Economics (all)

This course is designed for graduate students interested in international trade, labor economics, development economics, applied econometrics, and the Chinese Economy. Our first goal is to provide an introduction to a set of important topics related to the economic development of China so that students have a good understanding of the Chinese Economy and China’s impact on the rest of the world. A tentative list of topics includes China’s trade liberalization, the US-China trade war, China’s population control policy and its impact on China’s economic development, labor market dynamics (wage, employment, and human capital accumulation), agricultural reforms, and firm reforms.

The second goal is to present the empirical tools used to test related economic theories in the context of China and to discuss the empirical relevance of related theories. We will emphasize the conceptual issues and basic statistical techniques, such as instrumental variable strategy and differences-in-differences-type strategies. Students will also get familiar with several widely used Chinese datasets and learn how to conduct empirical analysis.

Our third goal is to introduce frontier researches to students. We will draw on some recent academic papers from international trade, labor economics, finance, development economics, macroeconomics, and economic growth, which will allow students to have a good understanding of cutting-edge researches and help students outline future research questions.

This course aims to introduce students to the essential tools of text analysis (or: natural language processing, NLP). It turns unstructured text into quantitative data used for empirical research in economics, management science, and many other fields. We will use R for most of the course and Python to showcase some machine learning applications with text data at the end of the semester. Topics:

• Working with text: regular expressions/regex – text parsing – parts-of-speech tagging – web-scraping – dictionaries
• Text as data: – word-embeddings (from bag-of-words to word2vec/GloVe) – topic models – sentiment analysis – text-similarity
• ML with text (in Python): – ML fundamentals – BERT – text-classification We will use recent economics and management science literature applications to illustrate methods and concepts.