Hypothetical bias is a pervasive problem in stated-preference experiments. Recent research has developed two empirically successful calibrations to remove hypothetical bias, though the calibrations have not been tested using the same data or in a conjoint analysis. This study compares the two calibrations in a conjoint analysis involving donations to a public good. Results find the calibrations are biased predictors of true donations but that calibrated and uncalibrated models together provide upper and lower bounds to true donations.
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