Person: Taubinsky, Dmitry
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Taubinsky
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Dmitry
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Taubinsky, Dmitry
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Publication Essays in Behavioral and Experimental Economics(2014-06-06) Taubinsky, Dmitry; Laibson, David I.; Luca, Michael; Mullainathan, Sendhil; Roth, AlvinThis dissertation consists of three essays examining the implications of human psychology for economic behavior and market outcomes.Publication Individual Laboratory-Measured Discount Rates Predict Field Behavior(Springer-Verlag, 2008) Chabris, Christopher F.; Laibson, David; Morris, Carrie L.; Schuldt, Jonathon P.; Taubinsky, DmitryWe estimate discount rates of 555 subjects using a laboratory task and find that these individual discount rates predict inter-individual variation in field behaviors (e.g., exercise, BMI, smoking). The correlation between the discount rate and each field behavior is small: none exceeds 0.28 and many are near 0. However, the discount rate has at least as much predictive power as any variable in our dataset (e.g., sex, age, education). The correlation between the discount rate and field behavior rises when field behaviors are aggregated: these correlations range from 0.09–0.38. We present a model that explains why specific intertemporal choice behaviors are only weakly correlated with discount rates, even though discount rates robustly predict aggregates of intertemporal decisions.Publication The Allocation of Time in Decision-Making(Massachusetts Institute of Technology Press, 2009) Chabris, Christopher; Laibson, David; Morris, Carrie L.; Schuldt, Jonathan P.; Taubinsky, DmitryWe study the allocation of time across decision problems. If a decision-maker (1) has noisy estimates of value, (2) improves those estimates the longer he or she analyzes a choice problem, and (3) allocates time optimally, then the decision-maker should spend less time choosing when the difference in value between two options is relatively large. To test this prediction we ask subjects to make 27 binary incentive-compatible intertemporal choices, and measure response time for each decision. Our time allocation model explains 54% of the variance in average decision time. These results support the view that decisionmaking is a cognitively costly activity that uses time as an input allocated according to cost-benefit principles.