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The International Journal of Banking and Finance, Vol. 20, Number 1 (January) 2025, pp: 1-22
1
How to cite this article:
Onyele, K. O., Ikwuagwu, E. B., & Ibe, H. C. (2025). Macro-econometric analysis of inward capital flows: A case
of Nigeria in times of security challenges. International Journal of Banking and Finance, 20(1), 1-22.
https://doi.org/10.32890/ijbf2025.20.1.1
MACRO-ECONOMETRIC ANALYSIS OF INWARD CAPITAL FLOWS: A CASE
OF NIGERIA IN TIMES OF SECURITY CHALLENGES
1Kingsley Onyekachi Onyele, 2Eberechi Bernadine Ikwuagwu
&
3Happy Chukwudike Ibe
1Department of Banking and Finance, Rhema University Aba, Abia State, Nigeria
2&3Department of Banking and Finance, Michael Okpara University of Agriculture,
Umudike, Abia State, Nigeria
1Corresponding author: kingsleyonyele@gmail.com
Received: 26/6/2023 Revised: 19/2/2024 Accepted: 23/3/2024 Published: 27/10/2024
ABSTRACT
This study used quarterly data for the period of 2014Q1–2021Q2 to investigate the macro-econometric
implications of capital flows to Nigeria in the face of security challenges. The ARDL bounds test
technique identified the long-run relationships between macroeconomic dynamics, insecurity, and total
capital inflows to Nigeria, while the error correction mechanism (ECM) identified the short-run
relationships. The Toda-Yamamoto test was used to determine whether the model variables were
causally related or not. The findings pointed to a short-term negative relationship between insecurity,
exchange rates, lending rates, and inflows of capital, while a positive relationship was found between
industrial production capacity, the consumer price index, and the total inflows of capital to Nigeria,
with insecurity, exchange rates, the consumer price index, and lending rates being the most significant
variables. In the long run, insecurity, lending rate, and consumer price index had no significant impact
on inward capital inflows, while exchange rate and industrial production capacity exerted significant
impacts on capital inflows. The lending rate had a negative impact on overall inflows of capital, whereas
the exchange rate, industrial production capacity, and consumer price index had positive impacts. The
exchange rate and industrial production capacity were the most important variables that affected capital
inflows. Based on the ECM, it was realized that aggregate inward capital flows were stabilized by a
factor of roughly 47.2% per quarter in order to reach long-run equilibrium. The Toda-Yamamoto
causality tests indicated that the interactions between macroeconomic variables and insecurity strongly
influenced capital flows to Nigeria. The overall findings suggested that promoting macroeconomic
stability and combating insecurity could improve the investment climate, encouraging foreign capital
flows into Nigeria.
INTERNATIONAL JOURNAL OF BANKING
AND FINANCE
e-journal.uum.edu.my/index.php/ijbf
The International Journal of Banking and Finance, Vol. 20, Number 1 (January) 2025, pp: 1-22
2
Keywords: ARDL, macro economy, macro-econometric, insecurity, capital flows, Nigeria.
JEL Classification: E20, Q56.
INTRODUCTION
Nigeria is listed as one of the most terrorized countries in the world, ranking 143 out of 163 countries
in global peacefulness based on data from the Global Peace Index (GPI) released in 2022 (GPI, 2022).
As a result, net capital inflows to Nigeria has down trending due to heightened insecurity and exchange
rate depreciation that led to macroeconomic instability (Oji, 2021). The net capital inflows as reported
by the National Bureau of Statistics (NBS, 2021) depicts that capital inflows into Nigeria has depleted
in recent years (refer Figure 1). Financial experts predict that if the security constraints limiting the
nation's macroeconomic environment remain unaddressed, the risky state of the financial market could
intensify significantly (Ozoigbo, 2019). Experts have also stated that until the government develops
fresh plans to strengthen Nigeria's security and macroeconomic institutions, increased insecurity will
continue to hurt the naira and industrial output, causing inflationary pressure, and resulting in low
capital inflows. Due to these economic menaces, the World Bank has suddenly revised its economic
growth projection for Nigeria from 3.2 percent to 2.9 percent in June 2022 (World Bank, 2022).
Figure 1
Trend of Inward Capital Flow and Insecurity in Nigeria
Sources: National Bureau of Statistics, 2021.
\
Inward capital flows deals with inflows from abroad in the form of direct investments, portfolio
investments and other investments such as trade credits. These inflows, no doubt, help in ensuring
financial stability in any economy. Apart from being a major source of foreign exchange, it provides
the needed liquidity and jobs in the recipient economy. It also determines the level of economic growth
that would be achieved over period of time (Lee & Sami, 2019). A decrease in inward capital flows is
an indication that an economy is passing through tough times or losing investor confidence. The more
capital inflows a country receives, the more it has access to foreign exchange for international trade
0.00
1000.00
2000.00
3000.00
4000.00
5000.00
6000.00
7000.00
8000.00
9000.00
10000.00
2014Q1
2014Q2
2014Q3
2014Q4
2015Q1
2015Q2
2015Q3
2015Q4
2016Q1
2016Q2
2016Q3
2016Q4
2017Q1
2017Q2
2017Q3
2017Q4
2018Q1
2018Q2
2018Q3
2018Q4
2019Q1
2019Q2
2019Q3
2019Q4
2020Q1
2020Q2
2020Q3
2020Q4
2021Q1
2021Q2
₦ BILLION
The International Journal of Banking and Finance, Vol. 20, Number 1 (January) 2025, pp: 1-22
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(Cayir, 2021). According to Onyele et al. (2017), Nigeria experiences a consistent decline in its inward
capital flows because its foreign exchange policies have not been favourable to foreign investors
coupled with the persistent volatility of the naira - US dollar exchange rate, leading to excessive capital
flight. For most foreign investors, as long as the exchange rate remains unstable, they are not likely to
bring in investible financial resources. Hence, the macroeconomic instability discussed in this study is
a foreign exchange leadership problem.
Nigeria’s macroeconomic environment, the sociopolitical climate and investments are seriously
threatened by security issues (Awa, 2020). The heightened security challenges in Nigeria comes with a
cost. Rather than investing in Nigeria, investors now divest to economies where a return on investment
is assured. Many of the states in Nigeria have such high levels of insecurity that doing business is
financially disadvantageous and deters potential investors from making decisions in an environment of
uncertainty and turmoil. As a result, the aim of this study is to investigate the collective effect of
macroeconomic aggregates and insecurity on Nigeria's inward capital flows. This study is significant to
investment practitioners and the government as it explores the extent to which the macroeconomic
environment and insecurity influences the much needed capital inflows, which has important
implications for risk management and economic policy advancement. More specifically, knowledge of
capital flows is important because it is directly associated with the level of growth in the domestic
economy (Adebayo et al., 2021; Amire, 2021; Ayoola, 2022). As such, establishing the amount of
investments to make should be based on knowledge of the extent of macroeconomic stability and
security in the domestic economy.
This study aims to achieve three main objectives. The first is to capture comprehensively, the impact of
insecurity on capital inflows into Nigeria. The second objective seeks to examine the impacts of
macroeconomic dynamics on capital inflows into Nigeria and the third is to identify the direction of
causality between insecurity, macroeconomic dynamics, and capital inflows into Nigeria. This study
further divides into four sections, in addition to the introductory section. In Section 2 extant conceptual,
theoretical and empirical literature were reviewed while the methodological framework upon which the
empirical investigation would be conducted was provided in Section 3. In Section 4, estimations of
empirical model were undertaken and findings were discussed while conclusions were reached and
policy suggestions were proffered in Section 5.
LITERATURE REVIEW
Insecurity in Nigeria: Stylized Facts
The various dimensions of security threats in Nigeria pose diverse risks to investments. Cases of
kidnapping have gained prominence in Nigeria (see Figure 2). People have been kidnapped all over the
country recently (Ikezue, 2023). The most celebrated kidnapping was the 276 Chibok girls that were
abducted from their school in Borno State, Nigeria in 2014 by the Boko Baram terrorist group. Since
then, there have been a number of other significant kidnappings. The same terrorist organisation
abducted 110 Dapchi girls in March 2018. After that, criminal groups began kidnapping people at
random to extract ransom from the victims or their families.
According to the United Nations Development Programme (UNDP), Northeast Nigeria's insurgencies
had taken the lives of nearly 350,000 people as of the end of 2020 (UNDP, 2021). Tanko (2021) reported
that Boko Haram (a terrorist group in Nigeria) had seized several territories and levied taxes on
The International Journal of Banking and Finance, Vol. 20, Number 1 (January) 2025, pp: 1-22
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agricultural products, they now control the once, booming international fish market in the Chad Basin.
The challenge is made more difficult by Nigeria's ungoverned spaces--remote areas that are largely
ignored, where various militant groups have seized to torment rural communities without fear of reprisal
(Awa, 2020; Ebipre & Wilson, 2020). In the Southeast, the Indigenous People of Biafra (IPOP) gave a
sit-at-home order (every Monday) to demand the release of its leader, Nnamdi Kanu, from prison,
resulting in the loss of lives, properties of residents and businesses (Odeniyi, 2023). The violent
agitators for resource control in the Niger Delta include the activities of various militia groups such as
the Movement for Emancipation of the Niger Delta (MEND), the Ijaw Youth Council (IYC),
Mgbonyenbi and Emeni (2020) likened these struggles to the ‘resource curse’, the tendency of natural
resource reliance to hinder growth, increase inequality and poverty for a larger majority of the populace.
Figure 3 depicts the number of casualties from the various security challenges between 2014 and 2021
in Nigeria.
Figure 2
Number of Kidnapping Cases and Victims
Source: International Centre for Investigative Reporting (ICIR) https://www.icirnigeria.org
In response to the rising state of insecurity in Nigeria, the government has been allocating much of its
resources to defence and security-related expenditure (refer Figure 4). These allocations represent large
sums of money that should have been invested in key economic sectors like education and health
(Onyele & Ariwa, 2020). The exorbitant defence expenditure, which primarily relies on foreign
procurement, has a negative economic impact and further devalues the Naira (Ayodele & Tomisin,
2021). The country has also witnessed how agricultural produce in Benue (the food basket of the nation)
and a number of other states has been badly depleted by the incessant herdsmen-farmers clashes. This
protracted security challenge has affected businesses who that, due to the uncertainty created by
insecurity, put off investment decisions in Nigeria. Under such an economic climate, creativity is
discouraged as innovation takes a back seat despite being a driving force for economic advancement,
leading to a high rate of brain drain through mass emigration to western countries for safety (Aderemi,
2019). As such, the government’s inability to put an end to the security challenges has created a feeling
of hopelessness and helplessness, especially in the areas that are vulnerable (Omole, 2020).
31 84 111 137 141 157 331 439 590
351
897 926
347 532
1014 1421
2879
5287
0
1000
2000
3000
4000
5000
6000
2013 2014 2015 2016 2017 2018 2019 2020 2021
Number of cases and victims relating to
kidnapping
Cases Victims
The International Journal of Banking and Finance, Vol. 20, Number 1 (January) 2025, pp: 1-22
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Figure 3
Annual Number of Fatalities Arising from Security Challenges
Source: https://www.cfr.org/nigeria/nigeria-security-tracker/p29483
Figure 4
Annual Government Expenditure on Internal Security
Source: CBN statistical bulletin, Vol. 32, 2021
Theoretical Underpinning: The Neoclassical Theory vs. Lucas Paradox
The neoclassical theory holds that a liberalised financial market improves efficiency of capital
allocation and implies that capital should flow from wealthy to poor countries. Assume, for the purposes
of discussing economic growth, that the economy is small and open, with explicit production factors
for labour and capital (K and L), and that production follows a constant return to scale function of the
following form:
= (1)
37,118
108,025
246,291
431,016
528,182
587,141
653,708
754,472
860,275
977,780
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
NUMBER OF FATALITIES
273.14
410.20
417.66
397.95
489.65
668.63
728.83
679.96
2014
2015
2016
2017
2018
2019
2020
2021
₦ BILLION
The International Journal of Banking and Finance, Vol. 20, Number 1 (January) 2025, pp: 1-22
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Where,
= technology parameter
= output per worker
= capital per worker; and
= Labour
Each factor must be compensated at the value of its marginal product in a production of homogeneous
commodities under perfect competition. Because of this, the requirement for the capital market
equilibrium must also be met at steady state:
= (2)
Where,
= is the domestic interest rate associated with capital per worker ratio
= is the net depreciation of marginal product of capital per worker.
Imagine that capital is completely movable across national boundaries, allowing enterprises to tap into
international savings without home savings restricting investments. Here, the scenario of a small open
economy dealing with an indefinitely elastic supply of capital at the global interest rate (rw) is
considered. Foreign capital would flow into a nation if the initial level of the capital stock to labour
ratio's related rate of return on capital was higher than the world interest rate. Neoclassical theory, which
is based on liberal capital movements, states that significant capital transfers from industrialised to
developing countries should be seen as a result of the capital's diminishing returns (Solow, 1956).
Contrarily, Lucas (1990) noted that capital moved from developing to developed nations, refuting the
neoclassical postulation. The literature argues that the neoclassical assumptions do not apply in practice
because market failure and distortions may prevent efficient capital allocation, which explains the
paradoxical behaviour of capital flows. As a result, the capital influx to poor and developing economies
is less than the neoclassical model can account for. In the words of Alfaro et al. (2008), theoretical
explanations for the Lucas paradox are divided into those that emphasise flaws in the capital market
and those that focus on basic production issues. Numerous academics have focused on Lucas' claims,
and an extensive empirical study on the capital flow allocation conundrum has been conducted. Lucas
(1990) outlined his justification using the following one-sector model. Let equation 3 represent the
production function, with the variables labor (L) and capital (K) standing for the output:
(3)
Also, let represent the price of goods, and denote returns on capital and labour, respectively.
Then, firms profit maximisation gives:
(4)
The domestic price is equalised between nations with unrestricted trade flow. According to the theory
of diminishing marginal products, countries with lower capital intensity should have higher returns (r).
Lucas gave the example that the return on capital in developing nations needs to be larger than that in
developed nations. Lucas argued for significant capital transfers from rich (developed) to poor
The International Journal of Banking and Finance, Vol. 20, Number 1 (January) 2025, pp: 1-22
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(developing) countries with a return difference in favour of the latter. The Lucas paradox refers to the
fact that this claim is false in practice. Several approaches, such as seeing human capital as a novel
production element and taking sovereign risk into account, include thinking of a worker in a rich country
as effectively equivalent to many workers in a poor country (Ju & Wei, 2006). The dynamics of
macroeconomic elements and security threats, however, may be important aspects that support the
Lucas paradox, according to further studies.
Conceptual Framework
Conceptual framework shows the association between the independent and dependent. Insecurity
(measured by the number of fatalities), exchange rate, industrial production capacity, consumer price
index and lending rate are the independent variables, while the dependent variable is the total inward
capital flows. Figure 5 provides a diagrammatical illustration of the conceptual framework of
macroeconomic dynamics, insecurity and the inward capital nexus.
The fact that inward capital flows may dramatically decrease in a capital-scarce economy like Nigeria
for a number of obvious reasons is of importance to this study. When domestic investors move their
investments abroad, the economy experience low inward capital flow or capital flight (Onyele et al.,
2017). Macroeconomic instability accounts for this abrupt change in the direction of capital flows,
which causes low rates of return as investors move their money away from such an economy in pursuit
of higher yields (Al-Smadi, 2018). This will result in a loss of domestic investment capital, low
industrial productivity, high domestic prices, little foreign exchange activity, and a decline in the value
of the currency (IMF, 2016).
Figure 5
Conceptual Framework
In this scenario, due to the declining money supply and rising loan rate, there would be fewer
investments and a higher unemployment rate (Adekunle et al., 2020). If this scenario is allowed to
persist, the local economy would collapse, resulting in a high incidence of poverty, and people would
start to engage in all kinds of social vices like kidnapping, armed robbery, oil theft and pipeline
Predictor
variables
Insecurity
Macroeconomic
dynamics
Increase in the
number of
fatalities
Predicted
variable
Aggregate
inward
capital
flows
Changes in
exchange rate,
inflation rate,
industrial
production and
lending rate
The International Journal of Banking and Finance, Vol. 20, Number 1 (January) 2025, pp: 1-22
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destruction, thuggery, and other violent behaviours that would cripple the security apparatus and further
degenerate to serious security challenges over time, which is not good for a country like Nigeria seeking
inward capital (Obiekwe, 2018). Spending by the government on arms and ammunition to address the
issue of insecurity will instead cause capital outflows rather than inflows.
If a country is labelled as insecure, foreign investment would decline as a result of the loss of foreign
investors' confidence brought on by acts of insecurity that result in the loss of human life and injuries.
In addition to the immediate effects of insecurity, security issues generate economic disruptions that
may manifest days, weeks, or months after the loss of lives and property. Economic behaviour is altered
by insecurity, mostly by modifying patterns of consumption and investment as well as by diverting
domestic resources away from economically beneficial endeavours (Bardwell & Igbal, 2021).
Therefore, this study combines both macroeconomic factors and insecurity (number of fatalities) in a
single model.
Review of Related Empirical Literature
There are several empirical works on the subject discussed in this paper. Many of these studies provide
evidence as to why sufficient capital is not flowing from rich countries to poor countries, as predicted
by Lucas's (1990) paradox. The review contains studies that show how macroeconomic conditions
affect capital inflows, while the others show how insecurity affects capital inflows. The major gap found
in the empirical review is the fact that most of the Nigerian studies reviewed did not consider the effect
of insecurity, and the few studies like Ayoola (2022); Essien et al. (2015) and Igbadoo et al. (2023) that
looked at the effect of insecurity did not consider the macroeconomic perspective in their analysis.
Hence, the present study considered the collective effect of insecurity and macroeconomic variables on
capital flows to Nigeria. Also, most of the studies for Nigeria largely considered FDI, which is only an
aspect of inward capital flows1: hence, the current study focused on the aggregate figure of inward
capital flows to Nigeria. A summary of these studies highlighted in Table 1.
Table 1
Empirical Evidence of the Effects of Insecurity and Macroeconomic Aggregates
Model
Period
Country
Findings
Igbadoo et al. (2023)
Systematic
review
-
Nigeria
Insecurity and laws
significantly affect FDI.
Le et al. (2023)
GMM
1990 -2020
Asia-Pacific
nations
Political stability had a
negative effect on FDI.
Feng et al. (2023)
Panel
least squares
2010 - 2020
45 economies
FDI showed a flight to
safety phenomenon.
Nwagu (2023)
ARDL
1986 - 2020
Nigeria
GDP, exchange rate and
MPR determined FDI.
Magoane et al. (2023)
NARDL
1995-2020
South Africa
Political risk rating and
exchange rate affected FDI.
Ayoola (2022)
Descriptive
1999 - 2014
Nigeria
Insecurity discouraged FDI
inflows.
Hassan (2022)
Panel least
squares
1991-2020
Visegrád
countries
Country risk mattes for FDI
inflows.
Adebayo et al. (2021)
ARDL
1981 - 2018
Nigeria
Exports and trade openness
enhanced FDI.
Hogetoorn & Gerritse
(2021)
Panel least
squares
1995 - 2019
116 countries
Firms divest from countries
marred by terrorism.
(continued)
The International Journal of Banking and Finance, Vol. 20, Number 1 (January) 2025, pp: 1-22
9
Model
Period
Country
Findings
Odili & Onyele
(2019)
ARDL
1986 - 2019
Nigeria
Banking sector and stock
market development
discouraged capital flows.
Bardwell & Igbal
(2021)
Cost accounting
method
-
163 countries
Increase in terrorism
decreased investments.
Wijaya et al. (2020)
VECM
1981 - 2018
Gulf Cooperation
Council (GCC)
GDP, inflation rate, interest
rate, debt and exchange rate
influenced largely FDI.
Ukachukwu &
Odionye (2020)
ARDL
1981 - 2017
Nigeria
Volatilities of exchange rate
and crude oil price
significantly influenced
FDI.
Nassour et al. (2020)
Panel least
squares
1984-2017
MENA countries
There was a significant
negative relationship
between politics and FDI.
Artantaş & Sipahi
(2020)
OLS
1994 - 2018
Turkey
Government deficit and
exchange rate were the
major determinants of FDI.
Kambou & Khariss
(2020)
OLS
2015 - 2018
Burkina Faso
Terrorism did not explain
the changes in FDI inflows
Akhtaruzzaman
(2019)
Panel least
squares
2000 -2017
Developing
countries
The risk of expropriation
reduced FDI.
Aderemi (2019)
OLS
1990 -2016
Nigeria
Exchange rate volatility had
a marginal effect on capital
flows.
Mistura & Roulet
(2019)
Gravity model
1997 -2016
Sample of 60
countries
Reforms liberalizing FDI
had varying effects.
Tellez-Leon & Ibarra
(2019)
VAR
1995 -2018
Mexico
Increase in US interest rate,
higher global risk aversion
and liquidity shocks
decreased FPI.
Lipovina-Božović &
Ivanovic (2018)
SVAR
2005 - 2017
Montenegro
Foreign output, interest rate
differentials and Euro area
risk sentiment significantly
influenced FDI and FPI.
David & Ampah
(2018)
ARDL
1990 - 2012
SSA countries
Investors’ perception of
government policies and
macroeconomic swings
caused capital outflows.
Al-Smadi, 2018
OLS
2000 - 2016
Jordan
Unstable macroeconomic
environment and risk
diversification reduced FPI.
Jehan & Hamid
(2017)
GMM
1980 - 2013
Developing
countries
Exchange rate volatility
caused a diminishing
impact on physical and
financial inflows.
Nwokoye & Oniore
(2017)
ARDL
1994 - 2015
Nigeria
Capital flows was majorly
determined by money
supply, nominal exchange
rate, inflation rate and
interest rates spread.
Nwosa & Adeleke
(2017)
E-GARCH
1986 - 2016
Nigeria
GDP and trade openness
determined FDI while
interest rate and stock
market capitalization
explained FPI.
Kisto (2017)
VECM
1975 - 2015
Mauritius
Exchange rate diminished
FDI inflows while interest
rate increased it.
(continued)
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10
Model
Period
Country
Findings
Dembo & Nyambe
(2016)
ARDL
1984 - 2014
Namibia
Exchange rate volatility
diminished FDI in the
short-run.
Erkekoglu &
Kilicarslan (2016)
Panel least
squares
2002 -2012
91 countries
Heightened political risks
reduced FDI.
Waqas et al. (2015)
GARCH (1,1)
2000 – 2012
China, India,
Pakistan and
Sri Lanka
Inflation and GDP
significantly influenced
portfolio volatility.
Essien et al. (2015)
Descriptive
1999 -2013
Nigeria
Insecurity hindered FDI
inflows.
Iida (2015)
Descriptive
2000 -2013
East Asian
countries
Protests against Japanese
firms in China increased
divestments to Southeast
Asia.
Mokhele (2015)
Indigenous
method
2000 -2012
South Africa
Political risks did not
significantly influence FDI
Kinyanjui (2014)
OLS
2010 -2012
Kenya
Terrorism reduced FDI
METHODOLOGY
Sources and Description of Data
This study used quarterly data spanning from 2014Q1 to 2021Q2 to assess the relationship between
macroeconomic dynamics and inward capital flows to Nigeria in the face of security challenges. The
justification for using quarterly data was hinged on the fact that the incessant Nigerian security
challenges were more pronounced in 2014 with the adoption of the Chibok girls in Borno State.
Therefore, we used quarterly data to ensure sufficient observations between 2014 and 2021, which
aligned with the dynamics of time series analysis.
Table 2
Sources and Description of Data
Data
Sources
Description
Inward capital flows
(CPF)
NBS quarterly publication on
Nigerian capital importation
Measured as the logarithm of aggregate foreign
capital flows to Nigeria.
Insecurity (INS)
Nigeria Security Tracker
This is gauged by the number of deaths as a result
of security caused by Boko Haram and other
terrorist groups in Nigeria. The number of deaths
is a conservative estimate, based on numbers
reported by the press. High level of fatality scares
away investors.
Exchange rate (EXR)
CBN quarterly economic report:
The value of a domestic currency (the Nigerian
Naira) in relation to the U.S. dollar.
Industrial production
capacity (IPC)
CBN quarterly economic report:
IPC is determined by comparing the industrial
sector's actual production to its potential
production.
(continued)
The International Journal of Banking and Finance, Vol. 20, Number 1 (January) 2025, pp: 1-22
11
Data
Sources
Description
Consumer
price index (CPI)
CBN quarterly economic report:
High domestic prices actively reduce the real
worth of domestic assets, encouraging citizens to
invest abroad.
Lending rate (LDR)
CBN statistical bulletin (2020)
This refers to the cost of borrowing money from
financial institutions.
The predictor variables were insecurity and macroeconomic variables such as the exchange rate,
industrial production capacity, consumer price index, and loan rate. The predicted variable (the
dependent variable) was the total inflow of capital. Data for inward capital flows was extracted from
the National Bureau of Statistics' (NBS, 2021) Quarterly Capital Importation Report (various issues),
the data for insecurity (number of deaths) was sourced from the Armed Conflicts Locations and Events
database, and data for exchange rate, industrial production capacity, consumer price index, and lending
rate were sourced from the Quarterly Economic Report (2021) and CBN Statistical Bulletin (2021).
Table 2 contains the list of data sources and their descriptions.
Technique of Data Analysis
To ensure reliable and consistent empirical results, the data were transformed into a natural logarithm
(LN) which was applied to solve the potential problems of time and growth. The functional relationship
of the models was specified thus:
Before the model execution, it was mandatory to scrutinise the time-series properties of the data. As a
result, the stationarity condition of the data set was ascertained using the Augmented Dickey-Fuller
(ADF), Phillips-Perron (PP) and Kwiatkowski-Phillips-Schmidt- Shin (KPSS) approaches for unit root
testing (Zhong, 2015). As some variables were stationary at I(0) and others are I(1), the study was
directed to the application of the Autoregressive Distributed Lag (ARDL) bounds test (Pesaran et al.,
2001). The ARDL model was preferred over other traditional estimation methods of for testing
cointegration for some reasons. Firstly, this estimation approach technique can be applied when
variables are mixed between I(0) and I(1) levels of integration. Secondly, the ARDL method allows for
simultaneous estimation of the model's short-run and long-run linkages. Additionally, the ARDL model
considers endogeneity problems by adding lags of predicted as well as predictor variables to the model.
The ARDL model was specified as:
Where, stands for the white noise term and LNCPF (natural log of capital inflows), LNINS (natural
log of insecurity), LNEXR (natural log of exchange rate), LNIPC (natural log of industrial production
capacity), LNCPI (natural log of consumer price index), and LNLDR (natural log of lending rate) are
the model's explanatory (predictor) variables. The bounds test technique for cointegration requires the
ARDL model to undergo an F-test with appropriate lag lengths. The Akaike Information Criterion
limited the model to a maximum lag duration of three (3) days. (AIC). To confirm the existence of
cointegration, the conventional F-test was performed, which displayed two sets of crucial values, i.e.,
lower and upper bound values (Pesaran et al., 2001). The lower and upper critical values capture the
The International Journal of Banking and Finance, Vol. 20, Number 1 (January) 2025, pp: 1-22
12
assumption that all variables are either I(0) or I(1) and none is I(2) which makes the ARDL estimation
technique most appropriate. Hence, the critical values provided a restricted bound that incorporates all
possible categories of the variables. The null hypothesis of no cointegration (i.e., no long-run
relationship) was rejected if the upper bound critical value lies below the F-statistic generated from the
bounds test, and vice versa, the test fails to reject the null hypothesis. On the other hand, the test is
deemed inconclusive if the F-statistic value falls in between the lower and upper bounds. Once a long-
run relationship (cointegration) is ascertained, the short-run dynamics are captured by transforming
Equation (6) into an error correction model (ECM) as follows:
=
indicates the speed of adjustment, which measures how quickly a model returns to long-run
equilibrium following the occurrence of short-run shocks that cause disequilibrium (Shrestha & Bhatta,
2018). The ECM's sign must be negative, statistically significant, and have a coefficient between -1 and
0, signifying rapid and flawless convergence following a shock to the mechanism, to guarantee long-
run convergence.
Model diagnostic tests need to verify the validity of some important ARDL assumptions, such as serial
independence, homoscedasticity, and normal distribution. Therefore, the Jarque-Bera test was used to
test for normality, the Breusch-Godfrey serial correlation LM test was used to check for serial
independence, and the ARCH test was used to check for heteroskedasticity in the model. To determine
whether there was model misspecification, the Ramsey reset test was used. To determine whether the
model parameters are stable, the recursive CUSUM and CUSUM of squares are applied (Turner, 2010).
In the analysis, the outcome of the diagnostic tests showed that the ARDL model was free from
problems associated with serial correlation, heteroskedasticity, abnormal distribution of the residuals,
and model misspecification. The ARDL technique encapsulates the dynamics of the short-term
connection between variables, and the distributed lag component represents the lagged values of the
explanatory variables. The ARDL model captures the lagged effects of the explanatory variables on the
dependent variable. This paper also applied the Modified Wald test (MWALD), as recommended by
Toda and Yamamoto (1995), to give an understanding of the direction of causation among the variables,
with regard to the relationships between macroeconomic dynamics, insecurity, and inward capital
flows. The Toda and Yamamoto (1995) approach uses a standard vector autoregression (VAR) model
and level series, implying that there is minimal risk of wrongly identifying the order of integration of
the quarterly time series data under consideration.
RESULTS AND DISCUSSIONS
Descriptive Statistic
To show the statistical properties of the data, the descriptive statistic presented in Table 3 was deemed
necessary. The series, especially NINS, IPC and LDR showed moderate variation from their mean
values as seen by the standard deviation, suggesting characteristics of an abnormal distribution.
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13
Table 3
Descriptive Statistic
CPF
NINS
EXR
IPC
CPI
LDR
Mean
3255.866
1058.867
276.4577
113.2230
243.6143
15.22867
Maximum
8508.480
3456.000
381.0000
139.4500
355.7200
17.80000
Minimum
875.6200
293.0000
157.2900
97.60000
155.2000
8.200000
Std. Dev.
2060.371
683.5489
73.94426
12.98421
63.59783
2.817171
Skewness
0.697965
1.822484
-0.362293
1.138144
0.150688
-1.456906
Kurtosis
2.507433
6.649677
1.873477
3.064050
1.758777
3.899693
Jarque-Bera
2.739053
33.25742
2.242600
6.481983
2.039329
11.62469
Probability
0.254227
0.000000
0.325856
0.039125
0.360716
0.002990
Observations
30
30
30
30
30
30
Source. EViews Output
The skewness results confirmed this, revealing that the aforementioned variables' skewness values
significantly exceeded the threshold (0) average. This is an indication that the distribution of the series
was highly skewed, either negatively or positively, and nearly abnormal, with the exception of CPF,
EXR and CPI, which were only slightly skewed. The Jarque-Bera test revealed that while INS, IPC,
and LDR were not normally distributed, CPF, EXR, and CPI all support the null hypothesis of normal
distribution. The fact that the variables were not normally distributed led to a natural logarithmic
transformation of the data to make the moderately skewed data more normally distributed (to achieve
constant variance).
Lag Selection Criteria and Bounds Test Results
A system of Vector Autoregression (VAR) was formed to generate the optimum lag. The ideal lag was
three (3), which was in line with all the selection criteria such as AIC (Akaike Information Criteria),
SC (Schwarz Information Criteria) and HQ (Hanna-Quinn Information Criteria). As a result, the optimal
lag length of three was suggested by all the information criteria.
The ARDL bounds test for cointegration was also carried out. At the 1 percent (5.230000) level, the
reported F-statistic (5.998478) was greater than the upper bound, or I(1). As a result, the cointegration
precondition was established, and the null hypothesis of no cointegration (i.e., no long-run link) was
rejected. It was then ascertained that there was a long-term connection between inward capital flows,
macroeconomic dynamics, and insecurity in Nigeria. The implication of cointegration was the presence
of an equilibrium correction representation of the variables. The existence of cointegration also clarified
the problem of spurious regression, in which intrinsically unrelated time series data are highly
correlated. The number(s) of lag(s) chosen for LNCPF, LNINS, LNEXR, LNIPC, LNCPI, and LNLDR,
respectively, were implied by the ARDL specification of (1, 1, 2, 1, 2, 1).
Error Correction Model (ECM) and Short-run Estimates
The ECM confirmed that capital inflows, macroeconomic dynamics, and insecurity were linked in a
long-run equilibrium path. The results of the ECM and short-run estimates were captured in Table 4.
The ECM coefficient of the ARDL equation turned out to be -0.471775, meaning that the speed of
adjustment of lags from previous period’s errors was approximately 47 percent before the long term
The International Journal of Banking and Finance, Vol. 20, Number 1 (January) 2025, pp: 1-22
14
variables converged to the long-term equilibrium path. This suggests that the current quarter has
remedied approximately 47 percent of the inward capital flows (CPF) disequilibria from the previous
quarter. Thus, further indicated that it took approximately two (2) quarters
for inward
capital flows to re-adjust to long-run equilibrium after short-run shocks arising from macroeconomic
dynamics and insecurity issues.
Table 4
ECM and Short-run Estimates
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
-46.22349
6.568614
-7.003318
0.000000***
D(LNINS)
-0.798342
0.121000
-6.597851
0.000000***
D(LNEXR)
1.778956
1.942582
0.915769
0.376500
D(LNEXR(-1))
-7.429909
1.979080
-3.754223
0.002400***
D(LNIPC)
0.022777
0.880888
0.025857
0.979800
D(LNCPI)
0.389270
0.263136
1.479348
0.162900
D(LNCPI(-1))
0.858079
0.146814
5.844653
0.000100***
D(LNLDR)
-1.701142
0.509469
-3.339051
0.005300***
ECM(-1)
-0.471775
0.066830
-7.059285
0.000000***
R-squared
0.833526
Adjusted R-squared
0.750289
F-statistic
10.01387
Prob(F-statistic)
0.000023
***
Durbin-Watson stat
2.470739
Source. EViews Output.
Note. *** denote 1% significant level
The ECM results also indicated that in the short run, LNCPF was negatively and significantly affected
by LNINS, implying that an increase in the number of deaths arising from insecurity had an immediate
diminishing effect on inward capital flows. Also, a period lag of exchange rate, denoted as D(LNEXR(-
1), had a negative and significant effect on LNCPF, meaning that a rise in the naira – dollar exchange
rate in the previous period caused a decreasing effect on the current period’s inward capital to Nigeria.
Again, the short-run coefficient of LNIPC was found to be positive and non-significant, indicating that
an increase in industrial production capacity had a marginal short-term effect on inward capital flows.
LNCPI was seen to have had a positive and statistically significant lag effect on LNCPF, implying that
the previous period’s increases in industrial production capacity attracted inward capital to Nigeria in
the current period. The negative and statistical significance of LNLDR suggested that an increase in the
lending rate discouraged the inflows of capital in the short run.
The adjusted R-squared value for the model's overall goodness of fit was 0.750289, which meant that
the predictor variables (LNINS, LNEXR, LNIPC, LNCPI and LNLDR) collectively accounted for
around 75 percent of the variation in the predicted variable (LNCPF). Based on the probability, the
adjusted R-squared value was 0.750289 at a 5 percent significance level. (F-statistic) of 0.000023, it
was implied that the collective effect of the independent variables (LNINS, LNEXR, LNIPC, LNCPI
and LNLDR) on the dependent variable (LNCPF) was statistically significant. This indicated that the
interactions between macroeconomic variables and insecurity had a significant influence on Nigeria's
inward capital flows.
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15
Long-run Estimates and Residual Diagnostic Tests
The long-run estimated coefficients of the model and residual diagnostic tests are shown in Panel A of
Table 5. The LNCPF was found to have a lag effect on itself, implying that the inflow of capital into
Nigeria during the previous quarter had a significant impact on capital inflows in the current quarter.
The long-run coefficient of LNINS was found to be negative and insignificant, implying that a 1 percent
increase in insecurity caused inward capital to decrease marginally by 5.3 percent. Also, the long-run
coefficient of LNEXR turned out positive and statistically significant, implying that a percentage
increase in the naira-dollar rate led to a considerable change of approximately 5.5 percent in inward
capital. The estimated coefficient of LNIPC in the long-run was positive and significant, implying a
percentage increase in the industrial production capacity caused a considerable increase of
approximately 5.7 percent in inward capital flows. The long run coefficient of LNDR was found to be
negative and statistically non-significant, implying that a percentage increase in lending rate caused
inward capital flows to decrease marginally.
Table 5
Long-run Estimates
Panel A: Long-run Estimates
Variable
Coefficient
Std. Error
t-Statistic
Prob.
LNCPF
-0.471775
0.137350
-3.434825
0.004400***
LNINS
-0.533520
0.363459
-1.467895
0.165900
LNEXR
5.507285
1.691080
3.256667
0.006200***
LNIPC
5.699687
1.710190
3.332779
0.005400***
LNCPI
0.130207
0.615616
0.211507
0.835800
LNLDR
-0.795325
0.608674
-1.306651
0.214000
Panel B: Diagnostic test
Test
Statistic
Prob.
Remark
Serial Correlation
0.600848
0.565400
No significant serial correlation
residuals are homoscedastic
residuals are normally distributed model is well
specified
Heteroskedasticity
0.658169
0.776300
Jarque-Bera
0.674980
0.713500
Ramsey RESET
0.131682
0.723000
CUSUM
Stable
CUSUMSQ
Stable
Source. EViews Output. Note. *** denote 1% significant level
Figure 6
Plots of CUSUM and CUSUMSQ
-12
-8
-4
0
4
8
12
II III IV III III IV III III IV III
2018 2019 2020 2021
CUSUM 5% Significance
-0.4
0.0
0.4
0.8
1.2
1.6
IV III III IV III III IV III III IV III
2017 2018 2019 2020 2021
CUSUM of Squares 5% Signific ance
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16
The results of the diagnostic tests in Panel “B” of Table 5 suggest that the ARDL model's residuals were
uncorrelated and homoscedastic. Also, since the model passed the Ramsey RESET test, the null
hypothesis was accepted, and the long-run ARDL coefficients had little or no misspecification error.
The Jarque-Bera test confirmed the normal distribution of the residuals in the ARDL model.
Discussion of Findings from the ARDL Estimation
In the long-run and short-run, insecurity exhibited a diminishing effect on inward capital flows to
Nigeria. This was in consonance with the earlier established fact that heightened security challenges
causes investors to divest to other countries that are more secure. However, the effect of insecurity was
significant in the short run but non-significant in the long run. This implies that a sudden rise in
insecurity had a considerable instantaneous impact on capital flows to Nigeria, but its non-significance
in the long run could mean that foreign capital flows to Nigeria could be influenced by other factors
such as macroeconomic dynamics over time. A plausible reason for this discrepancy could be that
foreign capital flows to Nigeria were targeted at Nigerian states where insecurity was minimal. Also,
this could imply that investors had a short memory of the security challenges, which explains the long
run insignificance of insecurity. The observed negative effect of insecurity on inward capital flows
aligned with prior studies such as Le et al. (2023); Ayoola (2022); Bardwell and Igbal (2021);
Hogetoorn and Gerritse (2021); Essien et al. (2015) who had affirmed that a rise in insecurity hindered
capital inflows.
Exchange rate in the short run did not have an instantaneous significant effect on inward capital flows,
but its lagged effect was found to be negative and statistically significant, which varied with the long-
run outcome of positive and statistical significance. The lagged effect implied that the effect of the
exchange rate in determining the direction of capital flows was largely based on investors’ perceptions
of previous period changes in the naira-dollar rate. This could also mean that foreign investors were at
some point motivated to invest in Nigeria for other reasons, such as opportunities to make huge profits
or for resource exploitation. This finding is in consonance with Dembo and Nyambe (2016), who found
that the effect of the exchange rate on capital inflows varied with time.
With regards to industrial production capacity, it was realised that the variable maintained a positive
coefficient in both the short-run and long-run but its significance was recorded only in the long run.
This could imply that consistent improvement in industrial production capacity (which implies higher
domestic productivity) could attract foreign inflows of investible funds in the long run, meaning that
improvements in the production capacity of domestic industries could pull foreign investments. The
findings of Nwagu (2023); Wijaya et al. (2020); Nwosa and Adeleke (2017) lend credence to this study.
Although the short-run coefficient of the consumer price index yielded a positive and non-significant
effect on inward capital flows. However, its one-period lagged effect was negative and statistically
significant, implying that investors’ perception of the previous quarter’s rise in domestic prices
continued to diminish the potentials of attracting inward capital in the current quarter. This could also
imply that the loss of purchasing power previously experienced by investors does not encourage them
to invest more in the future. Studies such as Wijaya et al. (2020); Nwokoye and Oniore (2017); Waqas
et al. (2015) have come up with findings that are in consonance with this study.
There was a negative and significant effect of the lending rate on inward capital flows in the short run
but it turned out to be negative and non-significant in the long run. This indicated that a high lending
rate generally causes a decline in inward capital flows, especially in the short run. The marginal effect
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17
of the lending rate in the long run could be attributed to monetary adjustments (like a downward review
of the monetary policy rate) aimed at stabilising the macroeconomic to attract investors (Al-Smadi,
2018). The negative effect of the lending rate found in this study aligned with the studies of Nwagu
(2023); Wijaya et al. (2020); Tellez-Leon and Ibarra (2019); Nwokoye and Oniore (2017) that an
increase in the lending rate could lead to macroeconomic instability and reduce inward capital flows.
Toda-Yamamoto (T-Y) Causality Test
Compared to previous tests of causality, the Toda Yamamoto test is better since it may be applied in
situations where the variables are cointegrated in a random order, the same order, or not at all. The
ARDL co-integration bounds test, which illustrates the short- and long-term relationships between the
variables, does not reveal the direction of the causal relationships. The estimated results are therefore
not complete. This improves the estimation's results since it verifies the causal influence between the
variables. The findings of the T-Y causality test confirmed the association between macroeconomic
factors, inward capital, and insecurity, as indicated in Table 6.
Table 6
Toda-Yamamoto Approach to Granger Causality (Modified Wald) Test
VAR Granger causality/block Exogeneity Wald tests
Dependent variable: LNCPF
Dependent variable: LNINS
Cause - Effect
Prob.
Cause - Effect
Prob.
LNINS → LNCPF
0.048400**
LNCPF → LNINS
0.557700
LNEXR → LNCPF
0.628200
LNEXR → LNINS
0.047800**
LNIPC → LNCPF
0.020900**
LNIPC → LNINS
0.120700
LNCPI → LNCPF
0.173400
LNCPI → LNINS
0.113500
LNLDR → LNCPF
0.039000**
LNLDR → LNINS
0.324700
All
0.002000***
All
0.204300
Dependent variable: LNIPC
Dependent variable: LNCPI
Cause - Effect
Prob.
Cause - Effect
Prob.
LNCPF → LNIPC
0.273300
LNCPF → LNCPI
0.369200
LNINS → LNIPC
0.944400
LNINS → LNCPI
0.030700**
LNEXR → LNIPC
0.804800
LNEXR → LNCPI
0.006500***
LNCPI → LNIPC
0.832000
LNIPC → LNCPI
0.876900
LNLDR → LNIPC
0.500600
LNLDR → LNCPI
0.360300
All
0.356000
All
0.112600
Dependent variable: LNEXR
Dependent variable: LNLDR
Cause - Effect
Prob.
Cause - Effect
Prob.
LNCPF → LNEXR
0.118100
LNCPF → LNLDR
0.042200**
LNINS → LNEXR
0.010800**
LNINS → LNLDR
0.275800
LNIPC → LNEXR
0.335400
LNEXR → LNLDR
0.173300
LNCPI → LNEXR
0.017300**
LNIPC → LNLDR
0.007200**
LNLDR→ LNEXR
0.286000
LNCPI → LNLDR
0.105900
All
0.000100***
All
0.100300
Source. EViews Output.
Note. *** and ** denote 1% and 5% significant levels respectively
CONCLUSIONS
During the quarterly period from 2014Q1 to 2021Q2, the current study conducted a macro-econometric
assessment of inward capital flows to Nigeria in the face of ongoing security issues. Nigeria was chosen
for this study because of its issues with internal security, openness to global investments, and intense
internal macroeconomic fluctuations. In order to take into account, the potential diverse stimuli of
The International Journal of Banking and Finance, Vol. 20, Number 1 (January) 2025, pp: 1-22
18
inward capital flows to macroeconomic dynamics and insecurity in the short-run and long-run, the
ARDL approach to econometric estimation was used. The findings supported the earlier established
claim that foreign investors respond negatively to an unstable macroeconomic environment as well as
a lack of security of lives and properties, which results in insufficient inward capital. The general
deduction from the findings indicated that the collective effect of macroeconomic factors and insecurity
on inward capital flows was highly significant. However, the results notably demonstrated that, as
evidenced by the short-term estimates, both insecurity and the currency rate instantly hindered the
inflow of capital. Long-term fluctuations in the exchange rate and industrial production capacity were
clear indicators of how macroeconomic dynamics greatly influenced capital flows.
To effectively manage the domestic macroeconomic environment and build a more secure nation, it is
necessary to attract foreign capital. According to these findings, it is necessary to improve the ease of
doing business through proactive measures to attract enough foreign capital, which would create jobs,
eradicate poverty, prevent future insecurity issues, and create a stable economic system that would
attract more inward capital from abroad. This will improve the reliable macroeconomic conditions for
productive firms. Additionally, all governmental levels and significant policymakers should adopt firm
policy measures by developing more comprehensive strategies to reduce the level of insecurity and
encouraging a culture of transparency to ensure that funds intended for managing security challenges
are used properly and only for that purpose. Additionally, governments should request security aid from
developed nations for technological support and information to tackle insecurity.
Due to the lack of funding to access data from other geographical areas, this analysis was based on
Nigeria hence, it was recommended that future studies use cross country data to make a comparative
analysis with other countries that are having security challenges. Also, due to the lack of data on
different perspectives of on insecurity in Nigeria, this study focused on the number of casualties and it
was recommended that future studies consider regime change and recent occurrences of security threats
in the country. Also, this study was able to garner quarterly data for 2014 and 2021, and it was
recommended that upcoming studies use high-frequency data, such as monthly data. Again, since this
study used the linear ARDL model, it is advised that upcoming studies apply a nonlinear ARDL model
in future research.
ACKNOWLEDGEMENT
This research did not receive any specific grant from any funding agency in the public, commercial, or
not-for-profit organizations.
ENDNOTES
1
Other types of capital flows are foreign portfolio investment (FPI) and debt.
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