Incentives

The Cobra Effect

The story most often told to illustrate perverse incentives comes from British colonial India. As it goes, authorities concerned about venomous cobras in Delhi offered a bounty for every dead cobra brought in. At first it worked. Then enterprising residents began breeding cobras specifically to kill them and collect the reward. When the government discovered this and scrapped the program, breeders released their now-worthless snakes into the wild, and the cobra population ended up larger than before. The Delhi story is much repeated but poorly documented in primary sources, so it is best treated as a useful parable rather than confirmed history. The pattern it describes, though, is unmistakably real and has been documented in other contexts.

This pattern repeats with remarkable consistency across centuries and contexts. In Hanoi under French colonial rule, a similar bounty was placed on rats in 1902. Residents were required to bring in rat tails as proof of kill. People began cutting tails off live rats and releasing them to breed more tail-producing rats. In modern contexts, the same logic applies. When Fort Benning, a US Army base in Georgia, put a bounty on invasive wild pigs in 2007, the pig population grew while it ran. Bait fed the pigs, and hunters went after trophies rather than breeding stock, the researchers suspect. An insurance policy that pays for repairs but not for upkeep gives owners less reason to keep up the maintenance.

The cobra effect is not about stupidity on either side. The people breeding cobras were perfectly rational given the incentive structure. The policymakers were not foolish; they simply failed to anticipate how people would adapt to a new reward. This is the central challenge of incentive design: humans are creative optimizers. They find the shortest path to whatever is rewarded, and that path frequently diverges from what the incentive designer intended. Any system that rewards an output invites gaming by people who find cheaper ways to produce that output, whether or not the underlying goal is achieved.

A reward for collecting is not a reward for recycling

Goodhart's Law

In 1975 the British economist Charles Goodhart, writing about monetary policy, observed that any statistical regularity tends to collapse once pressure is placed on it for control purposes. The anthropologist Marilyn Strathern later restated his law in the form now usually quoted: when a measure becomes a target, it ceases to be a good measure. This sounds abstract until you see it in practice. A hospital is measured on wait times in its emergency department. To improve the metric, administrators might move patients from the waiting room into hallway beds. Technically, wait time drops. Practically, patients are lying in a hallway instead of a waiting room. The metric improved. The experience did not.

Police departments measured on crime statistics face Goodhart's Law constantly. When precincts are evaluated by crime rates, some have been caught reclassifying felonies as misdemeanors or discouraging victims from filing reports. The recorded numbers improve while actual crime does not. In academia, researchers are evaluated by publication count and citation metrics. This incentivizes publishing many small papers instead of fewer significant ones, splitting single findings across multiple publications, and forming citation rings where researchers cite each other to boost metrics. The measure was meant to identify productive scholarship. It instead reshaped scholarship to produce higher measures.

Goodhart's Law is not an argument against measurement. Without metrics, accountability is much harder. But it is a warning that any single metric will be optimized at the expense of everything it does not capture. Wells Fargo set aggressive targets for the number of accounts each employee should open. Employees responded by creating millions of unauthorized accounts in customers' names. The target was met. Customers were harmed. The more pressure is placed on a single number, the more creative effort goes into hitting that number by any means, and the less the number reflects the reality it was supposed to represent.

Measured by the clock and the queue, a call can get shorter without getting better

Teaching to the Test

Standardized testing was designed to measure educational quality. If students perform well, schools must be teaching effectively. But once test scores became the basis for school funding, teacher evaluations, and school ratings, teaching shifted toward test preparation. Teachers spend weeks drilling students on the specific format and content of upcoming exams. Subjects not tested, such as art, music, and physical education, receive less time. The curriculum narrows to match what is measured, not what education broadly requires.

This is not a failure of teachers. It is a rational response to incentive structures. When a teacher's job security depends on student test scores, allocating time to untested subjects is a professional risk. When a school's budget depends on standardized performance, administrators direct resources toward test preparation. The No Child Left Behind Act in the United States made this dynamic explicit: schools with persistently low scores faced escalating consequences, ending in restructuring, such as replacing the staff or reopening as a charter school. The result was narrower curricula, intensive test prep, and in some documented cases, outright cheating by teachers and administrators who altered answer sheets.

The debate is not whether testing has value. Assessments provide useful information about what students know. The question is what happens when assessment becomes the purpose rather than a tool. Students who excel at standardized tests may struggle with creative problem-solving, collaborative work, or applying knowledge in unfamiliar contexts, precisely the skills that testing does not capture. Finland, often cited for educational excellence, uses minimal standardized testing and gives teachers significant autonomy, though its international test scores have fallen since 2006. Singapore uses intensive testing but embeds it within a broader system of teacher development and curricular depth. Both still score above the OECD average through different approaches, suggesting that the incentive structure surrounding tests matters more than the tests themselves.

A perfect score on the target, while the wall behind it crumbles

Algorithms and the Outrage Machine

Social media platforms earn revenue from advertising. Advertising revenue depends on attention. Attention is maximized by engagement. And many studies find that content provoking outrage, fear, or moral indignation generates more engagement than content that is calm, nuanced, or balanced. This is not a conspiracy. It is an incentive structure. Nobody has to decide to make people angry. Engineers optimized for engagement because that is what the business model rewards. The algorithm learned, through billions of interactions, that emotionally charged content keeps people scrolling, clicking, and sharing.

The consequences ripple outward. Content creators learn what the algorithm rewards and produce accordingly. A thoughtful fifteen-minute analysis of a policy issue gets modest engagement. A provocative thirty-second clip with a misleading caption goes viral. Over time, creators who produce outrage-optimized content grow their audiences, while those producing measured analysis struggle for visibility. Researchers disagree about how much of this reflects what audiences want and how much the feed amplifies. Either way, the feed sorts and surfaces content based on engagement metrics, and outrage scores high on them.

Critics argue that this incentive structure has reshaped political discourse and left moderate voices struggling to be seen. According to a 2020 Wall Street Journal report, Facebook's own researchers found that 64% of joins to extremist groups came through its recommendation tools. Independent studies are more mixed. In a large experiment during the 2020 US election, a chronological feed on Facebook and Instagram changed what users saw but not their polarization over three months, and studies of YouTube find that extremist channels draw mainly viewers who already seek them out. The same experiment showed the cost to the platforms: without the algorithm, people spent substantially less time on them, and time is what advertisers pay for. Critics conclude that reform requires changing the business model, regulation, or both; others argue that feeds largely reflect what users already want.

The angry post draws thousands of shares, the calm one a handful

Perverse Incentives in Healthcare

In a fee-for-service healthcare model, doctors and hospitals earn money by performing services: tests, procedures, surgeries, follow-up visits. The more they do, the more they earn. This creates an incentive to do more, not necessarily to make patients healthier. A doctor who resolves your problem with a single visit and some lifestyle advice generates less revenue for the system than one who orders imaging, refers you to a specialist, prescribes medication, and schedules follow-ups. Both might be practicing good medicine. But the system rewards volume over outcomes.

The treating-versus-curing tension is even more stark in pharmaceutical economics. A drug that cures a disease eliminates future revenue from that patient. A drug that manages symptoms indefinitely generates recurring revenue for years. This does not mean companies deliberately withhold cures. Drug development is genuinely difficult, and many diseases are far easier to manage than to cure. But the financial structure does influence where research investment flows. Chronic conditions that affect millions of affluent patients attract more research funding than conditions prevalent in low-income countries, because the revenue potential differs enormously. Rare diseases were long neglected for the same reason, until the US Orphan Drug Act of 1983 added tax credits and seven years of market exclusivity; in 2025, half of the novel drugs approved in the United States were for rare diseases.

Some healthcare systems attempt to realign incentives. Value-based care models pay providers partly on patient outcomes rather than on service volume alone. Capitation models give providers a fixed payment per patient per year, incentivizing them to keep patients healthy and out of the hospital. Kaiser Permanente, which both insures and provides care, has an incentive to prevent illness because it bears the cost of treatment. These models are not perfect, and they create their own perverse incentives, such as avoiding the sickest patients who are expensive to treat. But they demonstrate that different incentive structures produce measurably different patterns of care.

Under fee-for-service, each test, visit and procedure is billed separately

Principal-Agent Problem

Whenever you hire someone to act on your behalf, you face a fundamental problem: their interests may not align with yours. You are the principal. They are your agent. A real estate agent earns a commission based on sale price. You might expect this aligns their incentive with yours: sell your house for the most money possible. But research by Steven Levitt and Chad Syverson found that homes owned by real estate agents stay on the market about 9.5 days longer and sell for about 3.7% more than similar homes the agents sell for clients. The agent's incentive is not to maximize your price. It is to close the deal. An extra $10,000 on your sale price adds only a few hundred dollars to their commission, not worth weeks of additional effort. But that same $10,000 on their own home goes directly into their pocket.

Financial advisors face a similar misalignment. An advisor paid by commission earns more by recommending products that generate higher fees, regardless of whether those products are best for you. Even fee-only advisors who charge a percentage of assets managed are incentivized to keep your money under their management rather than recommending that you pay off your mortgage or invest in your own business. The advice you receive is filtered through what benefits the advisor, and that filter is often invisible to you.

The principal-agent problem appears in corporate governance, politics, law, and medicine. Corporate executives (agents) may pursue short-term stock price gains that benefit their bonuses while undermining long-term company health for shareholders (principals). Politicians (agents) may put their own reelection ahead of the interests of voters (principals). Lawyers paid by the hour have less incentive to resolve your case quickly than lawyers on flat fees. In every case, the solution is not to eliminate agents, which is impractical, but to design compensation structures, monitoring systems, and accountability mechanisms that narrow the gap between what the agent wants and what you need. Perfect alignment is usually impossible. Good enough alignment is worth pursuing.

The expert who diagnoses the problem also bills for the fix

Designing Better Incentives

If incentives are so powerful, can they be designed intentionally to produce better outcomes? Nudge theory, developed by Richard Thaler and Cass Sunstein, argues yes. A nudge is a change in how choices are presented that influences behavior without restricting options. The classic example is workplace pension enrollment. Where employees must actively sign up, participation typically sits somewhere between half and two-thirds. Where enrollment is automatic and they must actively opt out, it rises above 90%. The best-documented case is the US 401(k) plan, but the UK's automatic enrollment reform from 2012 produced a similar jump, to about 90% of eligible employees in Great Britain by 2025. Same plan, same options, same freedom. Different default, dramatically different outcome.

Choice architecture, the design of environments in which people make decisions, extends this principle. Placing healthier food first or within easy reach in a cafeteria can steer choices toward it without removing unhealthy options. Putting a fly sticker in a urinal is reported to reduce spillage by giving people something to aim at. Showing homeowners how their energy use compares to their neighbors cuts consumption by about 2% on average. These interventions work because they align the path of least resistance with the desired outcome, rather than requiring willpower or conscious effort.

But nudge theory has limits and critics. At scale, effects are often small: in 126 trials run by two US government nudge units, the average nudge raised take-up by 1.4 percentage points, against 8.7 in published academic studies, a gap the authors trace to publication bias. Thaler and Sunstein's libertarian paternalism aims to make people better off "as judged by themselves"; critics argue that it still assumes choice architects know what is best. Who decides which direction the nudge should push? Governments and corporations can use the same principles to steer behavior toward outcomes that benefit them, not you. Dark patterns in web design, like making unsubscribe buttons hard to find or pre-checking boxes for marketing emails, are nudges that serve the designer's interest at your expense. The same tools that make good incentive design possible also make manipulation more sophisticated. Understanding incentives is not just about designing better systems. It is about recognizing when systems are designed to work against you, and having the awareness to push back.

One more thing about incentive design that the dominant economic framing tends to underemphasize: not all behavior responds to material rewards, and in some contexts, introducing material rewards actually crowds out the intrinsic motivation that was already producing the behavior. Decades of research in self-determination theory, associated with psychologists Edward Deci and Richard Ryan, find that expected tangible rewards can erode interest in tasks people already find interesting. A 2014 review of four decades of studies adds a distinction: intrinsic motivation better predicts the quality of work, and incentives its quantity. A famous study of Israeli daycare centers found that when some started fining parents for picking up children late, lateness increased instead of falling. In the researchers' reading, the fine reframed lateness from a moral failing into a transactional choice. A 2020 survey-based replication did not reproduce the effect, so how general it is remains open. In a Swedish experiment, a small cash payment nearly halved the share of women willing to become blood donors, though rewards at American Red Cross drives raised donations, partly by drawing donors from drives without them. In trials in more than 200 urban US schools, paying students for grades, test scores, or books read had no measurable overall effect on achievement. Status, recognition, autonomy, and identity often outperform money as motivators above a baseline of material adequacy. A well-designed incentive system understands when to use money, when not to, and when the cheapest path to better behavior is to remove obstacles rather than add rewards.

Healthier options placed first and at eye level, with nothing taken off the menu

AI, Automation, and the Future of Work

"Will AI replace my job?" is a question many people have asked by now, and it is usually framed too broadly. A more useful question is which tasks within a given job will be automated, and what new tasks will emerge to fill the gap. History offers a consistent pattern here. When ATMs appeared in the 1970s, banks were supposed to need fewer tellers. Instead, ATMs reduced the cost of operating a branch, so banks opened more branches, and teller employment grew for several decades before eventually declining as digital banking took over more of the work. What changed was what tellers did: less cash handling, more relationship management and product sales. Spreadsheets did not eliminate accountants. Email did not eliminate office workers. Each wave of automation destroyed certain tasks, transformed others, and created entirely new roles that nobody predicted before they existed.

What makes this current wave different is its target. Previous automation waves mostly affected manual and routine cognitive tasks: assembly lines, data entry, basic bookkeeping. AI systems now operate in domains once considered exclusively human: legal research, medical image analysis, software development, translation, and creative writing. Lawyers who spent years learning to review contracts face tools that do it in seconds. Radiologists who trained for a decade find algorithms matching their diagnostic accuracy on certain imaging tasks. Programmers watch AI assistants generate functional code from plain-language descriptions. This does not necessarily mean these professions will vanish, but it does mean the value proposition of each role is shifting. Skills that complemented previous technology become less valuable, while skills that complement AI, such as judgment under ambiguity, ethical reasoning, and creative problem-solving in unstructured environments, become more important.

Who benefits and who loses from this transition depends heavily on policy choices, not only on the technology itself. Identical AI capabilities can concentrate wealth in a handful of platform owners or distribute productivity gains broadly, depending on how labor markets are regulated, how education systems adapt, how tax policy treats capital versus labor income, and whether workers have bargaining power to negotiate their share. Scandinavian countries face automation pressures similar to those in the United States but treat displaced workers very differently, through their institutional choices around retraining, social insurance, and collective bargaining. Much of the anxiety behind automation fear is not really about machines. It is about whether gains from increased productivity will be shared or concentrated, and recent decades give people legitimate reasons for skepticism on that front.

One attendant now oversees several checkouts that shoppers run themselves

When a system produces baffling outcomes, the explanation is often sitting in the incentive structure rather than in the character of the people inside it. Noticing what gets rewarded, rather than what gets announced, is one of the most useful lenses you can carry through any institution, workplace, or relationship. It also raises a harder question: who designs the incentives and who lives inside them? That is the territory of institutions, the rules, organizations, and habits that decide whose incentives actually shape outcomes.