UROP Proceedings 2022-23

School of Business and Management Department of Economics 167 Firms in Globalization: Evidence from China Supervisor: LI, Yao / ECON Student: HE, Jiawei / ECON WANG, Yingfan / MAEC Course: UROP1100, Fall UROP1100, Fall This article studies the changes and impacts of the China-United States trade war in the context of the global COVID-19 pandemic. We first selected the data of imports from China and the U.S. in the last twenty months and did descriptive statistics and analysis. Next, we predict that there is an inevitable link between the increase in tariffs and the trade on balance situation between the two countries and conduct calculations and analysis on a large number of data to test our hypothesis. Firms in Globalization: Evidence from China Supervisor: LI, Yao / ECON Student: LU, Yueyang / MAEC SOL, Dong Min / ECON TSOI, Chun Ki / RMBI Course: UROP1000, Summer UROP1000, Summer UROP1100, Summer This research study aims to examine the trading patterns of China, Korea and Hong Kong over the past 10 years, with a particular focus on the period before and after the trade war between the United States and China in 2018. By utilizing goods trade data obtained from a website called Trade Map, this research analyzes monthly import and export data at hs8 product level using Stata software. The study seeks to identify any significant changes in trading patterns, explore the impact of the trade war on China, Korea and Hong Kong's trade relationships, and provide insights into the evolving dynamics of international trade. Firms in Globalization: Evidence from China Supervisor: LI, Yao / ECON Student: OUYANG, Yuxuan / MAEC Course: UROP1100, Fall Continued with the study in UROP 1000 in this summer, I have come out the result that the credit constraint has impact on the innovation level of Chinese firm by using the year 2004’s data. The main purpose of this report is to do a regression that efficiently shows how predictive the credit constraint is for forecasting the overall change of firms’ innovation level in China. This time I would apply the Extreme Boosting model to do the rough validation of their relationship. Same as the process in the last report, I use the firm-level production data of Chinese manufacturing firms conducted by the National Bureau of Statistics of China (NBSC) to capture firms’ innovation level. Also I use factors that proxy credit constraints from Monova (2015) and Fan (2015). But the difference is, I would pick the data from 2001 to 2009 to form a simple time series data set. The whole timeline is short and the number of components are limited, so it is just an initial attempt of applying such model into this problem.