Fast Food, Slow Deliveries: Uncovering the Hidden Economics of Uber Eats

 

Seriously, why is my food taking so long?

We’ve all be there, stomach growling, staring at the Uber Eats map as our driver tales a suspicious detour or handles multiple orders. It’s easy to call it “poor service”, but it reflects a deeper economic issue behind the platform’s convenience.

The frustration comes from asymmetric information, as customers cannot observe how much effort drivers put into delivering orders. So, outcomes do not always match expectation, leading to delays and inconsistent service quality.

In this post, we explore how these hidden information problems lead to inefficiencies and why simple feedback tools don’t fully solve them.

Figure 1: Image of the order from Uber Eats (Group Member, 2026).

What’s actually going on behind your deliveries?

Have you ever tracked your food delivery on Uber Eats, only to notice your driver took a longer route or stopped multiple times along the way? While this may only seem like poor service on the surface level, there is a deeper economic problem concealed behind the convenience of such food delivery apps.

At the heart of the issue is a situation where one party in a transaction knows more information than the other, which is known as asymmetric information in economic theory. In this context, the driver knows exactly how much effort they are putting into delivering food, but both the platform (Uber Eats) and customers do not. Therefore, from the outside, the driver’s actions and efforts are not directly visible, suggesting that you pay for the service without fully understanding what is happening behind closed doors.

This produces what economists call a principal-agent problem. In other words, the platform Uber Eats, which is the principal, wants to provide fast and consistent food deliveries, while the driver, which is the agent, may have different objectives. Given that high effort requires time and energy, drivers may choose to avoid putting in high efforts to benefit themselves.

Therefore, once the driver accepts an order, their behaviour might change such that they may take longer routes or handle multiple orders at the same time, especially when their actions are not closely supervised which inevitably results in delayed food deliveries. This is known as moral hazard, where people change their behaviour when they realise they are not being closely monitored. This is evident from how “order staking” has become increasingly normalised in Uber Eats, often causing longer wait times for customers, and in some cases cold food which heaving damages customer satisfaction (Nott, 2024). This highlights the motivations of workers in gig economy platforms, such that they adjust their efforts depending on how they are rewarded (Skrzek Lubańska & Szban, 2024).

Overall, because drivers have more information about their own actions, they do not always fully bear the consequences of reduced effort, such as the reputation of Uber Eats amongst customers. This can lead to an inefficient outcome where not only delivery times are longer but also the quality of service is lower. To put it simply, the outcome is not as good as it could be when compared to the outcome if the efforts of drivers were fully observable. 

Figure 2: The GPS of the Uber Eat’s delivery (Group Member, 2026).

When Feedback Fails

A key way Uber Eats attempts to solve the problem behind your deliveries is by tipping and ratings acting as signals of driver’s effort and reliability, both would usually be unobservable. Without them there would be market inefficiency, where good drivers are not getting rewarded and poor service isn’t being punished.

Firstly, with a rating system, this allows customers to produce feedback of the drive to Uber Eats. This tries to improve allocative efficiency, as higher rated drivers are rewarded to “higher earning opportunities”, which incentivises drivers to work at a better quality (Uber, 2025). Additionally, the drivers are discouraged from low ratings as they “may lose access to the app”, which could act as a strong deterrent to drivers of Uber Eats (Uber, 2026a).

Rating systems may not be entirely accurate. Imagine a driver delivers a McDonald’s order on time and still warm, but McDonald’s forgets a milkshake they ordered. Even though the driver put their utmost effort into this order, the customer can give them a low rating for things out of their control. Uber Eats does have a policy in place to avoid misleading or fake reviews that are not based on “genuine experiences” (Uber, 2025b). However, there is still room for inaccuracies in the rating system, and so potentially Uber Eats needs an updated policy to better reflect true signals. This could potentially include allowing drivers to dispute low ratings with their side of the story.

Moving onto tipping, this can be considered a signal from customers of the overall quality of the driver. This brings back the idea that better service is rewarded, when both sides have equal information. Therefore, drivers who do go above adequately doing their job get financially rewarded, so this creates an incentive to do so.

In contrast, tipping varies from individuals, irrespective of effort (Saayman & Saayman, 2015). For example, one customer might tip largely for a McDonald’s delivery round the corner, but someone else didn’t tip anything despite the driver ensuring their fries weren’t cold. This means tipping doesn’t always reflect the driver’s effort and there is room for improvement with tipping as a signal for Uber Eat driver’s quality.

Overall, tipping and ratings can be considered as two signals for Uber Eats to lower asymmetric information. Despite this, due to the disadvantages of both signals, neither of them eliminates all of the unequal information about driver’s effort.

So, What’s Next?

In conclusion, although the efforts put in by the drivers are not fully observable in the context of food deliveries on Uber Eats, the solutions that were discussed in this blog have allowed these efforts to be incentivised to an extent. To further prevent poor service, such food platforms should further update their policies to better reflect a signal that would encapsulate the hidden effort of drivers.

References

Nott, G. (2024) ‘Cold food and poor pay: order stacking booming among food delivery apps’, The Grocer, 31 July. Available at: https://www.thegrocer.co.uk/news/cold-food-and-poor-pay-order-stacking-booming-among-food-delivery-apps/693908.article (Accessed: 12 April 2026).

 

Saayman, M. and Saayman, A. (2015) ‘Understanding tipping behaviour – an economic perspective’, Tourism Economics, 21(2), pp. 247–265. Available at: https://www.researchgate.net/publication/275528939_Understanding_Tipping_Behaviour_-_An_Economic_Perspective (Accessed: 6 April 2026).

 

Skrzek-Lubańska, M. and Szban, J.M. (2024) ‘Motivation in the Gig Economy: The Incentive Effect of Digital Platforms. A Literature Review’, Kwartalnik Nauk o Przedsiębiorstwie, 72(2), pp. 95–116. Available at: https://www.researchgate.net/publication/383969194_Motivation_in_the_Gig_Economy_The_Incentive_Effect_of_Digital_Platforms_A_Literature_Review (Accessed: 12 April 2026).

 

Uber (2025) Introducing the new Uber Eats Pro – better service, better status, better earnings. Available at: https://www.uber.com/us/en/blog/uber-eats-pro-preferred-deliveries/ (Accessed 6 April 2026).

 

Uber (2026a) How ratings work for delivery people. Available at: https://www.uber.com/gb/en/deliver/basics/tips-for-success/delivery-ratings-explained/ (Accessed: 26 March 2026).

 

Uber (2026b) Ratings Policy – Great Britain. Available at: https://www.uber.com/legal/en/document/?name=ratings-policy&country=great-britain&lang=en-gb&uclick_id=0bce070e-1317-49f1-b268-231963d2b630 (Accessed: 26 March 2026).