erer (version 4.0)

daRoll: Voting records for the Healthy Forests Restoration Act in 2003 and the associated characteristics of congressmen

Description

This data set contains the voting records for the Healthy Forests Restoration Act in 2003, as used in Sun (2006). The characteristics of individual congressmen are also included. There are 537 observations and 22 variables.

state	state name for a congressman
district	district for a congressman; 0 for senators
name	Family name of a congressman
voteMay	voting record in May 2003 in the House; 1 if yes, 0 if no, and NA if not voted
voteNov	voting record in Nov 2003 in both the House and Senate
RepParty	Dummy equals one if Republican
East	Regional dummy for 11 northeastern states
West	Regional dummy for 11 western states
South	Regional dummy for 13 southern states
PopDen	Population density - 1000 persons per km2
PopRural	Population density per km2
Edu	Percentage of population over 25 with a Bachelor's degree
Income	Median family income ($1,000)
FYland	Percentage of federal lands in total forestlands 2002
Size	Value of shipments of forest industry 1997 (million dollars)
ContrFY	Contribution from forest firms (1,000 dollars)
ContrEN	Contribution from environmental groups (1,000 dollars)
Sex	Dummy equals one if male
Lawyer	Dummy equals one if lawyer
Member	Dummy equals one if a committee member for the HFRA
Year	Number of years in the position
Chamber	Dummy equals one if House and zero if Senate

Usage

data(daRoll)

Format

A data frame object with 537 rows and 22 variables. This is a cross-sectional dataset that are generating from merging several raw datasets.

Details

This is the combinded final data set used in the study of Sun (2006).

References

Sun, C. 2006. A roll call analysis of the Healthy Forests Restoration Act and constituent interests in fire policy. Forest Policy and Economics 9(2):126-138.

See Also

glm; maBina.

Examples

# generate four datasets used in Sun (2006)
data(daRoll)
xn <- c('RepParty', 'East', 'West', 'South', 'PopDen', 
  'PopRural', 'Edu', 'Income', 'FYland', 'Size', 
  'ContrFY', 'ContrEN', 'Sex', 'Lawyer', 'Member', 'Year', 'Chamber')

f1 <- daRoll[!is.na(daRoll$voteMay), c('voteMay', xn)]
f2 <- daRoll[!is.na(daRoll$voteNov) & daRoll$Chamber == 1, c('voteNov', xn)]
f3 <- daRoll[!is.na(daRoll$voteNov), c('voteNov', xn)]
f4 <- daRoll[!is.na(daRoll$voteNov) & daRoll$RepParty == 0, c('voteNov', xn)]
rownames(f1) <- 1:nrow(f1); rownames(f2) <- 1:nrow(f2)
rownames(f3) <- 1:nrow(f3); rownames(f4) <- 1:nrow(f4)
colnames(f1)[1] <- colnames(f2)[1] <- 'Vote'
colnames(f3)[1] <- colnames(f4)[1] <- 'Vote'
dim(f1); dim(f2); dim(f3); dim(f4)
tail(f3)