How dairy farmers unions in Canada are distorting the facts about supply management

Under heat recently as President Trump has criticized supply management in Canada and retaliated against it, the different provincial associations representing dairy farmers have moved on the offensive. To promote the virtues of this system meant to reduce production in order to prop up prices through the use of trade tariffs, production quotas and price controls (how can we call those virtues), these unions have produced numerous infographics to make their case. It is even part of what they dub their These-infographics-show-that-diary-prices-are-lower-in-Canada-than-elsewhere, that milk is still a cheap drink relative to other type of drinks and those prices, supposedly, increase more slowly than elsewhere. All of these graphics are dishonest and must be dismantled.

The most egregious of these infographics – present in the “lobby day kit” – shows the price of milk in Australia (1.55 CAD), Canada (1.45 CAD) and New Zealand (1.65 CAD). They are seemingly using 2014 prices. First of all, they use data that conflicts massively with the reports of Statistics Canada that suggest that milk prices hover between 2.33$ to 2.48$ per liter.  Their data is provided by AC Nielsen but no justification is presented as to why they are better than Statistics Canada. The truth is that it is not better. Participants in Nielsen surveys come from a self-selected pool of storeowners who wish to participate and are then selected by Nielsen to be part of the data collection. Then, they can record prices. It should be mentioned that not all regions of Canada are covered in the data. Although the Nielsen data does have some uses (especially with regards to market studies), it hardly measures up Statistics Canada when comes the time to evaluate price levels. This is because the government agency collects information from all regions and tries a broader sweep of retailers in order to create the consumer price index.

But an even larger problem is that, in their comparison of prices, they don’t mention that New Zealand taxes milk. In New Zealand, all food items are subjected to sales tax, which is not the case in Canada and Australia. Hence, when they compare retail prices, they are comparing prices that exclude taxes and prices that include taxes. One would like to find if they acknowledge this fact in the methodological mentions, but there are none!

Using prices available at and and the exchange rates made available by the Bank of Canada, we can correct for this problem of theirs. Simply changing prices source leads to a massively different result with regards to Australia whose milk prices are lower than in Canada. Secondly, once we adjust for the sales tax in New Zealand, we find that prices in New Zealand are lower than in Canada. In fact they are lower than in one of Canada’s cheapest market, Montreal (let alone Toronto or Vancouver).  So the infographic they show in order to lobby governments is a fabrication.

Table 1: The real price of milk

Using (regular milk)
Unadjusted Adjusted for taxes
 Australia  $           1.59  $                 1.59
 New Zealand  $           2.26  $                 1.97
 Canada  $           1.99  $                 1.99
 Using (whole milk)
 Unadjusted  Adjusted for taxes
 Sydney  $           1.82  $                 1.47
 Wellington  $           2.42  $                 2.10
 Montreal  $           2.87  $                 2.87

Source: and, consulted May 16th 2014 and the Bank of Canada’s currency converter. Note: using the Statistics Canada price would make Canada’s situation even worse by comparison.

This is part of a pattern of deceit since they also massage data for numerous other graphs that are presented to Canadians in efforts to convince them of the virtues of supply management. One other example is an infographic that presents a figure of nominal milk prices in Australia before and after the abolition of supply management. Given that prices seem more volatile after 2000 and that they increase more steeply, they try to make us believe that liberalization was a failure. This is not the case. Any sensible policy analyst would deflate nominal prices by the general price index to control for inflation. When one does just that using the data from the Australian Bureau of Statistics, one sees that real prices stabilized in the first ten years of deregulation after increasing roughly 15% in the decade prior. And since 2010, real prices have been falling constantly.

Other examples abound. In one instance, the Quebec union of dairy farmers circulated an infographic meant to show that nominal prices for dairy products increased faster in the United States than in Canada. Again, they omit inflation. Since 1990 (their own starting date), prices of dairy products have risen more slowly than inflation – indicating a decline in real prices. In Canada, the opposite occurred – inflation increased more slowly than dairy prices indicating an increase of the real price.

The debate around supply management is complicated. The policy course to adopt in order to improve agricultural productivity and lower prices for Canadians is hard to pinpoint. But whatever position one may hold, no one is well-served by statistical manipulations offered by the unions representing dairy farmers.

On Evonomics, Spelling and Basic Economic Concepts

I am a big fan of exploring economic ideas into greater depth rather than remaining on the surface of knowledge that I accumulated through my studies. As such, I am always happy when I see people trying to promote “alternatives” within the field of economics (e.g. neuroeconomics, behavioral economics, economic history, evolutionary economics, feminist economics etc.). I do not always agree, but it is enjoyable to think about some of the core tenets of the field through the work of places like the Institute for New Economic Thinking. However, things like Evonomics do not qualify for this.

And this is in spite of the fact that the core motivation of the webzine is correct: there are problems with the way we do economics today (on average). However, discomfort towards the existing state of affairs is no excuse for shoddy work and holding up strawmen that can be burned at the stake followed by a vindictive celebratory dance. The most common feature of those who write for Evonomics is to hold such a strawman with regards to rationality. It presents a caricature where humans calculate everything with precision and argue that if, post-facto, all turns out well then it was a rational process. No one, I mean no one, believes that. The most succinct summary  of rationality according to economists is presented by Vernon Smith in his Rationality in Economics: Constructivist and Ecological Forms. 

Such practices have led me to discount much of what is said on Evonomics and it is close to the threshold where the time costs of sorting the wheat from the chaff outweighs the intellectual benefits.

This recent article on “Dierdre” McCloskey may have pushed it over that threshold. I say “Dierdre” because the author of the article could not even be bothered to write correctly the name of the person he is criticizing. Indeed, it is “Deirdre” McCloskey and not “Dierdre”. While, ethymologically, Dierdre is a variant of Deirdre from the Celtic legend that shares similarities to Tristan and Isolde, the latter form is more frequent. More importantly, Dierdre is name more familiar to players of Guild Wars. 

A minor irritant which, unfortunately, compounds my poor view of the webzine. But then, the author of the article in question goes into full strawman mode. He singles out a passage from McCloskey regarding the effects of redistributing income from the top to the bottom. In that passage, McCloskey merely points out that the effects of equalizing incomes would be minimal.  The author’s reply? Focus on wealth and accuse McCloskey of shoddy mathematics.

Now, this is just poor understanding of basic economic concepts and it matters to the author’s whole point. Income is a flux variable and wealth is a stock variable. The two things are thus dramatically different. True, the flux can help build up the stock, but the people with the top incomes (flux) are not necessarily those with the top wealths (stock). For example, most students have negative net worth (negative stock) when they graduate. However, thanks to their human capital (Bryan Caplan would say signal here), they have higher earnings. Thus, they’re closer to the top of the income distribution and closer to the very bottom of the wealth distribution.  My grandpa is the actual reverse. Before he passed away, my grandpa was probably at the top of the wealth distribution, but since he passed most of his time doing  no paid work whatsoever, he was at the bottom of the income distribution.

Nevermind that the author of the Evonomics article misses the basic point of McCloskey (which is that we should care more about the actual welfare of people rather than the egalitarian distribution), this basic flaw in understanding why the difference between a stock and flux leads him astray.

To be fair, I can see why some people disagree with McCloskey. However, if you can’t pass the basic ideological Turing test, you should not write in rebuttal.

Differences in life expectancy within Canada, 1921 to 2011

I’ve been playing around with some data for a paper I have been trying to write about the economic history of Canada in the 20th century. In the process, I assembled the data from the Base de données sur la longévité canadienne regarding life expectancy at birth. Then, I thought that it would be interesting to see how large were the differences between the provinces and how fast did they close. They closed pretty dramatically during the 20th century – see for yourself.


The GDP, real wages and working hours of France since the 13th century

Every few years, an economic historian in training spends thousands of hours in archives assembling a long quantitative essay. It’s the work of monks (in fact, when you go far back in history, you also end up working with monks and nuns – which was my case on Canadian economic history). It’s the kind of work that requires patience, attention to details and (did I say it already?) patience.

I did that for my own work on Canadian economic history. For two years, I locked myself in the archives of two religious congregations to collect and transcribe close to a million price and wages information. For these two years, I did not write one single paper. I just collected the data and constituted a list of the papers I could write. However, once its finished, you may party like a sailor fresh off the boat because you end up with a wealth of data to answer hundreds of questions. When I finished my own thing on Canada, I was thrilled as I thought it constituted a great advance in quantitative knowledge (which I could use to assess tougher historical questions).

However, compared to the work of Leonardo Ridolfi, my own work looks like a dwarf (I confess envy here).  Ridolfi spent hundreds of hours assembling a quantitative essay on France’s economy since 1250. This is monumental!  France has generally been a statistical abyss (except for demography and some price series) especially when compared to England. Yet, the country is highly relevant to western economic history. After all, the question of why did the Industrial Revolution take place in Britain is the mirror of asking why it did not happen in France. As a result, Ridolfi’s work fills one of the largest voids in the field of economic history and will end up being one of the most cited dissertations for the next ten years I expect.

He constructed estimates of real wages, prices, incomes and working hours. As such, he provided the widest possible statistical portrait possible which (I wont get into details here) circumvents tons of empirical complications that may limit the quality of each variable taken separately (see for example the manner in which GDP is calculated and the role that estimating working hours plays).

I invite anyone interested in economic history to read his work. But, I will give you the main conclusion I gathered: France was not as poor as many believed. I recently pointed this out in an article which I am trying to get published, but Ridolfi’s work proves my point beyond my wildest expectations. I assembled the most relevant figures below.


On the reversal of fortune, urbanization and Canada

One of the more famous articles of economist Daron Acemoglu is his 2002 article on the reversal of fortunes where he points out that countries colonized by Europeans in 1500 that were relatively rich then are relatively poor now. In the paper, they use urban density as a proxy for economic development at that point in time.

I was not particularly convinced by this because of the issue of ruralization in colonial economies. I am still not convinced in fact. As many scholars interested in American colonial history point out, the country de-urbanized (ruralized) during the colonial era as cities grew at a slower pace than the general population. As such, the share of the US population in rural areas increased. But Jeffrey Williamson and Peter Lindert documented that in 1774, the United States were the richest place in the world (beating England on top of being more egalitarian). 

This is normal. Economies on the frontier had land to labor ratios that were the exact opposite of those in Europe. The opportunity cost of congregating in one area was high given the abundance of land that could be brought under cultivation. This is why the Americas (North America at least) was the Best Poor Man’s Country. As such, areas with low population density are not necessarily poor (even if urbanization is a pretty strong predictor of wealth).

This is where Canada comes in. Today, the country easily fits in the “relatively rich” group. According to the figures 1 and 2 in the work of Acemoglu, Johnson and Robinson, it would have been in the “relatively poor” group well behind countries in Latin America. However, I recently finished compiling the Canadian GDP figures between 1688 and 1790 which I can now compare with those of Arroyo Abad and Van Zanden for Peru and Mexico. With my Canadian data (see the figure below), we can see that Canada was as poor as Latin America around 1680 (the start date of my data).


So, Canada was a relatively poor country back which was equally poor (or moderately richer) than Latin American countries. Why does that matter to the reversal of fortune story? Well, with the urbanization data, one shows that the non-urbanized of 1500 are the rich of the today. With the GDP data for the 1680s, we see that the more urbanized countries were also poorer than the less urbanized countries.

Now, my argument is limited by the fact that I am using 1680s GDP rather than 1500 GDP. But, one should simply extend the urbanization series to circa 1700 and the issue is resolved.  In any case, this should fuel the skepticism towards the strength of the reversal of fortune argument.

Dear Mr. Pirie, refrain from using the “neoliberal” label

A few days ago, Madsen Pirie of the Adam Smith Institute announced the publication of the Neoliberal Mind.  Basically, Pirie accepts the grab-everything-we-don’t-like tag that many would-be thinkers have tried for decades to stick upon what we can refer to as the “liberal right” (I prefer the French expression of droite libérale). All he does is take the same message that classical liberals have been using for centuries and puts a new label on it.

It is a PR stunt. To be fair, I have often made the joke that there should be a New Liberal Party of Canada so that its members may be called the “neoliberals” so as to ridicule those who use the word. As such, I am poorly placed to frown upon Pirie’s book. Nonetheless, I wish that Pirie (and the folks at the Adam Smith Institute) would refrain from using the label.

Why? Because for years, the word “neoliberal” has been the most efficient sorting tool to separate the wheat from the chaff.

There is no generally agreed upon definition of “neoliberalism”. Everyone has its own spin on it. Sometimes, academics who use that word sometimes to mean what classical liberalism entails. In other instances, they speak about subsidies to certain companies as “neoliberalism”. Once, and I am not joking, I debated a policy analyst from a left-wing think tank who told me that rising levels of public spending to GDP could be qualified as  part of a “neoliberal” agenda.

A concept without a concise definition which is meant to collect into a bag everything that is not liked is not a relevant one.

Generally, those who use the word have this épouvantail (the word strawman has a scarier sound in French) of the beast they claim to slay. But it is generally a caricature that does not hold basic scrutiny. They argue that “neoliberals” value profit and are “cold utility maximizers” who draw everything they believe from the cold hands of the economic sciences. They are generally unaware that economists (which are often lumped in the same bag as the main promoters of “neoliberalism”) adhere to no such simplicity. One merely needs to read James Buchanan, Vernon Smith, Elinor Ostrom, Deirdre McCloskey, Max Hartwell, William Easterly to be cleansed of this simplistic (and simpleton) view of the human mind. Using a concept that is ill-defined and does not even survive the most basic of ideological Turing tests has no value.

In the end, the sole value of those who spew the word “neoliberalism” is that they signal to readers and scholars that their work might be worth avoiding. To be fair, some of those who use the word produce interesting research and comments. Generally, they tend to use the word parsimoniously and they make it a point of honor to define it in clear and unambiguous terms. They are an exception and, generally, good research tends to be absorbed in the mainline if the point is valid. As such, the word “neoliberalism” is useful because it sorts out the wheat from the chaff.

I understand the PR value of accepting the cloak – which is what Pirie is doing. However, are we not forsaking the best weapon to identify bad social science in so doing?

Did Inequality Fall During the Great Depression ?


The graph above is taken from Piketty and Saez in their seminal 2003 article in the Quarterly Journal of Economics. It shows that inequality fell during the Great Depression. This is a contention that I have always been very skeptical of for many reasons and which has been – since 2012 – the reason why I view the IRS-data derived measure of inequality through a very skeptical lens (disclaimer: I think that it gives us an idea of inequality but I am not sure how accurate it is).

Here is why.

During the Great Depression, unemployment was never below 15% (see Romer here for a comparison prior to 1930 and this image derived from Timothy Hatton’s work). In some years, it was close to 25%. When such a large share of the population is earning near zero in terms of income, it is hard to imagine that inequality did not increase. Secondly, real wages were up during the Depression. Workers who still had a job were not worse off, they were better off. This means that you had a large share of the population who saw income reductions close to 100% and the remaining share saw actual increases in real wages. This would push up inequality no questions asked. This could be offset by a fall in the incomes from profits of the top income shares, but you would need a pretty big drop (which is what Piketty and Saez argue for).

There is some research that have tried to focus only on the Great Depression. The first was one rarely cited NBER paper by Horst Mendershausen from 1946 who found modest increases in inequality from 1929 to 1933. The data was largely centered on urban data, but this flaw works in favor of my skepticism as farm incomes (i.e. rural incomes) fell more during the depression than average incomes. There is also evidence, more recent, regarding other countries during the Great Depression. For example, Hungary saw an increase in inequality during the era from 1928 to 1941 with most of the increase in the early 1930s. A similar development was observed in Canada as well (slight increase based on the Veall dataset).

Had Piketty and Saez showed an increase in inequality during the Depression, I would have been more willing to accept their series with fewer questions and doubts. However, they do not discuss these points in great details and as such, we should be skeptical.