Hi, guys. Today we get to talk about the digestive system, which might be one of the coolest systems on the planet because it allows you to drop a giant doo-doo on the beach if you are a Y chromosome and are so inclined. Digestive system, in addition to the phenomenal ability to drop a giant doo-doo on the beach, the digestive system is tightly linked to all metabolic processes. Metabolism is essentially the sum of all chemical reactions that happen in your body. So let's take a second to compare digestive processes with metabolic processes, and then we're going to look at some of the inputs to the digestive system and perhaps conclude whether or not we think this young laddie could actually drop that size of a doo-doo on the beach if he so wanted to. Would he want to? I do not know the answer to this question, but it does not matter because we need to know what is this digestive system? In fact, what is digestion when compared to metabolism? They're not the same thing, and often they're kind of referred to in similar contexts. Digestion, the digestive system is responsible for breaking down food, break it down, break it down, break it down, break it down, and absorbing it into the blood.  So the lumen of the digestive system is actually outside your body, because remember, there it is, outside my body. And that tube goes all the way through my body and then comes out the other end and is like in the middle of my body, but it isn't actually in me. It isn't actually inside because inside is like the blood of my cells and my interstitial fluid, and this stuff is not that. So the other task, I mean, if you have food in the lumen of your digestive system, well, you're somehow going to have to absorb that into the blood, and that's ultimately the value of the digestive system.  If you don't break down your ice cream cone into smaller pieces, it's going to be too big to absorb into the bloodstream. Now, another interesting thing that happens, this is more of like a mechanism, a tool to help you do all of this. We have a great deal of secretion going on in the digestive system, and in fact, I've got a slide in just a second where I'm going to show you how much secretion is happening. Now, secretion is putting stuff from the blood or from the cells or from the glands surrounding the digestive system into the lumen. Why would we be secreting stuff into the lumen? Dude, the secretions help you break it down, break it down, break it down, break it down. And once you break it down, that's where metabolism comes in, breaking down your pieces so that you can put them into your bloodstream. Here are my little nutrient pieces. Thank you very much. What are we going to do with those nutrient pieces? Well, in this lecture, we're actually going to look at glucose and what we do with glucose, how we get glucose into our cells so that our cells can use it to go through cellular respiration and make energy. And hopefully at this point in the semester, you're like, dude, energy is kind of important. We can buy that we would have an entire system dedicated to getting stuff in so that we can make energy. Cellular respiration is an example of a metabolic process, a series of chemical reactions that enable your body to function. Cellular respiration is a metabolic process that breaks down substances, complex molecules, and it actually gets energy out. It's an example of catabolism. Catabolism. Catabolic reactions break things down into smaller pieces. Building muscle. This is another example of a metabolic process. You're increasing your muscle mass. You're putting together more myofibrils. You're increasing the diameter of your myofibers. That is an example of an anabolic chemical reaction or anabolism, anabolism. And anabolic processes build things out of building blocks. So these little molecules that you just absorbed, these are your building blocks. You're either going to use those building blocks to create energy through catabolic pathways, or you can use those building blocks to make more stuff through anabolic pathways.  That's what's going on in you. Digestion, the digestive system fuels your resource needs so that you can carry out all your metabolic processes. We're going to look, first of all, at digestion, and then we're going to look at specific mechanisms by which molecules are absorbed into the bloodstream. And then we're going to look at glucose homeostasis and how we maintain proper glucose concentrations in the blood.  Now, take a look at this. Here's just a rough diagram of the anatomy of the digestive system. And what I want you to notice here is that, you know, your digestive system is pretty amazing. You, on average, I don't know how they figured out this number, but we'll just go with it. You ingest two liters of fluids and food in a day. Whatever, give or take, however, whatever. If you ingest two liters of food and fluid in a day, I want you to look at these numbers and think about this. You actually secrete holy seven liters of stuff into your digestive system in a day. That's crazy, Doc. 1.5 liters. That's almost a two-liter soda pop bottle of spit from your salivary glands. Gets secreted into the lumen of your digestive tube every day.  You'd almost fill the thing with the saliva that you produced in one 24-hour period. That blows my mind right there. I love having my mind blown. The gallbladder makes half a liter of bile and secretes that into your mix. Now, we're going to look at bile and why we're grateful that our gallbladder does this for us and why we shouldn't eat a five-pound tub of butter. Yes, not a wise thing to do. Your stomach is producing two liters. There's another pop bottle of gastric juices, digestive enzymes, acids, mush, gush, mucus. Awesome. Two liters of the stuff. Then go to our pancreas. Just your little friendly pancreas is doing another liter and a half. And your intestines secret another liter and a half of stuff for a total of seven liters. This means that with the two liters that you put in here, you end up with nine liters of stuff in your digestive system in the lumen that's outside your body every 24 hours. Now, you should be very comfortable with the fact that, dude, if you don't replace that, number one, you're going to get really thirsty.  So let's replace it the easiest way. Well, we could ingest more, but if you're going to poop out seven liters of fluid, that's going to kind of be an unpleasant experience. So let's not replace it. Let's actually just reabsorb it. Your small intestine absorbs seven and a half liters of stuff every 24 hours.  Every one liter, that's a tenth of a liter. That's not even close to a two liter pot bottle. That's what, a 20th of that? So you tell me, is, oh boy, is this, is this possible?  I'm just going to let you think on that. In the next section, we're going to look at how nutrients, the mechanism by which specific nutrients are broken into smaller pieces for absorption.


Hello everybody, welcome back to another Adobe Premiere Pro CC 2021 tutorial and this one I'm gonna show you how to create a watermark inside of your video how to put a watermark on your video And I'll even show you how to animate it. Why would you want to do this? A couple reasons one as you can see here This is a music video if I hit spacebar. We've got a guy playing music and The music industry is especially notorious for people You know stealing clips and taking music and uploading it and pretending You know, it's their music or you know trying to monetize other people's stuff the second reason you might want to do a Watermark like this is because if you've made a video as a video editor or a graphic designer and you know You show your end product to somebody and they look at it and go Hey, this is great and rather than pay you for it They right-click save as or they download it and you don't get paid for it So watermark protects your work and it makes sure that people know who did it Alright that little preamble out of the way. Let's go now. You're gonna see here that I've got the watermarks Look right in the bottom right here super easy to do if you stick with me to the end I'll even show you how to animate it to make it even a little better. All right, cool All right, let's go through this step by step So I'm gonna delete everything here and we're gonna go through it front to back mandate Deleting everything starting from scratch. Okay first step is you go to your finder or your windows Explorer Wherever you have your footage and I'm gonna drag and drop this footage This is stock footage of just some guy playing the drums as you saw Drag and drop it into your project panel second step take the image and You're also going to drag and drop that into the project panel now when we go to the project panel We're gonna have both of these assets in here The next step is you want to left-click on the footage Drag and drop it into your timeline. That's gonna go ahead and load it up Now I'm only gonna want to say let's work with five seconds of footage. I'm gonna cut this a little shorter So I'm just gonna drag this to the left So we've got five seconds because if you go longer, it just takes more time to process it Okay, so we've got the footage we hit space bar and Bam, there he is doing his thing. I'll make it a little bigger so you can see it All right, the second step is you want to take the Image or the logo or in this case the watermark Drag and drop that on to the second track on top of V1 so on to V2 and You're gonna see that it went yellow here So that means it's a little bit processor heavy And you're gonna see a few things one that the image is too small because it's a small image and B It doesn't go through the end all the way to the end of the video so we're gonna solve those things right away first step take this top image and Slide it out to the end So now it goes all the way to the end the next step is you want to double click like I just did there on the Watermark that you're gonna be using and when you do that you're gonna get the bounding box like I have here now I'm going to expand the size of it So I'm pulling down on the on the bottom right corner. I'm also holding down shift so that it Expands proportionately so it doesn't go too high or too wide And boom that looks like a pretty good size I'm now going to left-click on it and I'm going to put it into place I'm gonna say that's watermark on the bottom right again You can watermark anywhere you want you can make it huge whatever works for you But for this tutorial we're gonna do bottom right, okay?  I take the Playhead moved to the back at the beginning and I hit space bar the watermark is in place That might be all you need to do and if that's the case your job is done However, some things you may want to consider is if you click on the watermark when I do that You want to go to your effect controls panel if you do not see it by default you should but if you don't You want to go down to window you make sure there's a check mark beside effect controls Go to your effect controls panel and in this place you may want to either reduce the opacity So let's say you don't want it to be too like way out there So maybe you want to go to something like 50% I don't know something like that, right? So it's not Super salient, so it sort of blurs into the background Fine that looks pretty cool, too, right?  I'm gonna go back to a hundred percent Secondly, you may want to animate your watermark now. This is like Bonus learning like this is this isn't gonna be on the test guys but if you want to do that make sure your playhead is at the start and What I'm gonna do is I am gonna go ahead and left click on The toggle animation I'm gonna rotate it So I'm gonna do a rotation animation just to show you what it would look like so left click on this Set the rotation is set to zero. I'm gonna move forward to about one second Let's say at one second. I'm gonna rotate it 25 degrees to the left. So I went to minus 25 And then when I hit two seconds, I'm gonna rotate it back to zero And then I'm just gonna show you really quickly here when I get to three seconds I'm gonna just move the time head up to three seconds. If you're not quite there we go I'm gonna move it to 25 to the right and Then at the four second mark. I'm gonna go back to zero just to show you a very quick animation for your Watermark pardon me. Who's my voice now? I've done it and you'll see here that you've got some keyframes, right? There we go So if we go back to the beginning and this is just extra marks guys not on the test and we hit spacebar Or we hit play here watch what happens We've got a very subtle watermark animation in your video that way, you know what there's no way in heck Anybody's gonna be able to take that out of the video. So the video is forever yours And if anybody uses it, you'll know it because your watermarks there guys. Thanks for watching this tutorial ton more stuff coming up Stay tuned


Good morning cyber traders happy Friday everybody on this 22nd of March has everybody doing good to see you happy Friday Donna Lewis Michael John everyone. All right. Good. Good. Good Army one. Hopefully everybody's ready to enjoy the weekend pouring over here in New York Looks like it's gonna be been like that last couple of days.  There's some good pre markets going on But before we get started guys and going through it Just remember you got about another week left to enjoy the on-site super early early bird special for the trader summit for Cybertrain University 10th or 10th to the 12th So remember we have it's three days regard two days actually a full training We're gonna have some great guest speakers. We're gonna have a soiree a blue carpet dinner So, you know, it's one of our most exclusives. We do it once a year So make sure you talk to the education advisor to get ready for that And also don't forget upcoming events starting April 4th. We got the cyber expo that's coming up So hopefully you get to see all you guys there, too And hopefully everybody everyone's been enjoying their classes, which by the way just want to bring it up Fran was just kind of made a comment in the trading room was kind of asking what is a shake and Fran listen, you just and this goes for everyone you guys have to understand This is not a treat a teaching room This is a trading room if you want to know what that stuff is you should be learning it in class If you're not a student you should be talking to an education advisor to find out how because it's not something You could just put in in one sentence a shake is a whole lesson of a class itself And now you could see how dangerous some of these shakes could be that this is one of the reasons Why people blow up their accounts. They just don't know how to handle a shake and that's where the issue comes in So hopefully you learned a lot from that friend and in the meantime guys Let's go over and talk about what happened yesterday before we go through our watch list There were a couple of decent stocks. We're gonna talk about the Levi's that went public Regarding that had a couple of little shakes going here, but looks like Levi's been doing good in pre-market Let's start off first with the SR and e-trade That one had a great push and you could see she's moving again this morning So she's kind of picking up where she left off She does got resistances in the after hours where it hit eight five eighty But the SR and e is going back on the watch list nice little move nice little pop right there A lot of you guys they're really well You could see how it had a nice move from 380 all the way to 460 and just kind of went up And then obviously bit its last-hour push, which is why we only trade part-time DSAN Another nice little move right there you could see this stock very slow-moving stock 480 to 550 Not the biggest mover, but she did move pretty nicely. She was okay The AMD was awesome yesterday that one you could see went from like 25 80 all the way to 28 bucks Reppin Brighton tell one o'clock you see 10 o'clock it went away to 26 60 and you know with with AMD you also had micron micron was just about the wrong one Mastercar well mastercard did pretty well I spelt the wrong wrong symbols micron was actually pretty good. It came out earnings micron You could see had a really nice pop right there right at the gate went from 41 50 all the way to 44 dollars You just got to remember guys that when we're trading these stocks right here regarding the These trends a lot of these stocks you got to be Remember we talk about levels what level are you are you a level one trader level two level three level four level five?  We just talked about The SRE and trade just basically made himself about 45 cents on that one. Good job. Give him a round of applause Good way to go. Oh, it's nice. You know, it's so nice to start before the mark even opens up being positive Can just calling out the MMP right now Right there nice little push right there a nice little movement of 55% You know, I didn't really had it on my watch list I kind of like had second thoughts on this but I'm gonna throw it on there Ken now because listen It did it doesn't have them doesn't have really the good pre-market As some of the other ones that we're gonna call out the MMP, but because it is gapped up We'll throw it on there But yeah, she's got this nice little trading range from like 320 to about 340 got major resistance levels there And if you look over here, you probably see and not really that many resistance You got a big buyer out here for 4800 at 337. That's a good sign. Look at a getting some um, you're also getting some whatchamacallit What is this right here gold, you know, we got yellows we got yellows going on Dark prints right there. Absolutely. All right, so let's go through some of the other watches Okay, so everyone's doing pretty well on this one. Listen. We said this yesterday. You can't go broke taking a profit SEL, you know, this one where in pre-market one from 235 all the way to 280 This actually was one of my big winners last week where it ran from three dollars to eight dollars Everyone did really well on it guys go back to your journals.  We just follow the money. This stock went from 18 all the way to 75 in an hour and a half I know a lot of you guys are trading it now Um making some nice little moves. Just be very careful with the stock right here the BPth because It is a level four stock. I know a lot of you're looking at like wow 21 to 25 You know, my god, if I had a thousand shares of that, you know, I'd be up about 4000 That's about a million dollars a year. Listen, there's only certain Certain traders here could trade the stock this stock has some nasty shakes And I think that's where fran was bringing up. He's she's asked me what's a shake fran You want to know what a shake is you did well about it being class All right, because if you got shaked out of this stock, this thing would take you to the cleaners All right, and they do it on every single stock It's one of the biggest lessons that we teach next to reading level three and everything else level three is just Learning level three is basically just first great stuff when you want to really start learning strategies You got to learn you got to learn the shakes Uh, what else we got h i bb That one's moving pretty nicely too. I called that one out a little bit earlier The only issue I have with this h i bb the spread was a little big once again One of the biggest things we teach you in uh In the first lesson of edge class. It's the first tea Tradeable is the stock tradeable got to look at the spread because I think it could crush you So we got that one see um c e n a uh at That one also getting destroyed Look at the name guys Pharmaceutical stock will we know about pharmaceutical stocks? They don't come back even though she's testing a major support levels right here Which you can see here in a big at uh at the end of this of december of last year I don't know maybe we are testing supports, but Down 54% I got that I mean that literally just showed absolutely no mercy But I want to keep an eye on it, you know it is supports Maybe a couple of days maybe you could turn into a swing trade. She did go from like a dollar 30 oi back to three bucks, you know, but uh, obviously whatever happened there didn't work out, but we'll we'll see leave eyes That one uh one public yesterday once again Minimum minimum There's a uh when you are trading a level when you're trading an IPO you have to be a level three trader period period, okay Everyone hears about these ipOs you got pinterest that's coming out. You got lyft.  Let me tell you you make money on an IPO Okay, that's the worst thing that could happen to you I remember teaching a student says I love IPOs.  It was this great lovely girl You know, she was doing great with IPOs. She took her 25 thousand dollar account to about 75 thousand And she's just she was just an IPO an out an IPO girl that just loved It's all she wanted to trade and sure enough She lost the 75 thousand and she ended up being in debt for another 50 Because she thought she knew how to trade IPOs. Trust me. We've traded IPOs I'd seen IPOs go from go from 25 to 150 right back down to 10 down to zero So just be careful IPOs You got to be a level three trader and I know there's a lot of them coming out But and you'll see us trade them in the room, but Try to get up to speed sre ends the other one we just called out earlier. I think that's about it China has 13 tech IPOs in the pipeline. Yeah, I know Wayne listen There's a lot of IPOs coming out, but you got to be very very care Remember you got to learn how to trade The smaller stocks before you could start trading them. Listen, there's 25,000 stocks out there. How about this?  Uh I am mp. I never heard of the stock. What does it do? Does it even matter? You know, who cares? I know people always scrutinize with you. What do you mean? You don't care because I only care about making money Just that's why we're all here. So focus on the prize and not on the symbol. All right Uh, Marybeth says I don't like china stocks. They have popped and dropped a lot Well, listen, we traded a lot of them right the inl That was a that was a china stock. Remember that went public. That was the uh company No, it was not a inl uh and uh Nio Right nio. That's what that was that china stock remember it went from five to 13. We did pretty well with that That's like china's uh tesla What else did we have that we did well on hoya? Hoya is another one remember that was a great ipo. Uh, I gotta change the calendar here on this one In a weekly so you remember this one ran from 20 to about 50. That was a great ipo did well on that one Uh, what else did we have we had a bunch of them that were with some good ipos out there But anyway, hey You can't fall in love with them The i l but I think the only one iq that was the other one. That's right. This was an ipo 10 to 22 iq is the other one Thanks a lot steve that one. We did actually I did really well on iq. We all did really well on iq But you know, listen, they would you got to look where to support levels I think uh, I think the only one that's really even even uh By do did really well Um, you know after finally popping this one's doing pretty well Tcc this one looks pretty good. Keep an eye on this one Put that on a watch list dc, uh cccl. Yep. That's another one Yep So anyway guys listen, you know, you know why all these china the china stocks have come into the united states Why don't we public here because remember that's still a communist country and we're not gonna hold it against them They just know that here in the united states people the money's very secure, you know being the united states That's why a lot of these companies going public here on the new york stock exchange and not on the on the shanghai exchange So, um, but anyway Regardless of what they are that's okay. Keep bringing them in and we'll trade them, you know Got a happy wife. Happy life. You know, that's why you know got to make money. All right guys So listen, there's your watch list Um, for some of you here. Hope you've been joining it so far students have been a great Month so far. I told you once after new years is over. This is where it's a hump months come in It's like the one of the best times of the year to trade so and don't forget you got classes that are coming up Remember you got to be in class. You got to learn the game But there's only a very small window of opportunity to make money And that's the first hour of the open which is obviously the pre-market included and the last hour of the close This is not a full-time job So but you can make it a full-time job But for some of us If you can make half as much and and and and have a you know, enjoy the rest of the a lot Enjoy the rest of the day. That's why a lot of us do it But you got to learn before you get on so everyone enjoy your weekend If I'll see you back at 2 30 some of you did pretty well so far in pre-market like I always say Don't worry about it. The stock market's not going to business. We'll see you back on Monday And you know what you could do that. Good luck everyone. Happy trading


Well, good afternoon everyone and welcome. I'm Barry Rabe. I'm a professor here at the Ford School and the director of the sponsor of today's event close-up the Center for Local State and Urban Policy. I'm very pleased to have all of you with us and very very delighted to be able to welcome our team of colleagues who've been working on this project for some time a Presentation that will be made by George Fulton and then we'll be transitioning toward Q&A issues Let me begin by introducing George who's certainly known to many of you and it's really played an Extraordinary role in a number of arenas including Illuminating a range of issues concerning the Michigan economy and the future of Michigan economies What are really a number of years a tremendous public service? George is the director of the research seminar in quantitative economics holds a number of other roles He joined today in Coauthorship of this report by Don Grimes who's the assistant the assistant director of the Center for Labor and Market Research They are two of three co-authors of a report which close-up is releasing today You're going to be hearing more about in a moment called transformation of America's metropolitan area economies This is such an interesting time I'd be thinking about the future challenges and issues of urban political economy if I understand it correctly This started initially as a project comparing Detroit and Pittsburgh a smaller direct comparison to very interesting Political economies rust belt systems that had shifted but has now become a much larger project with far more than Detroit and Pittsburgh In it, although both of those are there and really stretching over quite an extended period of time So this is a very very rich analysis We will turn to George for the overview and then allow For questions and answers we will follow our standard format Which is to invite you to submit questions on note cards that are being distributed or have been distributed We do this in large part to make sure that the voice is picked up for videotaping and the like so begin to think even now about questions that you would want to raise and my close-up colleague Tom Ivaka will be working with me to sort through some of those questions as they come in and also Tom will be also offering a couple of additional questions as well with that delighted to welcome George Fulton George Well, good afternoon, and thank you for that. Thank you for that introduction First I want to thank close-up for the its funding of this project and also for arranging You all hear me for arranging this this event today and also the the office of the Provost kicked in a few funds, and so I want to thank I thank them as well and Welcome So I first I want to Acknowledge my co-authors as as Barry already did first we have Don Grimes who's sitting right here and After I'm finished. He's going to answer all of your questions and Then the third author is you on Lee's zoo and he he was The technician in this paper and he has moved on to Ford Motor Company still there. I think right. Yes, okay, and so he's not not with us. He's not with us today I just I want to do also Thank Jackie Mary Murray for editing the paper that is on the working paper that's now on the the close-up website for all the layout and For these really pretty slides you're going to see today so the title of our report is Transformation of America's metropolitan area economies lessons from four decades and Before I before I go into that I wanted to show you a cartoon which I thought was relevant you see There it is Yeah I paid you enough I Yeah, so I spent the I spent the last hour trying to get this on on on this show. Well, but you know I'm technically not proficient here, but I thought it was relevant and show some guy that has just got clubbed over the head with a With with a a sledgehammer and the guy who's who slugged him is this real brutal guy with a shirt on that says January and the guy is just coming and ready to turn around the corner and he doesn't see a similar-looking guy carrying a sledgehammer that says February And I just wanted it it it made me it made me think that I really do need to thank you for coming today in this weather and Also, if this club E is a metro area is Getting clubbed in the prior period prologued to getting clubbed in the next period So we're we'll see we'll see in the presentation So just before I get into it I want to Tell you a little bit About me when I started my career I talked to a very esteemed colleague. It was not a colleague Actually was an academic at another place and he said to me, you know, I was trying to figure out what to do professionally and he said well You get what everyone gets one good idea in their life and whether you're successful or not depends on whether you Recognize it and cash in on it and so I'm still hoping I my one good idea is still coming but if if I did have one good idea, I guess it is to do this economic research on Metrop local and regional and state Economies and of course. I was advised then actually to not do that wasn't by Paul but and The three reasons were well, it doesn't have any cache a the national is much sexier to no one's ever going to care about regional stuff and Number three is you're going to spend your whole 80% of your career Dealing with data issues and constructing data well Only one of those three turned out to be true and that is the the data issue So I just want you to keep in mind today as We go through this that data issues were really a major issue in this study we spent months on it before we even looked at our first equation and It's why many of the concepts that might make sense to you and some of them that you recommended to me To explain economic success in these localities are not in our models because They didn't fit our strict measurement expectations, so I just want to I want to start start by that So in this study what we the questions we look at the questions of what leads metro Economies to function the way they do what makes some of them more successful than others and What policy handles if any can improve their profiles? and the primary tool we use is econometric modeling analysis and I will I think by and large make that accessible to everyone everyone here today So let's let's start By talking about what I think the innovations in this study are and then I will get into the results the first thing is that We extended the database for metro areas to 40 years 69 1969 to 2009 and that's much longer than is typical for small economic regions The data limitations in the stuff are so severe When you're analyzing these small economies that inferences on the effectiveness of economic drivers and policy Handles are often drawn from very narrow time intervals That will not reflect all the behavioral Relationships Outside the period so we said no we've got to go we've got to go with longer periods. That's one thing and so that means The data restrictions have to be have to be dealt with so we expended really a great effort to expand the time range of our data and This involved Ensuring that you had consistent metro area definitions over time because they change And we made sure that they were consistent We spent a lot of time in Paul's library looking up some of the old stuff and Copying numbers out of books and you might remember. That's how we did it back back then so then the second thing is We want to take advantage of the longer time period of available data to segment the estimation period Into sequential subintervals and I'm going to come back to that one in a minute Then we made a considerable investment in assembling new series for variables that we judge to be promising economic drivers For example We Individually hand-coded two million plus records on patents one by one and Almost as many on crime rates and those are just two of the variables So that was a that was a huge investment and you're always hoping that that'll pay off The fourth thing is we wanted to look at spatial differences among select regions of the country Okay, our things Do things look different in our region than they then they do in the nation? And we focused our our attention there on the rust belt, which we defined as the Midwest and Northeast regions of census regions, okay, and then due to my friend Paul Koran we We recognized that our models wouldn't fit all of the metro areas well What he called the tyranny of best practices when we assume that we estimate these things over 366 metro areas then assume that all of the favorable Statistical assumptions are met Random residuals and so on and so that it applies to all of them and He said well, why don't you? Why don't you see if that's if that's the case? So we did an analysis of the residuals to identify the metro areas that Showed the least confirmation to the general model. We found some interesting stuff. And so We'll we'll get into that in this talk Now just going back to the second point We estimated these equations for two Sequential 20-year subintervals. So we had 40 years of data to sequential 20-year subintervals and we wanted to ascertain What the changes in the what changes in the economic relationships could be identified between the earlier and the later periods? Well, while still avoiding The criticism of having intervals that are too too short. So we used our 20-year Our curiosity on this was motivated by observing how volatile These small economies were Over shorter segments of time That is any one decade is not prologue to the next decade Okay, so the guy that got clubbed on the head when he turned the corner isn't necessarily there got a guy waiting for him And that I can show you here on the next slide These are metro area rankings based on the change in personal income minus transfers per capita Okay, and I want you to note in particular the movements in the first two areas that are listed Bridgeport, Connecticut and Midland, Texas so over the whole period Bridgeport was first in the change in income But in 69 to 79 it was 73rd then it was first then it was second and then in the last decade list there It was 347 out of 366 Okay, another one is Midland, Texas 14 to 356 248 for Okay, and part of that of course is the is is is is the energy market and so If you look at the others There's more stability in a little more stability, but they still they still move around quite a bit so That's that's what we started with and then if you look at the paper This is all this analysis is all followed by a fairly extensive literature review In fact one of my colleagues that read the paper said there's too many pages on the literature review But and so so I'm not going to run through that I thought as a strategy here as I go right to Show you our estimating results and then when I'm done I can show you in a table how They compare with what the previous literature said to the extent that the variables were there Okay, so Let's start with the model. This is our general model specification We use two Dependent variables, which is change in why one was specifically real dollar change in Personal income minus transfers per capita and the second was the print percentage change in total metro area employment The independent variables that you see there are included at the beginning of each of these intervals and the dependent variable Measure this change over the time interval And so of course now the trick is to get proxy measures for each of those Variables that are in the parentheses In this equation and what I'm going to do is I take you through a few tables. You'll you'll you'll see what what those are So let's start and I'm not going to hit everything There's thousands of regressions in this project but Let's let's hit some let's hit some high points and so This is the this is the chart we designed So that I didn't put up all of these things with coefficients and standard errors and you can't even see them from the back So I hope this I hope this helps the way this chart is laid out is We have the variables in the first column and then in the next two columns are the results for our two income models early period and late period later period and then the last two columns are the are the early and later periods for employment and the sign of the coefficient is in in the cells and The purple cells indicate that it was significant at the five percent level using the using p values and The asterisk indicates that that particular variable didn't go in that equation Okay So if we look at the first line, which is the initial income levels You can see that they have a positive effect on the change in per capita income over the period but in fact the the Effect is dwindled over the two periods. Not only is it less significant the coefficient is is smaller Okay, so So that's an example. I think a more interesting one here is the initial population size in one of these areas in these areas and You can see that its effect on on per capita income is positive we interpret that as agglomeration economies in large areas But this effect seems to be dwindling over time as firms have a lessening need to congregate for production efficiencies So Let's go to the industry structure. I'm not going to do all of these in working in Many many in several decades. Let's say of working on These regional things. I think the most fundamental answer most fundamental thing that I've learned is the makeup The industry makeup of local economies is integral to their economic success patterns Okay, and for instance, you see the mind the mining share their variable and you can see that the In the income equation the signs flip And that reflects the vagaries of the oil market, so you can see if you just Estimated a decade you could get some misleading results on that If you look at manufacturing and durables collectively you can see that that is a negative sign Which shows the the negative impact of Manufacturing sector over time and I think one of the actually in many ways one of the best studies in the literature on this Just looked at one decade, which was the 1990s and they found a positive sign on the share of manufacturing because that decade happened to have a little resurgence in manufacturing and they made a deal of that and I think that Was misleading Okay Let's see what else we have here, okay. I'm just a couple others It's interesting that they share of finance and insurance is always positive always Significant and a few other studies that use that variable. It's always positive and it's always significant And I think that reflects the higher value service sector orientation there and then of course you see the the growing and influence of health services You know whether that's going to continue is is is a topic for discussion But we are the only ones that I know in this literature that actually looked at that particular industry Okay now I Think the demographic results Were in our results were largely puzzling or uninteresting, you know, I'd like to just click past this one The income employment results for the foreign born population Actually suggest a disproportionate numbers of lower paid Workers in this group, although it's not significant. What's interesting by the way in the rust belt. It's the other way around Higher paid The most puzzling result in this entire study was the positive and a Significant effect in the later pair a period on the share of the population in poverty So more people in poverty in the initial period led led to Higher growth and income higher employment So, you know, this is research. We don't hide anything here This could be a statistical problem And we realize that although we we hashed this around quite a bit If we're gonna take if we're gonna say, yeah That's the correct result and we're gonna try and think of something which we you know, we can think of something We can pause it that higher poverty levels initially Prompted more activity in programs to assist the poor Having said that I think that seems to be a bit of a bit of a stretch so Then we have the share of the population 65 or older and it's in there to represent the lower labor force participation rates of that cohort It wasn't a factor in our model and by the way It wasn't a factor in any study that I could find that looked at it. So I guess I guess Well, I won't make a comment about older people Especially especially I'm about to join them. Yeah So now let's look at the innovative environment here And an innovative environment is increasingly increasingly viewed to be an important driver of economic growth The argument is that scientific development promotes economic development So in terms of our new measures of patents granted two groups that stand out is our IT patents and Industrial patents and the impact of the industrial patents Strengthens in the later period so that was that was encouraging and We spent considerable effort on putting that series together So we were glad that it that at least it seems to have some some benefit to the model now the most disappointing result in our work was this research expenditures It's actually university research expenditures I'm glad you're still not the provost Now do I believe this? No, I Think the fact that we have educational attainment which you'll see in a minute and The granting of patents into our equations could be confounding the results here The interesting thing to me is other studies that have looked at this with other combinations of variable Have also found mixed results But it's really difficult to believe that the research and Technology creation functions of universities if they're isolated and measured properly Do not result in enhanced regional economic development that otherwise would not occur now Of course in a place like the world-famous University of Michigan a lot of these benefits are are not just contained in washtenaw County So they they spill out Okay Here's I think an interesting an interesting slide these are the variables related to policy decisions Gerald R. Ford school we should Take a look at this. I Think the two most compelling variables in this set are educational attainment and crime Educational attainment is measured by the share of the population with a bachelor's degree or higher Now we tried other measures I know it's not all about just bachelor's degrees and that but this one really this one really is the one That that stood out our results are consistently positive But only see and by the way in other studies. They're always positive But in our study only significant for income And I think that's consistent with the general rationale Which I will put out here that more educated reason regions are becoming more economically successful because they're becoming more productive and And productivity of course is a double-edged sword Now the crime rate Is one of the strongest variables in our model? And in a way, I'm I'm happy for as from a research point of view because we spent an enormous amount of time putting this together And I think it emphasizes the importance of deterring crime Especially considering that the effect on income was actually larger over time Now we thought well Maybe we also had we put together series on violent crime as well and property crime so we thought well, maybe we should test out the violent crime and see if that that's that's even more significant and We just ended up settling on on total crime As a measure for various reasons, which I won't get into now But we did we did certainly look at violent crime Now we get into now we get into the more controversial variables State and local government tax as a percent of personal income and so this is an age age old problem in State legislatures and that whenever I we go and do testimony twice a year It always comes up and Half of the people say we should be doing more of this and the other half say we shouldn't be doing this And I say I don't have any information on it It turns out that the effect of state and local taxes on the change in income employment was usually negative That's true of other studies, too But I have to say talking to people that are that specialize in this which obviously we don't That this Representing this is really fraught with complications very complicated to put a really good tax burden Number together, so I do have to I do have to give you that caveat but that's That I will Okay, we'll skip over the next one No, we won't We included in our equations a dummy variable for location in a right to work state So is the metro area in a right to work state if the metro area across border estate boundaries We assigned it to the state Where its major city resided, so we had to do a lot of fiddling So This is a dummy variable with a value of one Representing the presence of the legislation so for legislations there. It's one You can see that this variable was usually positive and It was significant for both income and employment in the later period we interpret this variable more generally as Representing a more general business friendly environment rather than more narrowly as a union avoidance measure and Indeed if if if you're going to argue it as a union avoidance measure you might expect a negative sign on on income and A positive sign employment, which is the argument as I understand it I think on this variable this one this one is one It's so politically charged and one really needs to be very careful about about this because It really does take some time after the enactment of the legislation I think for the effect to actually be reflected in results like this So if one we're going to dig deeper here, and we didn't but if one were to go dig deeper here I think it makes some sense to go back and look at the tenure of this legislation over states and Take a look at the states that have had the legislation in place for longer periods of time and see Try to determine What effect those have on Economic outcomes because these things don't help happen instantly our right to work state. Okay next day you know everything's positive or Employment is positive incomes negative or you know all of the arguments So I just I I leave with those thoughts on it The other thing of course that's that's become Increasingly important is the economy is becoming more global and The connectivity to the outside world has become more important and we measure that by air traffic we looked at freight as well didn't work as well and We find a positive and significant effect on employment in both periods there and We'll come back to that when we look at the rest of the literature. Okay. How about amenities? I Didn't think they were so funny the Problem is and we chatted with Barry and Tom about this before we we came here is that you have Person-made amenities and You have natural amenities a personal Person-made amenities are very difficult to measure Especially if you're trying to get a measure for 40 years over 366 metro areas that's consistent and good Now the But that hasn't stopped people of course from trying to look at what they call natural amenities and Until our study I could find no study that did anything different than putting in the July temperature Or the January temperature or both temperatures in our case We we got more mileage out of looking at the the difference in the two and arguing that More moderate temperatures are are are attractive and you can see all the negative signs but There is a positive sign for employment at the end and I think That's just in My view Too narrow a measure of climate, but I understand why people do it because you can get it Well, we stumbled across this natural amenities scale It's published by the US Department of Agriculture and it looks at natural amenities In six different dimensions Okay, so do you have water? You have mountains are you are you're summer you're summer temperatures You're you know and and it had had lots of lots of things that I think we would all agree are attractive and so we put that in and indeed It it worked in the employment equation It was positive positive amenities in the earlier period and positive but not significant in the second period and One wonders perhaps If those kind of natural amenities are becoming less important for people That are our businesses that are seeking locations, but that's That I don't know So let me see now what I want to do quickly is How do we line up with previous studies? So I kind of summarize these pages and pages of literature review So what what I did here is put a brief summary table together this isn't in the paper It is non-rigorous But I thought it would be helpful in the discussion in the first column after the variables P stands for expected positive sign and for expected negative sign and the question mark for It the fact that it was indeterminate in the in the Apriori in the literature Okay, the second column indicates whether the variable was usually significant And a stands for no presence in the literature or very little presence in in the literature Other than our study the next column is Indicates whether the variables in our study were significant Earlier means earlier period later means later period usually means usually and and then then I have some notes so For the initial period population size for for example In a time interval our results are usually significant It indicates a positive effect on employment change as I told you before that represents agglomeration economies and a negative effect on employment Which suggests that larger metro areas are more prone to declining employment over time Although that seems to be weakening over time as well so let's go to the structure and I think all I want to I Point out here is that the The share of health services, which isn't in any other studies Appears to becoming more prominent over time Often variables besides Manufacturing and finance matter and then these studies that's just about all you get and Of course, I've talked about mining Is it where's mining? Oops that's in other and Often these share variables are significant so we have Mining in for the energy metropolitan areas we have military For those that have large military bases and those those all matter in those areas okay What about I? Think what stands out in the next slide is the contribution of our patent variables to represent innovation especially for IT and Industrial patents and as I said before that's heartening because we spent so much time putting those together Now we go to the policy related variables My first impression here is that among these policy related variables. There are a lot of consistencies in Of the findings among previous studies and our study And I think the second thing is the largest contribution I think here for our study is the crime rate variable which is strong in our model and not present in other models And again that was heartening for us because we spent a lot of time putting that one together and Then off to the amenities again We have our more comprehensive measure Then the traditional measure of temperature with the national amenities in scale from the Agricultural Department and You can see how we can no one else has that one and And then there was one study because I thought a question would come up. What about corruption? I don't know why that would enter your mind about local economies But someone thought about this and so they somehow had a measure for it and They found that there was a week. There was a there was a negative relationship between corruption and economic Outcomes but it was not significant And so I'm not I'm not touting that I'm just I'm just saying that's that's that's what the study said Okay Now if the other thing one other thing we did as I told you is we repeated this entire Estimation effort for two sub regions of the country one is the rust belt Which we defined as being made up of the Midwest and Northeast census regions of the nation and then the rest of the country And our purpose there was to explore whether there were important spatial differences in our results fear not I'm not going to step through all of these results. They're all in the paper Instead, I'm just going to make a few summary observations First we did find that there were sufficient differences in national and regional results that there is benefit in estimating a regional equations when the region is a primary interest and Then we also found that there were effects of some policy related variables that were consistent across geographies The consistent effects for income growth across these geographies were supporting education and deterring crime and The consistent effects for employment growth were providing an innovative environment for industry Enhancing airport connectivity and being good stewards of the natural environment Okay, so now now we go to the Paul inspired analysis and so What we did next is investigate the pattern of the residuals generated by our four National models in other words the earlier and later periods for the change in both real per capita income and the change in employment and we did this for two reasons No, we did it for three. The first one was to try to further validate the model Which a lot of people don't do and the second was of course to identify Those metro areas that did not conform well to the fit of the general model And the third reason is Paul told us to do it. So, you know, that's what we do We don't know of any other study of this genre that did this Which in a way implies that these models those models there are Fought to be well behaved with random errors well When you're estimating the economic behavior of hundreds of metro areas across the country With the severe data Limitations that are inherent it really is unlikely that the models will be so well behaved So the question is where are the outliers? What didn't work so well and Is there any organized pattern to these outliers that can give us a clue of what we might be missing? Okay So I'll show you the graphical residual analysis of all four models But I'm really just going to focus on no I'm just going to focus on on the last one In all of them the residuals are plotted against the estimated change in the relevant dependent variable And the circled numbers you see in that in the graph are Associated with the key that lines up the outliers. These are more than two standard deviations from the mean and The then the the legend gives you the metro area names, and I'll show you just one example shortly Okay, got to say a technical thing The residuals for the 366 metro areas are studentized Which means the residual value is divided by an estimate of the standard deviation a Standard technique in the detection of outliers, so I'm sure I preempted lots of questions there a General observation is that the model fits the income change variables Better than the employment change variables at least in the sense that there appears to be no systematic pattern in the outliers in the in both income models in both income models the outlier residuals are evenly distributed in Sign and there are no clear geographic patterns Okay, here's the Here's the plot of the residuals for the later period in the in change in income And now I'll move to employment and this is this is the results for the employment change in the earlier period And these are a little different story, which makes them more interesting But also introduces more concerns We find two tendencies in these employment models first most of the outliers are positive Meaning that those metro areas are outperforming in employment what the models understand and Second most of the outliers are found in the south and west regions of the country So how come what's going on here? What are we missing in these areas? So I'm going to go to the later period, which is the one I'm going to look at a little here all 10 of the outliers as I defined it That we identified all 10 are are located in the south and west regions of the country and so Here they are Look at number one If someone wants to know what's going on in St. George, Utah, we have an expert in the room and so I'll pass that question on to him Now we didn't in the study try to uncover all that was going on here, okay at some point you got stopped We see that as a topic for future research But we said let's try to make an initial pass At trying to account for some of the strong employment growth in these areas That isn't being picked up adequately in our in our model so here we hypothesized that this rapid growth could be due in part to lesser geographic or legal restrictions on growth in these areas and Here are the data that we came up for these for those measures Okay It turns out that there are data available for 11 of the 21 outlier regions that we identified in either employment model The land use variable that's in the third column is a Z score Where smaller values including negative values represent looser regulation? So this is stringency of land use Regulations and we expected if it was loose that was more opportunity to grow the other column Is the land availability? Variable, and that's the percentage of land divided by a hundred if you like that's accessible to develop So you can't develop wetlands according to this measure. You can't develop land that's on steep slopes and So that's those are those are ruled out okay It turns out that these hot the those hype these hypotheses are fairly consistent for the seven areas that you see on this first slide They basically have loose regulations and available land Okay Miami at the bottom has a negative residual But it is appropriately combined with strong regulations and little available land Now the hypothesis works less well for the four areas on this for the four areas that are on this slide particularly the last two Okay, so we're only getting part of the way to understanding this One other possibility of course in these models is that we're dealing with outside or Exogenous shocks that are fairly Significant in these regions and you wouldn't expect them to be in the models for example in Laredo, Texas Which is a border is a town right on the board Mexican border? The introduction of NAFTA in the 90s gave this area a significant shift in the arm Another example would be the introduction of casino gaming in the 1970s Gave quite a boost to Atlantic City, New Jersey. Remember these are relative. We're not coming up the nation here These are relatively small areas and I can give you other examples So some of the large misses might not be due to internal modeling shortcomings And I'll just leave it at that Okay, so Let me After talking away here, let me consolidate What I think some of our main findings are this is the point to pay attention if you haven't been The strongest indicators of the well-being of a metro area, which are also found in previous studies are The initial conditions in the area the industry structure Educational attainment Business-friendly environment and airport connectivity We added the crime rate the innovative environment represented by industrial and IT patents and amenities associated with the natural environment The other thing we wanted to stress here is the impact of economic drivers can change over time That was the whole idea of doing these segments and following them through and See if all of these are at least intuitively pleasing to you and these were all that we pulled out of the model the shrinking effect of agglomeration economies does Consolidation for production is it as important as it used to be The growing influence of health services The increasing importance of industrial innovation The expanding negative impact of crime on regional income Those all rest well with me on the research method a couple of points I think estimation over longer time periods is important if you're going to interpret these Coefficients correctly More and better measures of local economies are called for And It's important to identify metro areas that are outliers to the estimates okay, finally Does public policy matter on this stuff? It's sort of the bottom line, right? and I'm standing here at the Gerald Ford School of Public Policy and Going to give my opinion on that I I've been coached prior to know I haven't They said say what you want some people say no Economic success and here's one two arguments economic success rests with decisions made by individual firms based on their products Process and location decisions reflecting personal preferences of company leadership Okay Second thing the second one is right out of the literature urban growth is largely based on idiosyncrasy fate and history What's our view Our view is that although many of the drivers of metro area economies have longer time or Horizons to affect change There is an opportunity to move the quantities onto a more favorable Long-term growth path with sensible policy induced change. What are some of these sensible things? That's how I'll close on such as educational attainment business-friendly environment airport connectivity crime deterrence innovative environment and amenities associated with the Industrial environment so I guess my conclusion is all of us in this room are relevant We're all validated so good night and good luck Don and Barry will now take your questions. Thank you George for saying what you wanted to say Don if you'll come up here for the panel And I'm delighted to welcome my close-up colleague Tom Ivaco to share a series of questions Many of which you've emerged here from the audience Tom. I'm not trying to dodge questions, but We're we're delighted to have discussion as well as just hitting us with questions That sounds good. Yeah, if there are any follow-ups as I put the questions to them, please feel free I just want to point out just so you all know the microphone here is not for the room That's actually just to pick up for the video So and first of all, thank you both very much. This was really almost a tour de force of Economic research the data set that you put together Was just an enormous amount of work and I think we'll go to very good use in the future as well So the first question is This is a complex study. This is this is kind of the big question here I think but you guys are the economic forecasters So can you speculate a little bit and tell us what you think? The implications are really for future growth in Southeast Michigan and Detroit over the next 20 years or so Yeah, okay, I'm gonna answer that you're on I think actually if you looked at the fact that everything sort of shifted every decade and That maybe we're entering a period that the growth in the Detroit area is going to be a little more positive than people Expect all I can think about is back when we were presenting our long-run forecast to SEM cog in the late 1990s we had a forecast for their performance for looking out 30 years, I think and Our results were a lot less favorable than what your Members expected they were thinking that the economy was continued to boom They had just gone through the 1990s Which were missed where the metro area of Detroit was in the top quartile of all metro areas in the country The unemployment rate was extremely low They thought things were just going to continue and when we presented results that were I mean continuing to grow But a lot slower pace than they expected they were unhappy And they thought we were wrong of course in the next 10 years things turned out to be even worse than we were expecting and Then a few years ago. We were presenting another long-run forecast revised into the SEM cog region and we had growth turning around in 2000 and starting when we were presenting us in 2010 2011 and everybody was sitting there thinking now We're going to go through another future that's been just like the last 10 years And of course as things have turned out Things have gotten better So I'm a little bit more optimistic I think things people have to realize take this longer term perspective as George emphasized That look at what's happened over 20 or 30 or 40 years and not look at sort of something that's going to happen over the last 10 Which can be misleading so I think that the overall things are going to look a little better for the next 10 years Then they have of course that's sort of maybe we get to the 1920s and maybe we'll get into a down cycle again But it does look like things are going to be doing better than the 2000s Yeah, I think I think our mindsets tend to be autoregressive. I remember A conference dinner In the late 90s and we had a speaker and he said the business cycle was dead and he took on everyone in the room and he was quite adamant about it and Well, you know the late 90s and then Now we move forward to after the first decade of the aughts and When we were turning out forecasts that's said Economies turning around in Michigan. We got trashed How can that be was terrible and eight was terrible and nine How can it be better? And so I think Just just to elaborate on Don's point Also, it's the only question. We know how to answer. So we're just dragging it on a bit Well, then I'm gonna throw a tough question at you Technical question regarding state and local taxes in the model Do you also control for state and local expenditures? One would expect the partial relationship To be negative for tax but positive for spending Yeah, we we don't have that in the model, but that is that is certainly a good point You know, obviously there are people that argue and I think argue correctly that Firms and people are attracted to places that are going to offer them good services and Some of those services have to be paid for by tax revenues. And so I think that's So so I I think that is The the the other the other side of the other side of that coin and that may explain why the Tax variable is not significant because obviously some of that also creates a positive effect through this government expenditure And so that essentially All other things equal maybe lower taxes But if lower taxes pay for lower services, then you don't get as big an effect as some of the theoretical I mean theoretically it should have a negative effect But you have to control for everything else, which means what do you do with the with the money? Or if you don't have the money what you don't do with it The study found somewhat of a mixed impact from natural amenities such as bodies of water and so on as you discussed In terms of job growth in the rust belt The question is for Michigan given given the growing importance of water in the future Do you think that the Great Lakes will have a larger impact in the future than perhaps? They've had in the past in terms of economic growth for the state and if so Are there any arguments there for more policy action to protect the lakes now since I didn't put the rust belt questions out That question must come from someone that has read the report I Think I think the and then Don can follow me. I think I think the Great Lakes are one of our biggest assets for our biggest natural asset, I think and I think in in the future. It's going to be even a more significant asset As long as we don't trash it We've got to be good stewards here, I think that the natural amenities are one of The comparisons you have to take outside the region especially the Great Lakes That's essentially remember that was a regression of the Great Lakes in the Northeast Midwest region So a lot of state you know states Are have the natural amenity of the Great Lakes as part of it So that's where you want to look the natural amenities or something where you're trying to compare a broader geography And I think the other thing going back to your actually your first question. I Think what's also Important is to look at how the coefficients are likely to become more significant over time You could sort of see some things that were trending as George said upward that were more important in the more recent period and lesson Less important than in the earlier period. So there's some Possibility that that actually represents sort of this this change so Looking again going back to the next 20 years agglomeration seems to matter less For example, crime seemed to matter More in an adverse effect Education attainment seemed to matter more in the in the current period that seem to be picking up a fact in other words So I think that's the other question is try to identify that the trends in what variables are gonna matter more for the next 20 years We continue to answer that question to avoid To what extent is The right to work dummy variable picking up on how poor the south was relative to the north In the late 1960s and how the wealth gap has narrowed over time perhaps due to factors besides right to work I think that's true To some extent because you go back to 1929 and you see a narrowing of the of the Income gap by states I think the other aspect was that the industrial structure in the southern states particularly benefited in the latter period You know the expansion of military activity for example the presence of those military bases Which were helping some of those most of military bases are located in the south and of course that helped oil production At least through 2009 tended to be located in southern states Loma, Texas So I think there was some Louisiana Some tendency for the south to benefit from some of the industrial structure issues in the Especially in the second latter period Yeah, I'm not I'm yeah, okay. I'm I'm not as sure I guess I just point out ultimately it is a dummy variable And so it's it's not a find it's not a fine-tuned. It's not a fine-tuned measure So we really it was important for us in this study You know we both have preconceived philosophical positions Some of them are not always the same actually But we really wanted to come in here and ask what What the data told us And and I think we stuck I think we stuck very closely to that. So I just want to add that So we're not advocating a position The next question is does richard florida have anything valid and significant to say and if so then what? I Forgotten about richard florida Um We think so but we can't prove it because a lot of his stuff would deal with the sort of the man-made amenities issues And we couldn't we couldn't construct sort of a consistent man-made amenity metric Going back in in in time over metro areas I think he's also looking at a at an issue of the of cities versus sort of suburbs He's looking at specifically at cities as opposed to the The metro area and we were of course using metro areas as opposed to central cities I think that's an important measure to try to put together though. I think that's I i'm really very intrigued by that But the thought of spending four more months constructing data at the moment Has reduced my motivation, but I think it's a if if someone here wants to do that That's cool. Let me know Just to follow up on that question It's one thing that we discussed a little bit Close up just recently put out a report from the michigan public policy survey That found that local governments around the state of michigan are increasingly Pursuing placemaking which is fairly along the lines of what richard florida discusses creating Places where people want to live work and play with man-made amenities, whether it's nightlife or art museums and theaters and so on Uh Can you speculate at all what you think if michigan is kind of limited in the natural amenities that we have in terms of days of Sunshine in the winter whether man-made amenities may be able to uh close some of the gap Well, just to remember one thing about the man-made amenities There's a lot of people who are reading richard florida's book ground country in his papers and so it's going to be If they matter and if everybody is trying to You know follow his prescription of creating this place It that the you're not going to gain. This is this is a zero-sum game in some sense. And so You know the problem is essentially if you create Uh a more positive environment in in michigan But so does uh, but that also happens in in illinois and chicago or Seattle or boston, you know, you're not going to gain anything. It's not going to show up in our data So I mean in that case it would be the counterfactual as you'd lose more I guess you'd lose ground So that's the problem with trying to if everybody follows the same policy prescription Um, you don't uh, you're not going to change the results. Well, I do think that higher amenity areas Can experience faster growth and I think uh a number of them have But I think there has to be some level of value-added Development that's required to to realize that growth I don't think people are going There's a great flow to places that are Just nice I'm turning to you now because you're the amenity person. So two quick comments on that. Yes, please One is a lot to speak for Um, one is you've found strong evidence of one which is yes Yes, prime is and you've got the right sign. You got the right sign. Yes, those variables. Yes, that seems right Yeah, it was very significant true for crime and can in principle be true for ballet Yes The other is with amenities They have opposite effects or they can't have opposite effects on On income and employment. So you'd expect nice places to actually have lower wages Because people are permanently happy to be there And therefore and and that makes them attractive For more employment and that seemed to mean it'd be also close to consistent with the amenity variable Yeah, we had now we're not at the level of granularity. Yeah, which are Florida amenities But yeah, they can't make a difference that there aren't compensating wage differentials for nice places I think that's pretty well established. Yeah Thank you this Follows up Chicago Is certainly one of the draws for michigan youth graduating with BA degrees This may be out of your Your area though, but let me just throw it What five things or let's say what three things would you recommend to rama manuel that he'd do to improve chicago's economy? Well don lived in chicago, so I'll toss that one to him first. It is really cold in chicago In the winter Yeah, I'd do something about that Yeah, the uh, whether there is is uh, not so nice. Um, it's also very expensive.  So it's a good place to visit Expensive place to live. What was that? Repeat that again. What five things? What two things including whether yeah around manual can do for chicago Can he do that though? What make it a less expensive place to live Yeah, the other thing that may be in and I it's not in our paper Transit may matter actually Mass transit stuff may matter. There's some work that sort of indicates that there may be more benefit to that in terms of attracting young people More than ballet Don't attend The study found a positive impact on employment growth based on the economy share of agriculture given that Michigan agriculture is a strength in michigan Are there any policy implications statewide? I know the data is uh, is not state based But this is a strength for the state of michigan Do these findings say anything to policy makers about the agricultural industry? If you ask me and want to go to lancing and do testimony to the legislature twice a year And have been doing this since 1991 if there is one question we get asked every time It's about agriculture purportedly the second largest Industry in the state and I just remember your quote is one of the second largest industries in the state A lot. Yes healthcare and yes, we could yeah, I know uh You answered it Well, the the you know the the question You know agriculture is important. It's actually done Well in the state of michigan It's probably going to affect smaller metro areas more than suburban detroit metro area Um, you know, what are the how do you enhance agriculture? That I don't know I remember we're talking about urban areas here too. And I know you know that tom But yeah, but it also depends on what you define as agriculture. Yes. I mean, is it farming? Is it urban farming or or uh, is it like food processing which is in non-durables Manufacturing we we do pretty well in food processing in this in this state So I you know part of the problem of answering these agriculture problems and questions that I never had a very good definition of it I mean some people say people who shelve groceries in grocery stores are agricultural workers And I don't strike me that they're agricultural workers, but Anyway, I'm going far afield. Sorry Uh, the last question is about immigration. Uh, some previous studies have found that uh immigrants tend to um Be more entrepreneurial than native born, um, americans Uh, but this study, uh, found a mixed impact or um Less of an impact in terms of employment growth from the foreign share Share of the population foreign bond Governor snider certainly is um making a big push for immigration in detroit Does your study say anything to state policymakers about trying to boost immigrants to michigan governor sniders talking about Called skilled immigrants It's going to be higher paid immigrants And that's what he's trying to attract You know people are worried that they uh This is more competition for jobs here I will say, um That in our long-term forecasting It's really very clear to us That there are going to be fairly significant labor shortages In in the economies that make up the u.s And I would not be surprised if at some point Uh these regions got very competitive to try to get these skilled immigrants um How you do that I'm a canadian I dealt with the immigration authorities and uh Boy, he's got to have some tricks up his sleeve to To deal with those people. They almost booted me out You know says Anyway, don you have some thoughts on that. I know, you know, uh This is just all immigrants are the same and the one thing I've seen in other studies with with data is that there's actually A wide variation in the educational attainment of of immigrants in in michigan and in a metro detroit area Immigrants are actually much better educated the foreign born are much better educated than the native born population In some of your southwestern states where a lot more more than mexican migration The undocumented mexican migration comes in you get a less educated Workforce and has a that I think is where you begin to see the negative effect on the income Side and if you saw the results for the great lakes area, you actually saw a positive effect on the per capita income Yeah, and I think that that's Yeah, I think it's a You could tie immigration better to the per capita income stuff than you can to the employment Numbers In their mix of educational attainment and and how they affect the weight average wage in the community than than on the employment stuff So I think that was just sort of a bad bad fit on the employment I think we have time for maybe one last question if we have uh anyone from the audience They want to go home Or eat You know, there's a lot of compounds that start to occur in terms of migration to the south Which was the reversal of maybe migration to the north You know And industrialization in the United States or the migration west But if I look at cities in terms of what really drove them And I don't know if your study really addresses it. Is there It's almost like there's sometimes there's maybe an individual that may Sort of create that city in the sense of detroit Henry Ford But you can look out into seattle and say maybe bill gates Yes, or you can look into so it's almost one person that creates an incredible Economic driver, which then spills out and the rest of the stuff No, it's certainly true in the early 1900s and we could list six others some of them in michigan That were recreated because of that I don't I don't see that as much as much now But no our research didn't didn't really address that Yeah, I don't know who's the guy in st. George utah It's driving your country. They're the outlier on our employment There's one person in this room. I know stayed at the hotel in st. George utah, so I know I know I know um, I want to thank tom Don and George and it also invite all of you to join us out in the great hall for a reception where you can talk about st George utah or anything that you like Before closing would only note that Remind you that the full paper is indeed posted on the close-up website Which gives you a chance to follow this conversation and look directly at that The information on that is on the back of your program With that would note that a preview of coming attractions There'll be two major events that the center will be sponsoring in march One is it was about this time last year Then we work with colleagues in the in urban planning To launch a book called the city after abandonment Which raised questions very pertinent to certainly detroit But a number of the jurisdictions of when you have population economic contraction What happens with land one issue that emerged from that was discussion of youngstown ohia Which many have held up as a possible model for detroit We'll actually have a panel conversation that we'll build on what we did last winter And talk about lessons from the youngstown experience Then we'll also be joined in late march march 25th I believe by kevin or emergency manager for the city of detroit Well, who'll be taking a break from his very busy schedule And joining us in that case not here in wild hall, but in the michigan union ballroom So that's a preview of things to come before we close and head out for refreshment Please join me in thanking our panelists


Well, I think Yuri's greatest challenge is how to turn over management and responsibility for the Institute to the Asian countries that are the primary beneficiaries. There are hundreds of millions of people in Asia who are still in need of the benefits of new technology and higher productivity, but there are also hundreds of millions in other places in the world who are a lot worth shape. Rice research is at a high level of development in Asia. This is something that Asian countries should take responsibility for. If they don't feel like there's any value to them of having a regional research institute, then I personally don't think the rest of the world should be supporting the whole thing. So that's the biggest challenge, I think.