Dear Applied Statistics Workshop Community, Our next meeting will be on Wednesday, February 21 (12:00 EST). Ross Mattheis presents "Spurious Mobility in Imperfectly Linked Data Trials" (joint with Jiafeng Chen). <When> February 21, 12:00 to 1:30 PM, EST Lunch will be available for pick-up inside CGIS K354. <Where> In-person: CGIS K354 Zoom: https://harvard.zoom.us/j/93217566507?pwd=elBwYjRJcWhlVE5teE1VNDZoUXdjQT09 <Abstract> Estimating intergenerational mobility often requires linking data across multiple sources. However, mistakes in record linkage can introduce biases in subsequent estimates. This paper re-examines the history of intergenerational mobility in the United States with emphasis on bias from imperfectly linked data. In particular, data corrupted by incorrect links will typically attenuate estimates of linear estimands towards zero. When the estimand is the intergenerational elasticity of status, this bias will tend to exaggerate levels of mobility. We propose two complementary methods to address bias from imperfectly linked data. Building on a large literature on Bayesian entity resolution, our first approach samples from a convenience prior and reports the ratio of the posterior and implicit prior distributions for the target parameter. Our second approach takes advantage of the availability of repeated measurements and identification results in settings with misclassified data due to Hu (2008). Consistent with bias from data-corruption, our estimates suggest that levels of mobility in the U.S. were lower than previously believed, with conventional estimates of the father-son elasticity of occupation status 10% to 40% lower than our estimates. The gap between ours and conventional estimates is largest in the mid-nineteenth century and declines in more recent years, resulting in relatively stable levels of mobility over the period. <2023-2024 Schedule> GOV 3009 Website: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Calendar: https://calendar.google.com/calendar/u/0?cid=Y18zdjkzcGF2OWZqa2tsZHJidTlzbm… Best, Jialu -- Jialu Li Department of Government Harvard University jialu_li(a)g.harvard.edu