Behind many production function estimators lies a crucial assumption that the firm's choice of intermediate inputs depends only on observed choices of other inputs and on unobserved productivity. This assumption fails when market frictions distort the firm's input choices. I derive a test for the assumption, which is rejected in several industries. Using weak identification asymptotics, I show that when the assumption fails, a simplified dynamic panel estimator can be used instead of choice-based methods because it requires choices to be distorted. I propose criteria for choosing between estimators, which in simulations yield lower error than either estimator alone.

A party’s influence over redistricting increases discontinuously when its seat share in the state legislature exceeds 50%. We apply bunching tests to show that, in the election before redistricting, parties systematically win narrow majorities in legislatures of states where they have lost recent U.S. House races. This trend of losses is reversed after redistricting despite no change in overall House vote shares in states near the cutoff. The pre-to-post-redistricting change in regression discontinuity estimates implies that the party that controls redistricting engineers an 11 percentage point increase in its probability of winning a House race.