Teaching

This course provides an in-depth examination of the contemporary North Korean economy, focusing on its unique economic system and the challenges associated with transition economics. Given the scarcity of reliable information and credible macroeconomic data, the course offers a systematic analysis structured into three modules: (1) theoretical foundations — socialist economic systems from a broad comparative perspective; (2) characteristics of the North Korean economy — key features, market dynamics, and policy implications; and (3) transition dynamics and economic integration — potential economic integration between North and South Korea. Suitable for students from diverse academic backgrounds; no prior expertise in North Korean studies or economics is required.

This course explores why some countries achieve sustained economic growth while others remain stagnant. It examines how capital accumulation, education, and demographic factors contribute to income differences; how technological progress and institutional efficiency drive productivity; and how political, cultural, and geographical factors shape long-term development. A central focus is South Korea — a striking example of rapid economic transformation — where investment in human capital, industrialization, and export-driven policies enabled a transition from poverty to prosperity within a generation. By integrating theory with real-world cases, including Korea's growth trajectory and contemporary challenges such as geopolitical risks and global trade tensions, the course provides a comprehensive understanding of economic development.

An introduction to statistics focusing on the essential skill of extracting information from data. Topics include descriptive and inferential statistics: organizing and analyzing data using descriptive methods (histograms, mean, median, mode, standard deviation), understanding probability theory, and applying hypothesis tests to means, proportions, and variances. Course delivery includes 3 hours of weekly lectures supplemented by lab sessions providing hands-on experience with Microsoft Excel. Students completing the course will be equipped to handle advanced courses requiring data analysis skills.

An application-focused extension of core econometrics courses, concentrating on empirical applications and the use of STATA. Students develop an understanding of econometric models, learning to estimate and test economic relationships using real datasets. The course also introduces deep learning, expanding the types of data that can be analyzed (images, text). Google Colab is used for deep learning practice. Upon completion, students will be able to interpret statistical results, conduct appropriate data analysis, understand the basics of neural network operations, and implement deep learning programs for natural language processing. The course includes 3 hours of weekly lectures and lab sessions with small-group work on STATA and Colab assignments.