Dear Applied Statistics Workshop Community,
Our next meeting of the semester will be on March 8 (12:00 EST). Cory
McCartan will present "Estimating Racial Disparities when Race is Not
Observed."
<Where>
CGIS K354
Bagged lunches are available for pick-up at 11:40 (CGIS K354).
Zoom:
https://harvard.zoom.us/j/99181972207?pwd=Ykd3ZzVZRnZCSDZqNVpCSURCNnVvQT09
<Abstract>
The estimation of racial disparities in health care, financial services,
voting, and other contexts is often hampered by the lack of
individual-level racial information in administrative records. In many
cases, the law prohibits the collection of such information to prevent
direct racial discrimination. As a result, many analysts have adopted
Bayesian Improved Surname Geocoding (BISG), which combines individual names
and addresses with the Census data to predict race. Although BISG tends to
produce well-calibrated racial predictions, its residuals are often
correlated with the outcomes of interest, yielding biased estimates of
racial disparities. We propose an alternative identification strategy that
corrects this bias. The proposed strategy is applicable whenever one’s
surname is conditionally independent of the outcome given their
(unobserved) race, residence location, and other observed characteristics.
Leveraging this identification strategy, we introduce a new class of
models, Bayesian Instrumental Regression for Disparity Estimation (BIRDiE),
that estimate racial disparities by using surnames as a high-dimensional
instrumental variable for race. Our estimation method is scalable, making
it possible to analyze large-scale administrative data. A validation study
based on the North Carolina voter file shows that BIRDiE reduces error by
up to 84% in comparison to the standard approaches for estimating racial
differences in party identification.
<2022-2023 Schedule>
GOV 3009 Website:
https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
Calendar:
https://calendar.google.com/calendar/embed?src=c_3v93pav9fjkkldrbu9snbhned8…
Best,
Shusei