Chicago Taxi Trips (Tipping)

This dataset accompanies a paper examining how tipping practices changed during the pandemic. I download data on unique taxi rides taken in Chicago freely available from the Chicago data portal for Jan 2018-March 2021. The data include a number of variables for each taxi trip, including the fare amount, the tip amount, and the passenger's location. I merge in demographic data available from CMAP for the community area the passenger came from in Chicago as well as daily data on COVID-19 hospitalizations to enrich the analysis. I filter the dataset to taxi rides payed with credit card, and remove trips with exceptionally weird data (e.g. 0 second trip duration, fares greater than $1000). I use the dataset to estimate the effect of the pandemic on whether passengers tip and if so, the average percent tipped. I find that the likelihood that a passenger leaves a tip declines by roughly 5 percentage points during the pandemic but the average non-zero tip left increased by roughly 2 percentage points higher. I exploit geographic and temporal heterogeneity in the data to explore the possible mechanisms behind these patterns in tipping.

本数据集配套于一篇探究疫情期间小费支付行为变化的学术论文。笔者从芝加哥数据门户（Chicago Data Portal）免费获取了2018年1月至2021年3月期间芝加哥市的唯一标识出租车行程数据。该数据集包含每笔出租车行程的多项变量，涵盖行程费用、小费金额以及乘客乘车位置。笔者并入了芝加哥大都会规划署（CMAP）提供的与乘客出发社区相关的人口统计数据，以及每日新型冠状病毒肺炎（COVID-19）住院人数数据，以丰富分析维度。笔者将数据集筛选为仅包含信用卡支付的出租车行程，并剔除了存在异常数据的行程（例如行程时长为0秒、车费超过1000美元的记录）。本数据集被用于评估疫情对乘客支付小费行为的影响，以及在支付小费的情况下，平均小费占车费的比例变化。研究发现，疫情期间乘客支付小费的概率下降了约5个百分点，而有小费支付的样本中，平均小费占比提升了约2个百分点。笔者借助数据中的地理与时间异质性特征，探究上述小费支付模式背后的潜在作用机制。

搜集汇总

背景与挑战

背景概述

该数据集基于芝加哥出租车行程数据（2018年1月至2021年3月），融合了社区人口统计和COVID-19住院数据，旨在分析疫情期间小费行为变化。研究发现，疫情期间乘客给小费的概率下降约5个百分点，但非零小费的平均比例增加约2个百分点，揭示了小费行为的复杂变化趋势。