Wallace E. Huffman; Just, Richard E.

WP #10022, June 2010

The objective of this paper is to develop an optimal incentive system for multitasking

scientists in universities or professors under repeat contracting. With the aid of a principalagent

model under repeat contracting, we show that (i) when a second task is assigned to a

professor and the two tasks are related, the size of the optimal incentive rate for the first task is

reduced in some situations but not others relative to that of a single task, (ii) with an increase

in the noise in the technical relationship of the second task or imprecision in output

measurement, the optimal incentive rate for that task is reduced and for the first task may be

reduced or increased , (iii) with greater efficiency of the professor in producing the second

output, as reflected in ability relative to cost of effort, the optimal incentive rate for the first

task generally decreases, (iv) if the output of the professor’s two tasks are negatively

correlated then the optimal incentive rate on the first task declines as the size of this

correlation increases. The size of the guarantee is always reduced as the professor’s ability for

a task increases, but is increased as his cost of effort, noisiness of the technology or

measurement of output, or correlation between the two outputs increases. It is also possible

that, as a professor undertakes several difficult-to-measure tasks, the incentive rate will be

reduced to the point that an optimal compensation system will involve only a guaranteed

salary, which is a very weak incentive for effort. Selective audits may be useful in these

situations.

Keywords: incentives, Principal-agent model, Multitask, scientists, professors, respeat contracting, linear contracts

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