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Why you Can’t Find a Taxi in the Rain and Other Labor Supply Lessons from Cab Drivers*

机译:为什么你在雨中找不到出租车和其他劳动力供应课程的出租车*

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摘要

In a seminal paper, Camerer, Babcock, Loewenstein, and Thaler (1997) find that the wage elasticity of daily hours of work New York City (NYC) taxi drivers is negative and conclude that their labor supply behavior is consistent with target earning (having reference dependent preferences). I replicate and extend the CBLT analysis using data from all trips taken in all taxi cabs in NYC for the five years from 2009-2013. Using the model of expectations-based reference points of Koszegi and Rabin (2006), I distinguish between anticipated and unanticipated daily wage variation and present evidence that only a small fraction of wage variation (about 1/8) is unanticipated so that reference dependence (which is relevant only in response to unanticipated variation) can, at best, play a limited role in determining labor supply. The overall pattern in my data is clear: drivers tend to respond positively to unanticipated as well as anticipated increases in earnings opportunities. This is consistent with the neoclassical optimizing model of labor supply and does not support the reference dependent preferences model. I explore heterogeneity across drivers in their labor supply elasticities and consider whether new drivers differ from more experienced drivers in their behavior. I find substantial heterogeneity across drivers in their elasticities, but the estimated elasticities are generally positive and only rarely substantially negative. I also find that new drivers with smaller elasticities are more likely to exit the industry while drivers who remain learn quickly to be better optimizers (have positive labor supply elasticities that grow with experience).
机译:在一项开创性的论文中,Camerer,Babcock,Loewenstein和Thaler(1997)发现纽约市(NYC)出租车司机的每日工作小时工资弹性为负,并得出结论,他们的劳动力供给行为与目标收入相符(具有参考相关的偏好设置)。我使用从2009年至2013年这五年中纽约所有出租车的所有出行数据来复制和扩展CBLT分析。使用Koszegi和Rabin(2006)的基于期望的参考点模型,我区分了预期的和未预期的每日工资变化,并提供了证据表明只有一小部分的工资变化(约1/8)是不可预测的,因此参考依赖性(只有在应对意外变化时才有意义),充其量只能在决定劳动力供应方面发挥有限的作用。我的数据中的总体模式很明确:驱动程序倾向于对意外和预期增加的收入机会做出积极反应。这与劳动力供给的新古典优化模型是一致的,并且不支持依赖参考的偏好模型。我探讨了不同驱动因素在劳动力供给弹性方面的异质性,并考虑了新驱动因素与有经验的驱动因素在行为上是否有所不同。我发现驱动程序的弹性存在很大的异质性,但是估计的弹性通常是正的,而很少是负的。我还发现,弹性较小的新驾驶员更有可能退出该行业,而仍保持敏捷的驾驶员会很快学会成为更好的优化器(随着经验的增长,劳动力供给的弹性会逐渐增强)。

著录项

  • 作者

    Henry S. Farber;

  • 作者单位
  • 年度 2015
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
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