pscl (version 1.02)

seatsVotes: A class for creating seats-votes curves

Description

Convert a vector of vote shares into a seats-vote curve object, providing estimates of partisan bias.

Usage

seatsVotes(x, desc = NULL, method = "uniformSwing")

Arguments

x

a vector of vote shares for a specific party (either proportions or percentages)

desc

descriptive text

method

how to simulate a seats-vote curve; the only supported method at this stage is uniformSwing.

Value

• An object of class seatsVotes, with components
• s
  Estimated seat shares over the range of simulated average, district-level vote shares
• v
  Simulated average district-level vote shares
• x
  observed seat shares, with missing data removed
• desc
  user-supplied descriptive character string
• call
  a list of class call, the call to the function

Details

Simulation methods are required to induce a seats-votes curve given a vector of vote shares from one election. The uniform swing method simply slides the empirical distribution function of the vote shares up and down, computing the proportion of the vote shares that lie above .5 (by construction, the winning percentage in a two-party election) for each new location of the vector of vote shares. That is, as the empirical CDF of the observed vote shares slides up or down, more or less seats cross the .5 threshold. A seats-votes curve is formed by plotting the seat share above .5 as a function of the average district-level vote share (a weakly monotone function, since the empirical CDF constitutes a set of sufficient statistics for this problem). The simulation is run so as to ensure that average district-level vote shares range between 0 and 1.

The extent to which the seats-votes curve departs from symmetry is known as bias. More specifically, the vertical displacement of the seats-votes curve from .5 when average district-level vote share is .5 is conventionally reported as an estimate of the bias of the electoral system.

Different methods produce different estimates of seats-votes curves and summary estimands such as bias. The uniform swing method is completely deterministic and does not produce any uncertainty assessment (e.g., confidence intervals etc).

References

Tufte, Edward R. 1973. The Relationship Between Seats and Votes in Two-Party Systems. American Political Science Review. 67(2):540-554.

Gelman, Andrew and Gary King. 1990. Estimating the Consequences of Electoral Redistrictring. Journal of the American Statistical Association. 85:274-282.

Jackman, Simon. 1994. Measuring Electoral Bias: Australia, 1949-93. British Journal of Political Science. 24(3):319-357.

See Also

plot.seatsVotes for plotting methods.

Examples

data(ca2006)
x <- ca2006$D/(ca2006$D+ca2006$R)
sv <- seatsVotes(x,
                 desc="Democratic Vote Shares, California 2006 congressional elections")