One of the most robust findings in economics is that, with few exceptions, people respond to incentives, rather than intentions or moral principles. Individuals operate under constraints of time, information, and risk, and as such, they will predictably and understandably adjust their behavior to whatever metrics ensure success. To do otherwise is irrational. When performance is evaluated and rewarded using metrics like quotas, behavior shifts toward satisfying those quotas to secure the benefits thereof. This happens in firms, schools, hospitals, police departments, and regulatory agencies, even when everyone understands, at least in the abstract, that the metric is distinct from the goals to be achieved.
Immigration enforcement provides a vivid case study of this general institutional failure mode. Under recent policy changes, US Immigration and Customs Enforcement has operated under explicit arrest targets in the form of daily and annual numerical goals meant to demonstrate enforcement intensity and resolve. The political rationale for these targets is straightforward. It is to signal to voters and political supporters that the current administration is serious about protecting the border and clamping down on illegal immigration.
But economics teaches that what gets measured gets optimized and gamed for various reasons, mostly having to do with incentives. In the case of immigration enforcement, when success is defined in numerical terms, agents will pursue the cheapest path to those numbers, rather than pursuing individuals and groups that are harder to find and detain. That is a rational given the incentives created by the Administration, namely rewarding aggressive arrest quotas. It makes sense that whenever institutions or individuals face quotas, they are likely to focus on the low-hanging fruit. Time spent achieving an easy unit of output eats up time spent pursuing a hard one. Effort that is devoted to high-risk targets, like violent criminals and well-entrenched gangs, threatens performance metrics in ways that low-risk targets do not. When failure to meet quotas carries professional consequences, agents will avoid activities that jeopardize the count, even if those activities are more closely aligned with the stated mission.
The logic is straightforward. Violent criminals, gang leaders, and professional smugglers are difficult to locate and expensive to apprehend, often relying on networks of other people to help them evade detection. Pursuing such criminal organizations requires investigations, coordination across jurisdictions, surveillance, and uncertain outcomes, making it easy for agents to come up empty-handed. By contrast, unauthorized immigrants who are otherwise law-abiding are comparatively easy to find. They have fixed residences, work regular jobs, and their children often attend the local school. Many are already interacting with the state through legal channels, including standard immigration check-ins.
When arrest quotas rise, then, it’s no surprise that arrests have accelerated disproportionately among those who are easiest to find and arrest rather than those who pose the greatest threat. Recent data confirm this pattern. Enforcement activity has surged, but the majority of arrests involve individuals without prior criminal convictions, a distribution consistent with quota-driven optimization rather than threat-based prioritization. And given the career and political incentives behind meeting those quotas, it is what we should expect. This behavior is rational given the incentives; it would be surprising if agents behaved otherwise.
There is a deeper problem here, though, that Hayek can help us diagnose. Quotas assume that central authorities know in advance how enforcement effort should be allocated across a vast and heterogeneous landscape. They assume that arrests are sufficiently homogeneous, such that merely counting them captures what matters. They assume that the marginal value of the next arrest is roughly constant across contexts. And they make these assumptions, often, without the salient local knowledge needed.
Here the analogy to central planning becomes illuminating. Central planners, like those in Cuba or the former Soviet Union, fail because they lack access to the dispersed, tacit, and constantly changing knowledge required to allocate resources efficiently. As Hayek argued, markets work not because anyone knows the right answer in advance, but because competition allows agents to discover it through decentralized experimentation and feedback information that would otherwise be unavailable. Enforcement environments share this complexity because, among other reasons, threats vary by region, network, industry, and time. A centralized quota cannot incorporate this information, partly because it treats arrests as interchangeable units in the same way that central plans treat tons of steel or bushels of grain as interchangeable.
This helps explain why quota-driven enforcement is insensitive to conditions on the ground. It cannot adapt to local threat profiles because it does not reward adaptation. It cannot prioritize effectively because prioritization is costly and quotas reward speed, and it cannot learn from failure because in most cases it lacks the local knowledge needed for the adjustment. Of course, politicians can pivot when citizens and voters push back, but it is necessarily a less detailed and efficient process than, for example, markets and prices.
Worse still, enforcement that deliberately and disproportionately targets working, embedded individuals produces sudden and uneven labor supply shocks. Industries that rely heavily on immigrant labor, like construction and agriculture, experience disruptions that cascade via prices, output, and complementary employment. These are downstream consequences of enforcement choices shaped by quotas. When enforcement prioritizes ease of arrest over social cost, it predictably targets workers rather than criminals, disrupting productive relationships that markets had already coordinated. The result resembles what happens when planners disrupt supply chains without understanding their internal complementarities.
A common defense of quotas appeals to accountability. Without numerical targets, agencies may underperform, selectively enforce, or drift away from their mandates. That said, the existence of a real problem, namely accountability, is hardly a defense of a flawed solution based on quotas that measure a single dimension without the necessary local knowledge.
The central lesson is rooted in institutional design and incentive structures under which these immigration agents operate. When complex, knowledge-intensive activities are governed by centralized numerical targets, agents will rationally pursue targets in ways that undermine the broader purpose of the institutional effort. Perverse incentives and poor institutional design are not the only explanatory factors here —personal choice and moral character matter, too—but they are a big part of the explanatory pie.
