Meet Our New Faculty : Maura Coughlin and Matt Thirkettle
We are delighted to welcome two new assistant professors to the Economics Department faculty , Maura Coughlin and Matt Thirkettle .
Maura specializes in applied microeconomics and applied econometrics , with a focus on empirical industrial organization , especially in health care and insurance markets . She earned her Ph . D . from Cornell University where she won two teaching awards as well as a graduate student research award . Her current research includes an analysis of consumer choice among alternative health plans when consumers consider only a limited set of the available options . She is also exploring regional and demographic variation in consumer choices regarding Medicare Part D . Maura will be teaching ECON 441 , Empirical Methods for Industrial Organization , and ECON 598 , a readings course designed to prepare our second-year Ph . D . students for independent research , in the spring .
Matt is an econometrician who has interests in the economics of networks and industrial organization . He also earned his Ph . D . from Cornell ( indeed , he and Maura shared an office ), and won a teaching prize and two graduate student research awards . Matt ’ s current research examines estimation issues that arise when social networks have missing links , as well as models of discrete choice under conditions of risk and limited consideration of available choices . He is also working on an analysis of how social networks among software developers influence decisions to contribute to open source software on GitHub . Matt is teaching both parts of our first-year graduate econometrics sequence – ECON 510 this fall and ECON 511 in the spring .
For more information on Maura and Matt and the rest of our faculty , check out the
faculty page on our website .
Pathways to Research and Doctoral Careers – PREDOC Program
The Pathways to Research and Doctoral Careers ( PREDOC ) is a consortium of universities and research institutions that aims to foster a talented , diverse , and inclusive population in the quantitative social sciences , especially Economics . Consortium members offer graduating students a variety of post-baccalaureate , full-time , paid pre-doctoral research positions , where students work directly for faculty and are mentored and prepared for top doctoral programs and eventual careers in the academy , institutes of policy research , and industry . Institutions participating in the PREDOC program are committed to reaching out to , mentoring and recruiting individuals who are underrepresented in their fields with the goal of diversifying the pool of pre-doctoral candidates . Consortium members include Harvard , Princeton , MIT , University of Chicago , Stanford and Columbia among others , as well as the National Bureau of Economic Research and several Federal Reserve banks . If you are interested in the program , please check out
PREDOC .
Menu
Main Course : ECON 320 , Evaluation of Social Programs
Appetizers : ECON 307 / STAT 310 or STAT 315 or SOSC 302
Master Chef : Professor Flávio Cunha
Dinner is Served : Fall 2020 , Tuesdays and Thursdays , 11:20 a . m .
Come enjoy a gastroeconomical delight !
New Course on the Menu
Evaluation of Social Programs , ECON 320 , examines various quantitative methods that researchers use to evaluate public policies or the impacts and costs of social programs . We cover experimental and quasi-experimental methods designed to assess program effects . The course ’ s approach is through “ learning-by-doing ” as students implement in practice different experimental and quasi-experimental estimators using real-world data . In the second part of the course , we present and review the ingredients method of cost estimation . We describe the procedure to specify a program ’ s input and consider different approaches to assigning prices to each element , whether there are markets for the inputs or not . We designed the course to attract students interested in data analysis , especially those interested in innovation in public policy and social programs . Students should have studied statistical tools such as ordinary least squares ( OLS ) and should be able to use R , Python , STATA , SAS , or SPSS ( among other alternatives ).
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