Akio Yamazaki

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Green Fiscal Reforms and the Demographic Squeeze: Lessons from Japan

Thu, 20 Nov 2025 00:00:00 +0000

Innovating Towards Carbon Neutrality: The Long-Run Responses of Carbon Pricing Policies

Sun, 08 Dec 2024 00:00:00 +0000

Do Carbon Taxes Kill Jobs? Firm-level Evidence from British Columbia

Tue, 31 Jan 2023 00:00:00 +0000

How Do Carbon Taxes Affect Emissions? Plant-level Evidence from Manufacturing

Sun, 10 Apr 2022 00:00:00 +0000

Environmental Taxes and Productivity: Lessons from Canadian Manufacturing

Sat, 01 Jan 2022 00:00:00 +0000

Comparing applied general equilibrium and econometric estimates of the effect of an environmental policy shock

Fri, 15 May 2020 00:00:00 +0000

Who Bears More Burdens of Carbon Taxes? Heterogeneous Employment Effects within Manufacturing Plants

Mon, 01 Jul 2019 00:00:00 +0000

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Canada’s aging population

Mon, 28 Jan 2019 00:00:00 +0000

Recently released data from Statistics Canada allows us to explore demographic trends in the age make-up of the Canadian population. The trend is clear: Canada is getting old.

In this chart-blog, we will take a look at population by age group – both across regions and over time – and how median age has evolved. (Spoiler: It’s risen. A lot!)

You can find the chart-blog here

Trucks! Trucks! Trucks!

Mon, 17 Dec 2018 00:00:00 +0000

Quick post to show how easy it is to do quick data exploration using R. This post walks us through making a (nice!) chart of new vehicle sales in Canada, breaking it down by vehicle type and region.

Step 1: Load the three packages we will use for this plot:

tidyverse : because always tidyverse
cansim : great package to easily extract Statistics Canada data
ggthemes : not necessary, but a quick way to pretty up your ggplot

library(tidyverse)
library(cansim)
library(ggthemes)

Step 2: Data wranglin’

Pull in the data
Filter to what we want in the data
Fiddle with some labels

data <- get_cansim(20100001) %>% normalize_cansim_values()
plotdata <- data %>%
filter(GEO!="Canada",
Sales=="Units",
`Vehicle type` %in% c("Passenger cars","Trucks"),
`Origin of manufacture`=="Total, country of manufacture")
# Change some GEO labels to shorten them
plotdata$GEO[plotdata$GEO=="Newfoundland and Labrador"] <- "Nfld & Lab."
plotdata$GEO[plotdata$GEO=="British Columbia and the Territories"] <- "BC & Terr."
plotdata$GEO[plotdata$GEO=="Prince Edward Island"] <- "PEI"
plotdata$GEO <- factor(plotdata$GEO, levels=c("BC & Terr.","Alberta","Saskatchewan","Manitoba","Ontario","Quebec","New Brunswick","PEI","Nova Scotia","Nfld & Lab."))
# Change "passenger cars" to "cars" for plot label
plotdata$`Vehicle type`[plotdata$`Vehicle type`=="Passenger cars"] <- "Cars"

Step 3: Plot it!

ggplot(plotdata, aes(Date,VALUE,color=`Vehicle type`))+
geom_point(size=.1,alpha=.2)+
geom_smooth(se=F)+
facet_wrap(~GEO, scales="free_y", nrow=2)+
scale_color_ptol(name="")+ # nice color scheme from ggthemes package
scale_x_date(limits=c(as.Date("1980-01-01"),as.Date("2020-01-01")),
breaks = c(as.Date("1980-01-01"),as.Date("2000-01-01"),as.Date("2020-01-01")),
date_labels = "%Y")+
theme_tufte(12,"Shree Devanagari 714")+ # font similar to 'Avenir'
theme(legend.position = "top",
title=element_text(face="bold"),
plot.caption = element_text(face="italic"),
legend.text = element_text(size=14))+
labs(x="", y="",
subtitle="New vehicle sales (monthly)",
title="Trucks! Trucks! Trucks!",
caption="Fitted line through monthly (seasonally unadjusted) data.\nSource: Statscan NDM Table 20-10-0001.")

There you go. A nice quick plot showing a pretty stark shift in consumer preferences towards trucks across all regions in Canada. An interesting extension of this data exploration would be to see what this means in terms of fleet-wide fuel economy. Are people switching from Civics to F-150s? Or Crown Vics to Subaru Crosstreks? The latter would mean a shift from cars to “trucks” is improving overall gas mileage… More data needed!

Happy Charting!

-Blake

Making animated charts with gganimate

Fri, 14 Dec 2018 00:00:00 +0000

What’s better than making charts? Making them animated, of course! While animation isn’t always (ok, rarely) necessary, it can help tell a story static charts can’t.

Recently there has been a lot of discussion in Canada related to equalization. This is a federal program to transfer funds to provinces with below average fiscal capacity. What is “fiscal capacity”? Others can tell you a lot more about it, but for simplicity, we can think of it like GDP.

So I wanted to take a look at GDP per capita, by province, going back several years. A little learning, a lot of online digging, and I was able to create this nice chart:

I made a chart. #cdnecon #cdnpoli pic.twitter.com/Ks8o5E4qir
— Blake Shaffer 📊 (@bcshaffer) December 13, 2018

People seemed to like it and some wanted to know more about how it was made. This post explains how to make an animated chart like the tweeted one above using gganimate.

##Let’s go!

To begin, let’s load our usual packages, but add in the latest version of gganimate from David Robinson which has been updated by Thomas Lin Pedersen. Thomas has added some new syntax which allows for really nice transitions to the animation.

library(tidyverse)
library(lubridate)
library(ggthemes)
library(ggrepel)
# Package to pull in cansim data, courtesy of Jens von Bergman and Dmitry Shkolnik
library(cansim)
# Now update to the newest gganimate version
#install.packages("devtools")
#devtools::install_github('thomasp85/gganimate')
library(gganimate)

With packages loaded, it’s time to wrangle the data. I could probably do this cleaner, but meh - it works! I wrote a function below to change the full province names to their 2 character abbreviations that we will use in the plots.

##Let’s start by pulling in annual GDP data by province and to test it’s working do a quick plot.

provlist <- c("British Columbia","Alberta","Saskatchewan","Manitoba","Ontario","Quebec","New Brunswick","Nova Scotia","Prince Edward Island","Newfoundland and Labrador")
short_provs <- function(df){
df <- df %>%
filter(GEO %in% c("British Columbia","Alberta","Saskatchewan","Manitoba","Ontario","Quebec","New Brunswick","Nova Scotia","Prince Edward Island","Newfoundland and Labrador"))
df <- df %>%
mutate(GEO.short = case_when(
GEO=="British Columbia" ~ "BC",
GEO=="Alberta" ~ "AB",
GEO=="Saskatchewan" ~ "SK",
GEO=="Manitoba" ~ "MB",
GEO=="Ontario" ~ "ON",
GEO=="Quebec" ~ "QC",
GEO=="New Brunswick" ~ "NB",
GEO=="Prince Edward Island" ~ "PE",
GEO=="Nova Scotia" ~ "NS",
GEO=="Newfoundland and Labrador" ~ "NL"
))
df$GEO.short <- factor(df$GEO.short, levels=c("BC","AB","SK","MB","ON","QC","NB","PE","NS","NL"))
}
GDP <- get_cansim(36100222) %>% normalize_cansim_values()
df.GDP <- GDP %>%
mutate(year=as.integer(REF_DATE)) %>%
filter(GEO %in% provlist,
Prices=="Chained (2012) dollars",
Estimates=="Gross domestic product at market prices") %>%
dplyr::select(GEO,year,GDP=VALUE)
df.GDP$GEO.short<-short_provs(df.GDP)
ggplot(df.GDP, aes(GEO.short, GDP/1e9, fill = GEO.short, label=GEO.short)) +
geom_col()+
geom_text(vjust=-.1)+
scale_fill_viridis_d(name="")+
scale_y_continuous(labels=scales::dollar)+
theme_tufte(14,"Avenir")+
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.ticks = element_blank())+
# Here comes the gganimate specific bits
labs(title = "Year: {frame_time}", x = "", y = "GDP (2012$ billions") +
transition_time(year) +
ease_aes('linear')

Ok. Good! We have an animated chart! But it’s not too informative given the BIG and small size of provinces. So to look at per capita GDP we need to pull in annual population data and merge it with our GDP data.

pop <- get_cansim(17100005) %>% normalize_cansim_values()
df.pop <- pop %>%
mutate(year=as.integer(REF_DATE)) %>%
filter(GEO %in% provlist,
Sex=="Both sexes",
`Age group`=="All ages") %>%
dplyr::select(GEO,year,pop=VALUE)
df.GDPpercap <-left_join(df.GDP,df.pop, by=c("year","GEO")) %>%
mutate(GDPpercap=GDP/pop)
df.GDPpercap$GEO.short<-short_provs(df.GDPpercap)

##Now, let’s create our plot.

I’m going to use the animate function here so that I can change the speed of the animation a bit, and add a pause at the end.

p<-ggplot(df.GDPpercap, aes(GEO.short, GDPpercap, fill=GEO.short, label=GEO.short)) +
geom_col()+
geom_text(vjust=-.1)+
scale_fill_viridis_d(name="")+
theme_tufte(14,"Avenir")+
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.ticks = element_blank())+
# Here comes the gganimate specific bits
labs(title = 'Year: {frame_time}', x = '', y = 'GDP per capita ($2012)') +
transition_time(year) +
ease_aes('linear')
animate(p, fps=5, end_pause = 30) # Use anim_save(filename) to save the plot

Not bad. But we can do better! It would be fun to get the provinces to rank themselves every frame. And let’s flip it horizontally too to look like they’re climbing over one another.

A little digging on stackoverflow took me to this post by Jon Spring: (https://stackoverflow.com/questions/52623722/how-does-gganimate-order-an-ordered-bar-time-series)

Basically, we add a rank variable, grouping first by year and ordering the provinces. We will use this as our x axis (vertical axis in the flipped coordinates). We will also use geom_tile instead of geom_col because this object creates smoother transitions in the size of the bars.

The tweeted chart!

plotdata <- df.GDPpercap %>%
group_by(year) %>%
mutate(ordering = rank(GDPpercap)) %>%
ungroup()
p<-ggplot(plotdata,
aes(ordering, group = GEO.short,color=GEO.short,fill=GEO.short)) +
geom_tile(aes(y = GDPpercap/2,
height = GDPpercap,
width = 0.9), alpha = 0.9) +
# text on top of bars
geom_text(aes(y = GDPpercap, label = GEO.short), hjust = -0.4) +
# text in x-axis (requires clip = "off" in coord_cartesian)
geom_text(aes(y = 0, label = GEO.short), hjust = 1.4) +
coord_flip(clip = "off", expand = FALSE) +
scale_color_viridis_d(name="")+
scale_fill_viridis_d(name="")+
scale_y_continuous(labels=scales::dollar)+
theme_tufte(14,"Avenir")+
guides(color=F,fill=F)+
labs(title='{frame_time}', x = "",y="GDP per capita ($2012)") +
theme(plot.title = element_text(hjust = 1, size = 22),
axis.ticks.y = element_blank(),
axis.text.y = element_blank()) +
transition_time(year)+
ease_aes('cubic-in-out')
animate(p, nframes = 100, fps = 5, end_pause = 20) #again, use anim_save(filename) to save

Cool. If i do say so myself :-)

But wait, we can do more! Rather than bars, let’s plot points as in a scatter plot and watch them float over time. We can start by looking at the co-movement of GDP per capita and unemployment rates across provinces over time. I pull in the annual LFS data from NDM 14-10-0090, merging the annual unemployment rate values with our GDP per capita data.

Let’s chart it!

p<-ggplot(df.full,
aes(GDPpercap,Unemp_Rate,
color=GEO.short, label=GEO.short)) +
geom_point(aes(size=pop))+
geom_text(hjust=-.2,vjust=-.2)+
scale_color_viridis_d(name="")+
scale_y_continuous(labels=scales::percent)+
scale_x_continuous(labels=scales::dollar)+
theme_tufte(14,"Avenir")+
theme(legend.position = "none",
plot.caption = element_text(size=9,face="italic"))+
guides(size=F,color=F)+
# Here comes the gganimate specific bits
labs(title = 'Year: {frame_time}',
x = 'GDP per capita ($2012)',
y = "Unemployment Rate",
caption="Bubble size proportional to population\nChart by @bcshaffer") +
transition_time(year) +
ease_aes('linear')
animate(p, nframes = 100, fps = 10, width = 600, height = 400, end_pause = 30)

Lastly, we can do the same with the employment rate. This time leads add some bread crumbs behind our roving points to get some history.

p<-ggplot(df.full,
aes(GDPpercap,Emp_Rate,
color=GEO.short, label=GEO.short)) +
geom_point(aes(size=pop))+
geom_text(hjust=-.2,vjust=-.2)+
scale_color_viridis_d(name="")+
scale_y_continuous(labels=scales::percent)+
scale_x_continuous(labels=scales::dollar)+
theme_tufte(14,"Avenir")+
theme(legend.position = "none",
plot.caption = element_text(size=9,face="italic"))+
guides(size=F,color=F)+
# Here comes the gganimate specific bits
labs(title = 'Year: {frame_time}',
x = 'GDP per capita ($2012)',
y = "Employment Rate",
caption="Bubble size proportional to population\nChart by @bcshaffer") +
transition_time(year) +
shadow_wake(wake_length = 1, exclude_layer = 2)+
ease_aes('cubic-in-out')
#ease_aes('linear')
animate(p, nframes = 100, fps = 10, width = 600, height = 400, end_pause = 30)

Happy charting!

-Blake

New Statscan data on victims of violent crime in Canada

Thu, 06 Dec 2018 00:00:00 +0000

Statistics Canada recently released new data on victims of violent crime across Canada from 2009 to 2017. In this post, I explore trends in this data by region, age and gender.

I find, unsurprisingly, big differences in incidence by gender. The rate of violent crime, notably sexual assaults, remains extremely skewed towards women. The rate of violent crime causing death and attempted murder is skewed towards men but remains relatively low in Canada. Alarmingly, the rate of sexual assaults towards young girls is high and climbing. Regional splits also show concerning trends in certain regions.

Let’s start by pulling in the data which can be found in NDM table 35-10-0051.

library(tidyverse)
library(cansim)
library(ggthemes)
data <- get_cansim(35100051) %>% normalize_cansim_values() %>%
mutate(year = as.numeric(REF_DATE))
data$GEO <- factor(data$GEO, levels=c("Canada",
"British Columbia",
"Alberta",
"Saskatchewan",
"Manitoba",
"Ontario",
"Quebec",
"New Brunswick",
"Prince Edward Island",
"Nova Scotia",
"Newfoundland and Labrador",
"Yukon",
"Northwest Territories",
"Nunavut"))

To start, let’s look at Canada-wide total violations, by gender:

plotdata <- data %>%
filter( `Sex of victim`!="Total, sex of victim",
`Sex of victim`!="Sex of victim unknown",
`Age of victim`=="Total, age of victim",
Statistics=="Rate per 100,000 population",
GEO=="Canada",
Violations=="Total, all violations")
ggplot(plotdata,
aes(Date,VALUE, group=`Sex of victim`, color=`Sex of victim`))+
geom_line(size=1.5)+
theme_light(12,"Avenir")+
scale_color_manual(name="",values=c("#CC6677","#4477AA"))+
theme(legend.position = "top")+
labs(x="",y="Rate per 100,000 population",
title="Victims of police-reported violent crime",
subtitle="Total, all ages",
caption="Source: Statistics Canada NDM 35-10-0051\nChart by @bcshaffer")

We can dig a little deeper and see how this breaks down by age group:

# by age
plotdata <- data %>%
filter( `Sex of victim`!="Total, sex of victim",
`Sex of victim`!="Sex of victim unknown",
`Age of victim` %in% c("Under 12 years",
"12 to 17 years",
"18 years and over"),
Statistics=="Rate per 100,000 population",
GEO=="Canada",
Violations=="Total, all violations")
plotdata$`Age of victim` <- factor(plotdata$`Age of victim`,
levels=c("Under 12 years",
"12 to 17 years",
"18 years and over"))
ggplot(plotdata,
aes(Date,VALUE, group=`Sex of victim`, color=`Sex of victim`))+
geom_line(size=1.5)+
theme_light(12,"Avenir")+
facet_wrap(~ `Age of victim`,nrow=1)+
scale_color_manual(name="",values=c("#CC6677","#4477AA"))+
theme(legend.position = "top")+
labs(x="",y="Rate per 100,000 population",
title="Victims of police-reported violent crime",
subtitle="Total, all ages",
caption="Source: Statistics Canada NDM 35-10-0051\nChart by @bcshaffer")

There is concerning growth in the rate of violent crimes against teens. For some perspective, let’s see the same data in numbers of victim rather than rate. The bulk of crimes remains in the greater than 18 category, although growth is larger for teens, especially on a per capita basis.

Now, let’s look at the rate of crime per 100,000 population by each violation type across each province. The list of violation types is:

unique(data$Violations)

## [1] "Total, all violations"
## [2] "Violations causing death and attempted murder"
## [3] "Sexual assaults, levels 1, 2, 3"
## [4] "Sexual violations against children"
## [5] "Assaults"
## [6] "Other violent violations"
## [7] "Criminal Code traffic violations causing death or bodily harm"

Breakdown by Province

We start by only looking at the provinces, because the territory values blow up the y-axis. I’ll come back to important data in the territories later. We aggregate across all ages, but split the data by sex.

# Function to plot by sex (all ages) with violation type as input
# Excludes territories due to much higher rate (y-axis issues)
plotbysex <- function(v){
plotdata <- data %>%
filter( Violations==v,
`Sex of victim`!="Total, sex of victim",
`Sex of victim`!="Sex of victim unknown",
`Age of victim`=="Total, age of victim",
Statistics=="Rate per 100,000 population",
!(GEO %in% c("Canada","Northwest Territories","Nunavut","Yukon"))
)
ggplot(plotdata, aes(year,VALUE, group=`Sex of victim`, color=`Sex of victim`))+
geom_line(size=1.5)+
facet_wrap(~ GEO,nrow=2)+
theme_light(12,"Avenir")+
scale_color_manual(name="",values=c("#CC6677","#4477AA"))+
scale_x_continuous(breaks=c(2010,2016))+
theme(legend.position = "top")+
labs(x="",y="Rate per 100,000 population",
title="Victims of police-reported violent crime",
subtitle=paste0(v,", all ages"),
caption="Source: Statistics Canada NDM 35-10-0051\nChart by @bcshaffer")
}

plotbysex("Total, all violations")

plotbysex("Violations causing death and attempted murder")

plotbysex("Sexual assaults, levels 1, 2, 3")

plotbysex("Sexual violations against children")

plotbysex("Assaults")

plotbysex("Other violent violations")

plotbysex("Criminal Code traffic violations causing death or bodily harm")

The Territories

Now, let’s look at the same data, but filtering only on the 3 territories and Canada for reference.

plotbysex.t("Total, all violations")

plotbysex.t("Violations causing death and attempted murder")

plotbysex.t("Sexual assaults, levels 1, 2, 3")

plotbysex.t("Sexual violations against children")

plotbysex.t("Assaults")

plotbysex.t("Other violent violations")

plotbysex.t("Criminal Code traffic violations causing death or bodily harm")

Breakdown by Age

We can look at how the incidence of violent crime breaks down by age. Here we’ll look at the Canada-wide numbers. A massive table (facet_grid) splitting it by age and region is possible, but gets messy quickly. I hide the function code here, but it is similar to the above function, with the exception that faceting is done by age rather than province, and I filter on Canada-wide data only.

plotbyage("Total, all violations")

plotbyage("Violations causing death and attempted murder")

plotbyage("Sexual assaults, levels 1, 2, 3")

plotbyage("Sexual violations against children")

plotbyage("Assaults")

plotbyage("Other violent violations")

plotbyage("Criminal Code traffic violations causing death or bodily harm")

The Bottom Line:

The rate of violent crime, notably sexual assaults, remains extremely skewed towards women.
The rate of violent crime causing death and attempted murder is skewed towards men but remains relatively low.
The rate of sexual assaults towards young girls is high and alarmingly climbing.

Gasoline demand: BC and the Rest of Canada

Fri, 30 Nov 2018 00:00:00 +0000

This post looks at gasoline demand across the Canadian provinces and territories. It was prompted by this tweet by @sjmuir, noting that gasoline demand per capita in BC is unchanged since 2008, the year of implementation of the BC carbon tax.

It's taboo to point this out, but the fact is that British Columbia residents used as much gasoline in 2017 as they did in 2008. That's why I'm skeptical whenever it's stated that carbon pricing has led to some kind of revolution in fuel usage, because it hasn't. pic.twitter.com/3IPILHxnTw
— Stewart Muir (@sjmuir) November 30, 2018

The data didn’t fit my prior as I had seen in a recent paper that shows BC gasoline demand per capita falling relative to the rest of Canada.

So what gives?

It turns out, a lot has changed since the above referenced paper. Namely, BC gasoline demand has risen quite a bit since 2015. Let’s dig in. I’ll first plot the change in total gasoline demand in each province since 2008:

library(tidyverse)
library(lubridate)
library(cansim)
library(ggrepel)
library(ggthemes)
## GET EMPLOYMENT DATA
data.gas <- get_cansim(23100066) %>%
normalize_cansim_values() %>%
filter(`Type of fuel sales`=="Net sales of gasoline",
GEO!="Northwest Territories including Nunavut") %>%
mutate(Year=as.numeric(REF_DATE),
Region = case_when(GEO %in% c("British Columbia","Alberta","Saskatchewan") ~ "West",
GEO %in% c("Manitoba","Ontario","Quebec") ~ "Central",
GEO %in% c("New Brunswick","Nova Scotia","Prince Edward Island","Newfoundland and Labrador") ~ "East",
GEO %in% c("Yukon","Northwest Territories","Nunavut") ~ "Territories",
GEO=="Canada" ~ "CANADA"),
GEO.abb = case_when(GEO=="British Columbia" ~ "BC",
GEO=="Alberta" ~ "AB",
GEO=="Saskatchewan" ~ "SK",
GEO=="Manitoba" ~ "MB",
GEO=="Ontario" ~ "ON",
GEO=="Quebec" ~ "QC",
GEO=="New Brunswick" ~ "NB",
GEO=="Nova Scotia" ~ "NS",
GEO=="Prince Edward Island" ~ "PE",
GEO=="Newfoundland and Labrador" ~ "NL",
GEO=="Yukon" ~ "YT",
GEO=="Northwest Territories" ~ "NT",
GEO=="Nunavut" ~ "NU",
GEO=="Canada" ~ "CAN")) %>%
group_by(GEO) %>%
mutate(Index=VALUE/VALUE[Year==2008]) %>%
dplyr::select(Year,Date,GEO,GEO.abb,Region,Index,"Gasoline"=VALUE)
data.gas$Region <- factor(data.gas$Region, levels=c("CANADA","West","Central","East","Territories"))
ggplot()+
geom_line(data=data.gas %>% filter(GEO!="Canada",GEO!="Nunavut",GEO!="British Columbia"),
aes(Year,Index,group=GEO,color=GEO),
size=1)+
geom_line(data=data.gas %>% filter(GEO=="British Columbia"),
aes(Year,Index),
size=2, color="firebrick")+
geom_label_repel(data=data.gas %>% filter(Year==2017,GEO!="Canada",GEO!="Nunavut",GEO!="British Columbia"),
aes(Year, Index, color=GEO, label=GEO.abb),
nudge_x=.5)+
geom_label_repel(data=data.gas %>% filter(Year==2017,GEO=="British Columbia"),
aes(Year, Index, label=GEO.abb),
nudge_x=.5, color="firebrick")+
geom_hline(yintercept = 1,linetype="dashed")+
scale_y_continuous(labels=scales::percent)+
scale_x_continuous(breaks=seq(2008,2018,2),limits=c(2008,2018))+
coord_cartesian(ylim=c(.95,1.4))+
scale_color_viridis_d(name="")+
theme_classic(14,"Gill Sans")+
theme(plot.caption=element_text(size=8,face="italic"),
legend.position = "none")+
labs(x="",y="",
subtitle="Gasoline demand relative to 2008 (unadjusted for population)",
caption="Statistics Canada NDM Table 23-10-0066\nChart by @bcshaffer")

It’s a busy chart but there’s a few things to note:

Sask is guzzling the stuff. This isn’t per capita yet, but still!
Meanwhile, BC, which showed a big dip in 2011-2014 has come roaring back.

The above chart isn’t per capita. So let’s get quarterly population estimates, annualize them, and create a gasoline_per_capita variable:

## GET POPULATION DATA
data.population <- get_cansim(0510005) %>%
normalize_cansim_values() %>%
mutate(Year=year(Date)) %>%
dplyr::select(Year,GEO,"Population"=VALUE) %>%
group_by(GEO,Year) %>%
summarise(Population=mean(Population))
## Merge and plot
plotdata <- left_join(data.gas,data.population,by=c("Year","GEO"))
plotdata$gas_per_cap <- plotdata$Gasoline/plotdata$Population/365
ggplot(plotdata, aes(Year,gas_per_cap,group=GEO,color=GEO,label=GEO.abb))+
geom_line(size=1.5)+
geom_vline(xintercept = 2008,linetype="dashed")+
geom_label_repel(data=plotdata %>% filter(Year==2017),nudge_x=.5)+
facet_wrap(~Region,nrow=1)+
scale_x_continuous(breaks=seq(2008,2018,4),limits=c(2008,2019))+
scale_color_viridis_d(name="",option="B")+
theme_classic(14,"Gill Sans")+
theme(plot.caption=element_text(size=8,face="italic"),
legend.position = "none")+
labs(x="",y="Litres per day",
subtitle="Per capita gasoline demand",
caption="Statistics Canada NDM Table 23-10-0066 and 17-10-0009\nChart by @bcshaffer")

Another question, is how different economic growth–rather than just population growth–across provinces impacts their gasoline demand. So here we look at gasoline intensity, the ratio of annual gasoline demand (in litres) per thousand dollars of GDP. (Note: I drop the territories as their scale gets wonky. Sorry Territories…)

## GET GDP DATA
data.GDP <- get_cansim(36100222) %>%
normalize_cansim_values() %>%
filter(Prices=="Chained (2012) dollars",
Estimates=="Gross domestic product at market prices") %>%
mutate(Year=year(Date)) %>%
dplyr::select(Year,GEO,"GDP"=VALUE)
plotdata <- left_join(plotdata,data.GDP, by=c("Year","GEO"))
plotdata$gas_intensity <- plotdata$Gasoline / plotdata$GDP * 1000
plotdata <- plotdata %>% filter(Region!="Territories")
ggplot(plotdata, aes(Year,gas_intensity,group=GEO,color=GEO,label=GEO.abb))+
geom_line(size=1.5)+
geom_vline(xintercept = 2008,linetype="dashed")+
geom_label_repel(data=plotdata %>% filter(Year==2017),nudge_x=1)+
facet_wrap(~Region,nrow=1)+
scale_x_continuous(breaks=seq(1995,2015,10),limits=c(1994,2020))+
coord_cartesian(ylim=c(16,45))+
scale_color_viridis_d(name="",option="B")+
theme_classic(14,"Gill Sans")+
theme(plot.caption=element_text(size=8,face="italic"),
legend.position = "none")+
labs(x="",y="L per $1000 GDP",
subtitle="Gasoline intensity\n(Gas demand / GDP)",
caption="Statistics Canada NDM Table 23-10-0066 and 36-10-0222\nChart by @bcshaffer")

It’s a little hard to tell in that graph, but in the West plot, BC is the line that starts highest and is falling rapidly throughout the period. I’ll re-plot it on one panel here:

ggplot()+
geom_line(data=plotdata %>% filter(GEO!="Nunavut",GEO!="British Columbia"),
aes(Year,gas_intensity,group=GEO,color=GEO),
size=1)+
geom_line(data=plotdata %>% filter(GEO=="British Columbia"),
aes(Year,gas_intensity),
size=2, color="firebrick")+
geom_label_repel(data=plotdata %>% filter(Year==2017,GEO!="Nunavut",GEO!="British Columbia"),
aes(Year, gas_intensity, color=GEO, label=GEO.abb),
nudge_x=1)+
geom_label_repel(data=plotdata %>% filter(Year==2017,GEO=="British Columbia"),
aes(Year, gas_intensity, label=GEO.abb),
nudge_x=.5, color="firebrick")+
geom_vline(xintercept = 2008,linetype="dashed")+
scale_x_continuous(breaks=seq(1995,2015,10),limits=c(1994,2020))+
coord_cartesian(ylim=c(16,48))+
scale_color_viridis_d(name="")+
theme_classic(14,"Gill Sans")+
theme(plot.caption=element_text(size=8,face="italic"),
legend.position = "none")+
labs(x="",y="L per $1000 GDP",
subtitle="Gasoline intensity\n(Gas demand / GDP)",
caption="Statistics Canada NDM Table 23-10-0066 and 36-10-0222\nChart by @bcshaffer")

To summarise, and make it clearer, let’s take a look at just BC and Canada side by side for both gasoline intensity and gasoline per capita:

data.summary <- plotdata %>%
filter(GEO %in% c("British Columbia","Canada")) %>%
dplyr::select(Year,GEO,GEO.abb,gas_per_cap,gas_intensity) %>%
gather(key,value,gas_per_cap,gas_intensity)
ggplot(data.summary, aes(Year,value,group=GEO,color=GEO))+
geom_line(size=1.5)+
geom_vline(xintercept = 2008,linetype="dashed")+
facet_wrap(~key,scales="free_y")+
scale_x_continuous(breaks=seq(1995,2015,5))+
scale_color_manual(name="",values=c("forestgreen","red"))+
theme_classic(12,"Gill Sans")+
theme(plot.caption=element_text(size=8,face="italic"),
legend.position = "top",
legend.text = element_text(size=14),
strip.text = element_text(size=12))+
labs(x="",y="",
subtitle="gas_intensity: L/GDP ($'000s)\ngas_per_cap: L/capita/day",
title="BC vs the Rest of Canada",
caption="Statistics Canada NDM Table 23-10-0066, 36-10-0222, 17-10-0009\nChart by @bcshaffer")

Bottom line: gasoline intensity (the amount of gasoline demand per $1000 of GDP) is falling across the board. That’s a good thing. B.C.’s gasoline intensity is falling more rapidly than the Canadian average. On a per capita basis, despite rising in the past 2 years on the back of strong GDP growth, BC is still falling relative to the Canadian average.

Social assistance and unemployment duration in Alberta

Fri, 30 Nov 2018 00:00:00 +0000

In this post, I consider the relationship between Social Assistance Caseloads and Unemployment Duration in Alberta.

Let’s start by calling the necessary libraries

library(tidyverse)
library(zoo)
library(cansim) #Big thanks to Jens von Bergmann and Dmitry Shkolnik for this package!

First, get CASELOAD data from open.alberta.ca

data.caseload <-read.csv("https://open.alberta.ca/dataset/e1ec585f-3f52-40f2-a022-5a38ea3397e5/resource/4f97a3ae-1b3a-48e9-a96f-f65c58526e07/download/GOAIncomeSupportCSVv61.02017-05-15.csv")
data.caseload <- data.caseload %>%
mutate(Date = as.Date(as.yearmon(Ref_Date,"%b-%y"))) %>%
filter(Geography=="Alberta") %>%
dplyr::select("Caseloads"=Value,Date,Measure)
data.caseload.total <- data.caseload %>%
filter(Measure=="Total Caseload") %>%
dplyr::select(Caseloads,Date)
ggplot(data.caseload.total, aes(Date,Caseloads))+geom_line() #check data

Second, get DURATION data from Statistics Canada

data.duration <- get_cansim(2820047) %>% normalize_cansim_values()
data.duration <- data.duration %>%
filter(GEO=="Alberta",
`Duration of unemployment`=="Average weeks unemployed, no top-code",
Sex=="Both sexes",
`Age group`=="15 years and over",
Date>="2005-04-01") %>%
dplyr::select("Duration"=VALUE,Date)
ggplot(data.duration, aes(Date,Duration))+geom_line() #check data

Now, gather into long format and do a quick plot

plotdata <- merge(data.duration, data.caseload.total, by="Date")
plotdata <- gather(plotdata,key,value,2:3)
ggplot(plotdata, aes(Date,value))+
geom_line()+
facet_wrap(~key,scales="free_y")

Now, do a pretty plot with scatter points and smoothes

plotdata$key[plotdata$key=="Caseloads"] <- "Caseloads (people)"
plotdata$key[plotdata$key=="Duration"] <- "Duration (weeks)"
ggplot(plotdata, aes(Date,value))+
geom_point(alpha=.5)+
geom_smooth(span=.3)+
facet_wrap(~key,scales="free_y")+
theme_light(12,"Avenir")+
theme(plot.caption = element_text(size=8),
strip.text = element_text(size=12),
panel.spacing = unit(1,"cm"))+
labs(x="",
y="",
title="Similar trends between caseloads and unemployment duration",
caption="Caseload data through Sep-18; Duration data through Oct-18
Sources: https://open.alberta.ca/dataset/income-support-caseload-alberta and CANSIM 282-0047
Chart by Blake Shaffer (@bcshaffer)")

We can also dig deeper into the caseload data

First, let’s look at caseload recipients by family types

# first filter on the family type categories
data.caseload.family <- data.caseload %>%
filter(Measure %in% c("Single Individuals Total","Lone-parent Families Total","Couples with Children Total","Couples without Children Total"))
# clean up the category names, not necessary
library(tm)
data.caseload.family$Measure <- as.character(data.caseload.family$Measure)
data.caseload.family$Measure <- removeWords(data.caseload.family$Measure,"Total")
# now plot it!
ggplot(data.caseload.family, aes(Date,Caseloads))+
geom_line(size=1.5,color="firebrick")+
facet_wrap(~Measure,nrow=1)+
scale_y_continuous(expand=c(0,0),limits=c(0,45000))+
theme_light(12,"Avenir")+
theme(plot.caption = element_text(size=8),
strip.text = element_text(size=12),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank())+
labs(x="",
y="",
title="Alberta social assistance caseloads by recipient's family type",
caption="Caseload data through Sep-18
Source: https://open.alberta.ca/dataset/income-support-caseload-alberta
Chart by Blake Shaffer (@bcshaffer)")

Now let’s look at the caseload data in terms of type of worker

data.caseload.worker <- data.caseload %>%
filter(Measure %in% c("ETW - Working Total",
"ETW - Not Working (Available for Work) Total",
"ETW - Not Working (Unavailable for Work) Total",
"BFE - Total"))
# clean up the category names
data.caseload.worker <- data.caseload.worker %>%
mutate(Measure=fct_recode(Measure,
"Working" = "ETW - Working Total",
"Available for Work" = "ETW - Not Working (Available for Work) Total",
"Unavailable for Work" = "ETW - Not Working (Unavailable for Work) Total",
"Barriers to FT employment" = "BFE - Total"))
# now plot it!
ggplot(data.caseload.worker, aes(Date,Caseloads))+
geom_line(size=1.5,color="royalblue")+
facet_wrap(~Measure,nrow=1)+
scale_y_continuous(expand=c(0,0),limits=c(0,45000))+
theme_light(12,"Avenir")+
theme(plot.caption = element_text(size=8),
strip.text = element_text(size=12),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank())+
labs(x="",
y="",
title="Alberta social assistance caseloads by recipient's worker type",
caption="Caseload data through Sep-18
Source: https://open.alberta.ca/dataset/income-support-caseload-alberta
Chart by Blake Shaffer (@bcshaffer)")

Bottom line:

The bulk of the growth in caseloads has been Single Individuals that are Available for Work. This also coincides with a large growth in Unemployment Duration. This suggests workers that are seeing their unemployment benefits expire may be turning to other forms of social assistance. More details on income support recipients would help policy makers respond effectively.

h/t to @ronkneebone for raising attention to this important data.

Blogging Charts!

Thu, 29 Nov 2018 00:00:00 +0000

I like to make charts.

I often post them on Twitter (@bcshaffer). I thought I might start posting some of them on my website as a way for me to (a) learn to use R Markdown and (b) share some of my R code that creates these charts for others who are interested in learning.

Note: Both my R code and especially my nascent R Markdown skills are still rough. I’m sure much of it isn’t “best practice”. Far from it. It’s what I like to call ‘gets it done’ code. So learn what you can, and apologies for creating bad habits!

- Blake

Resources:

I learned R primarily from Hadley Wickham and Garrett Grolemund’s book: R for Data Science. I highly recommend it. It assumes zero previous R knowledge, even guiding you through where to download the required software, and gets you up and running in no time.

For data visualization tips, I really like Claus Wilke’s online book: Fundamentals of Data Visualization (with code examples!).

The Effect of Climate Policy on Performance of Manufacturing Plants

Sun, 01 Jul 2018 00:00:00 +0000

Jobs and Climate Policy: Evidence from British Columbia's Revenue-Neutral Carbon Tax

Tue, 28 Mar 2017 00:00:00 +0000

It's not a job killing policy - The case of BC's revenue neutral carbon tax

Thu, 01 Sep 2016 00:00:00 +0000

External Project

Wed, 27 Apr 2016 00:00:00 +0000

Internal Project

Wed, 27 Apr 2016 00:00:00 +0000

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An example journal article

Tue, 01 Sep 2015 00:00:00 +0000

Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.

Click the Slides button above to demo Academic’s Markdown slides feature.

Supplementary notes can be added here, including code and math.

An example conference paper

Mon, 01 Jul 2013 00:00:00 +0000

Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.

Click the Slides button above to demo Academic’s Markdown slides feature.

Supplementary notes can be added here, including code and math.