Dear Applied Statistics Workshop Community,
Our next meeting of the spring semester will be on January 31 (12:00 EST).
Sooahn Shin presents "Measuring Issue Specific Ideal Points from Roll Call
Votes."
<When>
January 31, 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>
Ideal points are widely used to measure the ideology and policy preferences
of political actors, from voters and politicians to sovereign states. Yet,
the lingering challenge is to measure ideal points specific to a single
issue area. Scholars who wish to measure preferences in a specific area of
interest often resort to subsetting the voting data, resulting in the loss
of valuable information and rendering ambiguous comparisons across
different issue areas. To address this, I introduce IssueIRT — a
hierarchical Item Response Theory (IRT) model that estimates an
issue-specific axis representing a continuum extending from left to right
positions on the issue using roll-call votes and their issue labels. This
approach first estimates multidimensional ideal points using all available
voting data, which are then projected onto issue-specific axes to generate
single-dimensional, issue-specific ideal points. Furthermore, I develop a
measure of issue similarity to compare the alignment of different issue
areas on a unified left-to-right spectrum. I demonstrate that IssueIRT
effectively captures issue-specific voting behaviors through simulations
and a validation study that measures sectionalism in the US House of
Representatives during the 1890s gold standard era. Finally, I show that
polarization in Congress has markedly increased across 32 separate issues
from 1979 to 2023. IssueIRT is implemented in issueirt, an open-source R
package.
<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
Dear Applied Statistics Workshop Community,
Welcome back! Our first meeting of the spring semester will be on January
24 (12:00 EST). Hans Demetrio Gaebler presents "Overcoming Statistical
Challenges in Detecting Discrimination."
<When>
January 24, 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>
Outcome tests are a long-standing and widely used approach to detecting
discrimination in lending, hiring, policing, and beyond. For example, if
White loan recipients are found to default more often than racial minority
recipients, the outcome test would suggest that lenders impose a double
standard, preferentially lending to riskier White loan applicants. Despite
its popularity, outcome tests have long been known to be statistically
flawed, sometimes even suggesting discrimination against the group that in
reality received preferential treatment. We propose two methods for
remedying these statistical shortcomings. First, we show that a twist on
standard outcome tests leads to surprisingly strong statistical guarantees.
Our test is provably correct under a simple non-parametric assumption that
we show — both empirically and theoretically — likely holds in many common
scenarios. One limitation of this test is that it is, in some cases,
inconclusive. In light of this, we introduce an alternative test of
discrimination — which we call risk-adjusted regression — that can handle a
broader range of cases, but which requires a richer set of covariates. This
latter approach sheds light on the connection between statistical and legal
understandings of discrimination.
<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
