Empirical Analysis in Labor Market and International Finance
- Author(s): Zhu, Zijing;
- Advisor(s): Gu, Grace;
- et al.
This dissertation presents three empirical studies on topics in regional growth, international finance, and global commodity prices. The first chapter analyzes the labor market reallocation following the reduction of transportation costs. The second chapter discovers developing countries' external debt holdings abilities with different exchange rate regimes, and the third chapter reveals how commodity prices are affected by information from the news in high-frequency trading.
The first chapter discovers how labor is reallocated across cities as they become more connected to the High-Speed Railway (HSR) network in China. Specifically, by adapting graph theory from computer science, this chapter constructs a continuous connectivity index that captures the changes of 285 cities' indirect connections to the HSR network over ten years. Additionally, using China City Statistic Yearbook that covers these cities' labor market outcomes from 2003 to 2017, this chapter finds that increasing the indirect connectivity to the HSR network has insignificant effects on total employment. However, there are heterogeneous effects on employment in different industries. This chapter suggests that cities with higher indirect connectivity to the HSR network have more employment in skilled and non-service industries but less employment in service industries. Moreover, with or without direct connections to HSR generates different effects on labor reallocation. Cities have HSR contributes to the increase in employment in skilled industries, while cities near HSR has more increase in non-service employment.
The second chapter examines how exchange rate regimes affect external debt holdings for developing countries, both at normal times and during default episodes. In order to uncover the connection of more floating exchange regimes and external debt holdings, this chapter collects 57 countries' external debt level and exchange rate regimes from 1970 to 2007, including 232 default episodes. Using GMM regressions, the results reveal that compared to countries with more fixed exchange rate regimes, countries with more floating exchange rate regimes hold less external debts, especially during default episodes. Moreover, switching from fixed exchange rate regimes to floating regimes further reduces external debt holding, this can be driven by the abandonment of fixed exchange rate regimes commitment.
The third chapter is a group project with Yifei Sheng and Yunxiao Zhang. To uncover the news impact on the price of WTI crude oil futures, the third chapter applies supervised and unsupervised machine learning algorithms to conduct news sentiment and topic analysis. With the assumption that the crude oil futures market is efficient enough to respond quickly to new information, this chapter obtains high-frequency price and news from the Bloomberg terminal. Using results from logistic regression and K-means clustering, this chapter defines the positive score and topic for each news article as inputs for the final logistic regression. The regression results show that the "World Crude Oil" news is more positively correlated with price increase than other topics. Moreover, the "WTI Crude Oil" news has the highest correlation with the price increase as the positive score increases.