This study examines the role of key macro drivers - foreign reserves (FR), exchange rates (ER), and trade openness (TOP) - in shaping tourism (TOR) development across highly trade-open economies over the period 1995-2022. Grounded in an extended gravity model framework, the study employs pooled ordinary least squares (OLS), fixed-effects estimation (FEM), and the generalized method of moments (GMM), complemented by Pedroni and Johansen cointegration tests and Granger causality analysis to capture both long-run relationships and causal dynamics. The findings reveal that FR, ER, TOP, PCI (per capita income), foreign direct investment (FDI), population (POP), and labor force participation (LFP) significantly promote TOR development, whereas gross national savings (GNS) exert no significant effect. Furthermore, the findings confirm the existence of both short- and long-run relationships among the drivers. The study identifies a bidirectional relationship between population and TOR development, while TOP, PCI, ER, and FR exhibit unidirectional causal effects on TOR. These findings underscore the pivotal role of macro stability, global economic integration, and demographic dynamics in fostering TOR development in highly trade-open economies.
Introducing firm heterogeneity into a two-country general oligopolistic equilibrium (GOLE) model, we examine the welfare effects of market integration defined as a reduction in iceberg trade costs. A strand of GOLE literature without firm heterogeneity finds that market integration can lower welfare by increasing the welfare-relevant price dispersion across goods. In contrast, we show that lower trade costs decrease the domestic cost cut off in traded sectors and induce high-cost firms to exit the domestic market. Therefore, market integration reduces the welfare-relevant dispersion of product prices and raises welfare. We demonstrate that this result holds both in the short run with a fixed number of entrants and in the long run with free entry of entrants.
The flows of value-added and embodied carbon emissions are significant issues in international trade. This study utilizes the multi-regional database to construct an environmental input-output model from 1990 to 2021, analyzing the global manufacturing value-added trade network and embodied carbon emissions of 115 countries (regions). It also examines pollution trade conditions and export product situations of major countries. Results show that the network remains stable, with developed countries (including the United States, Japan and Germany) occupying central roles, while emerging economies (including China and Russia) have made significant progress. Trade-related carbon emissions have risen sharply, revealing carbon inequality as developing countries bear disproportionate environmental burdens. Furthermore, the flows of value-added and embodied carbon between the United States and China show divergent trends. These findings enhance understanding of the trade - environment nexus and provide insights for global carbon reduction.
Against the backdrop of technological dependence and a technology embargo, this study investigates the impact of digital product imports on firm innovation in China's manufacturing sector. Using data from Chinese listed manufacturing firms, we find that digital product imports significantly enhance both the quality and quantity of firms' patent output. Our analysis shows that digital product imports promote innovation by supporting digital transformation, lowering internal costs, and improving the efficiency of information exchange between firms, rather than via technology spillovers. This finding diverges from previous research on imported products and innovation, underscoring the distinctive intelligence-driven and information-rich characteristics of digital products. Furthermore, heterogeneity analyses reveal that the innovation-enhancing effect of digital product imports is more pronounced in firms with higher human capital, lower productivity, greater labor intensity, and non-state ownership. These findings provide valuable insights for policymakers in developing countries aiming to promote innovation through digital trade.
The global arms trade has received growing scholarly attention against the backdrop of increasing regional conflicts worldwide. Using social network analysis and the stochastic actor-oriented model, we test a theoretical framework for understanding the evolutionary mechanisms of the global arms trade network. Our study reveals the scale-free properties and hierarchical stratification of the global arms trade network. We find that the United States and Russia maintain their dominant positions in global arms exports, while countries such as China and India have gained significantly higher standing within the network. Endogenous network effects, bilateral relations, and national characteristics simultaneously shape the evolutionary trajectory of global arms trade. Transitivity, preferential attachment, geographic distance, defense cooperation, political stability, and military expenditure exert differential impacts on the evolution of the global arms trade network. Our findings reveal the evolutionary characteristics of global arms trade and provide a more explanatory analytical framework to elucidate the determinants of its evolution.
