Panel: Using Rapid, Rich, and Relevant Randomized Trials to Strengthen Metropolitan Development
(Sustainable Economic Development)

Friday, July 20, 2018: 1:45 PM-3:15 PM
Building 3, Room 210 (ITAM)

*Names in bold indicate Presenter

Panel Chair:  Cynthia Miller, MDRC

Using Rapid Cycle Experimental Impact Evaluation for Program Improvement
Irma Perez-Johnson, American Institutes for Research

Secure and resilient cities, with sustainable social services and economic development, require public policies that benefit the citizens of the metropolis. How should governments decide which housing, labor market, education, and criminal justice programs make a difference, in order to guide funding decisions? Tools for producing evidence on “what works?” have a vital role to play in strengthening metropolitan development. The best such tool is a random assignment experiment, which measures an intervention’s contribution to the well-being of the target population without bias by contrasting outcomes between randomly split treated and untreated (control) groups. To most substantially help city leaders make wise policy choices, experiments need to produce policy-rich, relevant evidence on a rapid time scale. The proposed panel will share how each of these goals can be achieved, engaging three experts whose recent scholarship has pushed the frontiers of experimental evaluation. First, Laura Peck will explain how creative design and analysis strategies make it possible to address a richer set of policy questions with randomized trails. Accomplishing this on a rapid time line will be addressed by Irma Perez-Johnson. Then Stephen Bell will describe how the findings of experimental trials can be made more relevant by producing evidence on the population of policy concern. Discussion, by the session’s formal discussants and with the audience, will follow. All participants will gain an appreciation of what it takes to test effectiveness of urban policies using randomized experiments and be introduced to state-of-the-art ways to conduct experiments to maximize the policy return.

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