Abstract:
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Publically-released data users often experience the challenge of computing regression estimates that are similar to those they would have attained were they able to access the internal data. When a variable is binned for being too sensitive for release, the task becomes more critical. This study investigates how releasing frequency data for a sensitive variable and unbinned data for a non-sensitive covariate affect the likelihood of estimating regression parameters that do not substantially differ from those obtained from the internal data. Simulations will determine whether the likelihood is impacted by the number of bins, the underlying distribution of the sensitive variable, and whether noise multiplication is introduced. An application to economic survey data follows.
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