Nancy Qian is a professor of MEDS. She is a native of Shanghai, China, and holds a Ph.D. in Economics from MIT. Prior to Kellogg, Professor Qian taught at Yale University and Brown University, and was a visiting scholar at the department of economics at Harvard University while she was post-doctoral fellow of the prestigious Harvard Academy Scholars program. 

Her research provides empirical evidence for a set of core questions in development economics that broadly fall into four sub-categories: demography and development, geography and development, institutions and development and culture and development. Her works in the first category include studies of the economic determinants of missing women, the effects of changes in family size on child educational attainment, the long-run effects of famine on health and labor supply, the historical effect of the Columbian Exchange on population growth and urbanization, and the extent to which human capital differences can explain cross-country income differences. Her work in the second category includes studies that explore the long-run effects of climate change on conflict and the long-run influence of agricultural productivity on economic growth and conflict. Her work in the third category includes a study of the institutional causes of China's Great Famine, the determinants and consequences of elections in autocratic regimes, the determinants and consequences of humanitarian aid, understanding the government's influence on the media, and the rapid economic development in China. In the final category, she has worked on understanding how the forces of social trust and political institutions interact to determine political and economic outcomes in the United States, China and across countries.

Professor Qian's work includes extensive analysis of survey data, as well as historical data and a recurring theme in in her research is to understand the difference between short and long-run effects, and endogenous responses to economic incentives. She is an expert of the Chinese economy. 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 numerous prestigious awards, such as the Alfred P. Sloan Fellowship, the Kiel Global Excellence Award and National Science Foundation grants. She serves in several editorial positions and has consulted for development agencies such as The World Bank, the Global Development Network and the China Development Bank.

In her spare time, she enjoys traveling, art, reading, TV, sports and cooking for her family and friends. She's a fan of the Red Sox, Patriots and Roger Federer.

Development Economics, Political Economy, Historical Development

• Doctor of Philosophy, 2005, Economics, Massachusetts Institute of Technology
• Bachelor of Arts, 2001, Economics, Government, Japanese and Mathematics, University of Texas at Austin, High Honors

• Associate Professor, Economics, Yale University, 2013-present
• Visiting Scholar, Economics, Stern School of Business, New York University, 2016-present
• Visiting Scholar, Economics, Booth Business School, University of Chicago, 2015-present
• Visiting Scholar, Economics, Stern School of Business, New York University, 2014-present
• Affiliate, Council on East Asian Studies, Yale University, 2014-present
• Affiliate, Leitner Center for Political Economy, Yale University, 2014-present
• Assistant Professor, Economics, Yale University, 2009-2013
• Visiting Scholar, Industrial Relations Section, Princeton University, 2011-2012
• Visiting Scholar, Booth Business School, University of Chicago, 2009-present
• Visiting Scholar, Economics, Harvard University, 2007-2009
• Harvard Academy Scholar, Harvard University, 2007-2009
• Assistant Professor, Economics, Brown University, 2005-2005
• Affiliate, Populations Studies and Training Center, Brown University, 2005-2009

• One Thousand Talents Plan, City of Shanghai, 3 years
• Keynote: “Tracking International Aid and Investment from Developing and Emerging Economies”, Heidelberg University

• Editorial Board, VOX China, 2017
• Editorial Board, American Economic Journal – Applied, 2017
• Editorial Board, Review of International Organizations, 2017
• Associate Editor, Economica, 2015
• Associate Editor, The Journal of European Economics, 2013
• Associate Editor, The Journal of Development Economics, 2010

Economic Development, Political Economy, Economies of the Population, Development Economics, Empirical Methods, Interdisciplinary Perspectives on Population

Political Economy IV: Topics in Development Economics (MECS-540-4)
This course introduces PhD students to three important topics within development economics and political economy, reviewing the frontier of the literature, the latest questions, methods most prevalently used, and the evidence thus far. The class focuses on empirical methods and their connection with theory. The course goal is assisting students as they transition into the research phase of their career.

Statistical Decision Analysis (MECNX-434-0)

Making Business Decisions with Big Data (MECN-935-0)
Big data are central to business decisions today. This class teaches the methods for the rigorous analysis of big data that can be used to obtain insights for business. It takes a practical view of statistics and big data analysis and provides students with a range of state-of-the-art statistical and machine learning tools to address economic questions. 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. The quality of the data and the method with which they can make inferences vary greatly across contexts. Moreover, handling big data requires programming sophistication using statistical software. The ultimate goal of this class is therefore to master a set of analytical tools and obtain confidence and competence in statistical programming. In order to maximize the amount and joy of learning, the 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 class in groups. Students will spend several weeks replicating famous empirical analyses with big data from the real world. The replication exercises will be guided by the instructor and TAs and done in class in groups. After the analysis, students will interpret the results, assess their credibility and applicability to the larger economic question which motivated the analysis, and present these findings to the class. This class aims to teach three complementary sets of tools: 1) big data creation; 2) big data management and 3) establishing causal relationships with big data. Data creation provides an overview of the new tools for creating big data, such as machine learning techniques, web scraping, the creation of text data for statistical analysis, and the creation of geographic data using spatial software. Data management provides tools for transforming big data into manageable samples, including new machine learning techniques as well as important traditional tools such as merging different datasets. The final component focuses on establishing causal relationships with the data. It complements the first two components and aims to provide students with a good sense of: which questions can be asked of the data given the economic question and data availability, which method to use, and the advantages and limitations of the chosen method. Some examples of the topics that we will study are: fame and political success, trade wars, air pollution and real estate prices, Chinese imports and the U.S. economy, or the Mexican drug war. By the end of the course, students will have had hands-on practice using a wide array of empirical estimation methods in a wide range of applications and big data types such as individual-level and firm-level data, cross-country data, numeric data, survey data, administrative data, text data and spatial data. The class will discuss a variety of new software in the context of applications such as ArcGIS, R, Python, SAS, but students are only required to program in Stata. The high level of competency in Stata programming that students achieve by the end of the course can be transferred to the programming of other statistical software. This class requires students to have taken business analytics and prepares students for independent data analysis and advanced topics courses using data.

Thought Leadership Seminar (MECN-484-5)
Objective: This thought leadership seminar is designed to facilitate an in-depth exploration of issues facing developing countries, which typically have worse institutions and more violent histories than rich countries. After an introduction about some key differences between rich and poor countries today, we will discuss the determinants and consequences of foreign aid, famine, conflict, democracy and economic growth. Each session will focus on a distinct topic to allow for a comprehensive, yet focused and digestible discussion of the subject matter from both a micro and a macro perspective. The course will discuss cross-country comparisons as well as focus on specific geographic areas such as China.

Format: This seminar consists of lectures, class discussions and group assignments. Source materials will include a combination of first-hand case studies, recent academic and policy research. Students will also be asked - in groups - to lead a discussion/debate on the most important and controversial development policy questions today. They will write a corresponding short opinion / editorial piece and write and present a focused paper in one of the sessions´ topic areas.

Requirements: Prior exposure to statistics/econometrics and micro economics. Interest in economic and institutional development. A high level of preparedness and participation is essential to ensure that the seminar is a rewarding experience for all participants.