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Have US Households Depleted All the Excess Savings They Accumulated during the Pandemic?
During the COVID-19 pandemic, US households accumulated a historically high volume of personal savings. As the crisis waned, personal savings started to decline. Economists disagree on whether households have drained their excess savings, and they disagree on which income group is more likely to have done so. The lack of consensus stems from different assumptions about today’s long-term saving rate, which is used as a benchmark to define excess savings. If households need to set aside a higher share of their income now relative to before the pandemic, then pandemic-era excess savings have ...
Breaking the Implicit Contract: Using Pension Freezes to Study Lifetime Labor Supply
This paper studies the elimination of traditional pensions and subsequent adoption of 401(k) plans by U.S. employers. Using thousands of firm-level natural experiments, it shows that unexpected losses in future compensation engendered by pension plan transitions induce premature retirement for some workers and delayed retirement for others. Observed heterogeneity in retirement behavior is indicative of differences in wealth and in preferences for leisure. Using credibly identified treatment effects as estimation targets, it fits a structural model of retirement and uses the model to evaluate ...
Bad Times, Bad Jobs? How Recessions Affect Early Career Trajectories
Workers who enter the labor market during recessions experience lasting earnings losses, but the role of non-pay amenities in either exacerbating or counteracting these losses remains unknown. Using population-scale data from Germany, we find that labor market entry during recessions generates a 6 percent reduction in earnings cumulated over the first 15 years of experience. Implementing a revealed-preference estimator of employer quality that aggregates information from the universe of worker moves across employers, we find that one-quarter of recession-induced earnings losses are ...
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. ...