  The leading strategy for analyzing unstructured data uses two steps. First,
latent variables of economic interest are estimated with an upstream
information retrieval model. Second, the estimates are treated as "data" in a
downstream econometric model. We establish theoretical arguments for why this
two-step strategy leads to biased inference in empirically plausible settings.
More constructively, we propose a one-step strategy for valid inference that
uses the upstream and downstream models jointly. The one-step strategy (i)
substantially reduces bias in simulations; (ii) has quantitatively important
effects in a leading application using CEO time-use data; and (iii) can be
readily adapted by applied researchers.
