (23): 3075–3087. doi:10.1080/

00036840903427240.

Kaufmann, D., A. Kraay, and M. Mastruzzi. 2011. “The

Worldwide Governance Indicators: Methodology and

Analytical Issues.” Hague Journal on the Rule of Law

3 (2): 220–246. doi:10.1017/S1876404511200046.

Khan, M. A., M. A. Khan, M. E. Abdulahi, I. Liaqat, and

S. S. H. Shah. 2019. “Institutional Quality and Financial

Development: The United States Perspective.” Journal of

Multinational Financial Management 49: 67–80.

doi:10.1016/j.mulﬁn.2019.01.001.

Khanna, R., and C. Sharma. 2021. “Does Infrastructure

Stimulate Total Factor Productivity? a Dynamic

Heterogeneous Panel Analysis for Indian Manufacturing

Industries.” The Quarterly Review of Economics and

Finance 79: 59–73. doi:10.1016/j.qref.2020.08.003.

King, R. G., and R. Levine. 1993. Financial Intermediation and

Economic Development. Vol. 156189. Cambridge:

Cambridge University Press.

Koetter, M., and F. Noth. 2013. “IT Use, Productivity, and

Market Power in Banking.” Journal of Financial Stability

9 (4): 695–704. doi:10.1016/j.jfs.2012.06.001.

Krarti, M., K. Dubey, and N. Howarth. 2019. “Energy

Productivity Analysis Framework for Buildings: A Case

Study of GCC Region.” Energy 167: 1251–1265.

doi:10.1016/j.energy.2018.11.060.

Krugman, P. 1981. “Trade, Accumulation, and Uneven

Development.” Journal of Development Economics 8 (2):

149–161. doi:10.1016/0304-3878(81)90026-2.

Lasagni, A., A. Nifo, and G. Vecchione. 2015. “Firm

Productivity and Institutional Quality: Evidence from

Italian Industry.” Journal of Regional Science 55 (5):

774–800. doi:10.1111/jors.12203.

Le, H. P., and S. A. Sarkodie. 2020. “Dynamic Linkage

Between Renewable and Conventional Energy Use,

Environmental Quality and Economic Growth: Evidence

from Emerging Market and Developing Economies.”

Energy Reports 6: 965–973. doi:10.1016/j.egyr.2020.04.020.

Lee, J. W., and Y. Xuan. 2019. “Eﬀects of Technology and

Innovation Management and Total Factor Productivity on

the Economic Growth of China.” The Journal of Asian

Finance, Economics, and Business 6 (2): 63–73.

doi:10.13106/jafeb.2019.vol6.no2.63.

Leitao, N. C. 2010. “Financial Development and Economic

Growth: A Panel Data Approach.” Theoretical and Applied

Economics 10(551): 15–24.

Li, S., and M. Marinč. 2018. “Economies of Scale and Scope in

Financial Market Infrastructures.” Journal of International

Financial Markets, Institutions and Money 53: 17–49.

doi:10.1016/j.intﬁn.2017.09.010.

Liaqat, I., Y. Gao, F. U. Rehman, Z. Lakner, and J. Oláh. 2022.

“National Culture and Financial Inclusion: Evidence from Belt

and Road Economies.” Sustainability 14: 3405. doi:10.3390/

su14063405.

Liu, Y., L. Luan, W. Wu, Z. Zhang, and Y. Hsu. 2021. “Can

Digital Financial Inclusion Promote China’s Economic

Growth?” International Review of Financial Analysis 78:

101889. doi:10.1016/j.irfa.2021.101889.

Luintel, K. B., and M. Khan. 1999. “A Quantitative

Reassessment of the Finance–growth Nexus: Evidence from

a Multivariate VAR.” Journal of Development Economics

60 (2): 381–405. doi:10.1016/S0304-3878(99)00045-0.

McKinnon, R. I. 1973. Money and Capital in Economic

Development. Washington, DC: Brookings Institution.

Menyah, K., S. Nazlioglu, and Y. Wolde-Rufael. 2014.

“Financial Development, Trade Openness and Economic

Growth in African Countries: New Insights from a Panel

Causality Approach.” Economic Modelling 37: 386–394.

doi:10.1016/j.econmod.2013.11.044.

Miller, S. M., and M. P. Upadhyay. 2000. “The Eﬀects of

Openness, Trade Orientation, and Human Capital on Total

Factor Productivity.” Journal of Development Economics 63 (2):

399–423. doi:10.1016/S0304-3878(00)00112-7.

Mlachila, M., and R. Ouedraogo. 2020. The Impact of Conict

and Political Instability on Banking Crises in Developing

Countries. Other Macroeconomics: ERN.

Nasreen, S., M. K. Mahalik, M. Shahbaz, and Q. Abbas. 2020.

“How Do Financial Globalization, Institutions and

Economic Growth Impact Financial Sector Development

in European Countries?” Research in International Business

and Finance 54: 101247. doi:10.1016/j.ribaf.2020.101247.

Ngo, M. N., and L. D. Nguyen. 2020. “Economic Growth,

Total Factor Productivity, and Institution Quality in

Low-Middle Income Countries in Asia.” The Journal of

Asian Finance, Economics, and Business 7 (7): 251–260.

Obstfeld, M., and K. Rogoﬀ. 1996. Foundations of

International Macroeconomics. MIT press.

Patel R., John W. Goodell, Marco Ercole Oriani, Andrea

Paltrinieri, Larisa Yarovaya,(2022). A bibliometric review

of ﬁnancial market integration literature, International

Review of Financial Analysis, Volume 80, 102035, doi:

https://doi.org/10.1016/j.irfa.2022.102035.

Pesaran, M. H. 2007. “A Simple Panel Unit Root Test in the

Presence of Cross-section Dependence.” Journal of Applied

Econometrics 22 (2): 265–312. doi:10.1002/jae.951.

Pesaran, M., and T. Yamagata. 2008. “Testing Slope

Homogeneity in Large Panels.” Journal of Econometrics

142 (1): 50–93. doi:10.1016/j.jeconom.2007.05.010.

Pradhan, R. P. 2019. “Investigating the Causal Relationship

Between Transportation Infrastructure, Financial Penetration

APPLIED ECONOMICS 17

and Economic Growth in G-20 Countries.” Research in

Transportation Economics 78: 100766. doi:10.1016/j.

retrec.2019.100766.

Pradhan, R. P., M. B. Arvin, M. S. Nair, J. H. Hall, and

S. E. Bennett. 2021. “Sustainable Economic Development in

India: The Dynamics Between Financial Inclusion, ICT

Development, and Economic Growth.” Technological

Forecasting and Social Change 169: 120758. doi:10.1016/j.

techfore.2021.120758.

Quijada, A. 2007. “Institutional Quality and Total Factor

Productivity in Latin America and the Caribbean:

Exploring the Unobservable Through Factor Analysis.”

Revista Ensayos Sobre Política Económica 25 (53. Enero,

2007): 66–119. doi:10.32468/Espe.5302.

Rajan, R., and L. Zingales. 1998. “Financial Development and

Growth.” The American Economic Review 88 (3): 559–586.

Rehman, F. U., M. A. Khan, M. A. Khan, K. Pervaiz, and I. Liaqat.

2019. “The Causal, Linear and Nonlinear Nexus Between

Sectoral FDI and Infrastructure in Pakistan: Using a New

Global Infrastructure Index.” Research in International

Business and Finance 52 (4): 101129. doi:10.1016/j.

ribaf.2019.101129.

Rehman, F. U., and Y. Ding. 2019. “The Nexus Between

Outward Foreign Direct Investment and Export

Sophistication: New Evidence from China.” Applied

Economics Letters. doi:10.1080/13504851.2019.1616056.

Rehman, F. U., A. A. Noman, and M. A. Khan. 2020. “China’s

Outward Foreign Direct Investment and Exports

Diversiﬁcation: An Asymmetric Analysis.” Journal of Chinese

Economic and Foreign Trade Studies. doi:10.1108/JCEFTS-12-

2019-0065.

Rehman, F. U., A. A. Noman, and Y. Ding. 2020. ”“Does

Infrastructure Increase Exports and Reduce Trade Deﬁcit

in Selected South Asian Countries?” by Using a New Global

Infrastructure Index.” Journal of Economic Structures 9 (1).

Article No. 10(2020). doi:10.1186/s40008-020-0183-x

Rehman, F. U., and A. A. Noman. 2021. “Does Infrastructure

Promote Exports and Foreign Direct Investment in

Selected Southeast Asian Economies? an Application of

Global Infrastructure Index”.” Journal of Economic Studies

48 (7): 1346–1370. doi:10.1108/JES-03-2020-0123.

Rehman, F. U., and K. Sohag. 2022. “Does Transport

Infrastructure Spur Export Diversiﬁcation and

Sophistication in the G-20 Economies? an Application of

CS-ARDL.” Applied Economics Letters 1–5. doi:https://doi.

org/10.1080/13504851.2022.2083554 .

Sanﬁlippo, M. 2015. “FDI from Emerging Markets and the

Productivity Gap—an Analysis on Aﬃliates of BRICS

Emnes in Europe.” International Business Review 24 (4):

665–676. doi:10.1016/j.ibusrev.2014.11.005.

Schumpeter, J. A., and A. J. Nichol. 1934. “Robinson’s

Economics of Imperfect Competition.” The Journal of

Political Economy 42 (2): 249–259. doi:10.1086/254595.

Schumpeter, J., and U. Backhaus. 2003. “The Theory of Economic

Development.” In Joseph Alois Schumpeter Magdeburger Allee

55, 99086 Erfurt , 61–116. Germany: Springer.

Sethi, P., S. Bhattacharjee, D. Chakrabarti, and C. Tiwari. 2021.

“The Impact of Globalization and Financial Development on

India’s Income Inequality.” Journal of Policy Modeling 43 (3):

639–656. doi:10.1016/j.jpolmod.2021.01.002.

Shaw, E. S. (1973). Financial Deepening in Economic

Development.

Solow, R. M. 1956. “A Contribution to the Theory of

Economic Growth.” The Quarterly Journal of Economics

70 (1): 65–94. doi:10.2307/1884513.

Song, Y., F. Hao, X. Hao, and G. Gozgor. 2021. “Economic Policy

Uncertainty, Outward Foreign Direct Investments, and Green

Total Factor Productivity: Evidence from Firm-Level Data in

China.” Sustainability 13 (4): 2339. doi:10.3390/su13042339.

Supekar, S. D., D. J. Graziano, M. E. Riddle, S. U. Nimbalkar,

S. Das, A. Shehabi, and J. Cresko. 2019. “A Framework for

Quantifying Energy and Productivity Beneﬁts of Smart

Manufacturing Technologies.” Procedia CIRP 80:

699–704. doi:10.1016/j.procir.2019.01.095.

Torres-Reyna, O. 2007. Panel Data Analysis Fixed and

Random Eects Using Stata (V. 4.2), 112. United States:

Data & Statistical Services, Priceton University.

Tugcu, C. T., and A. K. Tiwari. 2016. “Does Renewable And/

or Non-Renewable Energy Consumption Matter for Total

Factor Productivity (TFP) Growth? Evidence from the

BRICS.” Renewable and Sustainable Energy Reviews 65:

610–616. doi:10.1016/j.rser.2016.07.016.

Wang, X., and M. Dass. 2017. “Building Innovation

Capability: The Role of Top Management

Innovativeness and Relative-Exploration Orientation.”

Journal of Business Research 76: 127–135. doi:10.1016/j.

jbusres.2017.03.019.

Wei, Z., and R. Hao. 2011. “The Role of Human Capital in

China’s Total Factor Productivity Growth: A Cross-province

Analysis.” The Developing Economies 49 (1): 1–35. doi:10.1111/

j.1746-1049.2010.00120.x.

Wu, H., S. Ren, G. Yan, and Y. Hao. 2020. “Does China’s

Outward Direct Investment Improve Green Total Factor

Productivity in the “Belt and Road” Countries? Evidence

from Dynamic Threshold Panel Model Analysis.” Journal of

Environmental Management 275: 111295. doi:10.1016/j.

jenvman.2020.111295.

Xie, F., and B. Zhang. 2021. “Impact of China’s Outward Foreign

Direct Investment on Green Total Factor Productivity in “Belt

and Road” Participating Countries: A Perspective of

Institutional Distance.” Environmental Science and Pollution

Research 28 (4): 4704–4715. doi:10.1007/s11356-020-10849-2.

Xu, H., M. Lai, and P. Qi. 2008. “Openness, Human Capital

and Total Factor Productivity: Evidence from China.”

Journal of Chinese Economic and Business Studies 6 (3):

279–289. doi:10.1080/14765280802283576.

Zaman, M., C. Pinglu, S. I. Hussain, A. Ullah, and N. Qian.

2021. “Does Regional Integration Matter for Sustainable

Economic Growth? Fostering the Role of FDI, Trade

Openness, IT Exports, and Capital Formation in BRI

Countries.” Heliyon 7 (12): e08559. doi:10.1016/j.heli-

yon.2021.e08559.

18 F. U. REHMAN AND M. M. ISLAM

Appendices

Appendix-A. Financial infrastructure variables, descriptions and

sources

Finance Variables Descriptions Sources

Access Government listed

corporations

No. of government listed corporations, (population relative) World Bank- Global Financial

Development Database (WDI-

GFDD)

Bank accounts No of bank accounts, (relative to population). (WDI-GFDD)

traded Value Traded shares value outside of the major 10 traded corporations, as a % of

aggregated traded shares value in stock market exchange (logged).

(WDI-GFDD)

Depth Stock market

aggregated value

traded

In terms of GDP, the total number of shares traded on the stock exchange (logged). (WDI-GFDD)

quasi money and

Money (M2)

Money and quasi-money are made up of cash held outside of banks, demand

deposits other than those held by the central government, and time, savings, and

foreign currency deposits held by non-central government resident sectors. The

variable is expressed as a percentage of gross domestic product (GDP) (logged).

World Bank (WDI)

Private credit by

deposit money

banks

As a percentage of GDP, domestic money banks supply ﬁnancial resources to the

private sector (logged). Commercial banks and other ﬁnancial organizations that

take transferable deposits, such as demand deposits, are known as domestic

money banks.

(WDI-GFDD)

Eﬃciency turnover of Stock

market

The total value of shares exchanged over the time divided by the period’s average

market capitalization (logged).

(WDI-GFDD)

Stability Z-score Bank It expresses the likelihood of a country’s commercial banking sector defaulting. The

Z-score compares the buﬀer (returns and capitalization) of a country’s commercial

banking system to the volatility of those returns (logged).

(WDI-GFDD)

volatility of Stock

price

The average of the national stock market index’s 360-day volatility is used to

calculate stock price volatility (logged). The variable is multiplied by a factor (−1).

(WDI-GFDD)

APPLIED ECONOMICS 19

How Financial Inclusion Moderates the Curvilinear Nexus between Tangible Investment and Sustainable Firm Growth: New Evidence from the Middle East and North Africa Region

Article

Full-text available

Mar 2024

The aim of this study was to examine the curvilinear relationship between tangible investment and sustainable firm growth in the MENA region, as well as the moderating role of financial inclusion on this connection. To achieve this, we selected a sample of 465 firms over the period 2007–2020. Employing a system GMM model for the empirical analysis, the findings reveal that there is a curvilinear (inverted U-shaped) nexus between tangible investment and sustainable firm growth. Moreover, this study employs a moderating effect model to demonstrate that financial inclusion can enhance sustainable firm growth. The system GMM model further indicates that financial inclusion moderates the curvilinear relationship between tangible investment and sustainable firm growth. This study offers valuable insights for strategic firm planning and policy development, highlighting the role of financial inclusion in promoting firm sustainability.

Can two-way Foreign Direct Investment promote green technology spillover in Belt and Road countries? An analysis based on the moderator variable of the technology gap

Article

Full-text available

Apr 2024
ENVIRON ECOL STAT

The rapid growth of two-way foreign direct investment (FDI) is a striking feature of the Belt and Road (B&R). Can two-way FDI promote green technology spillover (GTS) from the B&R countries? Does the technology gap moderate GTS? And what is the underlying mechanism? To this end, this paper systematically analyses the impact of two-way FDI on GTS based on panel data of 64 B&R countries from 2001 to 2020. Three significant findings were obtained: first, there is a dynamic effect between two-way FDI and GTS, and the technology gap always adjusts this relationship positively. Second, "Iron needs to be hard." Whether "going out" or "coming in," GTS is confined to a certain extent. The B&R countries must rely on their capabilities to build higher-quality, independently controllable key technology chains. Finally, the technology effect of two-way FDI always inhibits GTS, and the technology gap between the B&R countries and technology frontier countries is the "valley of death" which is difficult to cross but has to cross. These findings have targeted policy implications for the B&R countries to capture better GTS.

Technological innovation, militarization, and environmental change: evidence from BRICS economies

Article

Full-text available

Mar 2024
ENVIRON SCI POLLUT R

In the complex international society, the economic development and defense construction of BRICS countries have attracted increasing attention. This article incorporates technological innovation, militarization, and environmental change into a unified analytical framework to assess the potential impact of technological progress and defense military expenditure on the environment in BRICS countries. Based on CSD tests, unit root tests, and cointegration tests, this study constructs a CS-ARDL model to examine the long-term and short-term relationships among various variables from 1990 to 2021. The results show that technological innovation, military expenditure, and economic growth can significantly increase ecological footprint in the long run, while in the short term, technological innovation and economic growth significantly increase ecological footprint, and the impact of military expenditure is not significant. It is suggested that BRICS countries should focus on supporting low-carbon technology policies and research and development investment, while also considering the use of cutting-edge technology to improve military intelligence capabilities in order to reduce the negative impact of technological innovation and military activities on the environment.

Investigating the simultaneous impact of infrastructure and geographical factors on international trade: Evidence from asian economies

Article

Full-text available

Dec 2023

Infrastructure and geography are the most important components of international trade. They provide trade-oriented amenities for the trade volume among trading partners. Thus, this study assesses the simultaneous impact of infrastructure and geographic factors on trade in Asian economies from 2004 to 2020. This work contributes to the existing literature by exploring the significance of infrastructure and geography in international trade. Furthermore, this research seeks to determine whether these factors have complementary or simultaneous effects. To examine these aspects, the augmented gravity model and cross-sectional autoregressive distributed lags are used in the current model. Then, the multilateral resistance terms are corrected. Results reveal that infrastructure has a significant and positive impact on trade. More precisely, transport infrastructure, communication infrastructure, financial infrastructure, and border-transport efficiency are productive influencers for trade over a certain period. Notably, the simultaneous impacts of infrastructure and geographic factors can lead to the deterioration of trade volume. The policy implications and future research direction of this study suggest that economies should improve infrastructure and geographic factors through gross domestic product.

Achieving carbon neutrality in West Africa: The impact of financial development and good governance

Article

Full-text available

Oct 2023
PLOS ONE

Achieving a net zero carbon has been one of the main agendas for all state and non-state actors. The political system of developing countries sometimes makes both internal and external actors question their efforts toward the agenda. Therefore, this study contributes to previous literature in analyzing the empirical effect of financial development and governance quality on carbon emissions. The study covers sixteen West African countries with data from 1996 to 2021. The study employs the Generalized Method of Moments for the analysis. Financial development in all the models contributes to carbon emissions. However, the effect of governance quality indicators varies depending on the model and the indicator(s) used. Nevertheless, economic governance and political governance in most models contribute to environmental pollution, but institutional governance helps promote environmental quality. Renewable energy and economic growth promote environmental quality through carbon mitigation. However, trade openness promotes environmental pollution by encouraging the release of carbon emissions. Finally, relevant policy implications are proposed based on the empirical findings of the study.

Self‐media information disclosure and firm productivity

Article

May 2024

Using Sina Weibo data obtained through crawler technologies from 2013 to 2021, this study investigates the relationship between self‐media information disclosure and firm productivity. We find that self‐media information disclosure promotes firm productivity, a conclusion that withstands various robustness and endogeneity tests. Particularly, original microblogs exhibit a more significant impact. Further analysis reveals that the promoting effect is more pronounced in listed companies with lower annual retweets, small companies, and those with lower turnover. Finally, a mechanism analysis indicates that self‐media information disclosure enhances enterprise productivity by alleviating financing constraints, enhancing corporate innovation, and strengthening corporate governance.

Non-native players in the domestic league: Foreign penetration and domestic banking sector in an emerging market

Article

Feb 2024
Pac Basin Finance J

Nhan Huynh

Effects of terrorism and economic policy uncertainty on economic complexity in Africa: A study of the moderating role of governance institutions

Article

Aug 2023

Following the paucity of empirical evidence on the effects of terrorism and uncertainty on economic complexity in Africa and the moderating role of governance institutions on these relationships, this study raised two important questions. First, how are terrorism and uncertainty impacting on economic complexity in Africa? Second, how are governance institutions moderating the effects of terrorism and uncertainty on economic complexity in Africa? To answer these questions, the study employed pooled ordinary least squares and dynamic system generalized method of moments (GMM) estimators, with a panel of 33 African countries over the period 2010–2021. We find that the unconditional effects of terrorism and uncertainty on economic complexity in Africa are predominantly negative and significant. We also find that governance institutions predominantly have unconditional positive and significant impact on economic complexity in Africa but failed to moderate the adverse effect of uncertainty on economic complexity. However, trade openness, international tourism and lagged economic complexity are potent factors promoting economic complexity in Africa, while physical capital stock remained a deterring factor. The study concluded that policymakers and leaders in Africa should engage in collaborative efforts at the African Union level to promote high‐quality institutions, while simultaneously addressing the detrimental effects of terrorism and uncertainty on the continent.

Balancing fiscal expenditure competition and long-term innovation investment: Exploring trade-offs and policy implications for local governments

Article

Full-text available

Nov 2023
PLOS ONE

The mobility of economic factors across jurisdictions has led to increased fiscal competition among decentralized subnational governments. This study examines the relationship between fiscal competition and long-term investment in innovation at the local government level. Panel data analysis, encompassing expenditures, taxes, and innovation inputs from 18 municipalities over a 10-year period, is employed using fixed effects regression. The results reveal a negative correlation between fiscal competition and expenditure on innovation, indicating that intensified competition for mobile capital diverts resources away from essential long-term investments crucial for knowledge-driven growth. Even after controlling for economic and institutional factors, a one standard deviation increase in competition corresponds to an average decline of 25% in per capita innovation investment. These findings highlight the unintended trade-off resulting from heightened competition and underscore the need for policy frameworks that promote localized flexibility while curbing uncoordinated competition that undermines innovation capacity. While fiscal decentralization aims to foster competitive governance, this study provides empirical evidence that short-term expenditure incentives often displace long-term innovation objectives without sufficient coordination. The insights contribute significant empirical evidence on the concealed costs of fiscal competition for regional development. Consequently, a re-evaluation of conventional perspectives on decentralization and competition is warranted, emphasizing the importance of developing cooperative policy solutions that strike a delicate balance between decentralized decision autonomy and strategic coordination. Adopting such an approach is essential to fully leverage the advantages of competitive governance while simultaneously nurturing innovation ecosystems.

Battling for net zero carbon: the position of governance and financial indicators

Article

Full-text available

Nov 2023
ENVIRON SCI POLLUT R

Africa, over the past years, has put various measures in place in the fight against carbon emissions. Achieving net zero carbon has caused the continent researchers to investigate various conditions required for a successful transition. Therefore, the political system cannot be left out since it plays a major role in decision-making. This study contributes to previous literature analyzing the empirical effect of financial development and governance quality on carbon emissions. The study is focused on 52 African countries with data from 1996 to 2021. Panel quantile and generalized method of moments are used for the analysis. The result indicates that financial development contributes to environmental degradation, government effectiveness, rule of law, and political stability which promote environmental pollution; however, control of corruption, renewable energy, and economic growth promote ecological sustainability. According to the aforementioned, it is crucial for governments to include financial development plans in national environmental strategies, particularly for those in African nations. Furthermore, governments should put restrictions on trade to control the trade of high-carbon technologies.

National Culture and Financial Inclusion: Evidence from Belt and Road Economies

Article

Full-text available

Mar 2022

Financial Inclusion is a key factor in achieving the sustainable development goals of the United Nations. The research in the area of financial inclusion is becoming more critical for scholars and policymakers. In previous studies, effects of formal institutions on financial inclusion have been explored. However, influence of informal institutions (culture) on financial inclusion remained untapped. To fill this gap, we investigate how national culture affects the financial inclusion of 81 Belt and Road economies using 17 years of data from 2004 to 2020. The empirical findings of the two-stage least square (2SLS) show that Hofstede's cultural dimensions are significantly associated with financial inclusion with different signs and levels of magnitude. We find that financial inclusion is lower in countries where uncertainty avoidance and power distance is high and that the opposite is true for individualism and masculinity. The overall results are reliable to a series of robustness checks and provide a useful basis for policymakers, regulatory agencies, and other stakeholders in achieving the sustainable development goal of financial inclusion in Belt and Road countries.

Does regional integration matter for sustainable economic growth? Fostering the role of FDI, trade openness, IT exports, and capital formation in BRI countries

Article

Full-text available

Dec 2021

IT exports, capital formation, FDI, and trade openness are the key elements to bring sustainable economic growth to any country, since these act as a lifeline to bring sustainability to countries. Moderating variable is regional integration, while the trade freedom index, investment freedom index, real interest rate, and inflation are control variables. The two-step system GMM technique is applied to the sample of 64 BRI countries from 2003 to 2018 as the number of moment conditions is greater than the number of parameters. The results suggest that FDI and gross capital formation have a substantial positive impact on economic growth, whereas IT exports and trade openness have a negative, insignificant impact. The overall results suggest that China’s outward FDI has boosted the economic growth in countries while trade openness has a negligible since most developing countries need to invest in industrialization and encourage exports based growth.

Trade openness and green total factor productivity: testing the role of environment regulation based on dynamic panel threshold model

Article

Full-text available

Jul 2022
Environ Dev Sustain

As an ecological barrier to ensure the high-quality development of China’s economy, the Yangtze River Economic Belt has attracted more and more attention from academic circles on how to promote the green development of the Yangtze River Economic Belt. This paper uses the SBM directional distance function and the Malmquist–Luenberger index to measure the green total factor productivity of 110 cities in the Yangtze River Economic Belt from 2006 to 2018, and then based on the panel threshold model to empirically explore the intensity of environmental regulations that induce trade openness and promote green total factor productivity. Studies have shown that: Firstly, trade openness has significantly inhibited the increase in green total factor productivity, but environmental regulations can play a positive regulatory role, that is increasing the intensity of environmental regulation can alleviate the adverse effects of trade openness, and it is mainly to improve the adverse effects by promoting the progress of green technology. Secondly, the results of the panel threshold model show that environmental regulations have a nonlinear regulatory effect on trade openness and green total factor productivity. When the intensity of environmental regulations crosses the second threshold, trade openness has a significant positive effect on the impact of green total factor productivity. Finally, the heterogeneity results show that in order to reverse the adverse impact of trade openness on green total factor productivity, the upper, middle and lower reaches of the Yangtze River Economic Belt should formulate relatively strict environmental regulations.

Does China's outward direct investment improve green total factor productivity in the “Belt and Road” countries? Evidence from dynamic threshold panel model analysis

Article

Aug 2020
J ENVIRON MANAGE

Does transport infrastructure spur export diversification and sophistication in the G-20 economies? An application of CS-ARDL

Article

Jun 2022

This study investigates the effect of transport infrastructure on export sophistication and diversification in the G-20 economies by applying the cross-sectional dependency autoregressive distributed lag (ARDL) approach. The empirical findings demonstrate that transport infrastructure spurs export sophistication and diversification in the short and long run. Besides, outward foreign direct investment, institutional quality and human capital contribute to exporting sophistication and diversification. Our results are robust in the common correlation effect, homogeneity and heterogeneity restrictions in the short- and long-run parameters, respectively.

Outward FDI Spillovers from Inward FDI: Evidence from Italian Firms

Article

Feb 2022
Struct Change Econ Dynam

Using data from Italian firms during the period of 2008-2012, this paper empirically explores how the presence of foreign-owned firms can affect the domestic firm's probability to invest abroad (i.e. Outward FDI spillovers from Inward FDI). We find positive spillovers via horizontal linkages, and negative spillovers through forward and backward linkages. However, an important role is played by both the origin of Inward FDI and the destination of Outward FDI.

A bibliometric review of financial market integration literature

Article

Jan 2022
Int Rev Financ Anal

We undertake a meta-literature review on the topic of financial market integration (FMI), covering 260 articles from 1981 to 2021. Our review consists of quantitative analysis of bibliometric citations concomitant with qualitative analysis of content, towards a goal of identifying primary research streams and proposing directions for future research. We identify five research groups: (1) portfolio diversification with financial market integration; (2) general equity market integration; (3) financial market linkage with respect to crises and events; (4) time-varying financial market integration; and (5) co-movements and spillovers between commodities and financial markets; as well as present a wide array of future research directions. We conduct an extensive review of FMI literature, answering several questions: (1) What is the domain of FMI research?; (2) What are the influential aspects of top journals and authors, and the characteristics of the most studied topics?; (3) What are the past and current key research streams in FMI literature?; and (4) What are the substantial future relevant research questions to explore regarding FMI? Given the ongoing attention on financial market integration by both academicians and policy makers, our results should be of great interest.

Renewable and non-renewable energy consumption in Bangladesh: The relative influencing profiles of economic factors, urbanization, physical infrastructure and institutional quality

Article

Dec 2021
RENEW ENERG

Many researchers investigate the socio-demographic and economic factors-induced energy consumption, particularly fossil fuel-based non-renewable energy consumption, but there exists a scarcity to study on the same factors-driven renewable energy consumption along with institutional quality factor. Given this, our research strives to analyse the relative influence of income growth, foreign direct investment, domestic investment, urbanization, physical infrastructure, and institutional quality on renewable and non-renewable energy consumption in Bangladesh for the period of 1990–2019 using the dynamic ARDL (DARDL) simulations method. The empirical findings divulge that income growth shows positive and negative effect on renewable and non-renewable energy consumption, respectively. Domestic investment has positive effect for renewable and non-renewable energy consumption. Foreign direct investment is found to have positive influencing factor for renewable energy consumption. Urbanization negatively impacts renewable energy consumption but urbanization affects non-renewable energy consumption positively. Physical infrastructure is a negative and positive determinant of renewable and non-renewable energy consumption, respectively. Institutional quality positively affects renewable energy consumption. The robustness of the results is also comparatively exhibited using the DARDL simulations area plots where 20% positive and negative shocks to the predictors on the predicted variables are presented.

Pesticide and agro-ecological transition: assessing the environmental and human impacts of pesticides and limiting their use

Article

Nov 2021

Can digital financial inclusion promote China's economic growth?

Article

Sep 2021
Int Rev Financ Anal

Can digital financial inclusion as an emerging and innovative financial service promote economic growth? Based on a Bayesian macroeconomic analysis framework, this paper introduces the level of Internet development as a threshold variable, analyzes the impact of digital financial inclusion on economic growth based on provincial panel data from 2011 to 2019 in China, and finally explores the mediating effect of digital financial inclusion on economic growth through a multiple mediation model. The results show that ① digital financial inclusion development has a significant contribution to economic growth. ② The impact of digital financial inclusion development on economic growth has a significant Internet threshold effect. ③Promoting small and medium-sized enterprise entrepreneurship and stimulating residents' consumption are two important channels through which digital financial inclusion development affects economic growth.

Financial infrastructure—total factor productivity (TFP) nexus within the purview of FDI outflow, trade openness, innovation, human capital and institutional quality: Evidence from BRICS economies

Abstract

Recommended publications

Agglomeration, Human Capital and Foreign Labour: The Case of Malaysia

Does infrastructural development allure foreign direct investment? The role of Belt and Road Initiat...

Does transport infrastructure make South Asian economies growth more inclusive? An application of a...

Does transport infrastructure spur export diversification and sophistication in the G-20 economies?...

Effects of Financial and Trade Globalization on Total Factor Productivity Growth in Emerging Economi...