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Comment by zdragnar | Hacker News Reader

> While we find some evidence of direct employment gains from attracting a firm, we do not find strong evidence that firm-specific tax incentives increase broader economic growth at the state and local level. Although these incentives are often intended to attract and retain high-spillover firms, the evidence on spillovers and productivity effects of incentives appears mixed.

So, would this mean that the study is analogous to a null result being spun to mean a negative result in the title? "We do not find strong evidence" is not the same as "no evidence".

I wonder if this is what would happen should more failure to reproduce papers were published to fix the reproducibility crisis- instead of encouraging more critical analysis, the abstracts will just get summed up into clickbait titles that continue to mislead.

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I do not understand. If you can't say "no evidence" when you can't find any strong evidence, then when can you? What should the article have concluded to make it OK for them to say "no evidence"?

Is it about the word "strong"? Because the way I understand it, the definition of "strong" in statistical analyses is "couldn't reasonably be coincidental". So then "no strong evidence" means the same as "all evidence we did find was so weak that it might've as well been a coincidence". I'm not a scientist, but it seems to me that "no evidence" conveys the right message.

I mean, the way I see it, let's I see two bees today and three bees tomorrow. This is weak evidence that the bee population is increasing. But "Weak evidence found that bee population is increasing" wouldn't be the more honest headline. It's no evidence at all, I saw some bees and that's it.

The way I see it, it's great that scientist report null findings and failures to reproduce as "no evidence found". The general public is not going to interpret "no strong evidence" the way scientists mean it.

From the second half of the quote:

> the evidence on spillovers and productivity effects of incentives appears mixed.

Without digging through their methodology, it seems that an interpretation of "no evidence" is unwarranted as there is a likelyhood of confounding factors that were not taken into account.

"No evidence" is a positive assertion and a misreading of what the paper indicates. This is not a critique of reporting a null result, but what appears to be a misinterpretation to make a catchier headline which is equally as bad as what we have now- uncritical acceptance of whatever gets published.

Not quite, a positive result would need to be obvious to justify spending. Give away a free factory and the most useful thing to use it for does not change thus the company employees the same number of workers. It’s a function of cheap capital making infrastructure subsidies really expensive and largely worthless.

There is probably some occasional benefit here and there, but that occurs with any kind of spending. But that’s offset by the usual inefficiency associated with command driven economies.

PS: I was personally hopping for the opposite result as these subsides can spread out jobs across the US.

Since the study compares subsidies by local and state governments, changing the location will benefit one community over another. The spillover effects of hiring 1500 people in a former mining town t will be very different from hiring 1500 in an outer suburb of NYC, for example, and I suspect this I where the mixed results data come from.

I think the issue is major cities like NYC also play the game, and they have far more money to spend. Amazon’s HQ2 ends up in the same place they would have without this fight, but the government hands out a huge subsidy.

But that’s just the nature of competition. Jurisdictions are in competition to attract investment as much as companies are.

HQ2 ended up in virginia- they added 1500 employees to an existing office in NYC but backed out of the 25,000 they had planned for a different office.

They claim that the decision was logistics rather than politics, but I don't know anyone who believes that the decision had anything to do with anything other than the loss of subsidies and bad publicity drummed up by local politicians.

The title seems to be intentionally misleading, the words "no evidence" are presented as if they are a quote from the paper, while in reality the phrase "no evidence" appears zero times in it.

Ignore the headline, read the abstract and if you have time the paper. It's much more interesting and accurately/fairly describes the data collected.

>"We do not find strong evidence" is not the same as "no evidence"

Yes it is? It's not literally the same, but it conveys the same meaning.

No, it doesn't. "No strong evidence" would convey the same meaning.

In the same way, "Yes it is" doesn't convey the same meaning as "Yes it is?", because just like the presence or lack of the word "strong" matters, the question mark matters.

If everyone is Humpty Dumpty, useful and accurate communication is not possible.

This isn't a study. The only study that the paper attempted was a narrative of Volkswagen...and, tbh, I didn't really understand that (they just looked at how many workers worked in the place that got the factory, versus second place...just no logic)...with a brief quantitative study (it is there, it isn't detailed enough).

Either way, this is basically a meta-analysis. Tbh, I only skimmed the paper because it just seemed very poorly put together. I would check out the papers they reference for more detail.

"Absence of evidence is not evidence of absence."

https://en.wikipedia.org/wiki/Evidence_of_absence

But the headline doesn't claim "evidence of absence". It merely claims "no evidence".

Yes, but saying "there is no evidence of X" is not claiming evidence of absence. That would be "There is evidence of not X", which the paper does not claim.

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