A meta-analysis of hypothetical bias in stated preference valuation examines the extent to which individuals overstate their economic valuation of goods. The study analyzes 28 stated preference valuation studies that report monetary willingness-to-pay and use the same mechanism for eliciting hypothetical and actual values, generating 83 observations. The median ratio of hypothetical to actual value is 1.35, with a severe positive skew. The study finds that a choice-based elicitation mechanism is important in reducing bias, though the exact mechanisms remain unclear. Some evidence suggests that student subjects may contribute to bias, but this variable is highly correlated with group experimental settings. There is weak evidence that bias increases when valuing public goods, and some calibration methods are effective at reducing bias. Results are sensitive to model specification, and a comprehensive theory of hypothetical bias is needed for further understanding. The study revisits the findings of List and Gallet (2001), which found that hypothetical bias is greater in WTA studies than in WTP studies. However, the study finds that the magnitude of hypothetical bias is statistically less for WTP than WTA applications, and for private than public goods. The study also finds that the WTP coefficient is no longer statistically significant after adjusting for certain factors. The study concludes that the significant difference between WTP and WTA in the original LG results is sensitive to extreme values that use different elicitation mechanisms. Private goods continue to have lower bias than public goods, and some elicitation mechanisms are significant, though most variables are based on a single study. The study uses a different data set and model to estimate the relationship between hypothetical and actual values. It finds that the primary factor explaining hypothetical bias is the magnitude of the hypothetical value. The study also finds that calibration techniques are effective at reducing hypothetical bias. However, the study notes that the significance of dummy variables is sensitive to model specification, data variability, and extreme values. The study concludes that hypothetical bias may not be as significant a problem in stated preference analyses as is often thought.A meta-analysis of hypothetical bias in stated preference valuation examines the extent to which individuals overstate their economic valuation of goods. The study analyzes 28 stated preference valuation studies that report monetary willingness-to-pay and use the same mechanism for eliciting hypothetical and actual values, generating 83 observations. The median ratio of hypothetical to actual value is 1.35, with a severe positive skew. The study finds that a choice-based elicitation mechanism is important in reducing bias, though the exact mechanisms remain unclear. Some evidence suggests that student subjects may contribute to bias, but this variable is highly correlated with group experimental settings. There is weak evidence that bias increases when valuing public goods, and some calibration methods are effective at reducing bias. Results are sensitive to model specification, and a comprehensive theory of hypothetical bias is needed for further understanding. The study revisits the findings of List and Gallet (2001), which found that hypothetical bias is greater in WTA studies than in WTP studies. However, the study finds that the magnitude of hypothetical bias is statistically less for WTP than WTA applications, and for private than public goods. The study also finds that the WTP coefficient is no longer statistically significant after adjusting for certain factors. The study concludes that the significant difference between WTP and WTA in the original LG results is sensitive to extreme values that use different elicitation mechanisms. Private goods continue to have lower bias than public goods, and some elicitation mechanisms are significant, though most variables are based on a single study. The study uses a different data set and model to estimate the relationship between hypothetical and actual values. It finds that the primary factor explaining hypothetical bias is the magnitude of the hypothetical value. The study also finds that calibration techniques are effective at reducing hypothetical bias. However, the study notes that the significance of dummy variables is sensitive to model specification, data variability, and extreme values. The study concludes that hypothetical bias may not be as significant a problem in stated preference analyses as is often thought.