Has previous loan rejection scarred firms from applying for loans during Covid-19?
Ontology highlight
ABSTRACT: Abstract
SUBMITTER: Cowling M
PROVIDER: S-EPMC8683813 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Based on a survey (7-13 April 2020) we evaluate the reaction of Swiss firms towards the COVID-19 crisis. Firms show little pro-active reactions towards the crisis, but decrease their business activities. The firms in the survey report that the decline in foreign demand is the single most important reason for their deteriorating business situation. Firms that faced a more difficult business situation before the crisis are affected more severely during the crisis. Moreover, we investigate the impact of the Swiss federal loan program (Bundeshilfe) on the business activities. To this end, we develop a stylized theoretical model of financially constrained heterogeneous firms. We find that policy makers face a trade-off between immediate higher unemployment rates and long-term higher public spending. The former arises from a combination of a too strong economic impact of the COVID-19 lockdown (demand drop) and too low levels of loans provided. Nevertheless, providing (too) high levels of loans to firms creates zombie firms that are going to default in the future leading to an increase in public spending. (JEL codes: D22, D25, D84, and G33).
| S-EPMC7799295 | biostudies-literature
Project description:The speed of the economic downturn in the wake of the COVID-19 pandemic has been exceptional, causing mass layoffs-in Germany up to 30% of the workforce in some industries. Economic rationale suggests that the decision on which workers are fired should depend on productivity-related individual factors. However, from hiring situations we know that discrimination-i.e., decisions driven by characteristics unrelated to productivity-is widespread in Western labor markets. Drawing on representative survey data on forced layoffs and short-time work collected in Germany between April and December 2020, this study highlights that discrimination against immigrants is also present in firing situations. The analysis shows that employees with a migration background are significantly more likely to lose their job than native workers when otherwise healthy firms are unexpectedly forced to let go of part of their workforce, while firms make more efforts to substitute firing with short-time working schemes for their native workers. Adjusting for detailed job-related characteristics shows that the findings are unlikely to be driven by systematic differences in productivity between migrants and natives. Moreover, using industry-specific variation in the extent of the economic downturn, I demonstrate that layoff probabilities hardly differ across the less affected industries, but that the gap between migrants and natives increases with the magnitude of the shock. In the hardest-hit industries, job loss probability among migrants is three times higher than among natives. This confirms the hypothesis that firing discrimination puts additional pressure on the immigrant workforce in times of crisis.
| S-EPMC8803145 | biostudies-literature
Project description:Manufacturing firms that continued production activities during the COVID-19 have been taking necessary measures to cope with the risks imposed by the pandemic. This study assesses the measures implemented by the Ready-Made Garments (RMG) sector in Bangladesh. With the increase in COVID-19 cases in Bangladesh, following government order, along with firms in other manufacturing sectors, the RMG firms had to shut-down their production between March 26 and April 25, 2020. Soon after the factories reopened, they had to take necessary actions to ensure employee safety, supply of raw materials, and purchase orders from buyers. Using a semi-structured interview approach, we identify 16 measures that have been implemented in the RMG sector in Bangladesh for the employees, suppliers and buyers. Then, we assess the degree of implementation of these measures using the Bayesian Best-Worst method. We find that providing healthcare safety, bringing previously outsourced activities in-house, and ensuring smooth delivery of existing orders were the three most implemented measures for employees, suppliers and buyers, respectively. On a higher level, the RMG industry professionals prioritised buyer-related measures the most, followed by employee and supplier-related. The analysed measures provide a blueprint for supply chain risk management during future waves of COVID-19 transmission and for other potential large-scale natural disasters.
| S-EPMC8776347 | biostudies-literature
Project description:This paper investigates the effect of firm-level operating flexibility on stock performance during the COVID-19 outbreak in China. We find that firm-level operating flexibility is significantly positively correlated with the cumulative abnormal stock returns that occurred during the event window, and this positive relation is more pronounced in firms in the provinces most affected by the epidemic. This positive relation is also more obvious in firms that have relatively fewer fixed assets. Therefore, our results provide direct empirical evidence that the real options embedded in operating flexibility played an important role during the COVID-19 outbreak.
| S-EPMC7574684 | biostudies-literature
Project description:ImportanceThere are concerns that suicide rates may have increased during the coronavirus disease 2019 (COVID-19) pandemic.ObjectiveTo assess whether suicide rates in Japan increased in April through November 2020 compared with previous years.Design, setting, and participantsThis cross-sectional study used national data obtained from the Ministry of Health, Labor and Welfare from 2016 to 2020 on the monthly number of individuals who died of suicide in Japan from January to November of 2016 to 2020.Exposure2020 vs previous years.Main outcomes and measuresThe main outcome was monthly suicide rates, calculated as the number of individuals who died of suicide divided by the total population. A difference-in-difference regression model was used to estimate the change in monthly suicide rates in April to November 2020 vs these months in 2016 to 2019.ResultsAnalyses included 90 048 individuals (61 366 [68.1%] men) who died of suicide from 2016 to 2020. The difference-in-difference analysis of men showed that there was no increase in suicide rates from April through September 2020 compared with these months in 2016 to 2019, but that suicide rates were increased in October (difference-in-difference, 0.40 [95% CI, 0.14 to 0.67] suicide deaths per 100 000 population) and November (difference-in-difference, 0.34 [95% CI, 0.07 to 0.60] suicide deaths per 100 000 population). Among women, suicide rates in 2020 compared with 2016 to 2019 increased in July (difference-in-difference, 0.24 [95% CI, 0.09 to 0.38] suicide deaths per 100 000 population), August (difference-in-difference, 0.30 [95% CI, 0.16 to 0.45] suicide deaths per 100 000 population), September (difference-in-difference, 0.29 [95% CI, 0.15 to 0.44] suicide deaths per 100 000 population), October (difference-in-difference, 0.62 [95% CI, 0.48 to 0.77] suicide deaths per 100 000 population), and November (difference-in-difference, 0.29 [95% CI, 0.15 to 0.44] suicide deaths per 100 000 population). In secondary analyses in which the suicide rates of 2020 were compared with the expected rates based on trends from 2011 to 2019, the increases in suicide rates were most pronounced among men aged younger than 30 years (eg, November: observed vs expected rate ratio [RR], 1.48 [95% CI, 1.26-1.71]) and women aged younger than 30 years (eg, October: observed vs expected RR, 2.14 [95% CI, 1.76 to 2.52]) and 30 to 49 years (eg, October: observed vs expected RR, 2.30 [95% CI, 2.01 to 2.58]).Conclusions and relevanceThese findings suggest that compared with previous years, suicide rates in Japan in 2020 increased in October and November for men and in July through November for women.
| S-EPMC7856546 | biostudies-literature
Project description:Anti-vaccination attitudes have been an issue since the development of the first vaccines. The increasing use of social media as a source of health information may contribute to vaccine hesitancy due to anti-vaccination content widely available on social media, including Twitter. Being able to identify anti-vaccination tweets could provide useful information for formulating strategies to reduce anti-vaccination sentiments among different groups. This study aims to evaluate the performance of different natural language processing models to identify anti-vaccination tweets that were published during the COVID-19 pandemic. We compared the performance of the bidirectional encoder representations from transformers (BERT) and the bidirectional long short-term memory networks with pre-trained GLoVe embeddings (Bi-LSTM) with classic machine learning methods including support vector machine (SVM) and naïve Bayes (NB). The results show that performance on the test set of the BERT model was: accuracy = 91.6%, precision = 93.4%, recall = 97.6%, F1 score = 95.5%, and AUC = 84.7%. Bi-LSTM model performance showed: accuracy = 89.8%, precision = 44.0%, recall = 47.2%, F1 score = 45.5%, and AUC = 85.8%. SVM with linear kernel performed at: accuracy = 92.3%, Precision = 19.5%, Recall = 78.6%, F1 score = 31.2%, and AUC = 85.6%. Complement NB demonstrated: accuracy = 88.8%, precision = 23.0%, recall = 32.8%, F1 score = 27.1%, and AUC = 62.7%. In conclusion, the BERT models outperformed the Bi-LSTM, SVM, and NB models in this task. Moreover, the BERT model achieved excellent performance and can be used to identify anti-vaccination tweets in future studies.
| S-EPMC8069687 | biostudies-literature
Project description: Not available
| S-BSST1055 | biostudies-other
Project description:BACKGROUND:The widespread death and disruption caused by the COVID-19 pandemic has revealed deficiencies of existing institutions regarding the protection of human health and well-being. Both a lack of accurate and timely data and pervasive misinformation are causing increasing harm and growing tension between data privacy and public health concerns. OBJECTIVE:This aim of this paper is to describe how blockchain, with its distributed trust networks and cryptography-based security, can provide solutions to data-related trust problems. METHODS:Blockchain is being applied in innovative ways that are relevant to the current COVID-19 crisis. We describe examples of the challenges faced by existing technologies to track medical supplies and infected patients and how blockchain technology applications may help in these situations. RESULTS:This exploration of existing and potential applications of blockchain technology for medical care shows how the distributed governance structure and privacy-preserving features of blockchain can be used to create "trustless" systems that can help resolve the tension between maintaining privacy and addressing public health needs in the fight against COVID-19. CONCLUSIONS:Blockchain relies on a distributed, robust, secure, privacy-preserving, and immutable record framework that can positively transform the nature of trust, value sharing, and transactions. A nationally coordinated effort to explore blockchain to address the deficiencies of existing systems and a partnership of academia, researchers, business, and industry are suggested to expedite the adoption of blockchain in health care.
| S-EPMC7511222 | biostudies-literature
Project description:To understand and analyse the global impact of COVID-19 on outpatient services, inpatient care, elective surgery, and perioperative colorectal cancer care, a DElayed COloRectal cancer surgery (DECOR-19) survey was conducted in collaboration with numerous international colorectal societies with the objective of obtaining several learning points from the impact of the COVID-19 outbreak on our colorectal cancer patients which will assist us in the ongoing management of our colorectal cancer patients and to provide us safe oncological pathways for future outbreaks.
| 2346925 | ecrin-mdr-crc
Project description: Not available
| S-BSST563 | biostudies-other