Welcome to the Canvas Data Science Meetup Group for the 15th of December, 2021.
We're heading quickly into 2022, and that has some big implications.
So one, we need to go back through the year and look at our forecasts.
So we made some forecasts throughout the year of how the market would shake out in 2021.
And so now it's time to check those forecasts.
So we made forecasts of Washington State sales and performance.
We looked at Oregon prices.
So let's, we'll need to, you know, look, check our forecasts there.
We looked at so many states.
We looked at Colorado, Oklahoma, Massachusetts.
Lately, we've been looking at Illinois.
And we've been curious about states such as Michigan, Maryland, and all the other states
with adult use and medicinal use.
So as we're here in the home stretch, I'll be working on, and you're happy to contribute
with, forecasts.
So we can work on one, checking our forecasts from 2021, making forecasts for 2022.
So should be a big time ahead with that.
Without further ado, so, Shane and Heather both joined us before.
So do either of you have any topics or questions, ideas, comments on your mind for the start
of today?
Starting with you, Shane.
I know I'm just glad to be here.
This is my second time and I'm relatively new to data and tech, but not so much new
to cannabis.
So I'm trying to bridge the gap and just kind of get the information and about the industry
and just get as much information as I can.
Happy to have you, Shane.
We're going to have a bunch of information today.
And so then Heather, would you be happy to just say a quick word and if you've got anything
on your mind for today?
Yes, I planned on observing this talk rather passively, but definitely, I know from last
week we were talking about Michigan, right?
So I guess I just, I know I want to analyze the Maryland data.
I just hasn't been in the cards for me just yet.
So that's really the angle.
I'm just, you know, I'm really intrigued by the state stuff because there's a different
level of control at each state.
So it's, you know, not something that we can ignore.
Anyway.
Exactly.
And so, as far as I'll speak to Michigan data real quick and then Maryland, so Michigan
data, they've got great data on their website.
They just have quite restrictive terms of use.
So you can't use their data for commercial use and you may not be able to access it programmatically.
I sent them an email, the Michigan regulatory agency to see what exactly is permissible.
So can we use their data in, you know, an open source public manner, the way we have
been.
So still waiting to hear back from that.
So we're going to actually have to pause on Michigan data, just till we get the green
light if we do, hoping we do.
Maryland will come up next week when we start doing state by state forecast for 2022.
We're going to really kick that off next week.
We're sort of just teasing that today.
And we may begin Saturday morning statistics.
We'll see.
We're doing a few like regressions there.
So if you're interested in regression analysis, definitely worth checking out.
And then next week, next Wednesday, we'll start the forecast.
Today I was just going to share with you some economics.
We've talked about some real interesting economics, talked about some of it during Saturday morning
statistics.
And I wanted to share some of that with you here today, on Wednesday with the KAMS data
science group, because I think it's valuable information and don't want to leave you out
of the mix.
So just wanted to share with you some real interesting economics real quick.
And then we can take a quick look at Washington State, because that was one of the states
that we first began and now analyzing at the beginning of the year.
So it seems fit to take a look here near the end.
So without further ado, let me share with you
the latest work and some little stash of economics just for fun.
So we saw a technical memorandum produced for the state of Nevada, where they were looking
at the relationship between dispensaries per capita and sales per dispensary.
And so we got a handle on this data, and we're going to take a look at it ourselves.
Awesome to have you join us Marjana.
Just quick update.
Today we're going to be honing in, we couldn't yet start wrangling Michigan data.
Waiting for the green light on that, just to make sure we abide by their terms of use
as far as accessing their data.
The next best thing, we can look at a comparable state.
So just so happens that the first state we looked at, and a comparable state to Michigan,
is Washington State.
So on the chart, as things shake out, Washington has a comparable number of retailers per capita
and sales per dispensary.
As far as the whole dichotomy goes, where you have Oregon and Colorado on this far end,
and then you have states that we've been looking at the past couple of weeks, such as Illinois
and Massachusetts on the far other side of the scale.
It's nice that we actually now have points all over the regression line, so that's nice.
Exactly, and as Marjana pointed out, there's only 10 data points here, so we would like
to add a lot more, especially equally distributed along this line here.
So we've added a lot of data points for Illinois and Massachusetts, so we're getting a little
heavy on this end.
So these states also have good data in Washington, Colorado, and Oregon.
So today we'll add a lot of data points for Washington State.
And once we get the green light, we'll work on Michigan, but for now we'll look at Washington.
And so we're starting with the figures, I'll zoom in here a bit, that we're expecting around
10 dispensaries per 100,000 adults here.
Notice world measure in population, so it'll be a little different.
And then we're also looking for around 2.2 or so million in annual revenue.
Okay, so why are we even looking at this data?
Just want to just go ahead and talk just real quick about why we're even looking at this
data, just to give the group a little bit of background.
So we've got a research question, this is essentially our literature review, and then
we'll get into our data, our methods, and our analysis and conclusion.
So essentially applying the scientific methods data.
So a lot of this work has been done for decades going on, starting at the beginning of what
was formerly termed economics.
And so the reason people are looking at this is, well, I'm just making sure there's not
any join these real quick.
Okay, so the reason people started looking at this is they wanted to start characterizing
markets that maybe there were some bad actors going on.
So maybe there was collusion, maybe there was some sort of regulatory capture, maybe
there were some sort of other factors going on to create these monopolies.
So in the early 1900s, you see a lot of the antitrust policy where they start to break
up the large oil companies.
And that's really where a lot of the focus started to go into characterizing markets
is, okay, are they competitive?
Are they anti-competitive?
And this is where the structured conduct performance paradigm came about, where they basically
start to say, okay, we need sort of a classification system for classifying markets.
So economic theory suggests, okay, if there's perfect competition, the market will shake
out one way.
If there's a monopoly, the market will shake out another way.
So this diagram shows how the market would shake out with a monopolist, with the monopolist
operating at QM in a perfectly competitive market.
All the firms would produce a total of QC.
So that's just really fundamental microeconomic models that make these predictions.
So we don't really ever observe a perfect monopoly.
We never really observe perfect competition.
Well, I take that back.
You may see a monopolist now and again, but it's actually kind of rare.
So you're really observing people in firms operating somewhere between oligopoly and
monopolistic competition.
And you could even split that up into gradients.
And generally, governments are concerned if they see really concentrated markets.
So if they see essentially oligopolies, they start to get a little concerned because the
firms start to get a lot of market power.
And as we saw, the consumers start to kind of get boxed out of surplus.
So the consumers, they lose a bit of their surplus.
And so the idea of the government is they're looking out for everyone.
And so they're looking out for producers, and they're looking out for consumers.
So that would be the justification for antitrust policy, is if there's a social preference
for there not to be a monopolist and for a more competitive market.
And so that's sort of been the justification for antitrust policy in the past.
And then I just point out things like regulatory capture, inclusion, and whatnot, because those
are factors that I think push the market in a more anticompetitive direction.
But long story short, you start to see different market structures.
They're affected by different things.
So for example, there may be barriers to entry.
So it may not really matter that much what government policy is per se, depending on
what the barriers to entry are.
So if there's high barriers to entry, that's going to push the market in a more concentrated
direction.
So long story short, we're interested in the cannabis industry.
And so it would be interesting to see if we can actually quantify this.
So in previous meetups, we've just been trying to just kind of make qualitative statements
about where we think the market's heading.
But we've got real good data here in Washington state.
So we can actually start looking at some of these metrics.
In one of these metrics that we can actually use to quantify this, so put an actual number
on how competitive the market is, this is metric called the HHI.
And so I'll come back to this table here in a second.
But let me go ahead and introduce this measure.
So I could have presented this more elegantly, but we'll power through this and get to the
data because it's an important concept.
Because in the past, we've just been trying to use other ways to judge how competitive
the market is.
But in Washington state, we actually know the market share of each retailer over time.
So we can actually use these standard measures of market competitiveness.
And the two standard measures are the end firm concentration ratio.
And so that's just the sum of the top largest firms.
So you just maybe look at the top five largest firms.
And the higher the number, the more concentrated the market is.
Similarly, the HHI, you look at the sum of all of the market shares squared.
And so if you were calculating the market share in terms of percentages, if there's
just one firm, the monopoly has 100%.
100 squared is 10,000.
Well if all the firms have effectively 0% of the market, so they're going to be each
firm is going to be approaching 0% of the market, then your HHI is 0.
And so note that there's an infinite amount of firms in the market.
Never really observe an infinite number of firms.
So it's approaching infinity.
So long story short, the Justice Department, the Department of Justice, through the Department
of Justice, they need a metric that they're going to use in courts, in legal disputes.
So they need a metric, a number.
So they defined the HHI.
And they set this threshold at 20.
And I recommend checking out this source here for the exact language.
But essentially, they say that, OK, any industry with an HHI above 2,500, we're going to define
as highly concentrated.
So that's what we would call an oligopoe.
And then basically they said, OK, anything below 2,500, if it's above 1,500, that's
a moderate concentration, but not really worth litigating over.
And then anything below 1,500 is fairly competitive.
So that's all of the nitty gritty out of the way.
But essentially, now we actually have a metric that we can.
Not really a metric, we've got like baselines that we can compare our metrics in Washington
State to.
So without further ado, let's calculate the HHI in Washington State.
And not only can we calculate it, but we've got fantastic panel data.
Here we've got data that varies over the individual.
So that would be over the different licenses.
And it varies over time.
So we can measure the HHI over time.
And so that way we can say, OK, is the market in Washington State becoming more competitive
or less competitive over time?
And the point to watch out for is essentially you're looking for jumps in the HHI of around
200 going up, positive increases.
Because those would be occurrences that increase market concentration.
So you just kind of got to keep an eye out for those.
So as diligent consumers, we can keep a pulse on the Washington market and sort of monitor
the HHI and see if it's spiking out of control or trending positively or negatively one way
or the other.
So real interesting metric that we can look at here.
So where can we get this data?
Well, Washington State has incredibly open public data.
And the Cannabis Observer is there, I guess you'd call them a news outlet, but they primarily
report on the cannabis industry in Washington State.
And they do periodic Freedom of Information Act requests to make these available to the
public.
So that way the public can get access to really incredible data here.
And so, for example, I'll share this data with you here.
I probably should have already shared it with you, but I will immediately after the talk
today.
Essentially, though, you now have access to all of the Washington State traceability data
through, you know, November of 2021.
And so I'm sure the Cannabis Observer will do another request here.
So Washington State is migrating off of their traceability system in this month, December.
What's interesting about that is it'll give us sort of a complete data set of this sort
of the their traceability data.
So that way we can just look at, you know, Washington State's experience with this date
with this traceability system.
And we actually have the entire population of data.
And so this is really powerful from a statistical lens, because, right, a lot of statistics
your underlying assumption is that you're operating with a sample of the data and you've
got much limited predictive power when you're working with the sample versus the entire
population.
And so here, you know, we actually have the entire population of all of Washington State's
data, you know, every single sale item.
So it's actually sort of overwhelming to deal with, because it's just these are zipped files.
So just be warned that once you unzip these, these are just ginormous TSVs.
So they're not even CSVs, they're tab separated values.
So they're even trickier to work with.
So just be warned.
Check out some of the source code from the cannabis data science group from the beginning
of the year where we are working with some of these data sets.
So user be warned, they're difficult to work with.
How many months of, like, I am, how long has cannabis been legal in Washington?
And does it have all of the data from when it was?
It doesn't have the very beginning.
So and I should know this off the top of my head, but oh yes, I want to say that Washington
State permitted cannabis use in 2012.
Oh, wow.
So that's a lot of data.
But this traceability system was adopted in formally of April of 2018.
So you really just have.
Yeah.
Okay.
So it's still a lot of data.
You don't have all of those formative years of the industry, which would actually be incredibly
interesting to look at.
The growth, yeah.
You can see how the industry came online over the years.
2018 still a good time frame.
So here's what we're given.
But essentially what happened is Washington State migrated from biotrack traceability
to leaf data systems traceability.
And we've got all the data from leaf data systems traceability system.
So it's awesome that Washington State makes this publicly available.
If any of you have clever ways to aggregate this data, have at it because we'll share
with you.
So for example, they put together a aggregate of sales by retailer by month.
And so I've already downloaded this here.
And once again, I'll share the link with you afterwards.
I think I already have it open.
But essentially, someone helpful has aggregated total sales by license number by month.
Well, here they're going back to November 2017.
But as I said, the system wasn't formally fully adopted until November, I mean, until
April of 2018.
So I would be cautious about using data prior to April of 2018.
OK.
And one more question.
Each license number is for one retailer.
And when they renew their license, it's the same license number.
I don't know.
That's why I'm asking.
It should.
I haven't actually dug into this data too, too deeply here.
But it should be like, so for example, like if we just look at one license.
OK.
Well, maybe that's not the best license to look at.
But if we just look at one license, well.
OK.
So this could be a problem here if these license numbers aren't unique.
Because it would be helpful to track these people over time.
Yeah.
OK.
So we don't actually have quite the exact data that I was hoping we did here.
Well, that sucks.
Yes, because it would be interesting to.
And like I said, the data exists.
We'll just have to compile it ourselves.
I think this will work for the analysis I wanted to do today, which was just look at
sort of aggregate this by license number by period.
So I think we just can't track these over time with this particular data set.
Unless, hold on here, unless we've just gotten really unlucky with my.
Right.
So this could be the problem with the non-random sample.
So let's try to pick out one.
OK.
Yeah.
Cool.
OK.
Awesome.
That's good.
OK.
As I just demonstrated, you have to be really careful about the conclusions you draw with
non-random samples.
But this is good.
Yeah.
So now we know that there are licenses that are going over years.
Yeah.
Cool.
So that way, I just picked a bad example.
So here is license 425498.
And what's cool is you could actually match that.
So you could grab the.
There should be.
So there's a licensees data set.
OK.
So you should be able to cross reference the licensees data and get granular information
about that license.
So another.
Where it isn't the state, what their name is.
Yeah.
Another interesting thing also is that it would be interesting to see like who did not
renew their how many how many retailers did not renew their license.
You know, and was it dependent on their total sales?
You are brilliant.
I love your thinking because you took it to the next step because not only is it interesting
to just see, OK, who's staying in the market to look at entries, exits over time.
But then seeing if that's dependent on sales, that's brilliant.
So brilliant idea, Marjana.
So I think that's worthy of a, you know, I always think things like this are worthy of
a paper because I mean, who else is looking at this?
But I mean, that's just.
You never want to write a paper.
I have a lot of experience writing paper, so I'm game with that.
Because I think you've got the data here to parse that out.
Yeah, absolutely.
So you could sort of.
I don't know how you would do your analysis here, but you'd almost do some sort of like
survival analysis.
So like this license.
Seventy nine or thirteen, you know, they only survived one period.
And then you could see, OK, you know, does like the number of periods that a license
survives, does that depend on.
Their average sales or something like that.
So you probably know better than I could do, but there's probably more sophisticated ways
to do that analysis.
But no, but I was going to say, Keegan, if you were interested in ever publishing even
for a state's results and you needed like help writing a paper, I can help you with
that.
I published several papers, but not in this field, obviously.
But we can look at journals that, you know, potentially this can be published in this.
This is this is a this is good.
This data set here.
Because that was something a professor told me once that stuck with me that, you know,
everyone's always so focused on market performance and entrance that no one that exit.
It is quite understudied.
Yes, absolutely.
And that that that actually answers a lot more questions that I feel like then entering
the market.
What is causing people to leave?
Yes, because we're risk averse because those are things you want to hedge against.
So if you know that there's some threshold of sales that you really have to hit.
So if you see that, oh, everyone below.
You know, 300,000 in monthly sales is exiting after six months, then that's a really that's
a, you know, a red flag, so to speak, that you'd want to watch out for.
Or other factors.
You know, you could have particular geographic regions that have high exit rates.
Which could be dependent on the taxes in that region, too.
I have a quick question.
Yes.
How I know if I'm sure it varies from state to state.
Are there different incentives or encouragement from the states once they decide to legalize
to encourage cannabis businesses from growers to retail to open up in the state?
Is there anything going on like that?
In Washington, I don't know off the top of my head.
In fact, I want to say it's difficult to operate because it's you really have to make sure
you're abiding by the regulations.
It can be real easy to get mad for some violation for just like not having appropriate security
cameras or this or that.
So I do know in certain states, that's sort of a new priority is to make sure that people
have the resources they need.
But I don't know per se the state the standard in Washington state.
I think Michigan has some incentives last I read, but I don't know what the incentives
are.
But yeah.
It's an interesting point because these companies are bringing in good tax revenue.
So you don't want the companies just not surviving for some good reason or another that could
be prevented.
But I love that you're thinking Marjana.
So I think that's going to entail a whole bit of analysis.
So we can perhaps put it on the docket and start looking at that.
Sure.
Look at exits every time.
So for today, I'll just show you how to just start calculating some of the standard retail
statistics that we've been calculating for other states.
And we can do that pretty quickly, just because we've got such good data here.
So essentially just going to read in this Excel spreadsheet.
So now we're just looking at this exact same data.
I've already kind of been filtering this, but looking at this data here.
And then I noticed there was just at least one blank row.
So just going to remove that.
And then just create a formal date column.
So nothing fancy yet.
Just making sure that the date matches the reporting period.
Then just going to grab Washington state's population real quick, just because we're
interested in knowing the number of retailers per capita, just to kind of put things in
perspective.
So without further ado, we can start looking at some of these statistics in Washington.
So for example, we can aggregate everything by period and just look at total sales over
time.
So this is sort of what we have to work with in a lot of the other states.
And so this is what's so cool about the Washington state data is we can go as granular or as
aggregated as we would like.
So for example, we could get each granular sale item, which we've had some entrepreneurial
members do and do studies on sale items.
And so to long story short, today we're just looking at a few aggregate statistics.
So we were calculating these statistics in Massachusetts and Illinois.
So it makes sense to go ahead and calculate them in Washington state too.
Okay, I knew there was someone trying to join.
We have one more member joining.
Happy to have you, Graham.
We've just been looking at statistics here in Washington state.
So happy to have you join.
Yeah, sorry guys, running late from a doctor appointment, but this is wonderful.
100% okay.
So essentially you joined at a good time.
Since we're centrally looking at the exact same metrics we were looking at in Illinois
and Massachusetts.
And this time we're looking at them in Washington state.
So we can see, okay.
And this is what's really cool is this is why I wanted to really compare Washington
state with Massachusetts.
Because I was in Washington state at the time.
And in April of 2020, you see this giant spike in retail sales.
And just anecdotally at the time, stores were getting rushed.
So people were stockpiling on products.
And it got to the point where basically all of the retailers were offering sales.
So they were basically, different retailers would say, oh, we'll have a Friday sale or
we'll do a Saturday sale.
Well, they said, okay, we're just going to do sales all the time.
And so effectively, that lowered the price.
And so that's going to be a whole nother analysis of its own.
We need to get the price data out of these sales items and look at prices over time in
Washington state.
So that's going to be a whole nother animal to wrangle.
But for now, we can at least see that sales spiked in April of 2020, which is sort of
the exact opposite of what we saw in Massachusetts where markets were closed for two months.
And so I think this offers a great opportunity for differential analysis where, you know,
you basically have two different policy decisions.
And basically what, you know, economics is all about is measuring sort of the impact
response of these policy decisions.
So basically, from my just naive observation, it looks like, okay, you know, in Washington
state, you know, the policy decision that they were going to keep stores open on top
of sort of the dynamics that led all the retailers to decide that they're going to do their discounts,
probably because there's an increase in demand, then, you know, you just see this is the whole
shift in supply.
So it just supplies shifted up and it's it stayed up.
And as we saw in Massachusetts, there was sort of a sort of a shift down in supply during
that time.
So just naive observations so far, nothing concrete yet.
But then we can start looking at retailers over time.
A little interested in what's happened in these these latest months.
Is this just like a reporting thing?
Like maybe these companies just haven't reported their sales yet.
And maybe there's measurement error, but there could have been a large well, and actually,
we have to look at the scale over here, right?
This is just a dip from 440 to 430.
So you know, having 10 retailers exit the market is not made on it really be that extraordinary.
But but anywho, this is the count of retailers over time.
So now we've got sales over time, we've got retailers over time, we can divide the two
and get sales per retailer over time.
And so once again, going along, and then you see the sales per retailer spike along with
sales.
So you know, although each retailer, you know, is seeing this increase in their revenue.
So good to be a retailer, if you're one of the ones that hasn't exited that is.
And then finally, just looking at the retailers per 100,000 people that tracks along with
retail retailers pretty closely.
The one thing I'm going to point out though, is our measurements here are quite different
than what's measured here in the technical memorandum produced for Nevada.
So they are they're estimating that Washington State has 9.8 dispensaries per 100,000 and
around 2.2 in annual revenue.
We're seeing okay, you know, if we calculate this exact same metrics, you know, these should
be identical.
But we're estimating, you know, 5.8 retailers per 100,000.
So it is a different metric.
So ours should be a little less, but it should still be comparable.
And then also our sales per retailer is a little higher.
So I think it depends on perhaps when this technical memorandum was prepared.
So you know, they may have prepared their memorandum, you know, early on in 2020, you
know, before we saw that spike in sales.
So that's a possibility I just realized.
Anywho, now we can do a little live coding here, because I'm still working on these metrics.
But we're real close.
So long story short, we're trying to calculate the HHI in the similar metric, the concentration
ratio over time.
So just to save us a little bit of time, I've already written this snippet of code right
here.
Let's see if we can't use this to our advantage.
So basically, I've got the market share for all the licenses on all the different days
here.
So let's try to calculate the HHI over time.
So bear with me while I code this, but I think we can do this within five minutes.
So basically, you know, if we're just going to iterate over this, so you know, so for
the date, we're basically going to iterate over the keys.
We're going to get the particular market shares for that date, actually, to make more sense
to iterate like this.
I think we can do it this way.
So bear with me, but just make sure this is...
Okay, cool.
So the HHI is going to be the sum of all of these market shares squared.
So it's share squared, right?
That's going to be S squared for SN shares.
And the HHI is just going to be the sum of the shares squared.
I may not be doing this correctly, but we've got to start somewhere.
And we want to just keep track of these over time.
So no promises here, but we may have a non-numerical value here.
Okay, although I did something wrong.
This should...
So now we should have the HHI over time.
But remember, we need this in percentages.
So we actually need the share times 100 squared.
And then we'll just put this into...
And pardon that I'm coding this up sort of haphazardly, but you're seeing how I'm hacking
this out.
So if we're lucky...
Not too lucky.
Okay.
One second, let me make sure that I'm doing this correctly.
We can just do a...
One second here.
Adding an index.
Let's see if we can add this data with everything else.
Okay.
Here is a rough attempt at calculating the HHI over time.
And so it looks like it's decreasing and it looks like it's really low at 40.
And so remember earlier we were looking for an HHI of below 2,500.
So I wanted to make you 100% sure I calculated this correctly, but it sure looks like there's
a quite competitive market in Washington state that's becoming more competitive over time.
Like I said, I'm super uncertain about this because I just calculated this sort of spur
on the moment here.
But let's just look at the logic real quick.
So we're getting the market shares for each day and then for each day we're calculating
the percentage squared and then we're summing those.
So I want to say it's just a competitive industry.
So you don't see like a oligopoly yet.
And that makes sense, right?
There's some like 400 some retailers, right?
If we were looking at the retailers over time, right?
There are some like 400 retailers in Washington state.
So if they were like one or I mean if there were like five or six retailers, you would
expect this HHI to be much higher.
So it looks like, at least to me, like I said, I want to recheck this metric, but it looks
like the HHI is well into the monopolistic competition level in Washington state.
Real quick, if you want to bear with us, bear with me, I think we could calculate the CR5
over time.
So here, this is just going to be the sum of the top market shares.
So I think we can just sort.
Okay, cool.
Here, let me, I should do this off the top of my head, but.
Okay, yeah, so I think we should just be able to just sort the shares.
And then just get the top five shares.
And then just do the sum of the top five.
That should be our CR5.
Yeah, let's just go ahead and multiply this.
Well, we don't really have to multiply it by 100, I suppose.
Okay, I don't think I did something right.
Okay, actually, let's.
So this would be, let's maybe multiply this by 100.
It looks like the CR5 may be slightly increasing, so this would be the concentration of the
top five largest retailers, but it still looks like an incredibly low number.
So it looks like, to me, like I said, I'm really skeptical about how I'm calculating
these statistics.
And so I'm, after this meetup, I'm going to make sure that I am calculating these correctly
and I'll send you out an email if I made any mistakes here.
I think you might have picked up all the shares except for the top five, because I don't know
if it's sort of descending or descending, but that looks like the same plots.
Yeah, I think I may have picked up the smallest five here.
So let's try this.
Oh, you're right.
Yeah, because like it should be for sort, you can do sorted.shares and put reverse equals
true because we want the highest shares, right?
Exactly.
So let's try one more time here.
Let's try this one more time.
Okay, so this looks a bit more plausible and it matches the movement of the HHI.
And so these numbers look about right.
Well, actually, I don't know.
I've actually never calculated this metric before, so I don't know if they look about
right or not.
If we calculated this correctly, then this would say that the top five retailers had
around 6.6% of sales and now they're down around 5.6% of all sales.
Keegan, I think you actually did that right because mathematically it makes sense.
Like this makes perfect sense and it's analogued in the Maryland system at least.
Like Maryland uses a sales system now because there was the only way the smaller retailers
could get customers.
So I think you actually did the math right.
Be a little more confident.
Well, I always got to double check and that's the whole point of the scientific process
too is reproducibility, right?
So not only should I calculate these statistics, but there really should be dozens if not hundreds
or thousands of other people such as yourselves.
You're brilliant data scientists calculating these too.
That way we're all sort of double checking each other.
So that way if I made a mistake in my logic, then someone else can point that out.
I'm going to be studying this script today and refining it and I would encourage you
all to as well.
So that way the more eyes on the data and the mechanism that generates the statistics,
the better.
So there you have it.
That was my, you know, rough attempt to calculate the CR5, the concentration ratio and the Hirfendale
Hirschman index, the HHI in Washington state.
And definitely want to double check everything, but on first glance, it looks like things
are quite competitive in Washington state.
So that would have been my guess, but I would have, I didn't think things were going to
that the HHI would be as low as it is.
So now it's piqued my curiosity to see what it may be in other states such as Illinois
or Maryland, where you have quite a fewer number of retailers per capita.
Good luck getting that data out.
Well, and so that's why, you know, we've got to work with the data that we're given
and you know, here I'm going to stop presenting for now.
And that's one of the reasons why we do this analysis is, so I was thinking about this
this morning.
So there's any of you smart entrepreneurial people one day end up as a statistician at
one of these regulatory agencies, or maybe one of these regulatory agencies, one of their
data scientists is listening in.
Well, you know, these are techniques that you can use in your markets and in your analysis.
So this is how it was done in Washington state.
So we were fortunate enough to have access to this data.
Well, I'm sure the regulators have access to this data in Massachusetts and Illinois
and California, or all these other states.
So people at those agencies can calculate these statistics.
Like I said, it'd be awesome, I think, to have many people be able to calculate them,
because as I pointed out, the more eyes on them, the better.
It's always awesome to have people double checking each other.
So I'm a big fan of sort of democratizing data.
And I heard somewhere that, you know, having your data be being reproducible.
So in our case, having our statistics be reproducible is critical.
Absolutely, even in science, even in biomedical sciences, right.
The problem with cancer studies is more 80% of them are not reproducible.
It's a problem in every science.
So yeah, I agree with you on that.
Well, I think that's going to be the lesson of the day is do what you can to make your
data reproducible.
That's sort of what we strive for here in the James Day Science Group.
So that's why I put our methods out there.
Ideally, you can run them yourselves, double check all the statistics.
And that's what we're all about.
So thank you all for coming.
And if you want to contribute, there's many different avenues.
So feel free to reach out and we can all start collaborating.
And as I said at the beginning, we'll start looking at our forecasts of 2021 just to see
how we did.
And then we can learn and improve and try to make even better forecasts for 2022.
So stay tuned.
That's great.
Thanks so much, Keegan.
This is great.
Awesome.
Marjana, Graham, Shann, thank you for coming today.
I hope you have a productive week and I'll speak with you all soon.
See you.
Bye everyone.
Bye now.