As part of a project to assess the influence, or impact, of Canadian provincial government ruling ideologies on provincial economic performance I have created a time-series cross-section summary of my party ideology variable across provinces over time. A time-series cross-section research design is one in which there is variation across space (cross-section) and also over time. The time units can literally be anything although in comparative politics they are often years. The cross-section part can be countries, cities, individuals, states, or (in my case) provinces. Here is the snippet of the data structure in my dataset (data frame in R):
canparty.df[1:20,c(2:4,23)]
Year Region Pol.Party party.ideology
1981 Alberta Progressive Conservative Party 1
1981 British Columbia Social Credit Party 1
1981 Manitoba Progressive Conservative Party 1
1981 New Brunswick Progressive Conservative Party 1
1981 Newfoundland and Labrador Progressive Conservative Party 1
1981 Nova Scotia Progressive Conservative Party 1
1981 Ontario Progressive Conservative Party 1
1981 Prince Edward Island Progressive Conservative Party 1
1981 Quebec Parti Quebecois 0
1981 Saskatchewan New Democratic Party -1
1982 Alberta Progressive Conservative Party 1
1982 British Columbia Social Credit Party 1
1982 Manitoba New Democratic Party -1
1982 New Brunswick Progressive Conservative Party 1
1982 Newfoundland and Labrador Progressive Conservative Party 1
1982 Nova Scotia Progressive Conservative Party 1
1982 Ontario Progressive Conservative Party 1
1982 Prince Edward Island Progressive Conservative Party 1
1982 Quebec Parti Quebecois 0
1982 Saskatchewan Progressive Conservative Party 1
To get the plot picture below, we use the R code at the bottom of this post. But a couple of notes: first, the year data are not in true date format. Rather, they are in periods, which I have conveniently labelled years. In other words, what is important for the analysis that I will do (generalized synthetic control method) is to periodize the data. Second, because elections occur at any point during the year, I have had to make a decision as to which party is coded as having been in government that year.
Since my main goal is to assess economic performance, and because economic policies take time to be passed, and to implement, I made the decision to use June 30th as a cutoff point. If a party was elected prior to that date, it is coded as having governed the province in that whole year. If the election was held on July 1st (or after), then the incumbent party is coded as having governed the province the year of the election and the new government is coded as having started its mandate the following year.
Here’s the plot, and the R code below:
library(gsynth)
library(panelView)
library(ggplot2)
ggpanel1 <- panelView(Prop.seats.gov ~ party.ideology + Prov.GDP.Cap, data = canparty.df,
index = c("Region", "Year"), main = "Provincial Ruling Party Ideology",
legend.labs = c("Left", "Centre", "Right"), col=c("orange", "red", "blue"),
axis.lab.gap = c(2,0), xlab="", ylab="")
## I've used Prop.seats.gov and Prov.GDP.Cap b/c they are two
## of my IVs, but any other IVs could have been used to
## create the plot. The important part is the party.ideology
## variable and the two index variables--Region (province) ## and Year.
## Save the plot as a .png file
ggsave(filename="ProvRulingParty.png", plot=ggpanel1, height=8,width=7)
Clouds, Clocks, and Sitting at Tables 
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