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Industrial Policies and Manufacturing Sector Output Growth in
Selected West African Countries
Abel G. Akpokorie, Peter C. Egbon
Department of Economics, Faculty of Social Sciences, Delta State University Abraka,
DOI: https://dx.doi.org/10.47772/IJRISS.2025.910000414
Received: 11 October 2025; Accepted: 16 October 2025; Published: 13 November 2025
ABSTRACT
The study investigated the impact of industrial policies on manufacturing sector output growth rate in selected
West African Countries. Data spanning 2000 to 2023 were collected on the manufacturing sector output
growth rate (MOG), exchange rate (EXCHR), domestic credit to the private sector (DCPS), trade openness
(TOP), and foreign direct investment (FDI). The fully modified ordinary least squares (MOLS) estimation
technique was used in the model estimation. The findings revealed that the exchange rate had a positive and
significant impact on the manufacturing sector's output growth rate. The finding implies that an effective
exchange rate policy would have a positive and significant effect on manufacturing sector output growth in the
selected West African Countries. Domestic credit to the private sector had a negative and significant effect on
the manufacturing sector's output growth rate in the selected African countries. implying that effective credit to
private sector has the potential of enhancing the manufacturing sector's output growth rate. Trade policy (TOP)
had a negative and insignificant effect on manufacturing sector's output growth rate. The study revealed that
industrial policy (exchange rate policy, credit policy, and trade policy) impacted manufacturing sector output
growth differently in selected West African countries. Based on the findings, the study recommended that the
governments of the selected West African countries should strengthen industrial sector’s policies to promote
the manufacturing sector's output growth rate.
Keywords: Industrial policies, manufacturing output, fully modified OLS, and West Africa.
INTRODUCTION
Industrial policies are a cornerstone of economic development strategies, particularly in regions striving for
structural transformation and sustainable growth. For decades, policymakers in West Africa have recognized
the potential of the manufacturing sector to drive economic diversification, reduce dependency on commodity
exports, and generate widespread employment opportunities. However, the region continues to grapple with
structural challenges, policy inconsistencies, and the global competitive pressures that hinder the realization of
its industrial potential (Juhasz, R. et al. 2023.
Globally, the role of industrial policies in driving economic transformation has been well documented. From
the success stories of East Asia's newly industrialized economies to the challenges faced by African countries,
industrial policy has proven to be a double-edged sword. While well-crafted policies can stimulate investment
in key industries and foster technological innovation, poorly designed or inconsistently implemented policies
can lead to inefficiencies and resource misallocation (Rodrik, 2019). In West Africa, industrial policies have
historically been influenced by colonial economic structures, which prioritized the extraction of raw materials
over industrialization. This legacy has left many countries with weak manufacturing bases, characterized by
low levels of value addition and limited integration into global value chains (UNECA, 2021).
The post-independence era saw many West African nations adopt import substitution industrialization (ISI)
strategies as a means to reduce dependence on foreign goods and promote local industries. These policies,
however, often fell short due to insufficient infrastructure, lack of skilled labor, and over-reliance on state-led
enterprises. The introduction of structural adjustment programs (SAPs) in the 1980s further compounded the
challenges, as these programs mandated the liberalization of trade and the privatization of state-owned
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enterprises, often at the expense of nascent industries (Olayemi, 2022). Consequently, the manufacturing
sector’s contribution to GDP in most West African countries has remained stagnant, at Nigeria 2020:28.22%
(industry share in GDP note that manufacturing is a subset of industry),2023 8.42% (manufacturing sector’s
contribution to GDP in Q3), with the sector contributing around 15.36% to GDP in 2023 (African
Development Bank 2023), Ghana 2020:29.74% ( industry’s share in GDP) 2023: industry growth rate was
1.6%, with the manufacturing sector contributing around 32% to GDP, Burkina Faso 2020 32.59%(industry’s
share in GDP), Senegal 23.22% (industry’s share in GDP), Guinea 2020 32.73% (industry’s share in GDP),
Cote d’Ivoire 2020 20.86% (industry’s share in GDP), note that the manufacturing sector’s specific
contribution might be lower than the industry overall share (African Leadership Magazine 2025), underscoring
the need for renewed policy focus.
The West African countries is not without challenges which includes political instability, bureaucratic
inefficiency and corruption and addressing these challenges requires strong institutional framework,
transparent governance and inclusive policy-making (WB, 2023). Despite the above hurdles the region has
industrialization prospect, that remain promising and by leveraging on the strength such as abundant natural
resources, growing consumer market and increasing interest from international investors, the region ( West
African countries) can implement industrial policies that not only enhance manufacturing output growth but
also contribute to the broader development goal such as poverty reduction, gender equality and environmental
sustainability (UNDP, 2022). The question now is to what level has the industrial policies formulated and
implemented have been able to drive industrialization in some selected West Africa’s countries.
Having reviewed relevant literature on industrial policies and its effect on manufacturing output growth rate in
the region found that, almost all their observations have similar challenges ranging from corruption,
infrastructure gabs etc., that limit the continued progress of the industrial policies implementation in the
region. Their studies are either country-based or cross-continent, such as Africa and Asian Countries. There is
lack of knowledge about industrial policies in West African countries. The broad topic of industrial policies on
country-based in African continent has received attention including from developed countries of the world but
West Africa as a region are not the focus of that attention, ( Eze c., and Uche 2019). While acknowledging that
industrial policies occur both in developing and developed clime, the previous focus is on country-based or
developed ones (Federal ministry of industry, trade and investment 2014). Consequently, their findings failed
to have a consensus, hence the current study become relevant to add to the discourse on the impact of
industrial policies on manufacturing output growth in selected West African countries. This study intends to
bridge these gap by investigating various industrial policies and how it has contributed to the region industrial
policies design.
The purpose of this study is therefore, to examine the impact of industrial policies on manufacturing sector
output growth in the selected West Africa Countries, while policies of growth and development to encourage
industrialization and promote investment in productive manufacturing activities in the selected West African
countries - Nigeria, Ghana, Côte d'Ivoire, Senegal, Burkina Faso, and Guinea are the target.
Objectives of the study
The main objective of the study is to examine the impact of industrial policies on the manufacturing output
growth in selected West African countries. The specific objectives are to:
Examine the impact of exchange rate policy on manufacturing output growth in selected West African
countries.
Investigate how credit to the private sector has impacted the manufacturing output growth in West African
Countries.
explore the impact of trade policy on the manufacturing output growth in West African Countries
determine the impact of industrial policies on the manufacturing sectors’ output growth rates in individual
selected West African countries
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Research Hypotheses
The hypotheses that would be tested in this research work are stated below:
H
01
: Exchange rate policy does not have significant impact on manufacturing output growth in selected West
African countries.
H
02
: Credit to the private sector does not have significant impact on the manufacturing output growth in the
selected West African Countries.
H
03
: Trade policy does not have an impact on the manufacturing output growth in the selected West African
Countries
H
04
: Industrial policies do not differently impact on the manufacturing output growth in the selected West
African countries.
This paper is structured as follows: Section 2 presents a literature and empirical review, Section 3 outlines the
methodology adopted for analysis, Section 4 discusses the results, and Section 5 provides a summary,
conclusion, and recommendations.
LITERATURE REVIEW
Conceptual Review
The conceptual review provides a foundation for understanding the key concepts and terminologies integral to
this study, ensuring clarity and alignment with the research objectives. Industrial policies and manufacturing
output growth are critical components of economic development, especially in emerging economies like those
in West Africa. This section explores the Industrialization and Regional Economic Integration, The Role of
Regional Trade Blocs in Industrial Development, the role of African continental free trade area (AfCFTA) and
Its Impact on Industrialization in West Africa, Synergies between Regional Integration and Manufacturing
Competitiveness, Challenges to Industrialization through Regional Integration concepts. Industrial policies
have long been recognized as pivotal in fostering structural transformation, particularly in economies
transitioning from agriculture-based systems to more diversified and industrialized structures. These policies
encompass a range of government interventions designed to stimulate industrial growth, enhance productivity,
and promote global competitiveness (Rodrik, 2021). The effectiveness of such policies often depends on their
design, implementation, and alignment with broader economic goals, as well as the socioeconomic and
infrastructural context in which they operate.
Industrialization and Regional Economic Integration
Industrialization and regional economic integration are intertwined processes that drive economic
transformation in developing regions. In West Africa, the pursuit of industrialization has been closely tied to
regional trade and economic cooperation. This relationship underscores the importance of fostering synergies
between industrial policies and regional frameworks to promote sustainable growth and enhance
competitiveness. This section explores the role of regional trade blocs in industrial development, the African
Continental Free Trade Area (AfCFTA). It also examines the synergies between regional integration and
manufacturing competitiveness.
The Role of Regional Trade Blocs in Industrial Development
Regional trade blocs are fundamental to industrial development in West Africa. They provide member states
with a platform for harmonized policies, collective bargaining power, and the creation of larger markets. By
addressing structural constraints such as fragmented markets, inadequate infrastructure, and trade barriers,
regional trade blocs enable countries to scale up manufacturing and achieve greater economic diversification.
Trade blocs also facilitate access to critical resources and inputs for manufacturing. For example, cross-border
trade agreements have enabled seamless supply chains in industries such as textiles, agro-processing, and
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automobile assembly. By fostering regional value chains, trade blocs contribute to industrialization while
creating employment opportunities and boosting income levels (Asiedu et al., 2024).
AfCFTA and Its Impact on Industrialization in West Africa
The African Continental Free Trade Area (AfCFTA) represents a significant step toward continental economic
integration. By establishing the largest free trade area in the world, AfCFTA aims to boost intra-African trade,
eliminate tariffs on 90% of goods, and foster industrial development across the continent. West African
countries, as part of AfCFTA, stand to benefit from increased market access, enhanced value chains, and
greater investment inflows (Okoro & Adebayo, 2024). One of the key provisions of AfCFTA is the
harmonization of standards and regulations, which reduces trade complexities and encourages the flow of
goods. For example, manufacturers in Nigeria’s automobile sector have leveraged AfCFTA to export vehicles
to Ghana and other African countries. Similarly, the agreement has spurred investment in value added
industries, such as cocoa processing in Côte d’Ivoire and Ghana, thereby reducing the region’s reliance on raw
material exports.
Synergies between Regional Integration and Manufacturing Competitiveness
Regional integration offers multiple avenues for enhancing the competitiveness of West Africa’s manufacturing
sector.
Value Chain Development: Regional integration fosters collaboration among countries to develop integrated
value chains. In the textile industry, for example, countries like Mali and Burkina Faso produce cotton, while
Ghana and Nigeria handle processing and garment manufacturing. This collaboration maximizes resource
utilization and creates jobs across the value chain.
Increased Foreign Direct Investment (FDI): Harmonized regional policies attract foreign investors seeking
stable and predictable business environments. By investing in manufacturing hubs, multinational corporations
(MNCs) contribute to technology transfer, skill development, and infrastructure improvements, which
collectively enhance competitiveness (Ademola & Yusuf, 2024).
Challenges to Industrialization through Regional Integration
While regional integration holds immense potential for industrial growth, several challenges persist. Political
instability, policy inconsistency, and weak governance undermine the effectiveness of trade blocs. For
example, disagreements over tariff reductions and market access have delayed the full implementation of
AfCFTA provisions in some West African countries (Adebiyi & Mensah, 2023). Inadequate infrastructure
remains a critical bottleneck. Despite efforts by members’ state and other stakeholders, transportation
networks, energy supply, and communication systems are often insufficient to support large-scale
manufacturing. Additionally, disparities in industrial development levels among member states create
imbalances that complicate regional cooperation.
Industrialization and regional economic integration are mutually reinforcing processes that have the potential
to transform West Africa’s economic landscape. Regional trade blocs like AfCFTA provide platforms for
harmonized policies, market expansion, and collaborative value chain development. By addressing structural
constraints and fostering synergies, these frameworks can enhance the competitiveness of the region’s
manufacturing sector. However, to realize the full benefits of regional integration, West African countries must
commit to improving governance, infrastructure, and policy coordination. These efforts will ensure that
industrialization becomes a sustainable driver of economic growth and development.
Empirical Review
This section extensively reviews both the empirical literature and the method used in industrial policies and
manufacturing output growth, particularly in West Africa, as it features on several country-specific studies.
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Osabuohien et al. (2023) examined the effects of industrial policies on manufacturing performance in Nigeria
under the Economic Recovery and Growth Plan (ERGP) from 2003 to 2022. Using an econometric analysis of
time-series data, the study found that while the ERGP provided a strategic framework for industrial growth,
inconsistent implementation and inadequate coordination between federal and state agencies limited its
effectiveness. Mensah and Okyere (2023) analyzed the impact of Ghana’s industrial policies on the
manufacturing sector’s contribution to GDP using a vector autoregression (VAR) model 2007 to 2019. The
study revealed that policies such as the One District, One Factory (1D1F) initiative had a positive effect on
manufacturing output in the short run, but structural constraints such as inadequate electricity supply and
limited access to credit weakened long-term growth prospects. The study recommended increased public-
private partnerships and improved access to finance for small and medium-sized manufacturers.
Diof and Sarr (2022) investigated the relationship between industrialization policies and manufacturing output
in Senegal using a computable general equilibrium (CGE) model from 2002 - 2021. Their findings suggested
that while policies such as the Plan Sénégal Émergent (PSE) had significantly increased industrial investments,
the country’s over-reliance on foreign capital and technology transfer limited the sustainability of these gains.
The study recommended a shift toward domestic capacity-building, investment in local research and
development, and targeted fiscal incentives to encourage indigenous manufacturing growth. Ouedraogo (2021)
explored the effectiveness of Burkina Faso’s industrialization strategy on manufacturing sector growth from
1998- 2019 using a fixed-effects regression model. The study found that while government interventions
increased manufacturing output, challenges such as high production costs, inadequate energy supply, and poor
transportation networks constrained progress.
Ibrahim and Ahmed (2021) evaluated the impact of Guinea’s industrialization policies on manufacturing sector
performance from 2010 – 2020 through an autoregressive distributed lag (ARDL) model. The results indicated
that although government efforts to promote industrialization had led to some improvements in production
capacity, the overall growth remained sluggish due to limited access to financing and weak institutional
support. Boateng (2020) studied the effects of Ghana’s industrial policy reforms on manufacturing productivity
from 1999 to 2019, using a stochastic frontier analysis. The findings revealed that while policy measures such
as tax incentives and reduced import tariffs had improved industrial efficiency, persistent challenges such as
inadequate skilled labor and poor access to modern technology impeded sustained growth.
Diakite (2014) analyzed the effect of Côte d'Ivoire’s industrial diversification policies on manufacturing
development from 1994 - 2013 using an input-output model. The study found that diversification strategies
improved industrial output and reduced dependence on agricultural exports. Toure (2013) studied the role of
regional integration in promoting industrial growth in Senegal using a gravity model from 1997 - 2010. The
findings indicated that trade agreements within the West African Economic and Monetary Union (WAEMU)
enhanced industrial competitiveness and expanded manufacturing output. However, weak institutional
coordination and inconsistent policy implementation slowed industrial growth.
Theoretical Framework
The theoretical framework for this study is based on the Structural Change Theory, which offers a
comprehensive explanation of the dynamics between industrial policies and manufacturing output growth in
developing regions, including West Africa. Structural Change Theory focuses on the transformation of an
economy’s structure, where resources are shifted from low-productivity sectors, such as agriculture, to high-
productivity sectors, particularly manufacturing. This transition is crucial for achieving sustainable economic
growth and reducing dependence on primary commodities (McMillan & Rodrik, 2011). West African
countries, like many developing economies, have traditionally depended on the export of raw materials such as
oil, cocoa, and metals. While these commodities provide foreign exchange earnings, they leave economies
vulnerable to price volatility and external shocks, limiting opportunities for industrialization and inclusive
growth (UNECA, 2013).
In Nigeria, for example, the National Industrial Revolution Plan (NIRP) seeks to shift focus from oil exports to
manufacturing, promoting industries such as agro-processing, petrochemicals, and technology. The Structural
Change Theory underpins such policies, advocating for government interventions to catalyze resource
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reallocation and build the foundational capacities of manufacturing industries (UNIDO, 2019). Similarly,
Ghana’s One District One Factory initiative reflects this theoretical perspective by emphasizing localized
industrialization to stimulate economic activities and create employment across the country. Burkina Faso’
agro-industrial development to enhance value addition and reduce reliance on raw material exports. Initiatives
like the National Plan for Economic and Social Development (PNDES) have aimed to modernize industries
and foster private sector participation. The construction of industrial zones and increased investments in
renewable energy havehas supported industrial expansion (Abdoulaye & Fofana, 2020).
Key to the Structural Change Theory is the acknowledgment of supporting factors that drive structural
transformation. These include technological innovation, skilled labor development, infrastructure provision,
and access to credit. For West Africa, however, these factors remain significant challenges. Infrastructure
deficits such as unreliable electricity and inadequate transportation networks continue to constrain the
productivity of manufacturing firms. These challenges underscore the theory’s assertion that structural change
requires a comprehensive policy approach addressing both sectoral and systemic bottlenecks (AfDB, 2020).
The theory also highlights the role of export diversification as a component of structural transformation. By
moving away from reliance on a narrow set of commodities, West African economies can achieve greater
resilience to external shocks and enhance their integration into global value chains. For instance, Côte
d’Ivoire’s industrial policy emphasizes processing agricultural products like cocoa locally, thereby adding
value before export and creating a more diversified economic base (World Bank, 2016).
Another vital aspect of Structural Change Theory is its focus on industrial policies that support backward and
forward linkages within the economy. Backward linkages, such as sourcing raw materials locally, and forward
linkages, including the establishment of processing industries, enhance value addition and stimulate the entire
economy. The implementation of these linkages aligns with the theory’s call for targeted investments in
manufacturing and complementary sectors to drive structural transformation (Rodrik, 2014).
In a nutshell, Structural Change Theory provides a relevant and robust framework for examining the impact of
industrial policies on manufacturing output growth in West Africa. By advocating for resource reallocation
from low-productivity to high-productivity sectors, the theory underscores the importance of deliberate, well-
coordinated industrial policies in achieving structural transformation. For West African economies, where
dependence on primary commodities remains high, the theory offers practical insights into policy design aimed
at fostering industrialization, promoting export diversification, and achieving inclusive growth. This theoretical
framework is instrumental in guiding this study’s exploration of industrial policy dynamics and their influence
on manufacturing growth across selected West African countries.
METHODOLOGY
This section carefully covers model specification and method of analyzing data.
Nature and Sources of Data
The study relies on secondary data collected from reputable and reliable international and regional databases to
analyze the impact of industrial policies on manufacturing sector output growths in selected West African
countries- Burkina Faso, Cote d’Ivoire, Ghana, Guinea, Nigeria and Senegal. The study used panel annual
data spanning a period of 24 years, from 2000 to 2023. The data sources provide comprehensive, consistent,
and accessible information necessary for the econometric analysis.
Model Specification
The econometric model for the current study was based on the theoretical framework as earlier statesd and
Akinwale’s (2019), empirical specification with minimal modification as follow:
INO
t
= λ
0
+ λ
1
TOP
t
+ λ
2
EXR
t
+ λ
3
GCE
t
+ λ
3
CPS
t
+ e
t
.
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In his specification, INO denoted industrial output, TOP denoted trade openness, EXR represented exchange
rate policy, CPS denoted credit to the private sector, and GCE represented government capital expenditure.
The adapted model replaced government expenditure in Akinwale (2019) with credit to private sector, based
on Yunanto and Medyawati (2014), who stated that,” monetary policy is more effective than fiscal policy”.
The econometric form of the model to analyse the influences of industrial policies on manufacturing output
growth is as follows:
Where: MOG
it
represents the manufacturing output growth for country i at time t, which serves as the
dependent variable. This variable captures the annual percentage change in manufacturing output, measured by
manufacturing value-added (MVA), employment in the manufacturing sector, and the share of manufacturing
in GDP.
EXCR
it
represents the exchange rates, as a tool of the government’s monetary policy.
CPS
it
. represent credit to the private sector in country i at time t
TOP
it
represents the trade policy of the government tends to protect the manufacturing sector. It is measured as
a percentage of trade in country i at time t
FDI
it
represents foreign direct investment inflows to country i at time t which is the control variable
are the coefficients that measure the impact of each independent variable (exchange rate, credit to the
manufacturing sector, trade openness and foreign direct investment) on manufacturing output growth. A
positive coefficient would suggest that an increase in the corresponding independent variable leads to an
increase in manufacturing output growth.
ϵ is the error term, which accounts for unobserved factors that may influence manufacturing output growth,
such as global economic trends, political stability, or unforeseen shocks. In this model, it is hypothesized that
industrial policies play a significant and positive role in manufacturing output growth. By specifying this
model, the study aims to empirically test the impact of industrial policies on manufacturing growth in West
Africa, providing important insights into how government interventions can help foster industrialization and
economic growth in the region.
Justification for Panel Data Analysis
Heterogeneity Control
Panel data analysis accounts for unobservable heterogeneity across the selected countries, such as institutional
differences, governance structures, and cultural factors. By introducing fixed or random effects, the model can
control for country-specific characteristics that may otherwise bias the estimates.
Dynamic Effects
The technique captures both time and cross-sectional dynamics, providing insights into how industrial
development influences economic growth across different countries and over the study period.
Efficient Estimation
The combination of cross-sectional and time-series dimensions increases the degrees of freedom and reduces
multicollinearity among explanatory variables, leading to more efficient and reliable parameter estimates.
(Baltagi, 2013).
Diagnostic Checks
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After estimation, diagnostic checks are performed to ensure the reliability and validity of the results. These
include:
Multicollinearity Test: To ensure that explanatory variables are not highly correlated.
Heteroskedasticity Test: To check for constant variance of errors across observations.
Autocorrelation Test: To detect correlations between residuals over time, which could affect the accuracy of
the estimates.
Software for Estimation: The analysis is conducted using EViews 12 or a similar econometric software,
which offers advanced tools for panel data analysis, diagnostic tests, and result interpretation.
This estimation technique ensures that the empirical findings are robust, reliable, and relevant for
understanding the industrial policies and manufacturing outputs growths in selected West African countries.
Diagnostic/Econometric Test
To ensure the reliability and validity of the results in this study, several diagnostic and econometric tests are
carried out. These tests help identify and address potential issues in the model, making the findings more
robust and credible.
First, stationarity tests like the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests are conducted
to determine whether the data used are stationary. If the variables are not stationary, differencing is applied to
make them suitable for analysis and to avoid misleading results. Next, multicollinearity is checked using the
Variance Inflation Factor (VIF). This ensures that the explanatory variables are not too highly correlated with
one another, as this could distort the estimates of their individual effects.
The model is also tested for heteroscedasticity to confirm that the variance of the error terms remains constant.
If the variance fluctuates, robust standard errors or alternative techniques are employed to adjust for this issue.
Similarly, autocorrelation tests, such as the DurbinWatson statistic, are performed to ensure that the residuals
(errors) from the model are not serially correlated, as this could undermine the reliability of the estimates.
The normality of residuals is verified using the Jarque-Bera test. This step ensures that the errors follow a
normal distribution, which is important for hypothesis testing and valid inference. Additionally, a model
specification test (such as the Ramsey RESET test) is conducted to confirm that the functional form of the
model is correct and no important variables are omitted.
For cross-sectional data, tests like Pesaran’s cross-sectional dependence test are used to check if there is any
dependence among the countries in the panel. If such dependence exists, adjustments are made to the standard
errors or modeling approach.
When analyzing panel data, unit root tests like Levin, Lin & Chu (LLC) and Im, Pesaran & Shin (IPS) ensure
that the data are stationary across both time and cross-sectional dimensions. Furthermore, cointegration tests
confirm whether there is a long-term relationship among the variables, and if so, methods like the error
correction model (ECM) are applied.
Lastly, the goodness-of-fit of the model is assessed using measures like the R² and adjusted R², which indicate
how well the model explains variations in economic growth. These diagnostic tests collectively ensure that the
analysis is thorough, reliable, and provides meaningful insights into the relationship between infrastructural
development and economic growth in the selected African countries.
To ensure the credibility and validity of the results in this study, several diagnostic and econometric tests are
performed to validate the model and the data used. These tests help identify potential issues and provide
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confidence in the conclusions drawn about the impact of infrastructural development on economic growth in
selected African countries.
First, stationarity tests, such as the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests, are
applied to examine whether the variables are stationary or exhibit trends over time. Non-stationary data can
lead to spurious regression results, so appropriate transformations, such as differencing, are used if needed.
Multicollinearity is assessed by calculating the Variance Inflation Factor (VIF). This ensures that the
explanatory variables are not highly correlated with each other, which could compromise the precision of the
estimated coefficients.
To check the consistency of the error terms, heteroscedasticity tests, like the Breusch-Pagan or White tests, are
employed. Heteroscedasticity can distort standard errors and affect the significance of the results. Robust
standard errors or other adjustments are made when heteroscedasticity is detected.
Autocorrelation in the residuals is tested using the Durbin-Watson statistic or other suitable methods,
especially given the time-series nature of the panel data. Autocorrelation, if present, can affect the reliability of
coefficient estimates and model inference.
The normality of residuals is verified through the Jarque-Bera test. This step ensures that the residuals follow a
normal distribution, which is critical for hypothesis testing and making accurate inferences about the
population.
Cointegration tests, such as the Pedroni or Kao tests, are conducted to determine if there is a long-run
equilibrium relationship between the variables. If cointegration is found, an Error Correction Model (ECM) or
other techniques are employed to capture both short-run and long-run dynamics.
Cross-sectional dependence, often present in panel data involving multiple countries, is tested using Pesaran's
test. Accounting for this dependence ensures that the model results are not biased by interconnections among
the countries studied (Osabuohien, Olayemi & Ogbu, 2023).
Finally, the specification of the model is verified through tests like the Ramsey RESET test. This ensures that
the model is correctly specified and does not omit relevant variables or include unnecessary ones. The overall
fit and explanatory power of the model are assessed using the R² and adjusted R² values. By conducting these
diagnostic and econometric tests, this study ensures the robustness of its findings and provides a solid
foundation for the conclusions on how infrastructural development influences economic growth in selected
African countries.
Method of Data Analysis
The study employs the Cointegration panel data analysis technique (Fully Modified OLS), which is appropriate
for examining data from multiple countries over several years. This method allows for controlling for both
cross-country differences and time-specific factors, thereby providing a more nuanced understanding of how
industrial policies influence manufacturing output growth. The fixed effects or random effects model will be
used, with the choice of model determined by the results of the Hausman test, which compares the suitability
of the three models. Diagnostic test checks are performed to ensure the reliability and validity of the results.
Unit root tests were performed using Levin, Lin &Chu (LLC) and Im, Pesaran &Shin (IPS) to ensure data
stationarity across both time and cross-sectional dimensions. Cointegration tests confirm whether there is a
long-term relationship among the variables, methods like error correction model (ECM) are applied. Pedroni’s
test examines the null hypothesis of no cointegration against the alternative hypothesis of a common or
individual autoregressive coefficient among the variables. Table 1 below presents the variables for the study.
Table 3.1: Variables’ Description and Sources
S/N
Variables
Proxy/
Description
Sources
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Symbols
1
Manufacturing Output
Growth
MOG
Manufacturing Output Growth
WDI,
2024
2
Monetary Policy
EXCR
The exchange rate is one of the monetary policy
tools used by the government to facilitate the
growth of manufacturing growth
WDI,
2024
3
Monetary policy
CPS
Credit to the private sector as another monetary
policy tool used by the government to help
manufacturing growth
WDI,
2024
4
Trade Policy
TOP
Trade Openness as trade policy to protect the
manufacturing sector
WDI,
2024
5
Commercial Policy
FDI
Foreign Direct Investment inflows to facilitate
the growth of the manufacturing sector
Source: Authors’ Compilation (2025)
ESTIMATION RESULTS AND DISCUSSION
This sub-section presents the results of the preliminary data analysis and selected countries estimation results
Table 4.1. Group Descriptive Statistics of Variables
Statistic
MOG
EXCHR
FDI
DCPS
TOPN
Mean
11.5647
1137.457
2.2932
14.7086
57.9123
Median
11.0512
502.6753
1.5343
13.2924
58.3976
Maximum
21.5869
9565.082
18.8280
32.3729
116.048
Minimum
0.0000
0.0000
-1.4788
0.0000
16.5142
Std. Dev.
3.8015
2145.143
2.7961
7.4778
21.4760
Skewness
0.1652
2.7257
2.6046
0.5191
0.5061
Kurtosis
3.8308
9.3417
12.6171
2.8815
3.2262
Jarque-Bera
4.7967
419.6081
717.7420
6.5521
6.4552
Probability
0.0909
0.0000
0.0000
0.0378
0.0397
Sum
1665.32
163793.8
330.2159
2118.03
8339.37
S. Sq. Dev.
2066.52
6.58E+08
1117.965
7996.22
65954.32
Observations
144
144
144
144
144
Source: Authors’ Computation (2025)
The mean value of MOG is 11.56, while its median is 11.05, suggesting that the data is fairly symmetrical with
slight variation. EXCHR (exchange rate) has a mean of 1137.46, which is significantly higher than its median
of 502.67, indicating a rightward skew in exchange rate fluctuations over the years. FDI (foreign direct
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investment) has an average value of 2.29, with a median of 1.53, reflecting an upward trend in foreign
investment. DCPS (domestic credit to the private sector) has a mean of 14.71 and a median of 13.29,
suggesting a relatively stable distribution. TOPN (trade openness) has an average value of 57.91, with a
median of 58.40, showing that trade openness has remained relatively stable over time. The standard deviation
indicates the degree of variability in the dataset. MOG has a standard deviation of 3.80, while EXCHR has a
much higher standard deviation of 2145.14, confirming the significant fluctuations in the exchange rate. FDI
exhibits a standard deviation of 2.80, suggesting that foreign investments have varied significantly. DCPS has
a standard deviation of 7.48, while TOPN has a moderate standard deviation of 21.48, highlighting the degree
of variations in financial and trade openness indicators.
The maximum and minimum values provide insight into the range of variation in the dataset. The highest
recorded value for MOG is 21.59, while its lowest value is 0.00. EXCHR fluctuates widely, ranging from 0.00
to 9565.08, indicating periods of extreme currency volatility. FDI ranges from -1.48 to 18.83, showing
instances of negative foreign direct investment. DCPS has a maximum value of 32.37 and a minimum of 0.00,
while TOPN ranges from 16.51 to 116.05, highlighting different levels of trade activity in Nigeria during the
study period. Skewness measures the asymmetry of the distribution. MOG, DCPS, and TOPN exhibit slight
positive skewness, indicating that the data has a rightward tail. In contrast, EXCHR and FDI display strong
positive skewness (2.73 and 2.60, respectively), confirming the presence of extreme values and sharp
fluctuations.
Kurtosis assesses whether the distribution is more peaked (leptokurtic) or flat (platykurtic) compared to a
normal distribution. MOG, DCPS, and TOPN have kurtosis values close to 3, suggesting a normal-like
distribution. However, EXCHR (9.34) and FDI (12.62) exhibit high kurtosis, indicating heavy tails and
extreme variations in exchange rate and foreign direct investment data. The Jarque-Bera test assesses whether
the data follows a normal distribution. The probability values for EXCHR and FDI are 0.0000, suggesting
significant departures from normality due to high skewness and kurtosis. MOG has a probability of 0.0909,
implying mild deviation from normality, while DCPS and TOPN have p-values of 0.0378 and 0.0397,
respectively, indicating moderate non-normality. The descriptive statistics indicate that EXCHR and FDI
exhibit high variability and extreme values, reflecting economic fluctuations in Nigeria. MOG, DCPS, and
TOPN display more stable distributions but still show signs of moderate non-normality. These insights are
crucial in understanding the behavior of key economic indicators and their potential impact on income
inequality, poverty, and economic growth.
Correlation matrix: The correlation matrix provides insights into the degree of association between the
variables used in the study. Correlation values range from -1 to +1, where a value close to +1 indicates a strong
positive relationship, a value close to -1 indicates a strong negative relationship, and a value around 0 suggests
no significant correlation. Table 3. presents the correlation coefficients among manufacturing sector output
growth rate.
(MOG), Exchange Rate (EXCHR), Foreign Direct Investment (FDI), Domestic Credit to Private Sector
(DCPS), and Trade Openness (TOPN). The results helped to identify potential multicollinearity issues that
may affect regression analysis.
Table 4.2. Correlation Matrix
Variable
MOG
EXCHR
DCPS
FDI
TOPN
MOG
1
-0.0768
0.3534
-0.1332
-0.1234
EXCHR
-0.0768
1
-0.3100
0.2031
0.3713
DCPS
0.3534
-0.3100
1
0.0431
0.0461
FDI
-0.1332
0.2031
0.0431
1
0.4378
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TOPN
-0.1234
0.3713
0.0461
0.4378
1
Source: Authors’ Computation (2025)
The correlation results show that MOG has a weak negative correlation with EXCHR (-0.0768), FDI (-
0.1332), and TOPN (-0.1234), suggesting that increases in these variables are associated with slight reductions
in money supply. On the other hand, MOG has a moderate positive relationship with DCPS (0.3534),
indicating that an increase in domestic credit to the private sector is associated with an increase in money
supply. Exchange rate (EXCHR) exhibits a weak negative correlation with DCPS (-0.3100) and a weak
positive correlation with FDI (0.2031) and TOPN (0.3713). The relationship between FDI and TOPN is
relatively strong (0.4378), implying that higher levels of trade openness are associated with higher foreign
direct investment inflows. Overall, the correlation matrix suggests that multicollinearity is not a major concern
among the independent variables, as none of the correlation coefficients are excessively high (above 0.8).
These results provide preliminary insights that will be further explored in the regression analysis.
Unit Root Tests
Unit root tests were conducted to examine the stationarity properties of the variables in the study. Non-
stationary data can lead to misleading inferences, making it necessary to determine the order of integration of
each variable. The tests employed include the Levin, Lin & Chu (LLC) and the Im, Pesaran, and Shin (IPS)
tests, applied at both level and first difference.
Table 4.3 Unit Root Tests Augmented Dickey-Fuller Unit Root Test
Variables
Level
1
st
Difference
Integration
Order
LLC
statistics
Prob.
Inference
LLC
statistics
Prob.
Inference
MOG
0.5296
0.7686
Non-
Stationary
-2.8218
0.0024
Stationary
I(1)
DCPS
0.6476
0.9271
Non-
Stationary
-5.3083
0.0000
Stationary
I(1)
EXCHR
0.8124
0.2503
Non-
Stationary
-5.7968
0.0000
Stationary
I(1)
FDI
-1.4284
0.0766
Non-
Stationary
-4.0035
0.0000
Stationary
I(1)
TOPN
-1.8212
0.0343
Non-
Stationary
-6.7914
0.0000
Stationary
I(1)
Source: Authors’ Computation (2025).
The results indicate that at levels, all variables are non-stationary as their probability values exceed the 5%
significance level. However, after first differencing, all variables become stationary, suggesting that they are
integrated of order one, I (1). This implies that subsequent econometric analysis, such as cointegration tests
and regression models, should be conducted with consideration for the stationarity properties of the variables.
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Table 4.4 Summary of Hypotheses Tests
Hypothesis
Variable
Coefficient
(FEM)
t-Statistic
p-Value
Decision
H
01
: Exchange rate does not affect
manufacturing output growth
EXCHR
0.001179
5.7274
0.0000
Reject H
01
H
02
: Credit to the private sector does
not impact manufacturing output
growth
DCPS
-0.166379
-3.1494
0.0020
Reject H
02
H
03
: Trade policy does not impact
manufacturing output growth
TOPN
-0.020534
-1.0360
0.3022
Fail to
Reject H
03
H
04
: Industrial policies do not impact
manufacturing output differently in the
selected West African countries
EXCHR,
DCPS, &
TOPN
Various
various
various
Reject H
04
Source: Authors’ Computation (2025).
The hypothesis tests confirmed that exchange rate and credit to the private sector have significant effects on
manufacturing output growth in selected West African countries. In contrast, trade policy (trade openness)
does not show a statistically significant impact. Also, the individual country estimations revealed that
industrial policies impact manufacturing output growth differently in the selected West African countries.
These findings provided empirical support for policies aimed at exchange rate stabilization and credit market
reforms to enhance industrial growth. Additionally, while trade policy alone may not significantly impact
manufacturing output, it should be complemented by domestic policies that strengthen the competitiveness of
local industries.
Table 4.5: Selected Countries Estimation Results
Dependent Variable: MOG
Results of Fully Modified Ordinary Least Squares (FMOLS)
Variable
Coefficient
Std. Error
t-Statistic
Prob.
EXCHR
0.001179
0.000206
5.727425
0.0000
FDI
-0.051429
0.104374
-0.492735
0.6230
DCPS
-0.166379
0.052828
-3.149434
0.0020
TOPN
-0.020534
0.019820
-1.036009
0.3022
R² 0.665511
Adj.R² 0.641993
Source: Authors’ Computation (2025)
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DISCUSSION OF FINDINGS
The coefficient of exchange rate (EXCHR) has a positive and statistically significant impact on manufacturing
sector output growth rate (MOG), reinforcing the strong link between currency fluctuations and monetary
expansion. The results showed that as the exchange rate goes up by 1%, the manufacturing sector output
growth rate goes up by 0.01179%. The result implies that a favourable exchange rate policy has the potential to
boost manufacturing sector output growth rate. The result on the impact of exchange rate and manufacturing
sector output growth rate is in line with the findings of Akinwale (2019). Also, the finding of this study has
implications for practice and future research direction. Conversely, domestic credit to the private sector
(DCPS) has a negative and significant impact on manufacturing sector output growth rate (MOG), indicating
that increased domestic credit reduces manufacturing sector output growth rate. This finding is also in line
with Akinwale (2019). Foreign direct investment (FDI) and trade openness (TOPN) have negative coefficients
models, but their impacts are statistically insignificant at conventional significance levels. This suggests that,
within the study period, FDI and trade openness did not significantly influence manufacturing sector output
growth rate in the selected West African Countries. The results of the findings have policy implications for
manufacturing sector growth rate in the selected West African Countries. The adjusted R-squared value was
estimated at 64%, indicating that all the independent variables included in the model explained about 64% of
the variation in the dependent variable manufacturing sector output growth rate. The estimated R-square at
64% showed a good fit of the regression line. Summary of findings from the individual country estimation
focusing on industrial policies and the manufacturing sector output growth rate in the selected African
countries. The implications of industrial policies in the selected West African countries differ. Domestic credit
to the private sector, which is one of the instruments of monetary policy, was significant in Côte d'Ivoire,
Nigeria and Senegal. The impact of domestic credit to the private sector was positive in Côte d'Ivoire and
negative in Nigeria and Senegal. Domestic credit was not significant in Burkina Faso, Ghana and Guinea. The
implication is that there is a need to strengthen monetary policy (credit to the private sector) in Burkina Faso,
Ghana and Guinea.
The impact of the exchange rate on the monetary sector was positive in all the selected countries for the study.
The impact was significant in Burkina Faso, Guinea and Nigeria. It was not significant in Côte d'Ivoire, Ghana
and Senegal. The impact of FDI was positive and insignificant in Burkina Faso, Côte d'Ivoire and Nigeria and
significant in Ghana. It was negative and insignificant in Ghana, Nigeria and Senegal.
Trade openness, which is trade policy, was significant and positive only in Guinea. It was negative and
significant in Burkina Faso and Ghana. It was negative and insignificant in Nigeria and Senegal. From the
above, it can be concluded that the implications of industrial policies differ in the selected West African
countries, possibly due to differences in policy formation and implementation.
CONCLUSION
The study investigated the impact of industrial policies on manufacturing sector output growth rate in selected
West African Countries. Data spanning 2000 to 2023 were collected on the manufacturing sector output
growth rate (MOG), exchange rate (EXCHR), domestic credit to the private sector (DCPS), trade openness
(TOP), and foreign direct investment (FDI). The fully modified ordinary least squares (MOLS) estimation
technique was used in the model estimation. The paper finds that exchange rate has a positive and statistically
significant impact on manufacturing output growth, domestic credit to the private sector has a negative and
statistically significant effect on manufacturing output growth, trade policy (proxy by trade openness) has a
negative but statistically insignificant effect on manufacturing output growth. It deduced major implication as
Exchange rate management is crucial for sustaining manufacturing output growth. Credit market reforms
should be implemented to ensure effective financing of productive sectors. Trade policies should be
complemented by domestic industrial policies to enhance competitiveness. The paper explained the challenges
facing industrial policies such as Political instability, policy inconsistency, and weak governance undermine
the effectiveness of the policy. The paper report that sustainable manufacturing output growth in West Africa
requires a balanced approach that integrates monetary policy, trade policies, and industrial development
strategies. Government of this region must implement measures that stabilize exchange rates, ensure efficient
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credit distribution, and develop comprehensive industrial policies that promote domestic production and
competitiveness. By adopting these strategies, West African economies can enhance their manufacturing
sectors’ contributions to economic growth, job creation, and long-term development.
RECOMMENDATIONS
Based on the findings of this study, the following recommendations are proposed to enhance manufacturing
output growth in selected West African countries:
The government of individual country should design and implement strategies to stabilize exchange rates, as
fluctuations significantly impact manufacturing output. Exchange rate volatility can be minimized through a
combination of managed float policies, foreign exchange reserves management, and monetary interventions to
prevent excessive depreciation that could lead to inflationary pressures.
Governments should establish credit guarantee schemes, reduce borrowing costs, and strengthen financial
regulations to ensure that loans are efficiently utilized for industrial growth.
Policy framework that domesticate industrial policies encourage complementary industrial policies should be
adopted to enhance domestic production capacity, improve infrastructure, and provide incentives for local
manufacturers such as Tariff and non-tariff measures should be designed to protect emerging industries while
maintaining competitiveness in global markets.
Governments of this region should increase investment in infrastructure projects to lower production costs and
enhance manufacturing productivity. A strong manufacturing sector requires adequate infrastructure, including
stable electricity supply, efficient transportation networks, and modern industrial zones.
(V) West African nations should harmonize trade agreements and industrial policies within regional blocs like
AFCFTA. By fostering regional supply chains and reducing trade barriers, countries can create a larger market
for locally manufactured goods and enhance industrial growth.
Government should coordinate monetary and fiscal policy to maintain price stability, control inflation, and
create an environment conducive to industrial expansion.
REFERENCES
1. Adebiyi, O., & Mensah, K. (2023). Policy Integration and Industrial Development in Africa: A
Focus on ECOWAS and AfCFTA. African Economic Review, 8(2), 45–63.
2. Abdoulaye, M., & Fofana, K. (2020). Industrial policies and agro-industrial development in Côte
d’Ivoire. West African Policy Journal, 12(2), 189–204.
3. African Development Bank (AfDB). (2020). African economic outlook 2020: Developing Africa’s
workforce for the future. African Development Bank Group.
4. African Leadership Magazine. (2025). Made in Africa: Rebuilding the Continent’s manufacturing
Backbone.
5. Ademola, B., & Yusuf, T. (2024). Industrial Growth and Regional Trade in West Africa:
Opportunities and Challenges. Journal of African Development Studies, 12(1), 67 -84
6. Akinwale, S. O. & Adekunle, E. O. (2019). Industrial Policy and Industrial Sector Productivity in
Nigeria Financial Markets, Institutions and Risks, 3(2), 44-51.
7. Boateng, A., & Duku, K. (2020). Ghana's Free Zones Act: Impact on Export-Oriented
Manufacturing. African Trade and Policy Journal, 18(2), 89–104.
8. Baltagi, B. H. (2013). Econometric analysis of panal Data. Wiley
9. Diakite, M. (2014). Effects of industrial diversification policies on manufacturing development in
Cote d’Ivoire. African Development Review, 26(2), 245-258. DOI: 10. 1111/1467-8268.12081
10. Diof, M. Sarr, A. (2022). Senegal’s Export Promotion Strategy: Achievements and Challenges.
Dakar: West African Economic Forum.
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Page 5059
www.rsisinternational.org
11. Eze, C., & Uche, K. (2019). Public-private partnerships and industrial development in Nigeria.
African Infrastructure Review, 7(1), 88–102.
12. Federal Ministry of Industry, Trade and Investment. (2014). National Industrial Revolution Plan
(NIRP). Government of Nigeria.
13. Ibrahim, M., Yusuf, T., & Ahmed, Z. (2021). Exchange rate dynamics and manufacturing growth in
West Africa. Journal of African Economic Studies, 18(3), 211–229.
14. Juhasz, R., Lane, N. & Rodrik, D. (2023). The new economics of industrial policies. Annual
Review of Economics, 16.
15. Lin, J. Y. (2012). New structural economics: A framework for rethinking development and policy.
The World Bank.
16. McMillan, M., & Rodrik, D. (2011). Globalization, structural change, and productivity growth.
National Bureau of Economic Research Working Paper No. 17143.
17. Mensah, K., & Okyere, E. (2023). Ghana’s Industrial Transformation Agenda and Sustainable
Development Goals. Journal of Development Studies in Africa, 14(2), 88–105.
18. Olayemi, F. (2022). Import substitution industrialization in post-independence West Africa:
Strategies and outcomes. Journal of African Economies, 31(3),345-367. DOI:10.1093/jae/ejac001.
19. Osabuohien, E., Olayemi, O., & Ogbu, M. (2023). Industrial policies and manufacturing
performance in Nigeria. Contemporary African Policy Review, 22(1), 99–116.
20. Rodrik, D. (2021). Industrial Policy for the 21st Century: New Challenges and Opportunities.
Journal of Economic Perspectives
21. Rodrik, D. (2019). Straight Talk on Trade: Ideas for a Sane World Economy. Princeton University
Press.
22. Rodrik, D. (2014). Economic diversification and growth. In M. Morris, R. Kaplinsky, & D. Kaplan
(Eds.), One thing leads to another: Promoting industrialisation by making the most of the
commodity boom (pp. 1–17). South Africa: University of Cape Town Press
23. Rodrik, D. (2011). Normalizing industrial policy. Commission on Growth and Development
Working Paper, No. 3.
24. Touré, I., et al. (2013). The Evolution of Manufacturing in West Africa: Policy and Prospects.
Dakar: West African Development Bank.
25. UNECA (2021). Economic Report on Africa: Innovation, Competitiveness, and Industrialization.
United Nations Economic Commission for Africa
26. UNECA. (2013). Making the most of Africa’s commodities: Industrializing for growth, jobs, and
economic transformation. Addis Ababa: UNECA.
27. UNIDO. (2019). Industrial development report 2019: Demand for manufacturing – Driving inclusive
and sustainable industrial development. UNIDO
28. World Bank (2023). Africa's Pulse: The Role of Manufacturing in Africa's Growth and Prosperity.
World Bank Publications.
29. World Bank. (2016). Côte d’Ivoire economic outlook: Why industrialization is key to Côte
d’Ivoire’s growth. Washington, DC: World Bank.
30. Yin, R. K. (2018). Case study research and application: Design and methods. Sage Publications
31. Yunanto, M. & Medyawati, H. (2014). Monetary and Fiscal Policy Analysis: Which one more
Effective? Journal of Indonesian Economy and Business, 29(3), 222 – 236.
