It’s details like these that you point out here, which make me SUPER hesitant when reading people making claims about correlating GDP/economy-based metrics with anything else.
What’s the original charts base definitions, assumptions, and their error bars? What’s their data sources, what assumptions are they making? To look at someone’s charts over GDP and then extrapolate and finally go “tech has made no effect”, feels naive and short-sighted, at least, from a rational perspective- we should know that these charts tend not to convey as much meaning as we’d like.
Having a default base of being extremely skeptical of sweeping claims based on extrapolations on GDP metrics seems like a prudent default.
“sounds like cope”? At least come in good faith! Your comments contribute nothing but “I think you’re wrong”.
Several people have articulated problems with the proposed way of measuring — and/or even defining — the core terms being discussed.
(I like the “I might be wrong” nod, but it might be good to note as well how problematic the problem domain is. Econ in general is not what I’d call a “hard” science. But maybe that was supposed to be a given?).
Others have proposed better concrete examples, but here’s a relative/abstract bit via a snippet from the Wikipedia page for Simulacra and Simulation:
Exchange value, in which the value of goods is based on money (literally denominatedfiat currency) rather than usefulness, and moreover usefulness comes to be quantified and defined in monetary terms in order to assist exchange.
Doesn’t add much, but it’s something. Do you have anything of real value (heh) to add?
”I think this is wrong and demonstrating flawed reasoning” would be more a substantive repudiation with some backing as to why you think the data is, in fact, representative of “true” productivity values.
This statement makes a lot more sense than your “sounds like cope” rejoinder brief explanation:
Having a default base of being extremely skeptical of sweeping claims based on extrapolations on GDP metrics seems like a prudent default.
You don’t have to look far to see people, um, not exactly satisfied with how we’re measuring productivity. To some extent, productivity might even be a philosophical question. Can you measure happiness? Do outcomes matter more than outputs? How does quality of life factor in? In sum, how do you measure stuff that is by its very nature, difficult to measure?
I love that we’re trying to figure it out! Like, is network traffic included in these stats? Would that show anything interesting? How about amounts of information/content being produced/accumulated? (tho again— quality is always an “interesting” one to measure.)
I dunno. It’s fun to think about tho, *I think*. Perhaps literal data is accounted for in the data… but I’d think we’re be on an upward trend if so? Seems like we’re making more and more year after year… At any rate, thanks for playing, regardless!
I’m going to be frank, and apologize for taking so long to reply, but this sounds like a classic case of naivete and overconfidence.
It’s routinely demonstrated that stats can be made to say whatever we want and conclude whatever the person who made them wants, and via techniques like the ones used in p-hacking etc, it should eventually become evident that economics are not exempt from similar effects.
Add in the replication crisis, and you have a recipe for disaster. As such, the barriers you need to clear: “this graph about economics- a field known for attracting a large number of people who don’t know the field to comment on the field- means what I say it means and is an accurate representation of reality” are immense.
It’s details like these that you point out here, which make me SUPER hesitant when reading people making claims about correlating GDP/economy-based metrics with anything else.
What’s the original charts base definitions, assumptions, and their error bars? What’s their data sources, what assumptions are they making? To look at someone’s charts over GDP and then extrapolate and finally go “tech has made no effect”, feels naive and short-sighted, at least, from a rational perspective- we should know that these charts tend not to convey as much meaning as we’d like.
Having a default base of being extremely skeptical of sweeping claims based on extrapolations on GDP metrics seems like a prudent default.
Sounds like cope to me.
It feels as if the data doesn’t support your position and so you’re making a retreat to scepticism.
I think it’s more likely that the position of yours that isn’t supported by the data is just wrong.
What are you supposed to conclude with data that doesn’t accurately reflect what it is supposed to measure?
“sounds like cope”? At least come in good faith! Your comments contribute nothing but “I think you’re wrong”.
Several people have articulated problems with the proposed way of measuring — and/or even defining — the core terms being discussed.
(I like the “I might be wrong” nod, but it might be good to note as well how problematic the problem domain is. Econ in general is not what I’d call a “hard” science. But maybe that was supposed to be a given?).
Others have proposed better concrete examples, but here’s a relative/abstract bit via a snippet from the Wikipedia page for Simulacra and Simulation:
Doesn’t add much, but it’s something. Do you have anything of real value (heh) to add?
“I think this is wrong and demonstrating flawed reasoning” is actually a contribution to the discourse.
I gave a brief explanation over the rest of the comment on how it came across to me like cope.
Contributes about as much as a “me too!” comment.
”I think this is wrong and demonstrating flawed reasoning” would be more a substantive repudiation with some backing as to why you think the data is, in fact, representative of “true” productivity values.
This statement makes a lot more sense than your
“sounds like cope” rejoinderbrief explanation:You don’t have to look far to see people, um, not exactly satisfied with how we’re measuring productivity. To some extent, productivity might even be a philosophical question. Can you measure happiness? Do outcomes matter more than outputs? How does quality of life factor in? In sum, how do you measure stuff that is by its very nature, difficult to measure?
I love that we’re trying to figure it out! Like, is network traffic included in these stats? Would that show anything interesting? How about amounts of information/content being produced/accumulated? (tho again— quality is always an “interesting” one to measure.)
I dunno. It’s fun to think about tho, *I think*. Perhaps literal data is accounted for in the data… but I’d think we’re be on an upward trend if so? Seems like we’re making more and more year after year… At any rate, thanks for playing, regardless!
I’m going to be frank, and apologize for taking so long to reply, but this sounds like a classic case of naivete and overconfidence.
It’s routinely demonstrated that stats can be made to say whatever we want and conclude whatever the person who made them wants, and via techniques like the ones used in p-hacking etc, it should eventually become evident that economics are not exempt from similar effects.
Add in the replication crisis, and you have a recipe for disaster. As such, the barriers you need to clear: “this graph about economics- a field known for attracting a large number of people who don’t know the field to comment on the field- means what I say it means and is an accurate representation of reality” are immense.