We examine the role of defaults in high-frequency, small-scale choices using unique data on over 13 million New York City taxi rides. Using a regression discontinuity design, we show that default tip suggestions have a large impact on tip amounts. These results are supported by a secondary analysis that uses the quasi-random assignment of customers to different cars to examine default effects on a wider range of fares. Finally, we highlight a potential cost of setting defaults too high, as a higher proportion of customers opt to leave no credit card tip when presented with the higher suggested amounts. (JEL D12, L92)
and the Virtual Market Design Seminar for helpful comments and suggestions. Cuimin Ba and Jihong Song provided excellent research assistance.The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.
L earning by Doing (LBD) is an economically important phenomenon which can affect several types of market activity. At the level of the individual worker, learning is a source of productivity improvements that can increase wages. At the firm level, LBD can be welfare-improving when it leads to cost reductions that increase output in competitive markets; less beneficial effects can exist in concentrated markets, where LBD may provide incumbents with cost advantages that deter entry. While there exists an extensive literature that documents learning effects in many settings (see Thompson 2010 and 2012, for surveys), in prior studies it has been difficult to learn which agents in a firm are improving their capabilities, what activities are being improved, and how strongly individual agents are encouraged to find improvements.We provide new evidence on LBD that addresses some of these gaps in the literature. We use a highly detailed dataset of New York City (NYC) yellow taxi rides to study how drivers make improvements overall, how their performance varies across measurably different situations, and how general and specific experience make different contributions to driver performance and strategies. Taxi service in NYC is characterized by several features that make it an interesting setting for studying
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