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Contact Information
650-814-3843 (mobile)
847-491-7001 (fax)
E-mail
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Scott Ogawa
Ph.D. Candidate
Department of Economics
Ph.D., Economics, Northwestern University, 2013 (expected)
M.A., Economics, Northwestern University, 2009
M.S., Mechanical Engineering, Stanford University, 2004
B.S., Mathematics and Mechanical Engineering (with distinction), Stanford University, 2003
Fields of Specialization
Applied Microeconomics, Experimental Economics
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Curriculum Vitae
Job Market Paper
“Do Students Who Pay More Study More?: Separately Identifying the Screening, Signaling, and Sunk-Cost Effects of Price” (coming soon)
Do people who pay more for something use it more intensely, and if so, why? There are at least three mechanisms – screening, signaling, and sunk-cost bias – that could generate a relationship between price and utilization, each with a different policy implication. I describe and implement a novel experimental design that separately identifies these three effects of price on product utilization. The design includes features from both lab and field experimentation. Results from two different studies refute the sunk-cost bias: People who pay more for a product do not use the product more intensely. However, there is some evidence that price may signal information to the consumer, thus creating a causal effect that is distinct from sunk-cost bias. In particular, students who pay more for an educational product sometimes study harder, but only due to changes in beliefs, not the actual amount paid. These findings, taken together, suggest that subsidies will not dampen total utilization so long as consumers are made fully aware of the non-subsidized price.
Other Papers and Work in Progress
“Placebo and Belief Effects: Optimal Design for Randomized Trials” with Ken Onishi. Download Show Abstract
The mere possibility of receiving a placebo during a randomized trial has the potential to alter behavior because it alters subjects' beliefs. This is distinct from the traditional "placebo effect" and complicates the identification of relevant parameters. We assume that two factors determine human-subject experimental outcomes: Treatment and the subject’s belief about the probability of treatment. Furthermore, we require that each subject must have correct beliefs about the probability of treatment. Given these two constraints, we investigate identification and optimal experimental design. Ultimately, we make three concrete optimal-design recommendations that maximize the statistical precision of various treatment effects. Most notably, we argue for a specific design in which the researcher introduces variation in the probability that each subject receives a placebo.
“Endogenous Class Size: Why it is Difficult to Observe Heterogeneous Ability among Cooperative Workers” (in progress) Show Abstract
Optimal behavior by principals and students should reduce the variation in teacher value-added scores relative to actual variation in teaching ability. In short, higher ability teachers will tend to have classrooms with more students, which in turn reduces their average output per student. This dynamic is the direct result a benevolent, output maximizing principal who optimally allocates more students to better teachers. (Alternatively, students may also seek out better teachers.) My empirical strategies look for three predictions of this model in data from North Carolina schools from 2007 to 2010. First, some teachers will have persistently more students. Second, there will exist characteristics of teachers that predict larger classes. And finally, more binding constraints on class-size at the school level will increase the variance of teacher performance. One immediate policy implication of this work: Teachers should be evaluated based on total number of student taught in addition to average student performance.
“Using Experimental Economics to Screen for Effective Teachers”, with John List, Sally Sadoff, and Phuong Ta. (in progress)
References
Prof. Diane Schanzenbach (Committee Chair)
Prof. Lori Beaman
Prof. Jonathan Guryan
Prof. Seema Jayachandran
Prof. Ian Savage (Teaching Reference)
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