Measuring the Market Power of China's Medical Product Exports.

During the COVID-19 pandemic, medical products have been crucial to the global fight against the disease. As a major manufacturing country, China occupies an important position in the medical products field. However, China's terms of trade are not commensurate with its status as a major exporter of medical products. Therefore, studying China's market power in medical product exports has important practical significance for determining China's value chain position in the global market and then proposing policies and measures to enhance China's market power. The findings of this paper, utilizing HS 6-digit data from 1992 to 2020, illustrate that China's market power is only in limited medical product export markets. Accordingly, we propose countermeasures to enhance the market power of China's medical product exports.

Reducing exposure to COVID-19 by improving access to fever clinics: an empirical research of the Shenzhen area of China.

BACKGROUND: The current 2019 coronavirus disease (COVID-19) pandemic is hitting citizen's life and health like never before, with its significant loss to human life and a huge economic toll. In this case, the fever clinics (FCs) were still preserved as one of the most effective control measures in China, but this work is based on experience and lacks scientific and effective guidance. Here, we use travel time to link facilities and populations at risk of COVID-19 and identify the dynamic allocation of patients' medical needs, and then propose the optimized allocation scheme of FCs. METHODS: We selected Shenzhen, China, to collect geospatial resources of epidemic communities (ECs) and FCs to determine the ECs' cumulative opportunities of visiting FCs, as well as evaluate the rationality of medical resources in current ECs. Also, we use the Location Set Covering Problem (LSCP) model to optimize the allocation of FCs and evaluate efficiency. RESULTS: Firstly, we divide the current ECs into 3 groups based on travel time and cumulative opportunities of visiting FCs within 30 min: Low-need communities (22.06%), medium-need communities (59.8%), and high-need communities (18.14%) with 0,1-2 and no less than 3 opportunities of visiting FCs. Besides, our work proposes two allocation schemes of fever clinics through the LSCP model. Among which, selecting secondary and above hospitals as an alternative in Scheme 1, will increase the coverage rate of hospitals in medium-need and high-need communities from 59.8% to 80.88%. In Scheme 2, selecting primary and above hospitals as an alternative will increase the coverage rate of hospitals in medium-need and high-need communities to 85.29%, with the average travel time reducing from 22.42 min to 17.94 min. CONCLUSIONS: The optimized allocation scheme can achieve two objectives: a. equal access to medical services for different types of communities has improved while reducing the overutilization of high-quality medical resources. b. the travel time for medical treatment in the community has reduced, thus improving medical accessibility. On this basis, during the early screening in prevention and control of the outbreak, the specific suggestions for implementation in developing and less developed countries are made.

Does Economic Policy Uncertainty Affect Cryptocurrency Markets? Evidence from Twitter-Based Uncertainty Measures

Using daily data from August 9, 2015, to July 7, 2020, this study examines the effects of economic policy uncertainty (EPU) on the returns of four cryptocurrencies: Bitcoin, Ethereum, Litecoin, and Ripple. To this end, two new measures of EPU (Twitter-based economic uncertainty and Twitter-based market uncertainty) are considered. A Granger causality test using the recursive evolving window approach shows a significant causality between the Twitter-based EPU measures and the BTC/USD exchange rate from October 2016 to July 2017. Moreover, a significant causality was noted from the EPU measures to the ETH/USD exchange rate from June 2019 to February 2020 and from the EPU measures to the XRP/USD exchange rate from January 2020 to February 2020. The Twitter-based EPU measures primarily positively affect the returns of the related cryptocurrencies during these periods. These results are robust to different measures of Twitter-based EPU and different econometric techniques. Potential implications, including the COVID-19 era, are also discussed.