Search Results (159)

Global value chains have recently changed structurally (“servicification”)—that is, service sectors’ involvement in global value chain processes has become more intensive. We quantify services trade restrictions’ contribution to underdevelopment of global value chain servicification across Asian economies—an underexplored area. The study applies the “structural” gravity trade model and constructs panel data based on the 2025 Trade in Value Added and the Services Trade Restrictiveness Index database developed by the Organization for Economic Co-operation and Development. The empirical analysis covers five major service sectors—trade, transport, I&C, finance, and professional services. First, global value chain servicification remains relatively underdeveloped in most emerging and developing Asian economies, particularly across several service categories. Second, services trade restrictions’ presence significantly and negatively affects global value chain servicification’s extent in these economies. Third, these restrictive measures account for approximately 30–60% of servicification’s observed underdevelopment. Regarding policy implications, removing or easing such trade restrictions could substantially promote global value chain servicification, enhancing productivity and integration for emerging and developing Asian economies. Full article

This study examines how regulatory heterogeneity in digital services trade relates to the carbon intensity of bilateral trade flows. Using a structural gravity framework estimated with Poisson pseudo maximum likelihood (PPML), we analyzed 10,719 bilateral observations from the Eora Multi-Region Input–Output (MRIO) database over 2014–2020. Bilateral gaps in the OECD Digital Services Trade Restrictiveness Index (DSTRI) were used as the main measure of regulatory heterogeneity, and the overall gap was decomposed into infrastructure-related hard barriers and institutional soft barriers. The results suggest that digital regulatory gaps are associated with a higher carbon intensity in trade while also being associated with lower total embodied emissions through reduced trade volumes. This indicates that lower aggregate emissions under regulatory divergence may reflect contraction in trade activity rather than genuine environmental improvement. The decomposition analysis further suggests that infrastructure-related misalignment is more closely associated with carbon inefficiency, whereas institutional divergence operates mainly through its association with trade volume. In addition, environmental policy stringency in the importing country appears to strengthen the positive association between institutional regulatory gaps and carbon intensity, consistent with the possibility of regulatory overload. The study contributes to the sustainability literature by showing that carbon intensity provides a more informative indicator of sustainable trade performance than aggregate emissions alone in fragmented regulatory environments. It also suggests that digital governance, trade policy, and environmental policy should be considered together in promoting more sustainable forms of international trade, particularly in the context of emerging policy frameworks such as WTO digital trade negotiations, OECD digital governance initiatives, and carbon border adjustment mechanisms (CBAMs). Full article

International tourism plays an important role in the global service economy, contributing to trade, employment, and regional development. For this reason, identifying the factors that influence tourist flows is an important issue for tourism policy, market strategy, and infrastructure planning. A large body of research has applied gravity models to analyze tourism flows between countries. While this approach provides a clear economic interpretation, it is usually based on linear specifications and may therefore capture only part of the relationships present in tourism data. This study examines the economic and geographic determinants of international tourism flows to Mongolia using a framework that combines a traditional gravity model with machine learning techniques. Mongolia serves as an instructive empirical setting, a landlocked, geographically peripheral destination whose inbound demand determinants have received limited systematic empirical attention. The analysis uses panel data for 27 origin countries covering the period from 2000 to 2024. In the first stage, a gravity model is estimated to assess how tourism flows relate to economic size and geographic distance. The results show that tourism flows tend to increase with the economic size of origin and destination countries, while greater geographical distance is associated with lower tourism flows. The estimated distance elasticity ranges from approximately −1.85 to −2.10 across model specifications, which is larger in absolute terms than the values typically reported in cross-country studies. This result is consistent with the relatively high travel cost barriers associated with Mongolia’s geographic location. These findings are consistent with the distance decay relationship commonly reported in the tourism literature. In the second stage, machine learning algorithms, including Random Forest, LightGBM, and XGBoost, are used as complementary interpretive instruments rather than forecasting tools to explore possible nonlinear relationships among the explanatory variables. To make the results more interpretable, the contribution of individual variables is examined using SHAP (Shapley Additive Explanations). The machine learning results indicate that some relationships in tourism demand may be nonlinear and not fully captured by the linear gravity specification. Specifically, distance sensitivity is approximately 6.5 times greater in nearby markets than in long-haul markets, with a structural inflexion at around 5700 km. Further analysis suggests that the influence of geographical distance is not uniform across all markets. In particular, tourism flows originating from middle-income countries appear to be more sensitive to increases in travel distance than those from higher-income countries. Overall, the findings indicate that economic size and geographical distance remain key determinants of international tourism flows to Mongolia. At the same time, the use of machine learning methods provides additional insight into potential nonlinear patterns in tourism demand. By combining econometric modelling with explainable machine learning techniques, the study offers an integrated analytical perspective for examining international tourism flows at geographically peripheral destinations where standard gravity assumptions may be insufficient. Full article

This paper provides a historical perspective on comparatively low levels of trade in the Middle East and North Africa (MENA) region, focusing on studies addressing the impact of the Israeli–Palestinian conflict. Our literature review identifies best practices and reviews trade potential estimates and finds that the last year for which a relevant trade potential estimate for the region accounting for the influence of the Israeli–Palestinian conflict is available is 1999. First, we replicate the seminal study that provided the earliest estimation of trade potential. Next, we extend and update this study, using a best practice panel PPML gravity model with ex(/im)porter-year fixed effects for 76 countries (1991–2019 inclusive). Finally, we use two alternative approaches to estimate the intra-MENA trade potential that could have been reaped as a consequence of a geopolitically more stable and open Middle East (ME). In the year 2019, this ‘pot of gold’ (POG) in per cent of intra-MENA trade amounted to 10% to 54% (import-based) and 21% to 48% (export-based), substantially lower than earlier literature reports. Full article

Regional trade liberalization via preferential agreements increasingly shapes economic outcomes in small open economies embedded in overlapping regional frameworks. This study evaluates the short-run economy-wide effects of tariff and non-tariff measure (NTM) reforms under the South Asian Free Trade Area (SAFTA) and the Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) using a Computable General Equilibrium (CGE) model calibrated to the GTAP 10 database. Gravity-based estimates of ad valorem equivalents (AVEs) of NTMs are integrated into the CGE framework, enabling explicit modeling of regulatory barriers alongside tariff reductions. Policy simulations examine scenarios involving a 90 percent tariff cut and a 50 percent NTM reduction, applied individually and jointly, under a short-run closure with fixed factor endowments and a trade balance for Nepal. Results indicate that combined liberalization yields positive macroeconomic adjustments, with real GDP rising by about one percent and exports increasing by over 14 percent, driven primarily by the manufacturing sector, particularly textiles, while agricultural responses vary by exposure to NTMs. These findings provide policy-relevant evidence on the relative effectiveness of tariff and regulatory reforms, informing strategies for deeper regional integration and enhanced competitiveness in small, structurally constrained economies. Full article

This paper proposes an innovative research algorithm “measurement—pattern—driving force—synergy” that determines the eco-efficiency of 83 Russian federal subjects (2000–2019) using the Slacks-Based Measure (SBM) model with non-desired outputs (incorporating comprehensive input indicators such as water resources and electricity input, and dual non-desired outputs of waste gas and wastewater). Combined with hot spot analysis, a gravity center model, and panel Tobit regression, we reveal the temporal-spatial evolution and driving mechanisms of eco-efficiency in resource-based economies. The research finds that the overall eco-efficiency of Russia is at a medium level and shows a dynamic correlation with the economic development stage. In the early stage of the period under review, there was a high degree of synergy, but the efficiency declined during the period of rapid economic growth. Later, it rebounded somewhat in tie with technological progress. Spatially, it presents a special pattern of low efficiency in the western European industrialized regions and high efficiency in the Arctic and Far East peripheral regions, reflecting the spatial heterogeneity of resource-dependent economies and the survival-constrained efficiency feature. The analysis of influencing factors indicates that per capita GDP has a significant positive driving effect on eco-efficiency, but the expansion of residents’ consumption, the improvement of education level and the dependence on foreign trade all have inhibitory effects, highlighting the path dependence of the current growth model on the structure of resource consumption. The research suggests that Russia should implement differentiated spatial governance in the future, promote the green transformation of consumption and trade structures, and strengthen the ecological orientation of the education and scientific research system to achieve a fundamental transformation of regional sustainable development from survival constraints to innovation-driven. Full article

Amidst the polycrisis of 2018–2024, Asia-Pacific trade flows exhibited a structural resilience that contrasts with traditional theoretical predictions of severe trade contraction under high uncertainty. This study investigates these resilience dynamics using a structural gravity model estimated via the Poisson Pseudo Maximum Likelihood (PPML) approach. The analysis utilizes a balanced panel of 14 key regional economies (N = 4914), explicitly disaggregated into geographic (ASEAN-6 vs. non-ASEAN) and global value chain (high vs. low GVC intensity) subgroups to capture heterogeneous responses. The empirical results confirm that economic policy uncertainty (EPU) acts as a significant trade friction (β = −3.371), consistent with the wait-to-invest mechanism of real options theory. However, this effect is heterogeneous and significantly mitigated by institutional frameworks. We identify a robust institutional shield effect, where participation in trade agreements effectively neutralizes the adverse transmission of policy shocks (interaction coefficient = 3.396). Furthermore, this study uncovers a structural break during periods of extreme geopolitical conflict, characterized by a convex U-shaped relationship between uncertainty and trade. This pattern provides macro-level evidence of a behavioral shift in regional supply chains from a just-in-time cost-efficiency optimization model to a just-in-case security maximization paradigm, consistent with precautionary inventory accumulation. These findings underscore the critical role of modern trade pacts as institutional credibility anchors and the necessity of adaptive strategies in navigating heightened macroeconomic volatility. Full article

This paper studies the dynamic adjustment of bilateral trade following Economic Integration Agreements (EIAs) and examines how financial development and labor market rigidity moderate the timing of trade responses. We approximate the event time adjustment path using a Piecewise Linear Trend (PLT) specification that relaxes global linearity restrictions common in dynamic gravity models. Event study evidence reveals heterogeneous pre-entry and post-entry slopes, particularly at the product-margin level. Split joint pre-trend tests show that aggregate trade satisfies long-run parallel trends, while product-level margins exhibit significant secular restructuring prior to implementation, motivating explicit slope parameterization. Within the PLT framework, financial development is associated with short-run anticipation effects, whereas labor rigidity corresponds to delayed post-entry adjustments. Industry-level interactions indicate that these dynamics vary systematically with sectoral characteristics. The results remain robust to zero-inclusive estimators, alternative institutional proxies, and alternative event time discretizations. Overall, the findings demonstrate that institutional conditions shape the temporal profile of trade adjustment and that flexible slope modeling is essential for identifying dynamic responses to trade liberalization. Full article

Assessing urban resilience under compound shocks requires observable and comparable process evidence that can inform resilient land governance and cross-jurisdiction planning. Using China’s Pilot Free Trade Zones (PFTZs) as a staged institutional setting, this research examines whether institutional exposure is associated with deviation–recovery trajectories of urban activity during the 2020 COVID-19 shock and whether these associations propagate through spatial spillovers with an identifiable scale profile. Institutional exposure is operationalized by the prefecture-level cities actually covered by PFTZ functional areas. With harmonized administrative boundaries, we construct an annual city-level VIIRS nighttime light (NTL) series for 2013–2024 and treat NTL as an activity-change signal rather than a direct proxy for output. We trace shock deviation in 2020 and subsequent recovery via staged differencing. Spatial interaction frictions are represented by least-cost path distance (LCPD) derived from a multi-source cost surface, which is used to build a gravity-based spatial weight matrix. Estimation relies on the Spatial Durbin Model (SDM), with LeSage–Pace impact decomposition to distinguish direct and spillover effects, complemented by distance-threshold diagnostics to map attenuation patterns. Results indicate persistent clustering within the PFTZ-related urban system. The shock year is characterized by compressed connectivity and fragmented brightening, whereas recovery proceeds in a layered manner with earlier core repair, partial corridor reconnection, and weaker adjustment at the periphery. Spatial dependence in activity change is statistically significant. Associations linked to institutional exposure are realized primarily locally, while structural and scale conditions more readily operate through spatial externalities. Spillovers are most detectable at meso-scales and attenuate gradually across distance thresholds. Overall, the integrated earth-observation and spatial-econometric framework provides replicable geospatial evidence to support resilient land governance and regional coordination under common shocks. Full article

High-resolution (HR) marine gravity data are critical for geophysical modeling, seafloor mapping, and tectonic analysis. However, acquiring such data remains challenging due to the inherent trade-offs between distinct measurement sources. While shipborne gravity surveys offer high accuracy and resolution, they are spatially sparse and geographically restricted; conversely, satellite altimetry provides global coverage but comes at the expense of reduced resolution and increased noise. To address this challenge, we propose a semi-supervised dual regression learning (SDRL) framework for gravity field super-resolution (SR) that synergizes the strengths of both data types. By jointly training on a limited number of paired shipborne-satellite samples and a large set of unpaired satellite observations, SDRL leverages cycle-consistent learning to preserve cross-domain structural integrity and enhance generalization. Extensive experiments under varying data conditions—including noisy, ideal, and label-scarce scenarios—demonstrate that SDRL consistently outperforms purely supervised models in terms of structural similarity and error reduction. Moreover, SDRL exhibits strong robustness against data imperfections and generalizes effectively to geophysically distinct test regions. These results highlight the practical advantages of semi-supervised learning for global marine gravity field reconstruction, particularly in real-world settings where high-quality labeled data are scarce. Full article

This study develops a data-driven predictive framework integrating hybrid feature selection, interpretable machine learning, and uncertainty quantification to forecast Olympic medal performance among elite nations. Focusing on the top ten countries from Paris 2024, the analysis employs a three-stage feature selection procedure combining Spearman correlation screening, random forest embedded importance, and the Caterpillar Fungus Optimizer (CFO) to identify stable long-term predictors. A novel test variable, rank, capturing historical competitive strength, and a refined continuous host-effect indicator derived from gravity-type trade models are introduced. Two complementary modeling strategies—a two-way fixed-effects econometric model and a CFO-optimized random forest—are implemented and validated. SHAP, LIME, and partial dependence plots enhance model interpretability, revealing nonlinear mechanisms underlying medal outcomes. Kernel density estimation generates probabilistic interval forecasts for Los Angeles 2028. Results demonstrate that historical performance and event-specific characteristics dominate medal predictions, while macroeconomic factors (GDP, population) and conventional host status contribute marginally once related variables are controlled. Consistent variable rankings across models and close alignment between 2028 projections and 2024 outcomes validate the framework’s robustness and practical applicability for sports policy and resource allocation decisions. Full article

Vietnam’s wooden forest products industry is an important export sector, contributing to industrial growth and employment. However, it is facing increasing pressures related to challenges such as forest and export sustainability. Despite its potential, Vietnam’s export performance remains uneven across destination markets, related to the presence of significant unrealized trade potential. This study examines the determinants of export efficiency in Vietnam’s wooden forest products sector by moving beyond traditional gravity variables to incorporate institutional and cultural dimensions. Using a panel of 70 trading partners between 2004 and 2023, covering more than 93% of Vietnam’s total wood exports, this study employs an instrumental-variable single-stage stochastic frontier gravity model (IV-SFGM) to estimate trade potential. The results show that economic size, favorable exchange rates, and shared borders significantly enhance export performance. Furthermore, geographical distance and land enclosure remain persistent structural barriers, particularly relevant for bulky and logistics-intensive wood products. Institutional and cultural distance constitute substantial non-tariff barriers, significantly reducing export efficiency across markets. Conversely, regional trade agreements, trade freedom, and foreign direct investment play a critical role in mitigating inefficiencies and facilitating market penetration. Export efficiency in Vietnam’s wooden forest products sector indicates considerable improvement, rising from approximately 25% in the mid-2000s to over 55% in recent years, indicating notable progress in the market and highlighting considerable untapped potential. So, integrating institutional and cultural factors into a frontier-based gravity framework, this study offers novel empirical evidence from an emerging, biodiversity-rich economy with evolving governance institutions. The findings provide important policy implications for aligning export growth with institutional reform and trade liberalization, thereby contributing to the achievement of SDGs such as Decent Work and Economic Growth. Full article

High-quality agricultural products from the Lambayeque region have contributed to the growth of Peru’s agro-export sector and increased international trade. However, the need for agricultural exports to be more resilient and sustainable is demonstrated by the fact that markets are still concentrated, logistical costs are high, and global demand is constantly shifting. The purpose of this study is to use a gravity-based trade model and market intelligence techniques to analyse the agricultural exports from the Lambayeque region between 2015 and 2024. Using official data from the World Bank, AZATRADE, CEPII, and MINCETUR, we employed a quantitative explanatory approach. The results show that the concentration of businesses has significantly decreased while the value of exports has increased steadily. The Herfindahl–Hirschman Index increased from 6209 in 2015 to 1349 in 2024, and export destinations have become slightly more diverse. Exports are negatively impacted by geographic distance, but free trade agreements greatly benefit them. There is a lot of export potential in markets like Finland, Indonesia, Austria, Bolivia, and Vietnam. However, Israel and Hong Kong appear to be full. Overall, the findings indicate that Lambayeque’s export performance has improved, but it still runs the risk of becoming overly focused on a single sector. Long-term sustainability of the region’s agricultural exports depends on enhancing logistical infrastructure, bolstering market intelligence, and promoting regional diversity. Full article

This study examines the potential implications of the EU–Mercosur free trade agreement for the Polish horticultural sector, with particular emphasis on sustainability, trade competitiveness, and structural complementarities between the regions. Drawing on production, trade, and demographic data for the EU, Poland, and Mercosur countries, the analysis evaluates the alignment of horticultural supply and demand structures, the degree of intra-industry exchange, and the economic conditions shaping bilateral trade. The research applies the Grubel–Lloyd index and a Poisson Pseudo-Maximum Likelihood (PPML) gravity model to assess the determinants of Poland’s horticultural exports to Mercosur. The results indicate that trade remains predominantly inter-industry, reflecting substantial differences in agricultural specialisation and regulatory frameworks. At the same time, rising income levels in Mercosur, together with selected product-level complementarities, indicate emerging export opportunities for Poland. Poland’s trade with the Southern Common Market remains mainly as inter-industry, with the greatest export potential concentrated in high-value-added processed goods. Divergent sustainability standards, particularly in pesticide use, environmental regulation, and carbon-intensive transport, pose structural challenges that may affect the competitiveness and environmental footprint of expanded trade. Overall, the findings provide evidence that closer integration with Mercosur may support export diversification, but requires careful alignment with the EU’s sustainability objectives to ensure resilient and environmentally responsible development of the horticultural sector. Full article

This paper extends the gravity model to financial markets by examining how distance and bilateral linkages influence stock market correlations between the United States and selected emerging economies. To this end, the Poisson Pseudo Maximum Likelihood (PPML) estimator is used to account for heteroskedasticity and zero-value observations. Results show that greater economic distance weakens equity market correlations, while larger combined economic mass strengthens them, suggesting that bigger economies foster deeper financial linkages. Moreover, the results show that higher trade intensity between the U.S. and emerging markets results in negative correlations, which are explained by portfolio diversification motives—investors view these markets as substitutes, reallocating funds in opposite directions under varying conditions. The findings highlight how structural factors, distance measures, and trade intensity influence international equity market correlations, providing key insights for portfolio allocation and diversification strategies. Full article