  "Big data" gives markets access to previously unmeasured characteristics of
individual agents. Policymakers must decide whether and how to regulate the use
of this data. We study how new data affects incentives for agents to exert
effort in settings such as the labor market, where an agent's quality is
initially unknown but is forecast from an observable outcome. We show that
measurement of a new covariate has a systematic effect on the average effort
exerted by agents, with the direction of the effect determined by whether the
covariate is informative about long-run quality or about a shock to short-run
outcomes. For a class of covariates satisfying a statistical property we call
strong homoskedasticity, this effect is uniform across agents. More generally,
new measurements can impact agents unequally, and we show that these
distributional effects have a first-order impact on social welfare.
