People are remarkably creative when it comes to bending the rules. Parents register fake addresses to get their children into better schools. Firms under-report their employee headcount to avoid legal obligations. Doctors manipulate records to move their patients up the waiting list for transplants. Whenever a score or measure is used to decide who gets something valuable – a school place, a grant or even an organ – people have an incentive to manipulate it.
That creates a basic problem for policy-makers. If allocation depends on a score, the score stops being just a measure or proxy for worth, and becomes a target – an idea that economists call Goodhart’s Law. Once that happens, people start investing time, money and effort into changing the score rather than improving the underlying characteristic that the score is supposed to capture.
Our research asks two questions. Can allocation systems be designed so that they effectively allocate resources to the most worthy but induce no incentive to game them? And if they can, are such systems simply a compromise, or can they actually be the best policy?
Our answer is strikingly simple: yes, honest allocation rules can be designed. What’s more, under meaningful conditions (when gaming is costly and there are increasing returns to scale in gaming), they are not a second-best compromise: they are the optimal policy in the sense that they target worthy individuals while eliminating all negative consequences of gaming.
Building more honest assessments
In recent work conducted with Eduardo Perez-Richet, we study situations in which resources are allocated without prices, using some measurable characteristic to decide who should receive them. This includes settings such as school admissions, public housing, access to training programmes, waiting lists for organs, environmental labels and professional certifications. In all these cases, policy-makers cannot or do not want to use markets. Instead, they rely on scores, thresholds or rankings.
The standard instinct is to design the rule to maximise allocative efficiency: give the resource to whoever looks most deserving according to the score. But that logic ignores a crucial fact: scores can often be manipulated. Once that possibility is recognised, the design problem changes.
A ‘naïve’ rule creates strong incentives to game the system. But even a more sophisticated rule that takes manipulation into account may still rely on gaming in an uncomfortable way. It may improve allocation on paper, but only by pushing people to incur costs in order to alter their scores. That wastes resources and can create wider social harm.
Our research characterises the best allocation rules that eliminate this incentive altogether. These are rules under which no one wants to falsify their score. In that sense, they make honesty optimal.
At first glance, this may sound like a concession. It is tempting to think that an honest rule sacrifices efficiency in order to discourage manipulation. But that view is too narrow.
We find that once a policy-maker cares about the wellbeing of the people involved and the broader social costs of gaming, the honest rule can be fully optimal. In other words, it solves the planner’s overall problem, not merely a constrained version of it.
This matters because falsification can be harmful. It is privately costly, since people spend effort and resources trying to improve their scores artificially. It is also socially harmful, as it can distort who receives the resource, rewarding those who are better able to game the system rather than those who are more deserving. It can make the score less informative, weakening the very tool on which policy-makers rely. And once manipulation becomes visible, it can erode trust in the system itself.
Our analysis identifies four forces that make honest allocation more likely to be optimal:
- Gaming must be genuinely costly. If falsification takes real effort, time or money, eliminating it becomes more valuable.
- Honesty is more attractive when the policy-maker places weight on the wellbeing of those subject to the rule.
- Honesty is also more attractive when gaming creates broader social harm, such as mistrust, distorted information or spillovers that encourage dishonest behaviour elsewhere.
- Honest allocation becomes especially appealing when the observed score is only an imperfect proxy for what the policy-maker truly cares about. In those cases, allowing people to game the score is particularly damaging.
We also show that the cost of honesty can be surprisingly small. In some environments, the best honest rule comes very close to the first-best benchmark. So, honesty is not just desirable in principle: it can also be remarkably cheap in practice.
Implications for policy-makers
The policy lesson is broader than any one application. Across education, health, environmental regulation and social policy, institutions increasingly rely on measurable indicators to decide who gets access to scarce goods and services. These tools are attractive because they seem objective and transparent. But their apparent precision can be misleading if they are easy to manipulate.
One practical implication is that policy-makers should be cautious about sharp thresholds. Hard cut-offs are easy to explain, but they create large rewards for small acts of manipulation. A family just outside a school catchment area has a strong incentive to fake an address. A firm just above a regulatory threshold has a strong incentive to understate its workforce. A hospital just below a priority threshold may feel pressure to adjust the data. In settings like these, a smoother allocation rule can reduce gaming incentives at relatively little cost.
A second implication concerns fairness. Systems that tolerate gaming tend to reward those who are best equipped to manipulate them. Those may be the people with more money, more information, better legal advice or better connections. An honest allocation rule removes that hidden advantage. It protects efficiency as well as the legitimacy of the allocation process.
A third implication is that policy-makers should treat the design of indicators and the design of allocation rules as inseparable. It is not enough to ask whether a score predicts the right outcome. We must also ask whether the allocation rule built on that score gives people an incentive to distort it.
Next steps for integrating honesty into decision-making
The first step is diagnostic. Institutions should audit score-based allocation systems for manipulation incentives as well as predictive performance. A score can contain useful information and still produce bad policy if it is too easy to game.
The second step is institutional design. When a system depends on a score, policy-makers should build robustness into the rule from the start. That may mean replacing sharp thresholds with smoother rules, combining several indicators or choosing allocation criteria that are harder to falsify.
The third step is empirical. More evidence is needed on the costs of gaming and on how those costs vary across people and organisations. Our theory shows that this matters greatly. The shape of the manipulation technology helps to determine whether honest allocation is merely attractive or fully optimal.
The broader message is simple. Rules that create incentives for honesty are often seen as a compromise: fairer perhaps, but less efficient. Our research shows that this view misses something important. Once the real costs of manipulation are taken seriously, honesty need not be a concession at all. In many settings, it is the best design.
Score-based systems can help institutions to allocate resources fairly and effectively. But that promise only holds if the score still means something. Our research shows that in many settings, the best way to protect that meaning is to design rules that make honesty the optimal strategy.
