Contents:

6 min read

    6 min read

    In this guest post below, Matthew Tarduno explores how cities can regulate and draft policies that address congestion, while not losing economic efficiency, in the age of rideshare.

    In 2015, Travis Kalanick imagined ridesharing ushering in “a world where there’s no more traffic in Boston in five years.” It would be an understatement to say that this vision has failed to materialize: Mounting evidence from both academics and the companies themselves suggests Uber and Lyft are actively worsening urban traffic. This additional stress to already- clogged city streets has governments the world across crafting policies to address congestion.

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    Naturally, some of these policies are better designed than others. In this post I’ll cover a few common approaches to dealing with congestion in the ridesharing age, with an eye on economic efficiency: How does each policy address the causes of congestion? What are the costs of the policy, and who bears these costs?

    But first, it may be useful to lay some groundwork on congestion policy. To economists, solving congestion is fundamentally an issue of providing the right incentives. In most cities across the world, drivers don’t take into account the impact that their driving has on the travel time of other road users.

    Through this lens, a well-designed congestion policy will provide incentives for drivers to avoid driving at congested times or places.

    So who are our candidates, and how well do they check these boxes?

    First up:

    Cap or Ban Uber and Lyft

    From NYC’s ridesharing cap to Vancouver’s decade-long abstention to the never-ending saga in London, policies that limit or outlaw ridesharing activity have been the most popular approaches by city governments to address negative side effects of ridesharing activity.

    From an economists’ viewpoint, these policies are — to put things nicely — imperfect. One might imagine that city governments are both worried about congestion and care about the happiness of city residents. An outright ban doesn’t score well in either of these dimensions.

    Absent transportation network companies (TNC), urban environments will still suffer from congestion if other drivers don’t face some sort of incentive to push them away from peak periods. Banning TNCs also means losing substantial benefits: flexible work, increase mobility, reduced drunk driving, etc.

    In my working paper, I put some numbers on the cost-benefit of a TNC ban. I find that in Austin, TX, TNCs cause slowdowns that cost Austinites 60 million dollars annually. That’s about the same size as estimates of the benefits to consumers in Austin.

    If one were to also take into account other harder-to-quantify benefits, like flexible work and safety, it is likely the case that banning TNCs is, on net, a loss for city residents.

    So while restricting TNC activity will likely curb urban congestion, outright bans throw out the baby with the bathwater. And, as robotic economists, we have empirically determined that the baby is indeed worth more than the bathwater.

    Our second contestant:

    Tax Uber and Lyft

    Policies to tax ridesharing companies directly have gained traction recently, and these laws have some compelling features. Lack of information is a major hurdle in designing efficient congestion policies: It’s hard for policymakers to tell what kind of trips contribute the most to traffic.

    Because Uber and Lyft have mountains of data at their disposal, a tax levied directly on TNCs might be able to better target congestion than could a toll on a bridge, or a charge for using the city center. It might also be easier politically (we see you, San Francisco!) to levy a tax on companies rather than drivers.

    There are, of course, drawbacks to this kind of congestion policy. Namely, taxing Uber and Lyft leaves many drivers (70-95%, depending on the city) without any incentive to avoid driving at congested times. Uber and Lyft will also have significant incentives to build their local strategy with around metric is used to levy the congestion tax (or just lie).

    As a side note, some commentators have advertised taxing (or banning) TNCs directly on the grounds that these companies, not individual city dwellers, ought to pay the congestion price tag. The economic concept of pass-through, though, suggests this story is not so simple.

    As the name suggests, this is the idea that firms, if faced with a tax, will to some extent pass costs through to consumers and workers. Exactly what fraction of these costs is borne by different groups is governed by some unbearably complicated formulas that contain distressing numbers of greek letters.

    Ultimately, though, you should be skeptical of the idea that Uber and Lyft would accept a tax and not take any measure (like adjusting prices or wages) to spread out these costs: If cities tax TNCs, some share of that cost will come out of the pockets of TNC users, and some fraction will come out of the pocket of drivers.

    And finally:

    Tax Everyone

    This is what tweed-wearing economists dream of when they dream their tweedy dreams. Why? It’s not that only Uber and Lyft cause congestion. Every driver causes congestion. Taxing all drivers would give everyone on the road a personal incentive to avoid (by re-routing, rescheduling, or not taking) trips that cause congestion.

    So what exactly does tax everyone mean, and why in god’s name would anyone be excited about that prospect? Taxing everyone means a congestion price: drivers (all drivers!) pay for using the road, and pay higher rates when they drive in times or places that are congested. Congestion prices are usually levied on chokepoints (think the Bay Bridge) or in city centers (as in London).

    Economists recommend road pricing because it addresses the root cause of overcongestion by attaching a dollar figure to the cost that a given trip has to other road users. Drivers weigh their personal benefit of a trip against the congestion fee when choosing to drive. This leads drivers to forgo or reschedule low-priority or low-value trips, and nudges the traffic system closer to an efficient functioning.

    Importantly, when compared to a cap on ridesharing, congestion pricing raises revenue. This revenue can be used for other city projects, or to ameliorate the distributional costs of the policy itself. If, for example, city planners decide that the tax provides an undue burden to low-income individuals, then they can target rebates (e.g. tax breaks or credits) to those groups.

    The Upshot

    Just because TNCs have changed the urban transit ecosystem recently doesn’t mean that congestion policies should be biased toward their regulation or taxation. If cities want comprehensive policies to address persistent congestion problems, they should start with tolls aimed at all road users.

    Matthew Tarduno is a Ph.D. student at UC Berkeley and a graduate student researcher at the Energy Institute at Haas Business School. His research focuses on transportation and climate policy. In his latest working paper, The Congestion Costs of Uber and Lyft, Tarduno investigates how ridesharing companies impact traffic speeds.

    Drivers, what do you think of these options? Which one would you be in favor of?

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    -Harry @ RSG

    Harry Campbell

    Harry Campbell

    I'm Harry, the owner and founder of The Rideshare Guy Blog and Podcast. I used to be a full-time engineer but now I'm a rideshare blogger! I write about my experience driving for Uber, Lyft, and other services and my goal is to help drivers earn more money by working smarter, not harder.