Anna Zamojska

This paper investigates whether uncertainty shocks weaken technology-trade performance across technology categories, through which channels they operate, and whether their effects differ across economies and time horizons. We develop a mechanism-based sectoral framework that links final goods, non-strategic components, and strategic components to distinct uncertainty-transmission channels. The panel VARX estimates are then interpreted against this framework to explain heterogeneous responses across technologies and economies. Focusing on the US, China, and European economies over 1998–2024, we estimate annual sector-specific panel VARX models for eight SITC Rev. 3 technology sectors, using two complementary trade outcomes: the export-to-import ratio (EI) and revealed comparative advantage (RCA). Operational uncertainty is proxied by Global Supply Chain Pressure Index, trade-policy uncertainty by Trade Policy Uncertainty, and geopolitical uncertainty by US-China Tensions Index. The results indicate strong own persistence and mean reversion in EI and RCA, with generally weak EI-RCA cross-transmission. Uncertainty effects are heterogeneous and mechanism-specific: operational shocks most consistently weaken trade performance in strategic component sectors, especially semiconductors, whereas trade-policy and geopolitical uncertainties primarily reallocate relative specialization rather than uniformly compress technology trade. Cross-economy patterns identify China as an exporter outlier and semiconductors as Europe’s key bottleneck, implying that resilience policies should be targeted by technology type and uncertainty dimension.

This article examines China–Africa economic relations as embodied in technology transfer, using African imports of Chinese high-technology products as a proxy. It argues that technology trade is heterogeneous and distinguishes two streams of manufactured products: electronics and electrical ( MTCE ), and other high-tech products ( MTCO ). Using a gravity model estimated with Poisson pseudo-maximum likelihood ( PPML ) on a panel of 53 African economies across five UN regions from 2016 to 2021, it relates imports to partner size, trade frictions and structural conditions (labour supply, productivity, resource rents and the development gap vis-à-vis China). Gravity drivers remain important, but their significance and magnitude differ sharply by stream and region: distance is negative for MTCO yet positive for MTCE , while GDP and structural controls display stream-specific and region-specific effects. The results imply that aggregate high-tech import measures only partially capture technology transfer dynamics and that “technology” enters African economies through distinct trade channels with different implications for upgrading and dependency.

This study examines how perceptions of stakeholder salience shape user attitudes and behaviors towards the Pomeranian Metropolitan Railway (PKM) in northern Poland, focusing on the moderating role of transportation connectivity. Utilizing repeated cross-sectional survey data collected in 2019, 2020, and 2021, the study analyzes how user perceptions of power, legitimacy, and urgency impact attitudes and rail usage behavior. The results highlight a significant shift in the moderating effect of perceived connectivity, which transitioned from a positive influence in 2020 to a negative one in 2021, indicating increasing disillusionment with the rail system’s responsiveness to users’ expectations. This study provides key insights into how external disruptions, such as the COVID-19 pandemic, interact with connectivity perceptions to influence regional rail usage patterns.

The global aviation sector underwent an unprecedented shock due to the COVID-19 pandemic, severely disrupting the passenger flows, flight operations, and revenues of Polish airports. In response, the government launched protective measures under the Anti-Crisis Shield and the COVID-19 Counteraction Fund. This study examines the financial impact of such public support on 12 Polish airports between 2016 and 2022, applying a two-step cointegration ECM framework with Driscoll–Kraay inference. Profitability (ROA, ROE, OM), liquidity, debt, and operational activity indicators were analysed, with particular attention to methodological distortions arising from including subsidies in operating revenues. The results indicate a material decline in profitability from 2020 to 2022, albeit with pronounced heterogeneity across airports. Larger hubs (Warsaw–Chopin, Kraków, Gdansk, Katowice, Poznan, and Wroclaw) demonstrated relative resilience, while many smaller, regionally owned airports (e.g., Bydgoszcz, Lodz, Lublin, Olsztyn-Mazury, Zielona Gora) remained structurally unprofitable despite substantial subsidies. In several cases, profitability, liquidity, and operating activity recovered by 2021–2022, yet the improvement was not uniform: for fiscally dependent airports, transfers merely masked persistent inefficiencies. Passenger volumes, flight operations, and employment emerged as the primary performance drivers, while capital expenditure, turnover of current assets, and liquidity were particularly relevant for ROE. The novelty of this research lies in disentangling the stabilising effect of subsidies from underlying profitability, revealing how non-market revenues distort standard performance metrics and accelerate short-run adjustment dynamics once netted out. The findings demonstrate asymmetric impacts of state aid across ownership structures, i.e., central state control at Warsaw versus regional self-government involvement elsewhere, and highlight structural inefficiencies that weaken systemic resilience. These insights underline the importance of subsidy-adjusted financial indicators, more selective allocation of support, and reporting standards that separate operating from non-market revenues to enhance resilience and ensure sustainable airport operations.

The study aims to investigate what determinants had an impact on the imports of different categories of technology goods and to verify whether the determinants such as level of sustainable development and quality of institutions had also an impact on the imports of different categories of technology goods. The study covers 53 African economies and 17 categories of technology goods. To investigate the determinants of the Chinese technology goods imports with the use of the trade gravity in levels approach in a panel data set covering the period 2016–2021, we have applied the semimixed effect method using the Poisson pseudo-maximum likelihood estimator. The study proved that most of the explanatory variables functioned in line with expectations and economic theory. Several anomalies were also identified in the behavior of variables such as productivity, quality of institutions and Internet technology advancement.

This article explores the concept of the ‘Trump effect’, defined by a technological disconnection and evaluated through alterations in export specialization among various economies. It scrutinizes if there were any shifts in the techno-nationalist trends of the EU, China and the US in terms of domestic value-added exports in technology sectors after 2017. The research covers the period from 1995 to 2020 and utilizes a fluctuating difference-in-differences approach. The findings suggest that technological global value chains (GVCs) were minimally affected by Trump’s policies, showing scant evidence of significant disconnection in the economies under study. Consequently, the research underscores the robustness of GVCs against the measures taken by the Trump administration.

Motives: The factors that affect technology imports from China have never been examined in the literature, and this study was undertaken to fill in this knowledge gap. The factors that drive high-tech imports from China to Africa, including those that negatively impact imports, were identified. The results can be used to implement changes in the trade strategies of African countries, including industry 4.0 strategies or trade agreements with China, and to influence the behavior of companies importing high-tech from China.Aim: The primary goal is to identify the factors that influence technology transfer, in particular the transfer of electronic and electrical technologies, in the areas of high-tech manufacturing. The second goal is to determine whether these factors have equal strength and direction of influence on different streams of technology transfer.Results: The study demonstrated that both economic and geographical factors influence technology transfer, defined as two streams of high-tech manufactures: electronic and electrical, as well as other high-tech manufactures. However, the two import streams behaved differently, and different factors affected Chinese imports into Africa.