[logicalfails]

My first thought is wondering if it's one of two things:

A. The bottom half of PhD Economists are not being trained in the data science/Big Data side of analysis increasingly needed

B. There is less demand for Theory-sided Economists over computationally trained ones

[ActorNightly]

Its C. the market doesn't follow traditional models anymore.

The whole profession was basically centered around putting a dollar amount on risk.

For example, lets say I give you a chance of either taking $1k now, or playing a game where you have 1 in 10 chance to win $200k. What would you do? The right answer is "sell" the risk to someone. For example, on the average, if I "buy" the game from 10 people, at a price of $10k each, I can realistically win twice what I spend.

Repeat that over x number of steps and more complex games, and that is what the PhDs worked on in terms of pricing.

For most of the time it worked ok. In a few instances (most notably the Gaussian Copula that was a large reason for the subprime house market crisis in 2007) it didn't.

The problem is that now, its impossible to predict whether orange man is going to throw a hissy fit and cause the market to go up or down, or if large investors are going to artificially prop up stock like they did with Tesla.

[loire280]

You're right that the orange man has been a big factor, but not because of his effect on the stock market. The stock market isn't the economy, and most Econ PhDs are not working on modeling stock prices.

As the article indicates, a huge portion of the market for hiring PhDs is directly or indirectly dependent on federal funding. Universities are freezing hiring and reducing PhD cohort sizes, institutions like the IMF and World Bank are in crisis, and US government agencies have been reducing staff sizes. There was hope that the tech industry would provide another big source of jobs for PhD economists, but that hasn't panned out.

Source: the article, and my wife works in the UChicago economics department.

[mothballed]

There was an electric vehicle stock I was watching for awhile (WKHS) hoping for a good time to short. All their reports showed what I believed was an unviable vehicle, there was simply no way to produce it that was anywhere near gross margin positive and they didn't have the money nor access to capital to lose hundreds of millions proving that out. All the economics of the company was going to zero, and they had a ticking time bomb of debt they were about to default on.

Shortly before this debt time-bomb went off, Trump magically showed up tweeting in support of the company and alluded to a deal getting pulled off with GM. [] Of course, this ended up being spun off as Lordstown motors, a company that has failed horribly, including Hindenburg Research publishing a video of one of the few trucks they had literally catching on fire on the road while the CEO was simultaneously claiming they had hundreds of millions of dollars in solid orders (later fined by the SEC for that and barred from being an executive of a company for N years).

I still don't understand how Trump magically got involved with this penny stock at the 11th hour, but I can tell you I feel something very fishy happened there.

https://www.cnbc.com/2019/05/08/trump-tweet-sends-penny-stoc...

[eej71]

Its the old axiom best expressed by the philosopher Sir Michael Tyson - "Everyone has a plan until they get punched in the face".

[jgamman]

Orange Guy is the official mascot for NZ elections.

[bigstrat2003]

To the extent that a single person can cause economists' predictions to be off, those predictions were never good in the first place. We will always have unstable people in power, it's just human nature. If your predictions can't hold up in the face of that, then you need to refine them.

[asciident]

CS and DS people are getting more applied and gaining domain expertise, and can do a lot of economics work now. Academic economists, especially those who primarily do data science / big data, seem to basically be doing Masters-level data science projects for their Ph.D. The hard part in their Ph.D.s is collecting the data, which used to be a very manual job that relied on connections, but more of them are getting them or imputing them from public sources so it's not that impressive anymore.

Speaking as someone who has attended 3 economics Ph.D. defenses in the past two years.

[1980phipsi]

Data science wasn't even a degree you could get 20 years ago. Twenty years ago if you were interested in what is now called data science, you were getting a degree with some kind of exposure to applied statistics. Economics is one of those disciplines (through econometrics).

[logicalfails]

In 2017, I listened to a highly cited Political Economist rant about how

"The whole damn field is turning into a bunch of Data Monkeys"

Referring to the rise of CS and DS minded economists in the field. His top student was a computer science major...

[narrator]

The bitter lesson is making it so all these people hand designing features with econometric models are being obsoleted by big models and lots of data.

[Eridrus]

The note about economists and data science in the article felt weird, because data science as a title was invented to get non-CS PhDs to do analyst work because they wanted smarter people doing it.

The point of hiring an economics PhD in industry is largely not because they learnt something but because it's a strong and expensive signal.