A firm pays a monthly bonus to its workers and adjusts the target every month according to how fast the worker worked in the previous month: if they worked faster, the target becomes harder. How do workers react to such an incentive system? Forward-thinking workers should work less hard than they potentially could to keep an easy-to-reach target. This unintended side effect results in lower worker effort and thus lower overall productivity. Our project aims to understand such “dynamic incentives”, i.e., positive or negative effects of incentives on behaviour in later periods.

It is important to understand both the direct and the indirect effects of incentives since they affect individual decisions on a daily basis. Incentive systems are widely used in the workplace, usually with the aim of achieving higher productivity. Incentive systems are also used outside the workplace, e.g., by governments that use tax and benefit systems to induce people to take up a job or to save. Understanding and making incentive systems more effective is thus of central importance for many aspects of society, in particular with regard to increasing productivity. Productivity growth in the UK has stalled for the last 10 years. Well-designed incentive systems could play a part in increasing overall productivity. Moreover, incentives that are more effective could help mitigating health-cost increases or improving educational outcomes.

While we already have well-developed theoretical models of behaviour under dynamic incentives, we have very little empirical knowledge about them. This project aims to fill this gap. We aim to find out how important dynamic effects of incentive on productivity really are and which characteristics of workers and of the workplace weaken or strengthen them. We will also design and test ways how firms can avoid negative dynamic effects or harness positive dynamic effects of incentives.

The research is undertaken in partnership with a firm that runs several warehouses with thousands of workers. The firm has allowed us to randomly allocate workers to different incentive schemes. This gives us the unique opportunity to test the effect of dynamic incentives in a very clean and direct way. In the first part of the project, some workers receive incentives that have direct (static) positive effects but negative dynamic effects. Others receive the same direct incentives but without any dynamic incentives. Comparing the output of the two groups will allow us to measure the importance of dynamic incentives. By collecting other data on workers and the workplace, we will be able to identify which factors weaken or strengthen dynamic incentives.

In the second part of the project, we use the fact that dynamic incentives are inherently harder to understand, as they need thinking about indirect effects. We will study whether the firm could reduce the negative dynamic effects by making the incentive system more complex. It might well be possible to do so while retaining the direct (static) incentive effect. Firms would benefit from this through increased productivity and workers would benefit through higher bonus payments. We use laboratory experiment with the same workers to study this question. This will also allow us to link the behaviour in the lab with behaviour on the shop floor.

In the last part of the project, we will study how we can use within-day changes of incentives to harness positive dynamic effects. The growing use of technology by firms has made such “real-time” incentive changes possible in many workplaces. For example, varying incentives during the day could influence agents to work hardest when a high productivity is particularly valuable or could enhance task engagement by making the task less boring. We will first try out different incentive schemes on an online casual-work platform like Amazon Mechanical Turk before rolling out the most successful schemes in the warehouse of our partner firm.


This project is funded by the Economic and Social Research Council