Individual laboratory-measured discount rates predict field behavior

Abstract

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.

Discounting future green: Money versus the environment.

“…In other words, discount rates are constructed based on the valence, domain, magnitude, time horizon, and other contextual features of a situation (Baron, 2000); correlations between situations will be higher to the extent that these factors are similar. The good news from the present research is that discount rates assessed in the lab for one domain should be applicable to other domains and contexts, even predicting real-world behaviors, as has been recently found (Chabris, Laibson, Morris, Schuldt, & Taubinsky, 2008).…”

Insurance decisions for low-probability losses

“…Larger samples may be needed in order to identify the relationships between the decision to insure in the lab and demographic characteristics or other (naturally occurring) insurance purchases. Recent studies linking field behavior to risk aversion and discount rates have found that sample sizes on the order of five times ours are needed for sufficient power (Anderson & Mellor, 2007;Chabris et al, 2008).…”

How Time Preferences Differ: Evidence from 45 Countries

“…It is also found that time preference can predict health-related behavior such as smoking and alcohol consumption, and nutrition intake (Khwaja, Sloan, & Salm, 2006;Chabris, Laibson, Morris, Schuldt, & Taubinsky, 2008;Weller, Cook III, Avsar, & Cox, 2008). In particular, Chabris et al (2008) and Sutter, Kocher, Glätzle-Rützler, and Trautmann (2013) find that time preference measure elicited from choices in experiments correlates with the body-mass-index (BMI) for adults and adolescents.…”

“…In particular, Chabris et al (2008) and Sutter, Kocher, Glätzle-Rützler, and Trautmann (2013) find that time preference measure elicited from choices in experiments correlates with the body-mass-index (BMI) for adults and adolescents. Consistent with their findings, the last two columns in Table 9 shows that the average weighting tendency can explain a certain degree of cross-country variation for the BMI.…”