Worthwhile Canadian Initiative

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. 

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)

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

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.

A Simple Micro/Macro Corona Tax Model

I want something you could teach to first or second year economics students. Using tools they already have in their toolkit.

MICRO

Start with a Demand and Supply model of the market for haircuts.

If we put a $1 per haircut tax on buyers of haircuts, the demand curve shifts vertically down by $1, reducing Q* and P*.

If we put a $1 per haircut tax on sellers of haircuts, the supply curve shifts vertically up by $1, reducing Q* and increasing P*.

If we put a tax on both buyers and on sellers of haircuts, both the demand and supply curves shift. We know that Q* falls. We don't know whether P* rises or falls. It depends on which tax is bigger; and it depends on whether demand or supply is more elastic.

The coronavirus is like a tax. Every haircut bought and sold comes with an extra cost to buyer and seller: the buyer risks getting sick; and the seller risks getting sick. (But the "tax revenue" isn't a gain to the government; it just gets thrown away as a net loss to everyone.)

I've drawn the above diagram with a roughly equal "Corona tax" on buyers and on sellers. Because I don't know which is bigger. But I have drawn a demand curve that is more elastic than the supply curve. Because I think that is roughly right, at least for the Short Run. And if demand is more elastic than supply, and the two taxes are the same, then P* falls.

And if the price of haircuts is sticky, and takes time to adjust to the new lower equilibrium price P*, we would see an excess supply of haircuts.

If the Corona tax is big enough, Q* may drop to zero, and the market for haircuts closes down completely.

Even if the Corona tax is not big enough to close down the market, the government might decide to close it down by law. Because the Corona tax also creates a negative externality, because if the buyer or seller gets sick, he or she might also infect other people who aren't buying or selling a haircut. So the Marginal Social Benefit of buying a haircut is less than the Private Marginal Benefit, and the Marginal Social Cost of selling a haircut is greater than the Private Marginal Cost. So the efficient quantity of haircuts Q^ might be zero, even if the market equilibrium quantity Q* is still positive.

Obviously, this will depend on the particular good. It might be efficient to ban the sale of haircuts, but allow people to buy and sell apples. Some markets close, or are closed; other markets stay open.

MACRO

That was Micro. Now lets do Macro, for those markets that do stay open, all lumped together in aggregate. So we are talking about Aggregate Demand and Aggregate Supply.

Eventually (we hope) the risks of buying and selling goods will go away, or at least get smaller. The Corona tax is a temporary tax. We can avoid the tax, by waiting to buy, or waiting to sell, until it goes away (or gets smaller). And the rate of interest is the extra price we pay for buying goods now, rather than waiting till next year; and it's the extra price we get for selling goods now, rather than waiting till next year. (Strictly, it's the real interest rate, which is the nominal interest rate adjusted for inflation.)

So put the real interest rate on the vertical axis, and relabel the curves "AD" and "AS".

Will the equilibrium rate of interest r* rise or fall? In other words, will the Bank of Canada need to raise or lower the rate of interest it sets to keep AD equal to AS (even though both AD and AS are lower than before). In other words, which curve shifts leftwards more, AS or AD?

That depends, on two things: it depends on whether the AD curve shifts vertically down more or less than the AS curve shifts vertically up (is the Corona tax bigger on buyers or on sellers); and it depends on which curve is more elastic (but here we are talking about intertemporal or interest-elasticity -- on how willing buyers and sellers are to postpone buying and selling until next year).

As before, I don't know whether the Corona tax is bigger on buyers or on sellers, so I've drawn it roughly equal. But I've assumed the AD curve is more interest-elastic than the AS curve, so r* falls, and the Bank of Canada needed to cut r. Because that seems plausible to me, and that is what the Bank of Canada actually did.

One big thing this model leaves out is this: buyers of goods expect to be able to buy a much wider variety of different goods (and services) in future than they can buy now. That also shifts the AD curve leftwards for the remaining goods. It's like they learn that they will be able to buy some fancy new device next year, that they can't buy now, and so want to save part of their current income because there are better things to spend their money on next year. See my previous post.

Update 1: A second big thing this model leaves out is that some sellers get hit much worse than others. Unemployed hairdressers might be borrowing constrained, and unable to buy as many apples as they want, because fully-employed apple producers aren't willing to lend to those with a bad credit risk.

[Update 2: I messed up slightly when drawing the two diagrams. If the "tax" on buyers is exactly equal to the "tax" on sellers, the point where the red S curve crosses the blue D curve should be exactly above the point where the blue S curve crosses the red D curve (same Q for both points). Which is the tax incidence irrelevance thing. Oh well.]

Relative supply shocks, Unobtainium, Walras' Law, and the Coronavirus

Here's the basic idea:

A temporary 100% output cut in 50% of the sectors (what the Coronavirus does) is very different from a 50% output cut in 100% of the sectors (what our intuitions might expect from supply shocks in aggregate macro models).

The former can easily lead to deficient demand in the unaffected sectors; the latter leads to excess demand in all sectors.

Here's the intuition:

(As they say: "Shit just got real". And one of my daft old thought-experiments just got very real.)

As you know, unobtainium is a very desirable good. Everyone wants to buy it. But nobody produces it. It doesn't exist.

You could say there's an excess demand for unobtainium, because people want to buy some of it, at any price. Maybe they want to spend half their income on unobtainium. But supply is zero. And if you do say there's an excess demand for unobtainium, and if you also believe in Walras' Law, you get into one helluva mess theoretically, because the excess demand for unobtainium must (by Walras' Law) be matched by an excess supply of other things. So even thinking about unobtainium creates a general glut -- a recession. Which is silly. Because Walras' Law is wrong. Because, as Clower said long ago, it's constrained not notional demands that matter. As I explained in my old post.

But now imagine that someone actually invents a way to produce unobtainium. They can't produce it just yet, but everyone knows they will produce it, and sell it at an affordable price, a few months from now.

What happens when they hear the news? Everybody (or everybody except borrowing-constrained Hand To Mouth agents) suddenly wants to save up to half their income, so they can spend it on unobtainium in a couple of months time. Their money will be worth more in a couple of months than it is now, because they will be able to buy unobtainium with it, and not just all the stuff they can already buy now. We get a general glut (deficient demand) now.

And that is pretty much what's happening with the Coronavirus, in the real world. Half the goods we used to buy are temporarily unobtainium. But we expect to buy them in a couple of months.

It would be very different if unobtainium were a perfect substitute for existing goods, so its invention is exactly like increasing the supply of existing goods in a couple of months. Then we would want to dissave now, like in standard aggregate macro models, because the Marginal Utility of Consumption is high when consumption is low, so we want to prepone consumption from the future to the present. But unobtainium isn't a perfect substitute for existing goods. High intertemporal substitution can beat low cross-section substitution.

(Sure, unlike in my thought-experiment, we used to be able to buy unobtainium in the past. But that's irrelevant; bygones are bygones. It's only the present and the expected future that matter.)

That's why we need temporarily looser monetary and fiscal policy. (Plus, we also need fiscal policy to redistribute between people differentially affected by the Coronavirus, but that's another story.)

Think that's (basically) right.

OK, I've oversimplified (of course). Much of the stuff that's still obtainable is the more essential stuff, like food. But not all. And, at the margin, we still face intertemporal vs cross section substitutibility.

Covid-19, Italy and Lessons for Canada

by Thomas Barbiero, Ryerson University and Livio Di Matteo, Lakehead University

As Italo-Canadese and members of the large Italian diaspora throughout the world, we have found the COVID-19 situation in Italy truly heart-wrenching.  As we write, Italy is the hardest hit country outside of China with over 60,000 confirmed cases and over 6,000 deaths. The hardest hit region is that of Lombardia in the north, which now has about half of the infected population and over 60 percent of the fatalities. The regions where our families originated, Campania and Abruzzo, have also been hit with about 1,500 cases combined. 

Hospitals in the hardest hit regions of Italy are overwhelmed, with mortality rates especially pronounced among seniors over the age 70.  The rapid spread of the virus and the high mortality rates in the last two weeks are particularly disconcerting with important lessons for us in Canada as we ramp up efforts to deal with the virus.  Based on current Canadian case numbers, we are approximately where Italy was in mid to late February. Canada currently has over 1,400 confirmed cases. A crucial difference is that we have had more warning and are taking things somewhat more seriously than they were at the start of their crisis.

We offer the following observations of the Italian situation and their application to Canada.  We believe the severity of the Italian situation is a function of several factors which have come together. 

First, Italy is an open economy heavily dependent on both trade and tourism and substantial international economic and travel ties. It is densely populated country -about the size of southern Ontario with 60 million people -  that receives over 60 million tourists a year. By comparison, Canada receives about 20 million international tourists a year but Canadians themselves are big international travellers especially to the US. Moreover, in just the last few days alone, about one million Canadians have returned home.

Ontario and British Colombia, together currently represent over half of those infected in Canada. Ontario, like Lombardia is an economic heartland with substantial international trade, travel and commercial ties.  B.C. has a thriving trade and commercial sector and again a major international travel destination as well as close links across the border. 

Canada’s decision to limit flights and close borders is an important one given that the virus spreads through personal contact and travel is the key factor in initial transmission and spread. Hopefully, this has not been delayed too substantially.

Unfortunately, Covid-19 spread in northern Italy before the authorities fully mobilized as there was reluctance to admit its seriousness.  A major politician in Italy in February even pronounced that there was no reason for Italians to give up their pleasurable aperitivo with friends in the evening. He was later found to have the virus and is now undergoing treatment.

Second, evidence has emerged that infection and mortality rates are higher in older Italian men. Smoking rates in Italian men are much higher than Canada.  Given COVID-19 has major impacts on the respiratory system, this is undoubtedly an aggravating factor.  Moreover, Italy’s population over age 65 is one of the highest in the world, representing almost a quarter of its population, compared to about 16 percent in Canada.  Mortality in COVID-19 is concentrated in the older population.

Third, Italian culture involves substantial social interaction outside of the house.  Italians are always out and about visiting the local bar to have a coffee or play cards, going for a passegiata and gathering on park benches after dinner. Italian culture is highly social and involves substantial physical contact when greeting.  Elders in Italian culture particularly expect the respect of warm physical embraces and greetings, and can be offended if that respect is not shown. Families are close and often get together for meals.

Indeed, the New York Times recently reported that 7 members of an Italo-American family in New Jersey were infected by the virus and 4 of them died Apparently, only one guest at one of the family dinners had the virus and spread it to these other family members.

Finally, Italians have a notorious irreverence for rules and compliance with government edicts, and the resistance to following quarantine and lockdown guidelines has been substantial based on the anecdotal evidence and news stories we are familiar with. Such behaviour has come with a price and is an important lesson to those of us in Canada who still are ignoring the need to social distance.

When it comes to what needs to be done to plank the curve in Canada, Italy in our opinion is a prime example of why “social distancing” combined specifically with “physical distancing” is paramount.  We all need to play our part.  It needs to be done immediately and it is important that the rules be complied with – now.

Rising Health Spending Is Not Just About Seniors

The Canadian Institute for Health Information (CIHI) has released its 23^rd annual report on health spending in Canada - National Health Expenditure Trends, 1975 to 2019.  As a member of the CIHI National Health Expenditures advisory panel, it is always great to see the wealth of data on trends in health spending across Canada.   Total health spending in Canada in 2019 is expected to reach $264.4 billion which represents an increase of 3.9 percent over last year and also accounts for 11.6 percent of Canada’s GDP – a figure also up slightly from last year.  After a period of zero average annual growth in real per capita total health spending from 2010 to 2014, the period since 2014 has averaged about 1.4 percent a year.  This, however is lower than the average annual growth rate from 1996 to 2010 at 3.3 percent.  Health spending growth has resumed but on what seems like a more sustainable trajectory given that real per capita GDP growth is closer to 2 percent. 

Much of the concern about rising health spending has focused on the effects of population aging.  Health spending does rise with age as Figure 1 below shows rather dramatically.  Aside from those aged less than 1-year, average per capita provincial/territorial government health spending is well below $5,000 until the 60-64 age group when it starts to rise above that threshold reaching over $30,000 for those aged over 90 years.  Yet, despite this surge after age 60, what is also interesting is that when the drivers to rising health spending are broken down, in 2019, aging per se only contributes 0.8 percentage points out of the 3.8 percent growth in public sector health spending – about 21 percent – with general inflation, population growth and other factors (eg. Technology and utilization) accounting for the rest.  It does lead one to wonder whether this is because today’s seniors are generally quite healthy compared to the past or perhaps there are unmet needs.

What is also interesting and seldom noted is that while provincial/territorial government per capita health spending is highest among seniors, over the last two decades, the rates of growth in per capita spending have not been for seniors.  Indeed, between 2000 and 2017, the highest average annual growth rates have been for children and youth aged 5 to 19, followed by children under age 1-year and adults aged 35-39 as shown in Figure 2. 

Indeed, per capita spending for adults between the ages of 35 and 64 has been growing at a faster rate than those aged 65 to 89.  While it is true that much lower per capita amounts are being spent on those below age 65, this demographic has been seeing faster growth in health spending.  Again, this leads one to wonder given scarce resources, whether there is an implicit transfer of resources underway away from seniors when it comes to new spending growth or whether younger people today have more health problems or utilize health care more than similarly aged groups in the past.  Given the epidemic of obesity and mental health issues among the young, perhaps this is having an impact on health spending needs and expenditures. 

If a significant cohort shift in health care needs and utilization is underway is an interesting question. I suppose fully knowing if this is a recent development or has been underway for the last 50 years requires per capita age spending data going back quite a ways - I am only aware of the CIHI data going back to the mid 1990s or so.  This is an important issue.  While an aging population may only be contributing 21 percent of the increase in health spending now , if younger cohorts at present have deteriorating health status or more health issues than in the past, they may be poised to be a more important driver of health spending both now and in the future.

What killed real wage growth?

This is the Project Link chart that most startles me:

What killed the growth in real wages in the early 1970s? I've been trying to come up with an answer to this question, and I think I have one. I'm not entirely sure that it's the correct answer, but I think it's a plausible conjecture.

Project Link update

It's past time for my annual update for Project Link, my attempt to piece together the fragments of Statistics Canada's published data into coherent time series.

Historical Canadian Government Data Sources

I recently received the following message from Ryan MacDonald at Statistics Canada:

“I recently came upon a number [of] scans done by our library to place the historical publications into pdfs.  They can sometimes be a little difficult to search for, so I thought I would pass along a few links that may be of use to you or your colleagues in your research.”

So, I think a good way to further disseminate these historical economic data sources more broadly is to post the links here on WCI. Feel free to share.

Canada Year Book

http://publications.gc.ca/site/eng/9.838186/publication.html

Tables of the trade and navigation of the Province of Canada for the year ... (1850-1908)

http://www.publications.gc.ca/site/eng/9.843286/publication.html

Report of the Department of Customs containing the tables of imports, exports and navigation of the Dominion of Canada for the fiscal year ended Mar. 31 ... (1909-1916, 1918)

http://www.publications.gc.ca/site/eng/9.843294/publication.html

Annual report of the trade of Canada (imports for consumption and exports) (1917, 1919)

http://publications.gc.ca/site/eng/9.852124/publication.html

Trade of Canada = Commerce du Canada (1920-1939)

http://publications.gc.ca/site/eng/9.851960/publication.html

Trade of Canada = Commerce du Canada (1939-1970)

http://publications.gc.ca/site/eng/9.809303/publication.html

Imports, merchandise trade / Statistics Canada, External Trade Division = Importations, commerce de marchandises / Statistique Canada, Division du commerce extérieur (1970-2002)

http://publications.gc.ca/site/eng/9.809322/publication.html

The Hospitalization Life Cycle

The Canadian Institute for Health Information has just released a report on Hospital Stays in Canada which provides a plethora of interesting tables on hospital stays in Canada at a national and provincial level.  Both the age-standardized hospitalization rate (per 100,000 population) and the age standardized average length of stay (in days) in Canada have fallen since 2013-14 going from 8,205 to 7,944 and 7.0 to 6.8 respectively.  The summary graphic for their report lists the top 5 reasons for inpatient hospitalizations as:1) Giving birth (362,700), 2) COPD and bronchitis (93,353), 2) Acute myocardial infarction (71,192), 4) Pneumonia (70,149) and 5) Heart failure (68,972) with the total number of inpatient hospitalizations in brackets beside each reason.  These five causes make up 21.6 percent of total hospitalizations in 2017-18.

Robinson Crusoe and the Carbon Tax Rebate

Suppose I impose a carbon tax on Robinson Crusoe. But I give him a rebate exactly equal to the tax he pays. That tax plus rebate will have no effect on Robinson Crusoe's behaviour. He knows that if he cuts carbon by 1kg, and pays $1 less tax, his rebate also falls by $1, so his net tax (= tax minus rebate) stays the same. He has no incentive to cut carbon.

Now imagine an archipelago of 100 identical islands, each with one identical Robinson Crusoe clone. And suppose I impose the same tax per kg on each of them, but give each one a rebate equal to 1% of the total tax I collect from all.

You might think the result would be exactly the same. But it's now very different.

Each of the clones knows that if he were to cut carbon by 1kg, and pay $1 less tax, while the 99 other clones kept doing the same thing, his rebate would fall by only $0.01, so his net tax falls by $0.99. Which gives him an incentive to cut carbon.

In the new equilibrium (we call it a "Nash Equilibrium") they all cut carbon by the same amount, and so each gets a rebate equal to the tax he pays. But each knows that if he alone were to move away from that equilibrium, by increasing carbon, his net tax would increase by $0.99 per 1kg of extra carbon. Which is sufficient incentive not to move away from that equilibrium. Which is what makes it an equilibrium.

It's like a competition, where each entrant has to put the same $10 into a pot for prize money, and the total pot is divided between them in proportion to how quickly they each run. That creates an incentive for each to try to run faster relative to all the other competitors. Only if they all collude, and collectively agree to walk the race slowly, and none cheats on the agreement, would the incentive fail. Except the carbon tax works in reverse, because they pay money into the pot in proportion to how much carbon they create, and share the pot equally. So they compete to create less carbon.

What is true for each of the parts is not necessarily true for the whole, and vice versa, even if each part is identical to the others. What is individually rational is not necessarily collectively rational, and vice versa, even if each individual is identical to the others. Prisoner's Dilemma is one example that illustrates this point. My carbon tax example is another.

This post is not really about the carbon tax. I know nothing useful about the carbon tax, except for this one point. It's about teaching economics. The point I am making above is true and important, but it is not obvious. Teaching is hard.

Maybe what non-economists don't get is not income effects vs substitution effects; it's the Fallacy of Composition and the difference between individual rationality and collective rationality. Free riders, collective choice problems, and all that. (If there were no difference we wouldn't need a carbon tax anyway.) And the best way to explain the difference is in a representative agent model, because the difference is at its starkest and clearest in a symmetric equilibrium.

[Update: I changed the title from "Robinson Crusoe and the Carbon Tax". Because it dawned on me that it's about the rebate, not the tax.]

Guest Post: Comparing Technological Change in the Health and Taxi Industries

Well, here is a guest post forwarded to me by Ruolz Ariste - a colleague whom I originally met during my annual interactions with the CIHI and with whom I have co-authored.  Ruolz Ariste is currently pursuing a PhD in Industrial Relations in a program run by run jointly by Université Laval and Université du Québec en Outaouais.  Enjoy.

Canada's Gross Domestic Income and trading gains: An update and estimates for the provinces

This builds off an earlier post about the terms of trade, trading gains, and Gross Domestic Income and it extends the analysis to the provincial level. It's probably a good idea to take a look at it before continuing on.

Hopefully, Tomorrow Won't Be Yesterday

A short quick post.  Yesterday’s job numbers for Canada were greeted with surprise.  With 55,900 jobs added in February, the sentiment as best summarized by Doug Porter, Chief Economist with BMO Capital Markets is that: “The economy clearly is not falling off a cliff by any means, arguably quite the opposite”.  This is despite what seems to be a slowdown in the Canadian economy as indicated by consumer spending, exports and capital investment by business in 2018.  The more entertaining headline was by BNN Bloomberg which led with the Canadian labour market is off to its best start since 1981 as “The two-month gain is the best start to a year since 1981.” Of course, if the economy is doing so well one wonders why we are holding off on interest rate increases but I digress.

More to the point is what those of us with more historical memory remember about 1981.  Take a look at Figure 1 which plots seasonally adjusted monthly employment gains from January 1980 to December 1982.  January and February 1981 were certainly the two biggest consecutive gains in that period and quite impressive at 66,500 and 76,800 jobs respectively.  But look at what happened next – employment shrank in March of 1981, growth resumed for a few months afterwards and then – well, the economy fell off a cliff into the 1981-82 recession which saw unemployment rates soar to peak at 13.1 percent in December of 1982.

 

 

If we compare monthly employment in the year leading up to January February 1981 to a similar range for January-February 2019 as in Figure 2 one sees that it is not inconceivable that we are again about to fall off a cliff especially given the accompanying aforementioned economic indicators for 2018.  However, a big difference between now and then is monetary policy and interest rates which peak at their post war high about this period.  We are currently still at pretty historic lows for interest rates.  However, based on what happened to employment in 1981, we should know by the end of summer if we are in store for some employment drops.

NAFTA - The “Worst” Trade Deal Ever

With time on my hands over the last week of the holiday season, I spent a bit more time than usual surfing news channels and watched a press conference by U.S. President Donald Trump in  which among other things he again reiterated how the United States had been hard done by NAFTA and that it was the worst trade deal ever.   Needless to say, while he has pronounced from the mount on this matter numerous times, for some reason it particularly annoyed me this time, so I decided to see what simple evidence could be mustered to weigh in on this point.