Source materials used in the study of population; standard procedures for the measurement of fertility, mortality, natural increase, migration, and nuptiality; and uses of model life tables and stable population analysis and other techniques of estimation when faced with inaccurate or incomplete data are studied.

Course examines how and why society can make us sick or healthy and how gender, race/ethnicity, wealth, education, occupation and other social statuses shape health outcomes. It looks at the role of social institutions, and environment-society interactions in shaping health outcomes and examines how these factors underlie some of the major causes of illness and death around the world including infant mortality, infectious diseases such as HIV/AIDS, and chronic diseases such as heart disease and cancer. The course draws on historical and cross-cultural material from the U.S. as well as global examples from different countries around the world.

This seminar explores important factors facing the field of global health today, as well as policy actions to address these factors. It examines demographic changes and rapid urbanization, climate change and its implications for global health, the increased importance of non-communicable diseases in low- and middle-income countries, the rise of social media and misinformation/disinformation, new health risk factors such as antimicrobial resistance, and the increased prominence of humanitarian emergencies due to conflicts, natural disasters, pandemics and other disease outbreaks.

This course uses the lens of reproductive justice to examine policy and politics around reproduction and family formation in the United States. The course explores the social, historical and cultural forces that shape reproduction, focusing on how inequalities based on gender, sexuality, race and ethnicity, class, and citizenship structure and influence reproductive opportunities and experiences. Topics include contraception and abortion, childbirth and maternity care, adoption and family policy, reproductive technology, eugenics, the maternal mortality crisis, and the role of law, medicine and activism in shaping contemporary reproduction.

This course combines a traditional public health course in epidemiology with a policy-oriented course on population health. Conventional topics include measurement of health and survival and impact of associated risk factors; techniques for design, analysis of epidemiologic studies; sources of bias and confounding; and causal inference. We also examine: models of infectious disease with an emphasis on COVID-19, inference and decision making based on large numbers of studies and contradictory information, the science underlying screening procedures, social inequalities in health, and ethical issues in medical research.

This seminar is designed to help graduate students in economics cultivate ethical research practices they may apply in future work at or beyond the University. Students are encouraged to discuss concerns that may arise during the conduct of their research with experienced faculty and devise solutions for dealing with these concerns. The course provides necessary training for newly mandated RCR training for graduate students supported by government grants, and is required for successful completion of the program.

This course begins with extensions of the linear model in several directions: (1) pre-determined but not exogenous regressors; (2) heteroskedasticity and serial correlation; (3) classical GLS; (4) instrumental variables and generalized method of moments estimators. Applications include simultaneous equation models, VARS and panel data. The second part of the course covers the bootstrap, nonparametric estimators, extremum estimators (including discrete choice models), and estimation of treatment effects.

The course surveys both the theoretical literature and the relevant empirical methods and results in selected current research topics in labor economics.

A continuation of ECO 551, with emphasis on current research issues. Topics vary from year to year.

This course studies topics in Development beyond those covered in ECO 562. Topics vary from year to year. The first half of the course focuses on issues in macro development. Specific topics include an overview of broad development patterns, development accounting, misallocation, structural change, premature deindustrialization, the role of agriculture in development, market imperfections, and risk-sharing. The second half of the course covers micro development. Specific topics include environment, education, gender inequality, intrahousehold allocation, and firms.

This is the first class of the quantitative methods field in the PhD. in Politics. It is a doctoral-level introduction to foundations of mathematical statistics for Ph.D. students in Politics and other social and behavioral sciences. The class covers rigorous foundations of classical point estimation and statistical inference, as well foundational topics in econometrics. It covers both finite-sample and large-sample theory and relies on linear algebra and multivariate calculus at the level of POL 502. POL 502 or equivalent is a pre-requisite of this class.

This course covers a range of advanced topics in statistical learning that are useful for empirical research in political science. These may include dimension reduction and regularized regression for large datasets; scaling models; models for text, audio, and image data; and spatial statistics among other topics. The course focuses in particular on estimation and inference to enable students to adapt and extend existing approaches.

This is a course on research methods for sociology PhD students. The seminar has four objectives: 1) to review foundational principles of research design and contemporary debates in sociological methodology; 2) to introduce students to the practice of different research methods (e.g., survey research, experiments, in-depth interviews, ethnography) while considering the strengths and limitations of various approaches; 3) to familiarize students with the components of a strong empirical paper and prepare them to identify a topic and data for their empirical paper; and 4) to train students in the conduct of responsible research.

This course teaches advanced statistical methods for social science in three segments: (1) causal inference, (2) categorical data analysis, and (3) replication analysis. Emphases are on research designs and practical applications rather than statistical theories or computations. Familiarity with basic probability theory, inferential statistics, and linear regression models for continuous dependent variables is assumed.

Preparation of quantitative research papers based on field experiments, laboratory experiments, survey procedures, and secondary analysis of existing data banks.