Applied Econometrics (Lanahan)
This applied econometrics course is designed for graduate students using quantitative, and particularly nonexperimental methods, in their research. We will cover data management, analysis, diagnostics, assumptions, and presentation of data in tables and graphics. Our focus will be on integrating and building your knowledge of research design and causal inference with data management and analysis skills that you can apply immediately to your work. The sequence is designed so that we will cover as many advanced quantitative analysis topics as possible. I assume that you have some familiarity with basic statistical concepts. I also assume you are familiar with the software package STATA.
Causal Inference - Methods (Livne-Tarandach)
This course complements the other doctoral seminars by delving more deeply into methodological issues. During the term, we take a fresh look at an old problem in the social sciences: causal inference. As Angrist and Pischke (2010) assert, the past few decades have witnessed a “credibility revolution” in empirical economics. We will cover key aspects of this revolution and discuss their implications for research design and the interpretation of empirical evidence in a variety of disciplines, including sociology, political science, history, economics, and management.
The purpose of this course is to improve the sophistication of students as budding scholars—both when designing the empirical component of their research and when critiquing evidence produced by others. By exposing students to useful methods and discussing linkages among them, this course also trains students to think more deeply and creatively about the “why should I believe your story?” question. In doing so, it leaves students better equipped to anticipate and pre-empt questions often asked by reviewers and seminar participants: “What, exactly, is your identification strategy?,” “Isn’t this relationship endogenous?,” and “Isn’t your evidence also consistent with…?” This seminar is not an applied econometrics course. Instead, it focuses on understanding the intuition of alternative research designs and the conditions under which such designs are particularly useful or limited.
Organization Theory and Strategy Seminar (Starbuck)
This course in organization theory covers selected theoretical perspectives on organizations and organizing, such as contingency theory, institutional theory, ecological and evolutionary theories, networks and virtual organizations, information processing, learning and knowledge, managerial and organizational cognition.
Networks and Institutions (Nelson)
As the title suggests, this PhD seminar introduces students to the study of organizations through the lens of network analysis and institutional theory. Network analysis is a form of structural analysis in the social sciences. It is based, therefore, on the assertion that the pattern of relations amongst individuals and organizations is responsible, at least in part, for the actions of these individuals and organizations. Institutional theory has enjoyed a powerful resurgence and is currently the dominant theoretical perspective (according to bibliometric analyses) for the study of organizations. Institutional theory is also a core component of economic sociology, which emphasizes the importance of social relations in shaping economic behaviors. Exciting work in organizational theory is taking place at the intersection of networks and institutions.
The purpose of the course is to provide students with a thorough grounding in the “classic” social science literature on these topics. The readings are organized to provide an introduction to fundamental concepts, followed by an elaboration and extension on these concepts. Over the course of the quarter, we’ll cover a number of topics, including embeddedness, institutional diffusion, institutional change, historical contingency, and network dynamics. Please note, however, that this is not a course on methods; in particular, while the final class provides an overview of select research methods, this course will not cover the logistics of collecting and manipulating network data. The course presumes no previous specialized background in organization theory.