Despite their documented importance in the labor market, little is known about how workers use social networks to find jobs and their resulting effect on earnings. I use geographically detailed US employer-employee data to infer the role of social networks in connecting workers to jobs in high-paying firms. To identify social interactions in job search, I exploit variation in social network quality within small neighborhoods. Workers are more likely to change jobs, and more likely to move to a higher-paying firm, when their neighbors are employed in high-paying firms. Furthermore, local referral networks help match high-ability workers to high-paying firms.

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 US 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.