Yes. I wonder how impact factor can be gamed…

The Scimago impact factor is 1. Know what that suggests?

MDPI?

Just because something is linked in the National Library doesn’t make it non-dubious…

I was mentioning yesterday the low quality, dubious nature of too many cannabis studies. And here’s another one…

And, unfortunately, the existence of unintended consequences to laws, as well as ineffective language, means that implementation often falls short of intention.

This is an important study.

Edit: your mindset would have derided follow up studies to Brown versus Board of Education, which almost uniformly found less than expected Black economic progress, because equal access did not imply equal resources. It’s a terrible mindset.

Cool study. Does suggest that using a signal to impute quality for consumers can have sizable losses, for those who wouldn’t get a license but would work, and for consumers.

The distributional impact is also important, as higher prices may preclude lower income Americans from performing needed repairs.

Note that this welfare loss does not take into account quality measures of work performed, which would mitigate the losses.

The point is that licensing imputing quality signals can and does have a net welfare loss before you take into account changes in quality.

And some of the early marijuana laws specifically targeted a substitution relationship between opioids and marijuana, which have largely evaporated in more recent studies. That’s certainly harm understatement.

Marijuana research suffers from both attenuation bias and bias that increases the magnitude of results. It’s a glorious mess in the sphere.

Again, early studies on this suffered from data limitations. We don’t really know much. This research sphere is filled with dubious claims.

They didn’t prove harm. Even the language of the author “may cause” suggests they understand it’s a correlation that could be more.

The growing lit suggests many of the benefits of marijuana are overstated, or that they disappear when correctly accounting for trends. It’s not some anti-cannabis conspiracy; it’s growth in an area with significant research limitations until recently…

Most of the findings for marijuana (both benefits and costs) are correlary, rather than causal. This sphere is filled with terrible studies pushing agendas.

AEL is for very short publications.

DiD is the methodology they use, which requires some pretty intricate statistical relationships.

No way you can determine this in AEL. Especially using a DiD methodology.

There are things called multiplier effects…

Which is why I think this is much more devastating than death. Especially for men who would be living in high mortality rural areas (or the convicts).

The mental, social, and physical healthcare costs of disability are astronomical. Caregiving further depletes additional labor from the market.

Death is an issue for the families, but disability imposes a much larger economic cost on society.

Short term GDP numbers mean nothing. The brain drain, the hollowing out of the middle class, and the long term disability consequences of soldiers are going to devastate Russia for decades.

Cannabis and the impact on opioids is one of the more consistent substance use disorder findings from the lit (maybe the one true impact).

Unfortunately, it doesn’t really translate into meaningful reductions (or consistently found reductions) in overdose death rates from opioids. Or admissions to the hospital with opioids detected as a primary substance.

The bigger question is the links between other SUD’s and cannabis, most of which rely on shoddy, preclinical studies.

A PhD and active research in the space?

There is consistent evidence that cannabis (marijuana and CBD) substitutes for opioids.

But the impacts are much smaller in magnitude than this survey.

Edit: these numbers are implausibly high for extrapolation to the general public.

Month-over-month numbers get revisions.

Not eliminating seasonality also means that your month-over-month numbers have an additional bias.

True. The joys of figuring out the peer review process.

Shouldn't be; it corresponds with a lot of the literature on the effectiveness of COVID shutdown policies to COVID cases (reduced it) and deaths (minimal impact).

Such a weird dichotomy. I have 2 COVID era papers; one was published after 8 months with a 21 page revision. Another is still under review at its 3rd journal.

I would imagine name recognition plays some role (if you couldn’t figure it out I don’t have it) but my experience certainly wasn’t of a faster process. Instead, it was more thorough than regular review.