Trade Data Research Articles

An analysis of how digital technology impacts trade costs—Based on the empirical data of RCEP member countries

<p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">This paper explores how digital technology reduces trade costs using bilateral trade data from RCEP member countries and a panel fixed effects model. The findings show that digital technology significantly lowers trade costs, a conclusion that holds even after accounting for lag effects and various robustness checks. The impact of digital technology on trade costs follows an inverted U-shape: the effect is most significant in the current period, especially with a one-period lag, and diminishes over time. Larger economies and higher export levels strengthen this negative impact due to their reliance on exports and continuous improvements in domestic digital technology. The study recommends investing in digital infrastructure, formulating reasonable internet access policies, supporting digital skills development, and enhancing digital connectivity to bridge the digital divide, thereby promoting trade facilitation and efficiency.</span></p>

Officials’ hometown connections and interprovincial trade in China: based on the perspective of hometown favoritism

ABSTRACT This study examines the impact of officials’ hometown connections on interprovincial trade in China, emphasising local officials’ favouritism towards their hometowns. Utilising the detailed curriculum vitae of provincial leaders along with railway trade data for province pairs, the paper finds that provincial governors significantly enhance bilateral trade with their hometowns by 14.0%. Further analysis reveals that governors augment final goods imports by 19.4% and raw resources exports by 17% with their hometowns, while exhibiting a less significant influence on final goods exports and raw resources imports. These findings suggest that local leaders may allocate portions of monopoly markets and raw resources to favour their hometowns. Moreover, differences in industrial structures appear to amplify the impact of hometown connections, while officials’ promotion pressures may mitigate this effect. This study contributes new insights into the spillover effects of hometown connections, thus combining the existing literature on hometown favouritism and local protectionism.

Globalization of wild capture and farmed aquatic foods

Aquatic foods are highly traded, with nearly 60 million tonnes exported in 2020, representing 11% of global agriculture trade by value. Despite the vast scale, basic characteristics of aquatic food trade, including species, origin, and farmed vs wild sourcing, are largely unknown due to the reporting of trade data. Consequently, we have a coarse picture of aquatic food trade and consumption patterns. Here, we present results from a database on species trade that aligns production, conversion factors, and trade to compute apparent consumption for all farmed and wild aquatic foods from 1996 to 2020. Over this period, aquatic foods became increasingly globalized, with the share of production exported increasing by 40%. Importantly, trends differ across aquatic food sectors. Global consumption also increased by 19.4% despite declining marine capture consumption, and some regions became increasingly reliant on foreign-sourced aquatic foods. To identify sustainable diet opportunities among aquatic foods, our findings, and underlying database enable a greater understanding of the role of trade in rapidly evolving aquatic food systems.

Assessing the legal, illegal, and gray ornamental trade of the critically endangered helmeted hornbill.

Monitoring wildlife trade dynamics is an important initial step for conservation action and demand reduction campaigns to reduce illegal wildlife trade. Studies often rely on one data source to assess a species' trade, such as seizures or the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) trade data. Each database provides useful information but is often incomplete. Combining information from multiple sources helps provide a more complete understanding of trade. A recent rapid increase in demand for helmeted hornbill (Rhinoplax vigil) casques (a brightly colored, solid keratinous rostrum) led to its uplisting to critically endangered on the International Union for Conservation of Nature Red List in 2015. However, there is little current information on what factors influence trade trends and what current levels of demand are. We combined data from CITES, seizure records, and previously underused, yet abundant, art and antique auction data to examine the global trade in helmeted hornbill casque products (HHPs). Three decades of auction data revealed that 1027 individual HHPs had been auctioned since 1992; total auction sales were over US$3 million from 1992 to 2021. The number of HHPs auctioned was greatest from 2011 to 2014, just after the global art boom (2009-2011), followed by a decline in volume and price. The auction data also revealed 2 possible markets for HHPs: true antique and speculative, defined by era, price, and trade patterns. Trends in illegal trade matched those of the auction market, but legal trade remained consistently low. Combining data sources from legal, illegal, and gray markets provided an overview of the dynamics of illegal trade in an endangered species. This approach can be applied to other wildlife markets to provide a more complete understanding of trade and demand at the market level to inform future demand reduction campaigns.

Challenges of regulating commercial use of marine elapid snakes in the Indo-Pacific.

Marine elapid snakes are a diverse, predominantly Indo-West Pacific species group. The persistent removal of some species has an unquantified but potentially dire impact on populations. We conducted the first comprehensive review of the trade in marine elapid snakes based on published literature (1974-2022) and trade data from the only species (i.e., Hydrophis [Lapemis] curtus) whose trade is monitored internationally. Some species and populations were subjected to targeted harvest for their meat and skins for at least the last century; fisheries are possibly the most significant threat to populations of marine elapids, with the highest numbers being exploited either accidentally, incidentally, or opportunistically in Southeast Asian fisheries targeting other seafood, including demersal trawl and squid fisheries. Southeast Asia is the core region for exploitation of marine elapids. Annual offtake is >225,000 individuals of at least 8 species in the Gulf of Thailand. Of 72 recognized marine elapids (all non-CITES [Convention on International Trade in Endangered Species of Wild Fauna and Flora] species), Hydrophis curtus and Hydrophis cyanocinctus dominate the skin trade. Skins of H. curtus are traded mainly in East and Southeast Asia and, to some extent, Europe. Despite some baseline information on the trade of these species, the sustainability of their harvests, particularly in the context of the burgeoning and unmanaged nature of fisheries in the region, remains the major challenge. In an era of declining fish stocks, there has been an increasing trend to commercialize the harvest and use marine elapids that were once considered accidental bycatch and discarded. This trend will continue to pose a significant risk to these snakes unless appropriate fisheries and trade regulations are enforced. Applying the precautionary principle to prevent the overexploitation of sea snakes is an indispensable measure in which trade in regional populations should be regulated through CITES. Accordingly, management plans to identify core distribution regions of exploited species would be crucial for assigning national responsibilities to sustain species and populations in the long term.

Data Monetization Strategies: The Construction Industry Market Analysis

Data monetization is leveraging data to obtain economic benefits. In the context of Big Data Analytics (BDA), data serves as a fundamental asset, enabling the transformation into valuable insights throughout various construction phases. Unfortunately, there is lack of studies focusing on data monetization within the construction industry, given the unique supply and demand characteristics among construction stakeholders. This study aims to address the objective of identifying data monetization strategies applicable to the construction industry, particularly for data sellers and data buyers. Data was gathered through a quantitative survey among 100 construction practitioners in Malaysia, encompassing developers, contractors, consultants, government agencies, technology providers, and academia. Respondents were further categorized into data sellers and data buyers. The data were analyzed using mean analysis, t-test and Spearman's rank correlation coefficient. The study identified six data monetization strategies, comprising 19 determinants. The analysis revealed high preference on all data monetization strategies and moderate differences in the preferred strategies between data sellers and buyers. Significant differences were found in 3 determinants which are (1) trading data to facilitate decision-making, (2) trading data for construction reports, benchmarks, and indices, and (3) selling visualized data on real-time platforms. The t-test indicated that data sellers are more inclined towards the three strategies for effective monetization. Furthermore, Spearman’s correlation coefficient revealed the 3 determinants also positively influence another 3 determinants of (1) data wrapping to reflect better service from data provider, (2) buying raw data with its inherit information and (3) monetizing internal data to optimize organization’s work process. The insights enable stakeholders to implement mechanisms that foster data monetization within project cultures accelerating BDA undertakings. Future recommendations include using larger sample sizes to enhance generalizability and to explore more areas such as construction contracts, cost, health, and safety.

How China can promote the outer circular economy under policy uncertainty

This paper utilizes the trade gravity model under the “double-cycle” pattern and selects the trade and export panel data of China in general and 31 provinces and cities located on mainland China specifically to the U.S. from 2008 to 2022 to analyze not only the reasons for the emergence of trade friction shocks between China and the U.S. but also the effects of trade friction shocks on trade between China and the U.S. and the characteristics of each market's export potential. The results of the study reveal that the volume of China's secondary industry GDP and the U.S. economic freedom index play a positive role in promoting China‒U.S. trade, while the volume of the U.S. GDP, the U.S. population, the trade distance between China and the U.S., and the number of U.S.‒China trade barrier notifications impede China‒U.S. trade. Furthermore, on the basis of the measurement of the trade potential equation, the provinces and municipalities of China's mainland are classified into the U.S. trade and export markets, and ways and means are sought to improve the potential of China's trade and exports with the U.S. Moreover, on the basis of the trade potential equation, China in general and the mainland provinces and cities specifically are divided into export markets to the U.S. to find ways to increase China's trade and export potential to the U.S. and promote China's “outward circulation” economy.

Stock Market Prediction in Nepali Stock Market using Machine Learning Model

The stock market, a key driver of the global economy, presents a formidable challenge for accurate prediction due to its intricate, chaotic, and dynamic nature. This study explores machine learning approaches, specifically comparing three prediction models—Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The analysis incorporates historical stock trading data, including open, high, low, and close prices, and technical analysis indicators such as moving average, Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and Commodity Channel Index (CCI) so on. The evaluation focuses on the performance of these models in predicting future trends in stock prices, utilizing data from the Nepal Stock market spanning from 2013 to 2023. The selected stocks for analysis include two stock NIC Asia Bank Limited and, NMB Bank Limited, two index NEPSE and Hydropower index. Through rigorous assessment, it is determined that LSTM demonstrates superior overall performance compared to GRU and BPNN. This research contributes valuable insights into the effectiveness of machine learning models in forecasting stock market trends, with implications for investors and financial practitioners.

DMD-based spatiotemporal superresolution measurement of a supersonic jet using dual planar PIV and acoustic data

The present study applies a framework of the spatiotemporal superresolution measurement based on the total-least-squares dynamic mode decomposition, the Kalman filter and the Rauch-Tung-Striebel smoother to an axisymmetric underexpanded supersonic jet of a jet Mach number of 1.35. Dual planar particle image velocimetry was utilized, and paired velocity fields of the flow with a short time interval were obtained at a temporal resolution of 5000 Hz. High-frequency acoustic data of 200,000 Hz were simultaneously obtained. Then, the time-resolved velocity fields of the supersonic jet were reconstructed at a temporal resolution of 200,000 Hz. Also, time coefficients of dynamic modes in high temporal resolution were calculated. The correlation between time coefficients implies that the mixing promotion by screech tone causes the lift-up of the high-velocity fluid from the jet center and accelerates at the downstream side.

The impact of changes in Russian log export tariffs on China's log imports

Russia is a major log supplier to China, and in order to control log exports, Russia has adjusted its log export tariffs. This paper analyzes the impact of changes in Russian log export tariffs on China's log imports using a gravity model based on China-Russia log trade data from 2003 to 2022. The conclusions show that the difference in forest resource endowment between the two countries promotes China's log imports, while the distance between China and Russia and Russia's log export tariffs harm China's log imports, and the effect of export tariffs is significant. The further research shows that the suppression effect of the changes in Russian log tariffs on China's log imports is very significant in the short term, but relatively weak in the long term. Based on the above conclusions, this paper recommends some policies from both short-term and long-term perspectives.

Monitoring the Sustainability of the EU Biomass Supply: A Novel Hybrid Approach Combining Tracing and Selected Sustainability Impacts

In an attempt to make a first step toward monitoring the sustainability of wood as (one of) the main element(s) of the EU biomass supply, a novel approach combining a physical accounting model with a material flow–life cycle assessment approach was used to trace the locations of origin of the wood and the associated sustainability impacts. Applying this approach to EU trade data from 2018, we found that around one-third of the wood fiber input in finished paper products consumed in the EU was imported. The main countries of origin were Brazil, the United States and Uruguay. We used Uruguay as a case study of an important country that provides wood pulp to assess the associated sustainability impacts. The results reveal synergies and trade-offs between employment, value added and environmental impacts. We highlight the need to analyze sustainability impacts in different dimensions of sustainability and consider not only territorial impacts in isolation but also from a global perspective in order to have a more holistic overview. Future extensions of the approach could include the coverage of other commodities, additional impacts along the global supply chain (e.g., post-use) and additional sustainability indicators.

Assessing Scotland’s self-sufficiency of major food commodities

BackgroundAnalysis of food self-sufficiency at the regional level helps to better understand its dependency on, and vulnerability to, the food system. Moreover, achieving food self-sufficiency has gained prominence in the present policy agenda to increase resilience to adverse events and lower food resource footprint. Using a novel method, we estimated the food balance sheets of Scotland, the second-largest region in the United Kingdom, and assessed the self-sufficiency of major food commodities for the first time.MethodsData from 2003 to 2019 were obtained from Economic Reports on Scottish Agriculture and the Department for Environment, Food and Rural Affairs (DEFRA)’s family food statistics. Food import and export data are not readily available for Scotland. Therefore, we developed a consumption-based approach to estimate the net trade values of each commodity. We also assessed the contribution of domestic production toward nutrient security.ResultsAccording to our estimates, Scotland is a net exporter of beef, exporting an average of 51.3 thousand tons per year. It is also a net exporter of cereal (wheat, barley, oats) and potatoes, although this was not always the case between 2003 and 2019. Scotland has a strong level of self-sufficiency in cereals, potatoes, lamb, beef, dairy, and eggs. However, there is room for improvement in achieving self-sufficiency in poultry and pork, where the self-sufficiency ratio is relatively low. Our analysis of nutrient security indicates that wheat and dairy sourced from Scotland play crucial roles in the food supply chain, markedly influencing total energy, dietary protein, and primary dietary fat sources.ConclusionsOur assessment can serve as a foundation for evidence-based decision-making in Scotland's agricultural and food policy. It offers insights into where investments and support may be needed to enhance domestic production and promote a resilient and sustainable food system. Estimated self-sufficiency ratios provide a more accurate assessment of the extent of food localization in Scotland. The method we developed in this study has the potential to be a valuable tool for future research studies, allowing for the estimation of regional-level food self-sufficiency even when trade data and food balance sheets are unavailable.

Green credit and firms’ span of global production stages

This paper investigates the impact of the green credit policy on firms' span of global production stages. We leverage a comprehensive dataset, including customs trade data, industrial firm data, and Chinese Input-Output tables, and employ a difference-in-differences design that accounts for the pollution spillover effect within the production network. Our findings indicate that firms in pollution-intensive industries tend to move downstream in the global production line, accompanied by an expansion in the span of production stages. We present evidence that the broader range of production stages can be attributed to increased firm productivity. The effect is more pronounced for hybrid processing firms, private firms, foreign firms, and those in industries that rely on external finance dependence. Our study provides valuable insights into the impact of green credit policies and sheds light on firms' strategies in the context of green development.

Global carbon neutrality and China's contribution: The impact of international carbon market policies on China's photovoltaic product exports

China's photovoltaic (PV) product exports satisfy the renewable energy demand for international carbon market construction, contributing to the global carbon neutralization process with China's power. Based on the panel data of PV trade from 2000 to 2020, this paper empirically investigates the impact of global carbon market policies on China's PV product exports and its potential impact mechanism using a multi-period DID model. The study results show that (1) International carbon market policies significantly promote the export of Chinese PV products. (2) Mechanism analysis shows that two essential transmission channels are increased demand for renewable energy and reduced consumption of traditional fossil energy in carbon market countries. (3) Carbon market maturity strengthens the positive effect of the international carbon market on China's PV product exports. (4) Further research finds that international carbon market policies affect China's PV exports more pronounced in high-income, trade-friendly, and high-technology countries. The results of this paper reveal the vital contribution of China's PV industry in promoting global climate governance and environmental protection and provide some insights into how to promote and improve the shared prosperity and sharing of the renewable energy industry in the context of global carbon neutrality.

In search of lost social finance: How do financial instability and inequality interact?

The 2008 crisis and COVID-19 pandemic have highlighted the impacts of financial shocks on the vulnerable, emphasizing the challenges posed by financial instability to social finance. However, academic studies on financial instability and income inequality present mixed results. They often focus on specific instances of financial crises rather than evolving effects, examine uni-directional causality rather than interactions, and rely on slowly changing annual data instead of higher-frequency data. This study fills these gaps by examining the time-varying bidirectional relationship between financial instability and income inequality in the US, using a newly developed monthly database from January 1990 to March 2023. We demonstrate the existence of a vicious cycle in which persistent financial instability leads to increased income inequality, which in turn triggers financial crises through the harmful effects of credit deregulation. Based on these findings, we argue that a “Robin Hood strategy” of progressive taxation to improve access to quality education and healthcare could offer a more effective solution to tackle inequality than easing credit access for low-income groups through deregulation.

A Retrospective Observational Study of Continuous Wireless Vital Sign Monitoring via a Medical Grade Wearable Device on Hospitalized Floor Patients.

Background: Continuous vital sign monitoring via wearable technology, combined with algorithm-based notifications, has been utilized for early detection of patient deterioration. In this retrospective observational study, we summarize a large-scale implementation of a continuous monitoring system in medical-surgical units of two hospitals over the course of fifteen (15) months. Methods: An FDA-cleared wireless monitoring device (BioButton®, BioIntelliSense Inc., Golden, CO, USA), was placed on each patient upon admission. The wearable device measures heart rate and respiratory rate at rest, skin temperature, and patient activity levels. High-frequency data (up to 1440 measurements per day) are transmitted to display in exception management software (BioDashboard™, version 2.9, BioIntelliSense Inc.). Algorithmic and rules-based notifications are triggered based on clinical and statistical trending criteria. We present (i) agreement of device readings with bedside charted measurements, (ii) the frequency of notifications, (iii) the occurrence of notifications prior to clinical deterioration events, and (iv) impact on clinical management, including early data on length of stay (LOS). Results: In total, 11,977 patient encounters were monitored at two sites. Bias ±95% limits of agreement were 1.8 ± 12.5 for HR and 0.4 ± 8.0 for RR. The rates of notifications were 0.97 and 0.65 per patient-day at Sites 1 and 2, respectively. Among clinical deteriorations, 73% (66%) had at least one notification within 24 h prior at Site 1 (Site 2). At Site 1, there were 114 cases for which a notification led to a new or changed physician's order. LOS in the first unit monitored by the system exhibited a decreasing trend from 3.07 days to 2.75 days over 12 months. Conclusions: Wearable continuous vital sign monitoring with the BioIntelliSense BioButton® system enables early detection of clinical deterioration.

The futility of economic sanctions in a globalized and interdependent world: a data-driven game theoretical study

Economic openness and globalization are under rising pressure from the proliferation of economic sanctions over the past decades. Data-driven evidence is urgently needed to inform the heated debates about the effectiveness of various sanctions and counter-sanction measures. In this research, we provide a game theoretical framework to model the interplays between the sanction sender and target at different stages of political conflicts. Using real-world international trade data, our game-theoretical framework assesses each country’s capability of and vulnerability to economic sanctions and quantifies the feasibility and effectiveness of different counter-sanction measures. Our findings suggest that expanded international trade creates the potential to weaponize economic interdependence for giant economies, imbalanced trade relationships with which is the main source of sanction risk for vulnerable economies. Complete decoupling from these highly capable countries is a costly and ineffective strategy to combat economic sanctions. Bloc-type collective retaliation against the common adversary can be effective under certain scenarios but provides little benefit for major contributors in the bloc, making it impractical in addressing real-world conflicts. Promoting cooperation and dialogue is the ultimate solution to maintaining economic and political stability and security.

Maximizing Trade Benefits Through Strategic WTO Dispute Filings: A Quantitative Analysis of Optimal Initiation

The study investigates the asymmetrical distribution of bilateral dispute initiations at the World Trade Organization Dispute Settlement System (WTO-DSS) between specific country pairs. The study introduces a novel concept of dispute depth to examine how the asymmetrical bilateral dispute initiation affects bilateral trade among disputing dyads and to optimize the number of disputes that a country should file to maximize its trade benefit. By using the augmented Structural Gravity Model of trade and the PPMLHDFE estimator with extensive trade data for the years 1995–2022, across selected WTO member countries, the article demonstrates that an increase in the dispute depth decreases the bilateral trade between disputing dyads. Further, the study finds that the WTO member countries should strategically file no more than three disputes per year to maximize their trade gains. The study has important ramifications in the ambiguous world of WTO-DSS and paves the way for implementing the optimal number of disputes into trade dispute strategy, policy-making decisions and business environment of the constantly evolving international trade landscape.

Design of Intelligent Financial System Based on Adaptive Learning Algorithm

The high-frequency trading system in the financial domain has long been a focal point of investigation. This study posits an intelligent financial system design framework predicated on a cross-adaptive self-entropy projection clustering model, aimed at enhancing the efficacy of high-frequency trading systems. A composite distribution model of financial data is formulated to derive sequences of financial data activities. And cross-adaptive learning algorithm is employed to ascertain the interrelated attributes of financial data. Following this, the support vector machine algorithm is applied for the classification processing of these interrelated features, yielding a set of financial data feature vectors, which are then fed into the gray correlation-based information feature extraction model. Through extensive empirical evaluations with authentic trading data, the proposed intelligent financial system design framework exhibits commendable performance, furnishing a viable solution for the intelligent optimization of high-frequency trading systems.

High-frequency monitoring reveals phytoplankton succession patterns and the role of cryptophyte in a subtropical river reservoir

Shifts in phytoplankton biodiversity drive frequent community successions within aquatic ecosystems. While significant progress has been made in understanding the factors influencing phytoplankton growth under controlled conditions, the processes driving phytoplankton succession in natural waters remain poorly understood. To address this gap, we conducted high-frequency monitoring of the phytoplankton community in the Jiangdong River Reservoir (Southeast China) from 2021 to 2022. High-frequency data captured 17 complete cycles of phytoplankton growth and decline. Our analysis showed that cryptophytes, though not the dominant group, played a key role in shaping the temporal dynamics of phytoplankton community structure and abundance. The relative abundance of cryptophytes consistently mirrored fluctuations in total chlorophyll-a concentrations. Rainfall-induced low-light conditions enhanced the competitive advantage of cryptophytes. Throughout each cycle of phytoplankton changes, cryptophytes leveraged this short-term advantage to proliferate by utilizing available ammonia nitrogen, ascending from the fourth to the second most abundant group. However, as ammonia nitrogen levels declined to 0.1 mg/L, cryptophyte populations began to decline. These new findings highlight the critical role of cryptophytes in driving phytoplankton succession patterns.