Institutional Affiliation: Geisinger Clinic
|Controlling for the Compromise Effect Debiases Estimates of Risk Preference Parameters|
with Jonathan P. Beauchamp, Daniel J. Benjamin, David I. Laibson: w21792
The compromise effect arises when options near the "middle" of a choice set are more appealing. The compromise effect poses conceptual and practical problems for economic research: by influencing choices, it distorts revealed preferences, biasing researchers' inferences about deep (i.e., domain general) preferences. We propose and estimate an econometric model that disentangles and identifies both deep preferences and the context-dependent compromise effect. We demonstrate our method using data from an experiment with 550 participants who made choices over lotteries from multiple price lists. Following prior work, we manipulate the compromise effect by varying the middle options of each multiple price list and then estimate risk preferences without modelling the compromise effect. T...
|Measuring intertemporal preferences using response times|
with David Laibson, Carrie L. Morris, Jonathon P. Schuldt, Dmitry Taubinsky: w14353
We use two different approaches to measure intertemporal preferences. First we employ the classical method of inferring preferences from a series of choices (subjects choose between $X now or $Y in D days). Second we adopt the novel approach of inferring preferences using only response time data from the same choices (how long it takes subjects to choose between $X now or $Y in D days). In principle, the inference from response times should work, since choices between items of nearly equivalent value should take longer than choices between items with substantially different values. We find that choice-based analysis and response-time-based analysis yield nearly identical discount rate estimates. We conclude that response time data sheds light on both our revealed (choice-based) preferences...
|Individual Laboratory-Measured Discount Rates Predict Field Behavior|
with David Laibson, Carrie L. Morris, Jonathon P. Schuldt, Dmitry Taubinsky: w14270
We 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.
Published: Christopher Chabris & David Laibson & Carrie Morris & Jonathon Schuldt & Dmitry Taubinsky, 2008. "Individual laboratory-measured discount rates predict field behavior," Journal of Risk and Uncertainty, Springer, vol. 37(2), pages 237-269, December. citation courtesy of