Poison or Cure? a Study on the Periodic Sponge Effect in Denmark’s Tourism Industry (preprint)

This paper investigates the crucial role of tourism with the periodic sponge effect played in the development of Denmark's industry structure. The existence of the periodic sponge effect between tourism and manufacturing is analysed by proposing an approach to calculate the periodic sponge effect index. This phenomenon describing wane and wax shifting between the industry sector and the service sector is jointly contributed by the natural seasonal variations in the tourism industry and the flexicurity labour market with flexible employment policies in Denmark. To prove that the service sector with periodic sponge effect will not cause the Dutch disease, the relationship between the service sector and GDP in Denmark are studied by adopting a unit root test, Johansen cointegration test and Granger causality test. The global disaster, including the global financial crisis, can be regarded as an outlier in the periodic economic oscillation. The impacts of the financial crisis of 2008 on the alteration of industry structure are revealed. Moreover, during this economic crisis, the de-industrialisation that is a replacement of the manufacturing proportion by tourism is discussed. In terms of the unemployment issue, the depression of manufacturing is the main factor to cause the unemployment problem. Tourism becomes a significant contributor to provide job opportunities. Furthermore, during the COVID-19 pandemic, the current situation of Denmark is discussed.

The COVID-19 Outbreak Decreases Residents’ Self-Reported Happiness (preprint)

Since erupting at the end of 2019, the COVID-19 outbreak has been the world’s most severe challenge since World War II. This challenge is reflected not only in the observable economic depression, but also in emotional damage (loss of well-being) which has been widely ignored. This paper estimates the effect of the epidemic on the well-being of Chinese residents by capturing the emotions conveyed via massive social media, combined with city-level epidemic data. We also use station-level weather data and spatial geographic distance from the epicenter (Wuhan) to construct instrumental variables to better overcome the endogeneity problems of the estimations. We confirm the significant negative impact of the outbreak on life satisfaction, with an extra 1% of cumulative death cases associated with a 0.54% decrease in expressed happiness. Meanwhile, we find that well-being drops the most in regions with high-level risk of COVID-19, especially for females. Further, happiness in regions with higher or lower income levels are more vulnerable to the effects of the epidemic, while intermediate-level income regions are relatively insensitive. We also found that public announcements regarding the WHO pandemic and city-unlocking significantly affect residents’ sentiment. The interaction between the outbreak and real-time expressed happiness provides an important opportunity for reflection on the current governance policies of epidemic containment.

Global economic footprint of the COVID-19 pandemic (preprint)

Countries around the world have sought to stop the spread of the 2019 novel coronavirus (COVID-19) by severely restricting travel and in-person commercial activities. Here, we analyse the economic footprint of such “lockdowns” using detailed datasets of global supply chains and a set of pandemic scenarios. We find that COVID-related economic losses are largely dependent on the number of countries imposing lockdowns, and that losses are more sensitive to the duration of a lockdown that its strictness—suggesting that more severe restrictions can reduce economic damages if they successfully shorten the duration of a lockdown. Our results also highlight several key vulnerabilities in global supply chains: Even countries that are not directly affected by COVID-19 can experience large losses (e.g., >20% of their GDP)—with such cascading impacts often occurring in low- and middle-income countries. Open and highly-specialized economies suffer particularly large losses (e.g., energy-exporting Central Asian countries or tourism-focused Caribbean countries). Supply bottlenecks and declines in consumer demand lead to especially large losses in globalized sectors such as electronics (production decreases of 13-53% across our scenarios) and automobiles (2-49%). Although retrospective analyses will undoubtedly provide further policy-relevant insights, our findings already imply that earlier, stricter, and thus shorter lockdowns are likely to minimize overall economic damages, and that global supply chains will magnify economic losses in some countries and industry sectors regardless of direct effects of the coronavirus.

Modeling the situation of COVID-19 and effects of different containment strategies in China with dynamic differential equations and parameters estimation (preprint)

This paper proposed a quarantine-susceptible-exposed-infectious-resistant (QSEIR) model which considers the unprecedented strict quarantine measures in almost the whole of China to resist the epidemic. We estimated model parameters from published information with the statistical method and stochastic simulation, we found the parameters that achieved the best simulation test result. The next stage involved quantitative predictions of future epidemic developments based on different containment strategies with the QSEIR model, focused on the sensitivity of the outcomes to different parameter choices in mainland China. The main results are as follows. If the strict quarantine measures are being retained, the peak value of confirmed cases would be in the range of [52438, 64090] and the peak date would be expected in the range February 7 to February 19, 2020. During March18-30, 2020, the epidemic would be controlled. The end date would be in the period from August 20 to September 1, 2020. With 80% probability, our prediction on the peak date was 4 days ahead of the real date, the prediction error of the peak value is 0.43%, both estimates are much closer to the observed values compared with published studies. The sensitive analysis indicated that the quarantine measures (or with vaccination) are the most effective containment strategy to control the epidemic, followed by measures to increase the cured rate (like finding special medicine). The long-term simulation result and sensitive analysis in mainland China showed that the QSEIR model is stable and can be empirically validated. It is suggested that the QSEIR model can be applied to predict the development trend of the epidemic in other regions or countries in the world. In mainland China, the quarantine measures can't be relaxed before the end of March 2020. China can fully resume production with appropriate anti-epidemic measures beginning in early April 2020. The results of this study also implied that other countries now facing the epidemic outbreaks should act more decisively and take in time quarantine measures though it may have negative short-term public and economic consequences.