Introduction

The logistics industry is an important puzzle in fulfilling the role of coordinating the primary, secondary, and tertiary industries. An economic system's smooth functioning depends on an organized logistics industry, without which the coordination of production, exchange, and consumption activities will be far-fetched. The success of the logistics industry is considered a key engine of economic expansion (Khadim et al., 2021; Pan et al., 2022; XiaofeI & Fen, 2021). Besides, the logistics industry is a strategic factor that indicates the competitiveness of economies and businesses and is a catalyst for job creation and incomes of people in an economy (Munim & Schramm, 2018; Sezer & Abasiz, 2017). The logistics industry is a system that offers services to customers through the fulfilment of six rights; ensuring that the (1) right goods, in the (2) right quantities, in the (3) right condition, are delivered to the (4) right place, at the (5) right time, for the (6) right cost(USAID, 2009).

Studies have shown that an improvement in the logistics industry of a country leads to an increase in economic prosperity (Khadim et al., 2021; Munim & Schramm, 2018). For this reason, various countries strategically invest in the logistics industry to expand and improve quality and efficiency. China for example, rolled out a five-year plan to develop the logistics industry. The efforts mainly target boosting the digital and smart upgrade of the transportation, storage, delivery, and packaging sectors and strengthening weak links in rural areas and cold chain logistics (Abdulla, 2023). In its ambition of becoming a US$26 trillion economy by FY48, India also pledges to renovate its infrastructure, digitization, and a heightened focus on sustainable logistics (Chakraborty et al., 2020). Similarly, after having suffered a double-edged sword of COVID-19 and the Brexit geopolitical disruption, the United Kingdom’s logistics sector is steadily evolving with the development of new technologies (Ali et al., 2023). Singapole and Finland are the top two highly efficient countries in logistics industry performance. While these countries share explanatory features, the efficiency of the logistics industry in Singapore goes down to its world-class technological infrastructure, efficient customs procedures, and strong and skilled logistics workforce professionalism (Munim & Schramm, 2018; Shao et al., 2023).

According to the rising corpus of information about Africa, the continent is advantageously evolving as a crucial trading bloc, especially for Asia and some regions of Europe(Cheru Fantu, 2010). The commercial emergence of Africa is necessitated by its enormous resource endowments, increasing wealth, and a monotonic concentration of the middle-income class (Kuteyi & Winkler, 2022). To realize this trade expansion potential, African countries should also invest in the logistic sector such as improving infrastructure and information technologies. Like any other macroeconomic objective, southern Africa’s logistics industry is seriously jeopardized by poor governance (Banda, 2023a, 2023b, 2023c; Mkandawire, 2007; Tawiah, 2023). Thus, the current study is of significant empirical essence for sub-Saharan Africa, considering that a greater proportion of the challenges thwarting its logistics industry and other development projects are governance-related in nature (See Figure 1). For instance, over 94% of the challenges – poor infrastructure, corruption, political instability, terrorism, piracy, and economic shocks, rest on public sector incapacitation. On the other hand, the remaining 4% can be handled by the corporate sector, such as managing inventory levels and cyber-attacks. The current study, therefore, seeks to examine the impact of good public governance on logistics industry performance and the moderation role of corporate governance in this relationship.

Figure 1: Main Threats to Sub-Saharan Africa’s Logistics Industry Click here to view Figure

Source: Statista (2023)

The remainder of the research paper is structured as follows: Section 2 interrogates existing literature on the importance of the corporate-public governance nexus in the logistics sector. Section 3 describes the materials and methods used to analyze the data. Section 4 presents the empirical findings and discussion. Lastly, section 5 concludes and offers policy implications.

Literature Review

Recently, there has been an increase in global logistics reports. For example, the World Bank first published the Logistics Performance Index (LPI) in 2007 (World Bank, 2022). Since its inception, the index has been increasingly used to detect challenges and opportunities in the trade logistics efficiency of regions and countries. Thus, the index provides insights to policy and decision-makers in the private and public sectors and various international agencies (Sergi et al., 2021). Existing research has investigated logistics performance from two angles. The first one examines the factors affecting logistics performance (Akdamah, 2022; Erkan, 2014; Islam & Siddiqui, 2019; Magazzino et al., 2021; Sergi et al., 2021; Suki et al., 2021), while the second examines the effects of logistics performance on macroeconomic aspects such as economic growth and environmental sustainability (Magazzino et al., 2021; Suki et al., 2021). However, this study concentrates on the first angle with an inquiry into the role of the sparingly examined good corporate-public governance nexus.

A similar study was conducted by Uyar et al. (2021), who also examined the relationship between public governance and logistics performance and the mediating effect of corporate governance. This study aggregated the six worldwide governance indicators (WGI) into a single composite public governance index. However, the coverage of their study was too extensive, covering 144 countries worldwide. The current study differs from Uyar et al. (2021) as it concentrates on countries from Sub-Saharan African with common socioeconomic and political status. In addition, their study employed a mediation analysis of data, posing a considerable risk of model misspecification and measurement error, especially with a large dataset (Lee et al., 2021). Uniquely, this study employs a dynamic panel model to see if corporate governance influences the effect of public governance on logistics industry performance.

Lund (2016) also came close to matching the needs of this paper when he revealed what he dubbed the "unsurprising" importance of governance on the logistics performance of countries. He strongly emphasized that public governance is the critical predictor of the efficiency and effectiveness of the logistics system of any country. However, this study used basic statistics such as correlation and scatter analyses to reach conclusions. On the other hand, Jen et al. (2020) investigated the impacts of corporate governance on knowledge sharing and logistics performance of a few firms in China. The study revealed that trust and risk-sharing contracting boost the possibility of knowledge dissemination among supply chain partners and enhance the logistics performance of the involved firms. Similarly, Pasko et al. (2020) observed that management ownership, institutional ownership, and the proportion of the largest shareholder are critical determinants of the logistics performance of ?hinese logistics-related listed companies.

Magazzino et al. (2021) examined six different determinants of logistics performance in 25 highly performing countries. The study revealed that technological innovations, human capital development, urbanization, and trade openness significantly and positively facilitate logistics performance in the selected countries. The study concurs with Jhawar et al. (2014), who also found that investment in human capital which increases the number of skilled workforces, significantly boosts logistics performance in India. Akdamah (2022) also noted that countries with very high human development register very high logistics performance than others in his study of the determinants of logistics performance in 160 countries.

Various determinants of logistics performance in three categories – infrastructure, human capital, and institutions, were examined for Africa, Europe, and Asia (Sergi et al., 2021). Using the analysis of variance (ANOVA) to analyze Logistic Performance Index (LPI) data, the paper discovered that each cluster has a critical contribution to logistics performance in one or two of the specified regions. For example, the human factor was deemed a more critical determinant for logistics performance in Europe, while infrastructure stands fundamental in Asia. Although all three clusters were observed to be critical determinants of the success of Africa's logistics performance, the study is not dynamic as it used cross-section analysis which is a snapshot of data that does not consider the effect of time.

Islam & Siddiqui (2019) examined the influence of transport policy, transport availability, and technology infrastructure on Pakistan's logistics performance. Interestingly, all the variables were found to be systematic factors that significantly and positively influence logistics performance in the country. Similarly, political factors that affect transport regulations and infrastructure, among other factors, were examined for 93 countries by Wong& Tang (2018). The study revealed a positive and statistically significant influence of infrastructure, technology, education, and the quality of labor on logistics performance. Erkan (2014) also contends that a country that intends to increase its logistics performance cannot do so when investment in modern, high-quality infrastructure is not emphasized.

Evidence shows that research related to logistics performance is overwhelming across the globe. However, the role of corporate and public governance on logistic performance remains a theoretical expectation and not empirically proven. We have also noted that most studies examining corporate governance's role on logistics industry performance are microeconomic and country-specific and limited to a few firms. Thus, the current research is macro-econometric and examines the role of the corporate-public governance nexus on logistics performance in Sub-Saharan Africa. The premise dwells on the argument that the quality of governance institutions shapes the development outcomes of a country (Acemoglu, Daron, 2012; North et al., 2009; Samarasinghe, 2019). We contend that an improvement in governance institutions will ensure the effectiveness of the logistics industry, which in turn provides good coordination of the industrial sector of an economy.

Materials and Methods

Data

Data from 32 Sub-Saharan African countries from 2007 to 2020 was used to execute our empirical approach. The list of countries is given in Table A1 (Appendix A). The selection of countries and year ranges were guided by the data availability for the dependent variable (Logistics Performance) and the two primary independent variables of interest (public and corporate governance) at the time of the study.

Variables, Sources and Expectations

Logistic industry performance is the dependent variable, while public and corporate governance are the two main explanatory variables of interest. Data for the dependent variable is measured by the Logistics Performance Index (LPI), whose data values range from 1 to 5, with 1 and 5 representing poor and optimum logistics performance extremes, respectively. Data for the variable was obtained from the World Bank data bank (World Bank, 2022). Unlike Uyar et al. (2021), who used the Worldwide Governance Indicators (WGI), the study employed the composite Mo Ibrahim Index of African Governance (MIIAG) data to measure public governance. The data was obtained from the Mo Ibrahim Foundation databank (Mo Ibrahim Foundation, 2020). According to Dassah & Tshishonga (2011), the MIIAG is crucial and suitable because its framework is built and aligned to the preconditions required for the survival of African nations.

We also adopted corporate governance proxied by the ethical behaviour of firms (World Bank, 2022) as another primary independent variable of interest. Due to massive privatization and decentralization in most countries following the 1980s structural adjustment programs, structures of logistics performance are mostly contracted out to private firms. Therefore, there is a need for transparent interaction among public officials, politicians, and other stakeholders from the private sector. However, corporate governance in most African countries is poorly developed and highly marred with malpractices such as bidding and procurement fraud and offering non-durable goods and services to the government (Kheil et al., 2022). The corporate governance score is scaled from "1" for the worst performance to "7" for the outstanding performance of corporations. Table 1 details the study variables.

Table 1: Variable description

Source: Authors’ analysis

Model Specification

The unexhaustive literature reviewed reveals endless determinants of logistics industry performance. However, this study focuses on the role of the corporate-public governance nexus on logistics industry performance. To control for omitted variables, we specify a model that incorporates various other determinants such as political instability, urbanization, and human capital development as control variables (Magazzino et al., 2021) as follows;

Econometrically, equation 1 can be systematically linearly specified as follows;

Where: LPI represents logistics industry performance; GGOV denotes public/good governance; URBN is urbanization; HDI represents human capital; POS is Political instability. On the other hand, ?₀ denotes the intercept; ?₁, ?₂, ?₃, ?₄, ?₅ and ?₆, are the coefficients to be estimated; ? depicts the country effect; ? represents the effect of time; ? is the random disturbance representation; i and t denote countries and time, respectively.

Our primary interest in this regression is the public governance, corporate governance variables, and the interaction term of the two variables (lnGOV = lnGGOV_it * lnCGOV_it. The nature of the logistics industry makes it classified as a quasi-public good or service, implying that the public sector possesses supreme power in investments such as sea and airports, roads, warehouses, trade and border regulations, import duties, and tariffs, among others. However, major private corporations will likely strongly influence the government to provide those amenities. Literature shows that public governance reduces the grabbing hand (Banda, 2023b; Liu et al., 2018), so government budget allocation for logistics infrastructural investment will likely yield the intended outcomes. Where there is good governance, fiscal policies and government trade regulations are likely to be considerate of the economy's welfare demands and hence effective performance of socioeconomic aspects such as logistics performance.

Estimation Techniques: Blundell/Bond System GMM Estimator

Distinctively, the current research uses a dynamic panel technique which helps to check for robustness and assurance of the consistency of the coefficient of the main parameters of interest. Considering the dynamics of adjustments was crucial because the performance of any economy’s variable at any point in time hugely depends on its immediate past performance. Besides, panel models contribute more informative data, more degrees of freedom, and, more importantly, allow for dynamics of adjustments to estimate intertemporal relations (Adeleye, et al., 2017; Hsiao, 2003).

As argued earlier, logistics performance is a function of, among other things, the immediate past performance, which means that logistics performance is dynamic. Consequently, a model of logistics performance should contain one independent variable in the name of logistics performance for the past period, as shown in the equation below;

Where i = 1,…,N for N number of countries (cross-sections) ad t = 1,…,T for T  time coverage of the study; is a scalar and x_it^' is a vector of 1 x k regressors. On the other hand, the error component is presumed to be one-way such that:

Where the error components are autonomous between one another, µ_i represents the unobservable individual-specific effect and ?_it is the disturbance term.

It is important to note that there is a correlation relationship between the dynamic component Y_i,t-1 with µ_i in equation (i) above. Such a correlation renders the use of ordinary least squares or static panel models (Random &and fixed effects produce biased and invalid or varying estimates. In the quest for unbiased and consistent estimates, Arellano and Bond, AB (1991) came up with a consistent generalized method of moments (GMM) estimator. Advantageously, the estimator employs orthogonality conditions of the lag components and the disturbance term to yield more instruments for estimation of a dynamic panel data model. The AB one-step system GMM consistent estimator of ? with W, a matrix of instruments, and G, M A(1) is illustrated as;

Nevertheless, if the autoregressive parameters are too large or the ratio of variance of the panel-level effect to variance of the idiosyncratic error is too large, then the AB estimator may yield poor results. To circumvent the problem, Blundell and Bond (1998)extended the model of Arellano and Bover (1995), to build a system estimator that employs more moment conditions. This is one known as the Arellano-Bover/Blundell-Bond linear dynamic panel-data estimator. Thus, the current study employs this estimator since it takes into account the dynamics of adjustment and outclasses the original AB estimator.

Another novel part of this study is the ability to compute and interpret long-run estimates for the robust GMM. The notion that the GMM model only yields short-run results is highly ignored in many papers (Adeleye, et al., 2017; Azam & Adeleye, 2023; Ejemeyovwi & Osabuohien, 2020; Manja et al., 2022; Naa et al., 2020). To generate long-run results, we run additional simulations that divide the coefficient of each significant variable in the short run by the coefficient of the lagged dependent variable subtracted from. The computation is shown in the equations below;

Considered a competing dynamic model to GMM that also uses a lagged dependent variable, the panel autoregressive distributed lag (ARDL) model could hardly be used in this study. Our constructed panel is micro with time (T = 14) less than the sample size (N = 32). Panel ARDL is more applicable in samples that have larger time series than cross-sections (T > N) because some of its estimators, such as pool mean group (PMG) and dynamic fixed effect (DFE), yield short-run country-specific estimations(Banda, 2023a; Nica et al., 2023; Roodman, 2009).

Empirical Results

Descriptive Statistics

Tables 2 and 3 present summary statistics and the Pearson correlation analysis. Summary statistics show that LPI for Sub-Saharan Africa is critically very low, averaging only 2.52 out of 5 from 2007 to 2020. The slight standard deviation of 0.44 shows that data points for logistics performance are highly clustered around the mean, implicitly implying that many countries are performing poorly on this index in sub-Saharan Africa. The maximum logistics index of 4.915 was recorded in 2018 in Uganda, while the lowest index of 1.53 was recorded in 2007 in Botswana. For the two independent variables of interest, public governance for Sub-Saharan Africa averaged 50.97%, while corporate governance averaged 3.71 out of 7. The maximum performance in public governance of 79.5 was recorded in Mauritius in 2017, while the lowest score of 30.7 was recorded in Chad in 2012. On the other hand, Liberia recorded the overall maximum corporate governance performance of 6.60 in 2007, while the minimum score of 1.87 for corporate governance was recorded in Angola in 2018.

Table 2: Summary statistics

Note: LPI = logistics performance index, PGOV = public governance, CGOV = corporate governance, GOV= PGOV*CGOV, * = moderation, HDI = human development, URBN = urbanization, and POS = political instability, T = time span, n = sample size, N = observations.

Source: Authors’ computations from study data

Table 3: Pairwise correlation Matrix

Note: *** p<0.01, ** p<0.05, * p<0.1; ln = natural logarithm, LPI = logistics performance index, PGOV = public governance, CGOV = corporate governance, * = moderation, HDI = human development, URBN = urbanization, and POS = political instability

Source: Authors’ computation from study data

Table 3 shows the strength of linear relationships among our series lnLPI, lnPGOV, lnCGOV, lnGOV, lnHDI, lnURBN, and lnPOS. We employed the Pairwise correlation matrix, which yields a value between -1 to 1 for any pair of variables, with a coefficient value of -1 meaning a complete inverse linear correlation, 0 meaning the absence of any correlation, and + 1 being a complete direct or positive correlation. Our results indicate that all independent variables lnPGOV (0.38), lnCGOV (0.15), lnGOV (0.24), lnHDI (0.25), and lnPOS (0.03) depict a positive relationship with the dependent variable, except lnURBN (-0.14). Importantly, these dependent variables, except political instability, are significant at a 1% level. All coefficients along the table's diagonal are meaningless because they depict a variable’s self-perfect correlation.

Bond, Hoeffler and Temple (2001) GMM Selection Criteria

There are two main types of GMM methods, namely, difference GMM (DiGMM) and systems GMM (SyGMM), each of which has one- and two-step sub-categories. It is always critical for researchers to decide whether to run a difference or systems GMM. To do this, we ran a pooled OLS and Fixed effect and observed the coefficient of the lagged dependent variables in each model. Then we ran the difference GMM and observed the coefficient of the lagged dependent variable. The decision criteria are to run a systems GMM if the lagged dependent variable of a difference GMM model is lower or close to the lagged dependent variable of the fixed effect model (Bond et al., 2001). The results are shown in Table 4 below:

Table 4: GMM Decision criteria

Source: Authors’ computation from study data

Results in Table 4 indicate that the coefficient of the lagged dependent variable of the difference GMM is lower or closer to the lower bound, such that a systems GMM model for is deemed suitable since interpreting DiGMM results will lead to biased and inconsistent estimation in this case.

Diagnostic Tests

A specification of the GMM method requires variables to be in their natural form or logarithmically transformed. The issue of non-stationarity of variables generates no worries with the Blundel/Bond dynamic panel model because the regression technique generates self-differencing variables (Roodman, 2007, 2009a). However, we optionally performed a stationarity test and confirmed that there is no variable integrated beyond first difference (See Appendix Table A3). Therefore, it was imperative to perform the Hansen test to check for over-identifying restrictions and the validity of moment conditions to ensure that GMM estimators are consistent. Under the null hypothesis that over-identifying restrictions are valid, results in Table 5 below show an insignificant p-value for our model. Therefore, moment conditions are valid. Simultaneously, the results suggest the absence of heteroskedasticity, which will further be dealt with by robust coefficients of the endogeneity system GMM model.

Table 5: Hansen test of over-identifying restrictions

Source: Authors’ computation from study data

Table 6: AB Test for autocorrelation

Further, to ensure the validity of moment conditions in the GMM model, researchers need to deal with serial correlation in the idiosyncratic errors. In this study, we employed the Arellano & Bond (AB) (1991) test under the null hypothesis that the first differenced errors depict no serial correlation. According to Roodman (2009), the AB test is appropriate for linear GMM regressions on panels when lags are used as instruments. The results of the AB test for our model indicate the absence of serial correlation in first-difference errors in both first AR(1) and second-order AR(2), with all p-values insignificant at convectional significant levels (See table 7 below). On the other hand, the variance-covariance matrix test in Table 3 revealed the absence of multicollinearity since no pair of variables were correlated above 0.8.

Results and Discussion

Model Estimation

Having passed all necessary diagnostic tests for a dynamic one-step systems GMM, we investigated the role of the public-corporate governance nexus on logistics industry performance in Sub-Saharan Africa. To begin with, the coefficient of the lagged dependent variable is desirably positive and statistically significant at a 5% level. That is, a percentage point improvement in the previous year’s performance of logistics industry performance will improve logistics performance by 0.51 percent in the immediate subsequent year.  Studies that examine the impact of the lagged dependent variable or the inertia such as Adeleye, et al. (2017), Manja et al. (2022), and  Azam & Adeleye (2023) showed that income inequality, demand for international reserves, and life expectancy are dependent on their previous years’ performances.

Table 7: Robust endogeneity system GMM results

Source: Authors’ computations from study data

The coefficients of corporate governance and public governance are negative and statistically significant in both time periods. For example, a percentage point improvement in corporate governance, public governance, and political instability deteriorates logistics performance by 3.92%, 1.01%, and 0.03%, accordingly. However, when moderated with corporate governance, public governance yields a positive and statistically significant effect on logistic performance with 1.01 and 2.09 elasticities in the short and long run, respectively. That is, a percent improvement in the corporate-public governance nexus improves logistics performance by 1.01% and 2.09% points, at ceteris paribus. The results concur with Uyar et al. (2021) who also found that corporate governance significantly mediates the relationship between public governance and countries' logistics performance in 144 countries. Other studies also found that corporate governance in form of trust and risk-sharing contracting, board effectiveness, and representation of minority shareholders helps to reduce the operating cost of businesses and to increase investment, thereby improving logistics performance (Ben-Amar & McIlkenny, 2015).

Unsurprisingly, political instability is negative and statistically significant in both the short (at 10% level) and long run (at 5% level). In the long run, a one-unit increase in political instability or violence will deteriorate logistics performance by 0.068%, holding other factors constant. That is, an increase in the instability of the political environment will worsen the logistics performance of Sub-Saharan Africa. This result aligns with the research expectation and the findings of Wong & Tang (2018), who observed that countries with political stability experience high logistics performance. However, a stable political environment may result from an oppressive government or limited political competition in a country. If the ruling party easily subdues its weak counterparts for re-election, the resulting peace may be a thorn in the fresh. The environment may give rise to the big man syndrome and neopatrimonialism – corruption, rent-seeking, and cronyism. Not uncommon in Africa, people believe that political leaders are the fathers of their nation, such that some political leaders even claim to be endowed with powers beyond the votes of the ruled, such as occult powers (Ellis, 1993; Kroesbergen, 2020). Though it may create a peaceful environment, the intimidating presence may allow politicians to plunder state resources, thereby worsening the logistics industry performance of the region.

Conclusions and Recommendations

The current study investigated the role of good public-corporate governance nexus on the performance of the logistics industry in sub-Saharan economies. The logistics industry coordinates supply chain partners among the three sectors of an economy – primary, secondary and tertiary industries. Therefore, the study is necessary because sound logistics management fills the puzzle that ensures the survival of an economic system and the maintenance of its international trade relations and competitiveness through the domino effect. For example, to function, the secondary and tertiary industries need supplies from the primary sector. This relationship is not a one-way chain because the primary sector also needs technical services from the secondary and tertiary sectors to improve efficiency. Thus, the logistics industry offers a desideratum coordination that improves the wellness of an economy and the people living in it.

The study data was obtained from the World Bank and the Mo Ibrahim Foundation. First, findings from the robust endogeneity GMM reveal that the lag of the dependent variable is statistically significant. The results indicate that a one-unit increase in the past year’s performance of the logistics industry will positively influence the present year performance by 0.518 percent, holding other factors constant. It entails, therefore, that logistics industry performance is persistence in a way that a past year performance will have a direct influence on the present year performance. Second, good governance depicts negative and statistically significant coefficients in both the short-and long run. For example, a one-unit improvement in good governance will worsen logistics industry performance by 1.008% and 2.09%, respectively. Similarly, corporate governance is found to be negative and statistically significant in both the short-and long run with the marginal effect of 3.93% and 8.14, accordingly. However, this relationship becomes positive with the moderation effects of corporate governance. For example, when moderated with corporate governance, good governance depicts a positive influence on the performance of logistics industry with a marginal effect of 1.01% and 2.09% in the short-and long-run, respectively.

The findings offer important information for private firms, logistics industry managers, and national policymakers. Firstly, African governments should be strongly alerted about the importance of implementing ethical standards by strengthening the ethics training of their labor force and enhancing honesty and transparency regarding resource allocation through national curricula or workplace training. The government should define and set structures and procedures for employees to voice their ethical concerns without indictments of persecution. Consequently, all engagements and interactions in the business arena will consider ethical issues. Secondly, African governments should strengthen governance structures such as the rule of law and justice, accountability and transparency, public administration, human rights, and other foundations for economic opportunity. All in all, Sub-Saharan African governments should not treat corporate and public governance as independent of each other due to their complementary positive influence on macroeconomic performance such as the logistics industry. Since political instability was found to negatively influence logistics industry, African governments should consider means to avoid political violence while maintaining healthy political competition.

While the study provides novel empirical, methodological insights, and robust results, little is known about how individual components of the Mo Ibrahim Index of African Governance (MIIAG) influence logistics industry performance. Similarly, our composite index of corporate governance shields the relevance of its components on logistics performance. Thus, we recommend that future researchers investigate the issues and dive deeper to understand which components of governance are critical for specific elements of logistics performance. In addition, the Generalized method of moments only gives short-run results. A separate simulation for long-run results is performed only on those variables that are significant in the short-run. This is a weakness because cointegration methods such as Panel ARDL models may have a variable insignificant in the short-run but desirable significant in the long-run. Future researchers should, therefore, consider employing cointegration and error correction methods.

Acknowledgement

The authors thank the anonymous reviewers for their insightful comments that improved the quality of the manuscript

Funding Sources

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Conflict of interest

The authors do not have any conflict of interest.

Data Availability Statement

The Mo Ibrahim Index of African Governance can be accessed at: https://mo.ibrahim.foundation/iiag, and the World Development Indicators can be accessed at: https://databank.worldbank.org/source/worlddevelopment-Indicators.

Ethics Statement

This research did not involve human participants, animal subjects, or any material that requires ethical approval.

Authors' Contribution

All authors designed the model and the computational framework and analyzed the data. Banda wrote the manuscript with input from all authors.

1. Abdulla, H. (2023). China Rolls Out Five Year Plan to Upgrade Logistics Sector. Just Style.
2. Acemoglu, Daron, and J. A. R. (2012). Why Nations Fail: The Origins of Power, Prosperity and Poverty (1st ed.). Crown: New York.
3. Adeleye, N., Osabuohien, E., & Bowale, E. (2017). The Role of Institutions in the Finance-Inequality Nexus in Sub-Saharan Africa. Journal of Contextual Economics, 137(1–2), 173–192. https://doi.org/10.3790/jce.137.1-2.173
4. Akdamah, E. (2022). The Effect of Human Development on the logistics efficiency of countries. Mehmet Akif Ersoy Üniversitesi ?ktisadi ve ?dari Bilimler Fakültesi Dergisi, 9(2), 871–896. https://doi.org/10.30798/makuiibf.912836
5. Ali, A., Cao, M., Allen, J., Liu, Q., Ling, Y., & Cheng, L. (2023). Investigation of the drivers of logistics outsourcing in the United Kingdom’s pharmaceutical manufacturing industry. Multimodal Transportation, 2(1), 100064. https://doi.org/10.1016/j.multra.2022.100064
6. Arellano, M., & Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. The Review of Economic Studies, 58(2), 277. https://doi.org/10.2307/2297968
7. Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29–51. https://doi.org/10.1016/0304-4076(94)01642-D
8. Azam, M., & Adeleye, B. N. (2023a). Impact of carbon emissions sources on life expectancy in Asia and the Pacific region. Natural Resources Forum. https://doi.org/10.1111/1477-8947.12303
9. Azam, M., & Adeleye, B. N. (2023b). Impact of carbon emissions sources on life expectancy in Asia and the Pacific region. Natural Resources Forum. https://doi.org/10.1111/1477-8947.12303
10. Banda, L. G. (2023c). Good Governance and Human Welfare Development in Malawi: An ARDL Approach. Malawi Journal of Social Sciences, 22, 89–119. https://doi.org/10.2139/ssrn.4235681
11. Banda, L. G. (2023a). State Capacity and Growth in Sub-Saharan Africa: A Dynamic Panel Examination of Smith’s Pillars of Prosperity . State Responsibility: Fourth Industrial Revolution, Sustainability and Good Governance , 1–33.
12. Banda, L. G. (2023b). The Institutions of Rule of Law and Inclusive Economic Development: Empirical Evidence from Southern African Development Community. SSRN Electroic Journal. https://ssrn.com/abstract=4386273
13. Ben-Amar, W., & McIlkenny, P. (2015). Board Effectiveness and the Voluntary Disclosure of Climate Change Information. Business Strategy and the Environment, 24(8), 704–719. https://doi.org/10.1002/bse.1840
14. Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143. https://doi.org/10.1016/S0304-4076(98)00009-8
15. Bond, S., Hoeffler, A., & Temple, J. (2001). GMM Estimation of Empirical Growth Models (21; Economics Group, Nuffield College).
16. Chakraborty, S., Sharma, A., & Vaidya, O. S. (2020). Achieving sustainable operational excellence through IT implementation in Indian logistics sector: An analysis of barriers. Resources, Conservation and Recycling, 152, 104506. https://doi.org/10.1016/j.resconrec.2019.104506
17. Cheru Fantu, C. O. (2010). The rise of China and India in Africa: Challenges, opportunities and critical interventions. Z Books.
18. Dassah, O., & Tshishonga, N. (2011). Good governance analyzing performance of Economic Community of West African States and Southern African Development Community members on Mo Ibrahim Index of African Governance. African Journal of Goverance and Development, 1(1), 1–27.
19. Douglass C. North, John Joseph Wallis, & Barry R. Weingast. (2009). Violence and the Rise of Open-Access Orders. Journal of Democracy, 20(1), 55–68. https://doi.org/10.1353/jod.0.0060
20. Ejemeyovwi, J. O., & Osabuohien, E. S. (2020). Investigating the relevance of mobile technology adoption on inclusive growth in West Africa. Contemporary Social Science, 15(1), 48–61. https://doi.org/10.1080/21582041.2018.1503320
21. Ellis, S. (1993). Rumour and power in Togo. Africa, 63(4), 462–476. https://doi.org/10.2307/1161002
22. Erkan, B. (2014). The Importance and Determinants of Logistics Performance of Selected Countries. Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB), 3(6), 1237–1254.
23. Hsiao, C. (2003). Analysis of panel data. Cambridge University Press.
24. Islam, Y., & Siddiqui, D. A. (2019a). The Effect of External Factors towards the Logistics Performance in Efficiency of Material Supply for the Pipeline Construction Based Projects: Evidence from the Oil and Gas Industry in Pakistan. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3510617
25. Islam, Y., & Siddiqui, D. A. (2019b). The Effect of External Factors towards the Logistics Performance in Efficiency of Material Supply for the Pipeline Construction Based Projects: Evidence from the Oil and Gas Industry in Pakistan. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3510617
26. Jen, C. T., Hu, J., Zheng, J., & Xiao, L. L. (2020). The impacts of corporate governance mechanisms on knowledge sharing and supply chain performance. International Journal of Logistics Research and Applications, 23(4), 337–353. https://doi.org/10.1080/13675567.2019.1691515
27. Jhawar, A., Garg, S. K., & Khera, S. N. (2014). Analysis of the skilled work force effect on the logistics performance index—case study from India. Logistics Research, 7(1), 117. https://doi.org/10.1007/s12159-014-0117-9
28. Khadim, Z., Batool, I., Akbar, A., Poulova, P., & Akbar, M. (2021). Mapping the Moderating Role of Logistics Performance of Logistics Infrastructure on Economic Growth in Developing Countries. Economies, 9(4), 177. https://doi.org/10.3390/economies9040177
29. Kroesbergen, H. (2020). African leadership and the kind of reality of the spirit world. HTS Teologiese Studies / Theological Studies, 76(2). https://doi.org/10.4102/hts.v76i2.5846
30. Kuteyi, D., & Winkler, H. (2022). Logistics Challenges in Sub-Saharan Africa and Opportunities for Digitalization. Sustainability, 14(4), 2399. https://doi.org/10.3390/su14042399
31. Lee, H., Cashin, A. G., Lamb, S. E., Hopewell, S., Vansteelandt, S., VanderWeele, T. J., MacKinnon, D. P., Mansell, G., Collins, G. S., Golub, R. M., McAuley, J. H., Localio, A. R., van Amelsvoort, L., Guallar, E., Rijnhart, J., Goldsmith, K., Fairchild, A. J., Lewis, C. C., Kamper, S. J., … Henschke, N. (2021). A Guideline for Reporting Mediation Analyses of Randomized Trials and Observational Studies. JAMA, 326(11), 1045. https://doi.org/10.1001/jama.2021.14075
32. Liu, J., Tang, J., Zhou, B., & Liang, Z. (2018). The Effect of Governance Quality on Economic Growth: Based on China’s Provincial Panel Data. Economies, 6(4), 56. https://doi.org/10.3390/economies6040056
33. Lund, J. (2016). The surprising importance of governance in the logistic performance of nations. LinkedIn. https://www.linkedin.com/pulse/surprising-importance-governance-logistic-performance-john-lund/
34. Magazzino, C., Alola, A. A., & Schneider, N. (2021). The trilemma of innovation, logistics performance, and environmental quality in 25 topmost logistics countries: A quantile regression evidence. Journal of Cleaner Production, 322, 129050. https://doi.org/10.1016/j.jclepro.2021.129050
35. Manja, L., Silumbu, E., & Chaweza, L. (2022). Determinants of demand for international reserves in the SADC region. African Review of Economics and Finance, (ISSN 2042.
36. Mkandawire, T. (2007). ‘Good governance’: the itinerary of an idea. Development in Practice, 17(4–5), 679–681. https://doi.org/10.1080/09614520701469997
37. Mo Ibrahim Foundation. (2020). 2020 Ibrahim Index of African Governance. Mo Ibrahim Foundation. https://mo.ibrahim.foundation/iiag
38. Munim, Z. H., & Schramm, H.-J. (2018). The impacts of port infrastructure and logistics performance on economic growth: the mediating role of seaborne trade. Journal of Shipping and Trade, 3(1), 1. https://doi.org/10.1186/s41072-018-0027-0
39. Naa Amua Dodoo, R., Appiah, M., & Tetteh Donkoh, D. (2020). Examining the factors that influence firm performance in Ghana: a GMM and OLS approach. National Accounting Review, 2(3), 309–323. https://doi.org/10.3934/NAR.2020018
40. Nica, E., Poliak, M., Alpopi, C., Kliestik, T., Manole, C., & Burlacu, S. (2023). Impact of Trade, FDI, and Urbanization on Female Employment System in SAARC: GMM and Quantile Regression Approach. Systems, 11(3), 137. https://doi.org/10.3390/systems11030137
41. Pan, W.-T., Jiang, B., Wang, Y., Cai, Y., & Ji, X. (2022). Comparison and Suggestions of Logistics Performance Index of Main Countries of Belt and Road Strategy Based on Bootstrap DEA Model. Computational Intelligence and Neuroscience, 2022, 2159578. https://doi.org/10.1155/2022/2159578
42. Pasko, O., Chen, F., & Yao, X. (2020). The relationship between corporate governance and financial performance in ?hinese logistics-related listed companies. Management and Entrepreneurship: Trends of Development, 4(14), 8–22. https://doi.org/10.26661/2522-1566/2020-4/14-01
43. Roodman, D. (2007). A Note on the Theme of Too Many Instruments (125).
44. Roodman, D. (2009a). A Note on the Theme of Too Many Instruments *. Oxford Bulletin of Economics and Statistics, 71(1), 135–158. https://doi.org/10.1111/j.1468-0084.2008.00542.x
45. Roodman, D. (2009b). How to do Xtabond2: An Introduction to Difference and System GMM in Stata. The Stata Journal: Promoting Communications on Statistics and Stata, 9(1), 86–136. https://doi.org/10.1177/1536867X0900900106
46. Samarasinghe, T. (2019). The relationship between institutions and economic development. MPRA Paper No. 97755.
47. Sergi, B. S., D’Aleo, V., Konecka, S., Szopik-Depczy?ska, K., Dembi?ska, I., & Ioppolo, G. (2021). Competitiveness and the Logistics Performance Index: The ANOVA method application for Africa, Asia, and the EU regions. Sustainable Cities and Society, 69, 102845. https://doi.org/10.1016/j.scs.2021.102845
48. Sezer, S., & Abasiz, T. (2017). The Impact of Logistics Industry on Economic Growth: An Application in OECD Countries. Eurasian Journal of Social Sciences, 5(1), 11–23. https://doi.org/10.15604/ejss.2017.05.01.002
49. Shao, S., Yang, Z., & Si, W. (2023). An analysis of logistics financing prospects in Singapore. SHS Web of Conferences, 163, 03032. https://doi.org/10.1051/shsconf/202316303032
50. Suki, N. M., Suki, N. M., Sharif, A., & Afshan, S. (2021). The role of logistics performance for sustainable development in top Asian countries: Evidence from advance panel estimations. Sustainable Development, 29(4), 595–606. https://doi.org/10.1002/sd.2160
51. Tawiah, V. (2023). The Effect of IPSAS Adoption on Governance Quality: Evidence from Developing and Developed Countries. Public Organization Review, 23(1), 305–324. https://doi.org/10.1007/s11115-022-00625-w
52. USAID|Deliver Project, T. O. 1. (2009). No Title. The Logistics Handbook: A Practical Guide for Supply Chain Managers in Family Planning and Health Programs.
53. Uyar, A., Fernandes, V., & Kuzey, C. (2021). The mediating role of corporate governance between public governance and logistics performance: International evidence. Transport Policy, 109, 37–47. https://doi.org/10.1016/j.tranpol.2021.05.023
54. Wong, W. P., & Tang, C. F. (2018). The major determinants of logistic performance in a global perspective: evidence from panel data analysis. International Journal of Logistics Research and Applications, 21(4), 431–443. https://doi.org/10.1080/13675567.2018.1438377
55. World Bank. (2019). Aggregated LPI 2012-2018. https://lpi.worldbank.org/
56. World Bank. (2020). Urban population (% of the total population). World Bank. https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS?view=chart
57. XiaofeI, L., & Fen, C. (2021). ImpactofLogisticsDevelopmentonEconomicGrowth:An EmpiricalResearchfromGuangdongProvinceinChina. Complexity, 2021, 12. https://doi.org/https://doi.org/10.1155/2021/9950935