The sharing economy: A futuristic taxi landscape (Part 2 – Modular regulation)

The sharing economy: A futuristic taxi landscape (Part 2 – Modular regulation)

Phuah Eng Chye (20 January 2018)

The taxi regulatory paradigm is under siege from formidable forces and is unlikely to survive in its traditional form. Sharing or the use of part-time assets and labour has triggered a massive shift in the patterns of participation, resource use, values, income and risks. Regulators and the industry face the challenge of managing a landscape characterised by evaporating boundaries, fragmentation, uncertainty, fare volatility, multiple roles, many-to-many relationships, transience and rising costs.

Hence, there is a need to evolve a new regulatory paradigm for the taxi industry. In my view, the new paradigm needs to be consistent with the theme of an information society. Hence, the new paradigm should aim to remove information constraints and to enhance the ability of drivers to self-organise. In tandem with this, I propose a shift from the current firm-centric regulatory paradigm to one based on modular regulation.

There are several forces underpinning the shift to modular regulation.

  • Fluidity in combinations of relationships between activities, assets, technology and people. Traditional regulation relies on segmentation and focuses on distinct organisational forms. But this approach is ineffective in a network due to the use of part-time labour and assets, the decline in the value of licenses, the ability to conduct transactions virtually and the permutations from many-to-many relationships and roles. The regulation of networks (as compared with vertically-organised industries) requires a framework that can accommodate the co-existence of a variety of organisational forms with varying combinations of relationships between activities, assets, technology and people. Hence, it would be more effective to focus regulation on the smallest unit in given the trend towards modularisation[1] and interchangeability.
  • The role of the firm is no longer central. In 1937, Ronald Coase justified the existence of firms with his transactions cost theory[2]; suggesting it was more cost-efficient to internalise production than to contract work to outside suppliers. But the landscape has changed. Many firms today prefer to outsource or transact for labour and tasks on the market. “Not only has information technology dramatically reduced transaction costs but the intangible economy has altered the nature of the markets and enlarged the range of transaction mechanisms, blurring the well-known distinctions between markets, hierarchies and networks…An alternative and broader rationale for the firm needs to be developed, which would stress the brand umbrella, the intellectual property repository and the control of distribution channels as key cohesion factors and functions of the firm.”[3] Currently, the ride sharing platforms have duplicated the non-physical functions of the traditional taxi firms because they are directly involved in managing drivers and outcomes. The coordination services platforms can be totally replaced by a mobile app that doesn’t attempt to manage the drivers and only charges a small transaction fee.
  • It is no longer possible to defend license values. The use of part-time labour and assets demolishes the ability of entry and fare regulation to protect license values. The concept of zero value of license is consistent with the diminishing role of the firm and the abundance of supply. In this regard, the notion of competitive neutrality is a non-starter since the rules are generic and policy can be agnostic to variations in business combinations.

It is evident that the traditional regulatory regime is indefensible because the traditional demarcations defining vehicles which can be used as taxis, individuals who could be drivers, services which would be considered as taxis and the setting of fares have been rendered inoperable in the sharing economy. There will be difficulty in focusing regulation at the firm level due to the growing complexity of business models.

In this regard, the taxi industry, unlike the airline or other industries, is a suitable candidate for self-organisation as the industry is local and is built around individual cars and drivers. This highlights the point that unlocking benefits from the sharing economy requires the reorganisation of the regulatory and commercial structures and processes.

Centralised administration can be cost-efficiently replaced by technology. Nikolaus Lang, Michael Rüßmann, Jeffrey Chua and Xanthi Doubara advocate “a digital mobility platform that aggregates all transportation modes should be at the heart of the new urban mobility ecosystem…an integrated mobility platform to help a city manage traffic volume and flows while at the same time offering consumers a single easy-to-use point of access to the city’s transportation network. Boston envisions just such a platform, which consumers will be able to use to plan and book trips, accessing it through a device such as a smartphone and receiving real-time information about delays and transportation alternatives. The system will manage payments centrally.”

The futuristic scenario of modular regulation will bring us closer to the sharing ideal that the drivers are individual entrepreneurs. In my view, a modular regulation paradigm would unbundle registration, maintenance, insurance and information from firms; shift regulatory oversight from manual processes to be based on crowd feedback, information transparency and audit trails; and address demand-supply imbalances through information-based bargaining processes. Note that the shift from firm-based paradigms to modular regulation relieves worries about boundaries demarcating jurisdictions and services and license value, profits and income.

Hence, the key objective of modular regulation should be to enhance self-organisation and to improve the welfare of drivers or customers. The ability of drivers to self-organise would reduce intermediation costs which would imply either higher income for drivers or lower fares for passengers. The rules should be tweaked to ensure a competitive environment that offers reasonable income, better service and coverage. The appropriate business models should be allowed to evolve based on the rules on drivers and cars.

The futuristic scenario of modular regulation is also based on the simplifying assumption that all cars, drivers and passengers are connected and that the information is freely accessible and transactions are seamless. The key features of modular regulation are as follows:

  • A generic regulatory framework built on the registration and profiling of drivers and cars. The boundaries segmenting different aspects of the car transportation business should be eliminated. Drivers and vehicles can be registered to “carry on the business of transporting passengers” which would allow them to earn income from picking up passengers. Registered drivers can use any registered car, take on multiple roles and be attached to a single firm or many firms. Note how direct registration reduces the dependence of drivers on firms. The reference to generic means that the same rules are applied equally to all individual cars and drivers regardless of their situation. Business issues are not considered in making policy decisions. Consideration should also be given to bringing illegal taxi drivers into the regulated space. For example, Aaron Reiss notes “dollar vans and other unofficial shuttles make up a thriving shadow transportation system that operates where subways and buses don’t – mostly in peripheral, low-income neighbourhoods that contain large immigrant communities and lack robust public transit.” Unlicensed operators “are in a constant legal tug-of-war with city authorities, dodging fines and repossession as they navigate the streets.” In this context, there will be firms and drivers against transparency or disclosure as they[4] may not want others to know who the drivers are. But the transparency of driver and car profiles are critical in a self-organising system. Profiles can include the driving track record, ratings and other feedback. Crowd feedback and ratings in terms of safety, cleanliness and service (pick-up reliability) are important tools for monitoring and building trust. The public can always report if the description of the driver or the car differs from the profile. Drivers and cars with bad feedback can be subject to additional checks while those with good feedback may be eligible for additional incentives. Suspensions and other penalties should be imposed on those providing false information or allowing third parties to use their registration.
  • Keep costs to drivers as low as possible and streamline processes. The only way to maximise drivers’ income and minimise fares is to keep regulatory and intermediation costs and inconvenience as low as possible. In this regard, firm profits, high regulatory costs and inconvenience will eventually cascade into higher fares while at the same time reducing the take-home for drivers. Opportunities to minimise costs include eliminating overlapping functions between firms and regulators; and reducing licensing and vehicle inspection fees and equipment requirements (meters and dispatch equipment can be replaced by a smartphone). Car inspection can be outsourced to registered workshops. Vehicle history and ratings by workshops should be published in the profile. Workshops can be subjected to inspection, suspension or fined if the actual state of the car did not match their report description. The costs of an app can be reduced to a nominal surcharge (cents per ride) with regulators playing a greater role in managing the information and algorithms.
  • Expand choice and incentives. Generally, for-profit platforms impose penalties to discipline its drivers to be responsive to customer needs – with features to coerce drivers into accepting assignments. To be fair, for-profits have limited levers except through punishment and by reducing choice. In contrast, governments are in a position to establish benevolent-based systems that expand drivers’ choice through increasing information transparency and relying on incentives to motivate drivers to be responsive to customer needs. In term of incentives, registered drivers and registered cars could be eligible for a broad range of incentives such as discounts on tolls, car parks, licensing fees and insurance; for tax deductibles and rebates as well as rights to use taxi signage, access and pick-up in special lanes or restricted areas (airports). Drivers should also be allowed to earn additional income from “non-taxi” services such as delivery as well as from advertisements or recommendations on places to visit, eat and shop. Expanding choice via incentives would improve the welfare of drivers. Incentives can be structured to incentivise drivers to respond to customer requests. Similarly, incentives and penalties can be provided to achieve other goals such as the use of electric cars or to improve safety.
  • The app should be integrated with the incentive system to ensure good two-way flow of information. By definition, a car is a taxi only when the driver logs on his app. Drivers may choose to switch off their app (i.e. to avoid taxes). Hence, drivers can be incentivised to turn on their app by linking incentives to reported data. For example, taxi drivers would need to turn on the app for commercial insurance coverage to be effective, for eligibility to toll discounts, for special road and parking access and for accepting customer payments. Good two-way flow of information is important for improving efficiency, reducing information asymmetry and building trust. The data can be used to analyse coverage and utilisation patterns and to establish benchmarks for pricing and efficiency to reduce unmatched demand and supply and to address service issues. For example, if demand in underserved neighbourhoods isn’t sufficiently dense, then the way to address this is to promote advance booking of trips. Passenger and driver patterns can also be analysed to guide taxi drivers to the locations with the best probability of getting fares, to reduce response time and the time taxis spend on the road searching for passengers. The certainty of a ride (information) is important for building trust in the system and therefore drivers and customers can be rated in terms of no-shows or delays from estimated pick-up time. Rather than differentiate the type of service (i.e. ride-hailing, dispatch, carpooling) it may be more practical to differentiate by hours and by geographical area covered. In this manner, incentives can be directed to those who rely on driving as a major source of income or to encourage a shift in driver coverage to underserved neighbourhoods.
  • Insurance and safety. The use of part-time assets not only blurs the distinction between commercial and private vehicles, but it also raises hard questions about usage as these distinctions (i.e. ride-hailing, dispatch, carpooling; public bus, school bus, factory bus) are important in relation to insurance coverage. For purposes of modular regulation, I suggest commercial insurance coverage applies only when the vehicle is in commercial use (when the driver has logged on the app). The premium can be calculated and paid monthly. Without valid insurance coverage, drivers would not be able to activate the app. The safety track record would be linked to insurance premiums; whereby insurance premiums would be sharply reduced for those with an excellent record and substantially increased for those with a bad driving record. The barring of drivers with a bad driving record should contribute towards lowering the risk premiums for registered drivers. Commercial premiums should also vary with the condition and safety features of the car and vary inversely with hours worked to favour full-time drivers and proportionately with the number of passengers carried. Taxi drivers will become the most careful drivers on the road when it is aligned with their economic interests. Bruce Schaller notes driver-related risks to safety could also be minimised through monitoring “driving records, use customer feedback to identify drivers with patterns of complaints and re-train or counsel drivers who have pattern of traffic crashes, violations or customer complaints. Companies can quickly spot patterns that point to higher risk of unsafe or abusive behavior.” The driver and car safety profiles – number of hours worked, accidents and traffic violation – should also be transparent.

There will be very little support for modular regulation from commercial firms. This is because it takes away control of information and registration from firms and passes it to drivers. Once drivers have a regulatory process and an app that provides them the most capabilities at the lowest possible costs, the bargaining power of drivers will improve relative to firms. Firms would then need to figure how to add value and they can compete by offering better terms to recruit drivers.

But regulators may also be uncomfortable with the self-organising process. First, they may not have the tolerance for the chaotic industry conditions which will be dictated by supply shifts (given demand is relatively inelastic). They will be reluctant to abandon a familiar model of restrictions that resulted in long-term commitment and stable (inflexible) supply.

In the traditional model, regulators could lean on taxi firms to expand capacity while providing them the laxity to hire cheap migrant labour and third-party use of vehicles to overcome driver shortages. Under the self-organising scenario, regulatory control is diminished by the fluidity of the many-to-many relationships. As a result, regulators won’t be able to lean on firms like before to impose discipline and coordination while agency resources will get limited by funding shortfalls. But the public would still expect regulators to intervene when things go wrong.

Hence, regulators need to change their approach and learn how to rely on information and transparency rather than firms to solve problems. In this regard, virtual activities are different from physical activities in that the information content is significantly higher. They are much easier to monitor. there is an audit trail and the necessary interventions can be shaped to influenced behavioural norms. Hence, regulation by decree (entry and fare regulation) is appropriate in a low-information environment where information asymmetry is prevalent. But this does not hold in a high-information environment.

Overall, we are unlikely to want to move back to a low information environment. I cannot imagine that we will try to arrange a taxi without the convenience of information – such as pick-up time, fare and driver’s identity and more. Sharing is already providing its ability to new ways to participate in economic activities. This will undermine the old ways. This is the real test of the sharing economy – the extent to which self-organising can be enabled to complement markets and governments in coordinating economic activities. Hence, governments will need to figure out their role and policies in managing information as this will have a tremendous impact on how people, assets and activities are connected. In particular, governments will need to pay greater attention to optimising social value and figure how to carefully balance this when it comes into conflict with the private pursuit of profits.

References

Aaron Reiss “New York’s shadow transit”. New Yorker Interactive. http://projects.newyorker.com/story/nyc-dollar-vans/

Bruce Schaller (20 September 2016) “Unfinished business: A blueprint for Uber, Lyft and taxi regulation.” Schaller Consulting. http://www.schallerconsult.com/rideservices/blueprint.pdf

Charles Goldfinger (2000) “Intangible economy and financial markets”.  Communications & Strategies, No. 40, 4th quarter 2000. http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=83F42CBBC58994AFC46DCD93DF9D0988?doi=10.1.1.461.6988&rep=rep1&type=pdf

Nikolaus Lang, Michael Rüßmann, Jeffrey Chua, Xanthi Doubara (17 October 2017) “Making autonomous vehicles a reality: Lessons from Boston and beyond.” BCG https://www.bcg.com/publications/2017/automotive-making-autonomous-vehicles-a-reality.aspx?utm_source=201711&utm_medium=Email&utm_campaign=Ealert

Phuah Eng Chye (2 December 2017) “The sharing economy: The challenge of regulating information”. Economicsofinformationsociety.com. http://economicsofinformationsociety.com/the-sharing-economy-the-challenge-of-regulating-information/

Phuah Eng Chye (6 January 2018) “The sharing economy: Sharing, social media and information”. Economicsofinformationsociety.com. http://economicsofinformationsociety.com/the-sharing-economy-sharing-social-media-and-information/

Phuah Eng Chye (13 January 2018) “The sharing economy: A futuristic taxi landscape (Part 1 – The future and the uncompromising economics of the past)”. Economicsofinformationsociety.com. http://economicsofinformationsociety.com/the-sharing-economy-a-futuristic-taxi-landscape-part-1-the-future-and-the-uncompromising-economics-of-the-past/

[1] Phuah Eng Chye “The sharing economy: Sharing, social media and the information society”.

[2] https://en.wikipedia.org/wiki/Ronald_Coase.

[3] Charles Goldfinger

[4] Part-time drivers may not want their employers to know they are moonlighting as taxi drivers. Firms may want the flexibility to use unregistered immigrants to drive their taxis or they may be concerned that others may poach their drivers.

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