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I talked about this a few months ago when I discussed the article by Ryan Cardwell et al. in Canadian Public Policy, but here is more, from a short article by the Montreal Economic Institute:

The main purpose of Canada’s supply management policies, implemented for dairy, poultry and eggs in the 1970s, was to protect farmers from price fluctuations. These policies have three main components: 1) fixing prices, 2) establishing tariff barriers in order to keep lower-priced foreign goods out, and 3) managing supply with quotas so as to avoid price-depressing overproduction.

The beneficiaries of these policies, at least at first glance, are Canada’s 13,500 dairy, poultry, and egg farms, representing about 1/8 of all farms in the country. However, supply management hurts all 35 million Canadian consumers by forcing them to pay consistently more for milk, chicken, and eggs, as well as for other products that use these foodstuffs as ingredients.

Importantly, supply management disproportionately hurts poor Canadians. According to a recent study by researchers from the University of Manitoba, supply management imposes an additional cost of $554 a year on the richest 20% of households, representing 0.47% of their incomes. In contrast, the corresponding burden for the poorest households ($339 a year) represents 2.29% of their incomes. These policies are therefore heavily regressive, hurting poor households almost five times as much as rich households.

Last week I talked about what to do what to do with an obviously endogenous control variable. This week, I answer a question received via email:

… [Y]ou should consider publishing a blog post about how you handle various types of missing data when you are working with secondary data. … I come across data with a lot of [missing] values when analyzing managing household data. I get confusing and contradicting responses when I search on Google as well as when I ask my peers about how to treat missing values. I feel how we handle missing values affects the reproducibility of one’s results hence I wanted to learn if you have any suggestions on how to manage missing values. I am of the view that I may not be the only one who can benefit from learning how you handle this issue when analyzing data for your various research projects.

That is a good question, and its object is something which is not discussed often in econometrics classes, where students are often presented with data sets that have been cleaned and have no missing values. As the email indicates, real-world data is often much messier.

This post is part of a continuing series on The Books that Have Shaped My Thinking.

It’s the summer, so I have time to read, both for work and for pleasure, and I have time to read books instead of just journal articles and blog posts. This made me realize that while a lot of my thinking has been shaped by things that I have read in journal articles (economics is an article-based field) and in blog posts (there is no better means of spreading important ideas quickly), a large part of my thinking has been shaped by books, which often contain more exciting ideas than journal articles–because they face less strict of a review process, books can be more daring in their claims, and thus have more chances of causing you to change how you view the world.

So I decided to write this series of posts on books that shaped my thinking. I talked about development books two weeks ago; I talked about food and agriculture books last week; this week I will talk about food and agriculture. Some recommendations are very general; others are eminently personal. I just hope you can find one or two that will also shape your own thinking. I’m sure I am forgetting a lot of important books I have read and which have also shaped my thinking, but I made this list by taking quick look at the bookshelves in my office. Conversely, some of the books in this list also appeared in my previous post on The Books that Have Shaped My Thinking.