[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.

[ActorNightly]

In the end, the need for a certain job sector drives demand. Its the same reason a new grad in CS in US could go get a six figure salary, because everyone was racing to monetize the web.

PhDs werent dealing with stock prices either. Nobody was trying to predict the stock market. The goal was to price volatility and sell volatility to the end party that would actually roll the dice.

[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...

[aprilthird2021]

> I feel something very fishy happened there.

The man scammed his own supporters twice with crypto scams. He, of course, is not at all opposed to market manipulation or other types of financial scams

[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.

[ActorNightly]

Look up the concept of computational irreduceability

[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).

[SJC_Hacker]

> Data science wasn't even a degree you could get 20 years ago.

It was called statistics

[lordnacho]

No, I did stats as part of economics around then, and it's nothing like modern DS. It overlaps a fair bit, but in practice the classical stats student is bringing a knife to a gunfight.

The practice of working with huge datasets manipulated by computers is valuable enough that you need separate training in it.

I don't know what's in a modern stats degree though, I would assume they try to turn it into DS.

[jltsiren]

Data science is basically a marketing title given to what would have been a joint CS/statistics degree in the past. Maybe a double major, or maybe a major in one and an extensive minor in another. And it's mostly taught by people with a background in CS or statistics.

Like with most other academic fields, there is no clear separation between data science and neighboring fields. Its existence as a field tells more about the organization of undergraduate education in the average university than about the field itself.

The Finnish term for CS translates as "data processing science" or "information processing science". When I was undergrad ~25 years ago, people in the statistics department were arguing that it would have been a more appropriate name for statistics, but CS took it first. The data science perspective was already mainstream back then, as the people in statistics were concerned. But statistics education was still mostly about introductory classes of classical statistics offered to people in other fields.

[mjhay]

No. Data science is different than statistics, because it is done on computers. It also uses machine learning algorithms instead of statistical algorithms. These advances, and the shedding of generations of restrictive cruft - frees data scientists to craft answers that their bosses want to hear - proving the superiority of data science over statistics.

[mathteddybear]

yeah, we called that data mining, decision systems, and whatnot... mapreduce was as fresh and hot as the Paul Graham's essays book... folks were using Java over python, due to some open source library from around the globe...

essentially, provided you were at a right place in a right time, you could get a BSc in it

[selimthegrim]

You might have missed the /s

[goalieca]

Actuarial science perhaps

[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...

[bonsai_spool]

> Political Economist rant about how

Political economists are explicitly less interested in the quantitative side of economics - which is why they call themselves political economists.

Thus, the comment about data makes a lot of sense, and isn't evidence of what is going on with economists

[logicalfails]

His work and department was very quant heavy. I'd say the majority of his students spend most of their time in Python/R cleaning datasets running models

[bonsai_spool]

I'm not disagreeing with you, and I also know political economists from that time who complain that their discipline is changing. It just has very little to do with what this article is discussing.

[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.