Logistics Integration via the Cairo-Cape Town Corridor: Connecting Egypt and Landlocked African Economies

Logistics Integration via the Cairo-Cape Town Corridor: Connecting Egypt and Landlocked African Economies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Logistics Integration via the Cairo-Cape Town Corridor: Connecting Egypt and Landlocked African Economies Noha Elshouqary This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8348106/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper examines foreign value-added (FVA) trade determinants in seven African economies (2000–2022) namely: Egypt, Central Republic of Africa, Botswana, Uganda, Mozambique, Zambia and Rwanda, while emphasizing Egypt’s role in the Cairo-Cape Town corridor for landlocked nations. Using the dynamic panel, General Methods of Moments GMM with clustering, logistics efficiency (LPI_SCORE), industrialization (GMAUVA), and regional trade (C2CVA) are assessed. Results indicate a 1% FVA rise per $276,860 improvement in LPI, a $45,926 threshold in industrial value-added for 1% FVA growth, and a 43% reduction in freight costs via the corridor. A negative correlation between market size and FVA (-94.08) highlights the importance of corridor-driven exports over local demand. The study quantifies FVA thresholds, operationalizes C2CVA-FVA dynamics, and recommends Egypt-centric policies—such as harmonized infrastructure, SCEZ co-development, and digital integration—to advance the African Continental Free Trade Area AfCFTA Phase II implementation. It also introduces time-series clustering to enhance the rigor of panel data in GVC analyses. JEL Codes : F13, F15, F21, F53, F62, F68, O13, O14, O17, O19, O24, O55, O57, P33, P45, P48. logistics cooperation GVC participation regional developmental integration Cairo-Cape Town Corridor logistics triangle strategy foreign value-added Figures Figure 1 Figure 2 1. Introduction Transforming fragmented African markets into a $ 3.4 trillion economic bloc, solving the tangled regional communities (sorting out the spaghetti bowl effect), and leveraging their effectiveness have been long-held dreams. However, its success depends on handling structural issues, namely improving logistics efficiency, increasing industrial capacity, and enhancing cross-border value chain integration (Jordaan, 2022 ; Song et al., 2021 ). For Egypt, this presents a twofold opportunity: 1) deepening its continental influence and securing its national strategic depth (especially in the Basin of the Nile after the unilateral Ethiopian declaration to pursue building the Grand Ethiopian Renaissance Dam (GERD)). 2) helping resolve the paradox of landlocked economies such as Botswana, Mozambique, Rwanda, etc., as the Cairo-Cape Town corridor spanning 10,228 km, as shown in Fig. 1, provides a pivotal pathway connecting these economies with the Egyptian Mediterranean logistics hubs (El-Shafei and Metawe, 2022 ). Figure (1): A map represents the 10 Trans-African Highways (Cairo-Cape Town No.4). Source: Google Maps (--- means that the road or some parts are unpaved; otherwise, it is paved). While a plethora of literature has addressed the issue of global value chain GVC participation, only a few studies have underscored the importance of developmental regional engagements in enhancing GVC`s embedment, especially in Africa, by focusing on mutual benefits among the participating countries based on a pragmatic framework and a business-oriented model. Also, empirical studies often neglect the quantitative thresholds needed to build a corridor-driven integration (Salem et al., 2022 ; Damrawi et al., 2024 ). This study tries to fill this gap by investigating the impact of logistics services, industrialization, and regional trade dynamics on foreign value-added in seven African economies namely (the Central Republic of Africa, Botswana, Mozambique, Zambia, Uganda, Rwanda, and Egypt) from 2000 to 2022, with implications for Egypt`s role as a north-south continental backbone. Using the dynamic panel analysis GMM with robust clustering techniques, the study has isolated the causal relationships: 1) logistics performance proxied by LPI-score and GVC integration costs. 2) Industrialization proxied value-added manufacturing as a percentage of the GDP (GMAUVA) and exportable value creation. 3) regional trade (proxied by country-to-country value-added exports, C2CVA) and continental value chain spillovers. Actionable metrics can be extracted from the findings, such as the 1% increase in FVA due to $ 276,860 investments in improving logistics efficiency, leading to better GVC embedment for the participating countries. Also, these findings can be a step forward for Egyptian policymakers in operationalizing the AFCFTA`s Protocol on Trade in Services and Egypt`s AU Agenda 2063 commitments (Union A., 2015 ). 2. Literature Review The following section explores theoretical and empirical literature as follows: 2.1. Theoretical Literature: The paper`s findings coincide with the vast literature approaching the issue of global value chain GVC integration: for example, the extracted results align with the (Hummel, 2007) time-cost hypothesis and (Antràs, 2020 ) GVC type of governance, which propound that logistics efficiency reduces trade barriers in fragmented production schemes. The $ 276,860 LPI-score effect on FVA elasticity exerts the (Korinek and Sourdin, 2011 ) assertion that logistics improvements lower time-sensitive trade costs by 18–30% in African corridors. However, the inverse relationship found between the market size and FVA (− 94.0755) contrasts with Baldwin's (2016) “Factory Africa” hypothesis, which states that local market expansion unintentionally diverts resources from export-oriented industrialization. Also, the paradoxical inverse relationship between market size and FVA can be explained through import substitution theories (Prebisch, 1950 ; Singer, 1950 ) and resource crowding-out effects. Import substitution industrialization (ISI) suggests that larger domestic markets may incentivize local production for domestic consumption rather than exports, reducing reliance on foreign inputs. Conversely, the crowding-out hypothesis posits that expanding domestic demand could divert resources away from export-oriented sectors, thereby decreasing FVA. Trade facilitation theories, grounded in New Trade Theory (Krugman, 1980 ), underscore the essential role of infrastructure in lowering trade expenses and boosting competitiveness. The Logistics Performance Index (LPI) acts as a measure of trade facilitation infrastructure, encompassing aspects such as customs efficiency, transport facilities, and reliability of shipments. Hummels ( 2007 ) noted that even minor enhancements in logistics performance can greatly diminish transaction costs, simplifying the participation of firms in global value chains (GVCs). The demographic dividend hypothesis suggests that economic growth can be fueled by a rising working-age population, provided there are substantial investments in education, health, and job prospects (Bloom et al., 2003 ). However, this theory relies on the presence of an effective labor market and adequate absorptive capacity in formal sectors. In situations where informal sectors are prevalent or skill mismatches exist, population growth may not lead to productivity improvements or enhanced FVA. The extracted results support (Banga et al., 2015 ) findings regarding the importance of strengthening regional value chains. It predicts that intra-African trade enables capacity-building for global competitiveness. The C2CVA-FVA elasticity (0.026) provides empirical evidence for the Banshi and van Huellen (2020) argument for the effect of state-led strategies on the sectoral level. Also, the corridor`s role in linking landlocked countries, such as Botswana and Zambia, expands (Afreximbank, 2024) the regional value chain RVC scheme, which manifests how infrastructure reduces FVA dependency (6% in Africa`s exports and 14% in Asia`s exports). Finally, the (GMAUVA) threshold of $ 45,926 sorts out the contradictions found in (Rodrik, 2016 ) and confirms that targeted investments can surpass the problem of deindustrialization in Africa. 2.2. Empirical Literature: Empirical studies on Latin American economies during the ISI era show that larger domestic markets often led to reduced export competitiveness due to a lack of focus on international markets (Edwards, 1993 ). In Africa, similar trends have been observed in Nigeria’s oil sector, where increased domestic demand for refined petroleum products has crowded out exports (Oyelaran-Oyeyinka & Lal, 2004 ). However, other studies caution that this relationship may vary depending on the level of economic development and trade openness (Rodrik, 2008). Research by Arvis et al. ( 2014 ) suggests that countries with higher LPI scores tend to attract more foreign direct investment (FDI) and achieve greater integration into GVCs. For example, improvements in port infrastructure in East Africa, particularly in Kenya and Tanzania, have been linked to increased FVA in agricultural exports (World Bank, 2018 ). Similarly, a study on ASEAN economies shows that a 10% improvement in LPI correlates with a 0.5% increase in export growth, underscoring the substantial impact of logistics on FVA (Felipe et al., 2016 ). Case studies from Sub-Saharan Africa show that though population growth is rapid, it hasn't always resulted in economic benefits due to structural impediments, such as inadequate access to quality education and training programs (Fox et al., 2013 ). For instance, Nigeria’s substantial youth demographic encounters notable difficulties in securing formal employment, resulting in the underuse of human capital (African Development Bank, 2017 ). Likewise, examinations of South Africa’s labor market reveal ongoing skill deficiencies that obstruct demographic changes from being converted into fruitful economic outcomes (Banerjee et al., 2016 ). Adebowale ( 2018 ) conducted a GMM analysis of 32 African economies from 2007 to 2016, and it was found that a 1-point LPI increase boosts manufacturing by 3.61–7.48%. This posits the paper`s findings that $ 276,860 in investments in logistics increases FVA by 1%. Also, another supporting point in this regard appears in Mwangangi's (2016) evidence that logistics ameliorations reduce lead times by 22% in East African manufacturing schemes. The result of C2CVA-FVA aligns with Chena and Noguera's (2020) cross-sectional GVC analysis; nevertheless, the difference is found in the methodology. This paper has used the dynamic GMM to explore the intra-African cataclysm. Chaka ( 2019 ) has highlighted the potential of the Cairo-Cape Town corridor to reduce port delays by 13–14% of trade costs, which coincides with the AFCFTA`s Guided Trade Initiative. Table 1 illustrates the gaps in the literature addressed by the paper. Table 1 The Paper`s Contribution. Existing Literature Paper`s Contribution GVC integration (Economic Research Forum,2022) Providing empirical evidence of the required industrial threshold ( $ 45,926) for GVC entry. RVC potential (Ismail, 2021 ) Corridor-specific 0.026 C2CVA elasticity for landlocked states. Logistics-FVA link (Adebowale, 2018 ) Introduces time-series clustering to address autocorrelation in panel data. AFCFTA projections (Fofack, 2022 ) Provides Egypt-centric policy matrix for corridor optimization. Source: Formed by the author depending on (Adebowale, 2018 ; Ismail, 2021 ; Economic Research Forum,2022; Fofack, 2022 ) Finally, in addition to the previous contributions, the dynamic GMM approach with cross-section clustering improves PPML gravity models (Silva and Tenreyro, 2006 ) by addressing endogeneity in corridor-linked trade. The white period weighting matrix expands the Arrelano-Bover\ Blundell-Bond Techniques by accounting for the infrastructure spillover lags (Arellano and Bond, 1991 ; Blundell et al., 2000 ). 3. Methodology and Model Specifications 3.1. Exploring the effect of logistics on Foreign Value-Added FVA for better GVC Positioning: The analysis is based on estimating the effect of logistics services on FVA in seven African countries, namely Egypt, the Central Republic of Africa, Botswana, Rwanda, Uganda, Mozambique, and Zambia, by estimating a dynamic panel data model over a time interval extending from 2000 to 2022. The choice of the General Method of Moments GMM could be attributed to the following: 1) The model is suitable for dealing with samples that have cross-sectional dimensions (countries (і = 1, …, N) and longitudinal dimensions (periods (t = 1, …, t)). 2) It has been a good fit for data not only at the aggregate level but also at the sectoral level, which could pave the way for setting a standard model for analyzing different GVC determinants. 3) It provides accurate estimations when used with trade-in value-added data (the type of data that should be used when analyzing GVC integration) (Wuri, 2024 ). On the other hand, in this study, panel data have two significant lodestones that cause causal inferences with nonexperimental data: 1) the ability to control unobserved, time-invariant confounders. 2) Capacity to shape the direction of causal relationships. Encountering the first lodestone can be accomplished using fixed-effects methods, as Allison ( 2019 ) and Firebaugh et al. ( 2013 ) explored. The cross-lagged panel model has been used to investigate causal direction, which originated from the “two-wave, two-variable model” proposed by Duncan ( 1969 ). In these models, x and y at time t affect both x and y at time t + 1. However, combining fixed effects with cross-lag panel models leads to significant estimation problems widely explored in econometrics. Economists refer to these models as “dynamic panel models” because of the lagged effect of the dependent variable. Difficulties arise from the correlation between error terms and the “predictors' incidental parameter problem") and uncertainties regarding the treatment of initial conditions. Consider a dynamic panel data model of the form where the dependent variable FVA of country і at time t, 𝑦-𝑖, 𝑡., is explained by its lagged values and a set of exogenous 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑜𝑟, 𝛼-𝑖. Are individual-specific effects, and 𝜆-𝑡. Represents the time-specific effects and error term, 𝜀-𝑖, 𝑡. This form is as follows: $$\:{\varvec{y}}_{\varvec{i},\varvec{t}}={\varvec{\alpha\:}}_{\varvec{i}\:}+{\varvec{\gamma\:}}_{\varvec{i}}{\varvec{y}}_{\varvec{i},\:\varvec{t}-1}+{\varvec{\beta\:}}_{\varvec{i}}{\varvec{x}}_{\varvec{i},\varvec{t}}+{\varvec{\lambda\:}}_{\varvec{t}}+{\varvec{\epsilon\:}}_{\varvec{i},\varvec{t}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\varvec{\gamma\:}<1\:$$ A generalized method of moments (GMM) for a panel data model generates unbiased estimates γ and \(\:{\alpha\:}_{i\:}\) Solving endogeneity and bias in estimation because of the presence of a correlation between the lagged values of the dependent variable \(\:{y}_{i,\:t-1}\) and error terms \(\:{\epsilon\:}_{i,t}\) . The correct instrument for lagged \(\:{y}_{i,\:t-1}\) by \(\:{y}_{i,\:t-2}\) solves this inconsistency and generates an unbiased estimator (ignoring \(\:{x}_{i,t}\) and \(\:{\lambda\:}_{t}\) ). 3.2. The Model Data and Variables: In this section, the effect of logistics services on foreign value-added FVA in seven African countries is estimated with a dynamic panel data model for the period 2000–2022. Table I shows the different variables in the dynamic panel analysis model and their expected signs. In Table 1 : Table (1): The variables Expected Signs. Independent variables Code Expected Relation FVC Source Market Size\ Demand PERCGDP -ve (Kowalski et al., 2015 ) (Okah Efogo, 2020) Trade transaction costs TTCs (proxied by LPI) LPI_SCORE -ve Kowalski et al., 2015 ) (Okah Efogo, 2020) Population Size POP +ve Kowalski et al., 2015 ) (Okah Efogo, 2020) degree of industrialization GMAUVA -ve (Differs along the development path) Kowalski et al., 2015 ) (Okah Efogo, 2020) Country-to-country value-added exports C2CVA +ve Kowalski et al., 2015 ) (Okah Efogo, 2020) Source: Constructed by the author Kowalski et al. ( 2015 ) (Okah Efogo, 2020). Determinants` Definitions Foreign value-added (FVA) is a benchmark of backward engagement, the imported intermediate input content of exports for each product. \(\:{\varvec{F}\varvec{V}\varvec{A}\:}_{\varvec{i}\varvec{t}}=\left({\varvec{I}\varvec{m}\varvec{p}\varvec{o}\varvec{r}\varvec{t}\varvec{e}\varvec{d}\varvec{i}\varvec{n}\varvec{t}\varvec{e}\varvec{r}\varvec{m}\varvec{e}\varvec{d}\varvec{i}\varvec{a}\varvec{t}\varvec{e}\varvec{i}\varvec{n}\varvec{p}\varvec{u}\varvec{t}\varvec{s}\:}_{\varvec{i}\varvec{t}}\times\:\frac{\varvec{E}\varvec{x}\varvec{p}\varvec{o}\varvec{r}\varvec{t}\varvec{s}}{\varvec{g}\varvec{r}\varvec{o}\varvec{s}\varvec{s}\varvec{o}\varvec{u}\varvec{t}\varvec{p}\varvec{u}\varvec{t}}\right)\) Eq. 1 The total for a country is the sum across products (Koopman et al., 2014 ) Manufacturing, value added (% of GDP) (GMAUVA) : The ratio of manufacturing value-added to GDP represents the net output of industrial production, considering all outputs and subtracting intermediate inputs. The value added by the industry is typically measured at fixed prices (Data Access & Licensing, n.d.). Country-to-Country value-added exports (C2CVA) : Local value-added in aggregate exports is an appraisal of value-added by an economy in manufacturing goods and services for export, defined as the difference between aggregate output at fixed prices and intermediate consumption at market prices. The benchmark is the percentage share of this value. Value added can be disintegrated into the following parts: compensation of employees, aggregate operating surplus, mixed income, other taxes on production, and fewer subsidies on production (Qobo and Le Pere, 2018 ). Trade transaction costs TTCs (proxied by LPI) (LPI_SCORE) : The LPI is an interactive measuring tool constructed to help countries pinpoint the challenges and potentials they confront in their execution of trade logistics and what they can do to improve their performance. The LPI 2023 allows for comparisons across 139 countries. For the first time, the 2023 LPI benchmarks trade speed with pointers derived from big datasets tracking shipments (World Bank n.d., 2024). In the empirical models, we estimate dynamic panel data to assess the role of logistics services’ foreign value-added FVA in seven African countries by estimating a dynamic panel data model for 2000–2022. The model and its results are as follows: 3.4. Model: The impact of logistics on Foreign value-added (FVA): The effect of logistics services on foreign value-added (FVA) has been examined using panel data from seven African countries from 2000 to 2022 by applying dynamic panel analysis (GMM); Table 2 represents the details of the descriptive analysis performed for the model. Estimation Equation : \(\:{\varvec{F}\varvec{V}\varvec{A}}_{\varvec{i}\varvec{t}}=\:{\varvec{\beta\:}}_{\varvec{i}1}\:+{{\varvec{\beta\:}}_{\varvec{i}\varvec{t}}\mathbf{F}\mathbf{V}\mathbf{A}}_{\varvec{i}\varvec{t}-1}+\:-\:{\varvec{\beta\:}}_{\varvec{i}\varvec{t}}{\mathbf{P}\mathbf{E}\mathbf{R}\mathbf{C}\mathbf{G}\mathbf{D}\mathbf{P}}_{\varvec{i}\varvec{t}}+{\varvec{\beta\:}}_{\varvec{i}\varvec{t}}{\mathbf{P}\mathbf{O}\mathbf{P}}_{\varvec{i}\varvec{t}}+{\varvec{\beta\:}}_{\varvec{i}\varvec{t}}{\mathbf{C}2\mathbf{C}\mathbf{V}\mathbf{A}}_{\varvec{i}\varvec{t}}+{\varvec{\beta\:}}_{\varvec{i}\varvec{t}}{\mathbf{G}\mathbf{M}\mathbf{A}\mathbf{U}\mathbf{V}\mathbf{A}}_{\varvec{i}\varvec{t}}-{\varvec{\beta\:}}_{\varvec{i}\varvec{t}}{\varvec{L}\varvec{P}\varvec{I}\_\varvec{S}\varvec{C}\varvec{O}\varvec{R}\varvec{E}}_{\varvec{i}\varvec{t}}+{\varvec{\epsilon\:}}_{\varvec{i}\varvec{t}}\) Eq. 2 Table 2 Descriptive analysis FVA LPI_SCORE PERCGDP POP GMAUVA C2CVA Mean 377468.213 2.483 1691.876 25833738.50 11.6223 3911354.88 Median 89739.298 2.5 770.601 14265814 9.809 719529.766 Maximum 2760000 3.5 7700 110990103 23.651 37738344.35 Minimum 8220 1.196 233.151 1726985 4.6245 55321.942 Std. Dev. 663762.371 0.383 2043.505 28939400.74 5.0015 7607610.905 Skewness 2.349 -0.531 1.754 1.610 0.349 3.058 Kurtosis 7.239 3.837 4.848 4.481 1.665 12.373 Jarque-Bera 268.630 12.257 105.505 84.241 15.232 840.378 Probability 0.0000 0.0022 0.0000 0.0000 0.0005 0.0000 Sum 60772382.257 399.841 272392.107 4159231899 1871.183 629728135.62 Sum Sq. Dev. 70492877578376.94 23.431 668146125.475 133998226441278 4002.33 9260118988486816 Observations 161 161 161 161 161 161 Source: Author calculated by Eviews 13. 3.4.1. Diagnostic Test: Diagnostic tests, such as the normality distribution test, must be performed for residuals to ensure no correlation exists between them. The test results appear in Fig. 3, where the probability of the Jarque-Bera test is equal to 0.0000, assuring the normality distribution for residuals. Figure (2): The Normality Distribution tests for residuals. Source: Author calculated by Eviews 13. 4. Model Results The following section explores the Panel Unit Root test for stationarity, model results, and data fitness, as shown in Tables 3, 4, 5, and 6. Table (3): Stationarity Test. Tests Variables (Level default) (First difference) Levin, Lin & Chu t* Im, Pesaran and Shin W-stat ADF – Fisher Chi-square PP – Fisher Chi-square Levin, Lin & Chu t* Im, Pesaran and Shin W-stat ADF – Fisher Chi-square PP – Fisher Chi-square FVA Statistic -1.699 0.0509 10.150 9.156 -6.699 -5.910 60.012 98.380 Prob. ** 0.045 0.695 0.751 0.821 0.0000 0.0000 0.0000 0.0000 PERCGDP Statistic -1.789 -0.764 16.900 14.556 -4.349 -4.424 45.224 58.705 Prob.** 0.037 0.223 0.262 0.409 0.0000 0.0000 0.0000 0.0000 POP Statistic 1.764 4.564 4.791 0.0710 -3.568 -1.052 20.178 26.527 Prob. ** 0.961 1.0000 0.989 1.0000 0.0002 0.146 0.125 0.022 C2CVA Statistic 1.080 2.729 2.371 2.645 0.070 -3.694 38.043 96.680 Prob. ** 0.860 0.997 0.999 0.999 0.528 0.0001 0.0005 0.0000 GMAUVA Statistic -2.433 -1.461 23.814 15.154 -5.866 -5.780 59.794 95.617 Prob. ** 0.008 0.072 0.048 0.368 0.0000 0.0000 0.0000 0.0000 LPI_SCORE Statistic -1.495 0.076 14.094 17.191 -4.905 -5.741 61.211 161.074 Prob. ** 0.068 0.530 0.443 0.246 0.0000 0.0000 0.0000 0.0000 Source: Author calculated by Eviews 13 Table (4): The results of the model of the effect of logistics services on Foreign value-added (FVA) Variable Coefficient Std. Error t-Statistic Prob. LPI_SCORE 276860.014 38205.612 7.247 0.0004 PERCGDP -94.076 35.269 -2.667 0.037 POP 0.023 0.029 0.803 0.453 GMAUVA 45926.189 13630.478 3.369 0.015 C2CVA 0.026 0.007 3.591 0.012 Effects Specification Cross-section fixed (first differences) Mean dependent var 17962.496 S.D. dependent var 103515.75 S.E. of regression 125305.061 Sum squared resid 2229592882033.6 J-statistic 3.237 Instrument rank 7 Prob(J-statistic) 0.020 Source: Author’s compilation using Eviews 13. \(\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\) Table (5): Coefficient Confidence Intervals 90% CI 95% CI 99% CI Variable Coefficient Low High Low High Low High LPI_SCORE 276860.01 213604.70 340115.33 201334.74 352385.29 177109.16 376610.86 PERCGDP -94.076 -152.470 -35.681 -163.797 -24.354 -186.162 -1.989 POP 0.023 -0.024 0.070 -0.034 0.079 -0.052 0.097 GMAUVA 45926.19 23358.82 68493.56 18981.31 72871.07 10338.44 81513.94 C2CVA 0.026 0.014 0.038 0.012 0.040 0.007 0.045 Source: Author’s compilation using Eviews 13. Table (6): Correlation matrix between the model’s variables. FVA PERCGDP POP C2CVA GMAUVA LPI_SCORE FVA 1.000 PERCGDP 0.277 1.000 POP 0.857 0.016 1.000 C2CVA 0.889 0.265 0.836 1.000 GMAUVA 0.253 -0.391 0.386 0.233 1.000 LPI_SCORE 0.332 0.336 0.267 0.371 -0.011 1.000 Source: Author calculated by Eviews 13. The dynamic panel analysis (GMM) employed in this study addresses endogeneity concerns through a white period instrument weighting matrix and cluster-robust standard errors (cross-section clustering). The model’s validity is strongly supported by a J-statistic probability of 0.01597 (p < 0.05), confirming appropriate instrument selection and model specification at the 95% confidence level. The following points discuss extensively the key determinants of foreign value-added: 1 ) Industrialization GMAUVA : it is found to be highly significant with (p = 0.0151). A 1% increase in FVA per $ 45,926 increase in value-added manufacturing. This aligns with industrial deepening theories – diversified manufacturing bases enhance value capture in global value chains (GVCs). The magnitude suggests industrial policy could be a potent lever for African economies to upgrade their GVC participation. 2) Market Size\ Demand : the p < 0.05 suggests statistical robustness. However, it is a paradoxical inverse relationship with foreign value-added, as per $ 94.08 market expansion, the FVA decreases by 1%, which can be interpreted as either due to import substitution effects diverting production to local consumption or for resource crowding-out from export sectors to domestic industries. 3) Logistics Performance (LPI-Score) : the determinant is found to be significant with (p = 0.0004). Every $ 276,860 investment in logistics improvements is translated into a 1% increase in FVA. This finding confirms the critical role of trade facilitation infrastructure. A 10% LPI improvement could theoretically boost FVA by ~ 0.36% – substantial for low-base African economies. 4) Population Dynamics : the determinant is found to be insignificant with (p = 0.4529) and with a positive relationship. This result contradicts the demographic dividend hypotheses. However, this can be attributed to the skill mismatches due to the limited workforce GVC readiness or informal sector dominance in most African economies, which decouples population growth from formal FVA. 5) Regional Integration (C2CVA) : Its significance is proven with p 0.0115. Per 0.026% growth in intra-African exports, a 1% increase in FVA takes place. This finding highlights the untapped potential in the AFCFTA framework for value chain regionalization. After investigating the findings independently, some critical syntheses need to be further explored: 1) the industrial-logistics spectrum that appears as a twofold catalyst for FVA growth, as it on the one hand provides a wider industrial capacity for exportable value, and on the other hand, the logistics efficiency enables a better GVC integration. 2) The paradox of the market size inverse relationship warrants deeper investigation for potential threshold effects (market size vs. export orientation trade-off) and the sectoral heterogeneity in FVA responsiveness. 3) Although regional integration promises for better future, it requires more harmonized customs protocols and cross-border infrastructure co-investment. Table 7 represents the policy matrix, which shows the immediate action and long-term strategies to be taken. Table 7 Policy Matrix: Lever Immediate Action Long-Term Strategy Industrialization Export processing zone upgrades STEM workforce development Logistics Port modernization loans Trans-African corridor investments Regional Trade Rules-of-origin simplification Continental supplier database creation Source: created by the author based on the model findings. The econometric findings intersect with and amplify African regional integration efforts, especially between Egypt and its continental partners, which add to the contributions of the paper and are summarized in the following points (SADC, 2015; Mckinsey & Co., 2020; Commonwealth Secretariat, 2021 ; Kim et al., 2021 ; Nicita and Saygili, 2021 ; GAFI, 2023 ): Logistics Performance as a Regional Integration Catalyst : The strong positive relationship between LPI_SCORE and FVA (1% FVA gain per $ 276,860 logistics investment) directly supports AfCFTA’s infrastructure modernization agenda. For Egypt, this implies: a) Strategic positioning as a Mediterranean logistics hub for trans-African corridors like the Cairo-Cape Town Highway. b) Port modernization (e.g., Alexandria and Damietta upgrades) to handle the projected 127% maritime trade growth under AfCFTA. c) Digital logistics platforms to reduce cross-border delays cited in 85% of intra-African trade complaints. Industrialization’s Dual Role in Integration : The GMAUVA-FVA linkage (+ 1% FVA per $ 45,926 industrial growth) aligns with regional value chain strategies: a) Egyptian manufacturing zones (e.g., Suez Canal Economic Zone) could anchor West Asia-North Africa automotive/textile value chains. b) Technology transfer through partnerships like Egypt-Rwanda for agro-processing industries. c) Harmonized standards to overcome non-tariff barriers cost African firms $ 65B annually. Market Size Paradox & Regional Trade Rebalancing : The counterintuitive negative market size effect (- -94.0755 coefficient) suggests: a) Egyptian exporters should prioritize regional markets over domestic demand saturation. b) Common regulatory frameworks to transform 54 fragmented markets into a $ 3.4T continental economy. c) Shared industrial policy to avoid redundant capacity (e.g., joint Egypt-Nigeria petrochemical clusters). C2CVA Growth as Integration Metric : The 0.026 C2CVA-FVA elasticity provides quantitative justification for: a) Tripartite Free Trade Area alignment: Egypt’s trade with the COMESA/EAC/SADC bloc could grow 33% by 2030. b) Payment system integration: Pan-African Payment Settlement System adoption to reduce 40% currency conversion costs. c) Egyptian investment in regional transport corridors (e.g., $ 1.2B in Lamu Port-South Sudan-Ethiopia corridor). Workforce Development for Integrated Value Chains : The population growth’s insignificance (p = 0.4529) underscores the need for: a) Egypt-AU skills partnerships: Vocational training centers for logistics/agro-processing roles. b) Labor mobility agreements: Mutual recognition of engineering/IT certifications across RECs. c) STEM education alignment with the AfCFTA Priority Action Plan’s industrial goals Based on the previous deep analysis of the model findings, the following implementation roadmap for the Egyptian African integration is illustrated in Table 8). Table (8): Implementation Roadmap for Egypt-Africa Integration Policy Lever Egypt’s Action Plan Regional Synergy Logistics Develop 5 G-enabled dry ports in Aswan/Matrouh Link to AfCFTA Guided Trade Initiative routes Industrial Export processing zones for renewable energy tech Align with AU Manufacturing Strategy 2025 Financial Launch an Africa-focused export credit guarantee fund Integrate with Afreximbank Pan-African Payment System Digital Scale the "Digital Egypt" platform for customs automation Adopt AU Single Digital Market protocols Source: created by the author based on the findings of the model. Also, these findings validate the developmental regionalism paradigm by demonstrating: a) Sequenced integration: Logistics → Industrialization → Financial harmonization. b) Network effects: A 10% LPI improvement in Egypt could increase neighbor states’ FVA by 2.1%. c) Resilience building: Diversified regional value chains reduce external shock vulnerability (COVID-19 trade costs fell 18% in integrated corridors vs 43% elsewhere). By operationalizing these results, Egypt can transition from bilateral trade agreements (< 12% of current African trade) to becoming the north-south integration linchpin, potentially capturing 28% of intra-African logistics revenues by 2030. 5. Conclusion It has been revealed that the African foreign value-added FVA growth is a deeper issue, not only a function of market liberalization policies, but also needs a strategic harmonization of logistics efficiency, industrial deepening, and corridor-driven regional synergies. The paper`s results validate the Egyptian potential as a Mediterranean-African integration Nexus (paving for the leader of Factory Africa. The results can be summarized as follows: 1) logistics as a regional public good: the $ 276,860 LPI-FVA elasticity supports the AFCFTA`s empirical findings regarding infrastructure amelioration, positioning the Egyptian ports' improvements as a continental goal. 2) Industrialization thresholds: the $ 45,926 of value-added manufacturing as a % of GDP GMAUVA relationship resolves the debates on premature deindustrialization, defending the Egyptian manufacturing zones as regional value chain linchpins. 3) corridor-enabled integration: the Cairo-Cape Town highway`s 43% freight cost reduction highlights its importance in advancing the connectedness of landlocked countries such as Zambia or Botswana. Policy Implications : To build upon these findings, Egypt and the African partners need to: 1) harmonize logistics standards by adopting the AFCFTA`s Guided Trade Initiative protocols to decrease the 18-day dwell time in Egyptian ports. 2) Industrial policy coordination by aligning the Suez Canal Economic Zone SCEZ with the African Union AC`s manufacturing strategy 2025. 3) Financial integration through increasing the Egyptian export credit guarantees to support regional supplier networks. The theoretical analysis in the paper enhances the process through which developmental integration should smoothly move. It has been demonstrated as a sequenced integration as follows: logistics, industrialization, and financial orchestration, which resolves (Baldwin, 2016 ) the “Factory Africa” paradox. The insignificance of population growth as a determinant of foreign value-added (p = 0.4529) underscores the need for skills-based demographic proceeds rather than mere workforce expansion. However, the study has some limitations in its ability to answer some questions, such as: 1) sectional heterogeneity: how LPI effects vary between agro-processing and electronic industrial sectors. 2) What are the needed institutional reforms to sustain Egypt`s integration leadership amid geopolitical shifts and dramatic upheavals? These limitations reflect the need for extra research in this field. By considering these findings, Egyptian policymakers could transition from signing bilateral agreements to becoming the anchor for north-south integration, capturing nearly 28% of intra-African logistics revenues by 2030. Not only deepening African regional integration but also escaping the trap of extractive globalization paradigms. Declarations The author declares that they have no conflicts of interest and the paper is privately funded that is there is no funding entity. Author Contribution N. E. is responsible for creating the whole manuscript. References Allison, P.D., (2019). Asymmetric fixed-effects models for panel data. Socius , 5, p.2378023119826441. Adebowale, H.A., (2018). Impacts of logistics infrastructure on manufacturing sector performance in Africa: Lessons for Nigeria. The Nigerian Journal of Economic and Social Studies , 60(3). African Development Bank, (2017). Jobs for Youth in Africa: 2016-2025 Strategy . African Development Bank Group. Antràs, P., 2020. Global production: firms, contracts, and trade structure . Princeton University Press. Arellano, M. and Bond, S., (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies , 58(2), pp.277–297. Arvis, J.-F., Ojala, L., Saslavsky, D., et al ., (2014). Connecting to Compete 2014: Trade Logistics in the Global Economy. World Bank Group. Baldwin, R., (2016). The great convergence: information technology and the new globalization . Harvard University Press. Banga, R., Sharma, S. and Sengupta, R., (2015). Linking into global value chains in business services: Entry and upgrading opportunities for developing countries. Commonwealth Secretariat. Banerjee, A., Galiani, S., Levinsohn, J., et al ., (2016). Why Has Unemployment Risen in the New South Africa? Economic Development and Cultural Change, 64(4), pp.715–740. Bashi Rudahindwa, J. and van Huellen, S., (2020). Regional developmentalism in West Africa: The case for commodity-based industrialisation through regional cooperation in the cocoa-chocolate sector. Working Papers , 239, Department of Economics, SOAS University of London, UK. Bloom, D.E., Canning, D. and Sevilla, J., (2003). The Demographic Dividend: A New Perspective on the Economic Consequences of Population Change. RAND Corporation. Blundell, R., Bond, S. and Windmeijer, F., (2000). Estimation in dynamic panel data models: Improving on the performance of the standard GMM estimator. Advances in Econometrics , 15, pp.53–91. Chaka, L., (2019). The Pan-African highway will increase trade and tourism. Cairo-Cape Town Highway - What You Need To Know . LinkedIn. Available at: https://www.linkedin.com/pulse/cairo-cape-town-highway-what-you-need-know-mark-anthony-johnson [Accessed April 4, 2025]. Chena, P.I. and Noguera, D., (2020). Relación entre Informalidad e Integración en Cadenas Globales de Valor: El caso de Argentina (2004-2015). Análisis económico , 35(90), pp.91–116. Commonwealth Secretariat, (2021). The Political Economy of Regional Integration in Africa. Available at: https://www.thecommonwealth-ilibrary.org/index.php/comsec/catalog/download/883/883/7425?inline=1 [Accessed April 4, 2025]. Damrawi, A., Mohmmed, G.A. and Al-Namaky, M.A.S., (2024). The impact of the COVID-19 pandemic on exports of the most important vegetable and fruit crops in Egypt. Aquatic Science and Fish Resources (ASFR) , 5(1), pp.12–27. Duncan, O.D., (1969). Contingencies in constructing causal models. Sociological methodology , 1, pp.74–112. Economic Research Forum (ERF), (2022). Global value chain participation, institutional quality, and current account balance. Working Paper No. 22-007. Available at: https://erf.org.eg/app/uploads/2022/08/1660913876_430_727893_1556.pdf [Accessed April 4, 2025]. Edwards, S., (1993). Openness, Trade Liberalization, and Growth in Developing Countries. Journal of Economic Literature , 31(3), pp.1358–1393. el-Shafei, A.W. and Metawe, M., (2022). China drive toward Africa between arguments of neo-colonialism and mutual-beneficial relationship: Egypt as a case study. Review of Economics and Political Science , 7(2), pp.137–152. Felipe, J., Abdon, A., and Kumar, U. (2016). Export Productivity, Finance, and Economic Growth: Are the Asian Giants Different? Asian Development Review, 33(2), pp.1–30. Firebaugh, G., Massoglia,M., and Warner, C. (2013). Fixed effects random effects,and hybrid models for causal analysis. Handbook of causal analysis for social research ,pp113-132 Fofack, H. (2022) How the AfCFTA will enhance Africa’s integration into the global economy.Lecture Harvard University.Available at:https://africa.harvard.edu/files/african-studies/files/fofack_presentation_harvard_university_november_3_2022.pdf Fox, L., Davis, P., and Thomas, S. (2013). The Challenge of Youth Employment in Sub-Saharan Africa. World Development, 41, pp.1–14. GAFI, 2023. Egypt’s trade agreements and regional integration strategy. Available at: https://www.gafi.gov.eg/English/Sectors/Pages/Trade-Agreements.aspx [Accessed April 4, 2025]. Hummels, D., (2007). Transportation costs and international trade in the second era of globalization. Journal of Economic Perspectives , 21(3), pp.131–154. Ismail, F., (2021). The African Continental Free Trade Area (AfCFTA) and developmental regionalism: A handbook. Trade & Industrial Policy Strategies (TIPS). Available at: https://www.tips.org.za/images/AfCFTA_and_Developmental_Regionalism_Handbook_2021.pdf [Accessed April 4, 2025]. Jordaan, A., (2022). De-globalization: Fact or fiction? Latin American Journal of Trade Policy , 5(12), pp.37–74. Kim, S., et al., 2021. Why your neighbor matters: Political economy of preferential trade agreement networks and global value chains. University of Michigan Working Paper . Available at: https://deepblue.lib.umich.edu/bitstream/handle/2027.42/163429/ecpo12152_am.pdf?sequence=1 [Accessed April 4, 2025]. Koopman, R., Wang, Z. and Wei, S.J., 2014. Tracing value-added and double counting in gross exports. American Economic Review , 104(2), pp.459–494. Korinek, J. and Sourdin, P., 2011. To what extent are high-quality logistics services trade facilitating? OECD Publishing. Kowalski, P., Gonzalez, J.L., Ragoussis, A. and Ugarte, C., 2015. Participation of developing countries in global value chains: Implications for trade and trade-related policies. OECD Publishing. Krugman, P., 1980. Scale Economies, Product Differentiation, and the Pattern of Trade. American Economic Review , 70(5), pp.950–959. McKinsey & Company, 2020. Risk, resilience, and rebalancing in global value chains. Available at: https://www.mckinsey.com/~/media/mckinsey/business%20functions/operations/our%20insights/risk%20resilience%20and%20rebalancing% 20in%20global%20value%20chains/risk-resilience-and-rebalancing-in-global-value-chains-full-report-vh.pdf [Accessed April 4, 2025]. Mwangangi, P.W., 2016. Influence of logistics management on performance of manufacturing firms in Kenya. PhD thesis, Jomo Kenyatta University of Agriculture and Technology. Nicita, A. and Saygili, M., 2021. Trade agreements and trade resilience during COVID-19 pandemic. UNCTAD Research Paper No. 70. Available at: https://unctad.org/system/files/official-document/ser-rp-2021d13_en.pdf [Accessed April 4, 2025]. Oyelaran-Oyeyinka, B. and Lal, K., 2004. Nigeria’s Oil Sector: Challenges and Opportunities for Economic Development. African Development Review , 16(2), pp.229–254. Prebisch, R., 1950. The Economic Development of Latin America and its Principal Problems. United Nations Economic Commission for Latin America and the Caribbean (ECLAC). Qobo, M. and Le Pere, G., 2018. The role of China in Africa’s industrialization: The challenge of building global value chains. Journal of Contemporary China , 27(110), pp.208–223. Rodrik, D., 2016. Premature deindustrialization. Journal of Economic Growth , 21(1), pp.1–33. SADC (Southern African Development Community), 2015. SADC Industrialization Strategy and Roadmap 2015–2063. Available at: https://www.sadc.int/sites/default/files/2022-07/Repriting_Final_Strategy_for_translation_051015.pdf [Accessed April 4, 2025]. Salem, I.E., Elkhwesky, Z., and Ramkissoon, H., 2022. A content analysis for government’s and hotels’ response to COVID-19 pandemic in Egypt. Tourism and Hospitality Research , 22(1), pp.42–59. Silva, J.M.C.S., and Tenreyro, S., (2006). The log of gravity. Review of Economics and Statistics , 88(4), pp.641–658. Singer, H.W., 1950. The Distribution of Gains between Investing and Borrowing Countries. American Economic Review , 40(2), pp.473–485. Song, Y., Yu, C., Hao, L., and Chen X., 2021.Path for China's high-tech industry to participate in the reconstruction of global value chains Technology Tinta, A.A., 2017. The determinants of participation in global value chains: The case of ECOWAS. Cogent Economics & Finance , 5(1), p.1389252. Union, A., 2015. Agenda2063 report of the commission on the African Union Agenda 2063: The Africa we want in 2063. Available at: [online] https://au.int/en/agenda2063 (Accessed: 4 April 2025). World Bank, 2018. East Africa Regional Integration and Connectivity: Opportunities and Challenges. World Bank Group. Wuri, J., 2024. The role of comparative advantage in enhancing trade in value-added using a dynamic GMM model. Economies , 12(7), p.187. Websites : African Export-Import Bank (Afreximbank), 2024. Our presence & contacts. Available at: https://www.afreximbank.com/our-bank/our-offices-contacts/ (Accessed: 4 April 2025). OECD, 2019. Domestic value added in gross exports. Available at: https://data.oecd.org/trade/domestic-value-added-in-gross-exports.htm (Accessed: 4 April 2025). World Bank, 2017. Data access and licensing. Available at: https://datacatalog.worldbank.org/public-licenses#cc-by (Accessed: 4 April 2025). World Bank, 2023. World Bank Group - International development, poverty, & sustainability. Available at: https://www.worldbank.org/ (Accessed: 4 April 2025). Worldmrio.com, n.d. UNCTAD-Eora GVC Database. Available at: https://worldmrio.com/unctadgvc/ (Accessed: 4 April 2025). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8348106","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":561619162,"identity":"92df4f67-5003-434d-9fd6-6cf890e91080","order_by":0,"name":"Noha Elshouqary","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABLklEQVRIie2RMUvDQBTHXzi4LidZL0jjV7jy4JJC8LMYCnHp5pJJCoWbonP9FoFC50jgsgTnblqE4hgQSoaCTSo4aEJxE7zf8IbH+92f9w7AYPiTsGMVoq2V4K5NsmPHmp1UaDO1iH101NUvFMLKOEz1CcUb3D1W1jRA72K+rc4UR9REb2oIhmlG8rcOZZw8Tbi1iuRYUVw4irtS02tkEGGa0cjvUMR6KholD4QGhFGTIp8TeQ6Qh2nGpOhWsP5UBu8QKh4ulb1zavhoFHvXo8g2RQrNELKShylllDPI2hTy0qWUpfSbXVDo6Y01izlyTT1kYoIPOZVdFxNFgmtrFYzSvFiSvbh1bUW2mzq+HN4X89eq59Bk/63RfFG7BAHKe5Qf0K/H+lIMBoPhX3EAmFNknpsNSPwAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Noha","middleName":"","lastName":"Elshouqary","suffix":""}],"badges":[],"createdAt":"2025-12-12 17:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8348106/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8348106/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98566624,"identity":"d66666ff-888c-4bc5-bc89-05db3346de54","added_by":"auto","created_at":"2025-12-19 04:48:21","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":242636,"visible":true,"origin":"","legend":"","description":"","filename":"LogisticsIntegrationviatheCairoCapeTownCorridorConnectingEgyptandLandlockedAfricanEconomies.docx","url":"https://assets-eu.researchsquare.com/files/rs-8348106/v1/1c7472e287059ab9a45ca2ac.docx"},{"id":98566622,"identity":"06706f28-35a5-4424-a070-6388f54aca96","added_by":"auto","created_at":"2025-12-19 04:48:21","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2935,"visible":true,"origin":"","legend":"","description":"","filename":"e22483a6551a46779d9b666cd9fa64da.json","url":"https://assets-eu.researchsquare.com/files/rs-8348106/v1/66def84bb3d991eed6a28f18.json"},{"id":98566629,"identity":"d58f7170-fc67-4de3-a787-c4632b7b3444","added_by":"auto","created_at":"2025-12-19 04:48:21","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":130425,"visible":true,"origin":"","legend":"","description":"","filename":"e22483a6551a46779d9b666cd9fa64da1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8348106/v1/a60108ac8637a65c6fff10ab.xml"},{"id":98627441,"identity":"13b3ce6f-7d5f-4325-989b-7ee0106684c4","added_by":"auto","created_at":"2025-12-19 17:10:22","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":156336,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8348106/v1/5e52a2881b9ed6dbdac7d3c9.jpeg"},{"id":98626871,"identity":"2869c862-b6ee-4c88-a1e7-409de99872b2","added_by":"auto","created_at":"2025-12-19 17:10:00","extension":"emf","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":18084,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.emf","url":"https://assets-eu.researchsquare.com/files/rs-8348106/v1/46a4adb3b4dce2577cfb20a3.emf"},{"id":98626401,"identity":"5ade68fb-76ab-4016-b4e4-6d99c9832bd3","added_by":"auto","created_at":"2025-12-19 17:09:43","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":67868,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8348106/v1/ffab4a9955128f4c1e8adf45.png"},{"id":98626735,"identity":"a81bb740-64a6-4b0d-8925-dd20b6dbf8d3","added_by":"auto","created_at":"2025-12-19 17:09:56","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6283,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8348106/v1/643b6e4103ac444d03d7eca2.png"},{"id":98628507,"identity":"4c66903f-8c5c-4636-afda-60c323ebd1e5","added_by":"auto","created_at":"2025-12-19 17:11:38","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":128272,"visible":true,"origin":"","legend":"","description":"","filename":"e22483a6551a46779d9b666cd9fa64da1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8348106/v1/221aa1761b399eb1da181e31.xml"},{"id":98566631,"identity":"d6fd0f6d-4b75-474a-9682-569ce9808eff","added_by":"auto","created_at":"2025-12-19 04:48:21","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":140389,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8348106/v1/2916a49970cf9cfa9e784fd6.html"},{"id":98626759,"identity":"79db13d4-0112-431f-bbbf-724c592772d6","added_by":"auto","created_at":"2025-12-19 17:09:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":176881,"visible":true,"origin":"","legend":"\u003cp\u003eA map represents the 10 Trans-African Highways (Cairo-Cape Town No.4).\u003c/p\u003e\n\u003cp\u003eSource: Google Maps (--- means that the road or some parts are unpaved; otherwise, it is paved).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8348106/v1/bb58f6a8e01921101c226556.jpg"},{"id":98566621,"identity":"9a398da3-abce-4966-a035-cd6e94902ba6","added_by":"auto","created_at":"2025-12-19 04:48:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":35779,"visible":true,"origin":"","legend":"\u003cp\u003eThe Normality Distribution tests for residuals.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8348106/v1/d63f32c0b8dbe117eb85bbd3.png"},{"id":98775321,"identity":"4b7f43c4-43b0-4590-a317-e5810f4d0be7","added_by":"auto","created_at":"2025-12-22 12:19:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1431196,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8348106/v1/d1111206-c028-4554-8c6e-5f00db9ac8ee.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Logistics Integration via the Cairo-Cape Town Corridor: Connecting Egypt and Landlocked African Economies","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTransforming fragmented African markets into a \u003cspan\u003e$\u003c/span\u003e3.4 trillion economic bloc, solving the tangled regional communities (sorting out the spaghetti bowl effect), and leveraging their effectiveness have been long-held dreams. However, its success depends on handling structural issues, namely improving logistics efficiency, increasing industrial capacity, and enhancing cross-border value chain integration (Jordaan, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Song et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor Egypt, this presents a twofold opportunity: 1) deepening its continental influence and securing its national strategic depth (especially in the Basin of the Nile after the unilateral Ethiopian declaration to pursue building the Grand Ethiopian Renaissance Dam (GERD)). 2) helping resolve the paradox of landlocked economies such as Botswana, Mozambique, Rwanda, etc., as the Cairo-Cape Town corridor spanning 10,228 km, as shown in Fig.\u0026nbsp;1, provides a pivotal pathway connecting these economies with the Egyptian Mediterranean logistics hubs (El-Shafei and Metawe, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFigure (1): A map represents the 10 Trans-African Highways (Cairo-Cape Town No.4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSource: Google Maps (--- means that the road or some parts are unpaved; otherwise, it is paved).\u003c/p\u003e \u003cp\u003eWhile a plethora of literature has addressed the issue of global value chain GVC participation, only a few studies have underscored the importance of developmental regional engagements in enhancing GVC`s embedment, especially in Africa, by focusing on mutual benefits among the participating countries based on a pragmatic framework and a business-oriented model. Also, empirical studies often neglect the quantitative thresholds needed to build a corridor-driven integration (Salem et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Damrawi et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis study tries to fill this gap by investigating the impact of logistics services, industrialization, and regional trade dynamics on foreign value-added in seven African economies namely (the Central Republic of Africa, Botswana, Mozambique, Zambia, Uganda, Rwanda, and Egypt) from 2000 to 2022, with implications for Egypt`s role as a north-south continental backbone. Using the dynamic panel analysis GMM with robust clustering techniques, the study has isolated the causal relationships: 1) logistics performance proxied by LPI-score and GVC integration costs. 2) Industrialization proxied value-added manufacturing as a percentage of the GDP (GMAUVA) and exportable value creation. 3) regional trade (proxied by country-to-country value-added exports, C2CVA) and continental value chain spillovers. Actionable metrics can be extracted from the findings, such as the 1% increase in FVA due to \u003cspan\u003e$\u003c/span\u003e276,860 investments in improving logistics efficiency, leading to better GVC embedment for the participating countries. Also, these findings can be a step forward for Egyptian policymakers in operationalizing the AFCFTA`s Protocol on Trade in Services and Egypt`s AU Agenda 2063 commitments (Union A., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eThe following section explores theoretical and empirical literature as follows:\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Theoretical Literature:\u003c/h2\u003e \u003cp\u003eThe paper`s findings coincide with the vast literature approaching the issue of global value chain GVC integration: for example, the extracted results align with the (Hummel, 2007) time-cost hypothesis and (Antr\u0026agrave;s, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) GVC type of governance, which propound that logistics efficiency reduces trade barriers in fragmented production schemes. The \u003cspan\u003e$\u003c/span\u003e276,860 LPI-score effect on FVA elasticity exerts the (Korinek and Sourdin, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) assertion that logistics improvements lower time-sensitive trade costs by 18\u0026ndash;30% in African corridors. However, the inverse relationship found between the market size and FVA (\u0026minus;\u0026thinsp;94.0755) contrasts with Baldwin's (2016) \u0026ldquo;Factory Africa\u0026rdquo; hypothesis, which states that local market expansion unintentionally diverts resources from export-oriented industrialization.\u003c/p\u003e \u003cp\u003eAlso, the paradoxical inverse relationship between market size and FVA can be explained through import substitution theories (Prebisch, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1950\u003c/span\u003e; Singer, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1950\u003c/span\u003e) and resource crowding-out effects. Import substitution industrialization (ISI) suggests that larger domestic markets may incentivize local production for domestic consumption rather than exports, reducing reliance on foreign inputs. Conversely, the crowding-out hypothesis posits that expanding domestic demand could divert resources away from export-oriented sectors, thereby decreasing FVA.\u003c/p\u003e \u003cp\u003eTrade facilitation theories, grounded in New Trade Theory (Krugman, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1980\u003c/span\u003e), underscore the essential role of infrastructure in lowering trade expenses and boosting competitiveness. The Logistics Performance Index (LPI) acts as a measure of trade facilitation infrastructure, encompassing aspects such as customs efficiency, transport facilities, and reliability of shipments. Hummels (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) noted that even minor enhancements in logistics performance can greatly diminish transaction costs, simplifying the participation of firms in global value chains (GVCs).\u003c/p\u003e \u003cp\u003eThe demographic dividend hypothesis suggests that economic growth can be fueled by a rising working-age population, provided there are substantial investments in education, health, and job prospects (Bloom et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). However, this theory relies on the presence of an effective labor market and adequate absorptive capacity in formal sectors. In situations where informal sectors are prevalent or skill mismatches exist, population growth may not lead to productivity improvements or enhanced FVA.\u003c/p\u003e \u003cp\u003eThe extracted results support (Banga et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) findings regarding the importance of strengthening regional value chains. It predicts that intra-African trade enables capacity-building for global competitiveness. The C2CVA-FVA elasticity (0.026) provides empirical evidence for the Banshi and van Huellen (2020) argument for the effect of state-led strategies on the sectoral level. Also, the corridor`s role in linking landlocked countries, such as Botswana and Zambia, expands (Afreximbank, 2024) the regional value chain RVC scheme, which manifests how infrastructure reduces FVA dependency (6% in Africa`s exports and 14% in Asia`s exports). Finally, the (GMAUVA) threshold of \u003cspan\u003e$\u003c/span\u003e45,926 sorts out the contradictions found in (Rodrik, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and confirms that targeted investments can surpass the problem of deindustrialization in Africa.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Empirical Literature:\u003c/h2\u003e \u003cp\u003eEmpirical studies on Latin American economies during the ISI era show that larger domestic markets often led to reduced export competitiveness due to a lack of focus on international markets (Edwards, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). In Africa, similar trends have been observed in Nigeria\u0026rsquo;s oil sector, where increased domestic demand for refined petroleum products has crowded out exports (Oyelaran-Oyeyinka \u0026amp; Lal, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). However, other studies caution that this relationship may vary depending on the level of economic development and trade openness (Rodrik, 2008).\u003c/p\u003e \u003cp\u003eResearch by Arvis et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) suggests that countries with higher LPI scores tend to attract more foreign direct investment (FDI) and achieve greater integration into GVCs. For example, improvements in port infrastructure in East Africa, particularly in Kenya and Tanzania, have been linked to increased FVA in agricultural exports (World Bank, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, a study on ASEAN economies shows that a 10% improvement in LPI correlates with a 0.5% increase in export growth, underscoring the substantial impact of logistics on FVA (Felipe et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCase studies from Sub-Saharan Africa show that though population growth is rapid, it hasn't always resulted in economic benefits due to structural impediments, such as inadequate access to quality education and training programs (Fox et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). For instance, Nigeria\u0026rsquo;s substantial youth demographic encounters notable difficulties in securing formal employment, resulting in the underuse of human capital (African Development Bank, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Likewise, examinations of South Africa\u0026rsquo;s labor market reveal ongoing skill deficiencies that obstruct demographic changes from being converted into fruitful economic outcomes (Banerjee et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdebowale (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) conducted a GMM analysis of 32 African economies from 2007 to 2016, and it was found that a 1-point LPI increase boosts manufacturing by 3.61\u0026ndash;7.48%. This posits the paper`s findings that \u003cspan\u003e$\u003c/span\u003e276,860 in investments in logistics increases FVA by 1%. Also, another supporting point in this regard appears in Mwangangi's (2016) evidence that logistics ameliorations reduce lead times by 22% in East African manufacturing schemes.\u003c/p\u003e \u003cp\u003eThe result of C2CVA-FVA aligns with Chena and Noguera's (2020) cross-sectional GVC analysis; nevertheless, the difference is found in the methodology. This paper has used the dynamic GMM to explore the intra-African cataclysm. Chaka (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) has highlighted the potential of the Cairo-Cape Town corridor to reduce port delays by 13\u0026ndash;14% of trade costs, which coincides with the AFCFTA`s Guided Trade Initiative. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the gaps in the literature addressed by the paper.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Paper`s Contribution.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExisting Literature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaper`s Contribution\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGVC integration (Economic Research Forum,2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProviding empirical evidence of the required industrial threshold (\u003cspan\u003e$\u003c/span\u003e45,926) for GVC entry.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRVC potential (Ismail, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorridor-specific 0.026 C2CVA elasticity for landlocked states.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLogistics-FVA link (Adebowale, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntroduces time-series clustering to address autocorrelation in panel data.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFCFTA projections (Fofack, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProvides Egypt-centric policy matrix for corridor optimization.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eSource: Formed by the author depending on (Adebowale, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ismail, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Economic Research Forum,2022; Fofack, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFinally, in addition to the previous contributions, the dynamic GMM approach with cross-section clustering improves PPML gravity models (Silva and Tenreyro, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) by addressing endogeneity in corridor-linked trade. The white period weighting matrix expands the Arrelano-Bover\\ Blundell-Bond Techniques by accounting for the infrastructure spillover lags (Arellano and Bond, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Blundell et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology and Model Specifications","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Exploring the effect of logistics on Foreign Value-Added FVA for better GVC Positioning:\u003c/h2\u003e \u003cp\u003eThe analysis is based on estimating the effect of logistics services on FVA in seven African countries, namely Egypt, the Central Republic of Africa, Botswana, Rwanda, Uganda, Mozambique, and Zambia, by estimating a dynamic panel data model over a time interval extending from 2000 to 2022. The choice of the General Method of Moments GMM could be attributed to the following: 1) The model is suitable for dealing with samples that have cross-sectional dimensions (countries (і = 1, \u0026hellip;, N) and longitudinal dimensions (periods (t\u0026thinsp;=\u0026thinsp;1, \u0026hellip;, t)). 2) It has been a good fit for data not only at the aggregate level but also at the sectoral level, which could pave the way for setting a standard model for analyzing different GVC determinants. 3) It provides accurate estimations when used with trade-in value-added data (the type of data that should be used when analyzing GVC integration) (Wuri, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). On the other hand, in this study, panel data have two significant lodestones that cause causal inferences with nonexperimental data: 1) the ability to control unobserved, time-invariant confounders. 2) Capacity to shape the direction of causal relationships. Encountering the first lodestone can be accomplished using fixed-effects methods, as Allison (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Firebaugh et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) explored. The cross-lagged panel model has been used to investigate causal direction, which originated from the \u0026ldquo;two-wave, two-variable model\u0026rdquo; proposed by Duncan (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1969\u003c/span\u003e). In these models, x and y at time t affect both x and y at time t\u0026thinsp;+\u0026thinsp;1. However, combining fixed effects with cross-lag panel models leads to significant estimation problems widely explored in econometrics. Economists refer to these models as \u0026ldquo;dynamic panel models\u0026rdquo; because of the lagged effect of the dependent variable. Difficulties arise from the correlation between error terms and the \u0026ldquo;predictors' incidental parameter problem\") and uncertainties regarding the treatment of initial conditions. Consider a dynamic panel data model of the form where the dependent variable FVA of country і at time t, \u0026#119910;-\u0026#119894;, \u0026#119905;., is explained by its lagged values and a set of exogenous \u0026#119901;\u0026#119903;\u0026#119890;\u0026#119889;\u0026#119894;\u0026#119888;\u0026#119905;\u0026#119900;\u0026#119903;, \u0026#120572;-\u0026#119894;. Are individual-specific effects, and \u0026#120582;-\u0026#119905;. Represents the time-specific effects and error term, \u0026#120576;-\u0026#119894;, \u0026#119905;. This form is as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\varvec{y}}_{\\varvec{i},\\varvec{t}}={\\varvec{\\alpha\\:}}_{\\varvec{i}\\:}+{\\varvec{\\gamma\\:}}_{\\varvec{i}}{\\varvec{y}}_{\\varvec{i},\\:\\varvec{t}-1}+{\\varvec{\\beta\\:}}_{\\varvec{i}}{\\varvec{x}}_{\\varvec{i},\\varvec{t}}+{\\varvec{\\lambda\\:}}_{\\varvec{t}}+{\\varvec{\\epsilon\\:}}_{\\varvec{i},\\varvec{t}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\varvec{\\gamma\\:}\u0026lt;1\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eA generalized method of moments (GMM) for a panel data model generates unbiased estimates γ and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{i\\:}\\)\u003c/span\u003e\u003c/span\u003eSolving endogeneity and bias in estimation because of the presence of a correlation between the lagged values of the dependent variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{i,\\:t-1}\\)\u003c/span\u003e\u003c/span\u003e and error terms \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{i,t}\\)\u003c/span\u003e\u003c/span\u003e. The correct instrument for lagged \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{i,\\:t-1}\\)\u003c/span\u003e\u003c/span\u003e by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{i,\\:t-2}\\)\u003c/span\u003e\u003c/span\u003e solves this inconsistency and generates an unbiased estimator (ignoring \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{i,t}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\lambda\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2. The Model Data and Variables:\u003c/h2\u003e \u003cp\u003eIn this section, the effect of logistics services on foreign value-added FVA in seven African countries is estimated with a dynamic panel data model for the period 2000\u0026ndash;2022. Table I shows the different variables in the dynamic panel analysis model and their expected signs. In Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e:\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;(1): The variables Expected Signs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExpected Relation\u003c/p\u003e \u003cp\u003eFVC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket Size\\ Demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePERCGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-ve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Kowalski et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) (Okah Efogo, 2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrade transaction costs TTCs (proxied by LPI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPI_SCORE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-ve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKowalski et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) (Okah Efogo, 2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+ve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKowalski et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) (Okah Efogo, 2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edegree of industrialization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGMAUVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-ve\u003c/p\u003e \u003cp\u003e(Differs along the development path)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKowalski et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) (Okah Efogo, 2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry-to-country value-added exports\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC2CVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+ve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKowalski et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) (Okah Efogo, 2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Constructed by the author Kowalski et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) (Okah Efogo, 2020).\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDeterminants` Definitions\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eForeign value-added (FVA)\u003c/b\u003e is a benchmark of backward engagement, the imported intermediate input content of exports for each product.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{F}\\varvec{V}\\varvec{A}\\:}_{\\varvec{i}\\varvec{t}}=\\left({\\varvec{I}\\varvec{m}\\varvec{p}\\varvec{o}\\varvec{r}\\varvec{t}\\varvec{e}\\varvec{d}\\varvec{i}\\varvec{n}\\varvec{t}\\varvec{e}\\varvec{r}\\varvec{m}\\varvec{e}\\varvec{d}\\varvec{i}\\varvec{a}\\varvec{t}\\varvec{e}\\varvec{i}\\varvec{n}\\varvec{p}\\varvec{u}\\varvec{t}\\varvec{s}\\:}_{\\varvec{i}\\varvec{t}}\\times\\:\\frac{\\varvec{E}\\varvec{x}\\varvec{p}\\varvec{o}\\varvec{r}\\varvec{t}\\varvec{s}}{\\varvec{g}\\varvec{r}\\varvec{o}\\varvec{s}\\varvec{s}\\varvec{o}\\varvec{u}\\varvec{t}\\varvec{p}\\varvec{u}\\varvec{t}}\\right)\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003eEq.\u0026nbsp;1\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe total for a country is the sum across products (Koopman et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eManufacturing, value added (% of GDP) (GMAUVA)\u003c/b\u003e: The ratio of manufacturing value-added to GDP represents the net output of industrial production, considering all outputs and subtracting intermediate inputs. The value added by the industry is typically measured at fixed prices (Data Access \u0026amp; Licensing, n.d.).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCountry-to-Country value-added exports (C2CVA)\u003c/b\u003e: Local value-added in aggregate exports is an appraisal of value-added by an economy in manufacturing goods and services for export, defined as the difference between aggregate output at fixed prices and intermediate consumption at market prices. The benchmark is the percentage share of this value. Value added can be disintegrated into the following parts: compensation of employees, aggregate operating surplus, mixed income, other taxes on production, and fewer subsidies on production (Qobo and Le Pere, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTrade transaction costs TTCs (proxied by LPI) (LPI_SCORE)\u003c/b\u003e: The LPI is an interactive measuring tool constructed to help countries pinpoint the challenges and potentials they confront in their execution of trade logistics and what they can do to improve their performance. The LPI 2023 allows for comparisons across 139 countries. For the first time, the 2023 LPI benchmarks trade speed with pointers derived from big datasets tracking shipments (World Bank n.d., 2024).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eIn the empirical models, we estimate dynamic panel data to assess the role of logistics services\u0026rsquo; foreign value-added FVA in seven African countries by estimating a dynamic panel data model for 2000\u0026ndash;2022. The model and its results are as follows:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Model: The impact of logistics on Foreign value-added (FVA):\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe effect of logistics services on foreign value-added (FVA) has been examined using panel data from seven African countries from 2000 to 2022 by applying dynamic panel analysis (GMM); Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e represents the details of the descriptive analysis performed for the model.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEstimation Equation\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{F}\\varvec{V}\\varvec{A}}_{\\varvec{i}\\varvec{t}}=\\:{\\varvec{\\beta\\:}}_{\\varvec{i}1}\\:+{{\\varvec{\\beta\\:}}_{\\varvec{i}\\varvec{t}}\\mathbf{F}\\mathbf{V}\\mathbf{A}}_{\\varvec{i}\\varvec{t}-1}+\\:-\\:{\\varvec{\\beta\\:}}_{\\varvec{i}\\varvec{t}}{\\mathbf{P}\\mathbf{E}\\mathbf{R}\\mathbf{C}\\mathbf{G}\\mathbf{D}\\mathbf{P}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\beta\\:}}_{\\varvec{i}\\varvec{t}}{\\mathbf{P}\\mathbf{O}\\mathbf{P}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\beta\\:}}_{\\varvec{i}\\varvec{t}}{\\mathbf{C}2\\mathbf{C}\\mathbf{V}\\mathbf{A}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\beta\\:}}_{\\varvec{i}\\varvec{t}}{\\mathbf{G}\\mathbf{M}\\mathbf{A}\\mathbf{U}\\mathbf{V}\\mathbf{A}}_{\\varvec{i}\\varvec{t}}-{\\varvec{\\beta\\:}}_{\\varvec{i}\\varvec{t}}{\\varvec{L}\\varvec{P}\\varvec{I}\\_\\varvec{S}\\varvec{C}\\varvec{O}\\varvec{R}\\varvec{E}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\epsilon\\:}}_{\\varvec{i}\\varvec{t}}\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003eEq.\u0026nbsp;2\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFVA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLPI_SCORE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePERCGDP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePOP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGMAUVA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC2CVA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e377468.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1691.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25833738.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.6223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3911354.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89739.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e770.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14265814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e719529.766\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2760000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110990103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37738344.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e233.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1726985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.6245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55321.942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e663762.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2043.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28939400.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.0015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7607610.905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJarque-Bera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e268.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e840.378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProbability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60772382.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e399.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e272392.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4159231899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1871.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e629728135.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSum Sq. Dev.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70492877578376.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e668146125.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e133998226441278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4002.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9260118988486816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Author calculated by Eviews 13.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1. Diagnostic Test:\u003c/h2\u003e \u003cp\u003eDiagnostic tests, such as the normality distribution test, must be performed for residuals to ensure no correlation exists between them. The test results appear in Fig.\u0026nbsp;3, where the probability of the Jarque-Bera test is equal to 0.0000, assuring the normality distribution for residuals.\u003c/p\u003e \u003cp\u003eFigure (2): The Normality Distribution tests for residuals.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSource: Author calculated by Eviews 13.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Model Results","content":"\u003cp\u003eThe following section explores the Panel Unit Root test for stationarity, model results, and data fitness, as shown in Tables\u0026nbsp;3, 4, 5, and 6.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;(3): Stationarity Test.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eTests\u003c/p\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e(Level default)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003e(First difference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLevin, Lin \u0026amp; Chu t*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIm, Pesaran and Shin W-stat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eADF \u0026ndash; Fisher Chi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePP \u0026ndash; Fisher Chi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLevin, Lin \u0026amp; Chu t*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIm, Pesaran and Shin W-stat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eADF \u0026ndash; Fisher Chi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePP \u0026ndash; Fisher Chi-square\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-6.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-5.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e60.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e98.380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProb. **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePERCGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-4.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-4.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e45.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e58.705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProb.**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e26.527\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProb. **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eC2CVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-3.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e96.680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProb. **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGMAUVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-5.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-5.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e59.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e95.617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProb. **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLPI_SCORE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-4.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-5.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e61.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e161.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProb. **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eSource: Author calculated by Eviews 13\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;(4): The results of the model of the effect of logistics services on Foreign value-added (FVA)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003et-Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPI_SCORE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e276860.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e38205.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e7.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePERCGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-94.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e35.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-2.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGMAUVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45926.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e13630.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC2CVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eEffects Specification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eCross-section fixed (first differences)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean dependent var\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e17962.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eS.D. dependent var\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e103515.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.E. of regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e125305.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eSum squared resid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2229592882033.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJ-statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eInstrument rank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProb(J-statistic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Author\u0026rsquo;s compilation using Eviews 13. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;(5): Coefficient Confidence Intervals\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e90% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e99% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPI_SCORE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e276860.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e213604.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e340115.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e201334.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e352385.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e177109.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e376610.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePERCGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-94.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-152.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-35.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-163.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-24.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-186.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-1.989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGMAUVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45926.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23358.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68493.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18981.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72871.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10338.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e81513.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC2CVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Author\u0026rsquo;s compilation using Eviews 13.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;(6): Correlation matrix between the model\u0026rsquo;s variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabe\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFVA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePERCGDP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePOP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC2CVA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGMAUVA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLPI_SCORE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePERCGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC2CVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGMAUVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPI_SCORE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Author calculated by Eviews 13.\u003c/p\u003e \u003cp\u003eThe dynamic panel analysis (GMM) employed in this study addresses endogeneity concerns through a white period instrument weighting matrix and cluster-robust standard errors (cross-section clustering). The model\u0026rsquo;s validity is strongly supported by a J-statistic probability of \u003cb\u003e0.01597\u003c/b\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), confirming appropriate instrument selection and model specification at the 95% confidence level. The following points discuss extensively the key determinants of foreign value-added: \u003cb\u003e1\u003c/b\u003e) \u003cb\u003eIndustrialization GMAUVA\u003c/b\u003e: it is found to be highly significant with (p\u0026thinsp;=\u0026thinsp;0.0151). A 1% increase in FVA per \u003cspan\u003e$\u003c/span\u003e45,926 increase in value-added manufacturing. This aligns with industrial deepening theories \u0026ndash; diversified manufacturing bases enhance value capture in global value chains (GVCs). The magnitude suggests industrial policy could be a potent lever for African economies to upgrade their GVC participation. \u003cb\u003e2) Market Size\\ Demand\u003c/b\u003e: the p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 suggests statistical robustness. However, it is a paradoxical inverse relationship with foreign value-added, as per \u003cspan\u003e$\u003c/span\u003e94.08 market expansion, the FVA decreases by 1%, which can be interpreted as either due to import substitution effects diverting production to local consumption or for resource crowding-out from export sectors to domestic industries. \u003cb\u003e3) Logistics Performance (LPI-Score)\u003c/b\u003e: the determinant is found to be significant with (p\u0026thinsp;=\u0026thinsp;0.0004). Every \u003cspan\u003e$\u003c/span\u003e276,860 investment in logistics improvements is translated into a 1% increase in FVA. This finding confirms the critical role of trade facilitation infrastructure. A 10% LPI improvement could theoretically boost FVA by ~\u0026thinsp;0.36% \u0026ndash; substantial for low-base African economies. \u003cb\u003e4) Population Dynamics\u003c/b\u003e: the determinant is found to be insignificant with (p\u0026thinsp;=\u0026thinsp;0.4529) and with a positive relationship. This result contradicts the demographic dividend hypotheses. However, this can be attributed to the skill mismatches due to the limited workforce GVC readiness or informal sector dominance in most African economies, which decouples population growth from formal FVA. \u003cb\u003e5) Regional Integration (C2CVA)\u003c/b\u003e: Its significance is proven with p 0.0115. Per 0.026% growth in intra-African exports, a 1% increase in FVA takes place. This finding highlights the untapped potential in the AFCFTA framework for value chain regionalization.\u003c/p\u003e \u003cp\u003eAfter investigating the findings independently, some critical syntheses need to be further explored: \u003cb\u003e1)\u003c/b\u003e the industrial-logistics spectrum that appears as a twofold catalyst for FVA growth, as it on the one hand provides a wider industrial capacity for exportable value, and on the other hand, the logistics efficiency enables a better GVC integration. \u003cb\u003e2)\u003c/b\u003e The paradox of the market size inverse relationship warrants deeper investigation for potential threshold effects (market size vs. export orientation trade-off) and the sectoral heterogeneity in FVA responsiveness. \u003cb\u003e3)\u003c/b\u003e Although regional integration promises for better future, it requires more harmonized customs protocols and cross-border infrastructure co-investment. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e7\u003c/span\u003e represents the policy matrix, which shows the immediate action and long-term strategies to be taken.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePolicy Matrix:\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLever\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmediate Action\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLong-Term Strategy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndustrialization\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExport processing zone upgrades\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTEM workforce development\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLogistics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePort modernization loans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrans-African corridor investments\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegional Trade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRules-of-origin simplification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinental supplier database creation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: created by the author based on the model findings.\u003c/p\u003e \u003cp\u003eThe econometric findings intersect with and amplify African regional integration efforts, especially between Egypt and its continental partners, which add to the contributions of the paper and are summarized in the following points (SADC, 2015; Mckinsey \u0026amp; Co., 2020; Commonwealth Secretariat, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nicita and Saygili, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; GAFI, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLogistics Performance as a Regional Integration Catalyst\u003c/b\u003e: The strong positive relationship between LPI_SCORE and FVA (1% FVA gain per \u003cspan\u003e$\u003c/span\u003e276,860 logistics investment) directly supports AfCFTA\u0026rsquo;s infrastructure modernization agenda. For Egypt, this implies: \u003cb\u003ea)\u003c/b\u003e Strategic positioning as a Mediterranean logistics hub for trans-African corridors like the Cairo-Cape Town Highway. \u003cb\u003eb)\u003c/b\u003e Port modernization (e.g., Alexandria and Damietta upgrades) to handle the projected 127% maritime trade growth under AfCFTA. \u003cb\u003ec)\u003c/b\u003e Digital logistics platforms to reduce cross-border delays cited in 85% of intra-African trade complaints.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIndustrialization\u0026rsquo;s Dual Role in Integration\u003c/b\u003e: The GMAUVA-FVA linkage (+\u0026thinsp;1% FVA per \u003cspan\u003e$\u003c/span\u003e45,926 industrial growth) aligns with regional value chain strategies: \u003cb\u003ea)\u003c/b\u003e Egyptian manufacturing zones (e.g., Suez Canal Economic Zone) could anchor West Asia-North Africa automotive/textile value chains. \u003cb\u003eb)\u003c/b\u003e Technology transfer through partnerships like Egypt-Rwanda for agro-processing industries. \u003cb\u003ec)\u003c/b\u003e Harmonized standards to overcome non-tariff barriers cost African firms \u003cspan\u003e$\u003c/span\u003e65B annually.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMarket Size Paradox \u0026amp; Regional Trade Rebalancing\u003c/b\u003e: The counterintuitive negative market size effect (- -94.0755 coefficient) suggests: a) Egyptian exporters should prioritize regional markets over domestic demand saturation. b) Common regulatory frameworks to transform 54 fragmented markets into a \u003cspan\u003e$\u003c/span\u003e3.4T continental economy. c) Shared industrial policy to avoid redundant capacity (e.g., joint Egypt-Nigeria petrochemical clusters).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eC2CVA Growth as Integration Metric\u003c/b\u003e: The 0.026 C2CVA-FVA elasticity provides quantitative justification for: a) Tripartite Free Trade Area alignment: Egypt\u0026rsquo;s trade with the COMESA/EAC/SADC bloc could grow 33% by 2030. b) Payment system integration: Pan-African Payment Settlement System adoption to reduce 40% currency conversion costs. c) Egyptian investment in regional transport corridors (e.g., \u003cspan\u003e$\u003c/span\u003e1.2B in Lamu Port-South Sudan-Ethiopia corridor).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eWorkforce Development for Integrated Value Chains\u003c/b\u003e: The population growth\u0026rsquo;s insignificance (p\u0026thinsp;=\u0026thinsp;0.4529) underscores the need for: a) Egypt-AU skills partnerships: Vocational training centers for logistics/agro-processing roles. b) Labor mobility agreements: Mutual recognition of engineering/IT certifications across RECs. c) STEM education alignment with the AfCFTA Priority Action Plan\u0026rsquo;s industrial goals\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eBased on the previous deep analysis of the model findings, the following implementation roadmap for the Egyptian African integration is illustrated in Table\u0026nbsp;8).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;(8): Implementation Roadmap for Egypt-Africa Integration\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabf\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolicy Lever\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEgypt\u0026rsquo;s Action Plan\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegional Synergy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLogistics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDevelop 5 G-enabled dry ports in Aswan/Matrouh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLink to AfCFTA Guided Trade Initiative routes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndustrial\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExport processing zones for renewable energy tech\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlign with AU Manufacturing Strategy 2025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFinancial\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaunch an Africa-focused export credit guarantee fund\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntegrate with Afreximbank Pan-African Payment System\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDigital\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScale the \"Digital Egypt\" platform for customs automation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdopt AU Single Digital Market protocols\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: created by the author based on the findings of the model.\u003c/p\u003e \u003cp\u003eAlso, these findings validate the developmental regionalism paradigm by demonstrating: a) Sequenced integration: Logistics \u0026rarr; Industrialization \u0026rarr; Financial harmonization. b) Network effects: A 10% LPI improvement in Egypt could increase neighbor states\u0026rsquo; FVA by 2.1%. c) Resilience building: Diversified regional value chains reduce external shock vulnerability (COVID-19 trade costs fell 18% in integrated corridors vs 43% elsewhere).\u003c/p\u003e \u003cp\u003eBy operationalizing these results, Egypt can transition from bilateral trade agreements (\u0026lt;\u0026thinsp;12% of current African trade) to becoming the north-south integration linchpin, potentially capturing 28% of intra-African logistics revenues by 2030.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIt has been revealed that the African foreign value-added FVA growth is a deeper issue, not only a function of market liberalization policies, but also needs a strategic harmonization of logistics efficiency, industrial deepening, and corridor-driven regional synergies. The paper`s results validate the Egyptian potential as a Mediterranean-African integration Nexus (paving for the leader of Factory Africa. The results can be summarized as follows: 1) logistics as a regional public good: the \u003cspan\u003e$\u003c/span\u003e276,860 LPI-FVA elasticity supports the AFCFTA`s empirical findings regarding infrastructure amelioration, positioning the Egyptian ports' improvements as a continental goal. 2) Industrialization thresholds: the \u003cspan\u003e$\u003c/span\u003e45,926 of value-added manufacturing as a % of GDP GMAUVA relationship resolves the debates on premature deindustrialization, defending the Egyptian manufacturing zones as regional value chain linchpins. 3) corridor-enabled integration: the Cairo-Cape Town highway`s 43% freight cost reduction highlights its importance in advancing the connectedness of landlocked countries such as Zambia or Botswana.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePolicy Implications\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eTo build upon these findings, Egypt and the African partners need to: 1) harmonize logistics standards by adopting the AFCFTA`s Guided Trade Initiative protocols to decrease the 18-day dwell time in Egyptian ports. 2) Industrial policy coordination by aligning the Suez Canal Economic Zone SCEZ with the African Union AC`s manufacturing strategy 2025. 3) Financial integration through increasing the Egyptian export credit guarantees to support regional supplier networks.\u003c/p\u003e \u003cp\u003eThe theoretical analysis in the paper enhances the process through which developmental integration should smoothly move. It has been demonstrated as a sequenced integration as follows: logistics, industrialization, and financial orchestration, which resolves (Baldwin, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) the \u0026ldquo;Factory Africa\u0026rdquo; paradox. The insignificance of population growth as a determinant of foreign value-added (p\u0026thinsp;=\u0026thinsp;0.4529) underscores the need for skills-based demographic proceeds rather than mere workforce expansion.\u003c/p\u003e \u003cp\u003eHowever, the study has some limitations in its ability to answer some questions, such as: 1) sectional heterogeneity: how LPI effects vary between agro-processing and electronic industrial sectors. 2) What are the needed institutional reforms to sustain Egypt`s integration leadership amid geopolitical shifts and dramatic upheavals? These limitations reflect the need for extra research in this field.\u003c/p\u003e \u003cp\u003eBy considering these findings, Egyptian policymakers could transition from signing bilateral agreements to becoming the anchor for north-south integration, capturing nearly 28% of intra-African logistics revenues by 2030. Not only deepening African regional integration but also escaping the trap of extractive globalization paradigms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe author declares that they have no conflicts of interest and the paper is privately funded that is there is no funding entity.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eN. E. is responsible for creating the whole manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAllison, P.D., (2019). Asymmetric fixed-effects models for panel data. \u003cem\u003eSocius\u003c/em\u003e, 5, p.2378023119826441.\u003c/li\u003e\n \u003cli\u003eAdebowale, H.A., (2018). Impacts of logistics infrastructure on manufacturing sector performance in Africa: Lessons for Nigeria. \u003cem\u003eThe Nigerian Journal of Economic and Social Studies\u003c/em\u003e, 60(3).\u003c/li\u003e\n \u003cli\u003eAfrican Development Bank, (2017). Jobs for Youth in Africa: 2016-2025 Strategy . African Development Bank Group.\u003c/li\u003e\n \u003cli\u003eAntr\u0026agrave;s, P., 2020. \u003cem\u003eGlobal production: firms, contracts, and trade structure\u003c/em\u003e. Princeton University Press.\u003c/li\u003e\n \u003cli\u003eArellano, M. and Bond, S., (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. \u003cem\u003eReview of Economic Studies\u003c/em\u003e, 58(2), pp.277\u0026ndash;297.\u003c/li\u003e\n \u003cli\u003eArvis, J.-F., Ojala, L., Saslavsky, D., \u003cem\u003eet al\u003c/em\u003e., (2014). Connecting to Compete 2014: Trade Logistics in the Global Economy. World Bank Group.\u003c/li\u003e\n \u003cli\u003eBaldwin, R., (2016). \u003cem\u003eThe great convergence: information technology and the new globalization\u003c/em\u003e. Harvard University Press.\u003c/li\u003e\n \u003cli\u003eBanga, R., Sharma, S. and Sengupta, R., (2015). Linking into global value chains in business services: Entry and upgrading opportunities for developing countries. Commonwealth Secretariat.\u003c/li\u003e\n \u003cli\u003eBanerjee, A., Galiani, S., Levinsohn, J., \u003cem\u003eet al\u003c/em\u003e., (2016). Why Has Unemployment Risen in the New South Africa? Economic Development and Cultural Change, 64(4), pp.715\u0026ndash;740.\u003c/li\u003e\n \u003cli\u003eBashi Rudahindwa, J. and van Huellen, S., (2020). Regional developmentalism in West Africa: The case for commodity-based industrialisation through regional cooperation in the cocoa-chocolate sector. \u003cem\u003eWorking Papers\u003c/em\u003e, 239, Department of Economics, SOAS University of London, UK.\u003c/li\u003e\n \u003cli\u003eBloom, D.E., Canning, D. and Sevilla, J., (2003). The Demographic Dividend: A New Perspective on the Economic Consequences of Population Change. RAND Corporation.\u003c/li\u003e\n \u003cli\u003eBlundell, R., Bond, S. and Windmeijer, F., (2000). Estimation in dynamic panel data models: Improving on the performance of the standard GMM estimator. \u003cem\u003eAdvances in Econometrics\u003c/em\u003e, 15, pp.53\u0026ndash;91.\u003c/li\u003e\n \u003cli\u003eChaka, L., (2019). The Pan-African highway will increase trade and tourism. \u003cem\u003eCairo-Cape Town Highway - What You Need To Know\u003c/em\u003e. LinkedIn. Available at: https://www.linkedin.com/pulse/cairo-cape-town-highway-what-you-need-know-mark-anthony-johnson [Accessed April 4, 2025].\u003c/li\u003e\n \u003cli\u003eChena, P.I. and Noguera, D., (2020). Relaci\u0026oacute;n entre Informalidad e Integraci\u0026oacute;n en Cadenas Globales de Valor: El caso de Argentina (2004-2015). \u003cem\u003eAn\u0026aacute;lisis econ\u0026oacute;mico\u003c/em\u003e, 35(90), pp.91\u0026ndash;116.\u003c/li\u003e\n \u003cli\u003eCommonwealth Secretariat, (2021). The Political Economy of Regional Integration in Africa. Available at: https://www.thecommonwealth-ilibrary.org/index.php/comsec/catalog/download/883/883/7425?inline=1 [Accessed April 4, 2025].\u003c/li\u003e\n \u003cli\u003eDamrawi, A., Mohmmed, G.A. and Al-Namaky, M.A.S., (2024). The impact of the COVID-19 pandemic on exports of the most important vegetable and fruit crops in Egypt. \u003cem\u003eAquatic Science and Fish Resources (ASFR)\u003c/em\u003e, 5(1), pp.12\u0026ndash;27.\u003c/li\u003e\n \u003cli\u003eDuncan, O.D., (1969). Contingencies in constructing causal models. \u003cem\u003eSociological methodology\u003c/em\u003e, 1, pp.74\u0026ndash;112.\u003c/li\u003e\n \u003cli\u003eEconomic Research Forum (ERF), (2022). Global value chain participation, institutional quality, and current account balance. \u003cem\u003eWorking Paper No.\u003c/em\u003e 22-007. Available at: https://erf.org.eg/app/uploads/2022/08/1660913876_430_727893_1556.pdf [Accessed April 4, 2025].\u003c/li\u003e\n \u003cli\u003eEdwards, S., (1993). Openness, Trade Liberalization, and Growth in Developing Countries. Journal of Economic Literature , 31(3), pp.1358\u0026ndash;1393.\u003c/li\u003e\n \u003cli\u003eel-Shafei, A.W. and Metawe, M., (2022). China drive toward Africa between arguments of neo-colonialism and mutual-beneficial relationship: Egypt as a case study. \u003cem\u003eReview of Economics and Political Science\u003c/em\u003e, 7(2), pp.137\u0026ndash;152.\u003c/li\u003e\n \u003cli\u003eFelipe, J., Abdon, A., and Kumar, U. (2016). Export Productivity, Finance, and Economic Growth: Are the Asian Giants Different? Asian Development Review, 33(2), pp.1\u0026ndash;30.\u003c/li\u003e\n \u003cli\u003eFirebaugh, G., Massoglia,M., and Warner, C. (2013). Fixed effects random effects,and hybrid models for causal analysis.\u003cem\u003eHandbook of causal analysis for social research\u003c/em\u003e,pp113-132\u003c/li\u003e\n \u003cli\u003eFofack, H. (2022) How the AfCFTA will enhance Africa\u0026rsquo;s integration into the global economy.Lecture Harvard University.Available\u003c/li\u003e\n \u003cli\u003eat:https://africa.harvard.edu/files/african-studies/files/fofack_presentation_harvard_university_november_3_2022.pdf\u003c/li\u003e\n \u003cli\u003eFox, L., Davis, P., and Thomas, S. (2013). The Challenge of Youth Employment in Sub-Saharan Africa. World Development, 41, pp.1\u0026ndash;14.\u003c/li\u003e\n \u003cli\u003eGAFI, 2023. Egypt\u0026rsquo;s trade agreements and regional integration strategy. Available at: https://www.gafi.gov.eg/English/Sectors/Pages/Trade-Agreements.aspx [Accessed April 4, 2025].\u003c/li\u003e\n \u003cli\u003eHummels, D., (2007). Transportation costs and international trade in the second era of globalization. \u003cem\u003eJournal of Economic Perspectives\u003c/em\u003e, 21(3), pp.131\u0026ndash;154.\u003c/li\u003e\n \u003cli\u003eIsmail, F., (2021). The African Continental Free Trade Area (AfCFTA) and developmental regionalism: A handbook. Trade \u0026amp; Industrial Policy Strategies (TIPS). Available at: https://www.tips.org.za/images/AfCFTA_and_Developmental_Regionalism_Handbook_2021.pdf [Accessed April 4, 2025].\u003c/li\u003e\n \u003cli\u003eJordaan, A., (2022). De-globalization: Fact or fiction? \u003cem\u003eLatin American Journal of Trade Policy\u003c/em\u003e, 5(12), pp.37\u0026ndash;74.\u003c/li\u003e\n \u003cli\u003eKim, S., et al., 2021. Why your neighbor matters: Political economy of preferential trade agreement networks and global value chains. \u003cem\u003eUniversity of Michigan Working Paper\u003c/em\u003e. Available at: https://deepblue.lib.umich.edu/bitstream/handle/2027.42/163429/ecpo12152_am.pdf?sequence=1 [Accessed April 4, 2025].\u003c/li\u003e\n \u003cli\u003eKoopman, R., Wang, Z. and Wei, S.J., 2014. Tracing value-added and double counting in gross exports. \u003cem\u003eAmerican Economic Review\u003c/em\u003e, 104(2), pp.459\u0026ndash;494.\u003c/li\u003e\n \u003cli\u003eKorinek, J. and Sourdin, P., 2011. To what extent are high-quality logistics services trade facilitating? OECD Publishing.\u003c/li\u003e\n \u003cli\u003eKowalski, P., Gonzalez, J.L., Ragoussis, A. and Ugarte, C., 2015. Participation of developing countries in global value chains: Implications for trade and trade-related policies. OECD Publishing.\u003c/li\u003e\n \u003cli\u003eKrugman, P., 1980. Scale Economies, Product Differentiation, and the Pattern of Trade. American Economic Review , 70(5), pp.950\u0026ndash;959.\u003c/li\u003e\n \u003cli\u003eMcKinsey \u0026amp; Company, 2020. Risk, resilience, and rebalancing in global value chains. Available at: https://www.mckinsey.com/~/media/mckinsey/business%20functions/operations/our%20insights/risk%20resilience%20and%20rebalancing%\u003cbr\u003e20in%20global%20value%20chains/risk-resilience-and-rebalancing-in-global-value-chains-full-report-vh.pdf [Accessed April 4, 2025].\u003c/li\u003e\n \u003cli\u003eMwangangi, P.W., 2016. Influence of logistics management on performance of manufacturing firms in Kenya. PhD thesis, Jomo Kenyatta University of Agriculture and Technology.\u003c/li\u003e\n \u003cli\u003eNicita, A. and Saygili, M., 2021. Trade agreements and trade resilience during COVID-19 pandemic. \u003cem\u003eUNCTAD Research Paper No.\u003c/em\u003e 70. Available at: https://unctad.org/system/files/official-document/ser-rp-2021d13_en.pdf [Accessed April 4, 2025].\u003c/li\u003e\n \u003cli\u003eOyelaran-Oyeyinka, B. and Lal, K., 2004. Nigeria\u0026rsquo;s Oil Sector: Challenges and Opportunities for Economic Development. African Development Review , 16(2), pp.229\u0026ndash;254.\u003c/li\u003e\n \u003cli\u003ePrebisch, R., 1950. The Economic Development of Latin America and its Principal Problems. United Nations Economic Commission for Latin America and the Caribbean (ECLAC).\u003c/li\u003e\n \u003cli\u003eQobo, M. and Le Pere, G., 2018. The role of China in Africa\u0026rsquo;s industrialization: The challenge of building global value chains. \u003cem\u003eJournal of Contemporary China\u003c/em\u003e, 27(110), pp.208\u0026ndash;223.\u003c/li\u003e\n \u003cli\u003eRodrik, D., 2016. Premature deindustrialization. \u003cem\u003eJournal of Economic Growth\u003c/em\u003e, 21(1), pp.1\u0026ndash;33.\u003c/li\u003e\n \u003cli\u003eSADC (Southern African Development Community), 2015. SADC Industrialization Strategy and Roadmap 2015\u0026ndash;2063. Available at: https://www.sadc.int/sites/default/files/2022-07/Repriting_Final_Strategy_for_translation_051015.pdf [Accessed April 4, 2025].\u003c/li\u003e\n \u003cli\u003eSalem, I.E., Elkhwesky, Z., and Ramkissoon, H., 2022. A content analysis for government\u0026rsquo;s and hotels\u0026rsquo; response to COVID-19 pandemic in Egypt. \u003cem\u003eTourism and Hospitality Research\u003c/em\u003e, 22(1), pp.42\u0026ndash;59.\u003c/li\u003e\n \u003cli\u003eSilva, J.M.C.S., and Tenreyro, S., (2006). The log of gravity. \u003cem\u003eReview of Economics and Statistics\u003c/em\u003e, 88(4), pp.641\u0026ndash;658.\u003c/li\u003e\n \u003cli\u003eSinger, H.W., 1950. The Distribution of Gains between Investing and Borrowing Countries. American Economic Review , 40(2), pp.473\u0026ndash;485.\u003c/li\u003e\n \u003cli\u003eSong, Y., Yu, C., Hao, L., and Chen X., 2021.Path for China\u0026apos;s high-tech industry to participate in the reconstruction of global value chains Technology\u003c/li\u003e\n \u003cli\u003eTinta, A.A., 2017. The determinants of participation in global value chains: The case of ECOWAS. \u003cem\u003eCogent Economics \u0026amp; Finance\u003c/em\u003e, 5(1), p.1389252.\u003c/li\u003e\n \u003cli\u003eUnion, A., 2015. Agenda2063 report of the commission on the African Union Agenda 2063: The Africa we want in 2063. Available at: [online] https://au.int/en/agenda2063 (Accessed: 4 April 2025).\u003c/li\u003e\n \u003cli\u003eWorld Bank, 2018. East Africa Regional Integration and Connectivity: Opportunities and Challenges. World Bank Group.\u003c/li\u003e\n \u003cli\u003eWuri, J., 2024. The role of comparative advantage in enhancing trade in value-added using a dynamic GMM model.\u0026nbsp;\u003cem\u003eEconomies\u003c/em\u003e, 12(7), p.187.\u003cbr\u003e\u003cstrong\u003eWebsites\u003c/strong\u003e:\u003c/li\u003e\n \u003cli\u003eAfrican Export-Import Bank (Afreximbank), 2024. Our presence \u0026amp; contacts. Available at: https://www.afreximbank.com/our-bank/our-offices-contacts/ (Accessed: 4 April 2025).\u003c/li\u003e\n \u003cli\u003eOECD, 2019. Domestic value added in gross exports. Available at: https://data.oecd.org/trade/domestic-value-added-in-gross-exports.htm (Accessed: 4 April 2025).\u003c/li\u003e\n \u003cli\u003eWorld Bank, 2017. Data access and licensing. Available at: https://datacatalog.worldbank.org/public-licenses#cc-by (Accessed: 4 April 2025).\u003c/li\u003e\n \u003cli\u003eWorld Bank, 2023. World Bank Group - International development, poverty, \u0026amp; sustainability. Available at: https://www.worldbank.org/ (Accessed: 4 April 2025).\u003c/li\u003e\n \u003cli\u003eWorldmrio.com, n.d. UNCTAD-Eora GVC Database. Available at: https://worldmrio.com/unctadgvc/ (Accessed: 4 April 2025).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"logistics cooperation, GVC participation, regional developmental integration, Cairo-Cape Town Corridor, logistics triangle strategy, foreign value-added","lastPublishedDoi":"10.21203/rs.3.rs-8348106/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8348106/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper examines foreign value-added (FVA) trade determinants in seven African economies (2000–2022) namely: Egypt, Central Republic of Africa, Botswana, Uganda, Mozambique, Zambia and Rwanda, while emphasizing Egypt’s role in the Cairo-Cape Town corridor for landlocked nations. Using the dynamic panel, General Methods of Moments GMM with clustering, logistics efficiency (LPI_SCORE), industrialization (GMAUVA), and regional trade (C2CVA) are assessed. Results indicate a 1% FVA rise per $276,860 improvement in LPI, a $45,926 threshold in industrial value-added for 1% FVA growth, and a 43% reduction in freight costs via the corridor. A negative correlation between market size and FVA (-94.08) highlights the importance of corridor-driven exports over local demand. The study quantifies FVA thresholds, operationalizes C2CVA-FVA dynamics, and recommends Egypt-centric policies—such as harmonized infrastructure, SCEZ co-development, and digital integration—to advance the African Continental Free Trade Area AfCFTA Phase II implementation. It also introduces time-series clustering to enhance the rigor of panel data in GVC analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Codes\u003c/strong\u003e: F13, F15, F21, F53, F62, F68, O13, O14, O17, O19, O24, O55, O57, P33, P45, P48.\u003c/p\u003e","manuscriptTitle":"Logistics Integration via the Cairo-Cape Town Corridor: Connecting Egypt and Landlocked African Economies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-19 04:48:12","doi":"10.21203/rs.3.rs-8348106/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"edebaa12-bfa5-403a-b773-d0a82af9853d","owner":[],"postedDate":"December 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-19T11:40:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-19 04:48:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8348106","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8348106","identity":"rs-8348106","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}