(1) Interpret the effect of being a male on the hourly earnings of drivers in Chicago i.e. interpret results in the first and third column of Table 4 in the paper on page 17 (Note that the outcome variable is log of hourly earnings).

(2) Comment about the sample size and the goodness of fit of the regressions in the first and third column of Table 4.

Table 4: Gender pay gap Note: This table documents both the base gender pay gap and the gender pay gap once controlling for experience, location, time, and speed. Data are at the driver-hour level. Further, it includes specifications with rider and driver cancellations. The outcome variable is log of hourly earnings. Hour of week controls for each of 168 hours. Geohash controls are a vector of dummies for whether a driver began a trip in a given geohash. Driver experience is measured by a driver's lifetime trips completed prior to a given date, where lifetime trips is binned into 0100 trips, 100500 trip, 5001000 trips, 10002500 trips, and >2500 trips. Driving speed is the speed driven while on trip in a given driver-hour. Standard errors (clustered at the driver-level) are in parentheses. Table 4: Gender pay gap Note: This table documents both the base gender pay gap and the gender pay gap once controlling for experience, location, time, and speed. Data are at the driver-hour level. Further, it includes specifications with rider and driver cancellations. The outcome variable is log of hourly earnings. Hour of week controls for each of 168 hours. Geohash controls are a vector of dummies for whether a driver began a trip in a given geohash. Driver experience is measured by a driver's lifetime trips completed prior to a given date, where lifetime trips is binned into 0100 trips, 100500 trip, 5001000 trips, 10002500 trips, and >2500 trips. Driving speed is the speed driven while on trip in a given driver-hour. Standard errors (clustered at the driver-level) are in parentheses