Welcome to the Canvas Data Science Meetup Group.
Happy to have you here as always.
So we've got an awesome day ahead of us.
So going to talk about economic surplus today.
So this is an introduction to economics that I've been wanting to do here for a little
while and just got input on the pause,
but the Python saying is now is better than never.
So we'll go ahead and kick off with that today.
Without further ado,
we'll just go ahead and do a quick round of introductions.
That way we can have a nice discussion throughout talking about economics,
plus Canvas data is always today.
So I'm going to start in my top left corner.
So Camila, you wouldn't mind introducing yourself real quick?
Sure. Hi. How are you?
Awesome.
Fine. So I'm Camila Coelho.
I'm an industrial designer.
I'm actually currently studying communication design.
I want to dig in data visualization.
So I was looking for different things around data,
and I'm a cannabis user,
so also it was matching.
So I'm here because of that.
You're in the right spot.
I'm happy to have you.
Ryan, would you introduce yourself, please?
Yes. I'm Ryan.
I'm from Long Island, New York.
I'm an applied math and economics graduate.
Currently doing a bootcamp in data analytics.
I'm a long-time medical cannabis user.
I'm actually winding down on that.
But it's funny, the less I'm using the medical stuff,
the more interested I am in how it works.
Well, awesome to have you, Ryan.
You can keep us on our toes with economics,
and also happy to hear about your experience with the New York market,
because they'll be coming online soon.
So interested to have your experience there.
Awesome. It's been a great review of economics for me.
Awesome. Well, like I said,
keep us on our toes.
Marjorie, would you please introduce yourself?
Hey, everyone. Nice to meet all of you.
I was an academic research scientist for 10 years.
I moved to software engineering and I enjoy cannabis and cannabis data.
So here I am. Nice to meet everyone.
Awesome to have you here, Marjorie.
Heather, would you please introduce yourself,
and then I can go last.
Hey, I'm Heather. I'm not really doing well today,
but just curious for Ryan,
if they still have that pre-packed ground flower in New York still.
We do.
That's unfortunate. I'm so sorry to hear that.
I know. It is what it is.
Okay. I'm so sorry. I'm not really well.
But it's nice meeting everybody.
I'm really happy to be here.
Awesome. Happy to have you here,
Heather. Then Graham, happy to see you here today.
We're just doing a quick round of introduction.
So you wouldn't mind taking
30 seconds and introducing yourself to the group program, and then I'm on.
Yeah. My name is Graham Manischewski.
I live in Maryland where we only have a medical market.
But I used to be a data scientist working in space.
I was a contractor with SpaceX.
But I got hit with a deadly disease and it's forever changed me.
As you can see, I shake while I talk.
But cannabis is my medicine.
I'd like to use my skills in data science.
I have a master's in mathematics and deep learning.
So I have a deep basis of understanding with,
I guess, the model of all this.
I've studied supply chain management,
economic PDEs, all that stuff.
I just don't know all the terminology
I guess with this and I'm just looking to see if I can search for
a job opportunity once I get through all this rehab to use my skills in cannabis.
I'm really interested to see what data science models we were looking for.
Because I was pretty confused on Saturday.
Exactly. So happy to have you.
Graham, you can keep us on our toes too because your experience with
data science will just keep everybody sharp.
There's a high demand for people with your skill set in the cannabis industry.
So happy to have you aboard.
Yeah. I should also say,
I am a heavy smoker and I've been smoking for about a decade.
I don't know your guys' experience,
but I've been around the game for a while.
Let's just say that.
Well, always happy to hear about your experience too.
Heather's also in Maryland too.
So yeah, it'll be interesting to have all of your experiences.
Just to get you into the conversation, Mel.
We're just doing a round of introductions.
So you wouldn't mind introducing yourself to the group real quick.
Hi everybody.
I just ran into this meetup.
I was just interested.
I've been working as a data scientist for the past few years.
So I just wanted to see what you can do with cannabis data.
Because in the recent years, cannabis market has been growing.
Personally, I don't smoke or do any.
Yeah. So I was just curious.
Well, happy to have you.
There's people from all walks of life here.
So we're a big married group of data scientists.
So this is awesome.
So a lot of skills here in this group today.
So we can definitely get a lot accomplished.
And then yet another guest.
So please correct me if I'm not pronouncing correctly, but Dharampal.
Yeah, thank you.
Yeah, it's Dharampal.
You can call me DP for the short one.
Actually, you know, first time I joined here.
And, you know, I just set up the Google Meet, in fact,
because I joined the meetup group recently.
And every time like some one or other group is being used to have these discussions.
So today I was late because of that one.
And I had to set it up like with my short name as DP.
So that is easy for everyone to pronounce.
Awesome. Thank you.
Well, happy to have you, DP.
So we're a big group of married data scientists here.
So my name is Keegan.
So I started a company, Cantlitics.
So my experience was working in the cannabis testing space.
And so I thought, well, we can probably utilize my experience as a,
you know, number cruncher statistician and help people set up data pipelines.
And so working in the industry for a while,
I realized there's a really big demand for data analytics, data science.
And so I thought I would share with you some of the issues at hand,
what people are talking about, where some of the data can be found,
and what analytics you can do with, you know, real public data.
So it's almost like there's just gold laying there right on the table.
So there's just, you know, treasure to be had.
And you all have the skill set to go out there and mine some data treasure.
So long story short, I can just show you some data sources today,
just to talk about a little bit of economics and just show you what you can potentially do.
And then I'm happy to hear from all of you about,
you know, what you may be curious about doing with the data,
because this is just my perspective on the data.
I come from an economics background.
So as Graham pointed out, so on Saturday, we talked about the statistical model,
the instrumental variable model.
So tune in on Saturday and we'll talk about this again likely.
And so long story short, Graham's keeping me on my toes, Mel, Camilla, Ryan,
DP, Marjana, and Heather.
So we can all keep each other on our toes here.
So without further ado, I'll just go ahead and spend a little bit of time here
and just introduce to you some economics and some data
that I've been looking at in Illinois and Massachusetts.
And then we can have a discussion about that.
Anyone want to say anything real quick?
Not as such, but, you know, I'm not sure like why, because I just, as I said,
like, OK, I'm for the first time here.
So I recently started doing this data science work.
And, you know, I tried to find out, like, if in my existing organization,
if I can find some opportunities to do some data analytics or this kind of work,
basically, like what data can tell me basically, because right now what we know
is what we know, but what we don't know is something like the patterns,
the hidden patterns within the data which can tell us, OK, hey, you may,
you know, work on certain things. So it's just my thing is like just a startup.
I really don't know, like, what I'm going to discover here.
So but you said, like the economics and like some of the other things.
I'm excited, basically. I'm sorry.
I would be like, OK, more curious to have a lot of questions.
But right now I don't have any questions, but we will see what exactly you present
and based on that, we will go forward.
Awesome, GP. I got a couple of questions.
Oh, yes, Graham.
I would like to know the any of the rules and regulations with like people growing and stuff.
I only say that because in Maryland, they are very strict.
You have to grow it in a specific place.
And there's only so many grow sites you can have in terms of this.
But I've heard in New England and stuff, they have a little free array
to have small like cannabis companies and small dispensaries to start up.
Yes, so we'll be looking today just to add, you know, the Massachusetts.
Cannabis Control Commission, so different
states have put together their own bodies to legislate cannabis.
So definitely a state by state basis at the moment, because as you're all aware,
cannabis is still not permitted federally.
So basically, certain states have enacted legislation to allow
or to permit people to operate in their states.
So. So exactly. So as Graham pointed out, so in Maryland, Maryland has their own legislative body.
You know, I don't know it off the top of my head, but, you know, you can just probably do a search,
you know, Maryland cannabis regulators, do you know, Heather, what the body may be?
By body, well, one thing I know is there's a number of there are only certain number of licenses
that can be distributed and you can actually see the names of all the testing,
the three testing sites as well in Maryland.
So there are very few of those, quite the monopoly at the testing site.
Just want to say there.
And then also certain brands like Colta will actually prevent dispensaries from giving coupons or other discounts.
That's something that they can do. So all this market data can be completely blown up by Colta.
Yeah. So just want to say that that's maybe a bias coming in there, but that's just my opinion is that,
well, it's my observation is that there are companies in Maryland that can dictate
when and how discounts are applied to the customer. Thank you.
And may I just add on to that too, there is a pretty heavy monopoly, oligopoly on the grow sites in Maryland as well.
There are only several small town grow operations that still have licenses to grow and sell.
G-Leaf has bought out over 75 percent of the market.
Yes. So it's very difficult to do price action with them.
That is basically what they said. Everyone has to follow.
It's really awful in Maryland. Thank you, Graham. Thank you.
I spend over twenty thousand dollars a year on my medicine. I have to use it constantly.
I believe you. I believe you. And I live that. So please know that.
Yeah, I hate Colta too. They have a bad rep involved more in Maryland now.
You've brought up an interesting point here. Serious. Thank you.
Because this is exactly what we're here to do is essentially we need to get this data that's locked up here with,
you know, the Maryland Medical Cannabis Commission.
And as you exactly said, try to quantify, you know, is Maryland sort of in the oligopolistic competition?
You know, so it's a long story short. You know, do these producers have a lot of monopoly power?
And, you know, can they raise prices on consumers like yourself?
So. So, look, long story short, we need to start getting this data and making it accessible.
This is sort of been a side project of Camp Lytics here is we would love to be able to get data in regulations for each of the states.
So and make it publicly available so that way. You know, we can just know off the top of our head what the regulations are in Maryland or Massachusetts or Illinois or where have you.
So if you want to contribute that started these two projects here.
So, you know, we've just started the Cannabis Data Science Meetup Group, where we just do a little bit of exploratory analysis,
try to find data from various states and just begin crunching statistics.
And then if you want a more formalized project, putting together Camp Lytics AI,
which will essentially just try to scrape all of the various states so that way we can collect data from Alaska, Arizona, California,
you know, all the way through Maryland and all the way to Washington state.
So that way we can actually compare how these different states are functioning here, because as you pointed out, it doesn't seem like Maryland's very competitive.
So it would be interesting to quantify that.
So thank you for bringing that point up, Graham.
Well, without further ado, let's talk about some of the economics while we're at it, and then we can show you how
or I can show you how I've begun to scrape data from Illinois and Massachusetts publicly available resources.
So, and Ryan, you can attest to this, so I would always tell people, you know, the reason you study economics is so that you don't get fooled by economists.
And so this quote goes back to an economist who was born a hundred years ago.
And, you know, make of it what you will, but I always get a good laugh out of this quote.
And so, you know, I'm going to be talking about economics today, but, you know, don't, you know, don't get fooled by me.
If anything sounds counterintuitive or doesn't make logical sense to you, then question it and think about it deeply.
Because, you know, good economics, you know, should be, you know, be able to be explained quickly and simply.
Can I just add quickly, I think that quote is so important, especially in a day where there's
where policymakers are just throwing up these big spending bills, big economics plans, saying stuff like, you know, this is not going to cost you anything.
And they cite these economists. This is why it's important that everyone kind of needs to know what exactly is happening.
I mean, obviously for here, but just in general, like, I think everyone having a basis in economics is so important.
I wish there'd be more done in the K through 12 education in relation to that.
Exactly. So that, you know, falls on the presenter a lot because it's a it's a high bar.
But, you know, I believe that, you know, if you firmly have concepts under your belt, that you should be able to convey them to to anyone.
So. So exactly. So, like you said, less hand waving and more, you know, making sure that everybody's on board and.
Yeah, like you said, don't just don't just take things for granted.
So. So. Awesome, Ryan.
So so as you can see, Ryan's schooled in economics and is pretty skeptical.
OK, anywho, we've begun to basically look at the number of retailers and then the.
Revenue per retailer as sort of a metric of profitability.
And so. What we're trying to uncover is, you know, are there essentially rules in place that may make certain places more profitable for retailers or producers to operate in?
And, you know, conversely, there's a other side of that coin there.
I'm actually going to to back up to this. I just going to start start here, I think.
So, you know, basically, you know, the reason we're talking about this is, you know, there's no essentially two sides to the coin here.
Right. So, yes, it's awesome, you know, for the producers to, you know, make a nice surplus here.
But, you know, the consumers are operating in the market, too.
And. You know, they get a certain amount of value out of their purchases.
And so you can begin to quantify this.
So, you know, if you just look at the quantity sold in the market and the prices,
if there weren't any anti-competitive forces and it was just free information and just,
you know, no transaction costs and just everything just worked out frictionless,
then you would experience what's called the perfectly competitive market equilibrium.
And so here, you know, marginal benefit is exactly equal to marginal cost.
And, you know, you've got what's called like an efficient market.
So this is sort of the idea that everything's compared to.
What you observe, you know, in reality is, you know, you observe things such as, you know, taxes.
So, you know, various market markets will be taxed.
And so, you know, cannabis is one of the goods that is taxed.
And there are various reasons why you may do a tax.
So, for example, you see do see taxes on other goods, such as, say, alcohol or cigarettes.
And one reason you can do a tax is just to discourage consumption.
So, you know, that's a way that people have discouraged cigarette consumption is just
by levying a high tax on it.
And then, you know, you'll, you know, reduce consumption and, you know,
you'll get this tax revenue and then you can spend that on other, in other markets
that you may think have what's called like, you know, public externality.
So, basically, you know, the government will sort of redistribute, you know,
this tax revenue from the cannabis market to another market.
So, you'll see maybe they'll, you know, subsidize some, you know,
so they'll add more funding to education.
And so, that's a whole other market of its own.
So, long story short, this is what the effect of a tax is.
And so, as you can see, some consumers are boxed out of the market and, you know,
the producers lose out on a bit of their surplus as well.
So, taxes is one thing.
The main thing that I was interested in was the comparable effect that you see just
from say quantity restrictions.
So, here there's no taxes levied, but you're just going to restrict the, you know,
the total quantity that can be allowed in the market.
And I see this happening in many states, and it's hard to, you know, think of, you know,
the social reason for this, except, you know, except if yet again, you know,
people were sort of interested in, you know, curtailing cannabis consumption.
And that would just be for, you know, social preferences.
And so, you know, maybe, but that's a conversation for.
Are you talking restrictions on the consumer to buy a certain amount of cannabis
or restrictions on the supplier of the cannabis to the market?
Exactly. This will just be on the actual supply.
So, basically, what my argument is, is if the state says that there can only be 20 cultivators,
well, normally there may, you know, in other states you observe, you know,
100 or 200 or more cultivators.
So, my argument is that you would normally see a larger quantity supplied,
but they're essentially, you know, restricting the quantity, you know, to be a certain amount.
Of course, I just realized this is maybe not the perfect example because you could just have, well,
the idea is this would, if this was a monopoly,
the monopoly list would choose to operate, you know, less than competitively.
So, that way they can, you know, set, you know, set prices to maximize their profit.
And so, the idea is if you're operating, you know, between, you know,
like the perfectly competitive in some way with more of an oligopoly,
then you would expect, you know, prices to be, you know, towards, you know, QM.
So, so my, so, so long story short is my argument is by restricting just the number of licensees,
that that's sort of effectively, you know, sort of restricting QC towards QM.
I'm not saying it's going to be perfectly at QM.
I'm just saying that I think that having restrictions is going to push things in that direction.
Is it safe to say that having more restrictions would make, I guess, the supplier kind of blanking out here,
but it would make it more inelastic, meaning that if there was a tax levied or something
that the price burden would be placed much more on the consumers due to these restrictions.
You do raise a good point.
So, in fact, that actually goes right back to this point of who bears the cost of the tax.
It depends.
So, so as you basically said, whoever basically has the more inelastic either demand or supply
typically bears the bigger burden of either a tax or the quantity restriction.
So that's a whole other question of its own is, you know, what exactly is the elasticity of demand
and what's the elasticity of supply?
And I actually don't know.
And so those are, you know, some metrics that I think are maybe they've been looked at in the literature,
but, you know, these are things that, you know, a group like ours could begin to estimate.
There is work on it in like botany circles.
On the elasticity of supply?
Yeah, in terms of how quickly you can turn it over, how consistent the supply can be versus how finicky can be.
Because we have diseases in here with the supply, there's outlier things,
and they're trying to clean out the outlier things so we can get a more foundationally appropriate estimate of this elasticity.
Graham, do you know if these studies were done on a state or federal level?
I believe I know NC State is doing research in hemp production and what does higher yield rate,
what's correlated with a more consistent or a higher yield rate product in hemp only.
But there's also research in Washington.
I know Washington State University.
They do a lot of research on studying diseases in cannabis, though, because it's more botany based, less economics.
But they are doing research to try and standardize production, both indoor and outdoor, on a grow level.
Awesome. OK, I need to pause for five minutes here.
So can you please continue this conversation and then I'll be right back to talk about the quantity restrictions here and maybe how we can parse this out in Illinois.
So bear with me. I'll be back in five minutes.
So short break here. So bear with me here.
All right.
Be right back.
So out of this group, how many people use cannabis here?
I do. I have to.
I absolutely have to every day. Otherwise, I will be in a massive amount of pain and suffering.
Thank you.
It's just interesting to see here because there's different perspectives when it comes to people use it versus outside perspectives.
And one thing I've realized through coming through these meetups is that you need both because users of it are biased to it.
But people that don't use it have no idea.
Because let me be honest, you really can't look at this as a purely economic thing because the grow part of it hasn't been figured out yet.
And in terms of that quantity, it varies randomly.
There is a company in Canada that got billions of dollars of funding and they went bankrupt because all of their crops were infected with mites and couldn't be used.
So we do have to be wary when we're talking about this.
But I understand. And it's really important that we get this economic thing down.
And that's why I'm really motivated to do this.
Because the Maryland, it's all for profit.
And it really stinks.
Thank you.
That's something that needs to be acknowledged.
And I'm glad that you're voicing that here.
Thank you so much.
But I will fully acknowledge I know nothing about economics of this stuff.
I know a lot about the chemical makeup of it, physics, and just the experience of it.
Because I've been taking data for the past two and a half years.
I was told I was going to die.
And here I am.
So, so yeah, just just background for you folks who don't know.
Thank you. Thank you, Graham.
Awesome.
Thank you for sharing your story.
Well, with migrating here, so just getting resituated.
It's awesome to have you, though, because, you know, essentially, you know, I can share what I know from economics.
You can bring your mathematics.
Ryan can keep us sharp with economics.
And then Heather's got the laboratory background.
Marjana knows statistics.
Camilla's got data science, EP got data science as well.
So I think with the group assembled just here today, there's so much value that we can add.
So so without further ado, I'll go ahead and just conclude real quick here with the economics and then show you how to get some data.
And then we can begin systematically collecting it for all the various states.
So. And I'll move through this quick.
So the idea is if you had a monopolist in the market that they would operate at QM.
You know, of course, we don't observe, you know, a.
An actual monopolist and we don't observe an actual perfectly competitive market, so we observe someplace in between.
So I'm just conjecturing that if you see things that restrict the number of players that can operate in the market,
that you may expect this to observe a queue that's closer to QM than QC.
This is just pure conjecture of mine.
And it may not be the case because as we were talking about, like it depends a lot on the elasticity.
So the elasticity is essentially the angle of the supply curve.
So the flatter the supply curve is, the more elastic it is.
So basically, the more elastic the supply curve, the producers aren't really going to be that affected one way or the other.
They're going to have to apply a restriction in.
In quantity caps. So if if, you know, supplies very elastic and they restrict it to one.
Producer that producer may still operate somewhere near the competitive market,
the competitive level, if the elasticity is really.
But if it's very inelastic.
The elasticity is very high.
But it's very inelastic. So if supply is close to vertical.
Or if there's any elastic demand, if demand is close to vertical.
Then they're going then either the consumers or the suppliers are going to bear a large cost from restrictions.
So there's various reasons why you may think supply may be inelastic.
And there's arguments why you could argue that demand would be inelastic.
Some people, right, they're going to consume whether the price is low or very high.
They're just going to arguably there'll be some people that get boxed out.
So once again, you can break the market up into different segments of demand.
But but long story short is.
My hypothesis is, and this is just going to be my beginning attempt at trying to to go about studying it.
So this is sort of just my rough draft, you know, just my rough analysis here in front of you.
But basically what I'm thinking is, right, we're not going to observe QC.
We're not going to observe QM. However, in all the various states,
I think we're going to observe some Q somewhere in between here, some price somewhere in between here.
With certain states being closer to the monopolistic side and certain states being closer to the competitive side.
Noting that all the states are going to be different.
So that right, all the states have different populations.
All the states have different factors that affect their supply.
So supply and demand are going to be different in all the various states, and they're going to be different over time.
So you're going to expect prices and quantities to either be shifting along the. Curve over time as policies may change, or you know, you may see.
You know, shift in demand curves or shifts in supply curve, so you may see a shift in the demand curve if the population increases,
or you may see entirely. Downward shift in the supply curve if there's like some brand new technology introduced into the market.
So you're going to see all these shifts all over the place.
And so in this chaotic world where we're seeing these data points moving all over the place,
is there any way that we can parse out where exactly states are?
In in the market and you know if they're potentially heading towards a competitive output.
Or if they potentially are heading towards a, you know, a monopolistic output.
So that's my big question is where are states now in say this dichotomy between monopolist and competitive?
And you know, what's their trajectory? So that's my grand research question.
And first steps first, we need to get the data.
So I'll go ahead and share these scripts with you. And so this is essentially how I was beginning to get the data from.
Illinois, so they have.
Data here in PDFs. So you've got licensee data and you've got sales data.
And. We just need a nice automated way to get this data out of these PDFs.
So all you data scientists out there who use Python, I think you're going to love this tool.
So I've been pretty excited about it myself, but it's PDF plumber.
So they use a clever way to extract data from PDFs. Essentially, they just look at.
Your PDF. And try to find any grid lines and then just return you the data as an array.
So that way, instead of just trying to parse all the text and you know, trying to think about tabs and this and that.
The you know, actually use the grid lines and return to you a nice array.
So long story short. We can now automate the collection of the Illinois data here.
So.
Let's.
See if we can't download this data here.
OK, so long story short, we're just going to download this data as a PDF.
Then we can read the data into an array.
So that way we can begin the cleaning process.
So just have. The giant 2D array here.
And so we can. Use our own column names and just create a nice data frame of licensees.
And so this is just retailers, however.
This is good data so we can clean this up a bit.
So then this can be refined a bit more, but this is essentially just parsing out.
Street city, state zip code and phone number from.
This block here.
So now, you know, we know how many retailers there are in Illinois and.
Graham and everyone else don't let me just get so fixated on certain states.
So as Graham pointed out, we need to unlock this data here from Maryland.
So perhaps this could be another good project is somehow trying to.
To unlock this data.
So, but for now. We've got. Retailer data in Illinois.
We can also unlock the sales data.
And once again, we read this as just a giant array of data, but will need to be cleaned up here.
So simple enough.
Once again, using our own column names.
Using a bit of logic to determine the year and month.
And then we've got a nice time series of data.
Well, not yet. Still have a bit of cleaning to do.
So basically. This these next few lines.
Create a time index and then remove all of the extraneous values.
So those can be removed and this this third block.
Handles the dollar signs, because as you can see, we've read the data in with dollar signs and.
Need to convert that to numeric values.
This line of code simply just converts.
The time index here. See how everything is indexed at the beginning of the month.
My personal preference is just to index things at the end of the month.
Because to me, that just makes more logical sense.
So, for example, when you're talking about monthly sales, it just makes sense.
Like, oh, that was the sales for the month.
This is entirely my personal preference.
But it makes a difference when you're doing the coding.
Long story short, we now have the sales data nicely parsed for us.
Awesome. And so we can begin to really calculate some interesting statistics here.
That you don't see many people calculating and let alone.
Showing you the methods, and that's what we believe a lot of the scientific process is all about, right?
You're you're all about showing your methods about how you get your results.
So we I personally believe, you know, if you're, you know, preparing statistics.
And presenting people with data, you know, it's important to show your processes and, you know, be upfront about assumptions made along the way.
Because as we we demonstrated, you know, there's a lot of cleaning that needs to be done along the way.
So long story short, we can begin to look at some real cool statistics here.
So we've got when the license were issued.
So given when they're issued, we can get a count of how many retailers were operating over time.
Because that's one of the dimensions we're interested in, right? We're interested in, right?
Quantity and price, but we're also interested in time and the state as well.
So we can basically get a nice count of the retailers over time.
And this is sort of where I was starting to get into the the quantity restrictions.
So in the in will compare Illinois to say Massachusetts and other states.
But, you know, in all the states, you see this gradual increase of retailers and cultivators as, you know,
people are coming online and setting up their shop and opening up shop.
In Illinois, though, you just see this just this cap, you know, they they they, you know, they can't do believe they've allowed more more licensees into the market as of late.
But, you know, the Illinois sort of, you know, one of the they were sort of famous for for capping their licenses.
And so, you know, here you do just observe this, you know, cap and retailers at at one hundred and ten.
And so that's sort of what I was talking about here.
So I feel like, you know, that that cap is almost kind of tapping the potential quantity that could be produced.
And so I don't think you're at QM per se. Right.
Because there's one hundred and ten retailers.
It's not a monopolist, which is one. But I don't feel that we're quite at QC.
So long story short is I think it would be interesting, you know, to measure.
You know, Illinois and maybe compare it to other states and try to conjecture, you know, what would be, you know, the competitive quantity and, you know, what would be competitive prices?
Like if you let more people into the market.
And like we said, we may not be able to just pinpoint what QC is, but, you know, a lot of what economics is about is marginal changes.
So we could basically say, you know, what would be the effect of, you know, allowing one more retailer into the market be?
So that's essentially what we're trying to uncover here is, you know, what would be the effect of allowing more people to operate in the market?
Or at least that's sort of the question I'm after.
And so a lot of this is or is or supporting statistics.
So I'll just walk through these briefly. So basically just grabbing the population.
And looking at retailers per capita.
And so this is a nice standardized metric that you can use to compare state to state.
So this is retailers per 100,000 people.
So that way we can start to compare Illinois to other states.
You know, controlling for factors such as population.
And then, as we pointed out, we can begin to look at things like sales per retailer to try to get a measure of profitability.
And. Join us.
Oh, yes. And here's I just did a few monthly.
I mean, a few annual statistics.
So here we just looked at retailers per capita in 2020.
And then sales per retailer in 2020.
And then in 2021, you do see you do actually see a doubling in retailers per capita, of course.
Although it doubled, it's the nominal value 0.82 is still quite low.
And so, you know, so. So long story short, we're starting to get a couple of data points here
about about the competitiveness in Illinois, but just scratching the surface.
And so there's still a lot more to do. And so just going to go ahead and tease.
Definitely recommend tuning in on Saturday because we've started to talk about instrumental variables
and how we could potentially. Actually try to estimate movement along the demand curve, potentially,
as people are allowed into the market. So that's what we're trying to measure.
But, you know, just to kind of show you where we are.
You know, we still are far from so. Right.
We're we're trying to draw a nice supply and demand curve with price and quantity.
And, you know, this is our best attempt at drawing a demand curve so far.
And it's got a positive slope. So. You know, Ryan can attest that, you know.
You know, a lot of demand insists that we have a negatively sloped demand curve here.
So the long story short. Actually, I'm not even certain I'm measuring the thing I'm thinking I am.
OK, I think I'm going to go ahead and pause it there because I think I just ran it.
I don't think I ran the correct regression here. Yes, this is an entirely different regression.
And so it's a part of me there. So long story short.
As you can see, I'm still sort of in the exploratory stages.
But, you know, we're starting to get scripts here that we can actually use to aggregate the data from Illinois.
And then I'll share this script with you where we can aggregate data from Massachusetts' Cannabis Control Commission.
And we'll keep at it. So I'm going to take a look at what we can do about Maryland data.
And then if any of you want to contribute, then think of a good state and jump right in.
And, you know, see if you can come up with a good collection scheme yourself.
So that's where we are today. But any thoughts, questions, comments here?
Mariana. Yeah, Keegan. Can we also look at Michigan recreational cannabis data?
Michigan. Yeah. Right now, they don't have data sets.
It's not as savvy as Massachusetts, the Cannabis Commission there.
They mostly have PDFs. Yeah. But I've been looking at that.
And their tables don't look as nicely set up as Illinois.
OK. So we may have to be creative. So we'll see if we can't use PDF Plumber.
And then we may have to find other tools in the toolbox. But yes, let's definitely look at Michigan.
So there's a lot of action going on in Michigan.
And I think we've only looked at it cursely before. So I'm happy to take a look at that.
So let's go ahead and put that on the agenda for next week.
So Michigan data and then Graham's mentioned Virginia data.
So if anyone else has recommendations. I would love Michigan because I have to travel
Michigan frequently. Oh, OK. And I've experienced their cannabis.
There is a huge opportunity there. Absolutely.
They grow grass. And by that, I mean stuff you feed cows, not stuff you want to use.
No, and Graham, you bring up a great point. I think the problem here is quality control.
Yeah. Just look at Oregon and Oklahoma. You do raise a good point.
So all the states have different challenges. So I just heard anecdotally, just from what I've read,
there's some heavy metal concerns. For example, in Oklahoma, right, there's
a lot of heavy metals in the air. And that's something that you have to be watch out for.
Because heavy metals are toxic to me. That's the disease. This is why I'm in this thing.
Exactly. And this is a big concern I have. So, for example, we looked at residual solvents
and concentrates. And it's just something that you're struck in accord with me that
you're doing. And we've got a big group of talented data scientists here. So
let's see what we can do and try to add some value.
This is motivating me to kind of dive into New York. One, because I know it has heavy regulations.
I think some of them are very much welcome, though. There's a big thing. I think it was
vitamin E. I could be mixing that up. But that's been so prevalent in some other states. But New
York regulations make sure that none of that went into the vaping products. Just recently,
they got into actual green marijuana, but it is crushed up, unfortunately. And now we just legalize
marijuana on a recreational level. So I'm just intrigued to dive in more to my state. And
I kind of want to get some data now before the recreational stuff goes into play,
so then I can see how the recreational marijuana affects the market.
Exactly. And that's something that we love to put together at CanLytics is basically just a nice
comparison of these regulations from state to state, because it's not apples to apples in all
the states. The limits are different in all the states. The actual compounds they're testing for
are different from state to state. So there's a lot of good standardization that can be done by
a talented group of data scientists like ourselves. So you're going to say something, Graham?
No, I'm sorry. I guess that was my bad.
Yes. Well, until next week, let's just all keep our nose to the grindstone. And then Graham,
Marjana, Ryan, Heather, Camilla, DP, if you make any discoveries along the way,
feel free to reach out. And so, or if you have any questions about any of the work we've done,
so let's try to make this a nice collaboration. And then on Saturday, we'll see if we can't
find any interesting instrumental variables to parse out supply and demand a bit more.
And then next Wednesday, we'll do a deep dive on Michigan.
Sounds great. Thanks.
Awesome. Thank you all for attending today. I hope you have an awesome, productive,
and enjoyable week and a day today. So enjoy yourselves. See you. Bye.
Bye now. Bye guys.
Thank you. Take care.