We leverage a large-scale incentivized survey eliciting behaviors from (almost) an entire undergraduate university student population, a representative sample of the US population, and Amazon Mechanical Turk (MTurk) to address concerns about the external validity of experiments with student participants. Behavior in the student population offers bounds on behaviors in other populations, and correlations between behaviors are similar across samples. Furthermore, non-student samples exhibit higher levels of noise. Adding historical lab participation data, we find a small set of attributes over which lab participants differ from non-lab participants. An additional set of lab experiments shows no evidence of observer effects.

Measurement error is ubiquitous in experimental work. It leads to imperfect statistical controls, attenuated estimated effects of elicited behaviors, and biased correlations between characteristics. We develop statistical techniques for handling experimental measurement error. These techniques are applied to data from the Caltech Cohort Study, which conducts repeated incentivized surveys of the Caltech student body. We replicate three classic experiments, demonstrating that results change substantially when measurement error is accounted for. Collectively, these results show that failing to properly account for measurement error may cause a field-wide bias leading scholars to identify “new” phenomena.

This paper studies, theoretically and empirically, the role of overconfidence in political behavior. Our model of overconfidence in beliefs predicts that overconfidence leads to ideological extremeness, increased voter turnout, and stronger partisan identification. The model also makes nuanced predictions about the patterns of ideology in society. These predictions are tested using unique data that measure the overconfidence and standard political characteristics of a nationwide sample of over 3,000 adults. Our numerous predictions find strong support in these data. In particular, we document that overconfidence is a substantively and statistically important predictor of ideological extremeness, voter turnout, and partisan identification.

The favorite–long shot bias describes the long-standing empirical regularity that betting odds provide biased estimates of the probability of a horse winning: long shots are overbet whereas favorites are underbet. Neoclassical explanations of this phenomenon focus on rational gamblers who overbet long shots because of risk-love. The competing behavioral explanations emphasize the role of misperceptions of probabilities. We provide novel empirical tests that can discriminate between these competing theories by assessing whether the models that explain gamblers’ choices in one part of their choice set (betting to win) can also rationalize decisions over a wider choice set, including compound bets in the exacta, quinella, or trifecta pools. Using a new, large-scale data set ideally suited to implement these tests, we find evidence in favor of the view that misperceptions of probability drive the favorite–long shot bias, as suggested by prospect theory.

People’s value of their own time is a key input in public policy evaluations—these evaluations should account for time taken away from work or leisure as a result of policy. Measuring this value for the self-employed is challenging, as, by definition, it is not priced by the market. Using rich choice data collected from farming households in western Kenya, we show that households exhibit non-transitive preferences. As a result, neither market wages nor standard valuation techniques correctly measure participants’ value of time. Using a structural model, we identify the behavioural wedges in participants’ choices and find that distortions appear when households exchange cash either for time or for goods. Our model estimates suggest that valuing the time of the self-employed at 60% of the market wage is a reasonable rule of thumb.