Dear Applied Statistics Workshop Community,
Our next meeting of the semester will be on February 1 (12:00 EST). Elliott
Ash will present "Televised Debates and Emotional Appeals in Politics:
Evidence from C-SPAN."
<Where>
Hybrid: CGIS K354 or Zoom (the presenter will join us via Zoom)
Bagged lunches are available for pick-up at 11:40 (CGIS K354).
Zoom:
https://harvard.zoom.us/j/99181972207?pwd=Ykd3ZzVZRnZCSDZqNVpCSURCNnVvQT09
<Abstract>
We study the effect of televised broadcasts of floor debates on the
rhetoric and behavior of U.S. Congress Members, focusing on a measure of
emotionality, relative to rationality, constructed using computational
linguistics methods. First, we show in a differences-in-differences
analysis that the introduction of C-SPAN broadcasts in 1979 increased the
use of emotional appeals in the House relative to the Senate, where
televised floor debates were not introduced until later. Second, we use
exogenous variation in C-SPAN channel positioning as an instrument for
C-SPAN viewership by Congressional district, and show that House Members
from districts with higher C-SPAN viewership are more emotive, rather than
deliberative, in floor debates. Contra accountability models of
transparency, C-SPAN has no effect on measures of legislative effort on
behalf of constituents, and if anything it reduces a politician’s
constituency orientation. We find that local news coverage – that is,
mediated rather than direct transparency – has the opposite effect of
C-SPAN, increasing legislative effort but with no effect on emotional
rhetoric. Looking to electoral pressures as a mechanism, we find the
emotionality effect of C-SPAN is strongest in competitive districts.
Finally, C-SPAN exposure increases the incumbency advantage, and the
incumbency effect is much larger among Congress Members who speak
emotionally. These results highlight the importance of audience and
mediation in the political impacts of higher transparency.
<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
Dear Applied Statistics Workshop Community,
Our next meeting of the semester will be on January 25 (12:00 EST). Justin
Grimmer will present "A Statistical Framework to Engage the Problem of
Disengaged Survey Respondents."
<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>
Researchers in academia, government, and industry increasingly rely upon
cheaper online surveys to measure public opinion. However, with their lower
cost, online surveys increase the risk of bias from inattentive or
disengaged survey respondents entering the sample – a risk that remains
even after survey firms and researchers use well-developed filters and
attention checks to exclude these disengaged respondents. In this paper, we
introduce a statistical framework for surveys with disengaged respondents
and tools to address the bias. First, we develop a partial identification
approach that clarifies the extent to which relevant estimands can be
identified in the presence of disengaged respondents. These bounds apply
regardless of how well attention checks uncover disengaged respondents.
Second, we show that simply dropping respondents who are flagged as
disengaged or inattentive from the analysis can lead to selection bias if
the scientific question is about the attitudes or beliefs in a general
target population (e.g., adults in the US). To correct for this, we
introduce partial and point identification approaches that adjust for this
selection bias. We apply our estimators to study the prevalence of extreme
anti-democratic attitudes and find that – despite alarming topline results
— that the survey data is consistent with there being effectively no
respondents who support these views.
<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
