(Weighted) mean distance from the most-liked party

Description

The (weighted) mean distance from the most liked party is a measure suggested by Wagner (2021), although he considers it inferior to the (weighted) spread. It measures the average discrepancy between like for the most liked party and like/dislike for all other parties. Wagner computes the (weighted) distance based on the CSES dataset, but in principle it can be applied to other datasets that contain the required items, i.e., like-dislike scores. ​

Operationalization

Wagner proceeds in two steps. First, they calculate the (weighted) mean distance for each respondent. Second, they aggregate to the party-system level by taking the mean of the respondents’ distance scores. The unweighted and weighted distance measures are calculated as follows:

Unweighted: $Distance_i = \sqrt{\frac{\sum_{p=1}^p (like_{ip} - like_{max,i})^2}{n_p}}$

Weighted: $Distance_i = \sqrt{\sum_{p=1}^p v_p (like_{ip} - like_{max,i})^2}$

The subscript $i$ denotes an individual respondent, $p$ denotes a party, $max$ is the most liked party, and $v_p$ is the percentage of votes received by a party. ​

polaR

# Import Data
cses_imd <- polaR_import(source = "cses_imd",
						 path = "path/to/dataset.dta")

# Use 'weighted' to toggle between the weighted and unweighted measure
# The data output can be provided with individual scores or aggregated to a country/year level with 'aggregate'
cses_imd <- distance(cses_imd,
					 weighted = c(TRUE, FALSE),
					 aggregate = c(TRUE, FALSE))

​

Visualization

Use cases

Publications that use this measure: