Productivity Beliefs and Efficiency in Science

TL;DR

This paper introduces a method to estimate scientists' productivity beliefs from labor supply data, revealing a skewed productivity distribution and significant potential gains from better talent identification and resource allocation.

Contribution

It develops a novel approach to infer productivity beliefs without observable output or input prices, applied to survey data to inform science policy and resource distribution.

Findings

Productivity beliefs are highly skewed among researchers.

More efficient resource allocation could yield billions in gains.

Developing new talent identification methods is highly valuable.

Abstract

We develop a method to estimate producers' productivity beliefs when output quantities and input prices are unobservable, and we use it to evaluate the market for science. Our model of researchers' labor supply shows how their willingness to pay for inputs reveals their productivity beliefs. We estimate the model's parameters using data from a nationally representative survey of researchers and find the distribution of productivity to be very skewed. Our counterfactuals indicate that a more efficient allocation of the current budget could be worth billions of dollars. There are substantial gains from developing new ways of identifying talented scientists.