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Thursday, January 25, 2018 at 11:40am to 1:10pm
Ives Hall, 111
B07 Tower Rd, Ithaca, NY 14853, USA
Neil Thakral - Harvard University
Daily Labor Supply and Adaptive Reference Points (joint w/Linh T. Tô)
Abstract: We document evidence of high-frequency reference-point adjustment in the field. Analyzing a dataset of all New York City cab fares in 2013 using non-parametric methods, we find reductions in cabdriver labor supply in response to higher accumulated daily earnings and stronger effects for more recent earnings. The income effect is inconsistent with the neoclassical model and the non-fungibility of daily income rejects models invoking daily income targets. To explain the evidence, we incorporate adaptive reference points into models of loss aversion and salience. While loss aversion tends to overstate the main quantitative features of the data, both models capture the qualitative features.