Welcome to the Cannabis Data Science Meetup Group. Fun times as always, big day ahead
of us, lots of ground to cover, and some exciting economic models to cover, some good cannabis
data to look at as always. And we've got a large group today. Well, Khalil, if you wanted
to start, we were going to maybe do a quick round of introductions since we've got a good
group today. So if you all just want to take 30 seconds just to say, or more, about yourself,
what brings you to the group and what you may like to get out of the group. So, Khalil,
you're on to start.
Hey everybody, my name is Khalil, and I'm actually a statistics major and a recent graduate.
I'm just looking to build my skill and also create a portfolio, a data statistics portfolio.
But when I found out about analytics, it's kind of like more specific towards, how do
you call it, cannabis. I don't know yet, but I'm just here to see where I could land from
you basically. And that's why I'm here.
Awesome. Happy to have you. Exactly. CanLytics is leading the space here with data science
applied to the cannabis industry. We're creating statistics that you don't see out there. We're
an open transparent box. So all the source codes available for you to use and start building
up your repository of statistics. So whether you're here just to learn data science or
learn about the cannabis industry or both, you're in the perfect place.
Awesome. So, Raelyn, are you interested in introducing yourself?
Sure. Can you hear me?
Yes.
Perfect. Good morning. My name is Raelyn Sawano. I came, so I just came out of a boot camp about
data science. And right now I'm kind of like in the job process, but I'm still obviously
want to keep learning with things with data science. And I just saw, I was looking up
meetups and I so happened to find this one. So I came here just to learn basically.
Too cool. Happy to have you. We get a lot of data science coming through. And what I
always tell everyone is there's a shortage of data scientists. So your skills will be
valued up.
Yeah, that'd be great.
Yes. A purr-by. Happy to have you today. You don't have to, but if you would like, you're
welcome to introduce yourself. So feel free to chime in. And then Heather, you're welcome
to introduce yourself to the group if you're interested as well.
Hey, I'm Heather. I've been coming to cannabis data science for a couple of months now. I
have been in the lab for over a decade, not currently, but I have research experience
and I have experience processing data. This is a refreshing experience for me now that
cannabis and data science have united for me personally. I have a personal interest in
this and also contributing if I can. Thank you.
And you definitely can. And so Heather is our laboratory whiz here. So you're the one
with the more stringent laboratory background. So Heather's always here to help us with the
real life experience and expertise of pest and cannabis. So without further ado, I'll
go ahead and share with you some of the work we've been doing. We've been looking at data
here in Massachusetts. We've got one last guest. We've been looking at Massachusetts.
And so today we can finish up. We've been doing sort of a rigorous analysis, but today
will be the third and final day and then we can start branching out into some other states.
Happy to have you, Barry. We just did a round of introductions. So you're welcome to, if
you would like, spend 30 seconds or so and introduce yourself if you would like. Well,
Barry, I'm not sure your mic's coming through. I believe you're muted.
There we go. How's that? Coming through. Okay. My name is Barry. Nice to meet you all. And
I'm just a gardener. I'm a home grower and just trying to master that. This is my second
year and already planning for third year. And I'm interested in this. I've been studying
the science for quite a while and the business part, I'm a businessman. So I'm looking for
those opportunities that stand out. Is that what you're all about? Well, we can definitely
use your perspective today. So we always like to have different people with different angles.
And so it will be awesome to have your perspective. So today we're actually going to be estimating
the rate of return that you would expect from cultivating a plant in Massachusetts. And
it would be interesting to hear your perspective if our estimations are in a reasonable ballpark.
We're growing in Massachusetts? We're just looking at cannabis data in Massachusetts.
So Massachusetts publishes good data. And so what we do is we essentially collect public
cannabis data and see if we can't calculate market statistics. So that way we can create
value for the public. We take data that's publicly available to everybody, show you
how you can create a pipeline and calculate statistics. And essentially you can turn this
treasure that's just laying there on the table into gold. And so you can get some, you'll
see, you can get some rich insights with data that's publicly available.
From all ears, I read your description and I'm very interested. Thank you for having
me everybody. Welcome aboard Barry. And without further ado, let's go ahead and show you how
it's done. Okay. Well, welcome to the meetup for the 17th. I can go ahead and just give
you a quick introduction to the presentation. Let's just get one final guest here. And Zab,
we may have to get your introductions at the end. Welcome to the group. We're about to
kick off. So you're welcome to give you a quick introduction if you would like.
Hi everyone. My name is Zabi. And I'm right now I'm joining you from Canada, Toronto.
Yeah. I have a bachelor's in business and I have a master's in education. I also have
one certificate and one diploma in data science. I have like the advanced data science, machine
learning, data analyzing and data cleaning. So I'm just new in the market and the industry
of data science. Awesome. Awesome to have you aboard. So you'll
be able to make sure everything's up to snuff. And so definitely feel free to share your
ideas as we go through. Sure. Thank you, Sonos.
Well, awesome to have you all. So just to give you a quick introduction and then we'll
just dive in. So we've been looking at Massachusetts data. So we were looking at sales in Massachusetts
and Massachusetts is an interesting state because they actually closed the cannabis
market for a two month period in 2020. Whereas in other states you saw a spike in sales.
In Massachusetts you see, you know, this period of no sales. So a different dynamic than other
states. And so we're going to essentially look at the market performance in Massachusetts
and predict what the performance may be in 2022. And then next week we'll start to do
interstate comparative analyses. So we can start to say, compare Massachusetts to Oregon
or Colorado. So then we can see how policies in different states affect the market performance.
And we'll try to uncover a handful of insights along the way. So the way we're doing all
of this is we're using this economic framework here where we're relying heavily on the Cobb
Douglas production function. I think we've got Donovan joining. So long story short,
we're using variables that we know we can get in Massachusetts. So that's sales per
week, labor per week, and we're proxying capital with flowering plants per week. And happy
to have you Donovan. You're welcome to chime in at any point to introduce yourself. So
don't let me just drone on to chime in at any point. But long story short, just to show
you a little bit of economics plus math real quick. So we'll move through this pretty quickly.
But long story short is, given the Cobb Douglas production function, you can take the derivative
with respect to labor. And this is an intermediary step. So basically, you just bring beta down
in front. You subtract y. And that gives you your marginal product of labor. Well, what
you can actually do is actually a times kt to the alpha times lt to the beta. Well, that's
just yt. So you can say, well, the marginal product of labor is beta times yt over lt.
And economic theory suggests that in equilibrium, the wage will be equal to your marginal product
of labor. So if the wage is higher than the marginal product of labor, then the employer
would actually lay off some workers. And then if the wage is above, I mean, if the wage
is below marginal product of labor, then firms will want to hire workers and then be gradually
raising and raising wages until wages reach the marginal product of labor. And so there'll
be forces in the economy that will prevent the wage from ever being competitive and actually
equal to the marginal product of labor. But we can still use it as an estimate. So we
can estimate what the competitive wage may be. Well, this is just for labor. We can also
do it for capital. So here is the same mathematics where we just take the derivative with respect
to capital this time. So we bring alpha down in front, subtract one. Well, that we can
just use math and say, OK, well, that's just alpha times yt over kt. And I am sort of running
through this. I would recommend you sit down with paper and pencil and actually take the
derivative. So this is something that I've spent years doing. So that's why I can move
through this quickly. It took me a long time to get to this point. So it took me three
plus years of doing this on paper and pencil over and over and over again and racking my
brain trying to figure this out. So I'm moving through this really quickly just to get to
the juicy bits for today. But if this is something that interests you, I would recommend sitting
down with paper and pencil and actually doing these equations yourself. But long story short,
once again, you can estimate what would be the competitive interest rate for capital.
And so this is the interest rate that investors would like to get for loaning out capital
equipment or money to invest in capital equipment. So that's the, you know, the scary mathematics
underpinning this just so we're not pulling things out of thin air. Keep in mind, this
is all coming from a pretty simple production function here. And so in subsequent weeks,
we can make this production function more complex. So for example, A is essentially,
they call it the total factor of productivity. That's basically the state of technology.
And we can expect that will vary over time. So you could let A vary over time by adding
a trend component. And there's many ways you could make the production function more complex
and realistic. And that's a lot of what the modern economics literature is about. And
so maybe if you tune in on Saturday morning statistics, I can go a bit more in depth into
this where we could talk about the history of the production function and some of these
additions that can be done to it. But this is the most simple model. And so everything,
so a lot of things are additions upon this. And just give a shout out to...
Well, when can I first meet? Maybe Robert. But Professor Solow at MIT was one of the
people who did a lot of the fundamental work on this. But we'll talk about this in Saturday
morning statistics. For today, we'll go ahead and estimate this. So let's go ahead and get
Cheyenne into the group here. And then we'll actually just go ahead and move on to the
coding part since we probably bored you to death with the mathematics. Let's actually
get our hands on the data here. And welcome to the group Cheyenne. We're just about to
get started with the coding. And I'm just going to go ahead and commit the code to GitHub
in case you want to follow along on the... So just so everybody knows, the source code
is online on GitHub. So here I just committed the code 19 seconds ago. So that way you can
follow along online as we go. So it may be a little quick to get started for today. But
in the future, if you want to follow along, now you know where the code base is and how
you can go about following along. But without further ado, I'm going to go ahead and move
through this part quickly since we've spent a bit of time on this in the prior week. So
check out YouTube or camlidx.com and we've got prior videos uploaded there. So we'll
basically read in data here for Massachusetts. And for those of you curious about where this
data is coming from, Massachusetts makes their data publicly available through an API. So
check out Socrata. We've got some links here that you can find some of the data points
that they publish. Some of the ones we've been looking at are sales. And so these are
daily sales. And we've talked in prior weeks about how a lot of our analysis since we're
looking at about the medium term, which is 2022, we can look at weekly sales. So we work
a lot with weekly data. So here are weekly sales moving along. And, you know, we've collected
a handful of data points here. I don't think we're using these today. But, you know, we
can calculate, you know, the total number of retailers and cultivators. So here's our,
you know, sales per retailer, flowering plants. It would vary. This is something that may
be of interest to you. So this is flowering plants per cultivator. And so on average,
you know, we're getting, right, you know, over time, there's an average of 300 plants
per cultivator. But as you can see, the trend is increasing. And, you know, these days,
I think, right, the last observation, people had around 500 plants on average per cultivation.
That's flowering plants. So that's what you're looking at in Massachusetts, or at least that's
what the data says. And I kind of want to go ahead and hedge all of our analysis here,
right, because data, you know, data in, data out, right. So a lot of our analysis depends
on how accurate the API data is. And I would like to think, you know, the Massachusetts
data is fairly accurate, but inevitably, I'm sure there's measurement error in the data.
So you know, is everybody measuring flowering plants the same or on the daily basis, right?
So maybe people may reconcile at the end of the week or so maybe there's measurement error
in here, but we'll just, you know, hedge all of our analysis, knowing that the date, what
I always like to say is the data is not perfect. But a measure is better than no measure. So
do people have exactly 495.9 plants? You know, probably not. But, you know, we can at least
know about how many plants somebody may have. So that way, you can at least know what may
be reasonable and what may not be reasonable. So with that said, we can start our forecasting
and check out some of the prior weeks, especially Saturday morning statistics, if you want to
get real in depth into the forecasting models. And so here, we're going to use an a theoretical
approach, where we basically just use past observations. And we were just saying, okay,
this series if it continues moving the way it has in the past, this is what forecast
would look like. So these forecasts can be improved upon. But it's a, so let's just go
ahead and do all the forecasts. So here we're forecasting sales, plants, and the total
number of employees that will be in the market, right, because we need to know, right, that
was one of our variables was labor. And here is the figure that we saw at the beginning
of the presentation, where we're measuring sales, plants, cultivators, retailers, and
so on and so forth. Check in last week for a bit more in depth discussion about this.
But because this week, I want to go ahead and move into this, the last aspect of market
performance, right. So we, we met right, because let's go back to our presentation. We're interested
in ensuring productivity, right, market performance here. And let's look at all our variables
here, right. We're of course interested in sales. We're taking productivity as exogenous.
So a we're not trying to explain labor we're interested in. So we've, you know, predicted
like the labor supply, you know, how much labor there's going to be capital, right, we've
predicted what how much capital is going to be supplied. Well, we know what supply is
going to be right. Well, in equilibrium, supply equals demand, and prices are set. So when
we're doing a competitive analysis, I mean, a market analysis here, a market performance,
we're basically going to assume that at every period t, the markets in equilibrium, supply
equals demand, and prices are set. So that means at each point t, there's a competitive
wage, and there's a competitive interest rate. And supply equals demand, the plants are grown,
the cannabis is sold. And those prices are as they were shaken out. Well, you know, we
actually have the price per gram here. So that was one of our variables that we're able
to pull from the API. But we're doing a market analysis here. So that means we need to characterize
the prices. So this is the price per gram in Massachusetts. Well, next week in our comparative
analysis, we'll compare the price per gram of Massachusetts versus the price per gram
in Oregon. And foreshadowing, there's quite a discrepancy. And once again, is it a measurement
error or it's probably got to do with the markets, right? The Oregon market is probably
different than the Massachusetts market. So we'll look at that. And when we look at prices,
right, there's a whole lot of ways you can characterize prices, right? What everyone's
talking about these days is, you know, inflation. And we've talked about, well, you know, cannabis
prices aren't rising, right? So you we can actually look at the change in prices. So
that would be what we would define as inflation. So, you know, to real quick, right, that's
basically, you know, price per gram, its percent change. And you can plot that over time. And
so this would be the inflation of cannabis prices in Massachusetts. And as you can see,
you know, it's actually kind of steady, except for this, you know, really rocky period here.
And we could even chalk some of this up to maybe there's some measurement error going
on during that period. So long story short, there is a whole lot of analysis you can do
just on prices. And we'll we'll do a bit of that next week. So the question I'm interested
in is, what's the effect on, say, inflation in Massachusetts versus Oregon, right? So
it's like, yes, and this is where we're going to use some statistical models and look at
the differences here, right? Because yes, their prices are different. But maybe this
closure in Massachusetts, did this have an upward or downward pressure on prices? And
we can perhaps look at the difference between Massachusetts and Oregon and maybe try to
disentangle these effects. I'll quit droning on about these prices. Because what we're
more interested in today are these prices. So people often don't think of, you know,
wage as a price. But economists like to just treat everything as being able to be bought
or sold. And labor is no exception. Labor can be bought or sold at the wage rate. So
that's how economists look at things. And so the wage is essentially the price of labor.
And the interest rate is essentially the price of capital goods. So basically, having capital
on hand, your economists are saying you're having to pay this interest rate, because
you're either having to pay an investor to get the funds you need to buy this equipment.
Or if you already own the equipment, you could be renting it out at the competitive rate.
And if you could have been making this revenue, and you're forgoing it, that's what's called
your opportunity cost. So even if you already own the equipment, your opportunity cost of
using the equipment to produce cannabis is your interest rate, which you could have been
earning by leasing out this equipment to somebody else. In reality, can you just lease out every
bit of equipment you own at the competitive interest rate? Not really. And this is where
there's imperfections in the market. There's transaction costs. Things aren't perfectly
liquid. So this is where theoretical abstractions of economics meets empirical reality. So that's
exactly what we're doing today. So we mix theory with empirics. So now that we've droned
on about prices, let's try to estimate them. And this is this is the fun part here. So
we've done all our forecasting here, we're going to get a couple, a couple more variables.
So check out the Federal Reserve Economic Data, Fred. So just do a Google search for
Fred, Fred, and you can get a bunch of economic series here. And I use these as fillers whenever
I can't get these data points from whatever data series we're working on. And this is
an awesome technique, right? supplement your data with other data sets. So here we've got
data from Massachusetts, Socrata, we're going to combine it with some data from Fred. So
what are we going to do? We're just going to get the average weekly hours here. So right,
we're not tracking this in the cannabis industry. So it would be awesome to know what people
in the cannabis industry were working each week. We don't have that data. So we're just
going to have to assume that people in the cannabis industry in Massachusetts are working
the same hours per week on average as everyone else in Massachusetts. Is this a stretch?
Maybe, maybe not. And so this is where when we're going through these estimations, right,
we're going to get our figure at the end. Well, we need to sort of include all of these
assumptions along the way in our footnotes or something, right? And not just in our footnotes,
right? If when you're presenting your findings to people, you need to be real explicit about
assumptions that were made along the way. And here we're making an assumption, maybe
it's minor, maybe it's major, that people in the cannabis industry work the same hours
per week as in other industries. And maybe they work more than average, maybe they work
less than average. And so we need to take that into consideration and say how that may
bias our results. So if people in the cannabis industry actually work greater than this,
maybe we're biased in our wage estimate either upwards or downwards. So that's something
to acknowledge here. I'm going ahead and grabbing these two series just for baseline. So what
these are, are so this is average earnings in Massachusetts per hour. Right? Yes. Yeah,
I think so. So this is average earnings per hour in Massachusetts. So going up, see the
spike here. That's interesting. So the average earnings went up in Massachusetts. Well, but
that's like, since this is where this is sort of a sad part of analyzing the data, right?
Because mathematically, right, this is what economists look at, right? Margins, what happens
at the margin, right? And so if the way margins were explained to me is, say you're getting
grades, and you've got a 90, the 92 average, well, if your next marginal grade is above
a 92, your average will increase. And if your next grade is below a 92, your average will
decrease. So it's, you know, that's how, you know, how you can kind of understand marginal
change here. Well, what happened in the market because the average earnings do increase?
Well, that means that either employees were added to the market with higher wages, or
unfortunately, what I fear is employees with below average wages were removed from the
market. And so unfortunately, it does look like a large number of lower income employees
may have lost their jobs at that point. Like I said, I don't want to read too much into
this. And this is where, if you weren't doing an analysis of wages, which we are essentially,
so you should hit the newspapers. So you should look at the newspapers in Massachusetts at
this time period here, which looks like this looks like the same period here, where, well,
these two series aren't going to plot too well on top of each other. But see, right
at this period where the market was closed, that's where wages increased. So long story
short, being an economist is basically being, you know, a historian, a statistician, you
know, a political science student, a full philosophy and psychology, all sort of bundled
into one. And, you know, that's sort of a data science, you know, data science takes
it to the next step. So all of you here today are, are having to having to do this. So,
you know, that's where we'll make you a good data scientist is if you can take skills from
other fields and incorporate them into your analysis. So incorporate some history, incorporate
some psychology into your analysis. So long story short, average earnings went up. The
minimum wage, we're just using this as a benchmark here just to know, okay, you know, whatever
we're predicting wages to be, our predictions really can't be below the minimum wage. Or
if they are, then you know, they're not, then people aren't getting paid. Or even people
are getting paid above their competitive. But long story short, this is a benchmark
here. So now we know what the minimum wage is here in Massachusetts. And we you know,
we can even plot these two together. So we can just kind of keep adding. So there's the
minimum wage, and there's the average wage in Massachusetts. And we want to know where
the cannabis industry wage shakes out. Well, let's estimate this. So we need to know beta,
y t and LT. Right. So we've got y t. We've got L. So we need hours worked. So this is
where I start to get a little creative. And so this is how I do things, you can do things
differently. And this is where science meets art. So I define labor as the total number
of employees times the average weekly hours. So that's the total people hours, the total
hours worked in the cannabis industry per week. Cool. Now we need to know beta. Well,
we've got a production function here. We can estimate it. So if you just take that's what
that's why people love the Cobb Douglas production function. Because if you just take the log
of both sides, you get a linear regression here, right, you just get log sales equals
log a, which is just a constant that we're taking as exogenous means we're not trying
to explain it. Plus alpha times KT. What's KT? We're proxying it as the flowering plants
per week. I'm going to show you here how this is a big assumption. And we need a better
measure of capital, but this is just the best measure that we're given. So we're basically
assuming that say you're growing 100 plants, you need a fixed amount of capital. If you're
growing 500 plants, you need a fixed amount of capital. If you need 1000 plants, you need
a fixed amount of capital that doesn't necessarily have to be linear, but but it does have to
be directly correlated. That's a huge assumption to make. But that's an assumption that we
have to make to or to estimate this thing. So so yeah, so this is a huge, huge estimate.
So basically, I'm just going to go ahead and hedge this is whatever numbers we spit out
here are going to be almost like a mental exercise. I'm not going to say that this is
what the competitive wages or is not what the competitive interest rate is, but this
is our exercise here. So we can at least attempt to have a measure since we don't actually
know what the wages, right? The trade rate, the whoever is doing taxes, the Department
of Revenue in Massachusetts, I'm sure, actually knows what the wage rate is for each individual.
But you know, that's private information. Right, so that's private information. And
so we're trying to get our best public estimate of what the wage rate is. So that way, one,
employees know what they should be asking for. And two, employers know what they should
be expected to pay. And same for interest rate. That way, investors know what to ask
for. And producers know what what fair rate of return, if a fair interest rate is to pay.
Right, because you want you want to know what a fair price is, like what are the fair prices.
And that's almost one of the hardest parts of operating in these industries is you don't
know what a fair price is. Right, when you're an employee, and you're bargaining for a wage,
you don't know what the fair wage is. Right, you have to sort right when you're in your
job interview, and they ask you if they do, like, you know, how much do you want to get
paid, like you're going to have to give them a figure. Or if they're, you know, writing
up the job, they're going to have to put a figure down. And you know, a lot of times,
people use these ad hoc methods. And here, we're trying to formalize it a little better.
But like I said, it's it's an imperfect measure as is. Okay, so we've got our model here,
we've got YK and L. Not yet, but we will shortly. All right, we've got YK and L. We're restricting
our analysis here to 2019 to 2021, the end of September. Just so we have a nice. Well,
I thought we were. Anyways, maybe we're. Oh, yes, that's right. So here, yes, our data
starting at 2019. And then I'm just going to assume that the market just closes. So
we're just going to assume, okay, the market's just going to close, and we're just going
to drop those observations for those two months there. This is sort of a violation of, like
time series analysis, right time series analysis is considering your t's are just incremented.
So this is a challenge here, having this break. But, you know, there's different ways you
can estimate this. But this is the way that I rationed was was the way to keep the most
data with, you know, with the least bias. So, like I said, there's probably ways to improve
upon this, but we're just going to exclude that time period there where sales were zero.
Okay. And so now we're going to take the log of Y, take the log of capital, take the log
of labor and fit a regression. So we just estimated our regression here, we have a really
poor fit. So this is not a very good regression model. So our production function can be improved
upon a lot. Our coefficients here aren't actually significant. So, so like I said, if you're
a frequentist, you couldn't conclude that these were statistically significant. We're
sort of more of a Bayesian in that I'm just trying to just get some numbers and use my
prior beliefs and knowledge to see if they look reasonable, right. And, right. And that's
the whole reason why we've been looking at things like average earnings, because I'm
a Bayesian and I'm trying to establish a prior, I'm trying to have a prior expectation of
what wages are going to be. If you're a frequentist, you don't want your prior beliefs creeping
into your analysis. The Bayesians just acknowledge that they inevitably do. So it's just saying,
okay, instead of, you know, pretending like your prior beliefs don't exist, just, you
know, acknowledge, okay, you know, this, this is what average earnings are, you know, this
is what the minimum wage is. My prior belief is that wages are probably between average
earnings and minimum wage, right, maybe they're above average earnings. But, you know, not
like that much above like, and they're not going to, it's not going to be like three
times above average, I don't think. And it's probably not going to be like that much below
minimum wage if it is below minimum wage. So, long story short, the significance of
the measure of capital may or may not matter. And this is where I'll show you here before
we end that our measure of capital matters quite a bit. So foreshadowing our measure
of capital matters. But long story short, we've measured, we've run our regression here.
Remember, alpha is just our coefficient on capital, beta is our coefficient on labor.
So here, you know, we just get those. We've got alpha, and we've got beta. Well, that's
cool because since we've got beta, we can now measure beta times yt over lt to get our
competitive wage. So let's do that. So here, we're estimating our historic competitive
wage. And we, we are noting that the competitive wage was really high at the start of the market.
And from the economic theory, this would make sense because there's not many people working
in the cannabis industry at this time, right? If you look at weekly employees, there's not
many people working. So each additional employee that you add is going to have a high marginal
product. A high marginal product is going to equal a high wage. Those are going to fall
over time as more and more people into the market. And it's going to stabilize. It looks
like there's just been downward pressure. Market closes. Well, if people can't sell
anything, then they can't be productive. So we're actually predicting that, you know,
their competitive wage at that time is zero, right? If you can't produce any value, then
no one's going to pay you for anything. And so that's where you saw people essentially
getting fired, because they're not, they're not able to produce any value because no cannabis
can be sold. And so they get laid off because the wage, their current wage is above their,
their marginal product. And then people come back to work. And then we're, you know, you're
going along here. Something happens here in, in like this spring of 2021. I'm, like I said,
I'm gonna have to hit the Massachusetts newspapers because I know I can't explain this dip in
sales. You don't see this in other markets. And it looks like it's a Massachusetts specific
dip. And I think some investigation needs to be done to figure out what, what, what
happened with sales during that period. So that's an interesting research question. So
long story short, wages, once again, they fall during that period because there's just
not many sales in that wages are, you know, bouncing back towards the level they were,
but they're volatile. So it's like, you know, when one week, you know, their productivity's
high. So that means, you know, firms are really going to be looking for workers, then the
next week, the productivity is low, you know, and they're not going to be looking for as
many workers. And then the next week is high, then low, high, high, then low. So that's
what this recent market's been characterized by this, this volatility. And especially,
you know, in a labor market, that's going to make things difficult, right? Because you
want to be accepting your wage rate, right? If you're a worker at the top of one of these
spikes versus the bottom, you know, and then employers are wanting the public peg wages,
you know, and they're low. And then so this is where you've just heard anecdotally, you
know, through the news and whatnot, how there's this tumultuous labor market. And then also,
you know, we've got our estimate for the rate of return on plants. And then Barry, you can
maybe speak to this. But basically, we were estimating that the rate of return on plants
was of course high in the beginning. And then maybe is decreasing over time. But it's maybe
wait, wait, six and nine. In the last year, last year, it's been around $62 per plant,
per flowering plant per week. So we're not certain if this is a very good measure of
a rate of return. But we can at least we at least like we need at least no wages dollars
per hour, people are pretty familiar with that measure. dollars per plant per week is
a bit more of an obscure measure. Right, just to go ahead and run through this last little
bit here, so we can be concluding on time, on time, I thought we could go ahead and use
our same analysis from above to predict what wages would be like in 2022. And I'm going
to run through this, but the codes online. So if you want to run through with me on the
one on one or something, just email me and we can maybe work something out to to go through
this, you know, line by line a bit more specifically. The long story short, let's go ahead and predict
wages into 2022. And there's our forecast, we can do a slightly better figure here. So
here's our competitive wage forecast into 2022 in Massachusetts. And so, you know, we
were saying that in the data science group is estimating that the wage for would be competitive
in Massachusetts to be $27 per hour on on average, so this would be everybody in the
industry. How does that stack out against everything? Well, here's everything plotted
together. So here is in blue, you have average earnings in Massachusetts. In red, you have
the minimum wage. In orange, you have our estimated competitive wage rate in the cannabis
industry. And then in green, you have our forecast for the competitive wage rate in
Massachusetts. Notice a couple things. One, the competitive wage rate we estimated was
above average. And now it's looking like it's either, you know, approaching average earnings,
or it may even be, you know, approaching a slightly below average median income. And
so that maybe, you know, if cannabis becomes a bit more of your know your standard agricultural
crop, you know, you may not actually see super high wage rates. But you notice our error
bounds here. And so you know, and plus we've got a lot of factors essentially by by seeing
our analysis downwards. And in fact, well, just to go ahead and pack in a bunch of things
here. If we just used alpha and beta from earlier, and then used our sales forecast
and our plant forecast, then you know, our forecast, it would be here in gray. So it
would be even, you know, even lower. So if we're just using the a theoretical approach,
it would be in green. If we're using a theoretical approach, it would be in gray, where we would
predict the lower competitive wage. And just to give you a complete uncertainty, if we
were going to change our measure of capital, to say instead of using flowering plants,
we just said, Oh, let's just use all plants. Oh, let's just estimate everything again real
quick. Maybe not super quick, but you know, quick enough in the grand scheme of things.
So here we're just, you know, changing our measure of capital. We get our forecasts.
Okay. And here is our forecast for wages. So here, if we change capital from flowering
plants to just all plants, which includes vegetative plants, well, it radically changes
our estimate of wage, right? Now all of a sudden, we're estimating wage was never as
high as average. And now it's fallen down to minimum wage. And we're predicting it's
going to just hover around minimum wage in 2022. So, you know, a lot of our prediction
here depends solely on our variables, like, you know, what, how do we want to measure
capital? You know, are we measuring it is flowering plants, are we measuring it is all
plants? You know, maybe we could measure, you know, you could, you know, you could cook
up some measure of capital that maybe incorporates floor plan, right, because we know the square
footage used by the producers. So there's a lot, you know, there's a lot of ways that
you can measure capital. And the way you do determines how your determines what you predict
the competitive wage in the markets going to going to be. So I just wanted to show you
this to give you a little bit of uncertainty here that, you know, this is not all set in
stone. This is, you know, it matters how you do your analysis here. And so, you know, are
we with you know, which graph do we present to the public? Do we show them the one with
the plant, the total plants through the flowering plants? You know, they both have their different
implications. My personal bias is towards flowering plants, because it just if you look
at these two series here, it just looks just on eyesight to be the more reliable. Sure.
But they're different. So, all right, all right, Donna, some people have got to go.
So we'll wrap up here. So thank you. Thank you all for attending. Thanks, Donovan. So
I always run a bit long. So I need to make an effort to conclude on time. So long story
short, next week, we'll pick back up and start to look at other states. But this is how you
can start to measure prices in the market, right? You can define your economic model.
You can conjecture that supply equals demand, and then estimate wages and the rate on return
on plants. And it matters how you do your analysis. And I was thinking there was going
to be a lesson for the day. But I think I'll let you all just maybe think about it and
maybe feel free to share your feedback about what your takeaways were for today. But until
next time, I'll clean up the code for you and share all these resources with you.