Welcome to the Cannabis Data Science Meetup Group.
Should be a really good day today.
We're going to pick up with looking at market performance.
We've got a good crowd today, so welcome to the group.
So normally I go on a spiel about economics, data science, and the cannabis industry.
We've got some new people here today.
So if you're interested, we could do a round of introductions either now or at the end.
So Andrew and our new guest, you're welcome to chime in and introduce yourselves because
we'd love to do a nice back and forth if you have any questions or want to steer the conversation
in any way.
Sure.
Hi everyone, I'm Andrew.
I'm just a student working on a master's in data science.
I was just looking forward to things to do and I saw this meetup and it fit my schedule,
so I decided to hop on in.
Well, awesome to have you, Andrew.
Always have a lot of people interested in data science.
So feel free to chime in at any point if you have any questions or ideas or anything you
want to talk about.
Without further ado, I'll go ahead and share with you what I've prepared today.
So we've been looking at predicting market performance.
So this is the data and forecasts we were looking at and preparing last week.
So we were looking at cannabis sales here in Massachusetts, which we saw are going up
and up and up.
However, there have been turbulent periods.
So we'll go into it, but our data could be biased by these past turbulent events.
So for example, this period, this dip in 2021 in January of 2021 was used in our forecasts.
And as you can see, our forecasts for 2022 also dip in January.
So if you're interested in doing forecasts of your own, it could be interesting to limit
your forecast training data to perhaps just the past six months.
You're going to have really wide confidence bounds because you're going to have only a
handful of observations, well, maybe more than a handful.
You could have a couple dozen if you're doing weekly.
So you could have a number of observations there that could be more representative.
And so this is where we were starting to realize that there's a lot of assumptions being made
when you start to forecast.
So we want to dive into some of these assumptions today and want to spend a bit of time today
on some of the economic theory to explain why we're looking at these data points.
So it makes sense why we may want to look at sales and plants grown here in Massachusetts.
We want to look at the retail side and the wholesale side, see how the producers are
doing.
And so we looked at, OK, how many retailers are there in Massachusetts and how many cultivators
are there in Massachusetts?
OK, bear with me.
We're going to use a different presentation and style next week, so we gave this one its
chance.
OK, so we're looking at retailers and cultivators.
And we did a forecast for each.
Note, these are difficult series to predict because there's actually not a lot of variability
in the series themselves.
So there could be other underlying factors which one would expect explaining the number
of retailers and the number of cultivators.
So this depends largely on the Cannabis Commission in Massachusetts, their licensing process,
who they choose to license.
Of course, it does matter to a certain extent on how many applicants are being submitted,
how many applications are being submitted.
Our prediction, crude as it may be, predicts that there may be a modest increase in retailers
or potentially even a modest decrease.
So we're predicting anywhere between around 350 to around 420, 2025 retailers or so in
the market by the end of 2022.
And right now there's around 375, 380 retailers.
And we...
Oh no.
OK.
So we're having...
It does not like the zoom.
So with cultivators, we're actually predicting a slightly higher growth in retailers.
So I forget the precise number, but we're hovering between 250 and 300 at the moment,
which is close to 300.
And we're predicting, as compared to retailers, higher growth and cultivation.
So we'll predict that at the higher end, you may see around 350 cultivators.
In Massachusetts, by the end of 2022, and on the lower end, we're still predicting around
300.
So we're predicting a growth in cultivation one way or the other.
So we'll see if that pans out.
And like we said, a lot of this depends on the regulation.
We've also got...
OK.
Pardon me for this poor presentation here.
Like we said, we're going to use a different presenting software next week.
All right.
So we're going to...
We're looking at sales, number of retailers, looking at plants and cultivators.
Well, we realized we can create a metric that perhaps not many people have looked at.
And it can provide some nice insights for us.
So we've got cannabis sales, we've got retailers.
Well, let's create a new statistic.
Average sales per retailer.
So it would be awesome if we actually had sales data for each of the retailers.
And the data exists out there.
I'm sure the state of Massachusetts has that data.
However, we'll make do with what we have.
And so we can just divide the average sales by retailers here.
Control L.
And this is going to be an interesting metric.
And I'll explain a bit more further on.
This is going to be our first metric where we sort of start to get at market performance.
So how is the market performing for these parties that are in the market?
And what are some of the parties?
Well, there's the retailers and the cultivators, or two of them.
Right.
And so how are these retailers doing over time?
And so we see that in the beginning, you have modest weekly sales at retailers.
So it starts around 20,000 per week per retailer on average.
Some are obviously above average, some are below average.
And this is climbing.
Right.
And you get around 50,000 per week per retailer.
Then of course, retail closes in Massachusetts for a two month span in April and May of 2020.
Average sales per retailer takes a hit when they reopen.
So when they reopen, they're below the 50K a week.
However, they get back up to the 50K in just a matter of a couple of weeks.
And then once again, you're on a modest trajectory up.
Once again, rocky boat in spring, well, I guess this would be winter slash early spring
of 2021.
You have a rocky time in the market here in Massachusetts.
Like I'm going to have to do a bit more historical digging.
Right.
And so this is what's so cool about economics.
Right.
Because not only are you studying economic theory, you're studying statistics, but you're
also a student of history.
Right.
Because we need to look in the history books, in the newspapers and find out what was happening
at this time.
In specific, what was happening in Massachusetts at this time.
So if you were, I want to use a better word than entrepreneurial, but if you're a go getter,
then you can go hit the local Massachusetts newspapers in this time period and try to
figure out, just kind of like look at the local stories and kind of see what's happening.
So this is where, right.
So they're not going to talk on a national scale.
They're not probably not going to talk in the national newspapers about cannabis markets
being closed in Massachusetts.
Right.
But right, if you look in the local newspapers or this or that, there could have been townships
or counties where businesses may have locked down or I'm just conjecturing, who knows what
happened.
There could have been the hots, late in Viroid may have hit particularly hard or who knows.
There could have been a tourism drought or there's a lot of factors.
So long story short, check out the history books and then try to incorporate history
into your analysis.
Our main takeaway was average sales per retailer.
It's actually, it's continued to increase and it's getting substantial, right.
You've got average sales around 75 or so thousand a week.
You know, not shabby.
And this is where if you're a student of economics, it would be worthwhile to compare this to
other industries.
So what's the average sale in the restaurant industry?
What's the average sales for, you know, an agricultural producer of another agricultural
crop?
So you can start comparing cannabis to other industries here.
But what we noticed and what we would like to measure, and if you tune into Saturday
morning statistics, we do this more rigorously, but you can actually see if there's a change
in factors or perhaps volatility during this period, right.
So there may have been an underlying structural change.
And what I notice is this period is much more volatile than the prior periods, right.
So especially 2019 to 2020, this, there was just a fairly steady period of growth, right.
I'm sure during that time they felt like things were tumultuous, but they're sure, they're
sure not as tumultuous, arguably as 2021.
So like, look at this volatility you have here.
It's like, yeah, average sales may be high or on average, but you have incredible volatility.
This may make it difficult for your general managers, may make it tricky to stock inventory
correctly, right.
If you're having sales peak one week and then they drop off the cliff the next week, well,
you know, you may overstock your stores, you may have your stores understocked when sales
spike.
So that's why it leads to the importance of forecasting.
And here we did a medium term forecast.
And like we, I think I brought this up in Saturday morning statistics, but if you're
a general manager, it would be helpful to do a in depth, say 30 day forecast.
So here we used weekly data.
Your general manager, it could be useful to just use daily data and just do a 30 day forecast.
So that way you can use day of the week effects and get a real nice prediction for the coming
month.
But here we just looked at the coming year because we're interested in kind of the bigger
scheme trajectory here in the Canvas data science meetup group, right.
So we've been looking at the, we're trying to look at the performance of these industries.
And so what we're noticing is the retailers are performing, one would argue better and
better, but kind of we've got some volatility issues that may need to get ironed out.
Okay, let's see if we can.
For the cultivation, cultivation is actually kind of got a different story going on where
you've got cultivation getting up to speed slowly.
And this is actually kind of what you see in a lot of cannabis markets, right.
So it's going to take your cultivation, you know, a good six months or so to get up to
speed for the market.
So this is essentially people getting plants in the ground.
So you don't, you know, it takes time to get these facilities built, get your doors opened,
get the lights on, get the employees hired, you know, get your HVAC system installed.
You know, there's a lot of preparation, get your permits, right.
So it's a whole long game, right.
So people think, oh, I'm just going to get my license and, you know, start growing a
thousand plants tomorrow.
Well, that's not, that's not the case.
You've got, you've got to jump through a lot of hoops.
You've got to get your local permits, you know, you've got to make sure your license
is locked in.
You have to get your employees, you have to get all your capital equipment, capital equipment
may require funding, right.
And the funding isn't entirely different.
Candle worms in the cannabis industry as it is in other industries, because, you know,
you can't just get conventional loans from large banks like you may be able to in other
agricultural industries.
Long story short, takes cultivation a little while to get what I would call up to speed,
right.
So here you just see this exponential growth in the number of plants in the ground.
And this is the tract number of plants.
You can break this down by vegetative and flowering.
So if you've got more insight than I do, then by all means, you can look at the data slightly
differently.
But in general, the trajectory of the number of plants is the same or similar.
Long story short, plants get up to around this, you know, steady level of 500 to around
600 plants per cultivator.
And that's quite steady.
So you do see what you could probably estimate, you know, a modest trend in that data.
But once again, you've got, you know, maybe minor cyclical business cycle trends going
on.
But even with what's interesting, right, is there's not a dramatic effect on the number
of plants in 2020 or really in January of 2021.
So the cultivators, and this would make sense, right, if you've got your cultivation set
up, you've got your plants in the ground, you know, it's not even really going to be
that big of a factor if, if retail is closed.
And in fact, you even see a little bit of an uptick.
Maybe people even took that took that time to get even more plants in the ground.
So you know, the plants per cultivator, so the cultivators, their production, it looks
like stayed steady.
And in fact, it looks like they realized they needed to turn on the gas in spring of 2021.
And then this makes me think that there's all of a sudden an increase in demand in the
Massachusetts market.
And so once again, time to hit the history books, time to hit the newspapers, what was
going on in Massachusetts in mid to late 2021.
Really interesting to see and you know, see if there's a lot of articles talking about
cannabis cultivation.
So once again, you see this large growth in the number of plants per cultivator, average
number.
And so once again, hedge this in that, right, there could be some cultivators that just have
a ginormous amount of plants and some that don't have very many.
So this could just be the entrance of mega growers, you know, that's not impossible.
That just like some mega growers just came online here in late 2021.
That's not impossible.
However, the story I generally been telling is that it looks like cultivators, that sort
of been exploiting economies of scale, there's increased demand, they're growing more plants.
However, it looks like they may have kind of reached a peak and maybe cutting back a
little bit.
So I think this is going to be one of the hardest series to predict coincidentally because
we don't it looks like things are changing kind of recently, right?
It looks like really, in the past month or two, there's been almost like a structural
change here in cultivation.
So the other series will same for plants.
So you know, the other series had their own structural breaks.
But for whatever reason, there's something going on in the cultivation side of the market.
And that's going to be interesting to see what happens in 2022.
Our forecasts because there's such a dip, we predict there's going to be a large dip
in 2022.
I would love to rework these forecasts and not have such a dramatic dip because just
my Bayesian prior is that there's not going to be this dramatic dip in January, like I
predict a modest dip, but not one that dramatic.
And this is what's cool about Bayesian statistics is you acknowledge your prior biases.
And you can kind of use them to make better forecasts if you want to boil it down that
way.
But tune into Saturday morning statistics and you can get a lot about the nerdy aspects
of the statistics.
Long story short, our predictions are that the average plants per cultivator is going
to fall back to its pre 2021 level, where you're going to see around 600 plants per
cultivator.
I think this is going to be one of like I said, I think this is going to be one of the
more interesting series to follow because right, the supply of cannabis is going to
be a major determination of the performance of the market rate.
So that's going to determine a lot of the set of the prices.
And, and essentially the profits for the retailers and the cultivators in a surplus that will
be had by the consumers.
Talking about profits and consumers, well, let's go ahead and do a little bit of a history
slash economics lesson, because we've been neglecting the economics that's underlying
our analysis.
And I wanted to give you a bit of a history lesson about where this all began.
Some of the people that pioneered the work pioneered sort of the amount type of analysis
that we're going to be doing and what people look at.
We don't have to look at these things necessarily, but it's just interesting to look at the history
of of where these tools came about from.
So long story short, you have a professor, Edward S. Mason at Harvard University.
He's the first one who's really extending upon sort of the theory of the firm.
And what's the theory of the firm?
Well, this is where you sort of determine, okay, you know, what gets produced internally
at an organization and what gets bought and sold on the open market.
Right.
So sometimes firms outsource and sometimes they do things in house.
Right.
And a lot of times economics sort of ignores, you know, things that are going on in house.
Right.
Because we're just looking at a lot of times economics is just looking at transactions
between parties.
So you're just looking at, say, producer A buying and selling it from producer B.
Well, you don't really take into consideration that, you know, producer A may be taking actions
so that they can then internally produce what producer B is producing and just do things
in house.
And you see companies do this.
So you see acquisitions, you see this a lot with the large technology companies where
large technology company A will acquire small, you know, small technology company B and then
just sort of bring that in house.
So that's the theory of the firm has a lot to do about costs.
So what's the cost of doing things internally versus buying it on the market?
Well, this leads to, okay, well, what's the layout of the market?
You know, what are the size of these players in the market and how do they conduct themselves
and what's the outcome, aka the performance?
So we're looking at how things are structured, how firms behave and people, and then what's
the outcome?
How do the firms perform and what's the consequence on like society as a whole?
So consumers.
And then that's where we sort of get, I'll talk a bit more about Joseph Bain and George
Stigler coming up, but those are two economists who really led the work forward in a similar
vein as we've been doing.
Where this is led?
Well, it's essentially led to antitrust policies, which, you know, may not have come about otherwise.
And so what they noticed was, okay, as they argued, markets become more concentrated.
Well, if there's only a small number of players, it's going to essentially be cheaper to collude.
So let's say there's a fixed cost, so you have to pay people off to collude.
Well, the more people there are, the more people you have to pay off, thus the higher
the cost of collusion.
So the argument is, the more concentrated the market is, the more likely collusion is
to occur because there's a lower cost.
And if collusion occurs, that can have an adverse effect on society.
So essentially, consumers are going to face a higher price and lose consumer surplus.
Of course, there's a counter argument, right?
The counter argument is, well, if you're naturally efficient, right?
So if you do things better than other people in the industry, well, you're just going to
naturally gain a market share, and just because you have a large market share, that doesn't
mean you're going to collude, right?
Just because you're the only or one of the only players in a market, that doesn't dictate
that you have to collude with people.
So that's sort of the counter argument, the argument is, well, that's inevitably what
we see.
And this is sort of where you kind of can get into psychology of groups.
And once again, economics, we like the dabble, and of course, you have to look at the psychology
of these agents.
And I think it's real interesting, right?
And so that's where psychologists can kind of help out is, okay, like, you know, do you
see like bad behavior slash collusion in, you know, small groups versus large groups?
And just my personal belief is, you know, the more people you add to a group, the harder
it is to say collude and do bad actions, right?
Because there's more people, you don't know which ones are going to turn you in.
I don't know.
And so I think there's more work that can be studied there.
But that's sort of, that's a major pinning point underlying market concentration analysis
is because the implicit argument you're making when you're measuring market concentration
is that an increase in market concentration could be bad for consumers.
Meanwhile, you know, a lower degree of market concentration could be beneficial for consumers.
Like I said, the counter argument is if you actively work to curtail concentration, then
you could be curtailing efficiencies that would otherwise been gained.
So the idea is you don't necessarily want to limit concentration just for the sake of
limiting it, but you may want to keep an eye out on concentrated markets because it may
be ripe for collusion.
And this was a lot of the work that was done by Bain, Joseph Bain.
Stigler looked a lot at regulatory capture.
And this is, of course, Stigler taught partially at the University of Chicago.
So this is quite a University of Chicago type of stance on regulation.
So just kind of keep that in mind.
University of Chicago school economists are typically not in favor of overarching regulations.
That's sort of a generalization, and it may be more true historically than present day,
but that's what you see a lot of with these economists.
They make good arguments, and so you can't just ignore them.
So that's sort of the point about history.
You have to study the people and the points they make, whether you agree with them or
not.
These were people that led a lot of the groundwork.
So it's just interesting and arguably important to study them.
That way you kind of know what you're talking about when we look.
So that way when we're talking about market performance in the cannabis industry, we actually
know about the history of the first people who studied market performance.
And one of these people was George Stigler.
And his argument was you have to watch out for essentially regulation for regulation's
sake, which kind of is part of this counter argument here where you don't want to just
curtail concentration for concentration's sake.
And what Stigler argued is the regulatory agencies, so in this case it would be the
Massachusetts Cannabis Commission, they can be captured.
So this would be lobbyists kind of persuading the government bodies to essentially enact
regulations at their behest.
So they're sort of nudging the politicians so they may take them out to lunch and say,
oh, don't you think it would be a good idea if we did X, Y, and Z?
Well X, Y, and Z may be easy for that producer to do, but it may be tough for their competition
to do.
So next thing you know, we've got a regulation that, so for example, there's a regulation
for delivery that you have to have four cameras on your delivery vehicle or something like
that.
But it does seem like one could argue, yes, that is for the public safety, but one has
to wonder, did somebody just already have a vehicle set up with four cameras?
And they say, oh, hey, don't you think it would be a good idea if everybody had four
cameras on their vehicle?
And then the regulator says, oh, yeah, that does sound like a good idea.
It sounds like that would be safe and for the best of everybody.
Now they pass a law that says, OK, all vehicles have to have four cameras on them.
Well now if you're starting a delivery company, that's essentially going to be a cost to entry.
You're going to have to install four cameras on your vehicle.
So that may not be the best example, but you may be able to think of better ones.
And so by all means, brainstorm for better or for worse.
But that's sort of the idea is, producers will advocate for regulations that they think
they can jump through, so they think they can meet that regulatory requirement.
But that regulatory requirement may box out competition.
And you don't have to take my word for it.
So a lot of times if you go to, so I've attended a handful of cannabis conferences and you'll
talk to your seminars, and people will be pretty upfront.
Producers will be quite upfront that they recommend going in, sitting in on the agency
meetings and being the squeaky wheel in these regulators' ears.
And I mean, one almost can't necessarily fault them.
I mean, they're parties in the industry and if they're trying to maximize their profits
and that's how they can do so, then that's how they're going to act.
So I think it just should be something kind of taken into consideration.
And long story short is, I think consumers should be at the table, right?
Because I heard once, if you're not at the table, you're for lunch.
So if they have these meetings and it's just the regulatory agency, their representatives,
and then you just have the producers.
Well the consumers aren't there at the table, right, and the consumers have a stake in the
market.
So they may be getting the short end of the stick, right?
And so I think that's ultimately up to the consumers to get to the table and to push
forth what they may wish.
Or for other producers, right?
If you're a smaller producer, then you may want to go to the board meetings and whatnot
and speak up for yourself too.
Because like I said, the large companies, you can count on it that they're going to
be at the meetings and they're going to be advocating for policies that may, they may
on their face seem like they may be beneficial to the public.
However, they may be cleverly designed to box out competition.
So I'm not going to hammer that home too much further, but something that's taken into consideration.
Well we talked about the economic theory, and I think it's kind of, it can only get
us so far.
You can kind of game theory it out so much.
What if player A does X?
What if player Y does Y?
And so on and so forth.
But at a certain point, you actually have to look at the empirics, right?
You actually have to look at how many firms are in the market.
What's the output?
What are the prices?
And that's what's called performance.
So how are we going to go about measuring market performance?
Well, we can look at profitability, how profitable are the firms.
This is tricky, but we've kind of shown that we can almost tackle this.
So we can make an estimated profitability.
It would be awesome to actually have everyone's balance sheets and actually have an actual
measure of everyone's profit or loss.
That would be cool to do really rich statistics, but no one's going to just turn over their
balance sheets like that.
So we can estimate it.
So we can look at the revenues.
We can look at average revenue, and we can try to estimate the prices of inputs, and
we can estimate profitability.
Once again, with concentration, the state of Massachusetts could look at this because
they probably have the sales per retailer and so on and so forth.
So they could see if the market's becoming more concentrated or not over time or less
concentrated over time.
We can just look at the number of players in the market, and we can even see if average
profits are going up or down.
So if average profits are going up, well, the market may be getting a bit more concentrated,
and these players may be finding a way to squeak price up and up and up above cost.
If average profits are going down, the market may be becoming more concentrated over time,
I mean less concentrated over time, and price is falling closer and closer and closer to
average cost.
Because that's sort of the theory of economics is given perfect competition, price will fall
to average cost.
However, we don't ever have quite perfect competition, right?
Because we don't ever have perfect information, or there's structural barriers to entry, or
there's transaction costs, and so on and so forth.
So all of that brings us to our next bullet point.
Well, it's useful to measure these barriers to entry.
And this is a bullet point I wanted to say is understudied.
So how do you even go about measuring barriers to entry?
This is difficult, can be arbitrary, yet valuable.
So the only idea I've had so far is maybe you could look at license fees by state and
see if that affects the structure, the performance in any way.
But yet again, it's really hard to measure, like the license fees, it's hard to get a
gauge of, right?
So for example, how do you compare apples to apples?
So the different states have different licensing structures, different fee structures.
How do you measure the difficulty of an application?
What if one application takes you six months, and the other one takes you a year?
How do you go about measuring some of these barriers to entry?
Is it worthwhile if you're up to the challenge?
And then finally, there's this measure of the total factor of productivity.
And so this is something, a measure that was put forth by Joseph Stigler.
He was at least one who utilized it.
And that's essentially what we've been estimating.
And that's just centrally sales per labor and sales per capital.
And so we've been getting at those measures.
And we actually kind of nearing the end, but just in the last bit of time here, let's get
at those measures.
So for those of you that are just joining us, we've been working with this data from
Massachusetts.
You can find the data through the Socrata API.
And please tune into prior episodes or Saturday morning statistics.
And I can help you or email me or what have you, and can help walk you through this.
But just since we only have a limited amount of time, long story short, we read in the
data here from Massachusetts.
We create forecasts using an ARIMA model.
And we forecast sales.
We forecast plants, employees, retailers, cultivators, and total licensees.
And that allows us to predict sales per retailer, plants per cultivator, and employees per licensee.
And so these are the forecasts that we have plotted that I showed you at the beginning.
And then, oh yes, the total factor of productivity.
And so this is where, remember I showed you the Cobb-Douglas production function in prior
weeks.
So here we're essentially estimating a Cobb-Douglas production function for the cannabis industry,
where we've got output, y, so this is going to be our sales, times technology.
And I want to stress this point real quick.
Technology we're taking as a constant.
Well, guess what?
Technology is not constant.
And this is where you can do interesting analysis with your production function.
So one of my favorites is just saying you can create what's called regime models.
So A0 and A1.
So you could say, OK, what if technology switches between two different regimes?
And so that's where we could say, oh, maybe we had one technology here from 2019 to 2020.
And we may have had a different technology regime going on here in 2021.
And technology is sort of this abstract word that economists use to just capture everything
else besides from capital and labor.
So this is sort of the state of the economy.
So A is sort of a big deal.
We're just going to take it as constant.
But I just want to stress on the fact that if you let technology be dynamic, you can
add a lot.
You can make your analysis even that much more in-depth.
But we're just going to take technology as constant.
And we're just going to say, OK, labor and capital, which we are proxying as plants,
is productive to a certain degree, alpha for the plants, beta for the labor.
And with this production function, which we expect to be concave, so to have diminishing
marginal returns, so the more and more we produce, the incremental amount is less and
less, yet always increasing.
So long story short, we expect alpha plus beta to be less than or equal to 1.
And then empirically, they've measured alpha.
There was somewhere here.
But like I said, Wikipedia is not the best source.
But it's just what I'm using for today.
But historically, they were measuring, I think they were seeing alpha to be 0.3 and beta
to be about 0.7.
So real quick, in just a matter of minutes, let's see if we can measure some of these
factors here.
So we're going to basically just supplement the data here with weekly hours from the Federal
Reserve.
So this is just the average weekly hours of somebody in Massachusetts.
So they're working around 33 hours per week.
Then they're going up to around 34.
And the reason we're doing this is because I'm using hours, total hours worked as a measure
of labor.
And the reason I'm doing that is because we can estimate the price of labor.
And if we use hours, well, that's dollars per hour.
And a lot of people get paid in dollars per hour.
Your wage.
So that's why we're estimating that.
So long story short, you've got Y, K, and L. We're going to limit it here to we only
have weekly hours through the end of August.
And we're going to exclude the turbulent period.
So actually, we're actually including the turbulent period.
We're basically starting from when things open back up after May of 2020.
So long story short, these are basically the total factors of productivity right here.
So that's YPK.
So you'll hear economists say, oh, YPL, KPL, or YPK and whatnot.
So these are measures that economists like to look at.
So here is YPL.
So that output per labor.
And so as you can see, labor is just incredibly productive right when the industry starts,
which you would expect.
Everyone's coming online, adding one more employee in those early days, which is really
adding a lot.
This is something that fluctuates, so something worth looking at.
And then you can also look at YPK.
That's essentially how productive your plants are going to be.
Once again, your plants are super valuable right when the industry starts.
If you look at the last 30 weeks, we've got a little bit of a negative trend here.
Let's look at the last year.
So here is this is basically sales per plant, so a crude measure of how productive your
plants are.
Well, let's estimate this Cobb-Douglas production function real quick.
Here I did it in a fancy way.
I'm going to not do it this way.
Let's do the log.
Let's do it this way.
Bear with me real quick, and then we'll be wrapping up here since we're at the end.
One second.
I think one second.
And I just have to figure out how to make a quick array here with a constant in it.
We may have to save this for next time, but I would like to run the regression.
Bear with me.
Okay, unfortunately, hold on.
Let's see if we can't do this real quick.
Okay, if I can make this in one minute, then we'll do it, if not, I'll save it for next
time.
I apologize that this wasn't already set up here.
Okay, I think I may be able to do this for you real quick.
So let's just define our regressors here.
Log A and log L. No promises, but we may be able to estimate alpha and beta real quick.
Okay, so unfortunately.
Unfortunately, I'm not able to code this up for you real quick on the fly.
But that's okay, because we can pick this back up next week.
Sometimes when I'm presenting, I just need to just sit down and think about the code
a little bit, a little bit more to just kind of know what I'm actually doing.
So my apologies, but I'm just going to need to code this up for you.
But long story short, we'll pick up with this next week.
And we're going to measure these total factors of productivity.
So that way we can get our estimates of alpha and beta.
And what's cool about measuring alpha and beta in the Cobb-Douglas production function
is it will allow us to estimate the competitive wage rate, as well as the rate of return on
capital.
Why is that cool?
Well, one, we can compare the competitive wage rate in the cannabis industry to other industries.
Right.
So you can look at the average wage of anything from say fast food to banking.
So that way you can see where the cannabis industry workers fall out in the grand scheme
of things.
You can look at the interest rate.
And this will be helpful for investors knowing, you know, should I invest in the cannabis
industry and what rate of return should we expect?
And it will let us measure or at least estimate profitability.
Right.
Because now if we have a measure of average sales, well, we can then estimate average
cost and we'll guess what the difference between average sales and average cost is expected
average profit.
This isn't going to be the profit everybody's getting, but it's going to be what we would
expect your average firm to make.
And so then you can compare that to your own profit.
So if you're a retailer in the space and we estimate average profits to be X, are you
above or below?
So then you can brag to your investors, hey, we're above expected profits or we're below
expected profits.
And you know, you wouldn't be bragging about that.
You may be nudging, you may be telling your general manager that and trying to bark at
them to to drum up sales a bit.
So it's really helpful, right?
Because you know, you're not operating in a silo here.
You know, you've got other players in the market.
And so as a retailer, you want to know how you size up against your competition.
Same with the cultivators.
You want to know how you size up.
So that's what we'll work on next week is actually measuring next week.
We'll estimate profitability by calculating these total factors of productivity.
And then we can make statements about what we think about concentration and barriers
to entry.
So stay tuned for next week, and I will iron out the estimation of these parameters for
us.
My apologies that I couldn't do it on the fly for today, but that's probably for the
best, because that way we can do it proper and thoroughly next week.
And so next week, we'll add at least two new series and forecasts will add the competitive
historic wage rate and the competitive interest rate for plants.
So you can know what rate of return on average having one plant in the ground is.
So that's where we'll pick up next week.
Want to go ahead and thank everybody for attending to rain a little extra today.
So any questions, comments, concerns, ideas from anyone from the group?
On that case.
I'm always here.
Send me a message.
Send me an email, get in contact with Canelitics.
We're always here to help the industry.
So until next week, keep your nose to the grindstone.
Stay productive.
Have fun and enjoy yourselves.
Thanks, Keegan.
Definitely, definitely, definitely.
Thank you very much.
Bye now.