1 00:00:00,000 --> 00:00:13,060 Welcome to the Cannabis Data Science Meetup Group. Fun times as always, big day ahead 2 00:00:13,060 --> 00:00:20,140 of us, lots of ground to cover, and some exciting economic models to cover, some good cannabis 3 00:00:20,140 --> 00:00:29,060 data to look at as always. And we've got a large group today. Well, Khalil, if you wanted 4 00:00:29,060 --> 00:00:33,500 to start, we were going to maybe do a quick round of introductions since we've got a good 5 00:00:33,500 --> 00:00:42,420 group today. So if you all just want to take 30 seconds just to say, or more, about yourself, 6 00:00:42,420 --> 00:00:48,600 what brings you to the group and what you may like to get out of the group. So, Khalil, 7 00:00:48,600 --> 00:00:50,100 you're on to start. 8 00:00:50,100 --> 00:00:58,460 Hey everybody, my name is Khalil, and I'm actually a statistics major and a recent graduate. 9 00:00:58,460 --> 00:01:07,820 I'm just looking to build my skill and also create a portfolio, a data statistics portfolio. 10 00:01:07,820 --> 00:01:17,060 But when I found out about analytics, it's kind of like more specific towards, how do 11 00:01:17,060 --> 00:01:25,700 you call it, cannabis. I don't know yet, but I'm just here to see where I could land from 12 00:01:25,700 --> 00:01:29,980 you basically. And that's why I'm here. 13 00:01:29,980 --> 00:01:36,980 Awesome. Happy to have you. Exactly. CanLytics is leading the space here with data science 14 00:01:36,980 --> 00:01:44,740 applied to the cannabis industry. We're creating statistics that you don't see out there. We're 15 00:01:44,740 --> 00:01:51,020 an open transparent box. So all the source codes available for you to use and start building 16 00:01:51,020 --> 00:02:00,460 up your repository of statistics. So whether you're here just to learn data science or 17 00:02:00,460 --> 00:02:05,620 learn about the cannabis industry or both, you're in the perfect place. 18 00:02:05,620 --> 00:02:12,420 Awesome. So, Raelyn, are you interested in introducing yourself? 19 00:02:12,420 --> 00:02:15,780 Sure. Can you hear me? 20 00:02:15,780 --> 00:02:16,780 Yes. 21 00:02:16,780 --> 00:02:25,380 Perfect. Good morning. My name is Raelyn Sawano. I came, so I just came out of a boot camp about 22 00:02:25,380 --> 00:02:29,980 data science. And right now I'm kind of like in the job process, but I'm still obviously 23 00:02:29,980 --> 00:02:34,820 want to keep learning with things with data science. And I just saw, I was looking up 24 00:02:34,820 --> 00:02:41,100 meetups and I so happened to find this one. So I came here just to learn basically. 25 00:02:41,100 --> 00:02:50,300 Too cool. Happy to have you. We get a lot of data science coming through. And what I 26 00:02:50,300 --> 00:02:57,820 always tell everyone is there's a shortage of data scientists. So your skills will be 27 00:02:57,820 --> 00:02:58,820 valued up. 28 00:02:58,820 --> 00:03:01,820 Yeah, that'd be great. 29 00:03:01,820 --> 00:03:08,740 Yes. A purr-by. Happy to have you today. You don't have to, but if you would like, you're 30 00:03:08,740 --> 00:03:19,620 welcome to introduce yourself. So feel free to chime in. And then Heather, you're welcome 31 00:03:19,620 --> 00:03:22,740 to introduce yourself to the group if you're interested as well. 32 00:03:22,740 --> 00:03:31,740 Hey, I'm Heather. I've been coming to cannabis data science for a couple of months now. I 33 00:03:31,740 --> 00:03:36,020 have been in the lab for over a decade, not currently, but I have research experience 34 00:03:36,020 --> 00:03:41,780 and I have experience processing data. This is a refreshing experience for me now that 35 00:03:41,780 --> 00:03:49,340 cannabis and data science have united for me personally. I have a personal interest in 36 00:03:49,340 --> 00:03:53,620 this and also contributing if I can. Thank you. 37 00:03:53,620 --> 00:04:01,340 And you definitely can. And so Heather is our laboratory whiz here. So you're the one 38 00:04:01,340 --> 00:04:10,940 with the more stringent laboratory background. So Heather's always here to help us with the 39 00:04:10,940 --> 00:04:22,780 real life experience and expertise of pest and cannabis. So without further ado, I'll 40 00:04:22,780 --> 00:04:27,020 go ahead and share with you some of the work we've been doing. We've been looking at data 41 00:04:27,020 --> 00:04:34,300 here in Massachusetts. We've got one last guest. We've been looking at Massachusetts. 42 00:04:34,300 --> 00:04:41,660 And so today we can finish up. We've been doing sort of a rigorous analysis, but today 43 00:04:41,660 --> 00:04:47,860 will be the third and final day and then we can start branching out into some other states. 44 00:04:47,860 --> 00:04:53,020 Happy to have you, Barry. We just did a round of introductions. So you're welcome to, if 45 00:04:53,020 --> 00:05:01,020 you would like, spend 30 seconds or so and introduce yourself if you would like. Well, 46 00:05:01,020 --> 00:05:12,060 Barry, I'm not sure your mic's coming through. I believe you're muted. 47 00:05:12,060 --> 00:05:25,820 There we go. How's that? Coming through. Okay. My name is Barry. Nice to meet you all. And 48 00:05:25,820 --> 00:05:37,660 I'm just a gardener. I'm a home grower and just trying to master that. This is my second 49 00:05:37,660 --> 00:05:44,780 year and already planning for third year. And I'm interested in this. I've been studying 50 00:05:44,780 --> 00:05:54,320 the science for quite a while and the business part, I'm a businessman. So I'm looking for 51 00:05:54,320 --> 00:06:03,580 those opportunities that stand out. Is that what you're all about? Well, we can definitely 52 00:06:03,580 --> 00:06:09,980 use your perspective today. So we always like to have different people with different angles. 53 00:06:09,980 --> 00:06:15,660 And so it will be awesome to have your perspective. So today we're actually going to be estimating 54 00:06:15,660 --> 00:06:22,380 the rate of return that you would expect from cultivating a plant in Massachusetts. And 55 00:06:22,380 --> 00:06:30,660 it would be interesting to hear your perspective if our estimations are in a reasonable ballpark. 56 00:06:30,660 --> 00:06:36,580 We're growing in Massachusetts? We're just looking at cannabis data in Massachusetts. 57 00:06:36,580 --> 00:06:44,380 So Massachusetts publishes good data. And so what we do is we essentially collect public 58 00:06:44,380 --> 00:06:52,780 cannabis data and see if we can't calculate market statistics. So that way we can create 59 00:06:52,780 --> 00:06:58,700 value for the public. We take data that's publicly available to everybody, show you 60 00:06:58,700 --> 00:07:07,220 how you can create a pipeline and calculate statistics. And essentially you can turn this 61 00:07:07,220 --> 00:07:15,340 treasure that's just laying there on the table into gold. And so you can get some, you'll 62 00:07:15,340 --> 00:07:21,820 see, you can get some rich insights with data that's publicly available. 63 00:07:21,820 --> 00:07:29,340 From all ears, I read your description and I'm very interested. Thank you for having 64 00:07:29,340 --> 00:07:37,180 me everybody. Welcome aboard Barry. And without further ado, let's go ahead and show you how 65 00:07:37,180 --> 00:07:53,340 it's done. Okay. Well, welcome to the meetup for the 17th. I can go ahead and just give 66 00:07:53,340 --> 00:08:15,260 you a quick introduction to the presentation. Let's just get one final guest here. And Zab, 67 00:08:15,260 --> 00:08:24,780 we may have to get your introductions at the end. Welcome to the group. We're about to 68 00:08:24,780 --> 00:08:31,260 kick off. So you're welcome to give you a quick introduction if you would like. 69 00:08:31,260 --> 00:08:39,820 Hi everyone. My name is Zabi. And I'm right now I'm joining you from Canada, Toronto. 70 00:08:39,820 --> 00:08:47,220 Yeah. I have a bachelor's in business and I have a master's in education. I also have 71 00:08:47,220 --> 00:08:53,820 one certificate and one diploma in data science. I have like the advanced data science, machine 72 00:08:53,820 --> 00:08:59,700 learning, data analyzing and data cleaning. So I'm just new in the market and the industry 73 00:08:59,700 --> 00:09:05,620 of data science. Awesome. Awesome to have you aboard. So you'll 74 00:09:05,620 --> 00:09:10,780 be able to make sure everything's up to snuff. And so definitely feel free to share your 75 00:09:10,780 --> 00:09:14,740 ideas as we go through. Sure. Thank you, Sonos. 76 00:09:14,740 --> 00:09:21,700 Well, awesome to have you all. So just to give you a quick introduction and then we'll 77 00:09:21,700 --> 00:09:35,140 just dive in. So we've been looking at Massachusetts data. So we were looking at sales in Massachusetts 78 00:09:35,140 --> 00:09:41,740 and Massachusetts is an interesting state because they actually closed the cannabis 79 00:09:41,740 --> 00:09:52,060 market for a two month period in 2020. Whereas in other states you saw a spike in sales. 80 00:09:52,060 --> 00:10:02,020 In Massachusetts you see, you know, this period of no sales. So a different dynamic than other 81 00:10:02,020 --> 00:10:08,940 states. And so we're going to essentially look at the market performance in Massachusetts 82 00:10:08,940 --> 00:10:16,260 and predict what the performance may be in 2022. And then next week we'll start to do 83 00:10:16,260 --> 00:10:23,660 interstate comparative analyses. So we can start to say, compare Massachusetts to Oregon 84 00:10:23,660 --> 00:10:31,940 or Colorado. So then we can see how policies in different states affect the market performance. 85 00:10:31,940 --> 00:10:41,940 And we'll try to uncover a handful of insights along the way. So the way we're doing all 86 00:10:41,940 --> 00:10:52,060 of this is we're using this economic framework here where we're relying heavily on the Cobb 87 00:10:52,060 --> 00:11:05,540 Douglas production function. I think we've got Donovan joining. So long story short, 88 00:11:05,540 --> 00:11:10,740 we're using variables that we know we can get in Massachusetts. So that's sales per 89 00:11:10,740 --> 00:11:27,180 week, labor per week, and we're proxying capital with flowering plants per week. And happy 90 00:11:27,180 --> 00:11:32,260 to have you Donovan. You're welcome to chime in at any point to introduce yourself. So 91 00:11:32,260 --> 00:11:45,260 don't let me just drone on to chime in at any point. But long story short, just to show 92 00:11:45,260 --> 00:11:55,740 you a little bit of economics plus math real quick. So we'll move through this pretty quickly. 93 00:11:55,740 --> 00:12:03,180 But long story short is, given the Cobb Douglas production function, you can take the derivative 94 00:12:03,180 --> 00:12:13,780 with respect to labor. And this is an intermediary step. So basically, you just bring beta down 95 00:12:13,780 --> 00:12:21,780 in front. You subtract y. And that gives you your marginal product of labor. Well, what 96 00:12:21,780 --> 00:12:30,940 you can actually do is actually a times kt to the alpha times lt to the beta. Well, that's 97 00:12:30,940 --> 00:12:45,140 just yt. So you can say, well, the marginal product of labor is beta times yt over lt. 98 00:12:45,140 --> 00:12:54,220 And economic theory suggests that in equilibrium, the wage will be equal to your marginal product 99 00:12:54,220 --> 00:13:04,900 of labor. So if the wage is higher than the marginal product of labor, then the employer 100 00:13:04,900 --> 00:13:14,860 would actually lay off some workers. And then if the wage is above, I mean, if the wage 101 00:13:14,860 --> 00:13:29,500 is below marginal product of labor, then firms will want to hire workers and then be gradually 102 00:13:29,500 --> 00:13:35,380 raising and raising wages until wages reach the marginal product of labor. And so there'll 103 00:13:35,380 --> 00:13:43,780 be forces in the economy that will prevent the wage from ever being competitive and actually 104 00:13:43,780 --> 00:13:49,540 equal to the marginal product of labor. But we can still use it as an estimate. So we 105 00:13:49,540 --> 00:13:59,580 can estimate what the competitive wage may be. Well, this is just for labor. We can also 106 00:13:59,580 --> 00:14:06,860 do it for capital. So here is the same mathematics where we just take the derivative with respect 107 00:14:06,860 --> 00:14:16,900 to capital this time. So we bring alpha down in front, subtract one. Well, that we can 108 00:14:16,900 --> 00:14:23,020 just use math and say, OK, well, that's just alpha times yt over kt. And I am sort of running 109 00:14:23,020 --> 00:14:29,100 through this. I would recommend you sit down with paper and pencil and actually take the 110 00:14:29,100 --> 00:14:36,160 derivative. So this is something that I've spent years doing. So that's why I can move 111 00:14:36,160 --> 00:14:42,300 through this quickly. It took me a long time to get to this point. So it took me three 112 00:14:42,300 --> 00:14:47,980 plus years of doing this on paper and pencil over and over and over again and racking my 113 00:14:47,980 --> 00:14:54,980 brain trying to figure this out. So I'm moving through this really quickly just to get to 114 00:14:54,980 --> 00:15:00,420 the juicy bits for today. But if this is something that interests you, I would recommend sitting 115 00:15:00,420 --> 00:15:09,100 down with paper and pencil and actually doing these equations yourself. But long story short, 116 00:15:09,100 --> 00:15:18,500 once again, you can estimate what would be the competitive interest rate for capital. 117 00:15:18,500 --> 00:15:27,500 And so this is the interest rate that investors would like to get for loaning out capital 118 00:15:27,500 --> 00:15:38,500 equipment or money to invest in capital equipment. So that's the, you know, the scary mathematics 119 00:15:38,500 --> 00:15:43,260 underpinning this just so we're not pulling things out of thin air. Keep in mind, this 120 00:15:43,260 --> 00:15:51,220 is all coming from a pretty simple production function here. And so in subsequent weeks, 121 00:15:51,220 --> 00:16:00,980 we can make this production function more complex. So for example, A is essentially, 122 00:16:00,980 --> 00:16:06,860 they call it the total factor of productivity. That's basically the state of technology. 123 00:16:06,860 --> 00:16:14,580 And we can expect that will vary over time. So you could let A vary over time by adding 124 00:16:14,580 --> 00:16:22,160 a trend component. And there's many ways you could make the production function more complex 125 00:16:22,160 --> 00:16:29,060 and realistic. And that's a lot of what the modern economics literature is about. And 126 00:16:29,060 --> 00:16:34,980 so maybe if you tune in on Saturday morning statistics, I can go a bit more in depth into 127 00:16:34,980 --> 00:16:43,500 this where we could talk about the history of the production function and some of these 128 00:16:43,500 --> 00:16:48,540 additions that can be done to it. But this is the most simple model. And so everything, 129 00:16:48,540 --> 00:16:56,420 so a lot of things are additions upon this. And just give a shout out to... 130 00:16:56,420 --> 00:17:10,140 Well, when can I first meet? Maybe Robert. But Professor Solow at MIT was one of the 131 00:17:10,140 --> 00:17:14,780 people who did a lot of the fundamental work on this. But we'll talk about this in Saturday 132 00:17:14,780 --> 00:17:23,980 morning statistics. For today, we'll go ahead and estimate this. So let's go ahead and get 133 00:17:23,980 --> 00:17:31,100 Cheyenne into the group here. And then we'll actually just go ahead and move on to the 134 00:17:31,100 --> 00:17:36,420 coding part since we probably bored you to death with the mathematics. Let's actually 135 00:17:36,420 --> 00:17:48,300 get our hands on the data here. And welcome to the group Cheyenne. We're just about to 136 00:17:48,300 --> 00:17:57,140 get started with the coding. And I'm just going to go ahead and commit the code to GitHub 137 00:17:57,140 --> 00:18:11,420 in case you want to follow along on the... So just so everybody knows, the source code 138 00:18:11,420 --> 00:18:23,500 is online on GitHub. So here I just committed the code 19 seconds ago. So that way you can 139 00:18:23,500 --> 00:18:35,420 follow along online as we go. So it may be a little quick to get started for today. But 140 00:18:35,420 --> 00:18:41,940 in the future, if you want to follow along, now you know where the code base is and how 141 00:18:41,940 --> 00:18:53,420 you can go about following along. But without further ado, I'm going to go ahead and move 142 00:18:53,420 --> 00:18:57,580 through this part quickly since we've spent a bit of time on this in the prior week. So 143 00:18:57,580 --> 00:19:08,180 check out YouTube or camlidx.com and we've got prior videos uploaded there. So we'll 144 00:19:08,180 --> 00:19:18,420 basically read in data here for Massachusetts. And for those of you curious about where this 145 00:19:18,420 --> 00:19:30,380 data is coming from, Massachusetts makes their data publicly available through an API. So 146 00:19:30,380 --> 00:19:37,300 check out Socrata. We've got some links here that you can find some of the data points 147 00:19:37,300 --> 00:19:51,940 that they publish. Some of the ones we've been looking at are sales. And so these are 148 00:19:51,940 --> 00:20:01,180 daily sales. And we've talked in prior weeks about how a lot of our analysis since we're 149 00:20:01,180 --> 00:20:10,100 looking at about the medium term, which is 2022, we can look at weekly sales. So we work 150 00:20:10,100 --> 00:20:23,820 a lot with weekly data. So here are weekly sales moving along. And, you know, we've collected 151 00:20:23,820 --> 00:20:29,100 a handful of data points here. I don't think we're using these today. But, you know, we 152 00:20:29,100 --> 00:20:40,540 can calculate, you know, the total number of retailers and cultivators. So here's our, 153 00:20:40,540 --> 00:20:51,420 you know, sales per retailer, flowering plants. It would vary. This is something that may 154 00:20:51,420 --> 00:21:01,900 be of interest to you. So this is flowering plants per cultivator. And so on average, 155 00:21:01,900 --> 00:21:11,740 you know, we're getting, right, you know, over time, there's an average of 300 plants 156 00:21:11,740 --> 00:21:19,460 per cultivator. But as you can see, the trend is increasing. And, you know, these days, 157 00:21:19,460 --> 00:21:32,780 I think, right, the last observation, people had around 500 plants on average per cultivation. 158 00:21:32,780 --> 00:21:41,180 That's flowering plants. So that's what you're looking at in Massachusetts, or at least that's 159 00:21:41,180 --> 00:21:48,180 what the data says. And I kind of want to go ahead and hedge all of our analysis here, 160 00:21:48,180 --> 00:22:00,660 right, because data, you know, data in, data out, right. So a lot of our analysis depends 161 00:22:00,660 --> 00:22:11,740 on how accurate the API data is. And I would like to think, you know, the Massachusetts 162 00:22:11,740 --> 00:22:19,020 data is fairly accurate, but inevitably, I'm sure there's measurement error in the data. 163 00:22:19,020 --> 00:22:29,020 So you know, is everybody measuring flowering plants the same or on the daily basis, right? 164 00:22:29,020 --> 00:22:36,940 So maybe people may reconcile at the end of the week or so maybe there's measurement error 165 00:22:36,940 --> 00:22:44,620 in here, but we'll just, you know, hedge all of our analysis, knowing that the date, what 166 00:22:44,620 --> 00:22:54,380 I always like to say is the data is not perfect. But a measure is better than no measure. So 167 00:22:54,380 --> 00:23:03,260 do people have exactly 495.9 plants? You know, probably not. But, you know, we can at least 168 00:23:03,260 --> 00:23:12,180 know about how many plants somebody may have. So that way, you can at least know what may 169 00:23:12,180 --> 00:23:23,260 be reasonable and what may not be reasonable. So with that said, we can start our forecasting 170 00:23:23,260 --> 00:23:29,060 and check out some of the prior weeks, especially Saturday morning statistics, if you want to 171 00:23:29,060 --> 00:23:34,820 get real in depth into the forecasting models. And so here, we're going to use an a theoretical 172 00:23:34,820 --> 00:23:41,780 approach, where we basically just use past observations. And we were just saying, okay, 173 00:23:41,780 --> 00:23:49,220 this series if it continues moving the way it has in the past, this is what forecast 174 00:23:49,220 --> 00:24:01,660 would look like. So these forecasts can be improved upon. But it's a, so let's just go 175 00:24:01,660 --> 00:24:19,180 ahead and do all the forecasts. So here we're forecasting sales, plants, and the total 176 00:24:19,180 --> 00:24:27,340 number of employees that will be in the market, right, because we need to know, right, that 177 00:24:27,340 --> 00:24:37,140 was one of our variables was labor. And here is the figure that we saw at the beginning 178 00:24:37,140 --> 00:24:52,580 of the presentation, where we're measuring sales, plants, cultivators, retailers, and 179 00:24:52,580 --> 00:25:01,700 so on and so forth. Check in last week for a bit more in depth discussion about this. 180 00:25:01,700 --> 00:25:09,060 But because this week, I want to go ahead and move into this, the last aspect of market 181 00:25:09,060 --> 00:25:18,660 performance, right. So we, we met right, because let's go back to our presentation. We're interested 182 00:25:18,660 --> 00:25:27,620 in ensuring productivity, right, market performance here. And let's look at all our variables 183 00:25:27,620 --> 00:25:37,260 here, right. We're of course interested in sales. We're taking productivity as exogenous. 184 00:25:37,260 --> 00:25:45,460 So a we're not trying to explain labor we're interested in. So we've, you know, predicted 185 00:25:45,460 --> 00:25:53,500 like the labor supply, you know, how much labor there's going to be capital, right, we've 186 00:25:53,500 --> 00:26:03,460 predicted what how much capital is going to be supplied. Well, we know what supply is 187 00:26:03,460 --> 00:26:17,020 going to be right. Well, in equilibrium, supply equals demand, and prices are set. So when 188 00:26:17,020 --> 00:26:24,100 we're doing a competitive analysis, I mean, a market analysis here, a market performance, 189 00:26:24,100 --> 00:26:33,400 we're basically going to assume that at every period t, the markets in equilibrium, supply 190 00:26:33,400 --> 00:26:43,180 equals demand, and prices are set. So that means at each point t, there's a competitive 191 00:26:43,180 --> 00:26:53,540 wage, and there's a competitive interest rate. And supply equals demand, the plants are grown, 192 00:26:53,540 --> 00:27:05,020 the cannabis is sold. And those prices are as they were shaken out. Well, you know, we 193 00:27:05,020 --> 00:27:13,940 actually have the price per gram here. So that was one of our variables that we're able 194 00:27:13,940 --> 00:27:23,620 to pull from the API. But we're doing a market analysis here. So that means we need to characterize 195 00:27:23,620 --> 00:27:35,740 the prices. So this is the price per gram in Massachusetts. Well, next week in our comparative 196 00:27:35,740 --> 00:27:42,900 analysis, we'll compare the price per gram of Massachusetts versus the price per gram 197 00:27:42,900 --> 00:27:53,580 in Oregon. And foreshadowing, there's quite a discrepancy. And once again, is it a measurement 198 00:27:53,580 --> 00:28:00,180 error or it's probably got to do with the markets, right? The Oregon market is probably 199 00:28:00,180 --> 00:28:09,220 different than the Massachusetts market. So we'll look at that. And when we look at prices, 200 00:28:09,220 --> 00:28:13,980 right, there's a whole lot of ways you can characterize prices, right? What everyone's 201 00:28:13,980 --> 00:28:22,220 talking about these days is, you know, inflation. And we've talked about, well, you know, cannabis 202 00:28:22,220 --> 00:28:32,180 prices aren't rising, right? So you we can actually look at the change in prices. So 203 00:28:32,180 --> 00:28:39,580 that would be what we would define as inflation. So, you know, to real quick, right, that's 204 00:28:39,580 --> 00:28:54,100 basically, you know, price per gram, its percent change. And you can plot that over time. And 205 00:28:54,100 --> 00:29:04,100 so this would be the inflation of cannabis prices in Massachusetts. And as you can see, 206 00:29:04,100 --> 00:29:11,540 you know, it's actually kind of steady, except for this, you know, really rocky period here. 207 00:29:11,540 --> 00:29:15,900 And we could even chalk some of this up to maybe there's some measurement error going 208 00:29:15,900 --> 00:29:23,940 on during that period. So long story short, there is a whole lot of analysis you can do 209 00:29:23,940 --> 00:29:32,940 just on prices. And we'll we'll do a bit of that next week. So the question I'm interested 210 00:29:32,940 --> 00:29:43,180 in is, what's the effect on, say, inflation in Massachusetts versus Oregon, right? So 211 00:29:43,180 --> 00:29:50,020 it's like, yes, and this is where we're going to use some statistical models and look at 212 00:29:50,020 --> 00:29:58,020 the differences here, right? Because yes, their prices are different. But maybe this 213 00:29:58,020 --> 00:30:08,180 closure in Massachusetts, did this have an upward or downward pressure on prices? And 214 00:30:08,180 --> 00:30:15,140 we can perhaps look at the difference between Massachusetts and Oregon and maybe try to 215 00:30:15,140 --> 00:30:25,700 disentangle these effects. I'll quit droning on about these prices. Because what we're 216 00:30:25,700 --> 00:30:31,620 more interested in today are these prices. So people often don't think of, you know, 217 00:30:31,620 --> 00:30:41,180 wage as a price. But economists like to just treat everything as being able to be bought 218 00:30:41,180 --> 00:30:50,620 or sold. And labor is no exception. Labor can be bought or sold at the wage rate. So 219 00:30:50,620 --> 00:30:58,900 that's how economists look at things. And so the wage is essentially the price of labor. 220 00:30:58,900 --> 00:31:10,780 And the interest rate is essentially the price of capital goods. So basically, having capital 221 00:31:10,780 --> 00:31:18,620 on hand, your economists are saying you're having to pay this interest rate, because 222 00:31:18,620 --> 00:31:27,820 you're either having to pay an investor to get the funds you need to buy this equipment. 223 00:31:27,820 --> 00:31:36,460 Or if you already own the equipment, you could be renting it out at the competitive rate. 224 00:31:36,460 --> 00:31:44,060 And if you could have been making this revenue, and you're forgoing it, that's what's called 225 00:31:44,060 --> 00:31:53,380 your opportunity cost. So even if you already own the equipment, your opportunity cost of 226 00:31:53,380 --> 00:32:02,700 using the equipment to produce cannabis is your interest rate, which you could have been 227 00:32:02,700 --> 00:32:11,060 earning by leasing out this equipment to somebody else. In reality, can you just lease out every 228 00:32:11,060 --> 00:32:18,500 bit of equipment you own at the competitive interest rate? Not really. And this is where 229 00:32:18,500 --> 00:32:28,060 there's imperfections in the market. There's transaction costs. Things aren't perfectly 230 00:32:28,060 --> 00:32:40,380 liquid. So this is where theoretical abstractions of economics meets empirical reality. So that's 231 00:32:40,380 --> 00:32:54,100 exactly what we're doing today. So we mix theory with empirics. So now that we've droned 232 00:32:54,100 --> 00:33:02,140 on about prices, let's try to estimate them. And this is this is the fun part here. So 233 00:33:02,140 --> 00:33:12,900 we've done all our forecasting here, we're going to get a couple, a couple more variables. 234 00:33:12,900 --> 00:33:24,660 So check out the Federal Reserve Economic Data, Fred. So just do a Google search for 235 00:33:24,660 --> 00:33:34,820 Fred, Fred, and you can get a bunch of economic series here. And I use these as fillers whenever 236 00:33:34,820 --> 00:33:41,820 I can't get these data points from whatever data series we're working on. And this is 237 00:33:41,820 --> 00:33:47,340 an awesome technique, right? supplement your data with other data sets. So here we've got 238 00:33:47,340 --> 00:33:54,020 data from Massachusetts, Socrata, we're going to combine it with some data from Fred. So 239 00:33:54,020 --> 00:34:06,900 what are we going to do? We're just going to get the average weekly hours here. So right, 240 00:34:06,900 --> 00:34:13,500 we're not tracking this in the cannabis industry. So it would be awesome to know what people 241 00:34:13,500 --> 00:34:20,500 in the cannabis industry were working each week. We don't have that data. So we're just 242 00:34:20,500 --> 00:34:26,380 going to have to assume that people in the cannabis industry in Massachusetts are working 243 00:34:26,380 --> 00:34:36,060 the same hours per week on average as everyone else in Massachusetts. Is this a stretch? 244 00:34:36,060 --> 00:34:41,740 Maybe, maybe not. And so this is where when we're going through these estimations, right, 245 00:34:41,740 --> 00:34:46,860 we're going to get our figure at the end. Well, we need to sort of include all of these 246 00:34:46,860 --> 00:34:52,780 assumptions along the way in our footnotes or something, right? And not just in our footnotes, 247 00:34:52,780 --> 00:34:58,860 right? If when you're presenting your findings to people, you need to be real explicit about 248 00:34:58,860 --> 00:35:04,660 assumptions that were made along the way. And here we're making an assumption, maybe 249 00:35:04,660 --> 00:35:11,260 it's minor, maybe it's major, that people in the cannabis industry work the same hours 250 00:35:11,260 --> 00:35:18,980 per week as in other industries. And maybe they work more than average, maybe they work 251 00:35:18,980 --> 00:35:25,180 less than average. And so we need to take that into consideration and say how that may 252 00:35:25,180 --> 00:35:32,660 bias our results. So if people in the cannabis industry actually work greater than this, 253 00:35:32,660 --> 00:35:39,660 maybe we're biased in our wage estimate either upwards or downwards. So that's something 254 00:35:39,660 --> 00:35:52,580 to acknowledge here. I'm going ahead and grabbing these two series just for baseline. So what 255 00:35:52,580 --> 00:36:06,900 these are, are so this is average earnings in Massachusetts per hour. Right? Yes. Yeah, 256 00:36:06,900 --> 00:36:16,260 I think so. So this is average earnings per hour in Massachusetts. So going up, see the 257 00:36:16,260 --> 00:36:27,820 spike here. That's interesting. So the average earnings went up in Massachusetts. Well, but 258 00:36:27,820 --> 00:36:35,420 that's like, since this is where this is sort of a sad part of analyzing the data, right? 259 00:36:35,420 --> 00:36:42,020 Because mathematically, right, this is what economists look at, right? Margins, what happens 260 00:36:42,020 --> 00:36:50,060 at the margin, right? And so if the way margins were explained to me is, say you're getting 261 00:36:50,060 --> 00:37:01,860 grades, and you've got a 90, the 92 average, well, if your next marginal grade is above 262 00:37:01,860 --> 00:37:10,500 a 92, your average will increase. And if your next grade is below a 92, your average will 263 00:37:10,500 --> 00:37:18,020 decrease. So it's, you know, that's how, you know, how you can kind of understand marginal 264 00:37:18,020 --> 00:37:29,860 change here. Well, what happened in the market because the average earnings do increase? 265 00:37:29,860 --> 00:37:42,980 Well, that means that either employees were added to the market with higher wages, or 266 00:37:42,980 --> 00:37:52,460 unfortunately, what I fear is employees with below average wages were removed from the 267 00:37:52,460 --> 00:38:01,460 market. And so unfortunately, it does look like a large number of lower income employees 268 00:38:01,460 --> 00:38:07,420 may have lost their jobs at that point. Like I said, I don't want to read too much into 269 00:38:07,420 --> 00:38:14,580 this. And this is where, if you weren't doing an analysis of wages, which we are essentially, 270 00:38:14,580 --> 00:38:23,340 so you should hit the newspapers. So you should look at the newspapers in Massachusetts at 271 00:38:23,340 --> 00:38:38,860 this time period here, which looks like this looks like the same period here, where, well, 272 00:38:38,860 --> 00:38:44,100 these two series aren't going to plot too well on top of each other. But see, right 273 00:38:44,100 --> 00:38:51,060 at this period where the market was closed, that's where wages increased. So long story 274 00:38:51,060 --> 00:39:00,980 short, being an economist is basically being, you know, a historian, a statistician, you 275 00:39:00,980 --> 00:39:08,980 know, a political science student, a full philosophy and psychology, all sort of bundled 276 00:39:08,980 --> 00:39:14,620 into one. And, you know, that's sort of a data science, you know, data science takes 277 00:39:14,620 --> 00:39:21,980 it to the next step. So all of you here today are, are having to having to do this. So, 278 00:39:21,980 --> 00:39:27,020 you know, that's where we'll make you a good data scientist is if you can take skills from 279 00:39:27,020 --> 00:39:33,140 other fields and incorporate them into your analysis. So incorporate some history, incorporate 280 00:39:33,140 --> 00:39:44,580 some psychology into your analysis. So long story short, average earnings went up. The 281 00:39:44,580 --> 00:39:52,180 minimum wage, we're just using this as a benchmark here just to know, okay, you know, whatever 282 00:39:52,180 --> 00:40:01,620 we're predicting wages to be, our predictions really can't be below the minimum wage. Or 283 00:40:01,620 --> 00:40:08,300 if they are, then you know, they're not, then people aren't getting paid. Or even people 284 00:40:08,300 --> 00:40:13,060 are getting paid above their competitive. But long story short, this is a benchmark 285 00:40:13,060 --> 00:40:19,500 here. So now we know what the minimum wage is here in Massachusetts. And we you know, 286 00:40:19,500 --> 00:40:28,940 we can even plot these two together. So we can just kind of keep adding. So there's the 287 00:40:28,940 --> 00:40:37,380 minimum wage, and there's the average wage in Massachusetts. And we want to know where 288 00:40:37,380 --> 00:40:54,500 the cannabis industry wage shakes out. Well, let's estimate this. So we need to know beta, 289 00:40:54,500 --> 00:41:15,660 y t and LT. Right. So we've got y t. We've got L. So we need hours worked. So this is 290 00:41:15,660 --> 00:41:26,100 where I start to get a little creative. And so this is how I do things, you can do things 291 00:41:26,100 --> 00:41:35,460 differently. And this is where science meets art. So I define labor as the total number 292 00:41:35,460 --> 00:41:50,340 of employees times the average weekly hours. So that's the total people hours, the total 293 00:41:50,340 --> 00:42:05,620 hours worked in the cannabis industry per week. Cool. Now we need to know beta. Well, 294 00:42:05,620 --> 00:42:13,500 we've got a production function here. We can estimate it. So if you just take that's what 295 00:42:13,500 --> 00:42:19,300 that's why people love the Cobb Douglas production function. Because if you just take the log 296 00:42:19,300 --> 00:42:27,140 of both sides, you get a linear regression here, right, you just get log sales equals 297 00:42:27,140 --> 00:42:33,580 log a, which is just a constant that we're taking as exogenous means we're not trying 298 00:42:33,580 --> 00:42:46,540 to explain it. Plus alpha times KT. What's KT? We're proxying it as the flowering plants 299 00:42:46,540 --> 00:42:53,460 per week. I'm going to show you here how this is a big assumption. And we need a better 300 00:42:53,460 --> 00:42:58,500 measure of capital, but this is just the best measure that we're given. So we're basically 301 00:42:58,500 --> 00:43:05,020 assuming that say you're growing 100 plants, you need a fixed amount of capital. If you're 302 00:43:05,020 --> 00:43:10,860 growing 500 plants, you need a fixed amount of capital. If you need 1000 plants, you need 303 00:43:10,860 --> 00:43:24,580 a fixed amount of capital that doesn't necessarily have to be linear, but but it does have to 304 00:43:24,580 --> 00:43:33,940 be directly correlated. That's a huge assumption to make. But that's an assumption that we 305 00:43:33,940 --> 00:43:43,260 have to make to or to estimate this thing. So so yeah, so this is a huge, huge estimate. 306 00:43:43,260 --> 00:43:49,340 So basically, I'm just going to go ahead and hedge this is whatever numbers we spit out 307 00:43:49,340 --> 00:43:57,100 here are going to be almost like a mental exercise. I'm not going to say that this is 308 00:43:57,100 --> 00:44:02,900 what the competitive wages or is not what the competitive interest rate is, but this 309 00:44:02,900 --> 00:44:11,420 is our exercise here. So we can at least attempt to have a measure since we don't actually 310 00:44:11,420 --> 00:44:18,900 know what the wages, right? The trade rate, the whoever is doing taxes, the Department 311 00:44:18,900 --> 00:44:26,380 of Revenue in Massachusetts, I'm sure, actually knows what the wage rate is for each individual. 312 00:44:26,380 --> 00:44:34,500 But you know, that's private information. Right, so that's private information. And 313 00:44:34,500 --> 00:44:41,460 so we're trying to get our best public estimate of what the wage rate is. So that way, one, 314 00:44:41,460 --> 00:44:48,500 employees know what they should be asking for. And two, employers know what they should 315 00:44:48,500 --> 00:44:55,820 be expected to pay. And same for interest rate. That way, investors know what to ask 316 00:44:55,820 --> 00:45:06,540 for. And producers know what what fair rate of return, if a fair interest rate is to pay. 317 00:45:06,540 --> 00:45:12,100 Right, because you want you want to know what a fair price is, like what are the fair prices. 318 00:45:12,100 --> 00:45:16,580 And that's almost one of the hardest parts of operating in these industries is you don't 319 00:45:16,580 --> 00:45:23,260 know what a fair price is. Right, when you're an employee, and you're bargaining for a wage, 320 00:45:23,260 --> 00:45:27,580 you don't know what the fair wage is. Right, you have to sort right when you're in your 321 00:45:27,580 --> 00:45:33,380 job interview, and they ask you if they do, like, you know, how much do you want to get 322 00:45:33,380 --> 00:45:39,400 paid, like you're going to have to give them a figure. Or if they're, you know, writing 323 00:45:39,400 --> 00:45:43,620 up the job, they're going to have to put a figure down. And you know, a lot of times, 324 00:45:43,620 --> 00:45:50,020 people use these ad hoc methods. And here, we're trying to formalize it a little better. 325 00:45:50,020 --> 00:45:58,540 But like I said, it's it's an imperfect measure as is. Okay, so we've got our model here, 326 00:45:58,540 --> 00:46:10,460 we've got YK and L. Not yet, but we will shortly. All right, we've got YK and L. We're restricting 327 00:46:10,460 --> 00:46:25,260 our analysis here to 2019 to 2021, the end of September. Just so we have a nice. Well, 328 00:46:25,260 --> 00:46:44,940 I thought we were. Anyways, maybe we're. Oh, yes, that's right. So here, yes, our data 329 00:46:44,940 --> 00:46:55,980 starting at 2019. And then I'm just going to assume that the market just closes. So 330 00:46:55,980 --> 00:47:00,220 we're just going to assume, okay, the market's just going to close, and we're just going 331 00:47:00,220 --> 00:47:10,780 to drop those observations for those two months there. This is sort of a violation of, like 332 00:47:10,780 --> 00:47:19,020 time series analysis, right time series analysis is considering your t's are just incremented. 333 00:47:19,020 --> 00:47:27,700 So this is a challenge here, having this break. But, you know, there's different ways you 334 00:47:27,700 --> 00:47:38,220 can estimate this. But this is the way that I rationed was was the way to keep the most 335 00:47:38,220 --> 00:47:49,220 data with, you know, with the least bias. So, like I said, there's probably ways to improve 336 00:47:49,220 --> 00:47:56,140 upon this, but we're just going to exclude that time period there where sales were zero. 337 00:47:56,140 --> 00:48:03,220 Okay. And so now we're going to take the log of Y, take the log of capital, take the log 338 00:48:03,220 --> 00:48:16,660 of labor and fit a regression. So we just estimated our regression here, we have a really 339 00:48:16,660 --> 00:48:23,700 poor fit. So this is not a very good regression model. So our production function can be improved 340 00:48:23,700 --> 00:48:46,620 upon a lot. Our coefficients here aren't actually significant. So, so like I said, if you're 341 00:48:46,620 --> 00:48:52,700 a frequentist, you couldn't conclude that these were statistically significant. We're 342 00:48:52,700 --> 00:49:02,660 sort of more of a Bayesian in that I'm just trying to just get some numbers and use my 343 00:49:02,660 --> 00:49:09,380 prior beliefs and knowledge to see if they look reasonable, right. And, right. And that's 344 00:49:09,380 --> 00:49:15,980 the whole reason why we've been looking at things like average earnings, because I'm 345 00:49:15,980 --> 00:49:23,740 a Bayesian and I'm trying to establish a prior, I'm trying to have a prior expectation of 346 00:49:23,740 --> 00:49:31,500 what wages are going to be. If you're a frequentist, you don't want your prior beliefs creeping 347 00:49:31,500 --> 00:49:41,460 into your analysis. The Bayesians just acknowledge that they inevitably do. So it's just saying, 348 00:49:41,460 --> 00:49:50,060 okay, instead of, you know, pretending like your prior beliefs don't exist, just, you 349 00:49:50,060 --> 00:49:56,060 know, acknowledge, okay, you know, this, this is what average earnings are, you know, this 350 00:49:56,060 --> 00:50:05,140 is what the minimum wage is. My prior belief is that wages are probably between average 351 00:50:05,140 --> 00:50:14,780 earnings and minimum wage, right, maybe they're above average earnings. But, you know, not 352 00:50:14,780 --> 00:50:19,700 like that much above like, and they're not going to, it's not going to be like three 353 00:50:19,700 --> 00:50:26,660 times above average, I don't think. And it's probably not going to be like that much below 354 00:50:26,660 --> 00:50:35,100 minimum wage if it is below minimum wage. So, long story short, the significance of 355 00:50:35,100 --> 00:50:42,940 the measure of capital may or may not matter. And this is where I'll show you here before 356 00:50:42,940 --> 00:50:53,740 we end that our measure of capital matters quite a bit. So foreshadowing our measure 357 00:50:53,740 --> 00:51:04,100 of capital matters. But long story short, we've measured, we've run our regression here. 358 00:51:04,100 --> 00:51:13,340 Remember, alpha is just our coefficient on capital, beta is our coefficient on labor. 359 00:51:13,340 --> 00:51:26,220 So here, you know, we just get those. We've got alpha, and we've got beta. Well, that's 360 00:51:26,220 --> 00:51:35,460 cool because since we've got beta, we can now measure beta times yt over lt to get our 361 00:51:35,460 --> 00:51:44,180 competitive wage. So let's do that. So here, we're estimating our historic competitive 362 00:51:44,180 --> 00:51:57,060 wage. And we, we are noting that the competitive wage was really high at the start of the market. 363 00:51:57,060 --> 00:52:03,280 And from the economic theory, this would make sense because there's not many people working 364 00:52:03,280 --> 00:52:11,260 in the cannabis industry at this time, right? If you look at weekly employees, there's not 365 00:52:11,260 --> 00:52:19,540 many people working. So each additional employee that you add is going to have a high marginal 366 00:52:19,540 --> 00:52:28,060 product. A high marginal product is going to equal a high wage. Those are going to fall 367 00:52:28,060 --> 00:52:36,340 over time as more and more people into the market. And it's going to stabilize. It looks 368 00:52:36,340 --> 00:52:44,300 like there's just been downward pressure. Market closes. Well, if people can't sell 369 00:52:44,300 --> 00:52:51,860 anything, then they can't be productive. So we're actually predicting that, you know, 370 00:52:51,860 --> 00:53:01,140 their competitive wage at that time is zero, right? If you can't produce any value, then 371 00:53:01,140 --> 00:53:07,500 no one's going to pay you for anything. And so that's where you saw people essentially 372 00:53:07,500 --> 00:53:18,140 getting fired, because they're not, they're not able to produce any value because no cannabis 373 00:53:18,140 --> 00:53:30,740 can be sold. And so they get laid off because the wage, their current wage is above their, 374 00:53:30,740 --> 00:53:38,780 their marginal product. And then people come back to work. And then we're, you know, you're 375 00:53:38,780 --> 00:53:49,220 going along here. Something happens here in, in like this spring of 2021. I'm, like I said, 376 00:53:49,220 --> 00:53:56,620 I'm gonna have to hit the Massachusetts newspapers because I know I can't explain this dip in 377 00:53:56,620 --> 00:54:05,380 sales. You don't see this in other markets. And it looks like it's a Massachusetts specific 378 00:54:05,380 --> 00:54:12,820 dip. And I think some investigation needs to be done to figure out what, what, what 379 00:54:12,820 --> 00:54:21,060 happened with sales during that period. So that's an interesting research question. So 380 00:54:21,060 --> 00:54:26,860 long story short, wages, once again, they fall during that period because there's just 381 00:54:26,860 --> 00:54:34,060 not many sales in that wages are, you know, bouncing back towards the level they were, 382 00:54:34,060 --> 00:54:43,420 but they're volatile. So it's like, you know, when one week, you know, their productivity's 383 00:54:43,420 --> 00:54:50,780 high. So that means, you know, firms are really going to be looking for workers, then the 384 00:54:50,780 --> 00:54:54,940 next week, the productivity is low, you know, and they're not going to be looking for as 385 00:54:54,940 --> 00:54:59,620 many workers. And then the next week is high, then low, high, high, then low. So that's 386 00:54:59,620 --> 00:55:07,300 what this recent market's been characterized by this, this volatility. And especially, 387 00:55:07,300 --> 00:55:12,460 you know, in a labor market, that's going to make things difficult, right? Because you 388 00:55:12,460 --> 00:55:19,140 want to be accepting your wage rate, right? If you're a worker at the top of one of these 389 00:55:19,140 --> 00:55:28,660 spikes versus the bottom, you know, and then employers are wanting the public peg wages, 390 00:55:28,660 --> 00:55:35,220 you know, and they're low. And then so this is where you've just heard anecdotally, you 391 00:55:35,220 --> 00:55:43,700 know, through the news and whatnot, how there's this tumultuous labor market. And then also, 392 00:55:43,700 --> 00:55:51,860 you know, we've got our estimate for the rate of return on plants. And then Barry, you can 393 00:55:51,860 --> 00:55:59,780 maybe speak to this. But basically, we were estimating that the rate of return on plants 394 00:55:59,780 --> 00:56:07,140 was of course high in the beginning. And then maybe is decreasing over time. But it's maybe 395 00:56:07,140 --> 00:56:28,020 wait, wait, six and nine. In the last year, last year, it's been around $62 per plant, 396 00:56:28,020 --> 00:56:38,140 per flowering plant per week. So we're not certain if this is a very good measure of 397 00:56:38,140 --> 00:56:44,820 a rate of return. But we can at least we at least like we need at least no wages dollars 398 00:56:44,820 --> 00:56:52,060 per hour, people are pretty familiar with that measure. dollars per plant per week is 399 00:56:52,060 --> 00:56:58,740 a bit more of an obscure measure. Right, just to go ahead and run through this last little 400 00:56:58,740 --> 00:57:04,460 bit here, so we can be concluding on time, on time, I thought we could go ahead and use 401 00:57:04,460 --> 00:57:15,220 our same analysis from above to predict what wages would be like in 2022. And I'm going 402 00:57:15,220 --> 00:57:23,180 to run through this, but the codes online. So if you want to run through with me on the 403 00:57:23,180 --> 00:57:28,820 one on one or something, just email me and we can maybe work something out to to go through 404 00:57:28,820 --> 00:57:36,700 this, you know, line by line a bit more specifically. The long story short, let's go ahead and predict 405 00:57:36,700 --> 00:57:47,900 wages into 2022. And there's our forecast, we can do a slightly better figure here. So 406 00:57:47,900 --> 00:57:58,260 here's our competitive wage forecast into 2022 in Massachusetts. And so, you know, we 407 00:57:58,260 --> 00:58:09,620 were saying that in the data science group is estimating that the wage for would be competitive 408 00:58:09,620 --> 00:58:17,980 in Massachusetts to be $27 per hour on on average, so this would be everybody in the 409 00:58:17,980 --> 00:58:29,580 industry. How does that stack out against everything? Well, here's everything plotted 410 00:58:29,580 --> 00:58:41,900 together. So here is in blue, you have average earnings in Massachusetts. In red, you have 411 00:58:41,900 --> 00:58:53,340 the minimum wage. In orange, you have our estimated competitive wage rate in the cannabis 412 00:58:53,340 --> 00:59:02,020 industry. And then in green, you have our forecast for the competitive wage rate in 413 00:59:02,020 --> 00:59:13,940 Massachusetts. Notice a couple things. One, the competitive wage rate we estimated was 414 00:59:13,940 --> 00:59:27,620 above average. And now it's looking like it's either, you know, approaching average earnings, 415 00:59:27,620 --> 00:59:35,220 or it may even be, you know, approaching a slightly below average median income. And 416 00:59:35,220 --> 00:59:41,060 so that maybe, you know, if cannabis becomes a bit more of your know your standard agricultural 417 00:59:41,060 --> 00:59:50,460 crop, you know, you may not actually see super high wage rates. But you notice our error 418 00:59:50,460 --> 00:59:59,820 bounds here. And so you know, and plus we've got a lot of factors essentially by by seeing 419 00:59:59,820 --> 01:00:15,380 our analysis downwards. And in fact, well, just to go ahead and pack in a bunch of things 420 01:00:15,380 --> 01:00:23,660 here. If we just used alpha and beta from earlier, and then used our sales forecast 421 01:00:23,660 --> 01:00:38,780 and our plant forecast, then you know, our forecast, it would be here in gray. So it 422 01:00:38,780 --> 01:00:46,500 would be even, you know, even lower. So if we're just using the a theoretical approach, 423 01:00:46,500 --> 01:00:56,700 it would be in green. If we're using a theoretical approach, it would be in gray, where we would 424 01:00:56,700 --> 01:01:08,300 predict the lower competitive wage. And just to give you a complete uncertainty, if we 425 01:01:08,300 --> 01:01:16,860 were going to change our measure of capital, to say instead of using flowering plants, 426 01:01:16,860 --> 01:01:23,900 we just said, Oh, let's just use all plants. Oh, let's just estimate everything again real 427 01:01:23,900 --> 01:01:33,060 quick. Maybe not super quick, but you know, quick enough in the grand scheme of things. 428 01:01:33,060 --> 01:01:51,460 So here we're just, you know, changing our measure of capital. We get our forecasts. 429 01:01:51,460 --> 01:02:01,340 Okay. And here is our forecast for wages. So here, if we change capital from flowering 430 01:02:01,340 --> 01:02:09,220 plants to just all plants, which includes vegetative plants, well, it radically changes 431 01:02:09,220 --> 01:02:16,980 our estimate of wage, right? Now all of a sudden, we're estimating wage was never as 432 01:02:16,980 --> 01:02:26,940 high as average. And now it's fallen down to minimum wage. And we're predicting it's 433 01:02:26,940 --> 01:02:38,780 going to just hover around minimum wage in 2022. So, you know, a lot of our prediction 434 01:02:38,780 --> 01:02:48,060 here depends solely on our variables, like, you know, what, how do we want to measure 435 01:02:48,060 --> 01:02:57,180 capital? You know, are we measuring it is flowering plants, are we measuring it is all 436 01:02:57,180 --> 01:03:01,820 plants? You know, maybe we could measure, you know, you could, you know, you could cook 437 01:03:01,820 --> 01:03:11,620 up some measure of capital that maybe incorporates floor plan, right, because we know the square 438 01:03:11,620 --> 01:03:17,140 footage used by the producers. So there's a lot, you know, there's a lot of ways that 439 01:03:17,140 --> 01:03:27,500 you can measure capital. And the way you do determines how your determines what you predict 440 01:03:27,500 --> 01:03:34,420 the competitive wage in the markets going to going to be. So I just wanted to show you 441 01:03:34,420 --> 01:03:40,780 this to give you a little bit of uncertainty here that, you know, this is not all set in 442 01:03:40,780 --> 01:03:49,140 stone. This is, you know, it matters how you do your analysis here. And so, you know, are 443 01:03:49,140 --> 01:03:55,180 we with you know, which graph do we present to the public? Do we show them the one with 444 01:03:55,180 --> 01:04:01,420 the plant, the total plants through the flowering plants? You know, they both have their different 445 01:04:01,420 --> 01:04:11,860 implications. My personal bias is towards flowering plants, because it just if you look 446 01:04:11,860 --> 01:04:26,220 at these two series here, it just looks just on eyesight to be the more reliable. Sure. 447 01:04:26,220 --> 01:04:38,860 But they're different. So, all right, all right, Donna, some people have got to go. 448 01:04:38,860 --> 01:04:43,220 So we'll wrap up here. So thank you. Thank you all for attending. Thanks, Donovan. So 449 01:04:43,220 --> 01:04:49,940 I always run a bit long. So I need to make an effort to conclude on time. So long story 450 01:04:49,940 --> 01:04:56,780 short, next week, we'll pick back up and start to look at other states. But this is how you 451 01:04:56,780 --> 01:05:06,380 can start to measure prices in the market, right? You can define your economic model. 452 01:05:06,380 --> 01:05:15,220 You can conjecture that supply equals demand, and then estimate wages and the rate on return 453 01:05:15,220 --> 01:05:29,860 on plants. And it matters how you do your analysis. And I was thinking there was going 454 01:05:29,860 --> 01:05:42,180 to be a lesson for the day. But I think I'll let you all just maybe think about it and 455 01:05:42,180 --> 01:05:51,460 maybe feel free to share your feedback about what your takeaways were for today. But until 456 01:05:51,460 --> 01:06:17,500 next time, I'll clean up the code for you and share all these resources with you.