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Exploring the nonlinear effect of Exploring thenonlinear
shadow economies on sustainable effect
development in Africa: does the
level of financial market
development matter? Received 6 June 2023
Revised 5 September 2023
Baah Aye Kusi Accepted 5 September 2023
Department of Finance, University of Ghana Business School, Accra, Ghana;
Department of Applied Finance and Policy Management, University of Education
Winneba, Winneba, Ghana and Institute of Statistical Social and Economic
Research, College of Humanities, University of Ghana, Accra, Ghana
Abstract
Purpose – This study aims to examine the nonlinear threshold effect of shadow economy on sustainable
development in Africa while providing additional evidence on how this nonlinear threshold effect play out in
economies with high and low developed financial/credit markets.
Design/methodology/approach – This study uses 37 African economies between 2009 and 2017 in a
dynamic GMMpanel model that controls for country, year and technological effects to ensure consistency and
reliability of results and findings.
Findings – The results reveal that there is an inverted nonlinear U-shape nexus between the size of shadow
economy and sustainable development in both short run and long run in Africa and across economies with
high and low developed credit/financial market. Also, the threshold points beyond which the size of shadow
economies dampens sustainable development is lower for economies with high financial/credit market
development and higher in the long run.
Practical implications – These results have policy implications and recommendations and suggest that
shadow economies can be beneficial to sustainable development particularly when the size of shadow
economies are restrained from increasing beyond certain thresholds/levels. Moreso, to restrict the adverse
effect of shadow economies on sustainable development, policymakers can rely on developing their financial/
credit markets to tame the destructive nature of shadow economies on sustainable development. These results
are robust to technological, year/time and country effects.
Originality/value – To the best of the author’s knowledge, this study examines for the first in the context
of Africa, the nonlinear effect of shadow economies on sustainable development under low and high
developed financial markets.
Keywords Financial markets, Sustainable development, Shadow economy
Paper type Research paper
JEL classification – E26, G1, G2, O470
Data availability statement: 1. Data is available on Global Financial Development (www.databank.
worldbank.org/reports.aspx?source5global-financial-development); 2. World Development Indicators
(www.databank.worldbank.org/source/world-development-indicators); and 3. Medina, L., and
Schneider, M. F. (2018). Shadow economies around the world: what did we learn over the last 20 years?.
International Monetary Fund.
Con ict of interest statement:The author states that there is no con ict of interest. Journal of Financial Economicfl fl Policy
Declarations – Availability of data and materials: Data will be made available on request. © EmeraldPublishingLimited
1757-6385
Funding:No funding has being received for this paper. DOI 10.1108/JFEP-06-2023-0146
JFEP Introduction
Sustainable development has recently emerged as one of the key and important economic
indicators that comprehensively captures the current consumption of the present generation
and that of the future generations in a manner that is economically, financially,
environmentally and socially friendly or sustainable (Boos, 2015; Kaimuri and Kosimbei,
2017; Arrow et al., 2003). Unlike economic growth/development which is short-term oriented,
considers only current consumption, do not take into account environmental sustainability
(depletion/damage) and future consumption of the next generation (Koirala and Pradhan,
2020; Kaimuri and Kosimbei, 2017; Pardi et al., 2015), sustainable development measured
with adjusted net savings (ANS) reflects “a development that meets the needs of the present
without compromising the ability of the future generations to meet their own needs”
(UNWCED, 1987, p. 17). Thus, sustainable development showsmassive concern for long run
and continuous/intergenerational development that comprehensively encompasses the “true
rate of savings in an economy after taking into consideration investments and human
capital, depletion of natural resources and damage the environment caused by pollution”
(World Bank, 2012, p. 1). In essence, sustainable development has become the economic
variable of interest for most economic policymakers and analysts who assess
intergenerational long-run developmental achievement of economies.
As such, many empirical studies have investigated sustainable development to
understand how to improve sustainable development. One factor that is recently gaining
attention in the economic literature is the shadow economy, which reflects informal
economic activities that are not captured in the computation of the official/national gross
domestic product of an economy (Medina and Schneider, 2020, 2018; Putni
nš and Sauka,
2015; Williams and Schneider, 2016; Orviska et al., 2006; Fleming et al., 2000; Schneider,
1994). Feige (1996, 1979) defines shadow economy as economic activities that circumvent
costs and are excluded from the benefits and rights incorporated in laws and administrative
rules covering property relationships, commercial licensing, labor contracts, torts, financial
credit and social systems. Interestingly, shadow economy activities directly or indirectly
influence the economic, social and environmental dimensions of an economy but empirical
evidence in the report on how shadow economy influences sustainable development is
limited to using environmental sustainability indicators (Tran, 2022; Dada et al., 2022, 2021;
Zhao et al., 2022; Sohail et al., 2021) as sustainable development although environmental
sustainability is just one aspect/dimension of sustainable development. Beyond this, prior
studies focus on comparing the effects of shadow economies on income inequality (Huynh
and Nguyen, 2020), economic growth (Baklouti and Boujelbene, 2020) and globalization
(Datsii et al., 2023) in developed and underdeveloped economies.
Again, the literature shows that although the dark side of shadow economies, which
includes reduced government revenues and disguised/understated national productivity,
has loudly been projected in the literature, the bright side of shadow economies as providing
a safety net for creating employment and generating income for citizens have gained little
recognition (Medina and Schneider, 2020, 2018; Putni
nš and Sauka, 2015). The existence of
both dark and bright sides of shadow economies presents a possibility of nonlinear effects of
shadow economies on sustainable development, implying that there would be a certain
level/size of shadow economies that may promote and or derail sustainable development.
However, empirical evidence examining shadow economies’ nonlinear effects is nonexistent,
especially in Africa. Interestingly, the few non-African studies on the nonlinear effect of
shadow economies on sustainable development (Saafi et al., 2022; Wu and Schneider, 2019)
use environmental sustainability indicators as measures for sustainable development, but
environment sustainability is just one dimension of sustainable development.
Interestingly, following prior studies on financial/credit market development within the Exploring the
shadow economies literature (Dada et al., 2022; Khan et al., 2021; Gharleghi and Jahanshahi, nonlinear
2020; Canh and Thanh, 2020; Berdiev and Saunoris, 2016), it is clear that financial/credit
market development lowers shadow economies through several ways (Berdiev and effect
Saunoris, 2016; Capasso and Jappelli, 2013; Blackburn et al., 2012; Bose et al., 2012). First, the
financial/credit sector/market, through its function of lending primarily/strictly lends to
formal business entities, which raises that opportunity cost for informal entities operating in
the shadow economy (Berdiev and Saunoris, 2016), and this pressurizes informal entities to
formalize and obtain legitimacy to gain access to credit (Berdiev and Saunoris, 2016;
Capasso and Jappelli, 2013; Blackburn et al., 2012). Second, the financial/credit sector allows
governments to monitor, supervise and implement policies and tax transactions
successfully. The financial/credit market/sector function helps lower the occurrence/
frequency and magnitude of tax evasion and other shadow economy activities (Capasso and
Jappelli, 2013; Blackburn et al., 2012). Clearly, these functions of the credit/financial market
toward the economy/government facilitate the formalization of businesses/enterprises,
increase the cost of remaining in the shadow/informal economy and therefore reduce the
activities/size of the shadow economy. Yet, empirical evidence to substantiate this logical
and intuitive claim is not documented in the empirical literature.
In addition, empirical studies that examine/show how shadow economies nonlinearly
affect sustainable development in economies with high and low development financial
markets are nonexistent. This study focuses on Africa because Africa is reported to have the
highest share of shadow economies (Medina and Schneider, 2020). Hence, this study takes
advantage of the measurement gap (using ANS as a comprehensive proxy for sustainable
development), issue gap (exploring the nonlinear effect of shadow economy on sustainable
development under economies with high and low credit/financial market development
indicators) and context gap (presenting African context evidence of a nonlinear effect of
shadow economy on sustainable development) to contribute to the “shadow economy-
sustainable development nexus” by:

 presenting first-time nonlinear effects of shadow economies on sustain development
in Africa;

 estimating the nonlinear effects (short run and long run) of shadow economies on
sustainable development in economies with developed and less developed financial/
credit markets in Africa; and

 obtaining the threshold nonlinear (short run and long run) turning points of shadow
economies on sustainable development under developed and less developed credit/
financial markets in Africa.
The rest of the study is organized into an overview, literature review, methodology, results
and discussions, conclusions, implications and recommendations.
Literature review: theoretical and conceptual
The literature on sustainable development posits that sustainable development has no
theories but rides on economic growth theory (Seiko, 2018; Steer and Wade-Gery, 1993).
Beyond using the economic theory to explain sustainable development, the modern theory of
financial intermediation is used in this study to explain the economic importance/relevance
of financial/credit markets to economies. Following economic theories, where sustainable
development is argued to derive its roots, it is clear that sustainable development is a recent
economic growth concept that has emerged as a result of the increasing importance of the
JFEP social and environmental sustainability/protection aspects of pursuing economic growth
activities (Castro and Lopes, 2022; Koirala and Pradhan, 2020). Arguably, the traditional
economic growth approach neglects the social and environmental implications of the quest
to pursue economic growth (Rutherford, 1997; Hardi et al., 1997). Specifically, pursuing
economic growth is argued to be short-term oriented on present consumption without
considering the social and environmental damages it poses to future generations’ ability to
meet their consumption (Castro and Lopes, 2022; Koirala and Pradhan, 2020). Therefore,
sustainable development arises to augment the failures/weaknesses of the traditional
pursuit of economic growth by incorporating social concerns and environmental damages
caused by pursuing economic growth. Put differently, how current consumption of human
and natural resources are carefully/adequately conserved without compromising the future
generation’s consumption can be conceptualized as sustainable development (Castro and
Lopes, 2022; Koirala and Pradhan, 2020).
In the conceptualization and computation of sustainable development (SUSDEV), the
World Bank advances that sustainable development can be measured with ANS because it
represents the “true rate of savings made by an economy after that economy has accounted
for investment in human capital, depletion of natural resources and damage caused by
pollution” (World Bank, 2012, p. 1). Thus, net national savings (NNS) adjusts for
intergenerational tradeoff between current and future consumptions through investment in
human capital, depletion of natural resources and damages caused by pollution. In practice,
higher current consumption reflects lower savings and capital stock accumulation and
hence lower future productivity, growth and standard of living (Koirala and Pradhan, 2020;
Boos, 2015; Kaimuri and Kosimbei, 2017; Arrow et al., 2003). Hence, the computation of
sustainable development is obtained by adjusted gross national savings (GNS) with
investment capital consumption (ICC) (fixed capital consumption), human capital
(educational expenditure), natural resource depletion (NRD) (minerals, forestry and energy)
and environmental depletion (ED) (CO2 and particulate emissions). Hence, sustainable
development is obtained and expressed as follows:
SUSDEVi;t ¼ ANSi;t (1)
NNSi;t ¼ GNSi;t  ICi;t (2)
ANSit ¼ NNSi;t þ HCit  NRDit  EDi;t (3)
or
ANSit ¼ GNSi;t  ICi;t þ HCit  NRDit  EDi;t (4)
Equation (1) projects that sustainable development (SUSDEV) is represented by ANS. To
obtain ANS, ICC is first deducted from GNS, which results in NNS in equation (2), then in
equation (3) human capital is added to NNS while natural resource and environmental
depletions are deducted from NNS to obtain ANS. Alternatively, equations (2) and (3) could
be combined immediately to obtain equation (4), where ANS is obtained by deducting IC,
NRD, ED and HC to GNS.
Focusing on the financial market development literature, the modern theory of financial
intermediation advances that financial intermediation activities undertaken by financial
institutions are aimed at facilitating economic activities and efficiency, implementing
government policies and granting creating credit to real economy for economic growth Exploring the
(Gyeke-Dako et al., 2021; Allen and Santomero, 1997). Thus, a modern theory of financial nonlinear
intermediation is focused on justifying economy-wide functions of financial institutions,
which include: effect

 providing a system for monitoring economic activities;

 executing government policies;

 collecting tax revenue for the government; and

 granting credit/loans to formal enterprises (Kusi et al., 2022; Lay, 2020; Cooray,
2009).
Interestingly, for financial markets and institutions to recognize and perform these functions
for economic agents (households, businesses and governments), the economic agents must
be legally formalized to enjoy such intermediation functions. Hence, developing/having
strong financial/credit markets can help formalize businesses and economic activities and
reduce shadow/black market activities. Thus, through the performance of these economy-
wide functions by financial institutions, the financial/credit market activities can suppress
informal/shadow economy activities, operations and size (see Dada et al., 2023; Ajide, 2021;
Canh and Thanh, 2020; Berdiev and Saunoris, 2019). Beyond the modern theory of financial
intermediation, the finance–growth nexus theory (Stolbov, 2013; Gurley and Shaw, 1955)
shows that the interaction between economic activities and financial activities, institutions,
markets and systems is critical for sustained economic growth and development. Thus,
financial institutions enforce environmental-friendly policies that can lower environmental
degradation and pollution to encourage sustainable development. Hence, financial
institutions andmarkets can suppress the effects of shadow economy activities.
Literature review: empirics
The empirical literature on how the shadow economy affects sustainable development exists
abundantly. However, such studies focusing on Africa and exploring the nonlinear effect of
shadow economies on sustainable development under developed and less developed credit/
financial markets are nonexistent. Arguably, most existent empirical studies use
environmental sustainability indicators (energy efficiency, energy consumption and air
pollution) as sustainable development measures. Meanwhile, environmental sustainability is
just one aspect of sustainable development. Given this discussion, the literature review
focuses on:

 studies on the effect of the shadow economy on sustainable development;

 studies that explore the nonlinear effect of the shadow economy on sustainable
development; and

 studies that use moderators on the nexus between shadow economy and sustainable
development.
Considering studies exploring the shadow economy’s effect on sustainable development,
Sohail et al. (2021) examined the effect of the shadow economy on sustainable development
in the form of environment sustainability (clean energy and air pollution). The study focuses
on South Asian economies between 1999 and 2019 and reports using ARDL models that
shadow economies increase clean energy consumption in Sri Lanka and Pakistan while
reducing clean energy consumption in India. These findings show that the effect of the
shadow economy on environmental sustainability is context-dependent. In addition,
JFEP institutional quality and tax revenue were identified to have critical effects on
environmental sustainability quality. Likewise, Hoinaru et al. (2020) investigated how
shadow economies and corruption affect sustainable development using 185 economies
between 2005 and 2015. The results show that while low-income economies have higher
thresholds of the shadow economy and corruption, corruption and shadow economies are
reported to have destructive effects on economic and sustainable development. These effects
are reported to be higher and stronger for high-income economies.
At the single-country level, Zhao et al. (2022) studied how shadow economy competition
influences sustainable development measured with environmental sustainability quality in
China using 2,848 public and private sector firms from 25 cities. The study uses
instrumental variable models and propensity score matching methods and reported that
shadow economy competition lowers energy intensity, reflecting environmental quality
sustainability improvement. Similarly, Pang et al. (2020) explored how the size of the
shadow economy in China affected sustainable environmental quality in three provinces of
China between 2000 and 2016. The dynamic models used reveal that shadow economy
directly and significantly contributes to pollution levels, whereas the larger pollution level
significantly impacts the size of shadow economies. Also, Benkraiem et al. (2019) examined
the asymmetric effect of the shadow economy on energy growth or energy consumption in
Bolivia, covering periods between 1960 and 2015. The study uses ARDL cointegration
method and the asymmetric causality test in a quarterly data structure and observes that
the shadow economy in Bolivia positively influences energy consumption, indicating that
the existence of a shadow economy lowers sustainable environmental quality.
Furthermore, Popescu et al. (2018) studied the determinants of the Romanian shadow
economy and additionally examined how it influenced sustainable development goals 8
(decent work and economic growth) and 16 (peace, justice and strong institutions). In
examining the drivers of the shadow economy, the study used multiple causes and multiple
indicator specification models and reported that tax avoidance, lack of trust in public
officials, bribery, poor business ecosystem, corruption and regulation uncertainties
determined the shadow economy at the micro level. In contrast, self-employment,
unemployment, part-time employment and lack of trust in institutions determined the
shadow economy. Their second stage results show that an increase in the shadow economy
has detrimental effects on sustainable development goals 8 and 16.
Focusing on the scanty empirical studies that examine the nonlinear threshold effect of
shadow economy on sustainable development, Saafi et al. (2022) studied the nonlinear
impact of shadow economies on sustainable development using 83 developing and
developed economies between 1996 and 2017. They use dynamic and static panel threshold
models and report that the size of the size economy initial has a positive effect on sustainable
development. Beyond a certain threshold, shadow economy size dampens sustainable
development. Thus, while this suggests an inverted U-shape effect between shadow
economy size and sustainable development, it simply implies that the growth and expansion
of shadow economies beyond certain thresholds is injurious/harmful to sustainable
development and results are consistent across both developed and developing economies.
This study measures sustainable development using individual aspects/dimensions of
sustainable development. Similarly, Wu and Schneider (2019) studied the shadow effect’s
nonlinear effect on development levels. The study uses an international sample consisting of
158 economies covering periods between 1996 and 2015. Using panel regression strategy,
the results show that the shadow economy has a direct U-shape effect on gross domestic
product per capita, implying that beyond a certain threshold of the shadow economy,
shadow economy can positively contribute to gross domestic product per capita.
Furthermore, in the light of studies that use moderators in the nexus/relation between Exploring the
shadow economy and sustainable development, Dada et al. (2022) used 30 economies in nonlinear
Africa between 1991 and 2017 to investigate the effect of shadow economy on environmental
sustainability using financial development as a moderator. Using both static and dynamic effect
panel models their results show that, although shadow economy and other variables,
including financial development, urbanization and economic growth and enhanced
ecological footprint, the interaction between shadow economy and financial development
had an adverse effect on sustainable development represented with environmental
sustainability indicator implying that shadow effect reduced sustainable development
through financial development. Similarly, Dada et al. (2021) examined the effect of the
shadow economy on environmental sustainability while focusing on how institutional
quality moderates the nexus between the shadow economy and environmental
sustainability. Using an array of estimation strategies of African economic between 1991
and 2015, the results show that while the shadow economy in Africa reduces environmental
sustainability, the weak institutional quality framework in Africa increases the reducing
effect of the shadow economy on environmental sustainability.
Likewise, Chen et al. (2021) investigated the interrelationship between technological
innovations and shadow economy on sustainable development in the form of energy
efficiency. The study used a sample of MENA economies between 1990 and 2016 and
reported using second-generation methodological approaches that technological innovations
promote energy efficiency while the shadow economy is detrimental to energy efficiency.
Furthermore, the results show that the shadow economy lowers technological innovations’
positive effect on energy efficiency. Furthermore, Canh and Thanh (2020) examined the
effect of shadow economies on energy use and renewable energy use as sustainability
indicators in 115 economies between 1991 and 2014. Applying dynamic models, their results
reveal that shadow economies increase the use of energy and renewable energy, comprising
environmental sustainability. However, it is observed that the official national income
lowers the comprising nature of the shadow economy on environmental sustainability by
lowering energy usage.
Literature review: gaps development
From the literature review, it is clear that while sustainable development is a recent
economic growth approach and has four main aspects/dimensions (investment
consumption, human capital development, environmental depletion and NRD), the
development of a comprehensive sustainable development indicator that captures all these
aspects/dimensions (ANS) is also new and less represented/used in the empirical literature.
Interestingly, most empirical studies have used environment-sustainable indicators (energy
efficiency, energy consumption and air pollution), which capture only one dimension/aspect
of sustainable development. Relying on ANS as a good and comprehensive indicator of
sustainable development (Castro and Lopes, 2022; Medina and Schneider, 2020; Koirala and
Pradhan, 2020), this study presents new perspectives on sustainable development using a
more comprehensive sustainable development indicator.
Again, despite the intuitive reasoning/argument that the shadow economy may have
nonlinear effects by serving as a safety net (by creating employment/income) for vulnerable
citizens and at the same time impeding tax revenue (for the government), there are limited
studies that explore this nonlinear effect of shadow economy on sustainable development
especially in Africa. Besides the limited African evidence, the few existing cross-country
studies (see empirical review section) fail to use the comprehensive measure of sustainable
development and ignore examining how shadow economies’ nonlinear effects play out in
JFEP economies with high and low credit markets in Africa. In effect, this study identifies
measurement gap (using ANS as a proxy for sustainable development), issue gap (exploring
the nonlinear effect of shadow economy on sustainable development under economies with
high and low credit/financial market development indicators) and context gap (presenting
African context evidence of the nonlinear effect of shadow economy on sustainable
development) that needs to be addressed. Hence, this study takes advantage of the gaps
identified to explore how the nonlinear effect of shadow economies in Africa affects
sustainable development in economies with high and low credit market development in
Africa.
Methodology and data
This study uses a panel data strategy to shed insights on the link between shadow
economies and sustainable development in Africa’s high and low financial/credit market
development economies. It is advanced that the panel data strategy provides more
convincing and robust results compared to the traditional time series and cross-sectional
data because the panel structure allows it to capture both time and entity variations (Baltagi,
2015; Baltagi and Baltagi, 2008). Furthermore, Imbens and Wooldridge (2009) posit that the
panel data strategy can control for omitted variable biases and its form/structure is
expressed as:
Yit ¼ ai þ gt þ bXit þ «it . . . . . . . . . : (5)
where Y is the dependent variable, a and g are the country’s fixed and time-specific effects,
respectively, X is a vector of control variables and b is also the sensitivity of the vector of
control variables. Data for this study is obtained from two different sources, including world
development indicators (WDI) and Medina and Schneider (2019). Specifically, data on the
shadow economy is obtained fromMedina and Schneider (2019), while other macroeconomic
data is obtained from WDI. For the purpose of this study, the modeling approach follows
that of Koirala and Pradhan (2020), who focused on the determinants of sustainable
development. However, the modeling of sustainable development in this study deviates from
that of Koirala and Pradhan (2020) in two main ways, which include:
(1) the inclusion of shadow economy in the modeling; and
(2) the use of dynamic GMMmodel instead of static models.
The structure of the dynamic GMM is expressed as:
Yit ¼ aYit1 þ gt þ bXit þ «it . . . . . . . . . : (6)
Yit is sustainable development, the past value of sustainable development determines Yit-1, gt
is the time dimension and Xit is a set of variables which include shadow economy and
control variables, a and b are the sensitivities of lag values of sustainable development and
control variables, respectively, and «it is the error term. The GMM panel data framework is
used for a number of data structures and econometric reasons. First, the literature advances
that the GMM is appropriate and yields robust results when there is high persistence
between the dependent variable (see Appendix 1) and its lag and the number of entities/
countries (37) is greater than the number of time series dimension (8 years) (Tchamyou,
2020; Asongu, le Roux et al., 2019). Similarly, there is evidence of a weak presence of cross-
sectional dependence (see Appendix 2) and the GMM is argued to control for cross-sectional
dependence (Tchamyou et al., 2019). Again, the GMM is used because it can control for
endogeneity, which has been shown to be present in sustainable development models Exploring the
(Zakari et al., 2022; Ahmed et al., 2021). Furthermore, in line with estimating long-run effects, nonlinear
which happens to be one of the objectives, the GMM offers the opportunity to estimate long- effect
run effects and hence the use of the GMM (Qudrat-Ullah and Nevo, 2021; Canh et al., 2019).
The dynamic GMMmodels to be estimated as expressed as:
SUSDEVi;t ¼ b1SUSDEVi;t1 þ b2SHAECOi;t þ b3SHAECO  SHAECOit
þb4LNGNIPCit þ b5INFLi;t þ b6LNMINREVi;t þ b7FINDEVi;t
þ «i;t . . . . . . . . . : (7)
SUSDEVi;t ¼ b1SUSDEVi;t1 þ b2SHAECOi;t þ b3SHAECO  SHAECOit
þb4LNGNIPCit þ b5INFLi;t þ b6LNMINREVi;t þ b7FINDEVi;t
þat þ «i;t . . . . . . . . . : (8)
SUSDEVi;t ¼ b1SUSDEVi;t1 þ b2SHAECOi;t þ b3SHAECO  SHAECOit
þb4LNGNIPCit þ b5INFLi;t þ b6LNMINREVi;t þ b7FINDEVi;t
þpi þ «i;t . . . . . . . . . : (9)
SUSDEVi;t ¼ b1SUSDEVi;t1 þ b2SHAECOi;t þ b3SHAECO  SHAECOit
þb4LNGNIPCit þ b5INFLi;t þ b6LNMINREVi;t þ b7FINDEVi;t
þat þ pi þ «i;t . . . . . . . . . : (10)
SUSDEVi;t ¼ b1SUSDEVi;t1 þ b2SHAERCOi;t þ b3SHAECO  SHAECOit
þ b4LNGNIPCit þ b5INFLi;t þ b6LNMINREVi;t þ «i;t . . . . . . . . . : (11)
SUSDEVi;t ¼ b1SUSDEVi;t1 þ b2SHAECOi;t þ b3SHAECO  SHAECOit
þb4LNGNIPCit þ b5INFLi;t þ b6LNMINREVi;t þ at þ «i;t . . . . . . . . . : (12)
SUSDEVi;t ¼ b1SUSDEVi;t1 þ b2SHAECOi;t þ b3SHAECO  SHAECOit
þb4LNGNIPCit þ b5INFLi;t þ b6LNMINREVi;t þ pi þ «i;t . . . . . . . . . : (13)
JFEP SUSDEVi;t ¼ b1SUSDEVi;t1 þ b2SHAECOi;t þ b3SHAECO  SHAECOit
þ b4LNGNIPCit þ b5INFLi;t þ b6LNMINREVi;t þ at þ pi
þ «i;t . . . . . . . . . : (14)
In models 7–10, the study estimates the nonlinear effects of shadow economies on
sustainable development while including financial development in the models. However, in
models 11–15, the effect of shadow economies on sustainable development under high and
low developed credit/financial markets in Africa. Thus, in models 11–15, we use the median
of financial development to split the sample into high and low developed financial/credit
markets, where those above the median are considered to be high developed credit/financial
market economies and those below are considered to be low developed credit/financial
market economies. Models 7 and 11 fail to control for time and country effects, while 8 and
12 and 9 and 13 control for time and country effects, respectively. However, in models 10 and
14, both time and country effects are controlled for. Thus, time and country effects are
controlled for to ensure the results’ reliability, accuracy and robustness. While the estimates
generated from these models represent the short-run effects, the long-run effects/estimates
are obtained following Qudrat-Ullah and Nevo (2021) and Canh et al. (2019), who expressed
the dynamic GMM approach of long-run estimates as:
bk=½1 f; (15)
where bk is the significant coefficient of shadow economy at linear or nonlinear level for
each estimated model and f is the coefficient of the lagged dependent variable for each
estimated model. To apply the long-run computation, both the variable of interest (in this
case, shadow economy) and the lag of the dependent variable must all be significant.
In testing for the U-shape nonlinear threshold, the approach of Lind and Mehlum (2010)
is used in which the computation of the nonlinearity threshold is computable only if both the
linear and nonlinear terms of the variable in question are significant. To obtain the nonlinear
threshold point, partial derivatives of equations (8)–(14) are taken for shadow economy and
set to zero. The formula for computing the threshold point is expressed as: [b2]  [2*b3].
While the coefficient of the short-run and long-run effects may all be significant, the study is
interested in estimating whether the difference in the short-run and long-run coefficients is
significant. This is relevant for policy because it indicates the regime (short run or long run)
with which shadow effects have the most effect. Thus, in terms of testing for significant
differences in the short-run and long-run coefficients, the approach of Clogg et al. (1995) and
Paternoster et al. (1998) [(jbm – bjj)/sqrt ((Standard Error of bm)2 þ (Standard Error of b 2j) )]
is used.
From the models estimated, sustainable development (SUSDEVit) is used as the
dependent variable and computed as adjusting net national saving with human capital
development (expenditure on education), mineral resource depletion (minerals, energy and
forest depletion) and environmental damages (particulate emission and CO2 damages)
following prior studies (Castro and Lopes, 2022; Koirala and Pradhan, 2020). SUSDEVit-1
represents the lag of sustainable development and from a theoretical perspective, it is
argued the past values of sustainable development inform/determine its current values.
Shadow economy (SHAECO) represents the value of informal activities scaled over gross
domestic product. It is the linear form of the shadow economy, while the extreme/nonlinear
form of the shadow economy is represented as (SHAECO*SHAECO or SHAECO2). It is
argued that the linear/initial level of shadow economy would promote sustainable
development because small-sized shadow economy serves as a safety net creating Exploring the
employment and generating income while large-sized shadow economies create more nonlinear
avenues for tax invasion and environment depletion/damage and hence impeding
sustainable development (Castro and Lopes, 2022; Koirala and Pradhan, 2020). Thus, an effect
inverted U-shape relationship is expected from the shadow economy to sustainable
development. Formal economy size is measured as the natural gross national income per
capital log. It is expected to promote sustainable development because the formal economy
generates more positive outcomes, including creating employment, generating income for
the citizenry and generating tax revenue for developmental projects.
Inflation (INFL) measures price stability and is measured with the consumer price index.
It is anticipated that inflation would lower sustainable development because inflation
reduces the purchasing value/power of savings and is consistent with the new economic
theory (Bruno and Easterly, 1998; Barro, 1995). Revenue from natural resources is measured
as the natural log of mining revenue and contributes to sustainable development. Following
prior studies, the effect of mining revenue on sustainable development depends on whether
the mining revenue exceeds its cost (Koirala and Pradhan, 2020). Thus, when mining
revenue exceeds its cost, a positive effect is expected, while if the mining cost exceeds its
revenue, a negative effect is expected. Financial development is measured as credit to the
private sector to gross domestic product. Following prior studies (Dada et al., 2022; Khan
et al., 2021; Gharleghi and Jahanshahi, 2020; Canh and Thanh, 2020; Koirala and Pradhan,
2020), financial/credit development has a positive effect on sustainable development.
However, if the credit advanced to the private is not efficiently used, it may lead to a
reduction in sustainable development. In addition, under a highly developed financial/credit
market, it is expected that financial development will force informal entities to formalize to
have access to credit. This reduces the size of the shadow economy and hence lowers the
threshold at which shadow economies impede/hurts sustainable development.
Empirical results and discussion
In estimating the results, the summary statistics table, correlation matrix table, ratios and
natural logs are used to ensure the results are accurate and robust. Using the summary
statistics (see Table 1), the mean, minimum, maximum and standard deviation values show
no evidence of outliers. Following econometric literature (Osborne and Overbay, 2004; Yuan
and Bentler, 2001), outliers can influence the reliability and accuracy of the results, but no
evidence of outliers is observed. Similarly, ratios and natural logs are used to control for
outliers and normality of variables. In Table 2, the correlations are used to screen for
Variable Obs. Mean SD Min Max
SUSDEV 236 41.647 2.648 33.789 49.704
SHAECO 306 35.363 7.568 21.9 56.7
LNMINREV 273 11.235 1.069 7.753 13.772
INFL 274 5.817 5.667 2.431 30.695
FORECO 274 7.168 0.949 5.394 9.549
FINDEV 274 29.605 16.425 0 104.626
Source: Computed using Stata 14 based on data from World Development Indicators (WDI) and Medina
and Schneider (2019) – SUSDEV (sustainable development), SHAECO (shadow economy), SHAECO2
(squared term of shadow economy), LNMINREV (log of mineral revenue), INFL (inflation rate), FORECO Table 1.
(log of GNI per capita), FINDEV (financial development) Descriptive statistics
JFEP multicollinearity. Following prior studies (York, 2012; Wichers, 1975) that set the
multicollinearity threshold to 0.5, there is no evidence of multicollinearity.
Table 3 presents the main results of this study. In all, 12 models are reported, where the
first four models capture results for the full sample, and the next two of four models capture
results for less and highly developed financial/credit market economies. As indicated earlier,
models 1, 5 and 9 fail to control for year and country effects, while models 4, 8 and 12 control
for both year and country effects concurrently. Also, models 2, 6 and 10 control for only year
effects, while models 3, 7 and 11 control for only country effects.
In the context of reliability and robustness of the results, the number of instruments, AR(2)
and Hansen tests provide evidence of reliability, validity and robustness (Roodman, 2009; Love
and Zicchino, 2006; Arellano and Bond, 1991) and these are reported in Table 3. Thus, in a two-
step dynamic GMM, the number of internally generated instruments must be lower than the
number of groups (countries), which is confirmed in Table 3. Similarly, the two-step dynamic
GMM controls assume the presence of autocorrelations, and the AR(2) is used to test for
autocorrelation (Windmeijer, 2005; Blundell and Bond, 1998). Given the AR(2) results and the
desirable outcome that there should not be autocorrelation present in the data, the hypothesis
that no autocorrelation is not rejected implies that autocorrelation is not present. In addition, the
Hansen test shows evidence of robustness and validity of the internally generated instruments,
confirming the Hansen test hypothesis that instruments generated are robust and valid
(Arellano and Bover, 1995). The results on the number of instruments, AR(2) and Hansen tests
are evidence of the reliability, validity and robustness of the results of this study.
From the main results in Table 3, the past value of sustainable development is persistent
on the current value of sustainable development, implying that the current level of
sustainable development is highly dependent on its previous values. This finding is
consistent with prior studies showing that sustainable development depends on previous
values (Zakari et al., 2022; Khan et al., 2019).
The results on the effect of the shadow economy on sustainable development show that
the shadow economy initially promotes sustainable development in both the short and long
runs. Still, the shadow economy impedes sustainable development in both the short and long
runs when the size of the shadow economy is doubled. Clearly, the results suggest an
inverted U-shape nonlinear short-run and long-run nexus from shadow economy to
sustainable development in Africa, implying that when shadow economy grows beyond a
certain threshold, it lowers/dampens sustainable development now and in the future (long
run). This finding is consistent across the full sample (Models 1–4), weak institutional
economies sample (Models 5–8) and strong institutional economies sample (Models 9–12).
Variables (1) (2) (3) (4) (5) (6)
(1) SUSDEV 1.000
(2) SHAECO 0.259 1.000
(3) LNMINREV 0.216 0.180 1.000
(4) INFL 0.177 0.059 0.039 1.000
(5) FORECO 0.319 0.163 0.307 0.148 1.000
(6) FINDEV 0.101 0.400 0.297 0.084 0.379 1.000
Source: Computed using Stata 14 based on data from World Development Indicators (WDI) and Medina
and Schneider (2019) – SUSDEV (sustainable development), SHAECO (shadow economy), SHAECO2
Table 2. (squared term of shadow economy), LNMINREV (log of mineral revenue), INFL (inflation rate), FORECO
Pairwise correlations (log of GNI per capita), FINDEV (financial development)
Exploring the
nonlinear
effect
Table 3.
Nonlinear effect of
shadow economy on
sustainable
development in
Africa
Low developed
Full sample financial/credit market
VARIABLES Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
L.SUSDEV 0.4440** (0.2107) 0.2645** (0.1068) 0.4377** (0.2088) 0.2551** (0.1009) 0.7116*** (0.1431) 0.7476*** (0.1349)
SHAECO 0.7057*** (0.2419) 0.9740*** (0.1918) 0.6993*** (0.2368) 0.9822*** (0.1898) 0.5855*** (0.1716) 0.5126*** (0.1733)
SHAECO2 0.0086*** (0.0031) 0.0121*** (0.0029) 0.0086*** (0.0030) 0.0122*** (0.0028) 0.0068*** (0.0020) 0.0060*** (0.0020)
LNMINREV 0.1710 (0.2190) 0.2525 (0.2908) 0.1610 (0.2134) 0.2330 (0.2845) 0.1708 (0.1473) 0.1115 (0.1327)
INFL 0.0107 (0.0308) 0.0103 (0.0390) 0.0124 (0.0296) 0.0105 (0.0377) 0.0072 (0.0533) 0.0108 (0.0410)
FORECO (LNGNICAP) 1.1505** (0.5610) 1.4418** (0.7029) 1.1895** (0.5538) 1.4558** (0.6835) 0.3088 (0.3341) 0.2619 (0.2964)
FINDEV 0.0212* (0.0114) 0.0265 (0.0168) 0.0209* (0.0113) 0.0257 (0.0171)
Year effects No Yes No Yes No Yes
Country effects No No Yes Yes No No
Short-run thresholds 41.029 40.248 40.657 40.254 43.051 42.717
Long-run thresholds 40.673 41.375 41.467 41.219 42.292 42.217
SHAECO long-run 1.269*** (0.201) 1.324*** (0.237) 1.244*** (0.180) 1.319*** (0.221) 2.030*** (0.531) 2.031*** (0.552)
SHAECO2 long-run 0.016*** (0.003)*** 0.016*** (0.004) 0.015*** (0.003) 0.016*** (0.004)*** 0.024*** (0.007) 0.024*** (0.007)
Observations 205 205 205 205 88 88
Number of codes 30 30 30 30 20 20
Instruments 11 13 12 14 9 10
F-Stats 2.65 (0.01) 3.16 (0.00) 2.65 (0.01) 3.19 (0.00) 2.58 (0.01) 2.62 (0.01)
AR(1) 0.44 (0.66) 0.18 (0.86) 0.43 (0.67) 0.17 (0.86) 0.41 (0.65) 0.43 (0.66)
AR(2) 1.70 (0.79) 5.27 (0.38) 1.70 (0.79) 4.90 (0.43) 0.80 (0.85) 0.23 (0.97)
Hansen 2.83 (0.59) 4.58 (0.49) 2.80 (0.59) 4.41 (0.49) 0.92 (0.82) 0.20 (0.98)
Notes: ***p < 0.01, **p < 0.05, *p < 0.1 L.SUSDEV (lag of sustainable development), SHAECO (shadow economy), SHAECO2 (squared term of shadow
economy), LNMINREV (log of mineral revenue), INFL (inflation rate), FORECO (log of GNI per capita), FINDEV (financial development); standard errors in
parentheses
Source: Computed using Stata 14 based on data fromWorld Development Indicators (WDI) and Medina and Schneider (2019)
(continued)
JFEP
Table 3.
Low developed
financial/credit market High developed financial/credit market
VARIABLES Model 7 Model 8 Model 9 Model 10 Model 11 Model 12
L.SUSDEV 0.6886*** (0.1522) 0.5827*** (0.1527) 0.5853* (0.2841) 0.4104* (0.2329) 0.6052** (0.2811) 0.7011** (0.2714)
SHAECO 0.5605*** (0.1626) 0.7744** (0.2808) 0.5165* (0.2799) 1.0943** (0.4705) 0.4870* (0.2793) 0.4127** (0.1803)
SHAECO2 0.0066*** (0.0018) 0.0093** (0.0035) 0.0066* (0.0035) 0.0149** (0.0072) 0.0062* (0.0035) 0.0054** (0.0023)
LNMINREV 0.0969 (0.1707) 0.0114 (0.2320) 0.1896 (0.2265) 0.3826 (0.5208) 0.2371 (0.2513) 0.1715 (0.2910)
INFL 0.0013 (0.0555) 0.0232 (0.0426) 0.0074 (0.0441) 0.0566 (0.0665) 0.0015 (0.0450) 0.0130 (0.0622)
FORECO (LNGNICAP) 0.3149 (0.3304) 0.2637 (0.3173) 0.7450 (0.6249) 0.3856 (1.1142) 0.6673 (0.6088) 0.4682 (0.5833)
FINDEV
Year effects No Yes No Yes No Yes
Country effects Yes Yes No No Yes Yes
Short-run thresholds 42.460 41.634 39.129 36.721 39.274 38.274
Long-run thresholds 42.857 42.182 38.938 37.12 38.563
SHAECO long-run 1.800*** (0.470) 1.856*** (0.249) 1.246*** (0.298) 1.856** (0.733) 1.234*** (0.318) 1.381 (0.776)*
SHAECO2 long-run 0.021*** (0.006) 0.022*** (0.004) 0.016*** (0.005) 0.025* (0.013) 0.016*** (0.005) 0.018 (0.012)
Observations 88 88 117 117 117 117
Number of codes 20 20 24 24 24 24
Instruments 10 13 9 13 10 9
F-Stats 2.56 (0.01) 2.21(0.03) 2.45 (0.01) 2.21 (0.03) 2.43 (0.02) 2.21 (0.03)
AR(1) 0.42 (0.68) 0.30 (0.77) 0.45 (0.65) 0.38 (0.70) 0.47 (0.64) 0.52 (0.61)
AR(2) 0.77 (0.86) 6.93 (0.23) 1.70 (0.64) 3.79 (0.71) 1.76 (0.62) 1.17 (0.28)
Hansen 0.92 (0.82) 2.49 (0.78) 1.25 (0.74) 8.71 (0.19) 1.26 (0.74) 0.99 (0.32)
In an attempt to determine and understand the threshold point beyond which the shadow Exploring the
economy impedes sustainable development, the full sample exhibits a threshold between nonlinear
40% and 41.7%, beyond which shadow economies dampen sustainable development (see effect
Appendix 3). However, under economies with high financial/credit market development,
shadow economy size beyond threshold points beyond 36.8%–39.3% results in a reduction
in sustainable development (see Appendix 5), while under economies with low financial/
credit market development, shadow economy size beyond threshold points beyond 41.6%–
43.1% results into reduction in sustainable development (see Appendix 4). These results
suggest that strongly developed financial/credit markets can lower/tame the size of shadow
economies that suppress sustainable development in Africa, and this effect is observed in
both the short-run and long-run. These results imply that the development of financial/
credit markets can shrink the size of shadow economies that impedes sustainable
development by:

 pressurizing informal businesses to formalize to gain access to credit (Berdiev and
Saunoris, 2016; Capasso and Jappelli, 2013; Blackburn et al., 2012); and

 providing the opportunity to monitor and supervise the shadow economy to reduce
value and frequency tax invasion which happens in the shadow economy (Capasso
and Jappelli, 2013; Blackburn et al., 2012).
Evidently, the importance of financial development in taming the adverse effect of shadow
economies on sustainable development cannot be overemphasized and demonstrated
through this result and supported by prior studies that show the relevance of financial
development between the nexus between shadow economies and sustainable development
(Dada, Ajide and Arnaut, 2022; Khan et al., 2021; Gharleghi and Jahanshahi, 2020; Canh and
Thanh, 2020; Berdiev and Saunoris, 2016).
In terms of the control variables, formal economy size measured as the natural log of
gross national income per capita has a significant positive effect on sustainable development
(see Models 1–4), implying that income from the formal economy propels sustainable
development in Africa. Similarly, financial development is reported to reduce sustainable
development in Africa (Models 1 and 3). These findings on the control variables are
consistent with prior studies.
Conclusions, policy implications and recommendations
The economic literature has long focused on evaluating, monitoring and reporting on the
formal economy through economic growth measures/variables such as gross domestic
product (growth or per capita) and gross national income (growth or per capita). However, in
recent times, economists and policymakers have increased their interest in understanding,
monitoring and reporting on the shadow/informal economy, given its positive and negative
implications for economic outcomes such as sustainable development. Thus, prior studies
show that shadow economies have both good and bad sides, providing a basis for a possible
nonlinear nexus from shadow economies to sustainable development. Yet, evidence of the
nonlinear effect of shadow economies on sustainable development is limited and scanty,
especially in Africa. At the same time, policymakers and economists are interested in
identifying and understanding the threshold point of shadow economies that yields
destructive effects on sustainable development to deploy effect tools/mechanisms that can
be used to tame the destructive threshold effects of shadow economies on sustainable
development. In this study, we attempt to explore the possible nonlinear threshold effect of
shadow economies on sustainable development in Africa while offering additional evidence
JFEP on how high and low financial/credit market development influences the threshold point of
shadow economies that yields destructive effects on sustainable development.
The study uses about 37 African economies between 2009 and 2017 on a dynamic GMM
panel model that controls for country, year and technological effects to ensure consistency
and reliability of results and findings. The key results show that:

 there is an inverted U-shape nexus between shadow economy and sustainable
development in both the short and long run;

 the threshold point beyond which shadow economies dampen sustainable
development is higher in the long run; and

 the size of shadow economies that presents destructive effects on sustainable
development is lower under economies with high financial/credit market
development.
These results have policy implications and recommendations for policymakers and financial
sector regulators. From the results, it is clear that shadow economies can benefit sustainable
development, especially when the size of shadow economies remains within certain
thresholds/levels. Similarly, it is clear from the results that policymakers and financial
sector regulators can rely on financial/credit market development to tame the size of shadow
economies, which creates destructive effects on sustainable development. Clearly,
developing the financial/credit market can improve/enhance sustainable development by
lowering shadow economies’ destructive effects on sustainable development, especially in
Africa. For future research direction, it will be insightful to understand how the
development of insurance and stock markets can influence the nonlinear effect of shadow
economies on sustainable development.
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Appendix 1 Exploring the
nonlinear
effect
Variables (1) (2)
(1) lnans 1.000
(2) L.lnans 0.735 1.000
Table A1.
Source: Computed using Stata 14 based on data from World Development Indicators (WDI) and Medina Matrix of
and Schneider (2019) correlations
Appendix 2: Cross-sectional dependence
H0: Errors are weakly cross-sectional dependent.
CD¼0.784
p-value¼ 0.433
Source: Computed using Stata 14 based on data from World Development Indicators (WDI)
and Medina and Schneider (2019)
Appendix 3
Curve fit for lnans
Observed
Linear
Logarithmic
20 30 40 50 60
Source: Plotted using Stata 14 based on data from World Development FigureA1.
Model 4 – full sample
Indicators (WDI) and Medina and Schneider (2019)
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JFEP Appendix 4
Curve fit for lnans
Observed
Linear
Logarithmic
FigureA2. 20 30 40 50 60
Model 8 – low
development Source: Plotted using Stata 14 based on data from
financial/credit
World Development Indicators (WDI) and Medina
market sample
and Schneider (2019)
Appendix 5
Curve fit for lnans
Observed
Linear
Logarithmic
FigureA3. 20 30 40 50 60
Model 12 – high
development Source: Plotted using Stata 14 based on data from World
financial/credit
Development Indicators (WDI) and Medina and Schneider
market sample
(2019)
Corresponding author
Baah Aye Kusi can be contacted at: baahkusi@gmail.com
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