Question

Table 4: Price Elasticities for Various Sub-Populations Elasticity All periods -0.5463 (0.0209) City Chicago -0.6618 (0.0367) Los Angeles -0.3270 (0.0458) New York -0.6084 (0.0514) San Francisco -0.5244 (0.0378) Time of Day Evening rush -0.4994 (0.0659) Morning rush -0.5206 (0.0776) Slow nighttime -0.5254 (0.0675) Weekday day -0.4611 (0.0557) Weekday evening -0.5418 (0.0571) Weekend day -0.6615 (0.0540) Weekend evening -0.5388 (0.0775) Weekend event -0.5480 (0.0433) Rides in Period 1 ride in period -0.4374 (0.1398) 1 < rides in period <3 -0.3822 (0.0914) 3<rides in period < 8 -0.4603 (0.0642) > 8 rides in period -0.5702 (0.0229) This table presents elasticity estimates for sub-populations within the data. The RD regression that generates these estimates instruments for expected wait time and includes all relevant controls; this specification corresponds to that presented in Column (5) of Table 3. Standard errors are reported in parentheses.

Recently a group of economists estimated price elasticities of demand for Uber rides for different riders, different cities, and different times of the day. The estimates are shown in the table. Ignore the standard errors in parenthesis and focus just on the elasticities of demand.

a) which city has the most inelastic demand for uber rides? What do you think explains this?

b) Do the elasticitiesreported above help explain surge pricing during rush hour? Why or why not?

c) What do you think explains the low elasticity during a weekday day?

Answer 1

a) The city with the lowest absolute (i.e. without the negative sign) value of price elasticity of demand will have the most inelastic demand. Looking at the elasticities for the cities given, this is Los Angeles. A reason for this could be that Los Angeles is a very busy city frequented by tourists. As there are a lot of tourists, the price of an Uber ride can be increased and the tourists will still take the ride to see the attractions. This makes the demand inelastic as it is not greatly affected by changes in the price.

b) If we look at the elasticities given for the rush-hour periods, we can see that the demand is inelastic during the rush-hours. This means even if the price is increased by a certain percentage during rush-hours, the demand for rides will fall by a lower percentage. As the percentage increase in price will be greater than the percentage decrease in demand, the overall revenue will increase. This justifies the surge pricing during rush-hours.

c) The elasticity given for a weekday day is also inelastic. Again this could be due to the fact that the demand for taxis in Los Angeles is always high. Also, another reason could be that the public transport system in Los Angeles is not good enough. This means that the people are forced to take an Uber regardless of the time of the day and also whether or not the price has increased. This makes the demand inelastic.