Kiasunomics 2: Economic Insights For Everyday Life. Sumit Agarwal

us to see whether there are differences during peak hours and end-of-school hours versus non-peak hours. Is this good?”

      “Yup, I got it so far. How about the second dataset?” enquired Teng.

      “The second dataset contains a panel of all taxis in Singapore. We also collected the data at every 30-minute interval. Here, we know for each taxi whether the taxi is available or hired, and its location. This allows us to understand the supply of taxis. ‘Available’ taxis means they are available for hire, which means there is excess supply since they are not used; while ‘Hired’ taxis means they have been booked already by demand. And we do this for each operator in each area every 30 minutes. Understand?”

      “Got it!” said an excited Teng.

      The young man continued. “So we know the supply of taxis. How about Grab’s supply of cars? Grab offers a range of ride services – GrabHitch, JustGrab and so on. We studied JustGrab. JustGrab allows commuters to book a ride from the closest private car and participating taxi at the same price. So there’s private cars with independent drivers not affiliated with a taxi company as well as taxis from participating fleets which included about half of taxis in Singapore at that time.

      “Now, remember that at the time of the study, not all taxi companies allowed their taxis to use Grab services. So the supply of taxis in our study is based on operators that forbid their taxis from participating in the JustGrab service.

      “Unfortunately, Grab doesn’t want to share with us their data. We understand. It’s proprietary. So what my professor did was to use a proxy for JustGrab supply.”

      “What is a ‘proxy’?” asked Teng, scratching his head.

      “Ahh . . . A ‘proxy’ means a ‘substitute’. In our study, because we could not get data from Grab, we used taxis from taxi operators that permit their taxis to participate as JustGrab cars as a measure of the supply of Grab cars.”

      “Is that accurate?” asked Teng. “JustGrab has other drivers too.”

      “You are right. Our proxy for Grab supply is not perfect because private drivers are not included. Also, taxi drivers from participating Grab operators can choose whether to drive as a Grab driver or not. However, since at the time of the study, participating operators do not have other ways of booking rides through a smartphone app, the affiliated taxi drivers are most likely to choose to drive as a Grab driver,” explained the young man.

      “So even though we do not have data from Grab on direct rides, we can study the relationship between Grab and taxi bookings due to the overlap of Grab cars and certain taxi operators in Singapore.

      “Moreover, since the taxis that operate using the Grab platform are able to observe the surge factor, their driving behaviour should be similar with those of private car drivers. But then like you said, we do not have all the data. Not having the private car data from Grab means that our Grab supply is likely to be underestimated. It’s not perfect. Ideally of course my professor would love to have Grab data. We wish Grab would share with us some of their data. As academics, my professor will rigorously analyse the data and help Grab and the society with the findings.

      “Anyway, I won’t go into the technical mumbo jumbo. The bottomline is that statistically, we can control for this bias in our analyses.”

      Teng wasn’t so sure that he could intelligently follow how the data were analysed. But he was eager to learn the findings. After all, he didn’t really care about technical details. He’d leave that to the experts. All he wanted was to know the results and how they affected him as a taxi driver, and his family and friends as commuters.

      “OK, are you ready?” asked the young man as he wanted so much to share the findings. “We found that Singaporeans are not very sensitive to the higher price of ride-hailing services when there is surge pricing. It’s like the ‘got no choice’ mentality. When the surge factor is high and there is a lack of available taxis as substitutes, people will just pay the higher fare.”

      “Yah. I agree. They have no choice. What else can they do?” agreed Teng.

      The young man continued, “But that’s not the interesting part. The interesting part is – we found that the number of taxis available do not match with the surge factor within an area during the time when there is surge pricing. Let me explain.

      “When we book a taxi, the supply of taxis comes from the same pool of taxis as those available for street pick-ups. So when people are booking a ride either through Grab or calling for a taxi, of course the taxi driver will prefer to take up the booking because he earns the additional booking fee of $3 to $5. Street pick-ups, on the other hand, becomes less attractive. And so the number of street pick-ups goes down because taxis are diverted to pick up passengers from such bookings.”

       [T]he number of taxis available do not match with the surge factor within an area during the time when there is surge pricing.

      “That makes sense,” thought Teng as he obviously would rather have more booking rides than street pick-ups because of the booking fee earned.

      The young man continued. “So, during the time of a taxi booking, the total number of taxis available in that area does not change. This means that the available taxis on the road in that vicinity can absorb these bookings, at least for a short period of time.”

      “But what about when it is raining?” asked Teng.

      “Good question. Rain is relevant because obviously rain affects demand. More people need a ride when it is raining, especially when it is raining heavily.

      “The problem with rain is that not all taxi drivers are able to observe the surge factor due to rain. Only drivers near the area where it is pouring can observe the rain and know whether a high demand for taxis is likely to occur. So whether it is raining or not is common information only to drivers in the rainy area.

      “Now, rain may increase taxi supply because drivers are anticipating more demand. But rain can also raise concerns about road conditions, safety and congestion. There are some drivers who avoid driving on rainy days.”

      Teng nodded as he knew of some taxi drivers who do not like driving when there’s a heavy downpour.

      “We found that in general, during rainy periods, taxi supply increases by 0.37 percent. But when it rains and the surge factor goes up by 10 percentage points, guess what happens? The total taxi supply goes down by 2.3 percent. Down, OK? Not up.

      “And as to be expected, we found that when the surge factor is high and people are booking either through Grab or directly from the taxi company, there are more booked taxis and fewer ‘Available’ taxis. Taxis on the street are diverted to absorb the demand from such bookings and there are less for street pick-ups.”

      “Yah, that makes sense,” nodded Teng.

      “But let me ask you this. If you were a smart passenger and there is surge pricing, how should you book your ride?”

      The young man paused for effect.

      “Let me tell you how much a passenger can save if he books a taxi directly instead of using Grab when there is surge pricing,” said the young man as he lowered his voice as if to share a secret.

      “Shh . . . It’s quite a lot, OK? We did a rough calculation based on the average distance of travel. We found that when the surge factor is higher than one, if a person switches from using ride-hailing to booking a taxi through the taxi operator, he saves about . . . You guess?” teased the young man as he felt Teng’s anticipation.

      “10 percent?” Teng hazarded a guess.

      “18 percent!” said the young man excitedly and yet trying hard not to raise his voice. “That’s a lot! If your ride cost $10, you’ll be saving $1.80!”

      Teng wasn’t quite sure whether to feel good or bad because obviously he’s a beneficiary of increased taxi bookings but he would also have made more driving on the Grab service.

      “The