  Statistical agencies face a dual mandate to publish accurate statistics while
protecting respondent privacy. Increasing privacy protection requires decreased
accuracy. Recognizing this as a resource allocation problem, we propose an
economic solution: operate where the marginal cost of increasing privacy equals
the marginal benefit. Our model of production, from computer science, assumes
data are published using an efficient differentially private algorithm. Optimal
choice weighs the demand for accurate statistics against the demand for
privacy. Examples from U.S.\ statistical programs show how our framework can
guide decision-making. Further progress requires a better understanding of
willingness-to-pay for privacy and statistical accuracy.
