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Working Paper
Finding Needles in Haystacks: Multiple-Imputation Record Linkage Using Machine Learning
This paper considers the problem of record linkage between a household-level survey and an establishment-level frame in the absence of unique identifiers. Linkage between frames in this setting is challenging because the distribution of employment across establishments is highly skewed. To address these difficulties, this paper develops a probabilistic record linkage methodology that combines machine learning (ML) with multiple imputation (MI). This ML-MI methodology is applied to link survey respondents in the Health and Retirement Study to their workplaces in the Census Business Register. ...
Working Paper
Impact of first-birth career interruption on earnings: evidence from administrative data
This paper uses unique administrative data to expand the understanding of the role women's intermittency decisions play in the determination of their wages. We demonstrate that treating intermittency as exogenous significantly overstates its impact. The intermittency penalty also increases in the education level of the woman. The penalty for a woman with a high school degree with an average amount of intermittency during six years after giving birth to her first child is roughly half the penalty for a college graduate. We also demonstrate the value of using an index to capture multiple ...