Kiasunomics 2: Economic Insights For Everyday Life. Sumit Agarwal

Kiasunomics 2: Economic Insights For Everyday Life - Sumit  Agarwal


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receive a full fare rebate if they exited the station before 7:45 AM. The long dashed line is the Late Treatment group where commuters receive the same rebate if they exited before 8:00 AM.

      “There are three time periods that we want to study. The two vertical lines demarcate the 10-week reward period, separating it from before the programme started and after the programme ended.

      “If you look at the graph, not everyone succeeded in exiting before the cut-off exit time, but you can see that they tried. Looking at the 10-week reward period, you can see the exit times for the Early and Late Treatment groups went down immediately in response to the reward. When the 10-week reward programme was over, we see the exit time went up for both groups. That’s to be expected because there was no more reward.

      “But what is interesting is that the difference in exit times between those who received the rewards and the Control group that didn’t receive any rebate persists. Look at the lines to the right of the graph during the Post-reward period. The short and long dashed lines continued to be way below that of the solid line. Remember that the solid line denotes commuters who didn’t receive any reward, and the short dashed and long dashed lines are those who received rewards for being early.

      “What this means is that having received the reward in the weeks prior, these commuters continued to exit the station earlier than those who did not receive any rewards, although not as early as they did when they were incentivised. So, incentivising them did work.”

      Mr Yang looked around the audience to make sure they understood.

      “Here’s another finding that shows the percentage of commuters who exited by 8:00 AM.

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      “After receiving the reward, the percentage of passengers who completed their trip by 8:00 AM went up compared to those who didn’t receive any reward. And this behaviour continued even after the reward was discontinued.

      “All in, during the 10-week reward period, we were able to shift 271 trips out of the peak period of 8:15 to 9:00 AM. After the reward period, we continued to shift 553 trips out of peak period. So congestion was largely alleviated.

      “So, you might ask – Why did commuters in the reward groups persist in using the MRT earlier? After all, there were no more financial rewards to be gained.

      “Now, we must bear in mind that the reward, a full rebate of fare, is really not a huge reward. On average, the fare is $1.30 and that is miniscule considering our average income. Public transportation in Singapore is relatively cheap compared to other developed countries.

      “So, here’s what we think. We think it’s because of this phenomenon called habit formation. During the reward period, not only did commuters receive financial rewards, as small as that might be for being an early bird, but they also learned through the experience the non-monetary benefits associated with early travel such as less crowded trains and less stress from being late for work. They began to enjoy these non-monetary perks and stayed on with this travel pattern even when there were no more financial rewards. A habit has been formed. And a good one at that!

       [O]ffering a temporary incentive or ‘nudge’ could induce lasting change in behaviour even well after the financial rewards are withdrawn.

      “In summary, our findings showed that offering a temporary incentive or ‘nudge’ could induce lasting change in behaviour even well after the financial rewards are withdrawn.

      “I see that many of you here are retirees. But still young at heart! Please encourage the working adults in your family to go to work earlier to beat the crowd so that more people, including themselves, can enjoy a more hassle-free journey. Thank you.”

      The discussion was then open to the floor.

      Someone behind Ah Kong and Ah Mah asked, “You carried your study only among 350 people and gave incentives for only 10 weeks. If the LTA were to give these rewards nation-wide to encourage non-peak period travel, it can be very expensive. You also mentioned that infrastructural improvements would require higher capital and the returns were not sustainable. How does giving rewards compare to the cost of adding a new train?”

      It seemed Mr Yang was well prepared for this question as he replied without hesitation.

      “Thank you for the question. We’ve asked this too among ourselves. Allow me to give you an example. A train, and let me be as specific as possible – the Kawasaki Heavy Industries C151 – with a capacity to hold 2,000 passengers costs about $13 million. The parallel scenario would be how much would it cost to shift 2,000 passengers to travel and exit earlier using such a reward programme? Does that seem to be a fair comparison?”

      Some in the audience nodded in agreement.

      “I had said earlier that the average train ride is about $1.30. Over the 10-week period, we paid $1,346 in rebates – $417 to Early Treatment commuters and $929 to Late Treatment commuters – to effectively shift them to use the train earlier and exit before the peak period to relieve congestion.

      “If we were to extend this to shifting 2,000 passengers daily, which is what adding a train would do, we would hit the $13 million cost of adding that train in slightly less than 3.5 years. Now, that’s not very cost effective, is it?”

      Mr Yang raised his eyebrow as he asked the last question. The audience, though mainly retirees, was fairly sharp when it comes to money matters and nodded.

      “But, think about it,” Mr Yang nudged. “We really do not need to reward people every time they travel earlier. We just need to reward them for only a limited period of time; in our study, it was 10 weeks. People’s commuting behaviour can change and we’ve shown that to be the case. They travel earlier. And importantly, the change persists for a while. Then when the early-travel behaviour dissipates, we can re-introduce the reward programme.

      “So, let’s say we conduct the reward programme for 10 weeks and discontinue it and re-introduce it some four to five months later. Then we can have a seven-month cycle including the 10 weeks of reward, right?

      “If that is the case, we find that under this scenario, we can enjoy similar benefits of adding a train for some 20 years before the cumulative costs of the reward is as much as the cost of buying a new train.

      “And mind you, this does not even take into account the operational costs of maintaining the train. Moreover, the reward programme is flexible and can be tweaked should travel behaviour change, while investing in a train is a sunk cost.”

      Another question was raised.

      “What plans does the LTA have then to encourage commuters to change their travel pattern?”

      Mr Yang paused for a while, debating in his mind how much he should reveal.

      “The LTA will certainly be collecting more data to ensure that whatever schemes we introduce will work and are cost effective. One possibility is the use of travel smartcards that have data on how a commuter travels. This will allow us to tailor individualised incentive schemes based on each commuter’s travel patterns instead of a generic reward system.”

      When the talk was over, Ah Mah told Ah Kong, “I think we should tell Siew Ling this just in case she has to take the MRT to work in the future.”

      Ah Kong turned to her, knowing how much she spoils the grandsons. “It seems that while being rewarded financially changes behaviour, people can learn and appreciate the non-monetary rewards also. And will continue their changed behaviour when there are no more financial rewards given. I think we can learn from this with regards to rewarding our grandsons. We cannot always reward them for good behaviour. They have to learn that there are other benefits to good behaviour.”

      Ah Mah nodded in agreement.

       WANT TO KNOW MORE?

      This chapter is based on the following research: Sumit Agarwal, Cheng Shih-Fen, Jussi Keppo and Yang Yang (2017), “The Impact of Air Pollution


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