congCombined {krige}

Congressional District Public Opinion Ideology in 2010

Description

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.

Format

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.

stateCD

Unique identifier for each congressional district by state. The first two digits are STATEA, and the second two are cd.

krige.cong

The ideology of the average citizen in the congressional district.

krige.state.var

The variance of ideology among the district's citizens.

cong

The term of Congress studied–112 for this dataset.

idno

Identification number for the House member–ICPSR numbers continued by Poole & Rosenthal.

state

The ICPSR code for the state.

cd

The congressional district number.

statenm

The first seven letters of the state's name.

party

Political party of the House member. 100=Democrat, 200=Republican.

name

Last name of the House member, followed by first name if ambiguous.

dwnom1

First 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.

STATEA

The FIPS code for the state.

Source

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’

References

Jeff Gill. 2020. Measuring Constituency Ideology Using Bayesian Universal Kriging. State Politics & Policy Quarterly. doi:10.1177/1532440020930197

Examples

    
# 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")