In our model, the optimal timing of specialization reflects a trade-off between

identifying one’s field of comparative advantage and acquiring specialized skills.

Each agent is best suited to one field, but the identity of that field is initially

unknown. Taking courses in many fields simultaneously provides agents with information

about their comparative advantage. College course choices solve an optimal

experimentation (bandit) problem: a student may choose multi-disciplinary

education, where he acquires skills and receives information about his match to

different fields; alternatively, he may choose specialized education, which conveys

field-specific skills at a faster rate but does not provide such information.

Students update their beliefs about their comparative advantage by filtering a

diffusion process in continuous time. Their course choices follow a stopping rule:

students start by enrolling in multi-disciplinary education, where they learn about

their field of comparative advantage. Once their confidence level is sufficiently

high – the belief that they belong in either field being sufficiently close to 1 – they

specialize. This bandit problem is not stationary: over time, as agents remaining

in the mixed-education stream acquire skills, the value of their foregone wages

rises and the expected length of additional specialized studies diminishes. This

reduction in the length of specialized education depresses the value of current

information and makes the agents less willing to experiment. Agents optimally

lower the confidence level they require in order to specialize.


Agents whose beliefs process drifts the fastest, and who therefore specialize

early, are more likely to specialize in the field of their comparative advantage.

Agents whose beliefs process remains close to their prior longer take their specialization

decision on the basis of weaker information. Late specializers are therefore

more likely to choose wrongly. Since these same individuals spent most of their

studies in the multi-disciplinary stream, their education is also relatively more

transferable. The model therefore delivers our reduced-form result: compared to

early specializers, late specializers are more likely to change occupations.

To separate the contribution of self-selection from the lock-in effect of early

specialization, the model is estimated structurally. Using data from a panel of

college graduates, we estimate the parameters of the model through simulated

method of moments. Detailed transcript data allow us to construct a proxy for

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