Welcome to the Cannabis Data Science Meetup Group.
Welcome to October.
So it's going to be a big month.
Croptober, as some call it.
And so we're going to be looking at cannabis data, as always.
And we should have an interesting day today.
So essentially, I thought we could recap some of the work
we've been doing the past few months
and apply it to Massachusetts.
So we've been calculating various economic statistics
here.
And so before we dive into that, let's just go ahead
and let Heather into the group.
And it's good to see you, Heather.
So good to have you.
Howdy.
Good morning.
Howdy, and doko as well.
Nice seeing you again.
And doko.
Not actually seeing you, I guess.
I think both of our cameras haven't
been on the last session.
So it makes two of us.
All right, on mute now.
We can definitely have more back and forth discussions.
So let me know if you just want to talk cannabis data
at any point.
Or if you just want to listen, I can show you
some of the current research I've been doing here
at Canelytics.
So essentially, what I've gathered and gleaned
is people are interested in data because they
need to do analytics.
They need to get good data.
Part of getting good data is curating the data.
So as we've seen, the data can be messy.
You may need to rename data points.
You may need to turn text into numbers.
You may need to handle text in numbers, for example,
less than signs, malformatted entries, double decimal places,
what have you.
So part of that's the data cleaning.
And then we've seen the augmenting of data,
where you supplement your data set with other data points.
So we've seen that, OK, we can grab data points such as GDP,
hours worked, employment from the Federal Reserve,
economic data.
So we can get those data points, add those to our data sets.
We can get the public data.
And we can standardize that.
And so there has to be some value added there.
And so that's, I think, analytics.
What we're sort of scoping out is our niche.
So we've seen, OK, people are doing some analytics.
So we can help there.
We can also help with the data pipelines.
And we can also provide data as a commodity.
So analytics is dipping our toes into offering some of this data
that we are curating to people in the industry.
And likewise, people in the industry
may be interested in offering their data,
albeit perhaps anonymized, to other parties who are interested.
So look for further action in that regard in the coming weeks.
So that's exciting news that I wanted
to let you know about, early access knowledge.
So without further ado, let's get into some of the data
curation that we're doing.
So all right.
So we've seen that, at least for Massachusetts,
you can access their public data, so public statistics,
daily totals, through an API.
And you can get a handful of interesting data points.
So you can get daily sales, daily number of plants, packages,
employees.
And we've been looking at the economic angle.
So just to recap some of the work that we've done.
So just to show you, so just running our analysis
in Python, using a handful of standard Python packages,
stats models, SciPy, pandas, requests, numpy.
These are just some helper functions.
And this is what we've been doing the past few weeks.
So we've basically aggregated the production data.
And we've also added the licensees data and prices.
So we've gone up through this point in previous weeks.
So last week, we worked with this data
to look at the GDP or total sales in Massachusetts.
So remember last week, we noticed
there were some outliers.
So here, I have simply coded anything greater than,
want to say this is, what's this one?
Yes, I've coded anything greater than 10 million sales
in a day as zero.
I was dividing them by 10, but that wasn't quite working.
So there's something going on with those outliers.
So I hate to just throw away the data point.
But that was throwing the analysis off too much.
Likewise, we shouldn't see sales below zero.
So there were a handful of observations
with negative sales.
So we want to figure out what was going on there as well.
Nonetheless, we can get this data and look at the data.
See, we've got data starting from October 15, 2018.
It's daily going up until October 5 of 2021.
So almost a full year's worth of data here.
So real interesting analysis we can do here.
Almost three years of data here.
Already then.
So we're looking at, right?
We were looking at sales.
This is what you would typically see from a time series sales
where you have what's called stochastic trending.
So you see this real sporadic movement, high variability.
There's still a trend.
And as we noted, it looks like there is this period
where reporting stopped.
So that will throw a bias into our analysis.
So we need to take that into consideration.
Unfortunately, I hate to just throw away the data,
because we've got all this valuable data
prior to April of 2020.
So I hate to throw away that data.
So we'll preface that as we move along.
We can go ahead and essentially just aggregate sales
into monthly and quarterly.
So I'll take the monthly average,
because that's how we'll want to look at employees, right?
The average number of employees at a given point in a month
versus sales, where we'll want to aggregate with a sum,
because we want to look at the total sales in a month.
So we can basically create both of these series here.
And we looked at these last week,
but we can look at them again.
So this is monthly sales, what we were calling GDP.
You see the business cycle prefaced with,
we do have missing observations right at this dip.
So that dip may not be as actual as we think it is.
So nonetheless, we can even look at quarterly
if we're interested in a bit smoother of a series here.
All right, so we created those series.
Just going to grab the licensees data,
just have it on hand.
And we also can grab the price data.
So prices.
In economics, you're often essentially calculating
or estimating the price for everything, right?
Everything has a price.
So that's what the competitive equilibrium is, right?
That's when the market supply equals demand,
everything shakes out, prices are determined,
and a certain quantity is bought or not bought
at a certain price.
So we're going to be pricing everything.
So we've got everything in the market, right?
So we've got the prices on the actual good here.
So this is average price per ounce.
Unfortunately, this is probably average price per ounce
of flour.
So this doesn't take into consideration
the myriad of products we have going on here.
So I'm going to go ahead and hedge my analysis in that.
And just once again, just say the work we do here
at the Canvas Data Science Meetup Group,
a lot of proof of concept.
So we're just saying, OK, the data exists.
You can crunch it in this manner.
If you're going to do this formally,
then you're going to want to be a bit more rigorous in your data
cleaning and what have you.
And we may have a new member.
So good to have you, Steve.
We're doing a market analysis of Massachusetts
at the moment using Canvas Data.
So happy to hear about your background
if you want to chime in.
So just feel free to speak up.
Or if not, then I'll just continue
droning on with this presentation.
So essentially, we're saying, OK, market analysis,
we're determining prices in the market.
We're given the price, presumably, of flour.
So that's awesome.
So we have some of our work already done for us.
So we've got the average price per ounce.
Well, we can put this in meaningful quantities here.
So we'll just say, OK, what's the average price per gram?
Given there's approximately 28 grams in an ounce,
then how many are in a 16th of an ounce?
Because that's a common measurement at the retail
level.
An eighth of an ounce, another common retail measurement.
And then a quarter of an ounce, another common retail
measurement.
And so these are just averages, so nothing glamorous.
In reality, you'd expect a quantity of 1.5 grams
of quantity discounts.
So it would be awesome to have more granular price data.
We'll work with what we're given.
I have a saying, when given peanuts, make peanut butter.
So in this case, we'll work with what we're given.
So we can see, OK, what's the average price
per gram?
Let's just hedge every dip here in August of 2020
with there is missing data.
And we just can't consider that month reliable.
So in fact, later on in the analysis,
I'm excluding, when we start running regressions,
I'm excluding August of 2020.
Because it's an outlier.
We're dropping other outliers so that they
don't bias our analysis.
And there's too many things that are wrong with this.
There are too many sources of bias in this month, right?
For starters, we're missing our data.
So that's bad.
And the relative percent difference, what have you.
Enough of that, we've got our price per gram.
And so this is where I'm going to say, OK,
let's start excluding August of 2020.
I'm not sure if this is.
Let's just say, OK, what was the?
Yeah, that'll exclude it.
Yeah, we'll just say, OK, what's the average price
per gram in the past 12 months?
Well, it's about 13.33 or so.
And if you look at just the price in the last year,
price per gram, this guy, and we've got
a visible negative trend here.
Of course, we're just going from 13.5 to 13.1.
But is this something to take into consideration?
Also, if you're more interested in looking at the price per.
You can look at the price per eight, about $46, $47.
I think this is interesting data.
So if you're a retailer, how are you pricing your products?
Are you above average, below average?
It's a benchmark.
Imperfect benchmark, but it's a benchmark nonetheless.
So we can continue our data curation.
So all of this is data curation, because first, we
had to go through the whole rigmarole
of getting the data through the API.
Not that complicated, but you have to do it.
Then you already saw that, OK, we're already
calculating a data point.
We're already having to curate that data point.
There are imperfections in the data.
So I have to buff those out.
And look, we're now creating entirely new series of data.
This is a whole series of data.
We've given daily data, and now we've
created monthly and quarterly series.
And we've indexed it by the date.
So value added, just got licensees data,
haven't really added much value there yet.
We've got the price data.
We've added a, we've created a few new series here.
So that's not nothing.
Moving on, we can now supplement the data.
So first, to supplement it, we have to actually
get the other data series.
So we saw that the Federal Reserve economic data
is a smashingly good source.
Once again, it has its own imperfections, right?
So nothing's perfect, but you can get some interesting data
points here.
The frequency may not be as we'd like it.
And we may not quite have up to real time, right?
So we only have up through August.
But we'll take what we're given.
So we'll supplement our data here,
initialize Federal Reserve client,
specify when we're starting our analysis.
And we can just grab a handful of data sets.
So this is awesome.
So we can grab the labor force.
We can get the total number of employees in Massachusetts.
We can get the population.
And we can go ahead and get the average weekly data.
And get the average weekly wage.
Not sure we'll need it, but we can get it nonetheless.
And then we can get the average weekly hours worked.
So we can get one, two, three, four, five data sets here.
So that's awesome.
So we already have one, two, three data sets.
So we'll add five more.
And so this is what I'm saying.
We're aggregating data and curating it
in order to provide value.
So we're now working with eight data sets.
We have touched each data set to make it a bit more user
friendly.
And we can now start calculating interesting novel statistics.
So we saw that we were just going to define GDP as sales.
So we can compare cannabis GDP to total GDP in Massachusetts.
And this is our analysis from last week.
So I'm not going to get into it too much.
Other than we saw, OK, cannabis as a percent of GDP
is increasing.
And there's a non-negligible amount of GDP per capita.
And so that's a crude measure of the well-being
of the people of Massachusetts.
So if you were going to try to quantify, OK,
from an economic perspective, how much better off
are people in Massachusetts because of permitting
adult-use cannabis?
Well, you can average it as about, OK,
they're about, I think, what did we say?
So now we're saying they're about $120 or so.
I thought we said it was $140 or so.
So we'll have to double check this.
Yeah, they're a little more than $100 a person better off.
And so it may not seem much, but remember,
this is every single person.
This is every child all the way up
to every retired person.
So it adds up.
It adds up.
And it's increasing, right?
So this is just the beginning.
We're going to essentially try to keep adding more data
points and statistics here.
So this is analysis that we did back in March.
And just to give you a refresher on the economics,
we're going back to March because we're
trying to keep using some of these statistics
that we've looked at along the way.
So here we're looking at employees.
Oh, yes.
And we're essentially saying, OK, we're measuring output.
Why?
So that's our cannabis sales.
And we're saying, OK, let's try to match an economic model
here.
So we said, OK, what if output is a function of plants
and labor?
So this is what's called a Cobb-Douglas production function.
So it's just taking capital and labor.
And they're each raised to the power function.
And then A is a production augmenting technology.
And then you've got a random production shock.
So that kind of encapsulates everything else.
So that's policy decisions or climate-related disasters,
like if a hurricane hits or lightning strikes.
So just kind of moving through this quick for a bit more
in-depth, definitely revisit our competitive wage
and competitive interest rate episodes.
So we're basically just saying, OK, sales
is a function of capital and labor.
And this allows us to basically, I'll have to lay out a,
if you look at my website, I've got the capital and labor
rate, how you derive the wage.
So maybe I'll do this in a future presentation.
So essentially, you can show that the wage rate is
a function of output and labor supply,
as well as our labor productivity beta.
So essentially, they're measuring
on how productive labor is in the production function.
This is a little messy.
Let's see if this one was a little cleaner.
Just canvas soil and plants.
Exactly.
And so then we were basically saying, OK,
given our Cobblet-Douglas production function,
you can estimate the parameters with a regression model.
So you just take the log of your equation,
and then you can basically say, OK, log of sales
is equal to a constant.
But you can also estimate the rate of sales
is exogenous to the model, which means it's not
explained by the model.
Then you have our parameters, so a parameter on capital
and our parameter on labor, and then a random shock
that you expect in a regression.
So that's the dirty economics lesson, quick and dirty.
So we'll do that a bit more in depth
and a bit more thoroughly in the future,
because I'm not satisfied with that explanation.
But nonetheless, we'll just keep the steam rolling along for now.
So we can essentially define those variables here.
So we're just saying, OK, why?
Well, that's our monthly sales.
We can say, so we're going to work with monthly data here
just to smooth out some of the stochasticness
of the daily data.
So capital, we're going to proxy capital
as just the number of total plants tracked.
And so this is going to be everything
from vegetative plants to immature plants
to flowering plants to potentially harvested
and destroyed plants.
So is this a perfect measure of capital?
Absolutely not.
We're just using it as a proxy, because you
have to proxy capital with something,
unless you actually have a measure of capital
at all of these firms.
So we'll do the best with what we have.
So once again, hedge would be much better
to have a better measure of capital.
Labor, we're essentially going to define labor
as the total number of hours worked.
And so we'll have to estimate that,
and we'll estimate that hours worked
as the average number of employees times
the average monthly hours worked times 4,
assuming there are four alarm going off.
So hopefully, we again, sorry for that.
It appears there's some sort of notice.
OK.
Wait one second.
Whenever you get a break.
OK.
Just again, you can see the numbers.
OK.
So we can define our economic variables, sales, capital,
and labor.
And we were defining labor as the average number
of employees times the average hours worked times 4,
assuming they're about four weeks in a month.
So we can define our economic variables.
And if we just take a quick look at these,
we'll see that, for example, we don't have a good measure
for October of 2018.
And furthermore, because of the Federal Reserve,
they only have data through August.
So we're limited with our measure of labor
through August.
Once again, we may retry this analysis
just using potentially a different measure for labor.
However, I'm trying to essentially calculate
the average hourly wage.
So that's why I'm trying to get everything
into a measure of hours.
So we'll work with what we're given.
And essentially, we'll drop October of 2018.
We'll also drop August of 2020, because it was an outlier
and there's missing data.
And then also drop September and October of 2021,
because we don't quite have recent data for these months
yet.
So we can exclude these missing observations.
And without further ado, we can estimate the economic model.
All right, so this is the regression that we just ran.
We just ran our dependent variable is sales,
the log of sales.
And our dependent variables were the log of capital
and the dependent variable was the log of capital.
The log of capital and the log of labor.
Oh, I see.
And we've estimated our coefficients here.
Excellent.
So this is interesting.
So I normally would expect alpha to be higher than beta,
but this may be contrary to our expectations.
So essentially, we're estimating that alpha is just
the coefficient on the log of capital
and beta is the coefficient on the log of labor.
So we're basically estimating that alpha
is about approximately 0.19 and beta is approximately 0.45.
For constant returns to scale, you
would expect alpha plus beta.
So if you're just looking at the production function here,
so this is just a mathematical concept.
Constant returns to scale, your power coefficients
would be equal to 1.
Decreasing returns to scale, your coefficients
are less than 1.
And then increasing returns to scale, they're greater than 1.
So here, our coefficients are alpha and beta.
So if alpha plus beta equals 1, you'd
have constant returns to scale.
Here, we've got decreasing returns to scale,
which essentially means we'll reach an equilibrium.
All righty then.
So without further ado, let's use these coefficients.
I'm not sure we need these data points,
but let's grab those statistics.
But yes, and I think I had to write.
So beta is actually our last parameter.
And then alpha is our first parameter.
That's right.
So we can actually go ahead and estimate the competitive wage.
So we already looked at the competitive wage in Colorado.
So now we can look at the competitive wage
in Massachusetts and see if they're comparable.
And this could be interesting, right?
So if you're an employee, do you want to go work in Colorado,
or do you want to go work in Massachusetts?
The cost of living may vary, but you
may want to go to where the wage is highest.
Likewise, if you're a lab director,
you may have to set a different wage rate in Massachusetts
than you may in Colorado.
So without further ado, let's estimate what the wage rate is.
So well, this is interesting.
So let's go back to our model here.
So remember that if we're going to declare
that these are statistically different than 0,
our confidence interval has to not include 0.
So as we can see, at the 5% confidence level,
which is pretty confident, well, if you're a frequentist,
so you wouldn't be able to declare that these
are statistically significant.
These are not statistically significantly different than 0
at the 95% confidence level.
The parameter beta appears to be statistically significantly
different than 0 at the 10% confidence level.
And then alpha is we can't conclude
any statistical significance.
Granted, we have a small data set here, 33 observations
after we've excluded the missing observations.
So not the greatest regression in the world.
And so that explains why we can't
be certain about the wage rate.
In fact, we're saying, oh, the wage rate, the minimum
could be less than 0.
Well, we know that's not actually possible.
Well, or is it?
And so this is where you run into interesting situations
in economics where maybe people would be, in some situations,
willing to work for a negative wage rate in that maybe they'll
commute and incur costs greater than their actual wage.
And why would they do that?
Maybe they're trying to seek experience.
So it's possible, but that's sort of going way out on a limb.
So let's just look at just the wage rate here.
And so first thing I noticed is it's
really high at the beginning.
So this is when the market is just starting up.
And from an economics point of view, this makes sense.
The industry just comes online.
There's not a lot of people working in the cannabis
industry.
The marginal product of labor is going to be incredibly high.
So that means the wage rate is going
to be high if it's a competitive industry,
because just adding one employee is
going to add a whole lot of value.
So you're willing to give them a high wage rate.
Adding the second employee is also
going to provide a lot of value.
So you're also willing to pay them a lot.
As more and more employees come into the industry,
each additional employee adds slightly less
marginal productivity.
So you would expect wages to decrease.
And sure enough, that appears what to have been the case.
So as the industry first comes online,
it would make sense for companies
to pay people hand over fist to come and work for them.
So if you're a producer, if you're a retailer, if you're
a lab, you're willing to attract talent.
You want to attract the best salespeople.
You want to attract the best farmers, the best processors,
the best manufacturers.
You want to attract the best chemists,
the best microbiologists, the best managers.
So you may pay them a premium.
You may pay them a higher wage rate
than they're getting paid in their current industry,
because they're going to add a lot of value
to your company in the cannabis space.
So you can pay them a high wage rate.
As time goes on, it looks like things stabilize.
So this is where we're starting to reach an equilibrium.
Have we reached an equilibrium yet?
Hard to say.
So we'll just say, OK, what's the wage
been in the past 12 months?
We're estimating that in the past 12 months,
the competitive wage in the cannabis industry
in Massachusetts is almost $40 an hour on average.
Are people getting paid the competitive wage?
Not necessarily.
So depending on market frictions,
people may not be getting paid their competitive wage.
If people have strong monopoly power,
then they may get paid more than their competitive wage.
So if there is barriers to entry for certain positions,
I'm not sure the regulations in Massachusetts
are going to be in place.
I know in Colorado, you have to get a license
to be in the cannabis industry.
So these are various frictions.
So employees can use these frictions
to ask for a rate above their competitive wage rate.
Conversely, I'll need to think a bit more about this.
If companies have stronger bargaining power,
then they may not be able to get their competitive wage.
I'll need to think more and more on this.
For now, let's just focus on the data here.
So just curious what the max wage.
OK, so we're saying, OK, they could be paid up to $40.
They could be paid up to $51 an hour.
And this is your average employee.
This is your retailer.
There are people working in retail.
But then again, remember, this is
a wildly inaccurate measure because we're saying,
oh, our lower bound is negative 18.
That's not even really possible.
So we've got a large room for errors here.
Just to continue, so remember, let's stay on track here.
So we've gotten the prices.
And then we said, OK, this is about the price per unit.
Well, we just priced labor.
In economics, everything has a price, and in equilibrium,
we determine the price.
So at each point in time, the market reached an equilibrium.
People decided how much labor they were going to supply.
Employers decided how much labor they were going to employ.
The wages were set, and the employees were paid,
and the revenue was gained.
So at each point in time, we've got an equilibrium.
And so the equilibrium competitive wage
will be fluctuating over time.
And that's essentially just the price for people's labor.
Like, how much does an employer have
to pay to get one hour of labor from the average employee?
So that is the price on labor.
So that's a cost.
But once again, we're determining
the prices in equilibrium.
That's the price on labor.
Moving on.
There's actually a price on capital, right?
So the investors, right?
So there were investors that invested
in these cannabis companies, the productions, the processors,
the retailers.
And they're expecting a return on their investments.
So you can think about this as, OK, so all the physical goods
that were invested in, how much return do those yield?
Well, we can estimate what the average competitive rate
of return is.
Once again, the market may not be competitive, right?
People may have patents on certain technologies.
It may be hard to move equipment from point A to point B.
There's a lot of factors that make the industry
uncompetitive.
And so people may have to pay a higher competitive rate
of return risk, right?
There's a non-negligible chance that your capital may get
seized.
Or there may be some serious losses in capital, right?
So if you invest a lot in this specific parcel of land,
and then all of a sudden the zoning changes,
well, you may incur a loss on some of your investments.
So there's a risk premium.
So these may not be factored into the competitive rate
of return.
Nonetheless, we always say A measure
is better than no measure, even if it's wildly inaccurate.
Well, that may not necessarily be true.
So just hedge that this could be wildly inaccurate.
Once again, it's a proof of concept.
So if you're going to do this on your own,
you can repeat this in a more rigorous manner.
So without further ado, let's see if this looks rational.
So it doesn't quite look like a rational number here.
Yes.
Now I'm struggling with units, right?
Because are they expecting a 110% return?
Are they expecting a 10% return?
Or is this a 1,010% return?
So I'm not happy with this measure here.
Let me think about this for a second.
So this is sales per plant.
So I think this measure must be, if given those units,
dollars per plant.
So this is maybe not the best measure of the rate of return.
Because we're basically saying they're expecting,
they're saying, yes, it's not the best measure.
Because we're basically saying, OK,
on average, you can expect $90 per plant per month
for your investment.
What does a plant cost per month?
It's left out of the picture.
So that's why we were kind of struggling with this.
So that's why we were kind of struggling with the units here.
So the units matter.
So I want to revisit this.
And I may think on this during the week.
And we can maybe discuss this more next week.
But I'm wanting to say that this is sales per plant.
So the rate of return is $90 per plant per month.
That is not a very logical measure of the rate of return.
And I may be wildly incorrect.
So I want to think about this measure a bit more.
One thing, so our measure on the rate of return per plant
is not, well, actually, maybe that's OK.
Maybe that's all right.
So the investor says, OK, we're going
to fund 100 plant room or 5,000 plant room.
Then we want $90 per plant back per month.
That sounds pretty high.
But maybe we'll keep visiting this next week.
And maybe next week we'll try to estimate
the cost of production.
So that way we can get this into percentages.
So what's the cost per plant per month?
So that way we could calculate what the, and what, well,
what we could maybe do is look at the sales per plant
per month.
OK, OK, this is how we can do this here.
So the rate of return, OK, right,
is around $90 per plant.
And each plant is grossing around,
well, what is each plant grossing?
Each tract, well, let's just look
at this in the last 12 months.
So in the last 12 months, each plant
has averaged about $475 per month in revenue.
So I wonder, so rate of return divided by the sales per plant,
the sales per plant.
Didn't do something right here.
Let's see why.
Oh, I see, I see, I see.
OK, so we're just backing out alpha right there.
OK, yeah, I'm going to need to think a bit more about this.
So let's see.
So I think we're going to need to think
a bit more about this.
So let's see.
Yeah, I'm going to need to think a bit more about this.
So price per plant.
Well, I guess that's basically it.
So if the cultivator has to pay the investor around $90 per plant
there, rate to the difference, basically.
I want to say that's profit per plant.
So this is sales per plant net, the rate of return.
So for each plant, they're going to be grossing so much.
But then they have to pay back their investor
a certain amount per plant.
So this would be, this is not quite profit per plant,
because you're not including labor costs here.
But we're kind of moving in the direction of the profit
margin per plant.
I'm going to want to think about this.
We've gotten a little messy here towards the end.
So I'm going to tidy this up for next week,
because we're trying to do the market analysis here
to determine the prices at equilibrium.
And we've kind of gotten a crude measure of, OK,
what's the price per plant?
And I want to say that's the rate of return.
So I want to say we've priced it.
I want to say we've just now priced the cost of a plant.
And we've priced the cost of labor.
So yes, so next week, we should be able to estimate profit
margins here.
So why don't we try to do that next week?
Next week, we try to estimate what
are the profit margins in the cannabis
industry in Massachusetts and try to just see, OK,
is the average profit per licensee,
or perhaps per producer or retailer,
is that increasing or decreasing?
My conjecture is just from what I've seen,
markets tend to get more concentrated over time, which
gives the firm's monopoly power.
But I don't know.
So I'm not going to make a conjecture yet.
So are prices, no, no, no, profits rising or falling?
I don't know.
It depends.
Is the industry becoming more competitive over time or not?
Because if the industry is becoming
more competitive over time, then we're
going to expect average profits to be heading towards zero.
So if the industry is becoming more competitive,
then we should see average profits heading to zero.
If they're heading in the other direction,
then there may be forces at play making the industry less
competitive.
So I think that's something to look at next week, hedging
that these are all crude measures.
But I want to say that we've now all determined prices.
We definitely have the prices of the products.
We have a crude measure of the price of labor.
And I want to say we now have the price for a plant.
I'm going to think about this during the week,
and we can revisit this next week.
But I think we may now have prices.
So now we can look at profits and make
a statement about market competitiveness
and how it may be changing over time in Massachusetts.
And we could even potentially compare that
to other states like Colorado.
And so this is what our industrial organization
economics is all about.
So we're saying, OK, how do these policies in Colorado,
how do they affect the outcomes and the structure
and the performance?
Likewise, how do these states compare?
So how do decisions made in Massachusetts,
how do those affect the market versus decisions
made in Colorado?
And maybe other states can learn from decisions
that these states have made.
Last but not least, we've also looked
at inflation in the past.
So you could just end the day by just saying,
oh, we can just see, OK, how are prices changing over time?
Looks like we have this wild outlier here.
But I just think it's interesting to see just how
prices are changing over time.
And the price of cannabis in Massachusetts
may have stabilized here.
So it'd be interesting to see what the trend is here.
So we'll start looking at that next week.
We'll start looking at the trend in prices
and try to look at market competitiveness
in Massachusetts.
So today, we've begun our analysis here,
our market analysis of Massachusetts.
So we've gotten the data.
We've analyzed the data.
We've curated the data and supplemented it
with other data sets.
And we've now begun our analysis.
We've calculated statistics.
And we are now using economic models, so economic theory,
to estimate what the competitive wage may be
or what the competitive interest rate may be,
or the competitive rate of return on plants
may be in Massachusetts.
So real thorough analysis.
And you're welcome to use all of this work
and do it on your own.
So you're welcome to do your own economic analysis
of another state or repeat my analysis
and try to do it better without some of these shortcomings
that I've run into.
So there's a lot of work to be done here.
So real exciting work here.
And as always, we're using economics, data, cannabis
know-how, and science to have fun
and try to make some interesting insights about the cannabis
industry.
So thank you for tuning in here.
And then are there any questions, comments, concerns,
or ideas, what have you?
So feel free to chime in.
On that note, you're always welcome to contact me.
So you can send me a message through meetup.com
slash cannabis data, cannabis hyphen data hyphen science
dot com.
Just do a Google search for Meetup Cannabis Data Science.
You can also check out the archive
for the latest information on cannabis data.
So if you're interested in the cannabis data science
and cannabis data science, you can
also check out the archive on canlyvix.com
forward slash videos.
So you can find the video archive there.
Do a search on GitHub for the cannabis data science.
And you will find the repository where we have all the meetups.
So you can find all the source code and all the data sets.
Awesome.
So it's not clean.
So as you'll see, some of the work we've done,
it's a little ad hoc.
And we're just kind of writing scripts as we go.
But it's there for you to use.
So you can at least grab snippets that we've written.
You can find data sets that we've compiled.
There are notes to where we've found data sources
and what have you.
So by all means, check it out.
Use anything that you find useful.
And definitely reach out if you want
to talk cannabis science or data science in general.
So always happy.
On that note, thank you for attending the Cannabis Data
Science Meetup Group here in Ohio.
So checking out the medicinal market here in Ohio.
And it's exciting.
So visiting a handful of laboratories here.
And this time, we're moving west.
And just going to keep visiting laboratories
and keeping my ear to the ground of what's
happening in the cannabis industry.
And so I'll let everybody know.
And until next week, we're going to be doing a bit more thinking
about the economics of our economy.
Of our market analysis in Massachusetts.
And think about directions for next week.
So until then, feel free to reach out.
And it's been awesome speaking with you all.
So stay productive, everyone.