Worthwhile Canadian Initiative

Good Chart Checklist

Note: this was prepared for my ECON 3403 students, and is a list of all of the mistakes I commonly see in student charts. Please add your suggestions for things to add to this list in the comments - or steal, modify, and use for your own purposes! 

Good Chart Check List:

Is my chart self-explanatory? Does it specify

• Who: The population the chart shows (e.g. eligible voters, Facebooks users, people over the age of 12, Canadian citizens, etc)
• Where: The geographic area the chart applies to (Ottawa, Canada, G7, G20, OECD)
• When the data was collected
• The data source (e.g. Statistics Canada Table 12-345-67, World Bank). Optional: give name of survey or statistical programme data came from.

Am I using an appropriate chart type?

• Line chart to show time trends (typically 4+ observations – for just two years of data use a bar chart).
• Bar chart to compare groups of things, e.g. countries, age groups, genders, occupations, industries, provinces
• Pie charts are rarely the appropriate chart type. Use pie charts when your only concern is distribution; you only want to know how each group’s share compares to other group’s shares. If you must use a pie chart, consider using the “doughnut” option.

Are my axes clearly labelled? Is it clear

• What each axis represents Exceptions:
  • do not have to label a “year” axis, it is usually obvious that 2005, 2010, 2015, 2020 etc are years.
  • If the chart title makes it clear what the graph shows, don’t have to label the axis as well.
• What units I am using (miles, km, tonnes, millions of tonnes, Canadian dollars, 2020 (constant) dollars, nominal (not inflation adjusted) dollars, etc)

Have I put my data into perspective?

• For data showing trends over time: are dollar amounts adjusted for inflation?
• For comparisons across provinces or across countries: am I showing numbers as a percentage of the population, to adjust for population size?
• For data focussing on a particular population subgroup: have I included data for other groups to put the numbers into perspective? E.g. for a chart showing trends in 12-15 year old’s mental health, consider including mental health trends for the entire population to put the numbers into perspective. For a chart showing poverty rates for elderly, include poverty rates for the non-elderly for comparison purposes.
• For data showing trends over time: have I tried the “year 1=100” approach – i.e. divide by the year 1 value*100?

Am I being honest?

• Does my vertical (y) axis start at zero?
  • Exception: sometimes, if you’re trying to highlight trends, it’s o.k. to have a line chart that doesn’t start at zero. But be very careful! For a bar chart, the vertical (y) axis must ALWAYS start at zero!
• Am I selecting my starting year carefully to show the results I want to show? (e.g. I am I showing stock market growth starting at 2009, when the market was at very low levels, instead of at 2007?).
• Am I distorting my results using fancy graphics, especially 3-dimensional shapes?
• Are the numbers on my chart accurate?
• Have I created a combo-chart, and manipulated my axes so that my series look closely related, when they really aren’t?

Is my chart accessible?

• Can someone who is colour-blind read my chart?
  • Am I using a colour-blind friendly colour scheme, e.g. a monochromatic scheme, or a scheme designed to be colour-blind friendly?
  • Am I using dashed lines, labels, or other tools to make it easier to tell my series apart? See here for tips on how to make a graph colour-blind friendly: https://towardsdatascience.com/two-simple-steps-to-create-colorblind-friendly-data-visualizations-2ed781a167ec
• Can someone who has trouble reading small print read my chart?
  • Flip a bar chart on its side (so the bars are horizontal, not vertical). That creates room for big-enough-to-read labels!
  • Consider putting legend labels on the chart itself, rather than to the side (requires advanced Excel skills)
• Does my chart contain any unnecessary information or other clutter?
  • Avoid cluttering up a graph with too many years worth of data. Zoom in on one year and compare population groups (e.g. age, sex, industry, occupation). Alternatively, download 5+ years of data and graph a time trend (challenge: can you explain the trend?).  Statistics Canada data tables often include 3 years of data by default. However a good graph rarely includes just three years of data (exception: the years 2019, 2020, 2021).
  • Only report 2 or 3 digits worth of data – e.g. report data as 1.2 million rather than 1,234,567.
  • Cut any unnecessary lines, words or graphics
• Am I using the right words to describe wages and incomes?
  • Wage rate=$ earned per hour worked
  • Wages=total amount earned. Also known as earnings
  • Income=earnings +other sources of income.
  • For income data, try to be as precise as possible.
    • Is the data pre-tax income or after-tax? If after-tax income, does it include benefits like Canada Child Benefit?
    • Individual income or family income or household income? If family or household income, whose income is counted? (e.g. if a 25 year old is living with a parent, whose income is included in the 25 year old’s “family income”?) Check to see if the income is “census family income” or “economic family income”.

13 facts every Canadian economist needs to know

1. Canada is a big country

...but not as big as one might think. Some maps show Canada as an enormous country, about twice the size of China or the US, similar in size to the entire continent of Africa:

However maps like the one shown above distort the size of northern countries, making them appear much larger than they actually are. (This video explains why all world maps are wrong.) The "Peters projection" world map distorts the shape of countries, but shows their true size:

Measured by total area Canada is the second largest country in the world, bested only by Russia. Measured by land area, however - so not counting lakes, rivers, and oceans - Canada is the fourth largest country, smaller than China and the United States.

If the focus is shifted to arable land - that is, land suitable for agriculture - Canada's ranking slips to sixth, well below India and Brazil, and only slightly ahead of Nigeria and Argentina.   This is because...

2. Most of Canada is rocks and trees (and water).

Much of Canada is dominated by the ancient rocky hills and mountains, lakes and rivers, that make up the "Canadian Shield", shown in pink on the map above. In the east, the Appalachians extend up from the United States, stretching as far north as Newfoundland, also known as "the Rock". The West is dominated by a series of mountain ranges - the Coast Mountains, the Rockies, and other smaller ranges. There are pockets of fertile land in the eastern and western parts of the country - the Fraser Valley, the Annapolis Valley, the Okanagan, other river valleys and deltas - but rocks and trees predominate. 

The abundance of rocks and trees means that resource-based industries such as softwood lumber and mining have always been a significant part of the Canadian economy. But being a resource-based economy creates certain challenges. For example, how is resource wealth distributed? In particular, how is the wealth shared between people living now and future generations, and between resource-rich and resource-poor parts of the country? Also, resource industries tend to be fairly cyclical. How can workers and others be protected from the negative impacts of resource industry booms and busts? What happens to people and communities when the resources are gone - when a fishery collapses, for example? Who pays for the environmental harms?

Movements in income inequality in Canada, 1944-2010

Here are the estimates for the Gini coefficients for Canada, taken from individual tax files (see here for more about where the data came from):

What happened to real incomes in the 1970s?

Here are the estimates for average and median total incomes based on tax file data (see this post for details):

Project Link update: The evolution of the distribution of income in Canada, 1944-2010

Project Link has been updated; the Excel file with the data updated through to June 2021 is available here.

I skipped the 2020 update for Project Link for a couple of reasons. There was obviously the distraction of the pandemic, but mainly because I hadn't yet finished the next extension. Every year, I try to extend the data base, and the latest extension took more time than I had originally expected.

This year's extension is the data set behind this animation I posted on twitter a while ago; the details are below the fold. 

Bitcoin as PAYGO Pension Plan. And Land.

Suppose the government starts a Pay As You GO (unfunded) pension plan. What the young pay into the plan today is what the old take out today. The first generation gets lucky; they get a pension paid out, but never put anything in.  And if the plan ever gets wound up, the last generation of old get unlucky; they paid in, but never get anything out.

Bitcoin is the same. What the young pay in to buy Bitcoin today is what the old take out when they sell Bitcoin today. The first generation gets lucky; they sell out at a high price, but bought in at a low price when it was just starting up.  And if the young ever stop buying Bitcoin, so the price collapses to zero, the last generation get unlucky; they paid to buy into Bitcoin, but never get anything out.

The big difference is that Bitcoin is a strictly voluntary PAYGO plan. You don't have to participate in the plan, and if you do choose to participate you can contribute however much you want, and you can pull your money out any time you like.

Now suppose the government starts a fully funded pension plan, that invests in land. If land is the only asset in the economy, and has a perfectly inelastic supply, it's not very different from a PAYGO plan. Or Bitcoin. It doesn't create any new assets for the economy as a whole; it just makes the existing land more valuable. What the young pay into the plan to buy land is what the old take out by selling land. The first generation of landowners gets lucky, because they bought land at a low price and sell it at a high price. And if the plan ever gets wound up, the last generation gets unlucky; they bought land at a high price and sell it at a low price.

At the macroeconomic level, the only difference between funded and unfunded (PAYGO) pension plans is due to the price elasticity of the supply of new assets ("investment"). If it's perfectly elastic, the total physical quantity of assets rises proportionately with a fully funded pension plan. If it's perfectly inelastic, the physical quantity of assets stays the same, but the price of assets rises proportionately with a fully funded pension plan. And you can think of PAYGO pension plans, and Bitcoin, as creating valuable assets ex nihilo, that satisfy the desire to save a stock of assets you can sell later, like a temporary abode of purchasing power.

[Maybe see my old post on the "Junker Fallacy", which isn't a fallacy in an OverLapping Generations model.]

Do the self-employed opt out of the Canada Pension Plan?

In 2017, anyone with earnings was required to pay $4.95 in Canada Pension Plan contributions for every $100 of earnings (up to a maximum earnings threshold of $51,800 - rates here). For the employed, their $4.95 CPP/QPP contribution was matched by an equal contribution by their employer. However self-employed individuals are required to pay the entire employer+employee contribution themselves, amounting to $9.90 for every $100 of earnings.

Now the self-employed have an out. They can incorporate, and pay themselves in dividends rather than salary. Since they are not required to pay CPP premiums on dividends, they can potentially avoid paying into the Canada Pension Plan. 

But do they?

There are both advantages and disadvantages of opting out of CPP - sure, you don't have to pay CPP premiums, but you don't get CPP benefits either. And CPP offers some attractive benefits, for example, it provides disability insurance, and is inflation protected. Moreover, there are other reasons why the self-employed might not want to minimize their employment income. Registered retirement savings plan contribution room is tied to employment income, and financial institutions may ask for proof of employment income before they will issue a mortgage. So ultimately it's an empirical question - do the self-employed opt out of the CPP, or don't they?

To some extent, this question can be answered with the Canada Revenue Agency's Income Taxation Statistics:

The above table shows average CPP or QPP premiums paid per taxfiler. The taxfilers are categorized according to their main source of income. So, for example, the "Employment" category includes all taxfilers whose main source of income was employment, even if those individuals had some self-employment income. Some groups of people, such as individuals whose main source of income was pension income, have been excluded. 

It does seem that individuals with professional income pay quite a bit less in Canada Pension Plan contributions than one would expect based on the total income assessed per tax filer. However there just aren't that many of those tax filers. The typical self-employed person is not a doctor or a lawyer or other professional. They're a small business owner or gig economy worker - someone with proprietorship or partnership income - and they're paying CPP contributions at a somewhat lower, but not massively lower, rate than employed individuals. (They're also, in many cases, supplementing their self-employment income with employment income, and pay into the CPP both as an employee and as a self-employed individual). Now it could be that these small business owners are working for cash, or making highly aggressive use of home office and other deductions, and that their total assessed income understates their actual income. But that's another matter entirely.

The big question, however, is how many of those taxpayers whose primary source of income is "investment" are self-employed individuals paying themselves in dividends? It's impossible to answer this question directly with the aggregate CRA data. However we can get at the issue directly by using information about the Canadian labour force. According to Statistics Canada Labour Force Survey data, there were 15.8 million employed workers in Canada - a number that almost exactly matches the number in the table above - and 2.9 million self-employed (here). Even if we add the 182,750 people with fishing and farming income to the ranks of the self-employed in the table above, there are still a significant number of self-employed individuals unaccounted for. That suggests there are indeed a significant number of self-employed individuals - possibly over a million taxpayers - who are not appearing as professionals, proprietors, and so on, and not making significant CPP contributions.

 

Click here to download Self employed cpp calculations

If you wanna run hot you gotta run steady

Canadian economist Richard Lipsey would always insist that the Phillips Curve is a curve, and not a straight line. Maybe because wages and prices are stickier down than up. Or maybe because unemployment can't go below 0%. Or both.

Here's why that matters for macroeconomic policy:

Suppose the central bank wants 2% inflation on average, over the longer term. One way to get that result would be to flip a coin, every year, or every decade, or whatever. If it's heads, the central bank "runs the economy hot", to get 3% inflation. And if it's tails, the central bank "runs the economy cold", to get 1% inflation. If the Phillips Curve were a straight line, you would get exactly the same average unemployment rate, over the longer term, as you would if the central bank kept inflation steady at 2%. But if the Phillips Curve is a curve, and curves the way we normally draw it, you would get higher unemployment on average with that random monetary policy than you would if you kept inflation steady at 2%. And the sharper the curvature of the Phillips Curve, the worse unemployment will be, on average, with a random monetary policy.

That's why macroeconomic demand stabilisation matters. Recessions are bad, and booms may be good, but the bad and good don't cancel out. Relative to where you would be if you stabilised demand, recessions are bigger than booms.

If you add "hysteresis" to this simple story, then macroeconomic stabilisation matters even more. Because a recession has costs that persist even after the recession is over, and those long term costs of the larger recession are bigger than the long term benefits of the smaller boom.

Now maybe 2% inflation is not the right inflation rate to target. Maybe it should be 3%. But that's a separate question, about the Long Run Phillips Curve and whether it's vertical or sloped, and the point I'm making in this post about the Short Run curvature still stands. It's better to target 3% every year than to flip a coin every few years between 2% and 4%.

And maybe inflation is the wrong thing to target. Maybe it's better to target a level-path of NGDP. But again that's a separate question. Or maybe only a partly separate question. Because if supply shocks cause the Phillips Curve to shift up and down, keeping NGDP on target would stabilise unemployment better than an inflation target. And the point of this post is that you need to stabilise unemployment if you want to keep unemployment low on average over the longer term.

And maybe it's not really about Phillips Curves at all. Maybe it's about Milton Friedman's "Plucking Model": there are no booms, only recessions. (What looks like a boom is just the absence of a recession, because recessions can be big or small but booms are all the same, because big recoveries follow big recessions, and small recoveries follow small recessions, but the size of the decline doesn't match the size of the "boom" it follows). Which is like an extreme version of my curved Phillips Phillips Curve story above, where booms are smaller than recessions. And Friedman's Plucking Model always reminds me of Frances' story about her old car: most of the time it ran OK, but if something unexpected happened it was always something bad. The only good news was absence of bad news. Which is how things are with designed systems: they sometimes break down and run worse than the Toyota designer intended, but rarely "break up" and run better than the designer intended. And market economies are a bit like that, though their "designer" is the invisible hand. There can be bad monetary shocks, but the only good monetary shock is no monetary shock.

You can read this post as a critique of "running the economy hot". What do you mean by that, and how can you do it sustainably over the longer term? Or you can read it as a comment on J.W. Mason's recent good post (which I partly agree with and partly disagree with, as always). Or you can just read it on its own.

The Economics of Increasing Immigration During an Economic Crisis

The following is a guest post by Mikal Skuterud of University of Waterloo

On February 14, the Federal Government issued 27,332 invitations for permanent residency to Canadian Experience Class (CEC) applicants in the Express Entry pool. To issue this unprecedented level of invitations, Immigration, Refugees and Citizenship Canada (IRCC) reached deep into the pool, providing invitations to applicants with record-low Comprehensive Ranking System (CRS) scores.  

The CRS is a points system used by IRCC to select the “best” candidates in the Express Entry pool. CRS scores are assigned to candidates based on their submitted profiles. Analogous to how universities screen their applicants using high school grades, IRCC sets a cutoff score in every Express Entry draw, held roughly every 2 weeks, and issues invitations to all applicants with scores above the cutoff.

The criteria and their relative importance in calculating scores are based on a statistical analysis of what immigrant characteristics best predict immigrants’ earnings during their first 10 years in Canada. The initial CRS points grid was based on the analysis of Bonikowska, Hou and Picot (2015). Adjustments have been made since. Most notably in November 2016, points were reduced for having pre-arranged employment in Canada and were increased for having Canadian (as opposed to foreign) post-secondary educational credentials. The criteria that receive the most points are age (the younger, the better); level of education (the more, the better); English/French language ability (standardized test scores are required); and Canadian work experience (more is better).

The Express Entry pool is comprised of all applicants who have met the minimum requirements of one of Canada’s economic-class immigration programs. The three biggest programs are the Federal Skilled Worker Program (FSWP), the Canadian Experience Class (CEC) Program, and Provincial Nominee Programs (PNPs). The CEC targets candidates with Canadian work experience. Hence, candidates in this stream are more likely to be currently residing in Canada, and this presumably explains why IRCC restricted its February 14 draw to these candidates.

The cutoff CRS score in the February 14 draw was 75, as shown in the chart above. Using IRCC’s CRS calculator tool (https://www.cic.gc.ca/english/immigrate/skilled/crs-tool.asp), one can get a sense of the profile of an applicant with a CRS score close to 75, the score of the lowest ranked candidate(s) to be invited:

- Never-married, single

- 39 years old

- Level of schooling below a high school diploma

- IELTS score of zero in speaking, listening, reading, and writing

- 1 year of Canadian work experience in a technical occupation (e.g. cook or car mechanic)

- Zero years of foreign work experience

- Not currently employed

    CRS score = 95

In a press release justifying February 14’s unprecedented Express Entry draw, IRCC stated:

“From large companies to major labour unions, Canadians agree that immigration is essential to our economy. One in three businesses with employees is owned by an immigrant, creating thousands of jobs from construction to retail. Throughout the pandemic, newcomers have played an outsized role in Canada’s response, accounting for over one third of our doctors and pharmacists. Put simply, immigration is crucial to Canada’s short-term recovery and long-term prosperity.” (emphasis added) 

As an economist, who has studied Canadian immigration for more than two decades, I struggle to understand how increasing immigrant entries in the midst of an economic crisis with historically high and rising levels of joblessness will aid our short-term economic recovery. As shown in the chart below, data from the most recent Labour Force Survey reveals there are now 1.7 million Canadian jobless workers who have been employed since February 2020 and say they want to work now. Moreover, 29% of them have been jobless for more than 6 months, which is especially troublesome as there is compelling evidence that jobless workers’ odds of getting back to work drops precipitously after 5-6 months.  

My concern is that the “increased immigration is crucial for our economic recovery” narrative overlooks a long and extensive economics literature documenting the challenges Canada has had, and continues to have, in leveraging immigration to boost economic growth. The simple fact is that Canadian immigrants experience significant labour market integration challenges, even compared to similarly educated immigrants from the same origin countries (e.g. China or India) who settle in Australia and the United States. It is hard to believe that these challenges will not be exacerbated for those entering Canada’s labour markets during the current economic crisis.

It is unusual for countries to significantly increase immigration levels during recessions, for good reason. There is, therefore, scant empirical evidence to tell us what happens. However, the Canadian experience does offer one telling episode. Between 1996 and 2001, Canada saw an exceptionally large inflow of immigrants with educational backgrounds and work experience in information technology (IT) and engineering. Employment in the computer and telecommunications sector grew by more than 50% over this period nationally compared to 12% across all sectors (think Nortel and Corel). However, the crash of the “dot.com bubble” in 2001 put an end to the growth, which Picot and Hou (2009) show had a disproportionately large adverse impact on the earnings of immigrants who entered Canada between 2002 and 2004. Even four years after landing, these immigrant IT and engineering workers had average earnings that were 25% lower than comparable immigrants who arrived in the mid-1990s.

It remains to be seen whether IRCC reaches its historically high target of 232,500 new economic-class immigrants in 2021, as shown in the chart below. But February 14’s Express Entry draw sends a strong signal that they intend to do so, even at the cost of foregoing its merit-based system for screening applicants.

 

While policymakers will be watching for a swift and speedy economic recovery in 2021, I’ll be continuing to track the economic integration struggles of Canada’s most recent immigrants.

For more of Mikal Skuterud's writing on immigration, see this WCI post and the papers on Mikal's website here. Also recommended: Riddell, Worswick and Green, “How Does Increasing Immigration Affect the Economy?” Policy Options (here), and the Bonikowska, Hou and Picot paper cited above (here).

 

Rapid Vaccinations Can Help Avoid the `Third Wave’ in Ontario

The following is a guest post by Miguel Casares (Universidad Publica de Navarra, Spain), Paul Gomme (Concordia University), and Hashmat Khan (Carleton University)

Of utmost importance is improving our understanding of the complex interactions between: (a) the epidemiology that describes the evolution of the coronavirus/COVID-19; and (b) the social and economic choices of individuals.  Towards this end, we have developed a model that captures some salient elements of this interaction. The parameters of the model are calibrated to match epidemiological and economic developments in Ontario in the first half of 2020. We then evaluate the contributions of health and socioeconomic policies and the lockdowns since last March. 

Interactions between epidemiology and socioeconomics. Epidemiology describes how the coronavirus spreads through the Ontario population. Key departure from the standard SIR (susceptible-infectious-recovered) epidemiological model: (a) the number of daily contacts (from the socioeconomic side of the model) affects the number of new COVID cases; and (b) asymptomatic transmission of the coronavirus. Socioeconomics characterizes how self-interested actors respond to their environment, including the prevalence of the coronavirus. These actors like consuming and socializing; they dislike working and the risk of catching COVID.

A key implication of our model is that individuals respond to the coronavirus even in the absence of government interventions. In fact, they did. For example, restaurant reservations in Ontario made through the Open Table website fell off more than a week before the declaration of a state of emergency on March 17, 2020.  This is depicted in Figure 1 (percentage change relative to previous year). These individual-level actions can collectively affect the spread of the disease.

Figure 1: Restaurant reservations in Ontario made through the Open Table website

 

Fear of death in preferences provides a simple, elegant way of describing how COVID affects individual decision making. The fear of death introduces two wedges. First, it affects the work-consumption margin: the fear of death reduces the value of consuming since buying more goods implies more daily contacts; and the fear of death increases the disutility of working, again due to increased daily contacts. Second, the fear of death reduces the value of social activity, again because of the effect of such activity on daily contacts. As the number of COVID cases increases, individuals reduce their hours of work, their consumption, and their social activity.

First lockdown. We model government policy to fit with events from March 24 and June 11. Health-related interventions include measures that reduce contagion, like mask mandates, social distancing, increased hygiene; and testing and contact tracing. Socioeconomic policies operate through: busines shutdowns, and restrictions on socializing. While the socioeconomic interventions are very effective at reducing case numbers, they also lead to higher unemployment. The importance of the health protocols is in reducing the growth in COVID cases in the longer term. In Figure 2, the yellow dots are daily deaths, the blue line is the model’s fit, and the grey shaded areas are the lockdown periods. 

Figure 2: Projected mortality with and without COVID lockdowns

Second lockdown. The model predicts rising COVID cases and mortality starting in the autumn, as is seen in the data. We then imposed a second lockdown: only essential businesses open and minimal social activity. The model predicts a rapid drop in COVID cases and mortality, saving about 4,000 accumulated deaths compared to the forecast from the scenario without the second lockdown (the red line in Figure 2).

Third wave. Absent vaccinations, the model predicts a third wave peaking in the summer. These results not only point to the importance of COVID vaccines, but also of getting as many people vaccinates as quickly as possible.

Miguel Casares (Universidad Publica de Navarra, Spain), Paul Gomme (Concordia University), and Hashmat Khan (Carleton University)

100 million is a really big number: a review of Doug Saunders' Maximum Canada

The following is a guest post by Mikal Skuterud, professor of economics at University of Waterloo 

Despite my interest in Canadian immigration policy, I’ve been hesitant to read Doug Saunders’ latest book Maximum Canada: Toward a Country of 100 Million. The idea that the solution to Canada’s economic challenges is more people, and a lot more people, seems naïve. In hindsight, I waited too long. Saunders’ account of Canada's immigration history is intriguing and his writing, as always, engaging. 

Canada's history of leaning towards protectionist trade policies and xenophobic immigration policies, what Saunders refers to as the "minimizing impulse," has almost certainly left a lasting legacy. It’s hard to believe that more ambitious and expansionist policies through the post-war years would not have put us on a stronger economic footing today, although big hypothetical questions like this are complex and extremely difficult to answer definitively.  

Nonetheless, the economic challenges facing Canada today are very different and Saunders’ overriding recommendation that a population of 100 million by 2100 should be a policy priority remains, in my view, remarkably naïve.

Saunders acknowledges, more than once, that the population level, in itself, is not a viable policy lever to boost economic growth. As he puts it: "more is not better." The challenge is having "the right people, in the right numbers" working together in the right settings (page 152).

But that's a massive challenge that the book doesn't do enough to describe. Canada has always struggled to attract and retain the most inventive, ambitious, and talented, as the book describes exceptionally well in its first chapters. But this challenge has, if anything, become bigger over time, as growing U.S. universities and salary premiums attract the world’s best and brightest and Canada’s relatively generous welfare state attracts migrants who are less sure of their talents seeking a landing pad should they fall through the economic cracks.

Chapter 7 touches on these challenges but overlooks a large Canadian literature examining them. Despite the much greater attention given to credential recognition issues, including in Saunders’ own discussion of the challenges, the evidence reveals that the challenge is largely not about how Canada integrates immigrants. Research shows that the literacy skills of Canadian immigrants are not only highly rewarded, but equally rewarded to their Canadian-born coworkers. In other words, Canadian immigrants struggle not because their skills are undervalued, but primarily because of shortfalls in the skills needed to be successful (Ferrer, Green and Riddell 2006; Clarke and Skuterud 2016).

Saunders’ claim that "we don't have great data on immigrant jobs" (p.195) is wrong. The quinquennial Canadian Census is a rich source of information on the occupations and industries that immigrants work in, as is the monthly Labour Force Survey. An extensive literature examining these data reveal a dramatic deterioration in the economic integration of Canadian immigrants since the 1980s, which Canada continues to struggle to reverse, despite extensive reforms in our immigrant selection and settlement policies.

The uncomfortable truth is that the first-order challenge has always been, and continues to be, the immigrants that Canada attracts and retains; what labour economists call "immigrant selectivity." This challenge is most stark when we compare economic outcomes of Canadian and U.S. immigrants. For example, Ferrer, Clarke and Skuterud (2019) compare the earnings of university-educated Chinese and Indian immigrants who settle in Canada and the U.S.. While those who settle in the U.S. earn more than similarly-educated U.S.-born workers, in Canada, immigrants earn substantially less.

Blit, Skuterud and Zhang (2019), on the other hand, replicate U.S. research showing large beneficial impacts of skilled immigrants on patents created per capita and find much smaller beneficial effects in Canada. They conclude that for most countries, skilled immigration is unlikely to be a panacea for sluggish innovation and that the U.S. experience may be exceptional.

The potential for increased immigration levels to boost economic growth is further complicated by the fact that the objective of selecting "the right people, in the right numbers" involves an unavoidable quality-quantity tradeoff. This tradeoff is explicit in Canada’s Express Entry System, which prioritizes economic-class applicants with the strongest educational background, work experience, and English/French language skills.

In October 2020, the government announced ambitious new immigration targets that, if reached, will put Canada on the path to reaching the 100-million population objective. As we are already seeing, increasing invitations for admission is producing a decline in entry standards. In other words, in order to increase entries, the government is being forced to reach deeper into the applicant pool and admit immigrants who are less likely to integrate successfully. And this is happening in the midst of an economic crisis with historically high levels of long-term joblessness.

As of December 21, there were 147,487 principal applicants in the Express Entry applicant pool. The challenge in reaching the 100-million objective is not immigrant supply. Saunders argues, probably correctly, that Canada’s historical “minimalist impulse” has kept us from achieving our productive capacity, but there is also an upper absorptive capacity limit, which is undoubtedly smaller in periods of high joblessness.

There is no question that raising immigration levels will boost Canada’s GDP; the size of our national “economic pie.” But what matters for the economic well-being of Canadians is not the size of the overall pie, but the size of the individual slices. As Saunders acknowledges, big countries are sometimes poor (India) and small countries rich (Norway). While increasing the productivity of the existing population unambiguously increases the size of the average slice, increasing the pie by adding more people has a much more ambiguous impact. Despite much wishful thinking about the impact of immigration on innovation and public finances, the Canadian evidence points to a neutral impact on GDP per capita. That is, immigration is unlikely to make those of us already here better off; but neither is it likely to make us worse off.

The narrative that immigrants make us all richer is appealing and goes a long way in convincing Canadians to embrace increased immigration levels as a national policy objective. Unfortunately, it is at best naïve and at worst dishonest. What is much more certain is that immigrants themselves experience significant welfare gains when they settle in Canada. This is particularly true for humanitarian and family-class immigrants, as well as the families they join and leave behind. But in trying to sell immigration by emphasizing how good it if for us, the narrative of how good it is for immigrants themselves is sadly lost.  

Mikal Skuterud, University of Waterloo

 

References:

Blit, Joel, Mikal Skuterud and Jue Zhang (2020), “Can Skilled Immigration Raise Innovation? Evidence from Canadian Cities,” Journal of Economic Geography 20(4): 879-901.

Clarke, Andrew and Mikal Skuterud (2016), “A Comparative Analysis of Immigrant Skills and Their Utilization in Australia, Canada, and the United States, Journal of Population Economics 2: 849–888.

Ferrer, Ana, David Green, and Craig Riddell (2006), “The effect of literacy on immigrant earnings,” Journal Human Resources 41(2): 380–410.

Clarke, Andrew, Ana Ferrer, and Mikal Skuterud (2019), “A Comparative Analysis of the Labor Market Performance of University-Educated Immigrants in Australia, Canada, and the United States: Does Policy Matter?” Journal of Labor Economics 37(S2): 443-490.

Loanable Funds Redux

Stop talking about "saving". Talk instead about "demand for assets". You will be happier, and your head will be clearer. Your students too will be happier, and their heads will be clearer.

Demand for assets is a thing; saving is a non-thing (a residual). Things are clearer than non-things. Saving is the part of your income you do not spend on consumption. OK, so what do you do with your income, if you don't spend it on consumption? You buy/accumulate assets.

Assets are things too. Like land, houses, machines, shares, bonds, IOUs, etc. And buying those assets is a thing people do. "Supplying loanable funds" sounds totally different, and much narrower, as well as confusing.

"Demand" is a familiar concept. And we are familiar with the distinctions between: demand curve (or function); quantity demanded; and quantity actually bought. There's no need for novel distinctions between ex ante/planned/desired/whatever vs ex post/actual/whatever.

Assets have prices. And if those prices are flexible, asset prices adjust so that demand equals supply. And if assets pay rents, or coupons, or dividends, or whatever you call those returns, a rise in asset prices means a drop in asset yields. You can call that drop in yields a "drop in interest rates" if you wish, but we don't normally call the rent/price ratio on houses or farmland a "rate of interest".

Talking about "demand and supply of assets", instead of "saving and investment", also clarifies the adding-up condition, so we can better understand and avoid fallacies of composition. Each individual can buy land to own more, but we can't all buy land to own more, unless the Dutch are creating more land ("investment"). So the price of land rises (and the rent/price ratio yield on land falls), until we stop all wanting to buy land. This is very standard; a downward-sloping demand curve shifts right, so it intersects a vertical supply curve at a higher price.

And of course there's a flow/stock distinction to be made when we talk about "demand for assets", just like there's a distinction between the flow of new saving and the stock of existing savings ("wealth"). The flow supply curve of new land is vertical at zero acres (unless the Dutch are doing their thing), but the stock supply curve is vertical at billions of acres.

Anyway: let's ditch that old "Loanable Funds" diagram, with "rate of interest" on the vertical axis, and "Saving" and "Investment" curves, and replace it with a new diagram, with "price of assets" on the vertical axis, and demand and supply curves for the flow of new assets. It's the same diagram. It's just that the old diagram was upside down, and had confusing misleading labels.

OK. That was the simple bit. Now for the difficult bits. Because doing it this way forces us to think about some difficult questions that we couldn't see before, because they had been swept under the rug.

1. Flow saving vs stock portfolio composition. It is standard practice (or at least common practice) for macroeconomists to separate out the Flow Consumption/Saving decision from the stock portfolio composition decision. What we are implicitly doing is taking a massively heterogeneous collection of different assets, then defining a "composite commodity" ("composite asset") that is supposed to let us treat choices about its magnitude and composition separately. Is it legitimate to do this? And if it is legitimate (if that "composite asset" exists), what does it look like? I don't know the answer to those questions. My guess is it probably isn't strictly legitimate to do this, but we can't stop ourselves from doing it anyway. And even if it were legitimate, the composite asset would probably look very different from the safe, liquid, nominal asset ("bonds"?) we have in mind when we talk about "the rate of interest".
2. Money. All assets are different, but the asset that gets used as Unit of Account and Medium of Exchange is more different than all the others. First, because Unit of Account money doesn't have a price of its own -- the price of money is just the reciprocal of the price of everything else. And (unless you believe the general price level is perfectly flexible, which I don't) that makes the price of money sticky. So even if the prices of other assets can easily adjust to equilibrate demand and supply, the price of money can't. Plus, there is only one way for an individual to accumulate more land, which is to buy more land, which means finding a willing seller, which you won't be able to do if everyone wants to buy land and the price of land is sticky. But there is a second way an individual can accumulate more Medium of Exchange money, simply by buying less of everything else, and nobody else can stop you doing that. But if everyone tries to accumulate money by buying less of everything else, we get a fall in the total volume of trade, including trade in newly-produced goods, which is what economists call "a recession". So the central bank needs to do the right thing with the money supply to prevent that happening.
3. Units. When we write down "C+S = Y = C+I" we must be measuring C, S, Y and I in the same units. And this works fine if assets and consumption goods always have the same relative price. But of course they don't (unless assets and consumption goods are perfect substitutes in supply, in which case we would just consume the existing stock of capital goods if we wanted too, and the asset supply "investment" curve would be perfectly elastic). So we can say the stock of land is fixed if we measure it in acres, but the stock of land is certainly not fixed if we measure it in units of the consumption good ("corn"). And if we think about "saving" this way, as a rising value of the stock of assets, measured in consumption goods, it is perfectly possible for everyone to save 10% of their incomes while consuming 100% of their incomes. All that's needed is for the price of land to be rising quickly enough for (realised or unrealised) capital gains on land to equal 10% of income from production of goods.

Anyway, that's enough for now.

Presidents: do you get what you pay for?

In 1915, Woodrow Wilson earned $75,000 per year, or $1.9 million in 2018 dollars, for serving as President of the United States. The current incumbent of that office receives only a fraction of that amount: $400,000 annually.

Congress has increased the presidential salary three times in the past 100 years: raising it to $100,000 in 1949, to $200,000 in 1979, and to $400,000 in 2001. These rises appear as sharp spikes in the graph below. After each one of these increases, the real value of the presidential salary has been gradually eroded by inflation, except during the deflationary 1930s. 

Can the Great Barrington proposal save the economy?

The Great Barrington Declaration argues against universal lock-downs:

Those who are not vulnerable should immediately be allowed to resume life as normal. Simple hygiene measures, such as hand washing and staying home when sick should be practiced by everyone to reduce the herd immunity threshold. Schools and universities should be open for in-person teaching. Extracurricular activities, such as sports, should be resumed. Young low-risk adults should work normally, rather than from home. Restaurants and other businesses should open. Arts, music, sport and other cultural activities should resume. People who are more at risk may participate if they wish, while society as a whole enjoys the protection conferred upon the vulnerable by those who have built up herd immunity.

Setting aside the epidemiological merits of the Declaration, the question remains: how much of an economic impact would a targeted easing up of restrictions have?

To answer that question, we need to figure out how much spending power is in the hands of "young low-risk adults", and how much is in the hands of "people who are more at risk." COVID-19 risk depends upon a variety of factors, including sex, blood-type, body mass index, and so on. However age appears to be a key risk factor - 40 to 49 year olds in the US are 10 times more likely to die from COVID than people between 18 and 29. The odds of COVID death for 50 to 64 year olds are 30 times the odds for someone between the ages of 18 and 29 (source). So, while age is not a perfect measure of COVID risk, we can use it to get a rough sense of how much of the purchasing power in our economy is in the hands of people who are vulnerable to COVID.

There are a couple of different data sets that can be used to estimate the distribution of after-tax income by age in Canada. The first is the Canada Revenue Agency's Taxation Statistics. These report the total amount of income declared by every Canadian taxfiler, broken down into five year age categories, as well as the taxes paid. They are not a perfect measure of after-tax income. They only include personal income reported for income tax purposes, not corporate income, nor any incomes not declared for tax purposes. Also, the taxation statistics do not include a measure of after-tax income, so I estimated it by calculating net income minus total taxes paid. Moreover, these numbers are from 2016, so are somewhat out of date.  But they serve to make a point.

First, even if young low-risk adults can work normally, it won't make much difference to the amount of money spent on restaurants, arts, music, sports and other cultural activities, because young low-risk adults don't earn much money. Many are in school or just starting out in the labour market. Young people's wages are relatively low, and their unemployment rates are relatively high.

In 2016, the age group that controlled the most economic resources was 50 to 54 year olds: the after-tax income of all Canadians in that age group added up to a total of $114 billion. The next highest earning age group 55 to 59 year olds, raking in a collective $110 billion after taxes, followed by 45 to 49 year olds. This should come as no surprise.  These cohorts are large. In 2016, people aged 50 to 54 were the single largest age group in Canada, followed by people 55 to 59. Moreover, people in their 50s and 40s are at the peak of their earnings potential.  

The next figure presents this same information a slightly different way. It shows the percentage of total income received by people under the ages of 20, 25, 30, and so on. For example, the graph shows that people under the age of 20, who reported a total of $9 billion of after-tax income in 2016, accounted for less than 1 percent of the total after-tax income reported that year. People under the age of 30 accounted for 11 percent of the reported after-tax income. Less than half of after-tax income was received by people under the age of 50. The bulk of income was received by people 50 and over.

As has already been noted, CRA's Taxation Statistics are not a perfect measure of after-tax income, so I double-checked the analysis using the 2016 Census PUMF. Again, the same pattern holds - roughly 10% of after-tax income is in the hands of people under 30, and the age group with the greatest after-tax income is 50-54 year olds:

Again, this data is slightly dated - but the aging of the baby boomers would be expected to push the bulk of purchasing power even higher up the age distribution.

It could be argued that people in their 20s and 30s don't earn that much money, but they're the ones who really keep the economy going. They're the ones buying homes, buying cars, buying consumer durables. This point has merit, but it only serves to strengthen the argument made in this post. Canada's housing market is on fire right now. Car sales are strong, as are sales of consumer durables. It's the service sector that's hurting - plays, music, restaurants, museums, art galleries, and so on. And, generally speaking, it's middle-aged people who have the disposable income to keep these sectors going.

Perhaps if middle-aged people were allowed to assess the risks of various activities and participate if they chose, enough would opt in to make a real difference to the health of the Canadian economy. But I wouldn't count on it.

Download data used to create this post here

Two Price-setting Monopolists Meet in the Street

Two price-setting monopolists meet in the street. One says to the other:

"I will buy 10 more of your overpriced bananas, but only if you buy 10 more of my overpriced apples in return. Deal?"

The second monopolist accepts the deal.

They meet in the street again the following day. One says to the other:

"Instead of me buying those 10 more overpriced bananas today, could I just lend you those 10 more overpriced apples, at the central bank's newly announced overpriced rate of interest, and you can repay the loan next year? Deal?"

The second monopolist refuses this second deal. So both go back to the first deal.

This is basic microeconomics. If a deal makes both parties better off, the deal happens. If a deal makes one of the parties worse off, it doesn't happen.

The first deal makes both monopolists better off (assuming the apple grower likes consuming bananas, and the banana grower likes consuming apples). Because profit-maximising monopolists set Price above Marginal Cost. So the Marginal Benefit of consuming an extra apple exceeds the Marginal Cost of producing it. And the same for bananas. The apple producer doesn't much want to buy an extra banana at the existing price, since his Marginal Benefit from consuming an extra banana equals the price he pays. But he really wants the banana producer to buy an extra apple at the existing price, since the price he gets exceeds his Marginal Cost of producing it. So he gains more from selling 10 more overpriced apples than he loses from buying 10 more overpriced bananas. Same for the banana producer. So the first deal happens. They can even get to the competitive equilibrium this way, even though both apples and bananas are overpriced, if everything is symmetric.

The second deal makes the first monopolist better off, but the second monopolist worse off. Because the central bank has set the rate of interest too high. At the old interest rate, neither wanted to borrow or lend to the other, by assumption. At the new higher rate of interest, each wants to lend to the other, but neither wants to borrow. So the second deal doesn't happen. (OK, they could do a third deal, where one agrees to borrow from the other in exchange for the other borrowing from him, but that third deal is just a wash: it's equivalent to the first deal). If the two monopolists are identical in their preferences for borrowing or lending, there will be no borrowing or lending between them, regardless of the rate of interest. The rate of interest is irrelevant; just like any price is irrelevant, for two identical individuals, who can't gain from trade anyway.

What happens when we introduce a third price-setting monopolist into the story? Now it gets more complicated. It all depends on who likes eating which fruit.

Suppose the apple producer likes eating bananas; the banana producer likes eating cherries; and the cherry producer likes eating apples. We have Wicksell's triangle, with no "double coincidence of wants". That first deal can't happen, unless all three price-setting monopolists meet in the street, at the same time and same place, and agree to a three-way deal. And that's much less likely to happen. And even if it did happen, it's harder to agree to a deal when three people must agree than when only two people must agree.

And that same Wicksellian triangle, which makes it hard for three price-setting monopolists to do the first deal, is also a very traditional economists' story for why people use a medium of exchange ("money"). Money lets them replace one three-person deal with three two-person deals. And that same story is even more compelling if we go from a 3-sided triangle to an n-sided polygon. Money lets them replace one n-person deal with n two-person deals.

And introducing money as medium of exchange into our story puts a totally new perspective on the second deal, where each price-setting monopolist wanted to lend to the other, but the deal didn't happen, because neither wanted to borrow. If you save part of your income by lending it to another person, you need that other person's consent to the deal. But if you save part of your income by simply not spending part of the money you earn, you don't need anyone else's consent. You just spend less money. Done. But if everyone spends less money, then everyone's money income falls too, so the attempts to save by accumulating money work for each but fail for all. The same amount of money just circulates more slowly. We get a recession. And if the n price-setting monopolists in my story had chequing accounts at the central bank, and used those chequing accounts to buy and sell everything else, and if the central bank set the interest rate paid on chequing account balances too high, so everyone wanted to accumulate money, we would indeed get a recession.

[You can read this as a critique, or as a reformulation/reinterpretation, of the canonical New Keynesian model, with its n identical price-setting monopolists, no Wicksellian triangles, with "money" used as unit of account but not, supposedly, as medium of exchange. The model's conclusions (monopoly power makes output too low, setting the rate of interest too high causes recessions) make no sense unless you introduce Wicksellian triangles and a medium of exchange.]

Pain, brioche, and the language of taxation

Ireland's Supreme Court recently ruled that the buns Subway uses in its sandwiches contain too much sugar to be considered "bread", and are thus subject to Value Added Tax (VAT). The decision lead to headlines and discussion along the lines of "Irish High Court Rules Subway’s Sandwich Bread Is Not Legally Bread" or "Ireland declares that Subway’s bread is basically cake". The emphasis was on how "confused" and "bizarre" the entire debate was, and the cost and arbitrary nature of distinctions made in tax legislation: "Having moral distinctions between foodstuffs in your tax regulations turns out to be an awfully expensive thing".

Yet the discussion of whether or not Subway buns are bread is only confused and bizarre in English, with our barren culinary language. In French, the discussion would not seem peculiar. French distinguishes between three broad categories of baked goods: unsweetened bread is "pain", sweetened bread (often made with eggs and milk) is "brioche", and cake is "gâteau." Hence the expression "Qu'ils mangent de la brioche" is (somewhat misleadingly) translated as "let them eat cake", because there is no English word for sweetened bread.

Subway's response to the ruling was that “Subway’s bread is, of course, bread.” Yet even though the English language does not distinguish between "pain"-type bread and "broiche"-type bread, the distinction does exist - and can hold up in court. Yet the fact that Subway believed they had a plausible case, and the bemusement the case generated in the English-language media reveal the extent to which language and culture influence the types of distinctions tax systems are able to make.

The national and first official language of Ireland is Irish, rather than English. I don't know whether "arán"  - the Irish word for bread - also encompasses brioche, or the extent to which various forms of bread are distinguished in everyday speech. If you know, please leave a comment! 

Inequality and competition in a digital economy: a case study

Pokémon Go is an economy in miniature. There is exchange: players trade Pokémons and swap gifts. There is scarcity: the balls required to catch Pokémons are scarce, as is Pokémon storage capacity and other items in the game. There is production: through various activities, such as walking a Pokémon egg, or leaving a Pokémon in a gym, players can produce new Pokémon, or earn tokens. There is status seeking and signaling: high rank players parade their skill by wearing special clothing, and displaying their game level. There are even exports and imports. Pokémon Go imports dollars from the real life economy, exporting in exchange game tokens, and the enjoyment and entertainment players get from using those tokens.

Yet Pokémon Go differs from the real-world economy in two crucial respects. First, prices, the distribution of initial endowments, and all rules are determined by the game manufacturer, Niantic, which acts as an omnipotent “social planner”.  Second, anyone who does not like the rules of the game is free to leave. The possibility of exit gives power to players: because more players=more revenue, the social planner has a strong incentive to set rules that keep players happy. Thus the rules that govern the Pokémon Go economy provide insight into what type of economic rules people would choose, if they actually had power to influence the social planner’s decisions in real life. 

One of the most notable features of the Pokémon Go economy is the number of rules that limit the extent of inequality. Players earn tokens by leaving their Pokémon in gyms – up to a maximum of 50 tokens a day. Players gain status by achieving higher levels – but the game is structured so that the overwhelming majority of players are somewhere between level 30 and level 40. Players gain the ability to defeat other players in battle by powering up stronger Pokémon – but each Pokémon’s maximum combat power is capped too.

But so what? Pokémon Go is just a game, after all.

Men, women, and the end of mandatory retirement

Back in 2014, I wrote a blog post on the end of mandatory retirement for university professors. I quoted a number of men who argued that having a standard retirement age hurts women. Here's an extract from that original post:

Thomas Klassen and David Macgregor, writing in the CAUT (Canadian Association of University Teachers) Bulletin, challenged ageism in academy on the grounds that "Mandatory retirement at an arbitrary age is devastating for female faculty who often began their careers later than males and may have had interruptions to raise children."

Gender-equity is also the basis of Lloyd Spurrell and Ahmed Hussein's arguments against what they call "Rule 65":

Because of their longer life expectancy, women must contribute significantly more to their pension plan by the time they are 65, if they are to be as financially secure as a man in the same situation. Women have to contribute enough for 20 years while men have to contribute enough for only 15 years. Why should women be discriminated against in this manner?

Yes, there are older female academics who will enjoy greater financial security as a result being able to work past 65. But let's think not about anecdotes - the stories of particular men and particular women. Overall, how many of the beneficiaries from the end of mandatory retirement are men, and how many are women? Who bears the costs of the transition?

Almost 15 years after the end of mandatory retirement, evidence on who takes advantage of the opportunity to work longer is mounting up. Unsurprisingly, it's those who were advocating for the elimination of a standard retirement age - men.

The CAUT almanac, available here, gives the number of full-time university professors in Canada by age.  Here is the number of male and female full-time university teachers by in 2007-8, shortly after the standard retirement age was eliminated (rounded to the nearest 10), along with the percentage who are male:

Number of full-time university teachers by age, 2007-08
Rounded to nearest 10. Source: Calculated from 2010-11 CAUT Almanac, Table 2.10.
	50 to 54	55 to 59	60 to 64	65 to 69	70+
Male	4,060	3,950	3,880	1,180	210
Female	2,340	1,910	1,240	230	30
% male	63.4%	67.4%	75.8%	83.7%	87.5%

Unsurprisingly, in 2007-8, men pre-dominated in the oldest age groups - a legacy of the barriers women once faced in accessing full-time, tenure-track university positions.

But let's age these people 10 years, and see how many of them are still employed a decade later. If we take the 50 to 54 age cohort, and age them up 10 years, we actually have slightly more attrition among men than among female. This is not really surprising - the mortality  rate for men in their 50s is higher than the mortality rate for women. Moreover there is a big financial hit associated with retiring before 65 in a lot of pension plans, and women may not be able to afford to take that hit.

However if we look at the 55 to 59 cohort in 2007-8, and compare it to the 65 to 69 cohort in 2017-18, we see that it has become more male-dominated over time. The over-representation of men continues to increase as we move up the age distribution: in 2017-18 the 70+ cohort was 78.8% male. 

Number of full-time university teachers by age, 2017-18.
Source: CAUT Almanac
	60 to 64	65 to 69	70+
Male	3,060	1,815	1,113
Female	1,902	801	300
% male	61.7%	69.4%	78.8%

It's not hard to figure out what's going on here. People make retirement decisions by weighing up the costs and benefits of continuing to be employed. 

There are lots of reasons to expect that female academics typically benefit less than men do from continuing in academic employment past the age of 65: they're less likely to have young or university-aged children to support, they're less likely to have a younger spouse who is still working (couples tend to synchronize retirement decisions), and they're less likely to be respected by colleagues and students as wise experts and leaders. Our society doesn't value older women, and universities reflect the values held by society as a whole.

There are also reasons to expect that the costs of continuing in employment will be higher for female professors -- the opportunity cost of not being able to care for a parent, partner, or grandchild, and the costs of meeting students' expectations that their female professors will be helpful, caring and organized.

I'm pretty sure that, for me, the costs of continuing to work past 65 will far outweigh the benefits, and I'll be happy to retire. But even if I was still enjoying teaching, I still believe retiring at 65 is the right thing to do.

First, there are juniors out there who are as smart and capable as me, if not smarter. They deserve the opportunity to have an academic position, and make an impact on the world.

Second, mandatory retirement is a bulwark against the end of tenure. If universities can't get rid of professors when they're old, they'll start finding other means to shed unwanted professors - and that's unlikely to be pretty.

Thomas Klassen and David Macgregor, writing in the CAUT (Canadian Association of University Teachers) Bulletin, challenged ageism in academy on the grounds that "Mandatory retirement at an arbitrary age is devastating for female faculty who often began their careers later than males and may have had interruptions to raise children."

Gender-equity is also the basis of Lloyd Spurrell and Ahmed Hussein's arguments against what they call "Rule 65":

Because of their longer life expectancy, women must contribute significantly more to their pension plan by the time they are 65, if they are to be as financially secure as a man in the same situation. Women have to contribute enough for 20 years while men have to contribute enough for only 15 years. Why should women be discriminated against in this manner?

Yes, there are older female academics who will enjoy greater financial security as a result being able to work past 65. But let's think not about anecdotes - the stories of particular men and particular women. Overall, how many of the beneficiaries from the end of mandatory retirement are men, and how many are women? Who bears the costs of the transition?

Almost 15 years after the end of mandatory retirement, evidence on who takes advantage of the opportunity to work longer is mounting up. Unsurprisingly, it's those who were advocating for the elimination of a standard retirement age - men.

The CAUT almanac, available here, gives the number of full-time university professors in Canada by age.  Here is the number of male and female full-time university teachers by in 2007-8, shortly after the standard retirement age was eliminated (rounded to the nearest 10), along with the percentage who are male:

Number of full-time university teachers by age, 2007-08
Rounded to nearest 10. Source: Calculated from 2010-11 CAUT Almanac, Table 2.10.
	50 to 54	55 to 59	60 to 64	65 to 69	70+
Male	4,060	3,950	3,880	1,180	210
Female	2,340	1,910	1,240	230	30
% male	63.4%	67.4%	75.8%	83.7%	87.5%

Unsurprisingly, in 2007-8, men pre-dominated in the oldest age groups - a legacy of the barriers women once faced in accessing full-time, tenure-track university positions.

But let's age these people 10 years, and see how many of them are still employed a decade later. If we take the 50 to 54 age cohort, and age them up 10 years, we actually have slightly more attrition among men than among female. This is not really surprising - the mortality  rate for men in their 50s is higher than the mortality rate for women. Moreover there is a big financial hit associated with retiring before 65 in a lot of pension plans, and women may not be able to afford to take that hit.

However if we look at the 55 to 59 cohort in 2007-8, and compare it to the 65 to 69 cohort in 2017-18, we see that it has become more male-dominated over time. The over-representation of men continues to increase as we move up the age distribution: in 2017-18 the 70+ cohort was 78.8% male. 

Number of full-time university teachers by age, 2017-18.
Source: CAUT Almanac
	60 to 64	65 to 69	70+
Male	3,060	1,815	1,113
Female	1,902	801	300
% male	61.7%	69.4%	78.8%

It's not hard to figure out what's going on here. People make retirement decisions by weighing up the costs and benefits of continuing to be employed. 

There are lots of reasons to expect that female academics typically benefit less than men do from continuing in academic employment past the age of 65: they're less likely to have young or university-aged children to support, they're less likely to have a younger spouse who is still working (couples tend to synchronize retirement decisions), and they're less likely to be respected by colleagues and students as wise experts and leaders. Our society doesn't value older women, and universities reflect the values held by society as a whole.

There are also reasons to expect that the costs of continuing in employment will be higher for female professors -- the opportunity cost of not being able to care for a parent, partner, or grandchild, and the costs of meeting students' expectations that their female professors will be helpful, caring and organized.

I'm pretty sure that, for me, the costs of continuing to work past 65 will far outweigh the benefits, and I'll be happy to retire. But even if I was still enjoying teaching, I still believe retiring at 65 is the right thing to do.

First, there are juniors out there who are as smart and capable as me, if not smarter. They deserve the opportunity to have an academic position, and make an impact on the world.

Second, mandatory retirement is a bulwark against the end of tenure. If universities can't get rid of professors when they're old, they'll start finding other means to shed unwanted professors - and that's unlikely to be pretty.

More Thoughts on a Better Income Support System for the Next Crisis

This post was written by Mike Veall of the Department of Economics at McMaster University.

I appreciated the comments on my earlier post, where I suggested that a small monthly Basic Income system would have the advantage of being able to be scaled-up in a crisis.

Incenting participation in the tax/transfer system is responsive to the work of Anna Cameron, Lindsay Tedds, Jennifer Robson and Saul Schwartz pointed out by Frances even though those authors seek a different, automatic-enrolment approach.

(Advertising: More on nonfiling by Robson and Schwartz is coming out in September’s Canadian Public Policy/Analyse de politiques; CPP/Adp is also currently working on expedited publishing/special issues for pandemic-related articles.)

The skepticism in the comments on my post is well-taken, and if we can get a better emergency benefit delivery system by other means, better is better.

But I would hope it would end up monthly. Especially during an emergency, “monthliness” is much superior to the quarterly delivery of the GST/HST credit and the CWB.

However it is obtained, there is a need for an improved starting point. While COVID policy so far has been the economics equivalent of meatball surgery, it already appears that CERB take-up was under-predicted. I fear overpayment will also prove an issue. While an automatic system with 100% coverage is unrealistic, the alternative has been 7 million CERB applications. With that many, CRA had to rubber stamp, and follow-up has to be at least questionable.

And a pure application system seems to require a lowest common denominator approach with consequent sharp edges. If someone’s 2019 income = $5K, CERB = $8K. If 2019 income = $4.9K, CERB =$0.

Finally, I have been thinking about whether emergency income support to a family could and should be conditioned on rent. Particularly in an emergency, rent looms large, and it varies a lot across the country. I’d be particularly interested in learning the thoughts of others.

A postscript: Privacy concerns sometimes come up, as mentioned in one comment. I think most taxpayers would accept this rather modest incremental risk to their privacy if it means better effectiveness for the substantial tax dollars being spent on programs like CERB.

A Small Basic Income as a Solution to the Magic List Problem

This post was written by Mike Veall of the Department of Economics at McMaster University.

What if in a future crisis there is again a need to distribute money? As Jennifer Robson put it, “government doesn’t have a magic list with everybody’s name and addresses and bank accounts.”

I was reminded of this when thinking about transmitting money to someone in a less developed economy. How can I be sure it will work? I could send a small amount, confirm receipt, and then send more.

The same approach could create a magic list for Canada. Every month the government could send a taxable $20 or $30 to everyone (except children and seniors, already in the system with monthly benefits). It would be enough money that most people would check receipt, at least occasionally, and hence continually test the system. I think this is much more likely to work than an enrolment system or having CRA try to construct a list.

Then, in a crisis, the government can quickly scale up the amounts, put in clawbacks, whatever it decides.

$30 per month can easily be financed by reducing the personal amount on the personal income tax. If desired, implementation could be made pretty close to distribution neutral by also reducing the GST/HST credit and a couple other tinkers. It would be cheap crisis preparation.

This post is about how to make a magic list, not on the desirability of a more sizable Basic Income. But I note that if a sizable BI would be a good policy, a small one could pave an incremental path. And if it would prove a bad policy, trying a small one first could help avoid a mistake.