Behavioral Biases are Temporally Stable / Victor Stango, Jonathan Zinman.

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Stango, Victor

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Material type: Text

TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w27860.Publication details: Cambridge, Mass. National Bureau of Economic Research 2020.Description: 1 online resource: illustrations (black and white)Subject(s):

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Abstract: 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.

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Item type	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Working Paper	Biblioteca Digital	Colección NBER	nber w27860 (Browse shelf(Opens below))	Not For Loan

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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.