Associate Professor Doowon Lee

© 2020 Objectives: To describe how health care crisis resulting from the COVID-19 pandemic in South Korea has led to innovation and changes to government policy. This paper presents the significant cluster events, relevant developments of innovation, and economical impact in Korea that could inform policy makers on how to respond to health crises in the future. Methods: Health care, economy, epidemiological data are collected from various sources including the Korea Centers for Disease Control and Prevention (KCDC) or other government sources. Results: The KCDC jointly with medical professionals developed a series of innovations such as 1) Full contact tracing and rapid testing with a 12 h turnaround and 10 min movement tracking systems, 2) transparent disclosure of all contract tracing data to the public through a central database, 3) Drive-Through and Walk-Through testing methods, and 4) a 4 tier patient severity index and community treatment isolation centers. Korea moved from the 4th in the world for total confirmed cases in March down to 76th in August. Conclusions: Expedited enforcement of amended legislation acts to protect the healthcare workforce resulted in only 10 healthcare professionals contracting the virus while caring for Covid-19 patients. This has resulted in minimal human capital loss and the government was able to re-direct existing medical workforce to areas in need. The quarantine strategies implemented resulted in little need to lock down the whole economy but also limited the cost spent to gain a year of life to 193,848 Won (US$163).

© 2019 Elsevier Inc. Predictive models of stock returns are often criticized for generating spurious predictability, unstable predictive relationship, and poor out-of-sample forecasting performance. This paper addresses these issues in the context of four major South Asian equity markets. We provide a bias-corrected estimate of the relationship of future stock returns to dividend yield and interest rate. We use a restricted vector autoregressive model, draw statistical inferences from a wild-bootstrap method with superior size and power properties, and allow model parameters to vary over time. Dividend yield is a significant predictor in both in- and out-of-sample (OOS)in two countries, while interest rate exhibits significant predictability in all four markets. Imposing theoretically motivated restrictions on model parameters appears to improve OOS predictability. Finally, time-variation in return predictability is found to be linked to countercyclical risk premium and persistence of the predictor variables.