In the 1990s, economists studying taxi markets in New York noticed something puzzling. Late at night, when demand for taxis was high, you might still see empty cabs driving around instead of immediately picking up nearby passengers. At first glance this looked irrational. Why would a driver ignore a paying customer?
Researchers discovered the explanation by analyzing driver behavior across entire shifts. Many taxi drivers appeared to follow what economists call Target Earning Behavior. Instead of maximizing income per hour, drivers often set a mental daily earnings target. Once they got close to that target, their motivation to accept additional passengers dropped.
This creates a strange pattern. On days when drivers earn money quickly—during busy periods or good weather—they often quit earlier. On slow days, they stay out longer trying to reach the same income goal. From a traditional economic perspective this behavior looks backwards. Standard theory predicts workers should work more when wages are high and less when wages are low.
But human psychology complicates the model. People frequently evaluate success relative to goals rather than absolute efficiency. If a driver mentally decides that $200 is “a good day,” reaching that number can trigger a sense of completion, even if working another hour would be financially beneficial.
The case became influential in behavioral economics because it showed how real-world labor decisions don’t always follow simple incentive logic. Later research found similar patterns among fishermen, bicycle messengers, and freelance workers. When income is unpredictable, workers often anchor their effort to a target rather than continuously optimizing.
The broader lesson is that economic behavior isn’t purely mechanical. Incentives matter, but so do psychological reference points. Sometimes a worker stops not because earning more money is impossible, but because their internal scoreboard already says they’ve won for the day.
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