Abstract
AbstractThis research adapts the Neyman‐Pearson testing protocol commonly used in biomedical research for ex post evaluation of the employment impacts of new ethanol bio‐refineries in the U.S. Great Plains and the Midwest. By calculating the power of the test, the suggested protocol may provide policy‐relevant information, even in the event of nonsignificant findings. The main obstacle to applying this protocol has been the need to posit an explicit alternative distribution, which runs counter to the empiricist tradition of mainstream econometrics. We resolve this problem by applying a data generating process with known parameters anchored to sample data to compute power.
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