Here's our abstract y'all. Any complaints, suggestions, advice? (esp.
does "socialize" make sense in this context?)
Yuki and Doru
_Winning Strategies_
_How Race and Economic Opportunity Shaped the California Electorate_
Wedge issues aim to polarize the electorate and secure a majority for
the proposing party. However, already committed electorates are less
susceptible to this strategy than emerging constituencies. Several
racially charged propositions on California ballots, aimed at polarizing
the preferences of Whites and Latinos, led the latter to join the
Democrats? camp while affecting little change on the former group.
Unlike previous research suggested, we find no backlash from Whites
against the racial wedge issues employed by Republicans. Employing a
multidimensional model of partisan identification, we find that, holding
constant economic opportunities in the ?80s and ?90s, White party
allegiances remained mostly unchanged, while Latinos became firmly
lodged in the Democratic camp. We attribute previously reported
variations in the partisan makeup of the electorate to changes in
economic opportunity. Thus, wedge issues appeal only to unstable,
emerging electorates, and fail to muster enough momentum to persuade
already socialized voters.
Hi all,
Please find our preliminary abstract below. We would appreciate comments and
recommendations. Thanks,
Jacqueline and Jen
The Media Followed the Parties, but Not Equally on All Issues: A Closer
Look at Agenda Formation in the 1997 British General Election
A small body of literature has attempted to determine the causal flow of
agenda-setting during political campaigns: do the media lead the parties or
vice versa? Using time-series cross-sectional data from the 1997 British
general election campaign, Brandenburg (2002) employs ordinary least squares
regression to conclude that the media tracked the parties' issue agendas. We
replicate these results but find evidence in first differences that parties
conceivably followed the media agendas as well. We therefore re-estimate
Brandenburg's five models using negative binomial regression in light of the
over-dispersed count-data. The results confirm Brandenburg's stated but
imperfectly supported conclusion that agenda formation was unidirectional,
from parties to media. In addition, however, the negative binomial models
uncover differences across policy dimensions in the magnitude of this party
impact, information that OLS suppresses. It appears that the parties
particularly affected the media's tendency to cover the economy, welfare,
education, and foreign policy.
dear all,
i'm coming across an error when trying to extract the matched dataset
after running matchit. using:
match<-matchit(TreatInd~ Age+ Gender+ Black+ Other+ Widow+ Div+ ...,
data=data)
which runs fine, i then get
matched<-match.data(match)
Error in cbind(data, object$distance): cannot coerce type closure to
list vector
I looked here
http://lists.hmdc.harvard.edu/lists/zelig/2006_03/msg00008.html
for help but there's no more information than this.
Any ideas?
Lucy
Hi,
What does exactly "all their multiplicative interactions" mean? Is is
all the possible interactions of pairs of covariates or are we supposed
to allow for higher order intercations of the type: X1*X2*X3*X4?
Juan
Hi everyone,
I just wanted to pass along that a new Zelig version was just released
today (version 2.6.1). The new version fixes a bug in the ordered logit
and ordered probit simulation procedures.
We've notified FAS and hopefully they will update the version on the
servers soon. But if you are on Windows, you may want to update your
Zelig version if you are using these models in your papers.
Best,
Ian
Kevin Quinn recommended a good package for this purpose, Graphviz,
www.graphviz.org. The code for simple directed graphs is pretty easy.
C
---------- Forwarded message ----------
From: Kevin Quinn <kquinn(a)fas.harvard.edu>
Date: Nov 29, 2005 11:06 PM
Subject: Re: [gov2000-list] Drawing flow charts in LaTex
To: nall(a)fas.harvard.edu
Cc: gov2000-list(a)fas.harvard.edu
Hi Clayton,
I'm afraid I don't see the error below. However, I can point you
toward a good open source tool for drawing directed (and undirected)
graphs. It is at:
http://www.graphviz.org/
Hope this helps.
Best,
KQ
On Nov 29, 2005, at 10:00 PM, Clayton Nall wrote:
> Hi all,
>
> The not so short intro to latex says that the text below should allow
> me to draw a flow chart of the structural relationships between the
> variables. When I run this text, I am getting the message "undefined
> control sequence" for my \xymatrix{}
> function. Any idea what I'm doing wrong here?
>
> Thanks,
> Clayton
>
> \begin{displaymath}
> \xymatrix{
> VV \ar[d] \ar[r] & UU \ar[d] \ar[dr]& & &\\
> & ZZ & & XX \ar[dll] \ar[d] &
> }
> \end{displaymath}
>
> _______________________________________________
> gov2000-list mailing list
> gov2000-list(a)lists.fas.harvard.edu
> http://lists.fas.harvard.edu/mailman/listinfo/gov2000-list
------------------------------------------------------
Kevin Quinn
Assistant Professor
Department of Government and
The Institute for Quantitative Social Science
1737 Cambridge Street
Harvard University
Cambridge, MA 02138
Hi all,
This isn't a strict 2001 question (though related). Does anyone know
where I can find ACU scores with ICPSR codes attached? I'd like
incorporate the ACU measure into a dataset I already have, but can't
think of any easy way to merge it in.
Thanks
John
--
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
John Gasper, PhD Candidate
Dept: Social & Decision Sciences
Carnegie Mellon University
www.hss.cmu.edu/departments/sds
Spring 2006:
The Institute for Quantitative Social Science
Cambridge, MA 02138
www.iq.harvard.edu
I agree with Dan. In a ddition, it would be good to know whether finding spillover effects matter in an avbsolute sense and also relative to what everyone else had estimated the program effect was.
Gary
-----Original Message-----
From: Dan Hopkins <dhopkins(a)fas.harvard.edu>
Date: Friday, Apr 28, 2006 3:15 pm
Subject: Re: [gov2001-l] Preliminary Abstract "Spillover Effects on Health Outcomes: Evidence from the Randomized Evaluation of PROGRESA"
This is a very nice abstract. Your substantive point is clear and
important. Depending on your audience, and certainly for our purposes, you might highlight the methodological contribution a bit more
clearly--precisely what methods are you bringing to bear on this problem? Also, the title could be more of a statement of the argument, rather than a statement of the topic.
Best,
Dan
On Fri, 28 Apr 2006, Sebastian Bauhoff wrote:
> Hi all,
> Comments welcome.
> Thanks,
> Sebastian and Holger
>
> Spillover Effects on Health Outcomes: Evidence from the Randomized
Evaluation of PROGRESA
> Holger Lutz Kern and Sebastian Bauhoff
> Abstract: We estimate spillover effects on health outcomes of PROGRESA, a
large-scale poverty alleviation program in Mexico. PROGRESA provides cash
incentives to eligible households if they participate in health education
and maternal and child health care. Exploiting the fact that the program
was assigned randomly across villages we find evidence for positive
spillover effects on non-eligible children aged 0-5 for objective and
subjective health measures. One year after the program began in 1998,
non-eligible children in treatment villages were on average [XX] taller,
[XX] heavier and [XX] per cent less likely to be anemic relative to
non-eligible children in control villages. They were also [XX] per cent less
likely to be reported sick. Previous evaluations have ignored these
spillover effects and hence understate the program's benefits.
>
_______________________________________________
gov2001-l mailing list
gov2001-l(a)lists.fas.harvard.edu
http://lists.fas.harvard.edu/mailman/listinfo/gov2001-l
_______________________________________________
gov2001-l mailing list
gov2001-l(a)lists.fas.harvard.edu
http://lists.fas.harvard.edu/mailman/listinfo/gov2001-l
Geoff Humphreys and Chris Long
Classfying Political Documents
In recent years, political methodologists, have produced innumerable automated
document classification systems. Many of these systems, such as those based on
the well-known Naive Bayes algorithm, treat each word as a distinct entity,
ignoring complex interactions between them. While for some applications this
approach may appear reasonable, the precise arrangements of words in political
documents often convey meanings which cannot be captured so easily. In this
paper, we investigate the success of such naive algorithms by comparing
Wordscores, a Naive Bayes derivative, to several well-known algorithms and a
new classification system based on vanilla recursive heirarchical
Dirichlet-multinomial mixture models, pointing out avenues for future
advancement. Surprisingly, we find that the assumptions of Wordscores
notwithstanding, it shows dramatically increased performance, comparable to
some of the latest developments in document classification, at carrying out a
small number of carefully selected classifications on meticuously arranged
collections of political documents, and discuss its use in practical
applications.