Showing results 1 to 5 of approximately 5.(refine search)
Exploring the use of anonymized consumer credit information to estimate economic conditions: an application of big data
The emergence of high-frequency administrative data and other big data offers an opportunity for improvements to economic forecasting models. This paper considers the potential advantages and limitations of using information contained in anonymized consumer credit reports for improving estimates of current and future economic conditions for various geographic areas and demographic markets. Aggregate consumer credit information is found to be correlated with macroeconomic variables such as gross domestic product, retail sales, and employment and can serve as leading indicators such that lagged ...
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 ...
Income and Earnings Mobility in U.S. Tax Data
We use a large panel of federal income tax data to investigate intragenerational income mobility in the United States. We have two primary objectives. First, we explore the determinants of two-year changes in individual labor earnings and family incomes, such as job or industry changes, marriage, divorce, and geographic mobility. Second, we evaluate how federal income taxes stabilize or destabilize post-tax income changes relative to pre-tax changes. We find a relatively high degree of income mobility, with almost half of workers exhibiting earnings increases or decreases of at least 25 ...
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. ...
Measuring Income and Wealth at the Top Using Administrative and Survey Data
Administrative tax data indicate that U.S. top income and wealth shares are substantial and increasing rapidly (Piketty and Saez 2003, Saez and Zucman 2014). A key reason for using administrative data to measure top shares is to overcome the under-representation of families at the very top that plagues most household surveys. However, using tax records alone restricts the unit of analysis for measuring economic resources, limits the concepts of income and wealth being measured, and imposes a rigid correlation between income and wealth. The Survey of Consumer Finances (SCF) solves the ...