Nancy Qian is the James J. O'Connor Professor at Kellogg MEDS and a professor of the Department of Economics by courtesy appointment. Nancy is a native of Shanghai, China, holds a Ph.D. in Economics from MIT, was a Harvard Univeristy Academy Scholar post-doctoral fellow, and an Associate Professor at the Dept. of Economics at Yale University prior to Kellogg.
Professor Qian's research uses data to understand the determinants of economic development, especially in relation to political economy and long-run growth. Her work examines the economic determinants and consequences of formal institutions, such as elections, and cultural norms, such as gender preference and racial identity. She uses economic frameworks and empirical evidence to resolve historical puzzles, such as the causes of the Great Chinese and Soviet Famines, or the presence of local democracy within autocratic regimes. Her work spans a large number of current and historical contexts such as former Eastern Bloc countries, the United States and sub-Saharan Africa.
Her research has been published in top academic journals and featured in media outlets such as the Wall Street Journal and National Public Radio. She is the recipient of many prestigious awards and grants. She serves in several editorial positions and has consulted for agencies such as The World Bank, the Global Development Network and the China Development Bank.
She regularly attends worskhops in development economics, political economy, and co-organizes of the Northwestern Economic History Seminar. She founded China Econ Lab, an independent international organization that promotes rigorous research about the Chinese Economy, and the China Cluster for Northwestern's Global Poverty Research Lab. She leads the development economics initiative for Kellogg.
In her spare time, she spends time with her family, writes opinion columns, appears on news outlets such NPR or Channel News Asia, and is working on her first book, which is planned for publication in 2023 with the University of Chicago Press.
Development Economics, Political Economy, Historical Development
Economic Development, Political Economy, Economies of the Population, Development Economics, Empirical Methods, Interdisciplinary Perspectives on Population
Inference with big data is central to business today, where evidenced-based decisions are highly valued. Doing this is difficult because real world situations are often complex and fast-paced, and data can be simultaneously "big" and yet imperfect. In the real world, one has to analyze data for different types of decisions and situations and is rarely in the position of choosing his/her ideal data or setting. This means that the quality of the data and the method with which one can make inferences vary greatly across contexts. Moreover, handling big data, where one cannot visualize the entire dataset and visually identify problems, requires knowledge of advanced regression modeling and post-estimation techniques. Business leaders in such situations need to extract useful insights from data with advanced statistical modelling, and to communicate these insights in a non-technical and intuitive way so that others can understand.
This class addresses these needs by teaching advanced statistical modelling with big data, and practicing communicating these ideas at all levels of technicality (or non-technicality). It takes a practical view of statistics and data analysis with large datasets and provides students with a range of advanced state-of-the-art statistical and basic machine learning tools to address economic questions. Two unique cases were developed especially for this class. One of the cases is an "open-ended" project in which students will be required to apply the tools they learn to build a statistical model for analysis and then design business strategy based on the evidence. Each case reflects a highly complex real-world situations and evolve across lectures in stages so that students learn advanced analytical skills in a concrete context-relevant setting. This class will benefit students who want to think creatively about how to apply the results of rigorous data analysis to economic decisions.
This course is case-focused and most of the analysis will be conducted in groups in class. Using a fun and hands-on approach, students build on the foundational tools they obtained in the Business Analytics courses and learn advanced applications by working with big data projects in a lab-like setting in class. Students will analyze the data using STATA, interpret the results, assess their credibility and applicability to the economic questions which motivated the analysis, and present evidence-driven business decisions in class. There are no exams. Prerequisite: Business Analytics II (DECS 431) (see syllabus).
Global Initiatives in Management (GIM) is an international experiential learning course designed to provide students with an introduction to the unique business opportunities, management practices and market dynamics of a specific region or global industry. The course combines in-class lectures, reading discussions and case studies during the winter quarter with ten days of international field research over spring break. Immersed in the culture and language of their host countries, students will have the opportunity to meet with local business and government leaders, conduct interviews and collect data for their group research projects, and experience some of the unique social and cultural facets of the region. Final presentations and written research reports are due in spring quarter after completion of the overseas portion of the class. Each class section is taught by a faculty member with deep knowledge of the region or industry and supported by an advisor from the Kellogg staff who assists students in planning the field experience. Students are financially responsible for their travel costs, and financial aid is available to those who qualify.