Behavioral Biases are Temporally Stable /
Stango, Victor.
Behavioral Biases are Temporally Stable / Victor Stango, Jonathan Zinman. - Cambridge, Mass. National Bureau of Economic Research 2020. - 1 online resource: illustrations (black and white); - NBER working paper series no. w27860 . - Working Paper Series (National Bureau of Economic Research) no. w27860. .
September 2020.
Social scientists often consider temporal stability when assessing the usefulness of a construct and its measures, but whether behavioral biases display such stability is relatively unknown. We estimate stability for 25 biases, in a nationally representative sample, using repeated elicitations three years apart. Bias level indicators are largely stable in the aggregate and within-person. Within-person intertemporal rank correlations imply moderate stability and increase dramatically when using other biases as instrumental variables. Additional results reinforce three key inferences: biases are stable, accounting for classical measurement error in bias elicitation data is important, and eliciting multiple measures of multiple biases is valuable.
System requirements: Adobe [Acrobat] Reader required for PDF files.
Mode of access: World Wide Web.
Behavioral Biases are Temporally Stable / Victor Stango, Jonathan Zinman. - Cambridge, Mass. National Bureau of Economic Research 2020. - 1 online resource: illustrations (black and white); - NBER working paper series no. w27860 . - Working Paper Series (National Bureau of Economic Research) no. w27860. .
September 2020.
Social scientists often consider temporal stability when assessing the usefulness of a construct and its measures, but whether behavioral biases display such stability is relatively unknown. We estimate stability for 25 biases, in a nationally representative sample, using repeated elicitations three years apart. Bias level indicators are largely stable in the aggregate and within-person. Within-person intertemporal rank correlations imply moderate stability and increase dramatically when using other biases as instrumental variables. Additional results reinforce three key inferences: biases are stable, accounting for classical measurement error in bias elicitation data is important, and eliciting multiple measures of multiple biases is valuable.
System requirements: Adobe [Acrobat] Reader required for PDF files.
Mode of access: World Wide Web.