| congCombined | R Documentation |
These data present measures of ideology in 2010 for 434 districts for the U.S.
House of Representatives, recorded as the variable krige.cong. Forecasts
are based on a kriging model fitted over the 2008 Cooperative Congressional
Election Survey (CCES), paired with predictive data from the 2010 Census. Each
district's public ideology is paired with the DW-NOMINATE common space score
of each of its representative in 2011 (update from McCarty, Poole and Rosenthal
1997). Eight districts have repeated observations in order to include the DW-NOMINATE
score when a member was replaced mid-term.
The congCombined dataset has 442 observations and 12 variables. 4
34 out of 435 congressional districts are covered, with eight districts duplicated
when a member was replaced mid-term.
stateCDUnique identifier for each congressional district by state.
The first two digits are STATEA, and the second two are cd.
krige.congThe ideology of the average citizen in the congressional district.
krige.state.varThe variance of ideology among the district's citizens.
congThe term of Congress studied–112 for this dataset.
idnoIdentification number for the House member–ICPSR numbers continued by Poole & Rosenthal.
stateThe ICPSR code for the state.
cdThe congressional district number.
statenmThe first seven letters of the state's name.
partyPolitical party of the House member. 100=Democrat, 200=Republican.
nameLast name of the House member, followed by first name if ambiguous.
dwnom1First dimension DW-NOMINATE common space score for the House member. Higher values are usually interpreted as more right-wing, with lower values as more left-wing.
STATEAThe FIPS code for the state.
Ansolabehere, Stephen. 2011. "CCES, Common Content, 2008." Ver. 4.
McCarty, Nolan M., Keith T. Poole and Howard Rosenthal. 1997. Income Redistribution and the Realignment of American Politics. American Enterprise Institude Studies on Understanding Economic Inequality. Washington: AEI Press.
Minnesota Population Center. 2011. National Historical Geographic Information System: Version 2.0. Minneapolis, MN: University of Minnesota. https://www.nhgis.org
Jeff Gill. 2020. Measuring Constituency Ideology Using Bayesian Universal Kriging.
State Politics & Policy Quarterly. doi:10.1177/1532440020930197
# Descriptive Statistics
summary(congCombined)
# Correlate House Members' DW-NOMINATE Scores with Public Opinion Ideology
cor(congCombined$dwnom1,congCombined$krige.cong)
# Plot House Members' DW-NOMINATE Scores against Public Opinion Ideology
plot(y=congCombined$dwnom1,x=congCombined$krige.cong,
xlab="District Ideology (Kriging)", ylab="Legislator Ideology (1st Dim., Common Space)",
main="U.S. House of Representatives", type="n")
points(y=congCombined$dwnom1[congCombined$party==200],
x=congCombined$krige.cong[congCombined$party==200],pch="R",col="red")
points(y=congCombined$dwnom1[congCombined$party==100],
x=congCombined$krige.cong[congCombined$party==100],pch="D",col="blue")
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