We take inspiration from Robert Higgs' 1992 "Myth of Wartime Prosperity" in the Journal of Economic History but shift from the USA to Canada.
Higgs had found that all claims of rising living standards were ill-founded. Most of this was the result of bad statistics, bad understanding of economics and wartime distortions to the meaningfulness of prices to convert quantities into "GDP" as a measure of wellbeing
Why Canada? Because it was in the war in 1939 rather than 1941 (i.e. more time to study) and it was also avoided the physical destruction. We also apply this to WW1. So, do we find the same results as Higgs?
Yes, we do. First, when we correct GDP numbers to concentrate on civilian well-being , we find that there was far smaller increases over the course of the war than the uncorrected figures do. This is true for WW1 as well.
When we correct for the disruptive effects of price controls on the deflator, we find that the war was merely a continuation of the Great Depression.
We also find that investments (private) were not above trends during either war. Similar finding for stock market data (from the TSX)
Thread: Let us be clear -- the work of Gabriel Zucman should be taken with a major/huge grain of salt. Largely because he and his colleagues have been sloppy as hell. I will not mince words here and list the litany of sloppiness #econtwitter
First, you have to understand the following thing: the work of Zucman with Piketty and Saez (henceforth PSZ) builds heavily on a 2003 paper in QJE. I revisited the data, assumptions and methods of that paper in two published papers -- one at Economic Inquiry and the other at EJ
Without that paper, the others dont follow. Its the essential building block and changing stuff to it changes the other papers (on optimal taxation, on progressivity, on wealth inequality)
This paper in NBER (summarized at WAPO) is not saying what many think it says. It says that the state can get more revenues by auditing the rich. It doesnt say the rich cheat more! #econtwitter
Why? I used to be interested in illegal work, sidelines, underground economy etc. I can tell you that cheating at the low-end of the ladder is easier because the sums are smaller and easier to hide.
After all, working a day for $200 cash for a moving company in Montreal (I never did that) when you are low-income is easier to hide than a large sum of unreported income or deliberately inflating expenses etc.
Thread: This is a poor reading of Hayek.
The Use of Knowledge in Society (AER, 1945) does not say what Acemoglu *thinks* it says. Prices are not aggregative devices. They are knowledge-economizing devices that are cheaper than *any* other devices. #econtwitter
The problem starts with fact that there is a great deal of knowledge that is simply tacit and impossible to code. Moreover, the *value* of that tacit knowledge is impossible to code as well. For example, riding a bike is hard to express into a book/code. So is coding its worth
But, the time-price, the price of an instructor, the price of a parent's time etc. are going to express the value of using this knowledge. Prices economize on all the knowledge we need to collect about bikes. They tell us how much knowledge is necessary.
2. No, Africa would be better off. We know that more rugged areas in Africa (ruggedness made the slave trade harder) are richer than less rugged areas. This is strong supportive evidence that where the slave trade could flourish, the worst off people were.
3. No, *America* was not made richer. It was made *poorer*. Let me elaborate a bit here.
Personal experience: I shopped a replication papers showing that the famous Piketty and Saez paper in the QJE was sloppily made, had tons of typos and tons of huge historical errors (like omitting that all state/local gov employees didnt file taxes) and told that it didnt matter
This was ultimately published in the Economic Journal -- doi.org/10.1093/ej/uea…. It showed that this was an immensely flawed paper.
In fact, that paper was strategically conceived. There were two problems with the P&S paper in QJE. The first was the assumptions and treatment of the data. The second was the data itself. We tried doing a paper that merged the two issues into one. It was too big.