1 00:00:00,000 --> 00:00:12,200 Welcome to the Canvas Data Science Meetup Group. 2 00:00:12,200 --> 00:00:14,160 Happy to have you here as always. 3 00:00:14,160 --> 00:00:16,760 So we've got an awesome day ahead of us. 4 00:00:16,760 --> 00:00:21,640 So going to talk about economic surplus today. 5 00:00:21,640 --> 00:00:27,720 So this is an introduction to economics that I've been wanting to do here for a little 6 00:00:27,720 --> 00:00:32,720 while and just got input on the pause, 7 00:00:32,720 --> 00:00:37,200 but the Python saying is now is better than never. 8 00:00:37,200 --> 00:00:41,280 So we'll go ahead and kick off with that today. 9 00:00:41,280 --> 00:00:44,200 Without further ado, 10 00:00:44,200 --> 00:00:47,200 we'll just go ahead and do a quick round of introductions. 11 00:00:47,200 --> 00:00:53,100 That way we can have a nice discussion throughout talking about economics, 12 00:00:53,100 --> 00:00:55,280 plus Canvas data is always today. 13 00:00:55,280 --> 00:00:57,960 So I'm going to start in my top left corner. 14 00:00:57,960 --> 00:01:03,240 So Camila, you wouldn't mind introducing yourself real quick? 15 00:01:03,240 --> 00:01:06,120 Sure. Hi. How are you? 16 00:01:06,120 --> 00:01:07,320 Awesome. 17 00:01:07,320 --> 00:01:11,040 Fine. So I'm Camila Coelho. 18 00:01:11,040 --> 00:01:12,680 I'm an industrial designer. 19 00:01:12,680 --> 00:01:16,760 I'm actually currently studying communication design. 20 00:01:16,760 --> 00:01:20,120 I want to dig in data visualization. 21 00:01:20,120 --> 00:01:23,600 So I was looking for different things around data, 22 00:01:23,600 --> 00:01:26,160 and I'm a cannabis user, 23 00:01:26,160 --> 00:01:28,560 so also it was matching. 24 00:01:28,560 --> 00:01:31,480 So I'm here because of that. 25 00:01:31,600 --> 00:01:34,120 You're in the right spot. 26 00:01:34,120 --> 00:01:36,240 I'm happy to have you. 27 00:01:36,240 --> 00:01:40,000 Ryan, would you introduce yourself, please? 28 00:01:40,000 --> 00:01:42,240 Yes. I'm Ryan. 29 00:01:42,240 --> 00:01:45,000 I'm from Long Island, New York. 30 00:01:45,000 --> 00:01:48,480 I'm an applied math and economics graduate. 31 00:01:48,480 --> 00:01:52,480 Currently doing a bootcamp in data analytics. 32 00:01:52,480 --> 00:01:56,200 I'm a long-time medical cannabis user. 33 00:01:56,200 --> 00:01:58,520 I'm actually winding down on that. 34 00:01:58,520 --> 00:02:02,360 But it's funny, the less I'm using the medical stuff, 35 00:02:02,360 --> 00:02:05,320 the more interested I am in how it works. 36 00:02:05,320 --> 00:02:07,400 Well, awesome to have you, Ryan. 37 00:02:07,400 --> 00:02:10,680 You can keep us on our toes with economics, 38 00:02:10,680 --> 00:02:15,880 and also happy to hear about your experience with the New York market, 39 00:02:15,880 --> 00:02:18,120 because they'll be coming online soon. 40 00:02:18,120 --> 00:02:24,600 So interested to have your experience there. 41 00:02:24,600 --> 00:02:28,040 Awesome. It's been a great review of economics for me. 42 00:02:28,040 --> 00:02:30,080 Awesome. Well, like I said, 43 00:02:30,080 --> 00:02:31,640 keep us on our toes. 44 00:02:31,640 --> 00:02:35,320 Marjorie, would you please introduce yourself? 45 00:02:37,600 --> 00:02:41,440 Hey, everyone. Nice to meet all of you. 46 00:02:41,440 --> 00:02:45,280 I was an academic research scientist for 10 years. 47 00:02:45,280 --> 00:02:51,720 I moved to software engineering and I enjoy cannabis and cannabis data. 48 00:02:51,720 --> 00:02:54,440 So here I am. Nice to meet everyone. 49 00:02:54,440 --> 00:02:57,800 Awesome to have you here, Marjorie. 50 00:02:57,800 --> 00:03:00,600 Heather, would you please introduce yourself, 51 00:03:00,600 --> 00:03:02,880 and then I can go last. 52 00:03:02,880 --> 00:03:05,360 Hey, I'm Heather. I'm not really doing well today, 53 00:03:05,360 --> 00:03:08,240 but just curious for Ryan, 54 00:03:08,240 --> 00:03:12,680 if they still have that pre-packed ground flower in New York still. 55 00:03:12,680 --> 00:03:14,160 We do. 56 00:03:14,160 --> 00:03:16,640 That's unfortunate. I'm so sorry to hear that. 57 00:03:16,640 --> 00:03:19,960 I know. It is what it is. 58 00:03:21,360 --> 00:03:24,440 Okay. I'm so sorry. I'm not really well. 59 00:03:24,440 --> 00:03:26,120 But it's nice meeting everybody. 60 00:03:26,120 --> 00:03:27,320 I'm really happy to be here. 61 00:03:27,320 --> 00:03:30,880 Awesome. Happy to have you here, 62 00:03:30,880 --> 00:03:34,880 Heather. Then Graham, happy to see you here today. 63 00:03:34,880 --> 00:03:36,880 We're just doing a quick round of introduction. 64 00:03:36,880 --> 00:03:38,360 So you wouldn't mind taking 65 00:03:38,360 --> 00:03:42,880 30 seconds and introducing yourself to the group program, and then I'm on. 66 00:03:42,880 --> 00:03:46,560 Yeah. My name is Graham Manischewski. 67 00:03:46,560 --> 00:03:51,440 I live in Maryland where we only have a medical market. 68 00:03:51,440 --> 00:03:57,240 But I used to be a data scientist working in space. 69 00:03:57,240 --> 00:04:00,400 I was a contractor with SpaceX. 70 00:04:00,400 --> 00:04:06,040 But I got hit with a deadly disease and it's forever changed me. 71 00:04:06,040 --> 00:04:08,920 As you can see, I shake while I talk. 72 00:04:08,920 --> 00:04:13,120 But cannabis is my medicine. 73 00:04:13,120 --> 00:04:17,360 I'd like to use my skills in data science. 74 00:04:17,360 --> 00:04:21,360 I have a master's in mathematics and deep learning. 75 00:04:21,360 --> 00:04:26,440 So I have a deep basis of understanding with, 76 00:04:26,440 --> 00:04:29,440 I guess, the model of all this. 77 00:04:29,440 --> 00:04:32,040 I've studied supply chain management, 78 00:04:32,040 --> 00:04:35,200 economic PDEs, all that stuff. 79 00:04:35,200 --> 00:04:38,640 I just don't know all the terminology 80 00:04:38,640 --> 00:04:46,440 I guess with this and I'm just looking to see if I can search for 81 00:04:46,440 --> 00:04:54,000 a job opportunity once I get through all this rehab to use my skills in cannabis. 82 00:04:54,000 --> 00:05:00,840 I'm really interested to see what data science models we were looking for. 83 00:05:00,840 --> 00:05:04,360 Because I was pretty confused on Saturday. 84 00:05:04,360 --> 00:05:09,280 Exactly. So happy to have you. 85 00:05:09,280 --> 00:05:13,440 Graham, you can keep us on our toes too because your experience with 86 00:05:13,440 --> 00:05:17,280 data science will just keep everybody sharp. 87 00:05:17,280 --> 00:05:23,640 There's a high demand for people with your skill set in the cannabis industry. 88 00:05:23,640 --> 00:05:26,000 So happy to have you aboard. 89 00:05:26,000 --> 00:05:28,480 Yeah. I should also say, 90 00:05:28,480 --> 00:05:34,320 I am a heavy smoker and I've been smoking for about a decade. 91 00:05:34,320 --> 00:05:37,240 I don't know your guys' experience, 92 00:05:37,240 --> 00:05:41,120 but I've been around the game for a while. 93 00:05:41,120 --> 00:05:43,400 Let's just say that. 94 00:05:44,080 --> 00:05:49,360 Well, always happy to hear about your experience too. 95 00:05:49,360 --> 00:05:51,760 Heather's also in Maryland too. 96 00:05:51,760 --> 00:06:00,760 So yeah, it'll be interesting to have all of your experiences. 97 00:06:00,760 --> 00:06:07,840 Just to get you into the conversation, Mel. 98 00:06:07,840 --> 00:06:10,480 We're just doing a round of introductions. 99 00:06:10,480 --> 00:06:14,200 So you wouldn't mind introducing yourself to the group real quick. 100 00:06:14,200 --> 00:06:16,880 Hi everybody. 101 00:06:16,880 --> 00:06:22,760 I just ran into this meetup. 102 00:06:22,760 --> 00:06:25,440 I was just interested. 103 00:06:25,440 --> 00:06:30,320 I've been working as a data scientist for the past few years. 104 00:06:30,320 --> 00:06:34,680 So I just wanted to see what you can do with cannabis data. 105 00:06:34,680 --> 00:06:38,760 Because in the recent years, cannabis market has been growing. 106 00:06:38,760 --> 00:06:42,880 Personally, I don't smoke or do any. 107 00:06:42,880 --> 00:06:46,720 Yeah. So I was just curious. 108 00:06:47,200 --> 00:06:49,200 Well, happy to have you. 109 00:06:49,200 --> 00:06:50,800 There's people from all walks of life here. 110 00:06:50,800 --> 00:06:56,960 So we're a big married group of data scientists. 111 00:06:56,960 --> 00:06:58,280 So this is awesome. 112 00:06:58,280 --> 00:07:01,960 So a lot of skills here in this group today. 113 00:07:01,960 --> 00:07:05,120 So we can definitely get a lot accomplished. 114 00:07:05,120 --> 00:07:09,000 And then yet another guest. 115 00:07:09,000 --> 00:07:15,320 So please correct me if I'm not pronouncing correctly, but Dharampal. 116 00:07:15,320 --> 00:07:18,480 Yeah, thank you. 117 00:07:18,480 --> 00:07:19,760 Yeah, it's Dharampal. 118 00:07:19,760 --> 00:07:22,920 You can call me DP for the short one. 119 00:07:22,920 --> 00:07:25,280 Actually, you know, first time I joined here. 120 00:07:25,280 --> 00:07:29,120 And, you know, I just set up the Google Meet, in fact, 121 00:07:29,120 --> 00:07:32,000 because I joined the meetup group recently. 122 00:07:32,000 --> 00:07:38,600 And every time like some one or other group is being used to have these discussions. 123 00:07:38,600 --> 00:07:42,000 So today I was late because of that one. 124 00:07:42,000 --> 00:07:45,400 And I had to set it up like with my short name as DP. 125 00:07:45,400 --> 00:07:49,720 So that is easy for everyone to pronounce. 126 00:07:49,720 --> 00:07:51,040 Awesome. Thank you. 127 00:07:51,040 --> 00:07:52,360 Well, happy to have you, DP. 128 00:07:52,360 --> 00:07:57,280 So we're a big group of married data scientists here. 129 00:07:57,280 --> 00:07:58,840 So my name is Keegan. 130 00:07:58,840 --> 00:08:00,800 So I started a company, Cantlitics. 131 00:08:00,800 --> 00:08:07,480 So my experience was working in the cannabis testing space. 132 00:08:07,480 --> 00:08:16,200 And so I thought, well, we can probably utilize my experience as a, 133 00:08:16,200 --> 00:08:21,880 you know, number cruncher statistician and help people set up data pipelines. 134 00:08:21,880 --> 00:08:26,160 And so working in the industry for a while, 135 00:08:26,160 --> 00:08:32,160 I realized there's a really big demand for data analytics, data science. 136 00:08:32,160 --> 00:08:38,440 And so I thought I would share with you some of the issues at hand, 137 00:08:38,440 --> 00:08:43,080 what people are talking about, where some of the data can be found, 138 00:08:43,080 --> 00:08:47,800 and what analytics you can do with, you know, real public data. 139 00:08:47,800 --> 00:08:52,800 So it's almost like there's just gold laying there right on the table. 140 00:08:52,800 --> 00:08:55,480 So there's just, you know, treasure to be had. 141 00:08:55,480 --> 00:09:03,200 And you all have the skill set to go out there and mine some data treasure. 142 00:09:03,200 --> 00:09:09,000 So long story short, I can just show you some data sources today, 143 00:09:09,000 --> 00:09:16,840 just to talk about a little bit of economics and just show you what you can potentially do. 144 00:09:16,840 --> 00:09:21,880 And then I'm happy to hear from all of you about, 145 00:09:21,880 --> 00:09:24,960 you know, what you may be curious about doing with the data, 146 00:09:24,960 --> 00:09:27,840 because this is just my perspective on the data. 147 00:09:27,840 --> 00:09:30,920 I come from an economics background. 148 00:09:30,920 --> 00:09:39,000 So as Graham pointed out, so on Saturday, we talked about the statistical model, 149 00:09:39,000 --> 00:09:40,880 the instrumental variable model. 150 00:09:40,880 --> 00:09:45,720 So tune in on Saturday and we'll talk about this again likely. 151 00:09:45,720 --> 00:09:53,240 And so long story short, Graham's keeping me on my toes, Mel, Camilla, Ryan, 152 00:09:53,240 --> 00:09:55,400 DP, Marjana, and Heather. 153 00:09:55,400 --> 00:09:59,560 So we can all keep each other on our toes here. 154 00:09:59,560 --> 00:10:05,720 So without further ado, I'll just go ahead and spend a little bit of time here 155 00:10:05,720 --> 00:10:11,440 and just introduce to you some economics and some data 156 00:10:11,440 --> 00:10:15,320 that I've been looking at in Illinois and Massachusetts. 157 00:10:15,320 --> 00:10:20,840 And then we can have a discussion about that. 158 00:10:20,840 --> 00:10:27,560 Anyone want to say anything real quick? 159 00:10:27,560 --> 00:10:33,240 Not as such, but, you know, I'm not sure like why, because I just, as I said, 160 00:10:33,240 --> 00:10:35,440 like, OK, I'm for the first time here. 161 00:10:35,440 --> 00:10:39,240 So I recently started doing this data science work. 162 00:10:39,240 --> 00:10:45,600 And, you know, I tried to find out, like, if in my existing organization, 163 00:10:45,600 --> 00:10:52,320 if I can find some opportunities to do some data analytics or this kind of work, 164 00:10:52,320 --> 00:10:57,160 basically, like what data can tell me basically, because right now what we know 165 00:10:57,160 --> 00:11:00,920 is what we know, but what we don't know is something like the patterns, 166 00:11:00,920 --> 00:11:07,160 the hidden patterns within the data which can tell us, OK, hey, you may, 167 00:11:07,160 --> 00:11:12,080 you know, work on certain things. So it's just my thing is like just a startup. 168 00:11:12,080 --> 00:11:15,120 I really don't know, like, what I'm going to discover here. 169 00:11:15,120 --> 00:11:22,000 So but you said, like the economics and like some of the other things. 170 00:11:22,000 --> 00:11:24,360 I'm excited, basically. I'm sorry. 171 00:11:24,360 --> 00:11:27,280 I would be like, OK, more curious to have a lot of questions. 172 00:11:27,280 --> 00:11:31,840 But right now I don't have any questions, but we will see what exactly you present 173 00:11:31,840 --> 00:11:34,200 and based on that, we will go forward. 174 00:11:34,200 --> 00:11:37,280 Awesome, GP. I got a couple of questions. 175 00:11:37,280 --> 00:11:38,800 Oh, yes, Graham. 176 00:11:38,800 --> 00:11:47,880 I would like to know the any of the rules and regulations with like people growing and stuff. 177 00:11:47,880 --> 00:11:51,720 I only say that because in Maryland, they are very strict. 178 00:11:51,720 --> 00:11:55,400 You have to grow it in a specific place. 179 00:11:55,400 --> 00:12:00,840 And there's only so many grow sites you can have in terms of this. 180 00:12:00,840 --> 00:12:07,080 But I've heard in New England and stuff, they have a little free array 181 00:12:07,080 --> 00:12:15,000 to have small like cannabis companies and small dispensaries to start up. 182 00:12:15,000 --> 00:12:25,840 Yes, so we'll be looking today just to add, you know, the Massachusetts. 183 00:12:25,840 --> 00:12:30,360 Cannabis Control Commission, so different 184 00:12:30,360 --> 00:12:37,640 states have put together their own bodies to legislate cannabis. 185 00:12:37,640 --> 00:12:44,400 So definitely a state by state basis at the moment, because as you're all aware, 186 00:12:44,400 --> 00:12:48,520 cannabis is still not permitted federally. 187 00:12:48,520 --> 00:12:57,360 So basically, certain states have enacted legislation to allow 188 00:12:57,360 --> 00:13:00,960 or to permit people to operate in their states. 189 00:13:00,960 --> 00:13:12,280 So. So exactly. So as Graham pointed out, so in Maryland, Maryland has their own legislative body. 190 00:13:12,280 --> 00:13:19,760 You know, I don't know it off the top of my head, but, you know, you can just probably do a search, 191 00:13:19,760 --> 00:13:27,160 you know, Maryland cannabis regulators, do you know, Heather, what the body may be? 192 00:13:27,160 --> 00:13:33,120 By body, well, one thing I know is there's a number of there are only certain number of licenses 193 00:13:33,120 --> 00:13:37,880 that can be distributed and you can actually see the names of all the testing, 194 00:13:37,880 --> 00:13:40,680 the three testing sites as well in Maryland. 195 00:13:40,680 --> 00:13:44,880 So there are very few of those, quite the monopoly at the testing site. 196 00:13:44,880 --> 00:13:46,840 Just want to say there. 197 00:13:46,840 --> 00:13:55,760 And then also certain brands like Colta will actually prevent dispensaries from giving coupons or other discounts. 198 00:13:55,760 --> 00:14:02,400 That's something that they can do. So all this market data can be completely blown up by Colta. 199 00:14:02,400 --> 00:14:09,000 Yeah. So just want to say that that's maybe a bias coming in there, but that's just my opinion is that, 200 00:14:09,000 --> 00:14:13,360 well, it's my observation is that there are companies in Maryland that can dictate 201 00:14:13,360 --> 00:14:18,320 when and how discounts are applied to the customer. Thank you. 202 00:14:18,320 --> 00:14:30,920 And may I just add on to that too, there is a pretty heavy monopoly, oligopoly on the grow sites in Maryland as well. 203 00:14:30,920 --> 00:14:41,760 There are only several small town grow operations that still have licenses to grow and sell. 204 00:14:41,760 --> 00:14:47,680 G-Leaf has bought out over 75 percent of the market. 205 00:14:47,680 --> 00:14:53,080 Yes. So it's very difficult to do price action with them. 206 00:14:53,080 --> 00:14:58,280 That is basically what they said. Everyone has to follow. 207 00:14:58,280 --> 00:15:03,680 It's really awful in Maryland. Thank you, Graham. Thank you. 208 00:15:03,680 --> 00:15:11,000 I spend over twenty thousand dollars a year on my medicine. I have to use it constantly. 209 00:15:11,000 --> 00:15:16,240 I believe you. I believe you. And I live that. So please know that. 210 00:15:16,240 --> 00:15:23,920 Yeah, I hate Colta too. They have a bad rep involved more in Maryland now. 211 00:15:23,920 --> 00:15:29,000 You've brought up an interesting point here. Serious. Thank you. 212 00:15:29,000 --> 00:15:38,400 Because this is exactly what we're here to do is essentially we need to get this data that's locked up here with, 213 00:15:38,400 --> 00:15:41,520 you know, the Maryland Medical Cannabis Commission. 214 00:15:41,520 --> 00:15:56,240 And as you exactly said, try to quantify, you know, is Maryland sort of in the oligopolistic competition? 215 00:15:56,240 --> 00:16:02,360 You know, so it's a long story short. You know, do these producers have a lot of monopoly power? 216 00:16:02,360 --> 00:16:09,080 And, you know, can they raise prices on consumers like yourself? 217 00:16:09,080 --> 00:16:19,480 So. So, look, long story short, we need to start getting this data and making it accessible. 218 00:16:19,480 --> 00:16:33,000 This is sort of been a side project of Camp Lytics here is we would love to be able to get data in regulations for each of the states. 219 00:16:33,000 --> 00:16:50,720 So and make it publicly available so that way. You know, we can just know off the top of our head what the regulations are in Maryland or Massachusetts or Illinois or where have you. 220 00:16:50,720 --> 00:16:56,200 So if you want to contribute that started these two projects here. 221 00:16:56,200 --> 00:17:03,520 So, you know, we've just started the Cannabis Data Science Meetup Group, where we just do a little bit of exploratory analysis, 222 00:17:03,520 --> 00:17:09,960 try to find data from various states and just begin crunching statistics. 223 00:17:09,960 --> 00:17:16,120 And then if you want a more formalized project, putting together Camp Lytics AI, 224 00:17:16,120 --> 00:17:30,480 which will essentially just try to scrape all of the various states so that way we can collect data from Alaska, Arizona, California, 225 00:17:30,480 --> 00:17:36,240 you know, all the way through Maryland and all the way to Washington state. 226 00:17:36,240 --> 00:17:52,480 So that way we can actually compare how these different states are functioning here, because as you pointed out, it doesn't seem like Maryland's very competitive. 227 00:17:52,480 --> 00:17:57,600 So it would be interesting to quantify that. 228 00:17:57,600 --> 00:18:05,680 So thank you for bringing that point up, Graham. 229 00:18:05,680 --> 00:18:15,680 Well, without further ado, let's talk about some of the economics while we're at it, and then we can show you how 230 00:18:15,680 --> 00:18:26,360 or I can show you how I've begun to scrape data from Illinois and Massachusetts publicly available resources. 231 00:18:26,360 --> 00:18:41,200 So, and Ryan, you can attest to this, so I would always tell people, you know, the reason you study economics is so that you don't get fooled by economists. 232 00:18:41,200 --> 00:18:49,520 And so this quote goes back to an economist who was born a hundred years ago. 233 00:18:49,520 --> 00:18:57,000 And, you know, make of it what you will, but I always get a good laugh out of this quote. 234 00:18:57,000 --> 00:19:05,400 And so, you know, I'm going to be talking about economics today, but, you know, don't, you know, don't get fooled by me. 235 00:19:05,400 --> 00:19:17,720 If anything sounds counterintuitive or doesn't make logical sense to you, then question it and think about it deeply. 236 00:19:17,720 --> 00:19:29,360 Because, you know, good economics, you know, should be, you know, be able to be explained quickly and simply. 237 00:19:29,360 --> 00:19:36,160 Can I just add quickly, I think that quote is so important, especially in a day where there's 238 00:19:36,160 --> 00:19:45,520 where policymakers are just throwing up these big spending bills, big economics plans, saying stuff like, you know, this is not going to cost you anything. 239 00:19:45,520 --> 00:19:53,040 And they cite these economists. This is why it's important that everyone kind of needs to know what exactly is happening. 240 00:19:53,040 --> 00:20:00,640 I mean, obviously for here, but just in general, like, I think everyone having a basis in economics is so important. 241 00:20:00,640 --> 00:20:07,640 I wish there'd be more done in the K through 12 education in relation to that. 242 00:20:07,640 --> 00:20:17,600 Exactly. So that, you know, falls on the presenter a lot because it's a it's a high bar. 243 00:20:17,600 --> 00:20:28,000 But, you know, I believe that, you know, if you firmly have concepts under your belt, that you should be able to convey them to to anyone. 244 00:20:28,000 --> 00:20:40,800 So. So exactly. So, like you said, less hand waving and more, you know, making sure that everybody's on board and. 245 00:20:40,800 --> 00:20:44,680 Yeah, like you said, don't just don't just take things for granted. 246 00:20:44,680 --> 00:20:49,840 So. So. Awesome, Ryan. 247 00:20:49,840 --> 00:20:59,360 So so as you can see, Ryan's schooled in economics and is pretty skeptical. 248 00:20:59,360 --> 00:21:10,480 OK, anywho, we've begun to basically look at the number of retailers and then the. 249 00:21:10,480 --> 00:21:18,080 Revenue per retailer as sort of a metric of profitability. 250 00:21:18,080 --> 00:21:36,240 And so. What we're trying to uncover is, you know, are there essentially rules in place that may make certain places more profitable for retailers or producers to operate in? 251 00:21:36,240 --> 00:21:43,880 And, you know, conversely, there's a other side of that coin there. 252 00:21:43,880 --> 00:21:53,120 I'm actually going to to back up to this. I just going to start start here, I think. 253 00:21:53,120 --> 00:22:01,560 So, you know, basically, you know, the reason we're talking about this is, you know, there's no essentially two sides to the coin here. 254 00:22:01,560 --> 00:22:09,880 Right. So, yes, it's awesome, you know, for the producers to, you know, make a nice surplus here. 255 00:22:09,880 --> 00:22:14,600 But, you know, the consumers are operating in the market, too. 256 00:22:14,600 --> 00:22:25,520 And. You know, they get a certain amount of value out of their purchases. 257 00:22:25,520 --> 00:22:29,320 And so you can begin to quantify this. 258 00:22:29,320 --> 00:22:36,400 So, you know, if you just look at the quantity sold in the market and the prices, 259 00:22:36,400 --> 00:22:45,440 if there weren't any anti-competitive forces and it was just free information and just, 260 00:22:45,440 --> 00:22:53,560 you know, no transaction costs and just everything just worked out frictionless, 261 00:22:53,560 --> 00:23:01,400 then you would experience what's called the perfectly competitive market equilibrium. 262 00:23:01,400 --> 00:23:08,040 And so here, you know, marginal benefit is exactly equal to marginal cost. 263 00:23:08,040 --> 00:23:13,000 And, you know, you've got what's called like an efficient market. 264 00:23:13,000 --> 00:23:21,440 So this is sort of the idea that everything's compared to. 265 00:23:21,440 --> 00:23:28,520 What you observe, you know, in reality is, you know, you observe things such as, you know, taxes. 266 00:23:28,520 --> 00:23:33,280 So, you know, various market markets will be taxed. 267 00:23:33,280 --> 00:23:38,280 And so, you know, cannabis is one of the goods that is taxed. 268 00:23:38,280 --> 00:23:42,520 And there are various reasons why you may do a tax. 269 00:23:42,520 --> 00:23:51,280 So, for example, you see do see taxes on other goods, such as, say, alcohol or cigarettes. 270 00:23:51,280 --> 00:23:58,360 And one reason you can do a tax is just to discourage consumption. 271 00:23:58,360 --> 00:24:06,320 So, you know, that's a way that people have discouraged cigarette consumption is just 272 00:24:06,320 --> 00:24:09,960 by levying a high tax on it. 273 00:24:09,960 --> 00:24:16,280 And then, you know, you'll, you know, reduce consumption and, you know, 274 00:24:16,280 --> 00:24:26,160 you'll get this tax revenue and then you can spend that on other, in other markets 275 00:24:26,160 --> 00:24:30,600 that you may think have what's called like, you know, public externality. 276 00:24:30,600 --> 00:24:36,560 So, basically, you know, the government will sort of redistribute, you know, 277 00:24:36,560 --> 00:24:41,600 this tax revenue from the cannabis market to another market. 278 00:24:41,600 --> 00:24:48,480 So, you'll see maybe they'll, you know, subsidize some, you know, 279 00:24:48,480 --> 00:24:52,560 so they'll add more funding to education. 280 00:24:52,560 --> 00:24:56,080 And so, that's a whole other market of its own. 281 00:24:56,080 --> 00:25:04,400 So, long story short, this is what the effect of a tax is. 282 00:25:04,400 --> 00:25:11,640 And so, as you can see, some consumers are boxed out of the market and, you know, 283 00:25:11,640 --> 00:25:18,160 the producers lose out on a bit of their surplus as well. 284 00:25:18,160 --> 00:25:22,840 So, taxes is one thing. 285 00:25:22,840 --> 00:25:33,320 The main thing that I was interested in was the comparable effect that you see just 286 00:25:33,320 --> 00:25:36,320 from say quantity restrictions. 287 00:25:36,320 --> 00:25:43,800 So, here there's no taxes levied, but you're just going to restrict the, you know, 288 00:25:43,800 --> 00:25:49,280 the total quantity that can be allowed in the market. 289 00:25:49,280 --> 00:26:00,680 And I see this happening in many states, and it's hard to, you know, think of, you know, 290 00:26:00,680 --> 00:26:07,960 the social reason for this, except, you know, except if yet again, you know, 291 00:26:07,960 --> 00:26:13,520 people were sort of interested in, you know, curtailing cannabis consumption. 292 00:26:13,520 --> 00:26:16,960 And that would just be for, you know, social preferences. 293 00:26:16,960 --> 00:26:24,320 And so, you know, maybe, but that's a conversation for. 294 00:26:24,320 --> 00:26:30,560 Are you talking restrictions on the consumer to buy a certain amount of cannabis 295 00:26:30,560 --> 00:26:36,600 or restrictions on the supplier of the cannabis to the market? 296 00:26:36,600 --> 00:26:39,880 Exactly. This will just be on the actual supply. 297 00:26:39,880 --> 00:26:52,600 So, basically, what my argument is, is if the state says that there can only be 20 cultivators, 298 00:26:52,600 --> 00:26:58,920 well, normally there may, you know, in other states you observe, you know, 299 00:26:58,920 --> 00:27:02,360 100 or 200 or more cultivators. 300 00:27:02,360 --> 00:27:09,920 So, my argument is that you would normally see a larger quantity supplied, 301 00:27:09,920 --> 00:27:18,160 but they're essentially, you know, restricting the quantity, you know, to be a certain amount. 302 00:27:18,160 --> 00:27:31,800 Of course, I just realized this is maybe not the perfect example because you could just have, well, 303 00:27:31,800 --> 00:27:37,520 the idea is this would, if this was a monopoly, 304 00:27:37,520 --> 00:27:43,400 the monopoly list would choose to operate, you know, less than competitively. 305 00:27:43,400 --> 00:27:51,760 So, that way they can, you know, set, you know, set prices to maximize their profit. 306 00:27:51,760 --> 00:27:57,760 And so, the idea is if you're operating, you know, between, you know, 307 00:27:57,760 --> 00:28:03,880 like the perfectly competitive in some way with more of an oligopoly, 308 00:28:03,880 --> 00:28:15,480 then you would expect, you know, prices to be, you know, towards, you know, QM. 309 00:28:15,480 --> 00:28:34,040 So, so my, so, so long story short is my argument is by restricting just the number of licensees, 310 00:28:34,040 --> 00:28:42,240 that that's sort of effectively, you know, sort of restricting QC towards QM. 311 00:28:42,240 --> 00:28:46,040 I'm not saying it's going to be perfectly at QM. 312 00:28:46,040 --> 00:28:57,240 I'm just saying that I think that having restrictions is going to push things in that direction. 313 00:28:57,240 --> 00:29:04,640 Is it safe to say that having more restrictions would make, I guess, the supplier kind of blanking out here, 314 00:29:04,640 --> 00:29:10,320 but it would make it more inelastic, meaning that if there was a tax levied or something 315 00:29:10,320 --> 00:29:23,000 that the price burden would be placed much more on the consumers due to these restrictions. 316 00:29:23,000 --> 00:29:24,520 You do raise a good point. 317 00:29:24,520 --> 00:29:31,600 So, in fact, that actually goes right back to this point of who bears the cost of the tax. 318 00:29:31,600 --> 00:29:32,920 It depends. 319 00:29:32,920 --> 00:29:41,800 So, so as you basically said, whoever basically has the more inelastic either demand or supply 320 00:29:41,800 --> 00:29:52,120 typically bears the bigger burden of either a tax or the quantity restriction. 321 00:29:52,120 --> 00:30:00,920 So that's a whole other question of its own is, you know, what exactly is the elasticity of demand 322 00:30:00,920 --> 00:30:07,400 and what's the elasticity of supply? 323 00:30:07,400 --> 00:30:10,120 And I actually don't know. 324 00:30:10,120 --> 00:30:18,920 And so those are, you know, some metrics that I think are maybe they've been looked at in the literature, 325 00:30:18,920 --> 00:30:28,400 but, you know, these are things that, you know, a group like ours could begin to estimate. 326 00:30:28,400 --> 00:30:34,160 There is work on it in like botany circles. 327 00:30:34,160 --> 00:30:39,000 On the elasticity of supply? 328 00:30:39,000 --> 00:30:50,800 Yeah, in terms of how quickly you can turn it over, how consistent the supply can be versus how finicky can be. 329 00:30:50,800 --> 00:30:56,720 Because we have diseases in here with the supply, there's outlier things, 330 00:30:56,720 --> 00:31:11,160 and they're trying to clean out the outlier things so we can get a more foundationally appropriate estimate of this elasticity. 331 00:31:11,160 --> 00:31:16,360 Graham, do you know if these studies were done on a state or federal level? 332 00:31:16,360 --> 00:31:28,680 I believe I know NC State is doing research in hemp production and what does higher yield rate, 333 00:31:28,680 --> 00:31:36,560 what's correlated with a more consistent or a higher yield rate product in hemp only. 334 00:31:36,560 --> 00:31:41,800 But there's also research in Washington. 335 00:31:41,800 --> 00:31:44,600 I know Washington State University. 336 00:31:44,600 --> 00:31:55,280 They do a lot of research on studying diseases in cannabis, though, because it's more botany based, less economics. 337 00:31:55,280 --> 00:32:07,120 But they are doing research to try and standardize production, both indoor and outdoor, on a grow level. 338 00:32:07,120 --> 00:32:11,720 Awesome. OK, I need to pause for five minutes here. 339 00:32:11,720 --> 00:32:26,520 So can you please continue this conversation and then I'll be right back to talk about the quantity restrictions here and maybe how we can parse this out in Illinois. 340 00:32:26,520 --> 00:32:31,160 So bear with me. I'll be back in five minutes. 341 00:32:31,160 --> 00:32:37,040 So short break here. So bear with me here. 342 00:32:37,040 --> 00:32:38,600 All right. 343 00:32:38,600 --> 00:32:40,000 Be right back. 344 00:32:40,000 --> 00:32:44,680 So out of this group, how many people use cannabis here? 345 00:32:44,680 --> 00:32:47,920 I do. I have to. 346 00:32:47,920 --> 00:32:53,360 I absolutely have to every day. Otherwise, I will be in a massive amount of pain and suffering. 347 00:32:53,360 --> 00:32:55,320 Thank you. 348 00:32:55,320 --> 00:33:08,800 It's just interesting to see here because there's different perspectives when it comes to people use it versus outside perspectives. 349 00:33:08,800 --> 00:33:19,480 And one thing I've realized through coming through these meetups is that you need both because users of it are biased to it. 350 00:33:19,480 --> 00:33:23,520 But people that don't use it have no idea. 351 00:33:23,520 --> 00:33:35,080 Because let me be honest, you really can't look at this as a purely economic thing because the grow part of it hasn't been figured out yet. 352 00:33:35,080 --> 00:33:41,920 And in terms of that quantity, it varies randomly. 353 00:33:41,920 --> 00:33:59,560 There is a company in Canada that got billions of dollars of funding and they went bankrupt because all of their crops were infected with mites and couldn't be used. 354 00:33:59,560 --> 00:34:06,960 So we do have to be wary when we're talking about this. 355 00:34:06,960 --> 00:34:14,880 But I understand. And it's really important that we get this economic thing down. 356 00:34:14,880 --> 00:34:20,080 And that's why I'm really motivated to do this. 357 00:34:20,080 --> 00:34:24,280 Because the Maryland, it's all for profit. 358 00:34:24,280 --> 00:34:27,320 And it really stinks. 359 00:34:27,320 --> 00:34:28,920 Thank you. 360 00:34:28,920 --> 00:34:31,200 That's something that needs to be acknowledged. 361 00:34:31,200 --> 00:34:33,200 And I'm glad that you're voicing that here. 362 00:34:33,200 --> 00:34:36,520 Thank you so much. 363 00:34:36,520 --> 00:34:43,200 But I will fully acknowledge I know nothing about economics of this stuff. 364 00:34:43,200 --> 00:34:54,040 I know a lot about the chemical makeup of it, physics, and just the experience of it. 365 00:34:54,040 --> 00:34:59,320 Because I've been taking data for the past two and a half years. 366 00:34:59,320 --> 00:35:01,560 I was told I was going to die. 367 00:35:01,560 --> 00:35:04,320 And here I am. 368 00:35:04,320 --> 00:35:09,880 So, so yeah, just just background for you folks who don't know. 369 00:35:09,880 --> 00:35:13,360 Thank you. Thank you, Graham. 370 00:35:13,360 --> 00:35:14,720 Awesome. 371 00:35:14,720 --> 00:35:16,560 Thank you for sharing your story. 372 00:35:16,560 --> 00:35:25,720 Well, with migrating here, so just getting resituated. 373 00:35:25,720 --> 00:35:32,240 It's awesome to have you, though, because, you know, essentially, you know, I can share what I know from economics. 374 00:35:32,240 --> 00:35:34,520 You can bring your mathematics. 375 00:35:34,520 --> 00:35:37,680 Ryan can keep us sharp with economics. 376 00:35:37,680 --> 00:35:40,720 And then Heather's got the laboratory background. 377 00:35:40,720 --> 00:35:43,400 Marjana knows statistics. 378 00:35:43,400 --> 00:35:48,040 Camilla's got data science, EP got data science as well. 379 00:35:48,040 --> 00:35:55,240 So I think with the group assembled just here today, there's so much value that we can add. 380 00:35:55,240 --> 00:36:06,840 So so without further ado, I'll go ahead and just conclude real quick here with the economics and then show you how to get some data. 381 00:36:06,840 --> 00:36:14,520 And then we can begin systematically collecting it for all the various states. 382 00:36:14,520 --> 00:36:27,400 So. And I'll move through this quick. 383 00:36:27,400 --> 00:36:38,560 So the idea is if you had a monopolist in the market that they would operate at QM. 384 00:36:38,560 --> 00:36:44,160 You know, of course, we don't observe, you know, a. 385 00:36:44,160 --> 00:36:55,640 An actual monopolist and we don't observe an actual perfectly competitive market, so we observe someplace in between. 386 00:36:55,640 --> 00:37:07,800 So I'm just conjecturing that if you see things that restrict the number of players that can operate in the market, 387 00:37:07,800 --> 00:37:16,280 that you may expect this to observe a queue that's closer to QM than QC. 388 00:37:16,280 --> 00:37:19,480 This is just pure conjecture of mine. 389 00:37:19,480 --> 00:37:28,400 And it may not be the case because as we were talking about, like it depends a lot on the elasticity. 390 00:37:28,400 --> 00:37:34,360 So the elasticity is essentially the angle of the supply curve. 391 00:37:34,360 --> 00:37:39,360 So the flatter the supply curve is, the more elastic it is. 392 00:37:39,360 --> 00:37:48,200 So basically, the more elastic the supply curve, the producers aren't really going to be that affected one way or the other. 393 00:37:48,200 --> 00:37:53,280 They're going to have to apply a restriction in. 394 00:37:53,280 --> 00:38:02,400 In quantity caps. So if if, you know, supplies very elastic and they restrict it to one. 395 00:38:02,400 --> 00:38:07,400 Producer that producer may still operate somewhere near the competitive market, 396 00:38:07,400 --> 00:38:13,080 the competitive level, if the elasticity is really. 397 00:38:13,080 --> 00:38:18,360 But if it's very inelastic. 398 00:38:18,360 --> 00:38:22,040 The elasticity is very high. 399 00:38:22,040 --> 00:38:28,560 But it's very inelastic. So if supply is close to vertical. 400 00:38:28,560 --> 00:38:34,360 Or if there's any elastic demand, if demand is close to vertical. 401 00:38:34,360 --> 00:38:42,800 Then they're going then either the consumers or the suppliers are going to bear a large cost from restrictions. 402 00:38:42,800 --> 00:38:50,400 So there's various reasons why you may think supply may be inelastic. 403 00:38:50,400 --> 00:38:55,760 And there's arguments why you could argue that demand would be inelastic. 404 00:38:55,760 --> 00:39:02,320 Some people, right, they're going to consume whether the price is low or very high. 405 00:39:02,320 --> 00:39:09,920 They're just going to arguably there'll be some people that get boxed out. 406 00:39:09,920 --> 00:39:18,120 So once again, you can break the market up into different segments of demand. 407 00:39:18,120 --> 00:39:22,200 But but long story short is. 408 00:39:22,200 --> 00:39:31,320 My hypothesis is, and this is just going to be my beginning attempt at trying to to go about studying it. 409 00:39:31,320 --> 00:39:38,000 So this is sort of just my rough draft, you know, just my rough analysis here in front of you. 410 00:39:38,000 --> 00:39:42,680 But basically what I'm thinking is, right, we're not going to observe QC. 411 00:39:42,680 --> 00:39:47,480 We're not going to observe QM. However, in all the various states, 412 00:39:47,480 --> 00:39:56,880 I think we're going to observe some Q somewhere in between here, some price somewhere in between here. 413 00:39:56,880 --> 00:40:06,840 With certain states being closer to the monopolistic side and certain states being closer to the competitive side. 414 00:40:06,840 --> 00:40:10,840 Noting that all the states are going to be different. 415 00:40:10,840 --> 00:40:15,120 So that right, all the states have different populations. 416 00:40:15,120 --> 00:40:19,600 All the states have different factors that affect their supply. 417 00:40:19,600 --> 00:40:26,840 So supply and demand are going to be different in all the various states, and they're going to be different over time. 418 00:40:26,840 --> 00:40:43,160 So you're going to expect prices and quantities to either be shifting along the. Curve over time as policies may change, or you know, you may see. 419 00:40:43,160 --> 00:40:51,000 You know, shift in demand curves or shifts in supply curve, so you may see a shift in the demand curve if the population increases, 420 00:40:51,000 --> 00:41:01,000 or you may see entirely. Downward shift in the supply curve if there's like some brand new technology introduced into the market. 421 00:41:01,000 --> 00:41:04,320 So you're going to see all these shifts all over the place. 422 00:41:04,320 --> 00:41:12,600 And so in this chaotic world where we're seeing these data points moving all over the place, 423 00:41:12,600 --> 00:41:22,040 is there any way that we can parse out where exactly states are? 424 00:41:22,040 --> 00:41:31,200 In in the market and you know if they're potentially heading towards a competitive output. 425 00:41:31,200 --> 00:41:38,160 Or if they potentially are heading towards a, you know, a monopolistic output. 426 00:41:38,160 --> 00:41:47,120 So that's my big question is where are states now in say this dichotomy between monopolist and competitive? 427 00:41:47,120 --> 00:41:56,080 And you know, what's their trajectory? So that's my grand research question. 428 00:41:56,080 --> 00:42:01,520 And first steps first, we need to get the data. 429 00:42:01,520 --> 00:42:11,560 So I'll go ahead and share these scripts with you. And so this is essentially how I was beginning to get the data from. 430 00:42:11,560 --> 00:42:20,360 Illinois, so they have. 431 00:42:20,360 --> 00:42:30,560 Data here in PDFs. So you've got licensee data and you've got sales data. 432 00:42:30,560 --> 00:42:39,840 And. We just need a nice automated way to get this data out of these PDFs. 433 00:42:39,840 --> 00:42:48,120 So all you data scientists out there who use Python, I think you're going to love this tool. 434 00:42:48,120 --> 00:42:55,240 So I've been pretty excited about it myself, but it's PDF plumber. 435 00:42:55,240 --> 00:43:04,120 So they use a clever way to extract data from PDFs. Essentially, they just look at. 436 00:43:04,120 --> 00:43:14,120 Your PDF. And try to find any grid lines and then just return you the data as an array. 437 00:43:14,120 --> 00:43:22,960 So that way, instead of just trying to parse all the text and you know, trying to think about tabs and this and that. 438 00:43:22,960 --> 00:43:29,160 The you know, actually use the grid lines and return to you a nice array. 439 00:43:29,160 --> 00:43:38,200 So long story short. We can now automate the collection of the Illinois data here. 440 00:43:38,200 --> 00:43:42,400 So. 441 00:43:42,400 --> 00:43:47,200 Let's. 442 00:43:47,200 --> 00:44:04,080 See if we can't download this data here. 443 00:44:04,080 --> 00:44:13,040 OK, so long story short, we're just going to download this data as a PDF. 444 00:44:13,040 --> 00:44:18,880 Then we can read the data into an array. 445 00:44:18,880 --> 00:44:25,120 So that way we can begin the cleaning process. 446 00:44:25,120 --> 00:44:34,560 So just have. The giant 2D array here. 447 00:44:34,560 --> 00:44:47,560 And so we can. Use our own column names and just create a nice data frame of licensees. 448 00:44:47,560 --> 00:44:52,640 And so this is just retailers, however. 449 00:44:52,640 --> 00:44:57,880 This is good data so we can clean this up a bit. 450 00:44:57,880 --> 00:45:05,720 So then this can be refined a bit more, but this is essentially just parsing out. 451 00:45:05,720 --> 00:45:13,080 Street city, state zip code and phone number from. 452 00:45:13,080 --> 00:45:18,120 This block here. 453 00:45:18,120 --> 00:45:27,680 So now, you know, we know how many retailers there are in Illinois and. 454 00:45:27,680 --> 00:45:32,520 Graham and everyone else don't let me just get so fixated on certain states. 455 00:45:32,520 --> 00:45:39,600 So as Graham pointed out, we need to unlock this data here from Maryland. 456 00:45:39,600 --> 00:45:45,600 So perhaps this could be another good project is somehow trying to. 457 00:45:45,600 --> 00:45:48,240 To unlock this data. 458 00:45:48,240 --> 00:45:55,240 So, but for now. We've got. Retailer data in Illinois. 459 00:45:55,240 --> 00:46:06,120 We can also unlock the sales data. 460 00:46:06,120 --> 00:46:18,560 And once again, we read this as just a giant array of data, but will need to be cleaned up here. 461 00:46:18,560 --> 00:46:22,840 So simple enough. 462 00:46:22,840 --> 00:46:27,600 Once again, using our own column names. 463 00:46:27,600 --> 00:46:32,720 Using a bit of logic to determine the year and month. 464 00:46:32,720 --> 00:46:38,920 And then we've got a nice time series of data. 465 00:46:38,920 --> 00:46:44,720 Well, not yet. Still have a bit of cleaning to do. 466 00:46:44,720 --> 00:46:50,600 So basically. This these next few lines. 467 00:46:50,600 --> 00:46:58,720 Create a time index and then remove all of the extraneous values. 468 00:46:58,720 --> 00:47:07,320 So those can be removed and this this third block. 469 00:47:07,320 --> 00:47:15,080 Handles the dollar signs, because as you can see, we've read the data in with dollar signs and. 470 00:47:15,080 --> 00:47:19,720 Need to convert that to numeric values. 471 00:47:19,720 --> 00:47:24,200 This line of code simply just converts. 472 00:47:24,200 --> 00:47:31,720 The time index here. See how everything is indexed at the beginning of the month. 473 00:47:31,720 --> 00:47:37,680 My personal preference is just to index things at the end of the month. 474 00:47:37,680 --> 00:47:41,960 Because to me, that just makes more logical sense. 475 00:47:41,960 --> 00:47:46,600 So, for example, when you're talking about monthly sales, it just makes sense. 476 00:47:46,600 --> 00:47:49,920 Like, oh, that was the sales for the month. 477 00:47:49,920 --> 00:47:53,840 This is entirely my personal preference. 478 00:47:53,840 --> 00:47:58,120 But it makes a difference when you're doing the coding. 479 00:47:58,120 --> 00:48:11,560 Long story short, we now have the sales data nicely parsed for us. 480 00:48:11,560 --> 00:48:19,480 Awesome. And so we can begin to really calculate some interesting statistics here. 481 00:48:19,480 --> 00:48:26,840 That you don't see many people calculating and let alone. 482 00:48:26,840 --> 00:48:34,600 Showing you the methods, and that's what we believe a lot of the scientific process is all about, right? 483 00:48:34,600 --> 00:48:39,360 You're you're all about showing your methods about how you get your results. 484 00:48:39,360 --> 00:48:47,400 So we I personally believe, you know, if you're, you know, preparing statistics. 485 00:48:47,400 --> 00:49:00,000 And presenting people with data, you know, it's important to show your processes and, you know, be upfront about assumptions made along the way. 486 00:49:00,000 --> 00:49:09,560 Because as we we demonstrated, you know, there's a lot of cleaning that needs to be done along the way. 487 00:49:09,560 --> 00:49:13,920 So long story short, we can begin to look at some real cool statistics here. 488 00:49:13,920 --> 00:49:18,040 So we've got when the license were issued. 489 00:49:18,040 --> 00:49:26,640 So given when they're issued, we can get a count of how many retailers were operating over time. 490 00:49:26,640 --> 00:49:31,600 Because that's one of the dimensions we're interested in, right? We're interested in, right? 491 00:49:31,600 --> 00:49:42,880 Quantity and price, but we're also interested in time and the state as well. 492 00:49:42,880 --> 00:49:57,280 So we can basically get a nice count of the retailers over time. 493 00:49:57,280 --> 00:50:04,440 And this is sort of where I was starting to get into the the quantity restrictions. 494 00:50:04,440 --> 00:50:12,920 So in the in will compare Illinois to say Massachusetts and other states. 495 00:50:12,920 --> 00:50:22,160 But, you know, in all the states, you see this gradual increase of retailers and cultivators as, you know, 496 00:50:22,160 --> 00:50:28,320 people are coming online and setting up their shop and opening up shop. 497 00:50:28,320 --> 00:50:42,920 In Illinois, though, you just see this just this cap, you know, they they they, you know, they can't do believe they've allowed more more licensees into the market as of late. 498 00:50:42,920 --> 00:50:51,600 But, you know, the Illinois sort of, you know, one of the they were sort of famous for for capping their licenses. 499 00:50:51,600 --> 00:51:01,920 And so, you know, here you do just observe this, you know, cap and retailers at at one hundred and ten. 500 00:51:01,920 --> 00:51:07,040 And so that's sort of what I was talking about here. 501 00:51:07,040 --> 00:51:19,320 So I feel like, you know, that that cap is almost kind of tapping the potential quantity that could be produced. 502 00:51:19,320 --> 00:51:22,680 And so I don't think you're at QM per se. Right. 503 00:51:22,680 --> 00:51:26,320 Because there's one hundred and ten retailers. 504 00:51:26,320 --> 00:51:33,640 It's not a monopolist, which is one. But I don't feel that we're quite at QC. 505 00:51:33,640 --> 00:51:40,600 So long story short is I think it would be interesting, you know, to measure. 506 00:51:40,600 --> 00:51:53,440 You know, Illinois and maybe compare it to other states and try to conjecture, you know, what would be, you know, the competitive quantity and, you know, what would be competitive prices? 507 00:51:53,440 --> 00:51:56,680 Like if you let more people into the market. 508 00:51:56,680 --> 00:52:06,160 And like we said, we may not be able to just pinpoint what QC is, but, you know, a lot of what economics is about is marginal changes. 509 00:52:06,160 --> 00:52:17,600 So we could basically say, you know, what would be the effect of, you know, allowing one more retailer into the market be? 510 00:52:17,600 --> 00:52:26,400 So that's essentially what we're trying to uncover here is, you know, what would be the effect of allowing more people to operate in the market? 511 00:52:26,400 --> 00:52:29,800 Or at least that's sort of the question I'm after. 512 00:52:29,800 --> 00:52:36,640 And so a lot of this is or is or supporting statistics. 513 00:52:36,640 --> 00:52:44,960 So I'll just walk through these briefly. So basically just grabbing the population. 514 00:52:44,960 --> 00:52:50,000 And looking at retailers per capita. 515 00:52:50,000 --> 00:52:58,800 And so this is a nice standardized metric that you can use to compare state to state. 516 00:52:58,800 --> 00:53:02,160 So this is retailers per 100,000 people. 517 00:53:02,160 --> 00:53:07,720 So that way we can start to compare Illinois to other states. 518 00:53:07,720 --> 00:53:13,000 You know, controlling for factors such as population. 519 00:53:13,000 --> 00:53:24,840 And then, as we pointed out, we can begin to look at things like sales per retailer to try to get a measure of profitability. 520 00:53:24,840 --> 00:53:29,000 And. Join us. 521 00:53:29,000 --> 00:53:33,960 Oh, yes. And here's I just did a few monthly. 522 00:53:33,960 --> 00:53:37,240 I mean, a few annual statistics. 523 00:53:37,240 --> 00:53:43,520 So here we just looked at retailers per capita in 2020. 524 00:53:43,520 --> 00:53:48,800 And then sales per retailer in 2020. 525 00:53:48,800 --> 00:53:59,440 And then in 2021, you do see you do actually see a doubling in retailers per capita, of course. 526 00:53:59,440 --> 00:54:08,080 Although it doubled, it's the nominal value 0.82 is still quite low. 527 00:54:08,080 --> 00:54:16,560 And so, you know, so. So long story short, we're starting to get a couple of data points here 528 00:54:16,560 --> 00:54:24,120 about about the competitiveness in Illinois, but just scratching the surface. 529 00:54:24,120 --> 00:54:29,840 And so there's still a lot more to do. And so just going to go ahead and tease. 530 00:54:29,840 --> 00:54:37,440 Definitely recommend tuning in on Saturday because we've started to talk about instrumental variables 531 00:54:37,440 --> 00:54:50,000 and how we could potentially. Actually try to estimate movement along the demand curve, potentially, 532 00:54:50,000 --> 00:54:57,760 as people are allowed into the market. So that's what we're trying to measure. 533 00:54:57,760 --> 00:55:09,440 But, you know, just to kind of show you where we are. 534 00:55:09,440 --> 00:55:16,080 You know, we still are far from so. Right. 535 00:55:16,080 --> 00:55:24,760 We're we're trying to draw a nice supply and demand curve with price and quantity. 536 00:55:24,760 --> 00:55:31,000 And, you know, this is our best attempt at drawing a demand curve so far. 537 00:55:31,000 --> 00:55:39,880 And it's got a positive slope. So. You know, Ryan can attest that, you know. 538 00:55:39,880 --> 00:55:44,360 You know, a lot of demand insists that we have a negatively sloped demand curve here. 539 00:55:44,360 --> 00:55:54,680 So the long story short. Actually, I'm not even certain I'm measuring the thing I'm thinking I am. 540 00:55:54,680 --> 00:56:03,000 OK, I think I'm going to go ahead and pause it there because I think I just ran it. 541 00:56:03,000 --> 00:56:12,200 I don't think I ran the correct regression here. Yes, this is an entirely different regression. 542 00:56:12,200 --> 00:56:18,200 And so it's a part of me there. So long story short. 543 00:56:18,200 --> 00:56:22,760 As you can see, I'm still sort of in the exploratory stages. 544 00:56:22,760 --> 00:56:31,560 But, you know, we're starting to get scripts here that we can actually use to aggregate the data from Illinois. 545 00:56:31,560 --> 00:56:41,640 And then I'll share this script with you where we can aggregate data from Massachusetts' Cannabis Control Commission. 546 00:56:41,640 --> 00:56:51,000 And we'll keep at it. So I'm going to take a look at what we can do about Maryland data. 547 00:56:51,000 --> 00:56:59,160 And then if any of you want to contribute, then think of a good state and jump right in. 548 00:56:59,160 --> 00:57:06,440 And, you know, see if you can come up with a good collection scheme yourself. 549 00:57:06,440 --> 00:57:13,000 So that's where we are today. But any thoughts, questions, comments here? 550 00:57:13,000 --> 00:57:21,480 Mariana. Yeah, Keegan. Can we also look at Michigan recreational cannabis data? 551 00:57:21,480 --> 00:57:26,520 Michigan. Yeah. Right now, they don't have data sets. 552 00:57:26,520 --> 00:57:33,800 It's not as savvy as Massachusetts, the Cannabis Commission there. 553 00:57:33,800 --> 00:57:42,360 They mostly have PDFs. Yeah. But I've been looking at that. 554 00:57:42,360 --> 00:57:48,680 And their tables don't look as nicely set up as Illinois. 555 00:57:48,680 --> 00:57:55,320 OK. So we may have to be creative. So we'll see if we can't use PDF Plumber. 556 00:57:55,320 --> 00:58:03,240 And then we may have to find other tools in the toolbox. But yes, let's definitely look at Michigan. 557 00:58:03,240 --> 00:58:08,360 So there's a lot of action going on in Michigan. 558 00:58:08,360 --> 00:58:14,520 And I think we've only looked at it cursely before. So I'm happy to take a look at that. 559 00:58:14,520 --> 00:58:18,120 So let's go ahead and put that on the agenda for next week. 560 00:58:18,120 --> 00:58:23,400 So Michigan data and then Graham's mentioned Virginia data. 561 00:58:23,400 --> 00:58:31,240 So if anyone else has recommendations. I would love Michigan because I have to travel 562 00:58:31,240 --> 00:58:37,400 Michigan frequently. Oh, OK. And I've experienced their cannabis. 563 00:58:37,400 --> 00:58:41,400 There is a huge opportunity there. Absolutely. 564 00:58:41,400 --> 00:58:48,520 They grow grass. And by that, I mean stuff you feed cows, not stuff you want to use. 565 00:58:48,520 --> 00:58:54,920 No, and Graham, you bring up a great point. I think the problem here is quality control. 566 00:58:54,920 --> 00:59:03,640 Yeah. Just look at Oregon and Oklahoma. You do raise a good point. 567 00:59:03,640 --> 00:59:12,280 So all the states have different challenges. So I just heard anecdotally, just from what I've read, 568 00:59:12,280 --> 00:59:18,840 there's some heavy metal concerns. For example, in Oklahoma, right, there's 569 00:59:18,840 --> 00:59:26,200 a lot of heavy metals in the air. And that's something that you have to be watch out for. 570 00:59:26,200 --> 00:59:33,880 Because heavy metals are toxic to me. That's the disease. This is why I'm in this thing. 571 00:59:33,880 --> 00:59:41,240 Exactly. And this is a big concern I have. So, for example, we looked at residual solvents 572 00:59:41,240 --> 00:59:48,600 and concentrates. And it's just something that you're struck in accord with me that 573 00:59:48,600 --> 00:59:54,120 you're doing. And we've got a big group of talented data scientists here. So 574 00:59:55,080 --> 00:59:57,240 let's see what we can do and try to add some value. 575 00:59:59,960 --> 01:00:07,000 This is motivating me to kind of dive into New York. One, because I know it has heavy regulations. 576 01:00:07,000 --> 01:00:13,160 I think some of them are very much welcome, though. There's a big thing. I think it was 577 01:00:13,160 --> 01:00:20,680 vitamin E. I could be mixing that up. But that's been so prevalent in some other states. But New 578 01:00:20,680 --> 01:00:27,800 York regulations make sure that none of that went into the vaping products. Just recently, 579 01:00:28,520 --> 01:00:37,400 they got into actual green marijuana, but it is crushed up, unfortunately. And now we just legalize 580 01:00:37,400 --> 01:00:45,640 marijuana on a recreational level. So I'm just intrigued to dive in more to my state. And 581 01:00:46,680 --> 01:00:51,080 I kind of want to get some data now before the recreational stuff goes into play, 582 01:00:51,080 --> 01:00:55,400 so then I can see how the recreational marijuana affects the market. 583 01:00:58,280 --> 01:01:03,880 Exactly. And that's something that we love to put together at CanLytics is basically just a nice 584 01:01:03,880 --> 01:01:09,000 comparison of these regulations from state to state, because it's not apples to apples in all 585 01:01:09,000 --> 01:01:17,320 the states. The limits are different in all the states. The actual compounds they're testing for 586 01:01:17,320 --> 01:01:24,920 are different from state to state. So there's a lot of good standardization that can be done by 587 01:01:24,920 --> 01:01:32,360 a talented group of data scientists like ourselves. So you're going to say something, Graham? 588 01:01:32,360 --> 01:01:35,560 No, I'm sorry. I guess that was my bad. 589 01:01:36,680 --> 01:01:42,600 Yes. Well, until next week, let's just all keep our nose to the grindstone. And then Graham, 590 01:01:42,600 --> 01:01:48,840 Marjana, Ryan, Heather, Camilla, DP, if you make any discoveries along the way, 591 01:01:48,840 --> 01:01:55,960 feel free to reach out. And so, or if you have any questions about any of the work we've done, 592 01:01:55,960 --> 01:02:02,840 so let's try to make this a nice collaboration. And then on Saturday, we'll see if we can't 593 01:02:02,840 --> 01:02:07,320 find any interesting instrumental variables to parse out supply and demand a bit more. 594 01:02:08,120 --> 01:02:11,400 And then next Wednesday, we'll do a deep dive on Michigan. 595 01:02:12,600 --> 01:02:13,640 Sounds great. Thanks. 596 01:02:15,000 --> 01:02:20,600 Awesome. Thank you all for attending today. I hope you have an awesome, productive, 597 01:02:20,600 --> 01:02:27,320 and enjoyable week and a day today. So enjoy yourselves. See you. Bye. 598 01:02:28,120 --> 01:02:29,000 Bye now. Bye guys. 599 01:02:29,000 --> 01:02:49,640 Thank you. Take care.