When Does Air Transport Infrastructure and Trade Flows Matter? Threshold Effects on Economic Growth in ASEAN Countries

Abstract

This study examines how air transport infrastructure and trade flows influence economic growth across ASEAN countries, with a focus on identifying the threshold levels at which these factors begin to enhance growth. Despite increasing investment in regional logistics and connectivity, policymakers often lack evidence-based thresholds to guide infrastructure and trade policy for long-term development. Addressing this gap, this study applies a Dynamic Panel Threshold Model to uncover the tipping points at which improvements in air cargo volume (lnCargo) and air transport infrastructure quality (lnQAir) translate into stronger economic growth. By employing System-GMM and First-Difference GMM estimations, the analysis captures the threshold effects of air cargo volume (lnCargo) and air transport infrastructure quality (lnQAir) on economic growth over varying regimes. The results reveal significant single-threshold effects for both lnCargo and lnQAir, indicating that their contributions to economic growth become substantial after surpassing specific critical levels. When air cargo volume exceeds approximately 267,067 tons per year (lnCargo > 5.5875), its positive effect on economic growth strengthens, particularly when accompanied by high-quality infrastructure. Similarly, air transport infrastructure quality exhibits a significantly stronger impact on economic growth once it exceeds the critical threshold of lnQAir = 1.5476 (≈4.7001 index points). These findings emphasize the complementarity between trade flows and infrastructure, aligning with endogenous growth theory, which suggests that infrastructure investments yield increasing returns when integrated with trade expansion. Policy implications suggest that ASEAN economies should adopt demand-driven infrastructure development aligned with trade dynamics, prioritizing regional connectivity, logistics efficiency, and investment attraction to sustain long-term economic growth.

1. Introduction

Endogenous growth theory emphasizes that sustained long-term economic growth is driven by continuous investment in key productive assets, including transportation infrastructure, which enhances trade and productivity [1]. Within this framework, both air transport infrastructure and trade are seen as mutually reinforcing drivers of growth: improved infrastructure facilitates trade by reducing costs and increasing connectivity, while expanded trade stimulates further economic activity and productivity gains [2,3]. However, the theory also suggests that these benefits do not materialize instantly; they require persistent investment and systematic improvement over time before translating into lasting development outcomes. This raises an important question for ASEAN economies: how much air transport infrastructure and trade integration are necessary to sustain growth? Addressing this question calls for identifying the critical threshold levels of both factors at which their contribution to economic growth becomes significant and self-reinforcing. This study aims to provide such an assessment for the ASEAN context.

Air transportation infrastructure and trade are vital catalysts for economic growth across the dynamic ASEAN region. By enabling the fast and reliable movement of goods, people, and services, air transport plays a central role in enhancing trade efficiency, attracting investment, and strengthening regional connectivity [4,5,6,7]. Improved air connectivity not only reduces transportation time and costs but also expands market access and facilitates smoother integration into global supply chains. This, in turn, supports ASEAN’s broader economic development and integration objectives [8,9]. The impact of air transport is even more pronounced for landlocked and island economies within the region, where enhanced air infrastructure helps overcome geographical isolation, lowers trade barriers, and encourages economic diversification [10]. Therefore, advancing air transport infrastructure is not merely a logistical improvement—it is a strategic enabler of inclusive and sustainable growth in ASEAN.

While air transportation infrastructure and trade are widely recognized as vital enablers of economic growth, the precise mechanisms through which they jointly contribute to sustained development remain complex, dynamic, and highly context-specific. A growing body of empirical research confirms the positive impact of air transport infrastructure on economic performance (e.g., [4,11]). Likewise, air-based trade, particularly in the form of air cargo, has emerged as a critical conduit for high-value goods, just-in-time production, and global supply chain integration. Despite this, the interdependent roles of air transport infrastructure and air trade flows have often been analyzed in isolation. Most existing studies emphasize the direct effects of individual factors—such as trade volume [12,13], infrastructure quality [14,15,16], and foreign direct investment [17,18,19], on economic growth. While these variables are undeniably important, this segmented approach overlooks the synergistic effects between infrastructure and trade, particularly within the air transport sector. For example, advanced airport infrastructure may only generate significant economic returns if matched by sufficient air cargo volumes; conversely, trade potential may be undermined if logistical infrastructure is inadequate. This suggests that the impact of air trade on growth is not only direct but also conditioned by the quality of supporting infrastructure, a largely absent from the current literature. Moreover, the overwhelming reliance on linear modeling frameworks in previous research fails to capture threshold effects or nonlinear dynamics that may define the true nature of these relationships. Economic returns from air transport investments may only materialize after certain critical thresholds, such as a minimum level of trade activity or infrastructure quality, are met. Ignoring these tipping points may lead to underinvestment or misaligned policy strategies.

This highlights the need for a more understanding of the interaction of direct and indirect effects, and the nonlinearities inherent in the relationship between air infrastructure and economic growth. Recent research highlights that the interplay between air infrastructure quality and air cargo volume plays a crucial role in driving economic growth within ASEAN. Improvements in air transport infrastructure facilitate increased trade flows, enhance connectivity to global markets, and boost the competitiveness of industries reliant on-air cargo transport [20,21,22,23]. According to Park, 2020 [14], high-quality infrastructure can enhance cargo handling capacity, leading competitiveness of comparative advantage. However, its full potential is only realized when cargo volumes surpass a certain threshold. This aligns with the findings of [24] who further revealed a nonlinear dynamic, and highlight an inverted U-shaped effect, where infrastructure initially has a negative impact, then strongly drives growth between thresholds, but weakens beyond a certain point due to diminishing returns. Similarly, [25] argued that without sufficient infrastructure, substantial freight volumes may not lead to meaningful economic gains. Thus, the economic benefit of improvements in air transport depends on sufficient cargo volumes, which indicates that infrastructure alone is not a panacea, but rather a catalyst dependent on trade dynamics. This issue is particularly relevant in the context of threshold effects, where the influence of air transport infrastructure on economic growth is not constant but varies depending on specific conditions, namely trade volume and infrastructure quality. While previous studies have acknowledged the benefits of air transport, they have not sufficiently investigated the critical threshold levels at which infrastructure development and air cargo volume begin to yield significantly positive and sustained economic returns. Understanding these thresholds is vital for guiding strategic investment decisions. When such interdependencies are ignored, countries risk inefficient allocation of resources, either through overinvestment that leads to diminishing marginal returns or underinvestment that constrains economic potential. This challenge is especially pressing in the ASEAN region, where disparities in infrastructure quality and economic development persist. In such heterogeneous contexts, conventional linear models fail to capture the nonlinear and regime-dependent dynamics inherent in the relationship between infrastructure, trade, and growth. What is needed is an analytical approach capable of identifying not just whether these variables matter, but when and under what conditions they exert meaningful influence on economic outcomes.

To address this gap, the present study employs a Dynamic Panel Threshold Model (DPTM), which enables the detection of regime-specific impacts and the estimation of empirically derived threshold values, such as the level of air cargo volume or infrastructure quality beyond which their effect on economic growth becomes statistically and economically significant. By uncovering these critical tipping points, this study provides actionable insights for policymakers seeking to optimize infrastructure investment and trade facilitation. This framework is especially relevant for ASEAN governments aiming to align air transport infrastructure policy with sustainable and inclusive economic development strategies.

Our results reveal that both air transport infrastructure quality and international air cargo volumes significantly enhance economic growth in ASEAN countries, but only once they surpass specific threshold levels. Below these thresholds, their effects are limited or insignificant. This suggests that policy efforts should focus on achieving and maintaining high-quality infrastructure and sufficient trade flow volumes to fully unlock their growth potential.

This paper is organized as follows: Section 2 reviews the existing literature on air transport infrastructure, and trade flows and its effects on economic growth. Section 3 details the data and econometric methodology employed in this study. Section 4 presents the analysis results and offers a comprehensive discussion of the findings. Finally, Section 5 concludes with recommendations for future research.

2. Literature Review

To better understand the complex and potentially nonlinear interactions between air transport infrastructure, trade flows, and economic growth, this section reviews the existing body of literature along two key thematic nexuses: the Air Transport Infrastructure–Growth nexus and the Air Trade Flows–Growth nexus. These streams of research offer important theoretical and empirical foundations for analyzing how improvements in aviation infrastructure and trade volume, particularly air cargo, influence macroeconomic outcomes.

2.1. Air Transport Infrastructure Growth

Air transport infrastructure is critical in driving economic growth by enhancing connectivity, trade, and tourism. Well-developed airport networks improve the efficiency of passenger and cargo transportation, reducing travel time and costs while facilitating business activities. Numerous studies have established a strong link between air transport infrastructure and economic performance, particularly in trade-driven economies. Ref. [14] highlighted those industries with a high dependence on logistics services derive substantial benefits from well-developed transport infrastructure and effective logistics systems, resulting in a comparative advantage conducive to economic expansion. Similarly, Ref. [26] emphasized that air connectivity enhances productivity by integrating economies into global supply chains, fostering investment, and increasing trade flows. The impact of air transport on economic growth is particularly pronounced in ASEAN countries, where regional integration and trade liberalization have heightened the demand for efficient air transport networks. Research by [27] found that ASEAN’s open skies policy has stimulated market competition, improved airline efficiency, and contributed to regional economic development by reducing barriers to air connectivity.

The relationship between air transport infrastructure and economic growth is not uniform across all ASEAN countries. While developed economies such as Singapore, Malaysia, and Thailand have leveraged strong aviation hubs to enhance trade, tourism, and investment, less developed nations like Laos, Cambodia, and Myanmar face challenges due to inadequate infrastructure and limited financial capacity for expansion [22,28]. Ref. [18] examined the long-term effects of transportation infrastructure on economic growth across various regions, revealing that while developed economies experience significant economic gains from air transport improvements, the impact is more constrained in developing nations due to regulatory inefficiencies, weak institutional frameworks, and lower levels of foreign investment. Furthermore, Refs. [16,29,30] found that enhanced air transport infrastructure leads to increased trade volumes, improved business competitiveness, and higher foreign direct investment (FDI), which are key drivers of economic expansion. Ref. [31] also highlighted that air transport growth creates employment opportunities and boosts local economies, reinforcing its role as a catalyst for economic development.

Various studies, such as those conducted by [15,16,29] have consistently shown a positive correlation between the quality of air transport infrastructure and trade. Strengthening air transport networks boosts economic activity, job creation, and foreign direct investment, with [18] finding that a 1% increase in transport infrastructure supports 0.2416% economic growth in East Asia, the Pacific, and South Asia. Ref. [14] emphasizes that efficient logistics are crucial for both exporting goods and sourcing intermediates, ensuring seamless trade flows, highlighting their significance in ensuring smooth product movement across borders and facilitating the procurement of essential materials. However, the impact varies by region. While air transport significantly drives growth in developed economies [28,32], its effect in developing countries is weaker due to infrastructure gaps and economic constraints. Ref. [33] found that air transport improvements had minimal impact on South Asia’s large, low-income economies, while Hong et al., 2011 [34] noted that air transport’s contribution to growth in Asian countries was often insignificant. This highlights the need for developing nations to strengthen air transport infrastructure to maximize its economic benefits.

To fully realize the economic potential of air transport infrastructure, ASEAN countries need to implement strategic policies that integrate aviation investment with overall economic objectives. Studies by [33,35] suggest that targeted investment in aviation infrastructure, particularly in emerging economies, can stimulate regional economic integration and unlock growth potential. However, caution is needed to avoid overcapacity and inefficiencies, as excessive investment in underutilized infrastructure can lead to diminishing returns [14,20]. This underscores the importance of examining the nonlinear effects of air transport infrastructure on economic growth in ASEAN, ensuring that investments are optimized for sustainable and long-term development.

2.2. Air Trade Flows Growth

Air trade is a critical component of the global economy, enabling rapid and efficient transport of goods across long distances, which facilitates international trade and drives economic growth. This speed and efficiency are crucial for facilitating international trade, driving economic growth, and supporting complex global supply chains. Furthermore, air trade fosters global connectivity, supporting industries such as tourism, manufacturing, and logistics. This crucial role of air trade is made possible by a robust transportation infrastructure. Studies have shown a strong link between high air cargo volume and economic growth, emphasizing the necessity of well-developed infrastructure [3,33].

Efficient air cargo infrastructure is a catalyst for international trade and economic prosperity, enabling faster and more reliable goods movement [33,36,37,38]. This efficiency attracts businesses, leading to increased trade and economic stimulation due to streamlined cargo handling [19,39,40,41,42]. Moreover, thriving air cargo activity fosters investment and job creation in related sectors, diversifying the economy [25,37,43]. Research suggests a positive correlation between cargo business share and airline efficiency [34,44,45,46]. Studies in developed economies (e.g., US, Brazil, and China) demonstrate a link between increased air cargo volume and economic growth [47,48].

The correlation between high air cargo volume and the need for robust transportation infrastructure emphasizes the critical importance of such infrastructure for facilitating trade and achieving efficient transport costs [3,33]. Also, such effective air infrastructure promotes international trade and economic expansion [36,37,38]. Consequently, high air cargo volume serves as a key indicator of a nation’s developed transport infrastructure and its contribution to economic prosperity [33]. The availability of reliable and efficient cargo handling within a strong air transport infrastructure proves attractive to businesses [19,39,40]. This, in turn, results in increased trade activity, contributing to a rise in overall cargo volume and further stimulating the economy. Additionally, the flourishing air cargo activity leads to investment and job creation in related industries such as logistics, warehousing, and manufacturing, diversifying the economic base [25,37,43,48].

However, the link between air cargo and economic growth is not universally positive, some studies have found no significant relationship in ASEAN [49]. In developing economies, research suggests a negative correlation, potentially due to several interconnected factors [32,33]. These factors include delayed infrastructure development [32,33] and disparities in infrastructure utilization, geographical conditions, skilled labor, governance, innovation, and technology compared to developed nations [50,51]. A key consideration within this complex interplay is the potential for diminishing returns on infrastructure investment. This means that beyond a certain point, increasing investment in air cargo infrastructure may not yield proportionally larger economic benefits. Therefore, optimizing air cargo volume, considering the complex interplay of factors and the risk of diminishing returns, is crucial for maximizing economic benefits, especially in developing economies.

Following the previous literature, the pivotal role of air transport infrastructure quality in facilitating increased air cargo volume is well established. Upgraded infrastructure reduces transportation costs, enhances operational efficiency, and contributes to varying levels of economic growth. However, past studies have typically examined either infrastructure quality or trade volume in isolation, overlooking their interconnected and mutually reinforcing effects on economic performance. The interaction between infrastructure quality and trade efficiency is critical for optimizing trade operations and maximizing economic returns. Yet, despite their strategic importance, no existing studies have empirically identified the critical threshold levels at which improvements in air transport infrastructure and trade flows begin to significantly and sustainably drive economic growth.

Building on the theoretical foundations of endogenous growth theory and the empirical literature linking infrastructure, trade, and economic performance, this study focuses on the role of air transport infrastructure and trade flows in ASEAN countries. Prior studies suggest that these factors can stimulate growth by enhancing connectivity, reducing transaction costs, and facilitating the movement of goods, services, and people. However, the magnitude and significance of their effects may depend on whether certain critical levels, referred to as thresholds, are reached, beyond which their contribution to growth becomes more pronounced. Thus, we propose the following hypotheses:

Hypothesis 1:

The development of air transport infrastructure and the expansion of trade flows have a significant positive impact on economic growth in ASEAN countries.

Hypothesis 2:

The impact of air transport infrastructure and trade flows on economic growth in ASEAN countries exhibits a threshold effect, becoming significantly positive only when these factors surpass specific critical levels.

3. Data and Econometric Methodology

3.1. Data

Air transport infrastructure and trade are critical for economic growth, and their importance has been widely recognized in studies of air transport development. The Air Transport Infrastructure Growth nexus highlights the role of high-quality air transport infrastructure in strengthening connectivity, reducing trade costs, and facilitating economic expansion [15,18,52]. Improved airport infrastructure enhances air cargo efficiency, which in turn boosts trade flows and economic performance. Empirical research indicates that air cargo volume significantly contributes to economic growth, with key metrics such as airport capacity and cargo volume serving as indicators of its impact [53,54]. To assess infrastructure quality, studies employ the Air Transport Infrastructure Quality Index, measured on a Likert scale from 1 (extremely underdeveloped) to 7 (world-class infrastructure) based on expert surveys and international benchmarks [33]. This index evaluates key factors such as airport efficiency, runway capacity, logistics performance, and technological advancements, which are essential for maintaining competitiveness in global trade. Additionally, international air cargo volume, measured in thousand tons, is considered a more precise indicator of air trade’s economic contribution compared to passenger throughput, as it directly reflects trade intensity and logistics efficiency [32,54]. These linkages emphasize the need for investment in air transport infrastructure to optimize trade flows and sustain long-term economic growth. To account for broader economic dynamics, the model includes foreign direct investment (FDI), income inequality (Gini coefficient), capital investment, and the labor force. These variables are consistent with economic growth theory [1,26,55] and supported by empirical studies linking infrastructure, trade, and macroeconomic performance. FDI fosters growth through capital inflows, technology transfer, and integration into global markets, complementing air transport development [8,18,19]. The Gini coefficient captures inequality, which can influence aggregate demand and the distribution of growth benefits [34,56]. Capital investment increases productive capacity and complements infrastructure [9,29], while the labor force reflects human resources essential for translating infrastructure and trade flows into productivity gains [11,32]. Finally, GDP per capita serves as the dependent variable, providing a measure of economic growth.

This analysis utilizes panel data from eight ASEAN countries—Brunei Darussalam, Cambodia, Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Viet Nam—spanning the period from 2008 to 2023. Myanmar and Lao People’s Democratic Republic are excluded due to data limitations. The data is sourced from multiple reliable databases, including the ASEAN database, the World Bank database, the SWIID database, and the Global Economy database, comprising a total of 160 observations. Descriptive statistical results for each variable are presented in

Table 1. Descriptive statistics of variables.

Variable	Description	Mean	S.D.	Min	Max	Data Source
lnGDP	Log of GDP per capita (constant 2015 USD)	8.4758	1.2673	6.5647	11.1265	World Bank
FDI	Foreign direct investment (% of GDP)	0.0762	0.1521	−0.0227	1.3036	World Bank
GINI	Growth of Gini coefficient	−0.0019	0.0321	−0.2497	0.1212	SWIID
lnCap	Log of capital investment (gross fixed capital formation, % of GDP)	3.2887	0.2004	2.6152	3.7152	Global Economy Database
lnLabor	Log of labor force	16.2947	1.7918	12.1416	18.7637	World Bank
lnCargo	Log of international air cargo (thousand tons)	4.5010	2.2385	−3.2188	7.0604	ASEAN Statistics
lnQAir	Log of air transport infrastructure quality index (1 = low, 7 = high)	1.5350	0.1978	0.7975	1.9315	Global Economy Database

.

3.2. Econometric Methodology

This study investigates the potentially nonlinear impact of air transport infrastructure, trade, and economic growth. We utilize a DPTM, using lnCargo and lnQAir as threshold variables, to examine how this relationship varies and to address endogeneity and reverse causality [57].

Our Dynamic Panel Threshold Model is

$$\ln {\mathrm{GDP}}_{it}={\displaystyle \sum _{j=1}^k{\beta }_j^{\left(1\right)}{X}_{jit}I\left(q_{it}\le \gamma \right)+}{\displaystyle \sum _{j=1}^k{\beta }_j^{\left(2\right)}{X}_{jit}I\left(q_{it}>\gamma \right)+}{u}_i+{\epsilon }_{it},$$

(1)

where the subscripts i = 1, …, N represents the country (N = 10) and t = 1, …, T indexes the time (T = 16). $X_{it}$ is the set of regressors, including an endogenous variable (lagged of lnGDP ($\ln {\mathrm{GDP}}_{it-1}$), as well as FDI, GINI, lnCap, lnLabor, lnCargo lnQAir). We note that lnCargo and lnQAir are both included as regressors to investigate their nonlinear impact on economic growth and to examine their roles relative to each other. ${\beta }_j^{\left(1\right)}$ and ${\beta }_j^{\left(2\right)}$ are the corresponding coefficients of regimes 1 and 2, respectively. $q_{it}$ is a threshold variable which can be either lnCargo or InQAir. $I(·)$ is an indicator function that takes the value of 1 if the condition is met, and 0 otherwise. $\gamma$ serve as threshold parameter. $u_i$ is the country-fixed effect, and ${\epsilon }_{it}$ is the random disturbance term.

To estimate our model, we use dynamic panel GMM estimators, viz the difference GMM [58], and system GMM [59,60] to address unobserved country-specific effects and endogeneity, especially with lagged dependent variables. The GMM estimators use instrumental variables to address endogeneity, with one set correcting for endogenous regressors and another handling the correlation between the lagged dependent variable and the error term. An assumption for GMM validity is the absence of second-order serial correlation, which Arellano and Bond, 1991 [58] test using an autocorrelation test, while the Hansen J test evaluates instrument validity. The system GMM, introduced by Blundell and Bond, 1998 [60], improves upon the difference GMM by incorporating both differenced and level equations, enhancing efficiency and reducing endogeneity bias. Refs. [56,61] validated its advantages, making system GMM a widely used method in dynamic panel data analysis for economic growth.

To estimate the threshold parameter $\gamma$, we use an endogenous search procedure based on minimizing the GMM criterion function. This function measures the discrepancy between the sample moment conditions and their theoretical expectations under the null hypothesis of instrument validity. For each candidate threshold value $\gamma$, the sample is split into two regimes, and the model is estimated using System-GMM. The corresponding GMM objective function is defined as

$$J\left(\gamma \right)=\widehat{g}\left(\gamma {)}^{\prime }{W}^{-1}\widehat{g}\right(\gamma ),$$

(2)

where $\widehat{g}\left(\gamma \right)$ is the vector of sample moment conditions, typically of the form as

$$\widehat{g}(\gamma )={\displaystyle \frac{1}{N}}{\displaystyle \sum _{i=1}^N{Z}_i^{\prime }}{\widehat{\epsilon }}_i(\gamma ).$$

(3)

Note that $Z_i$ is the instrument matrix for individual i, which includes lagged values of endogenous variables. ${\widehat{\epsilon }}_i\left(\gamma \right)$ is the vector of residuals from the System-GMM estimation for a given threshold $\gamma$ (Equation (1)) and $W$ is a positive–definite weighting matrix, often set as a consistent estimate of the variance–covariance matrix of the moment conditions.

The estimator $\widehat{\gamma }$ is chosen as the value of $\gamma$ ∈ Γ (the admissible set of threshold values) that minimizes the J-statistic, meaning the set of moment conditions is best satisfied under that threshold specification.

$$\widehat{\gamma }=\arg {\min }_{\gamma \in \Gamma }J\left(\gamma \right)$$

(4)

The intuition is that a lower value of $J\left(\gamma \right)$ indicates a better fit of the instruments to the model, conditional on a specific threshold split. The minimization of $J\left(\gamma \right)$ is therefore equivalent to selecting the threshold that best partitions the data into two regimes, such that the orthogonality conditions between instruments and residuals are most closely satisfied.

To ensure proper identification, we exclude extreme values by trimming the top and bottom 5% of the threshold variable’s distribution—whether lnCargo or lnQAir. In our study, these variables represent the thresholds themselves and are central to our main research question, “When Does Air Transport Infrastructure and Trade Flows Matter?” This trimming step serves two purposes. First, it ensures that both regimes (observations below the threshold and those above it) contain enough data points for meaningful estimation. If one regime is too small, model convergence becomes difficult, and the estimates risk becoming biased or unstable. Second, trimming prevents the threshold from being set at values driven by statistical outliers, which could distort the true relationship between air transport, trade flows, and economic growth. By limiting the search to thresholds within the central range of lnCargo or lnQAir, we focus on values that are both representative of real-world conditions and robust in statistical terms. This approach allows us to determine the specific points at which air transport infrastructure or trade flows begin to have significantly different effects on economic growth across ASEAN countries, a key novelty and contribution of our paper.

4. Empirical Results and Discussion

4.1. Stationarity Test and Multicollinearity Test

Before estimating the econometric models, we conduct pre-diagnostic tests to ensure the robustness of our analysis by examining panel variable stationarity and assessing multicollinearity among independent variables. For stationarity, we apply first- and second-generation unit root tests, including the Levin, Lin, and Chu (LLC) test Levin et al., 2002 [62], the IPS test, and the CIPS test [63]. Multicollinearity is evaluated using correlation analysis and the variance inflation factor (VIF) test, as shown in

Table 2. Correlation analysis and VIF test.

Variable	VIF	lnCargo	lnQAir	FDI	lnCap	lnLabor	Gini
lnCargo	3.62	1.0000
lnQAir	1.59	0.3008	1.0000
FDI	1.23	0.0975	0.1139	1.0000
lnCap	1.11	−0.1262	−0.0480	−0.0786	1.0000
lnLabor	2.29	0.4733	−0.1860	−0.2574	0.0319	1.0000
Gini	1.39	0.3998	0.2143	0.0088	−0.0096	0.2885	1.0000

. The highest correlation is observed between lnCargo and lnLabor, while the lowest is between lnCap and lnLabor, with all correlations remaining below 0.8, indicating weak interdependencies among the explanatory variables. The VIF test further supports this, with all variables exhibiting VIF values below 10 [56], confirming the absence of severe multicollinearity. Additionally,

Table 3. Results of panel unit root test at the level.

Variable	LLC	IPS	CIPS
lnGDP	5.5006 ***	−2.6284 ***	−3.084 **
lnCargo	−7.1188 ***	−6.5852 ***	−3.556 ***
lnQAir	−2.3240 ***	−2.1023 ***	−2.950 ***
FDI	−3.6864 ***	−3.6169 ***	−2.634 *
lnCap	−2.9637 ***	−2.3702 *	−2.898 *
lnLabor	−5.9814 ***	−3.4876 ***	−2.810 *
Gini	−8.7334 ***	−2.7801 ***	−3.096 **

Note: ***, ** and * represent statistical significance at the levels of 1%, 5%, and 10%, respectively.

presents the results of panel unit root tests, conducted with both intercept and trend specifications, where lag selection is based on the lowest Akaike information criterion. The findings confirm that all variables are stationary at level, validating their suitability for subsequent empirical analysis.

4.2. The Interaction of Air Transport Infrastructure and Trade Flows on Economic Growth in ASEAN

Our study examines the nonlinear relationship between air transport infrastructure, trade flows, and economic growth in ASEAN, focusing on the threshold effects of air transport infrastructure quality (lnQAir) and air cargo volume (lnCargo) on economic growth. To capture these dynamics, we employ DPTM to examine the threshold effects of lnCargo and lnQAir influence on economic growth across different regimes. The threshold effect tests, presented in

Table 4. Results of threshold effect significance tests.

Threshold Variable	Number of Thresholds	F-Statistic	Thresholds Value	95% Confidence Intervals
lnCargo	Single	42.240 ***	5.5875	[5.431–5.620]
	Double	13.242
lnQAir	Single	23.994 *	1.5476	[1.450–1.604]
	Double	10.834

Note: ***, ** and * represent statistical significance at levels of 1%, 5%, and 10%, respectively.

, provide empirical evidence of significant regime shifts. For the lnCargo variable, we observe a single threshold effect that is statistically significant at the 5% level, with an F-statistic of 27.834. The estimated threshold value for lnCargo is 5.5875, falling within a 95% confidence interval of [5.431, 5.620]. Similarly, the lnQAir variable also exhibits a single threshold effect, significant at the 10% level with an F-statistic of 23.994. Its threshold value is estimated at 1.5476, with a 95% confidence interval of [1.450, 1.604]. Notably, single threshold effects are significant for both variables; the double threshold effects do not demonstrate statistical significance for either lnCargo (F-statistic: 13.242) or lnQAir (F-statistic: 10.834). These findings provide strong evidence supporting the use of panel threshold regression estimation in our analysis.

To capture these nonlinear dynamics, we employ three estimation methods: Panel Threshold Fixed Effects, Dynamic Panel Threshold System-GMM, and Dynamic Panel Threshold First-Difference GMM to analyze the interaction between air transport infrastructure and trade flows on economic growth in ASEAN. Each method shows how lnCargo and lnQAir impact economic growth under different conditions. The estimation results of the model for the impact of air transport infrastructure and trade on economic growth are shown in Figure 1. Detailed information is presented in

Table 5. Parameter estimates from dynamic panel threshold model using air cargo volume (lnCargo) and air transport infrastructure quality (lnQAir) as threshold variables.

Threshold Variable	Air Cargo Volume (lnCargo)			Air Transport Infrastructure Quality (lnQAir)
Variable	Panel Fixed Effect	Dynamic SYSTEM-GMM	First Difference-GMM	Panel Fixed Effect	Dynamic SYSTEM-GMM	First Difference-GMM
Regime 1
lnGDPit-1		0.983 *** (0.022)	0.901 *** (0.056)		0.993 *** (0.005)	0.912 *** (0.017)
lnCargo	0.045 * (0.025)	0.012 *** (0.005)	0.040 ** 0.021	0.005 (0.013)	0.002 (0.003)	0.002 (0.003)
lnQAir	−0.132 (0.223)	−0.053 (0.028)	−0.029 0.094	−0.051 0.145	0.057 * (0.029)	0.056 ** (0.028)
lnK	0.086 (0.120)	0.031 *** (0.012	0.100 * 0.057	0.203 * (0.118)	0.137 *** (0.032)	0.116 *** (0.029)
lnFDI	0.438 *** (0.241)	0.027 ** (0.013)	0.305 *** 0.113	0.465 *** (0.171)	0.073 * (0.044)	0.051 (0.036)
lnGINI	0.394 * (0.212)	0.002 (0.046)	0.049 (0.451)	0.148 (0.172)	0.126 *** (0.043)	0.084 ** (0.036)
lnL	−0.110 *** (0.039)	−0.008 (0.012)	−0.006 (0.013)	−0.011 (0.019)	0.008 * (0.004)	0.005 (0.004)
Regime 2
lnGDPit-1		0.976 *** (0.004)	0.901 *** (0.018)		0.973 *** (0.014)	0.896 *** (0.018)
lnCargo	0.035 *** (0.011)	0.032 *** (0.008)	0.062 * (0.035)	0.005 (0.015)	0.015 *** (0.006)	0.004 (0.003))
lnQAir	0.0184 * (0.106)	0.045 * (0.024)	0.081 *** (0.022)	1.119 *** (0.251)	0.089 *** (0.032)	0.124 ** (0.054)
lnK	0.002 (0.145)	0.108 *** (0.033)	0.100 *** (0.035)	0.023 (0.110)	0.013 (0.022)	0.114 *** (0.028)
lnFDI	0.737 *** (0.139)	0.046 * (0.027)	0.077 ** (0.039)	0.038 (0.117)	0.001 (0.001)	0.055 * (0.031)
lnGINI	−1.208 *** (0.205)	−0.011 ** (0.004)	−0.138 *** (0.051)	−0.479 *** (0.1801)	−0.013 (0.051)	−0.174 *** (0.425)
lnL	0.253 *** (0.041)	0.027 * (0.008)	0.013 (0.010)	0.019 (0.018)	0.0019 *** (0.003)	1.094 *** (0.205)
AR(1) p-value		0.008	0.023		0.002	0.045
AR(2) p-value		0.783	0.882		0.304	0.634
Hansen J Test p-value		0.2004	0.593		0.2734	0.3239

Note: ***, ** and * represent statistical significance at the levels of 1%, 5%, and 10%, respectively.

.

Table 5 presents the estimation results, differentiating between models where lnCargo serves as the threshold variable (columns 1–3) and lnQAir acts as the threshold variable (columns 4–6). For each threshold variable, results are reported using Fixed Effects (columns 1 and 4), System GMM (columns 2 and 5), and First-Difference GMM (columns 3 and 6). The results reveal a consistent pattern across Regime 1 (below the threshold) and Regime 2 (above the threshold), confirming the robustness of our findings. As detailed in the methodology section, this study employs the System-GMM estimator as its primary analytical approach. Thus, the findings and conclusions presented below are derived from the System-GMM estimation results.

Considering lnCargo as the threshold, our findings indicate that when lnCargo is below the threshold of 5.5875 (exp(5.5875) ≈ 267,067 tons per year), its impact on economic growth is small positive, with a 1% increase in lnCargo corresponding to a 0.012% increase in economic growth. However, lnQAir remains negative, suggesting that infrastructure improvements alone do not drive growth at low trade volumes. This underscores the inefficiency of underutilized infrastructure in early-stage trade expansion, where insufficient cargo flow limits its economic benefits. Once lnCargo surpasses this threshold, its effect on growth more than doubles, with a 1% increase in lnCargo leading to a 0.032% increase in economic growth, highlighting the stronger role of trade expansion in high-cargo conditions. At this stage, lnQAir also turns positive and significant, a 1% increase in lnQAir contributing to a 0.045% increase in economic growth. This shift suggests that infrastructure investment becomes more effective when trade volumes reach a critical point, reinforcing the complementarity between trade flows and transport infrastructure. These findings align with Romer’s (1986 [55], 1990 [1]) endogenous growth theory, which suggests that infrastructure improvements yield increasing returns when integrated with productive economic activities. Additionally, they are supported by Karanki et al., 2024 [64], who found that high cargo volume airports experience greater productivity growth (4.1%) compared to low cargo volume airports (2.9%), consistent with [65] efficiency theory. Additionally, capital and FDI contribute positively to growth across both regimes, with their impacts increasing significantly in the high lnCargo regime, while income inequality and labor only become significant drivers of growth when trade volumes are high. These results suggest that air transport infrastructure and trade flows complement each other, where efficient infrastructure enhances trade capacity, and increased trade further amplifies the economic benefits of infrastructure investment, reinforcing ASEAN’s long-term growth trajectory.

Expanding our analysis by considering lnQAir as the threshold variable, we find that air transport infrastructure quality plays a crucial role in shaping economic growth, with a more pronounced effect in a high-quality air transport infrastructure regime. When lnQAir is below 1.5476 (exp(1.5476) ≈ 4.7001), a 1% increase in lnQAir leads to a 0.057% rise in economic growth, while surpassing this threshold amplifies the effect to 0.089%, indicating infrastructure-driven efficiency gains. This aligns with [21,66], who suggest that strong air transport infrastructure attracts businesses and investors by enhancing connectivity, ensuring efficient logistics, and reducing trade costs, thus promoting international trade and investment. Moreover, examining its moderating role on air cargo volume, we find that in a high-quality air transport infrastructure regime, a 1% increase in lnCargo contributes to a 0.015% rise in economic growth, while in a low-quality infrastructure regime, its effect is statistically insignificant. This reinforces [67,68], who highlight the importance of advanced logistics and infrastructure in facilitating trade efficiency. Additionally, we observe a structural shift in the significance of control variables across infrastructure quality regimes. In low-quality infrastructure, capital, FDI, inequality, and labor force significantly drive growth, indicating that economies rely on fundamental inputs when infrastructure is underdeveloped. However, in high-quality regimes, the significance of capital and FDI diminishes, while labor continues to play a modest role in both regimes, suggesting diminishing returns to traditional growth factors as infrastructure improves [69].

Overall, our findings confirm the critical role of air transport infrastructure and trade volume in driving economic growth in ASEAN, consistent with Hypothesis 1, which posits a significant positive relationship between these factors and growth. The results also support Hypothesis 2, which predicts a threshold effect—where the benefits of air transport development and trade flows become more pronounced only after surpassing specific critical levels. Specifically, when annual air cargo volume is below 267,067 tons (or lnCargo < exp(5.5875)), infrastructure improvements yield limited gains, reflecting the inefficiency of underutilized facilities. Similarly, when the air transport infrastructure quality index is below lnQAir = 4.7001 (or lnQAir < exp(1.5476)), its contribution to growth remains modest, as inadequate infrastructure fails to deliver connectivity and trade efficiency benefits. These findings align with prior studies highlighting that infrastructure impacts are contingent upon adequate utilization and complementary trade activity [9,11,29]. They also reinforce the complementarity identified in the literature between infrastructure investment and trade expansion, where developed infrastructure maximizes trade benefits, and greater trade volumes enhance the returns to infrastructure [5,34]. Moreover, our results reveal that capital accumulation and FDI become increasingly important in high-cargo regimes, echoing evidence from [18,54] that investment inflows amplify the growth effects of transport and trade.

Nevertheless, to refine country-specific policies for each ASEAN country, we apply threshold effects at lnCargo = 5.5875 and lnQAir = 1.5476, capturing to account for spatial and temporal heterogeneity in air cargo volume and transport infrastructure quality.

Table 6. Country regimes classified by thresholds of air cargo volume and air transport infrastructure quality.

Regime	Air Cargo Volume (lnCargo)		Air Transport Infrastructure Quality (lnQAir)
	lnCargo < 5.5875	lnCargo ≥ 5.5875	lnQAir < 1.5476	lnQAir ≥ 1.5476
Year	Country
2008	Brunei Darussalam, Myanmar, Cambodia, Lao PDR, Viet Nam, Indonesia	Malaysia, Philippines, Singapore, Thailand	Cambodia, Lao PDR, Philippines, Viet Nam, Indonesia	Brunei Darussalam, Malaysia, Myanmar, Singapore, Thailand
2009	Brunei Darussalam, Myanmar, Cambodia, Viet Nam, Indonesia	Lao PDR, Malaysia, Philippines, Singapore, Thailand	Cambodia, Philippines, Viet Nam, Myanmar, Indonesia	Brunei Darussalam, Lao PDR, Malaysia, Singapore, Thailand
2010	Brunei Darussalam, Myanmar, Cambodia, Viet Nam, Indonesia	Lao PDR, Malaysia, Philippines, Singapore, Thailand	Cambodia, Philippines, Viet Nam, Lao PDR, Indonesia	Brunei Darussalam, Malaysia, Singapore, Thailand, Myanmar,
2011	Brunei Darussalam, Myanmar, Lao PDR, Cambodia, Viet Nam, Indonesia	Malaysia, Philippines, Singapore, Thailand	Cambodia, Philippines, Viet Nam Indonesia	Brunei Darussalam, Lao PDR, Malaysia, Myanmar, Singapore, Thailand
2012	Brunei Darussalam, Myanmar, Lao PDR, Cambodia, Viet Nam, Indonesia	Malaysia, Philippines, Singapore, Thailand	Philippines, Viet Nam, Cambodia, Myanmar, Indonesia	Brunei Darussalam, Malaysia, Lao PDR, Singapore, Thailand
2013	Brunei Darussalam, Myanmar, Lao PDR, Cambodia, Indonesia	Malaysia, Philippines, Singapore, Thailand, Viet Nam	Cambodia, Lao PDR, Myanmar, Philippines, Viet Nam, Indonesia	Brunei Darussalam, Malaysia, Singapore, Thailand
2014	Brunei Darussalam, Myanmar, Lao PDR, Cambodia, Indonesia	Malaysia, Philippines, Singapore, Thailand, Viet Nam	Cambodia, Lao PDR, Myanmar, Philippines, Viet Nam, Indonesia	Brunei Darussalam, Malaysia, Singapore, Thailand
2015	Brunei Darussalam, Myanmar, Lao PDR, Cambodia, Viet Nam Indonesia	Malaysia, Philippines, Singapore, Thailand	Cambodia, Lao PDR, Myanmar, Philippines, Viet Nam, Indonesia	Brunei Darussalam, Malaysia, Singapore, Thailand
2016	Brunei Darussalam, Myanmar, Lao PDR, Cambodia, Viet Nam Indonesia	Malaysia, Philippines, Singapore, Thailand	Brunei Darussalam, Cambodia, Lao PDR, Philippines, Viet Nam Indonesia	Myanmar, Singapore, Thailand Malaysia,
2017	Brunei Darussalam, Lao PDR, Cambodia, Myanmar, Viet Nam Indonesia	Malaysia, Philippines, Singapore, Thailand	Brunei Darussalam, Cambodia, Lao PDR, Malaysia, Myanmar, Philippines, Viet Nam	Singapore, Thailand, Indonesia
2018	Brunei Darussalam, Lao PDR Cambodia, Myanmar, Indonesia	Malaysia, Philippines, Singapore, Thailand, Viet Nam	Brunei Darussalam, Cambodia, Lao PDR, Malaysia, Myanmar, Philippines, Viet Nam	Singapore, Thailand, Indonesia
2019	Brunei Darussalam, Lao PDR Cambodia, Myanmar, Viet Nam Indonesia	Malaysia, Philippines, Singapore, Thailand	Cambodia, Lao PDR, Malaysia, Philippines, Viet Nam	Brunei Darussalam, Myanmar, Singapore, Thailand, Indonesia
2020	Brunei Darussalam, Myanmar, Lao PDR, Cambodia, Philippines, Viet Nam, Indonesia	Malaysia, Singapore, Thailand	Brunei Darussalam, Malaysia Cambodia, Philippines, Thailand	Lao PDR, Myanmar, Singapore, Viet Nam, Indonesia
2021	Brunei Darussalam, Cambodia Lao PDR, Myanmar, Philippines	Malaysia, Singapore, Thailand, Viet Nam, Indonesia	Brunei Darussalam, Malaysia, Thailand, Cambodia, Myanmar, Singapore	Lao PDR, Philippines, Viet Nam Indonesia
2022	Brunei Darussalam, Cambodia Lao PDR, Philippines, Viet Nam Indonesia	Malaysia, Myanmar, Singapore, Thailand,	Lao PDR, Myanmar, Singapore, Viet Nam	Brunei Darussalam, Cambodia, Malaysia, Philippines, Thailand, Indonesia
2023	Cambodia	Brunei Darussalam, Lao PDR, Malaysia, Myanmar, Viet Nam Philippines, Singapore, Thailand, Indonesia	Lao PDR, Myanmar, Philippines, Thailand, Indonesia	Brunei Darussalam, Cambodia, Malaysia, Singapore, Viet Nam
Average 2008–2023	Brunei Darussalam, Myanmar, Viet Nam, Lao PDR, Cambodia, Indonesia	Malaysia, Philippines, Singapore, Thailand	Cambodia, Philippines, Viet Nam, Lao PDR, Myanmar, Indonesia	Brunei Darussalam, Singapore, Thailand, Malaysia

classifies ASEAN economies according to these threshold regimes, providing insights into their structural positioning over time and underscoring the necessity of tailored policy interventions rather than one-size-fits-all strategies. Our findings indicate that five ASEAN economies, Viet Nam, Lao PDR, Indonesia, Myanmar, and Cambodia, fall within the low-air cargo volume and low-air transport infrastructure quality category. These countries must prioritize air transport investments through public–private partnerships (PPPs), enhance logistics efficiency, and develop regional air cargo hubs to facilitate trade expansion. Brunei Darussalam, despite exhibiting low cargo volume, benefits from high infrastructure quality and should leverage its existing facilities by attracting logistics hubs and fostering trade agreements to stimulate air freight activity. Conversely, Singapore, Thailand, and Malaysia, which are positioned in the high cargo volume and high infrastructure quality regime, should focus on advancing smart logistics, expanding regional connectivity, and integrating sustainability measures to maintain their competitive edge in air transport. Meanwhile, the Philippines, characterized by high cargo volume but relatively low infrastructure quality, must prioritize air transport modernization, expand airport capacity, and strengthen regulatory frameworks to accommodate growing trade demands. By implementing targeted, demand-driven policies aligned with their respective threshold regimes, ASEAN economies can enhance the efficiency and competitiveness of their air transport sectors, fostering long-term economic growth and regional integration.

4.3. Verification Test

To verify the validity of our SYSTEM-GMM estimations, we conduct the Arellano–Bond tests for autocorrelation and the Hansen J test for overidentifying restrictions for both models. The results, reported in the last panel of Table 5, confirm that the AR(1) test is significant, indicating the expected presence of first-order serial correlation in dynamic panel models. More importantly, the AR(2) test is non-significant, suggesting the absence of second-order serial correlation in the original error terms. This finding validates using lagged dependent variables as instruments, ensuring their appropriateness in addressing endogeneity concerns [58].

Additionally, the Hansen J test fails to reject the null hypothesis in both models, confirming that our instrument set is valid and that the overidentifying restrictions hold. The test result suggests that our instruments are uncorrelated with the error term, reinforcing their exogeneity. To further assess instrument strength, we examine the instrument count relative to sample size, ensuring that our model avoids potential overfitting issues due to instrument proliferation. The diagnostic tests collectively support the efficiency and consistency of our SYSTEM-GMM estimations, confirming the robustness of our model specification and enhancing confidence in our empirical findings.

Table 7. Comparison of coefficient estimates between regimes under system-GMM estimation.

Threshold Variable	Air Cargo Volume (lnCargo)			Air Transport Infrastructure Quality (lnQAir)
Variable	Difference	SE of Diff	p-Value	Difference	SE of Diff	p-Value
lnCargo	0.020 **	0.009	0.034006	0.013 *	0.006	0.052
lnQAir	0.098 **	0.036	0.007875	0.032 **	0.013	0.458
lnK	0.077 **	0.035	0.028318	−0.124 **	0.038	0.001
lnFDI	0.019	0.029	0.526056	−0.072 *	0.043	0.100
lnGINI	−0.013	0.046	0.778291	−0.139 **	0.066	0.037
lnL	0.035 **	0.014	0.015232	−0.0061	0.005	0.222

Note: ***, ** and * represent statistical significance at the levels of 1%, 5%, and 10%, respectively.

presents a comparison of coefficient estimates across regimes defined by threshold levels of air cargo volume and air transport infrastructure quality, estimated using the System-GMM method. The statistically significant differences in several key variables confirm that the impact of air transport infrastructure and trade on economic growth is not uniform but instead varies significantly across regimes. These statistically significant regime differences demonstrate the validity of the dynamic panel threshold approach, rejecting the assumption of homogeneity in growth drivers. The results confirm that both air cargo volume and infrastructure quality exhibit threshold effects, beyond which their impact on economic growth becomes more (or less) pronounced. This supports the hypothesis that nonlinear and context-specific policy responses are essential for maximizing the growth effects of transport and trade infrastructure in ASEAN economies.

5. Conclusions

This study addresses a critical gap in the literature by examining the nonlinear relationship between air transport infrastructure, trade flows, and economic growth in ASEAN economies. While existing research has acknowledged the positive role of transport and trade in development, few studies have empirically explored the threshold conditions under which their impacts become meaningfully growth-enhancing. In response, this study applies DPTM to capture the regime-dependent effects and uncover the tipping points beyond which infrastructure and trade translate into sustained economic growth.

The empirical evidence confirms the presence of statistically significant threshold effects for both air cargo volume (lnCargo) and air transport infrastructure quality (lnQAir). These findings highlight the fact that scale and quality matter—not all infrastructure investment or trade expansion yields proportional economic returns unless certain foundational conditions are met. Specifically, when air cargo volume remains below approximately 267,067 tons per year (lnCargo < 5.5875), its contribution to growth is marginal, and returns on infrastructure investments are limited. However, once this threshold is exceeded, air transport and trade flows jointly exert a significantly positive effect on economic growth, revealing a strong degree of complementarity. This dynamic aligns with Romer’s (1986 [55], 1990 [1]) endogenous growth theory, which suggests that infrastructure investments stimulate productivity only when paired with active trade and market engagement. A similar pattern is observed for air transport infrastructure quality. When the index remains below lnQAir = 1.5476 (equivalent to 4.7001 index points), improvements have negligible growth effects. Yet once this quality threshold is surpassed, infrastructure not only becomes more effective on its own but also amplifies the positive effects of trade. This supports the idea that infrastructure must reach a certain performance level before it becomes a true enabler of sustainable development.

In addition, our results also reveal shifting roles of growth drivers: Capital and FDI remain significant in both regimes but exhibit stronger effects when trade volume is high. Income inequality and labor show regime-dependent effects, becoming significant in contexts of high cargo volume or superior infrastructure, suggesting that economic structure and factor productivity evolve with infrastructure improvements.

The country-specific policy approaches suggest that infrastructure investment must be complemented by trade expansion. ASEAN countries should adopt demand-driven infrastructure strategies aligned with their economic conditions. Low-cargo and low-infrastructure economies (e.g., Viet Nam, Lao PDR, Indonesia, Myanmar, and Cambodia) should prioritize upgrades and connectivity. Countries with strong infrastructure but low cargo (e.g., Brunei Darussalam) should leverage existing assets to attract trade. Advanced economies (e.g., Singapore, Thailand, and Malaysia) should invest in smart logistics and sustainability, while the Philippines must address infrastructure gaps to support trade growth.

Overall, the key contribution of this study is the identification of critical thresholds that define when air transport infrastructure and trade begin to foster meaningful and sustained economic growth. These findings offer valuable policy insights for ASEAN economies: rather than uniformly investing across all sectors, governments should target threshold-sensitive infrastructure upgrades and trade facilitation measures that push key variables beyond their tipping points. Doing so will help unlock the full potential of air transport as a catalyst for inclusive, resilient, and long-term economic development.

However, this study is not without limitations. First, the analysis focuses exclusively on air transport infrastructure and air cargo, whereas other modes of transport, such as maritime, rail, and road, may also play complementary or competing roles in shaping economic growth outcomes. Second, the infrastructure quality index employed may not fully capture institutional, regulatory, or operational aspects that influence transport efficiency. Third, due to data limitations, the sample is restricted to ASEAN countries and to periods with consistent reporting, which may limit the generalizability of the results. Fourth, while the dynamic panel threshold model addresses endogeneity and regime-specific effects, it does not account for potential spatial spillovers or multi-threshold dynamics, which could yield further insights.

Despite these limitations, the findings provide a strong empirical foundation for policy targeting and can serve as a basis for more refined transportation–growth strategies. Future research could extend the analysis by (i) incorporating additional transport modes to capture the full spectrum of connectivity effects; (ii) using richer, multidimensional indicators of infrastructure quality that account for governance, safety, and operational efficiency; (iii) expanding the sample to include other developing regions for comparative analysis; (iv) integrating spatial econometric methods to examine cross-border spillovers; and (v) applying alternative estimation strategies, such as instrumental variables (IV) estimation, other GMM variants, or bias-corrected least squares dummy variable (LSDVC) estimators, to provide additional checks on endogeneity and strengthen the robustness of the findings.