## Data Visualization #16—Canadian Federal Equalization Payments per capita

My most recent post in this series analyzed data related to the federal equalization program in Canada using a lollipop plot made with ggplot2 in R. The data that I chose to visualize—annual nominal dollar receipts by province—give the reader the impression that over the last five-plus decades the province of Quebec (QC) is the main recipient (by far) of these federal transfer funds. While this may be true, the plot also misrepresents the nature of these financial flows from the federal government to the provinces. The data does not take into account the wide variation in populations amongst the 10 provinces. For example, Prince Edward Island (PEI) as of 2019 has a population of about 156,000 residents, while Quebec has a population of approximately 8.5 million, or about 55 times as much as PEI. That is to say a better way of representing the provincial receipt of equalization funds is to calculate the annual per capita (i.e., for every resident) value, rather than a provincial total.

For the lollipop chart below, I’ve not only calculated an annual per-capita measure of the amount of money received by province, I’ve also controlled for inflation, understanding that a dollar in 1960 was worth a lot more (and could be used to buy many more resources) in 1960 than today. Using Canadian GDP deflator data compiled by the St. Louis Federal Reserve, I’ve created plotting variable—annual real per-capita federal equalization receipts by province, with a base year of 2014. Here, we see that the message of the plot is no longer Quebec’s dominance but a story in which Canadians (regardless of where they live) are treated relatively equally. Of course, every year, Canadian in some provinces receive no equalization receipts.

Here’s the plot, and the R code below it:

```require(ggplot2)
require(gganimate)

gg.anim.lol3 <- ggplot(eq.pop.df[eq.pop.df\$Year!="2019-20",], aes(x=province, y=real.value.per.cap, label=real.value.per.cap.amt)) +
geom_point(stat='identity', size=14, aes(col=as.factor(zero.dummy))) +  #, fill="white"
scale_color_manual(name="zero.dummy",
#      labels = c("Above", "Below"),
values = c("0"="#000000", "1"="red")) +
labs(title="Per Capita Federal Equalization Entitlements (by Province): {closest_state}",
x=" ", y="\$ CAD (Real—2014 Base Year)") +
geom_segment(aes(y = 0,
x = province,
yend = real.value.per.cap,
xend = province),
color = "red",
size=1.5) +
scale_y_continuous(breaks=seq(0,3000,500)) +
theme(legend.position="none",
plot.title =element_text(hjust = 0.5, size=23),
plot.subtitle =element_text(hjust = 0.5, size=19),
axis.title.x = element_text(size = 16),
axis.title.y = element_text(size = 16),
axis.text.y =element_text(size = 14),
axis.text.x=element_text(vjust=0.5,size=16, colour="black")) +
geom_text(color="white", size=4) +
transition_states(
Year,
transition_length = 1,
state_length = 9
) +

animate(gg.anim.lol3, nframes = 610, fps = 10, width=800, height=680, renderer=gifski_renderer("equal_real_per_cap_lollipop.gif"))

```

## Data Visualization #15—Canadian Federal Equalization Payments over time using an animated lollipop graph

There is likely no federal-provincial political issue that stokes more anger amongst Albertans (and is so misunderstood) as equalization payments (entitlements) from the Canadian federal to the country’s 10 provinces. Although some form of equalization has always been a part of the federal government’s policy arsenal, the current equalization program was initiated in the late 1950s, with the goal of providing, or at least helping achieve an equal playing field across the country in terms of basic levels of public services. As Professor Trevor Tombe notes:

Regardless of where you live, we are committed (indeed, constitutionally committed) to ensure everyone has access to “reasonably comparable levels of public services at reasonably comparable levels of taxation.”

Finances of the Nation, Trevor Tombe

For more information about the equalization program read Tombe’s article and links he provides to other information. The most basic misunderstanding of the program is that while some provinces receive payments from the federal government (the ‘have-nots’) the other, more prosperous, provinces (the ‘haves’) are the source of these payments. You often hear the phrase “Alberta sends X \$billion to Quebec every year!” That’s not the case. The funds are generated and distributed from federal revenues (mostly income tax) and disbursed from this same fund of resources. The ‘have’ provinces don’t “send money” to other provinces. The federal government collects tax revenue from all individuals and if a province has a higher proportion of high-earning workers than another, it will generally receive less back in money from the federal government than its workers send to Ottawa. (To reiterate, read Tombe for more about the particulars.)

Using data provided by the Government of Canada, I have decided to show the federal equalization outlays over time using what is called a lollipop chart. I could have used a bar chart, but I like the way the lollipop chart looks. Here’s the chart and the R code below:

The data source is here: https://open.canada.ca/data/en/dataset/4eee1558-45b7-4484-9336-e692897d393f, and I am using the table called Equalization Entitlements.

N.B.: The original data, for some reason, had Alberta abbreviated as AL, so I had to edit the my final data frame and the gif.

```## You'll need these two libraries
require(ggplot2)
require(gganimate)

gg.anim.lol1 <- ggplot(melt.eq.df, aes(x=variable, y=value, label=amount)) +
geom_point(stat='identity', size=14, aes(col=as.factor(zero.dummy))) +  #, fill="white"
scale_color_manual(name="zero.dummy",
values = c("0"="#000000", "1"="red")) +
labs(title="Canada—Federal Equalization Entitlements (by Province): {closest_state}",
x=" ", y="Millions of nominal \$ (CAD)") +
geom_segment(aes(y = 0,
x = variable,
yend = value,
xend = variable),
color = "red",
size=1.5) +
scale_y_continuous(breaks=seq(0,15000,2500)) +
theme(legend.position="none",
plot.title =element_text(hjust = 0.5, size=23),
axis.title.x = element_text(size = 16),
axis.title.y = element_text(size = 16),
axis.text.y =element_text(size = 14),
axis.text.x=element_text(vjust=0.5,size=16, colour="black")) +
geom_text(color="white", size=4) +
transition_states(
Year,
transition_length = 2,
state_length = 8
) +