Impacts of Reforestation on Stabilization of Riverine Water Levels in South Korea / Jaehyun Yoon, Saang Joon Baak, Min Young Seo, and Taejong Kim
We investigate how reforestation contributed to stabilization of riverine water levels in South Korea. For the purpose, we estimate an equation capturing dynamic relationships among rainfall, upstream-area tree stock, and downstream water levels in three river systems of Hongcheon, Mangyeong, and Hyeongsan, using daily observations of precipitation and water levels for the period from 1985 to 2005. Simulation based on estimation results shows that increase in the tree stock in a river basin leads to a significantly suppressed peaking in riverine water levels in response to an abrupt and concentrated rain in the upstream area. For instance, an hour-long concentration of 100mm rain results in 0.7m rise in water level if the volume of growing stock is 1 million m3, whereas the rise in water level stays below 0.27m with 5 million m3in the growing-stock volume.

Indirect Network Effect and Spillover Effect in Food Delivery Platforms / Gong Lee
I analyze how the food delivery market’s indirect network effect and spillover effect influence the spread of food delivery platforms in different counties. This study finds that there is a positive local indirect network effect and a positive spillover effect in the adoption of the platform by examining the food delivery platform market in South Korea as of 2020. As food delivery platforms secure consumers who use them, more restaurants on the other side of a two-sided market adopt such platforms (indirect network effect). The spillover effect would allow other restaurants in a region to become more likely to adopt food delivery platforms if there are a greater number of restaurants in the region that use such platforms. This study contributes to the comprehension of technology diffusion and the marketing strategies of platform providers by providing empirical evidence of both effects.

Estimating China’s Capital Flows-at-risk: The Case of Potential US Financial Sanctions / Daehee Jeong
The arena of strategic competition between the US and China is expandable from international politics, trade and commerce to finance. What would happen if financial sanctions against China are imposed by the US? Would US financial sanctions lead to a sudden outflow of foreign capital and a liquidity crisis in China? We try to address these questions by estimating China’s capital flows-at-risk with the CDS premium on Chinese sovereign funds. We follow Gelos et al. (2019) in setting up a quantile regression model from which China’s foreign capital flow-at-risks are estimated. Based on our analysis of China’s monthly capital flow data, we find that a rise in the CDS premium has statistically significant negative impacts on China’s foreign capital flows-at-risk, mainly in banking flows. However, the analysis also found that due to favorable global conditions, an increase in the CDS premium is unlikely to trigger a shift to a sudden outflow of foreign capital at the moment. Meanwhile, this study found no statistically significant correlation between Korea’s capital flows-at-risk and the CDS premium, suggesting that the negative impact of US financial sanctions on China would not increase the probability of capital flight from Korea in a significant manner.

Sentiment Shock and Housing Prices: Evidence from Korea / Dong-Jin Pyo
This study examines the impact of sentiment shock, which is defined as a stochastic innovation to the Housing Market Confidence Index (HMCI) that is orthogonal to past housing price changes, on aggregate housing price changes and housing price volatility. This paper documents empirical evidence that sentiment shock has a statistically significant relationship with Korea’s aggregate housing price changes. Specifically, the key findings show that an increase in sentiment shock predicts a rise in the aggregate housing price and a drop in its volatility at the national level. For the Seoul Metropolitan Region (SMR), this study also suggests that sentiment shock is positively associated with one-month-ahead aggregate housing price changes, whereas an increase in sentiment volatility tends to increase housing price volatility as well. In addition, the out-of-sample forecasting exercises conducted here reveal that the prediction model endowed with sentiment shock and sentiment volatility outperforms other competing prediction models.