Category: Democracy

Data Visualization #19—Using panelView in R to produce TSCS (time-series cross-section) plots

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)

Data Visualization #18—Maps with Inset Maps

There are many different ways to make use of inset maps. The general motivation behind their use is to focus in on an area of a larger map in order to expose more detail about a particular area. Here, I am using the patchwork package in R to place a series of inset maps of major Canadian cities on the map that I created in my previous post. Here is the map and a snippet of the R code below:

Created by: Josip Dasović

You can see that a small land area contains just under 50% of Canada’s electoral districts. Once again, in a democracy, citizens vote. Land doesn’t.

In order to use the patchwork package, it is helpful to first create each of the city plots individually and store those as ggplot objects. Here are examples for Vancouver and Calgary.

## My main map (sf) object is named can_sf. Here I'm created a plot using only 
## districts in the Vancouver, then Calgary areas, respectively. I also limit the
## districts to those that comprise the "red" (see map) 50% population group.

library(ggplot2)
library(sf)

yvr.plot <- ggplot(can_sf[can_sf$prov.region=="Vancouver and Northern Lower Mainland" &
                             can_sf$Land.50.Pop.2016==1 | can_sf$prov.region=="Fraser Valley and Southern Lower Mainland" &
                             can_sf$Land.50.Pop.2016==1,]) + 
  geom_sf(aes(fill = Land.50.Pop.2016), col="black", lwd=0.05) + 
  scale_fill_manual(values=c("red")) +
  theme(axis.text.x=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.x = element_blank(),
        axis.ticks.y = element_blank(),
        legend.position = "none",
        plot.margin=unit(c(0,0,0,0), "mm"))

yyc.plot <- ggplot(can_sf[can_sf$prov.region=="Calgary" &
                            can_sf$Land.50.Pop.2016==1,]) + 
  geom_sf(aes(fill = Land.50.Pop.2016), col="black", lwd=0.05) + 
  scale_fill_manual(values=c("red")) +
  theme(axis.text.x=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.x = element_blank(),
        axis.ticks.y = element_blank(),
        legend.position = "none",
        plot.margin=unit(c(0,0,0,0), "mm"))

I have created a separate ggplot2 object for each of the cities that are inset onto the main Canada map above. The final R code looks like this:

## Add patchwork library
library(patchwork)

## Note: gg50 is the original map used in the previous post.

gg.50.insets <- gg.50 + inset_element(yvr.plot, left = -0.175, bottom = 0.25, 
                            right = 0, top = 0.45, on_top=TRUE, align_to = "plot",
                            clip=TRUE) +
  labs(title="VANCOUVER") +
  theme(plot.title = element_text(hjust = 0.5, size=9, vjust=-1, face="bold"),
        panel.border = element_rect(colour = "black", fill=NA, size=1)) +
  inset_element(yeg.plot, left = -0.175, bottom = 0, 
                right = -0.025, top = 0.2, on_top=TRUE, align_to = "plot",
                clip=TRUE) +
  labs(title="EDMONTON") +
  theme(plot.title = element_text(hjust = 0.5, size=9, vjust=-1, face="bold"),
        panel.border = element_rect(colour = "black", fill=NA, size=1)) +
  inset_element(yyc.plot, left = -0.1, bottom = 0, 
                right = 0.20, top = 0.25, on_top=TRUE, align_to = "plot",
                clip=TRUE) +
  labs(title="CALGARY") +
  theme(plot.title = element_text(hjust = 0.5, size=9, vjust=-1, face="bold"),
        panel.border = element_rect(colour = "black", fill=NA, size=1)) +
  inset_element(yxe.plot, left = 0.125, bottom = 0, 
                right = 0.275, top = 0.15, on_top=TRUE, align_to = "plot",
                clip=TRUE) +
  labs(title="SASKATOON") +
  theme(plot.title = element_text(hjust = 0.5, size=9, vjust=-1, face="bold"),
        panel.border = element_rect(colour = "black", fill=NA, size=1)) +
  inset_element(yqr.plot, left = 0.225, bottom = 0, 
                right = 0.45, top = 0.175, on_top=TRUE, align_to = "plot",
                clip=TRUE) +
  labs(title="REGINA") +
  theme(plot.title = element_text(hjust = 0.5, size=9, vjust=-1, face="bold"),
        panel.border = element_rect(colour = "black", fill=NA, size=1)) +
  inset_element(ywg.plot, left = 0.375, bottom = 0, 
                right = 0.55, top = 0.175, on_top=TRUE, align_to = "plot",
                clip=TRUE) +
  labs(title="WINNIPEG") +
  theme(plot.title = element_text(hjust = 0.5, size=9, vjust=-1, face="bold"),
        panel.border = element_rect(colour = "black", fill=NA, size=1)) +
  inset_element(yyz.yhm.plot, left = 0.725, bottom = 0, 
                right = 1.15, top = 0.25, on_top=TRUE, align_to = "plot",
                clip=TRUE) +
  labs(title="TORONTO/\nHAMILTON") +
  theme(plot.title = element_text(hjust = 0.5, size=9, vjust=-1, face="bold"),
        panel.border = element_rect(colour = "black", fill=NA, size=1)) +
  inset_element(yul.plot, left = 1, bottom = 0, 
                right = 1.175, top = 0.175, on_top=TRUE, align_to = "plot",
                clip=TRUE) +
  labs(title="MONTREAL") +
  theme(plot.title = element_text(hjust = 0.5, size=9, vjust=-1, face="bold"),
        panel.border = element_rect(colour = "black", fill=NA, size=1)) +
  inset_element(yqb.plot, left = 0.95, bottom = 0.175, 
                right = 1.2, top = 0.375, on_top=TRUE, align_to = "plot",
                clip=TRUE) +
  labs(title="QUEBEC\nCITY") +
  theme(plot.title = element_text(hjust = 0.5, size=9, vjust=-1, face="bold"),
  panel.border = element_rect(colour = "black", fill=NA, size=1))

ggsave(filename="can_2019_50_pct_population_insets.png", plot=gg.50.insets, height=10, width=13)

Data Visualization #17—Land Doesn’t Vote, Citizens Do

As I have noted in previous versions of this data visualization series, standard electoral-result area maps often obfuscate and mislead/misrepresent rather than clarify and illuminate. This is especially the case in electoral systems that are based on single-member electoral districts in which there is a single winner in every district (as known as ‘first-past-the-post’). Below you can see an example of a typical map used by the media (and others) to represent an electoral outcome. The map below is a representation of the results of the 2019 Canadian Federal Election, after which the incumbent Liberal Party (with Justin Trudeau as Prime Minister) was able to salvage a minority government (they had won a resounding majority government in the 2015 election).

There are two key guiding constitutional principles of representation regarding Canada’s lower house of parliament—the House of Commons: i) territorial representation, and ii) the principle of one citizen-one vote. As of the 2019 election these principles, combined with other elements of Canadian constitutionalism (like federalism) and the course of political history, have created the current situation in which the 338 federal electoral districts vary not only in land area, but also in population size, and often quite dramatically (for a concise treatment on this topic, click here).

Because a) “land doesn’t vote,” and b) we only know the winner of each electoral district (based on the colour) and not how much of the vote this candidate received) the map below misrepresents the relative political support of parties across the country. As of 2016, more than 2/3 of the Canadian population lived within 100 kilometres of the US border, a total land mass that represents only 4% of Canada’s total.

I’ve created a map that splits Canada in two—each colour represents 50% of the total Canadian population. You can see how geographically-concentrated Canada’s population really is.

The electoral ridings in red account for half of Canada’s population, while those in light grey account for the other half. Keep this in mind whenever you see traditional electoral maps of Canada’s elections.

## This is code for the first map above. This assumes you 
## have crated an sf object named can_sf, which has these 
## properties:

```
Simple feature collection with 6 features and 64 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 7609465 ymin: 1890707 xmax: 9015737 ymax: 2986430
Projected CRS: PCS_Lambert_Conformal_Conic
```

library(ggplot2)
library(sf)
library(tidyverse)

gg.can <- ggplot(data = can_sf) +
  geom_sf(aes(fill = partywinner_2019), col="black", lwd=0.025) + 
  scale_fill_manual(values=c("#33B2CC","#1A4782","#3D9B35","#D71920","#F37021","#2B2D2F"),name ="Party (2019)") +
  labs(title = "Canadian Federal Election Results \u2013 October 2019",
       subtitle = "(by Political Party and Electoral District)") +
  theme_void() + 
  theme(legend.title=element_blank(),
        legend.text = element_text(size = 16),
        plot.title = element_text(hjust = 0.5, size=20, vjust=2, face="bold"),
        plot.subtitle = element_text(hjust=0.5, size=18, vjust=2, face="bold"),
        legend.position = "bottom",
        plot.margin = margin(0.5, 0.5, 0.5, 0.5, "cm"),
        legend.box.margin = margin(0,0,30,0),
        legend.key.size = unit(0.65, "cm"))

ggsave(filename="can_2019_all.png", plot=gg.can, height=10, width=13)

Here is the code for the 50/50 map above: 

#### 50% population Map
gg.50 <- ggplot(data = can_sf) +
  geom_sf(aes(fill = Land.50.Pop.2016), col="black", lwd=0.035) + 
  scale_fill_manual(values=c("#d3d3d3", "red"),
        labels =c("Electoral Districts with combined 50% of Canada's Population",
                "Electoral Districts with combined other 50% of Canada's Population")) +
  labs(title = "Most of Canada's Land Mass in Uninhabited") +
  theme_void() + 
  theme(legend.title=element_blank(),
        legend.text = element_text(size = 11),
        plot.title = element_text(hjust = 0.5, size=16, vjust=2, face="bold"),
        legend.position = "bottom", #legend.spacing.x = unit(0, 'cm'),
        legend.direction = "vertical",
        plot.margin = margin(0.5, 0.5, 0.5, 0.5, "cm"),
        legend.box.margin = margin(0,0,0,0),
        legend.key.size = unit(0.5, "cm"))
#       panel.border = element_rect(colour = "black", fill=NA, size=1.5))

ggsave(filename="can_2019_50_pct_population.png", plot=gg.50, height=10, width=13)

Data Visualization #6–US Counties are [essentially] Meaningless in Presidential Elections

The inspiration (so to speak) for this latest instalment of my Data Visualization series is a meme that I have been seeing spread across social media in the wake of the recent US Presidential Election. The meme, in essence, notes that numer of US counties (there are over 3000) that were “won” by the incumbent, Donald J. Trump. Indeed, it seems as though the challenger, Joe Biden, ironically won the most votes of any US Presidential candidate in US history while simultaneously having “won” the lowest percentage of counties (about 17%) of any winner of the Presidency ever.

Why did I place “won” in quotation marks? Two reasons: first, I am assuming that the authors of this meme suggest that Trump “won” these counties by having won (at least) a plurality of the vote in each. Which, I suppose, is true. The more important reason that I put “won” in quotation marks above is because US counties are effectively meaningless when it comes to determining who wins the US Presidency. They are only important insofar as receiving more votes than one’s opponent in any individual county helps increase the odds of winning what is important–a plurality of the vote in any individual state (or in Congressional Districts in the cases of Nebraska and Maine). Counties have no official weight when determining electoral college votes, and it doesn’t matter how many counties a candidate wins, as long as they reach at least 270 electoral votes. Counties in the USA vary in population from fewer than 100 (Kalawao County in Hawaii) to over 10,000,000 (Los Angeles County in California). So, discussing who “won” more counties is essentially meaningless.

Here’s an example of how absurd referring to counties won becomes. The aforementioned Los Angeles County is a county that Joe Biden handily “won” in November, by a margin of 72.5% to 27.5% for Donald Trump. In short, Trump was walloped by Biden in LA County. Yet, when you compare Trump’s vote in LA County (about 1.15 million) to his total vote in all of the states (and DC) it might shock you to learn that Trump won more votes in LA County than he won in 25 individual states (and in DC). For example, Trump won more total votes in LA County (which, remember, he lost 72.5%-27.5%) than he won in the state of Oklahoma, where he won all 6 Electoral College votes. Moreover, Biden won more votes in LA County alone than Donald Trump won in each of all but three states–Florida, Texas, and Ohio. To be clear, for example, Biden won more total votes in LA County (which, alone, didn’t win him a single Electoral College vote) than Trump won in North Carolina (for which Trump won 15 Electoral College votes).

Here is a bar plot that I’ve created to visualize these data (click on the image to open a larger version). The yellow bar at the far-right represents the number of votes won by Biden in LA County (just over 3 million). The other yellow bar represents the votes won by Trump in LA Country (just over 1 million). Every other bar is the number of votes won by Trump in each of the states (and DC) listed below (Texas, Ohio, and Florida are missing because Trump won more votes in each of those states than Biden won in LA County). The red bars are states won by Trump, while the blue bars represent states won by Biden. Remember, each of the bars (except for the one on the far-right) represent the number of votes Donald Trump won in that state (and LA County).

Created by: Josip Dasovic

Data Visualization #3–Cartograms as an alternative to standard area-based electoral maps

In my first post of this series I explained at length why basic geographically-based electoral maps are not very good at conveying the phenomena of interest (see that post for more detail), and alluded to the increased use of political geographers, and political scientists, of alternative methods of “mapping” the required information that were more clear about the message(s) contained in the data.

Let’s examine this further using the map above. This map shows the results of the Canadian federal (national) election of October 2019.The respective proportions of total area “won” by each political party as depicted in the map above are not easily translated into either the relative vote share of the parties, or the relative number of seats won. Someone ignorant about Canadian federal politics would see a relatively similar total amount of red, blue, and orange, and assume that these parties had relatively equal support across the country. The sizes (land mass), and populations of, federal electoral districts in Canada vary drastically and, as a result, these maps are not a good gauge of voter support for political parties.

Since this problem is widespread political scientists, and political geographers, have attempted to find solutions to this problem. One increasingly-common approach has been to use what are called cartograms. Cartograms are maps in which the elements (in this case, electoral districts) are usually transformed in such as way as to maintain their connections to neighbours (contiguous cartograms), but to either increase or decrease the area of the specific electoral district in order to match it to a common variable. A variable often used in the transformation of electoral maps is population size. Thus, in a completed cartogram, the size of the electoral districts is not the actual land mass of the electoral district, but is proportional to the population of the electoral district (sometimes the number of voters, or the size of the electorate is used instead of population). It’s no surprise, then, that cartograms are also called “value-by-area” maps.

Cartograms are used by geographers and social scientists to depict a wide variety of phenomena. Here are some examples. The first one is a global cartogram for which the size of the area in each country is equivalent to total public health spending by that country. We can easily see that most of the world’s spending on public health occurs in the rich countries of the global north.

Here’s one more, depicting the global share of organic agriculture, by country.

Below, I have created a cartogram that has transformed the standard electoral map of the 2019 Canadian federal election into one in which the size of the electoral districts is mostly proportional to their populations. By “mostly” I mean that they’re not perfectly proportional, since the difference in sizes between the largest and smallest districts is so large the algorithm eventually stabilizes without creating completely equal-sized electoral districts.

This map more accurately conveys the nature of political partisan support (at least as it relates to the winning of electoral districts) across the country during the 2019 election, and provides visual evidence for the reality of an election in which the Liberal Party (red) won a plurality of the seats in the federal parliament (House of Commons). Because urban districts are much smaller than rural districts, the strength of Liberal Party support in Canada’s two largest cities–Toronto and Montreal–is obfuscated by the traditional area-based electoral map, but becomes evident in this cartogram.

The next map in this series will analyze another approach to geographically-based electoral maps–the hexagon map.

Here’s the R code for the cartogram above. Here, the original R-spatial data object–can_sf–is the base for the calculation of the cartogram data. 

## Here is the code to generate the cartogram object:

library(cartogram)
can_carto_sf = cartogram_cont(can_sf, "Population_2016", itermax=50)

## Now, the map, using ggplot2
library(ggplot2)

gg.can.can.carto <- ggplot(data = can_carto_sf) +
  geom_sf(aes(fill = partywinner_2019), col="black", lwd=0.075) + 
  scale_fill_manual(values=c("#33B2CC","#1A4782","#3D9B35","#D71920","#F37021","#2B2D2F"),name ="Party (2019)") +
  labs(title = "Cartogram of Canadian Federal Election Results \u2013 October 2019",
       subtitle = "(by Political Party and Electoral District)") +
  theme_void() + 
  theme(legend.title=element_blank(),
        legend.text = element_text(size = 16),
        plot.title = element_text(hjust = 0.5, size=20, vjust=2, face="bold"),
        plot.subtitle = element_text(hjust=0.5, size=18, vjust=2, face="bold"),
        legend.position = "bottom",
        plot.margin = margin(0.5, 0.5, 0.5, 0.5, "cm"),
        legend.box.margin = margin(0,0,30,0),
        legend.key.size = unit(0.75, "cm"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5))

Data Visualization #2–Animations aid in Conveying Change

The first entry in my 30-day (it will actually be 30 posts over about 2 months) data visualization challenge argued that geographically-based electoral maps have many drawbacks as data visualization techniques. I demonstrated by using the results from the 2017 and 2020 British Columbia (BC) provincial elections as supporting evidence.

Although there were some significant political changes over the course of the two elections, these were poorly-represented by these maps. Only when we zoomed into the population centres of southwestern BC were we able to partially convey the changes that had occurred. We could have made our effort to convey the underlying movement in political party support between 2017 and 2020 a bit more obvious by using animated maps, rather than the static ones that were used.

When it comes to representing change over time, animated graphs can be very useful (as long as they aren’t too complicated and busy) and are advantageous to static maps.

Below we can find the maps in the original animated to more clearly show the changes over time. Here’s the map of the whole province:

The change between 2017 and 2020 is made clear by a jarring change in the map, where a bit more NDP-orange shows up, replacing the BCLP-blue (see the previous post for descriptions of the two parties). Otherwise, there doesn’t seem to be much change in the province overall.

We know, however, that the drastic changes that took place did so in the very tiniest southwestern corner of the BC mainland. Let’s zoom in there to have a look.

We can now more clearly see the change in results (in terms of electoral districts won) between 2017 and 2020 in this populous region. Not only did the NDP (orange) win many seats in the eastern Vancouver suburbs that had not only been won by the BCLP in 2017 but had been a bastion of support for the right-wing vote over many decades, but the NDP candidate in the Victoria-area district of Oak Bay-Gordon Head won a seat that had previously been held by the former leader of BC Green Party, Andrew Weaver (it’s the small piece of green, that changes to orange, in the eastern part of the lower orange horizontal band on the lower-left of the map) . Are these changes the harbinger of a sea-change in BC provincial politics, or are they just an anomalous blip?

Going back to my original point about these types of maps being poor representations of the underlying change in voters’ preferences, we don’t know much about the level of support for the respective parties in any of these electoral districts. All that we do know, based on the “first-past-the-post” electoral system used by BC at the provincial level, is the party whose candidate finished with the most votes in each of these electoral districts. We don’t know if a district newly-won by the NDP candidate was by one vote, or by 10,000 votes. In future posts, I’ll present graphs that will allow us to answer this question visually.

Our next posts will focus on alternatives to the basic electoral geographic maps that we’ve used in these first two posts.

‘Controlling’ for confounding variables graphically

As we’ve learned (ad nauseum) basing causal claims on a simple bivariate relationship is fraught with potential roadblocks. Even though there may be a strong, and statistically significant, relationship between an independent and dependent variable, if we haven’t controlled for potentially confounding variables, we can not state with any measure of confidence that the putative relationship between the IV and DV is causal. We should always statistically control for any (and all) potentially confounding variables.

Additionally, it is often desirable to dig deeper into the data and find out if the units-of-analysis are fundamentally different on the basis of some other variable. Below you may find two plots–each of which shows the relationship between margin of victory and electoral turnout (by electoral district) for the 2017 British Columbia provincial election. The first graph plots a simple bivariate relationship, while the second plot breaks that initial relationship down by political party (which party won the electoral district). It could conceivably be the case that the relationship between turnout and margin of victory varies across the values of political party. That is, the relationship may hold in those electoral districts where party A won, but not hold in those in which party B won.

We can see here that there is little evidence to suggest a difference in the relationship based on which party won the electoral district. Can you think of another `third’ variable that may cause the relationship between turnout and margin of victory to be systematically different across different values of that variable? What about rural-versus-urban electoral districts?

Here are the plots:

Polity IV Democracy Scores, Participation, and the Suffragettes

We noted today in lecture that Polity IV gives countries like the United States very high scores on the ”democraticness” variable, even during periods when a majority of the adult population–African-Americans, and women–were legally not allowed to vote. While Switzerland (1971) was the last European democracy to grant universal suffrage for women, Portugal was the last European country to do so (1976)–Portugal was run by a military dictatorship during in the early years of the 1970s.

In this era of social media abuse and bullying, it’s interesting to learn about some of the abuse hurled at the Suffragettes:

dvwcclyxcaewjpd

You may not be registered to vote even though you think you are.

I read a somewhat troubling story this morning about Canadian citizens who have previously not only been registered to vote, but who have voted, and are no longer registered with Elections Canada. Here is an excerpt:

Delaney Ryan is a 23-year-old anthropology student at Simon Fraser University. Unlike many of her contemporaries, she voted in both the last provincial and federal elections. She meets all the criteria for voter registration, having her driver’s licence and maintaining the same address since voting in those elections.

She can’t understand why, then, this time, she wasn’t registered on the voters list.

“I had heard rumours about people not being registered, and a friend’s Facebook page had postings on it of other people finding out they were suddenly no longer registered. A lot of these people seemed to be in the same demographic as me. So I went online (to the Elections Canada website) and checked, and I wasn’t on it, either.

“So I had to reapply for registration.”

So, go to the Elections Canada website and verify that you are registered to vote this October 19.

Should Canada have Mandatory Voting?

When political scientists engage in studies of political phenomena there are many approaches that they may take. One oft-used approach is the so-called most similar systems design. This approach tries to “control for concomitant variation.” What does that mean? In social research it is difficult to clear cause-and-effect relationships because phenomena are complex and multi-faceted. Thus, if we wanted to determine why, for example, Canada is a relatively enduring and stable democracy, and Azerbaijan is not, one potential reason could be the relatively different histories of the two countries–Canada is a former British colony, while Azerbaijan was a former republic in the Soviet Union (which was a communist state).

Figure_3_3

Could this be the reason? Possibly. But, there are so many other differences between Canada and Azerbaijan that could also be the cause of the divergent outcomes regarding present political regime. Which one of these myriad differences, then, is the true cause of the difference between Canada and Azerbaijan regarding the level of democracy in each? (Indeed, the answer may not be mono-causal, but more complex and multi-causal.)

This is why many comparativists use the most similar systems design. By selecting units (countries) that are as similar as possible, they can control for many other potential causes for the alleged divergence in outcomes across the political phenomenon of interest.

So, let’s look at Canada and Australia–two countries that are quite similar in many respects: former British colonies, large land masses with relatively small populations, multi-cultural, constitutional monarchies, parliamentary democracies, economies reliant on natural resources, neither of which has won FIFA’s World Cup (men or women), etc. The two countries, differ, however, in levels of voting participation. Whereas barely 60% of eligible Canadians vote in federal elections, the corresponding figure for Australia is well over 90%. Do Aussies simply value political participation more than Canadians? Hardly! Australia has a mandatory voting law, which penalizes (monetarily) those who do not vote.

Should Canada enact a mandatory voting law? What do you think? Is it anti-democratic to force citizens to participate in the democratic process?