Good afternoon everyone, and we'd like to welcome you to the webinar hosted by CPA, the Association for Contract Packagers and Manufacturers, and today we're highlighting the exciting story of automation. Today's session is titled, Packaging with Cobots a Success Story. I am Ron Pubek, the Mansion Director of CPA, and I'd like to just walk you through very briefly who CPA is. Ben, would you send the next slide please? Thank you. The CPA has been the voice for contract packages and manufacturers since 1992. We engage in providing knowledge and expertise within the contact packaging industry, and we try to raise the profiles of contract packages and their capabilities. Next slide please. Okay, just a little bit of statistics. We have an RFQ tool, request for quotation tool on our websites for anybody to use if you need help. We're happy to help you. You can lose at our member directory. We have a lot of networking opportunities up to 10 events a year. We do have a lot of training opportunities for folks. And also, just to let you know, we're growing at a rate of actually 12.2% for the last five years as an industry. Next slide please. So we invite you to join us one way or the other.  If you are a brand or an entrepreneur, we invite you to come and visit us at PAC Expo. You can come to our annual meetings, things like that. And it's just a few of the things we have as member benefits. But again, thank you so much for attending today. All right. Ben, next slide please.  If you have any questions during the webinar, you'd like to get an answer, please use the question box, type it in. We'll get as many as we can. This webinar is being recorded and will be posted on CPA website at contractpackaging.org within a few days. At the end of the webinar, you will see a link for a very brief survey. Please provide us your feedback. It's terribly important to have that. So a little bit today, I would like to introduce our presenter. I'm going to let him do some of his own introduction. Ben Courtwright is a sales development manager at Pacific Northwest Universal Robots. Ben is a subject matter expert on food and beverage and several other areas and also our subject matter expert on collaborative robots. Ben, please take it away. Thank you, Ron. And thank you everyone for attending the webinar today. I'm very excited to present this awesome case story out of Oregon with Derek. And I want to thank you, Ron, and CPA for having me here. Very excited. So we'll kick things off here. So the agenda today, we're going to briefly touch on market trends that are driving changes in packaging, who Derek is and the stakeholders involved that made this project a success, how they implemented their collaborative robots, the reasons they were successful in the return on investment Derek has seen since implementing their collaborative robots, and then where Derek is looking next in their facility for collaborative robots. About myself, Ron touched on this a little bit. I'm the sales development manager responsible for the Pacific Northwest with Universal Robots. I currently reside in Portland, Oregon. For anybody who hasn't been there, terrible place to live. Do not move there. Do not recommend it. I've been with you since early January 2018. So coming up on two years here and then major industries that I work in in the Northwest are aerospace electronics and food and beverage. So we're going to cover here the challenges of today's market environment, product variation and customization into high mix low volume. I'm sure a lot of you are seeing this from your customers or in your own facility is that you're going from a small quantity of skews to a large quantity of skews with lower volumes, which requires less fixed automation and more flexible automation. Electronics, shorter product life cycles. Everybody wants the newest iPhone. So in that trend is kind of went into other areas as well within electronics and even other industries. So always having the newest goods and everybody wants the newest thing on trend. Precision and consistency as products get smaller, you need to be more precise. You need to have the products packed in the same place, same orientation every single time with very little variation in your box or on your palette. So having a system in place that can achieve that is crucial nowadays. Customer delivery or customer trends, faster delivery. So everybody wants everything yesterday. You need to be able to scale up for large scale orders and have automation in place that can address those challenges, especially within this hiring environment, which we'll touch on with darex here shortly, which was a huge aspect for them when looking at automation and which collaborative robot to move forward with. So here, all these things together kind of add up to the following six installations into relocatable assets. So putting a collaborative robot on a movable carton, moving it between line one and line two throughout the day and having to do different tasks or 10 different lines as your needs change without human interaction into frequent interaction and shared space. So having humans work in conjunction with collaborative robots to achieve the desired throughputs. Repetitive work into frequent changes.  Long-term ROI into short-term ROI. This is something I'll touch on with the darex case study here in a second, but we're seeing very, very short return on investments from collaborative robots in the field right now because of the relocatable aspects of them because they do not require a cage around them. So now I'm going to touch on who darex is. Darex is a small family-owned manufacturer in southern Oregon that makes drill and knife sharpeners. Roughly 100 employees, depending on the time of year, very seasonal, located in a small rural community, very, very difficult to find labor down there. It's extremely competitive. There's a strong ag industry, and then there's a large university in town as well that hires away a lot of people. So they experience a lot of turnover, which really affects them during the busy times of year, especially around Christmas and that sort of thing. And so being able to scale for those production runs has been very difficult for them. So that's what led them into looking at automation and exploring which projects they wanted to tackle so they could really start ramping up for some of these larger scale orders they're experiencing and keep on with their growth demands. The stakeholders involved in this project, kind of from the top down, you have Matthew, he's a present owner, third generation owner, needs to address labor shortage in his company and improve employee satisfaction. There's a lot of tedious jobs within that manufacturing facility, and so finding automation that's friendly towards his workers and kind of kills two birds with one stone there, addresses a labor shortage, and improves the overall satisfaction of his workers by eliminating a very tedious position and moving them to something more rewarding. John, the director of operations, is then tasked by Matthew to increase production quantities and reduce scrap, so he needs to look at automation to increase his capacity and reduce the amount of scrap parts that are coming back because of misaligned screws or not torqued properly, etc. Sam, the manufacturing engineer has a limited budget to work with and experience with automation. They had very little automation in their facility going into these projects, and so that was a large factor, and this is that they needed something that they could start small with and build their skill set from there. Project SKILF, that was identified. Matthew, the owner of a company, identified that his company is facing a critical labor shortage and needed to adopt automation to address the problem. He tasked John, the director of operations, to put together a project to reduce some of the repetitive tasks like screw driving, case erecting, case packing, palletizing, take a look at those and what is the lowest hanging fruit that they could identify, which would be a fast return on investments and a quick win for them. That's a common thing we look for when talking to companies about collaborative robots is what is the low hanging fruit application that might not have the three month return on investment that something down the line has, but is the quickest to win and you can get it in there the fastest to start making that impact immediately instead of a few months from now because there's more work that needs to be done for some of these harder projects. So from this point, John and Matthew started researching automation and what really fits their needs and their experience and something that they could take ownership of and start small with and they came across Universal Robots and got connected with the local distributor and what's nice about collaborative robots in general is that it's a very, very low entry level piece of automation. You can start very, very small, very simple and build your skill set from there and Derek's the prime example of that. You'll see here in a second that they started small with a screwdriver application and then over time with their experience of having the co-bot in there is that they were able to build into something very complex end of line of having a single robot do case directing and case packing in one cell. So really from there, it's something that's phenomenal to see that skill set build because of the ease of use that our co-bots provide in co-bots in general. And like I said, the first application that took place was the screwdriver with the UR3. So here you can see an example of it in their facility. They have the sharpener bodies that come down the line. Robot drives in three screws. It's bullfed, fairly straightforward application, but it's reduced. The main aspect here is reducing a tedious job of driving those screws in and then workers were not driving them in properly every time. So they're having a lot of issues with scrap parts and that sort of thing or those having to come back and be reworked. So putting the co-bot into the line there is completely reduced. There's scrap parts because it does it the same every single time. He's to success. After the implementation of the screwdriver application, Derek's went to the end of line packaging and case erecting. Sam, the manufacturing engineer was responsible for sourcing and implementing the automation for this project. Sam had very little experience with automation at this point, so he really relied on doing a lot of research and working with the local distribution partner for Universal Robots to learn from their experiences with other customers and what products they recommend that work in conjunction with our collaborative robots. He was able to use his experience from screwdriver to build upon a skill set with UR. He used our online academy to continue to learn how to program and get more self-sufficient on the collaborative robots. He sourced more conveyors, a checkware, and he even 3D printed his own end effector for case erecting. He even made his own infeed for case erecting. For this application, the robot was actually the easiest part of the application. The hardest part was sourcing and putting together the conveyors, the checkware, the case sealer, and building and designing the infeed for his boxes for case erecting so they're in the same repeatable position every single time. This is something that we see with most applications within the collaborative robot world is that the robot is usually the easy part. It's the stuff around it that can be a little more complicated. How your boxes are presented, what's the orientation of your product that you're putting into your case? Where does the box go after it's filled? Do you push it through a case sealer? Does a worker come over and tape it? Those aspects are usually the hurdle or what does my end effector look like to grab the product or grab the part to put into this box? Sam was able to use the UR as a master controller for the entire end of line system instead of a PLC so he was able to connect the conveyor and the case sealer to the controller and was able to have them communicate with each other to know when a box is full and to push it through what the case sealer know that a box can be coming through the case sealer. Here's an overview of the complete line here at the end of line so you can see a 5kg collaborative robot there front and center. It has a pneumatic end effector on there that Sam actually 3D printed which I was very impressed with is that it actually has a solenoid hinge on there. So that solenoid hinge allows you, if you watch the case study on YouTube, so if you go on to YouTube and look up UniversalRobotsDarex you can watch the full video here but you'll see that the robot goes over to the stack of boxes, grabs it off and that solenoid hinge there comes on to the side of the box and is able to pull the box open and then it puts it on to the slide there to close the bottom flaps of the box and then it's stationary there while boxes of products come down the line to the checkware and it's either a go or no-go. If it's a no-go, there's a light that goes on that tells the worker to come over and grab the case if it wasn't the correct weight. If not, the robot grabs it, puts it into a box once there is four into the master case. The robot then uses a solenoid to push down one of the flaps and then it pushes it through the 3M case sealer which then it goes kind of like a horseshoe down to the end of the line where it is then put on to a pallet by a worker at the end of line. This whole system, the robot was sourced very quickly the rest of the stuff was just waiting on conveyors and the case sealer and finishing up design on his in-feed for the boxes to then assemble it but it was a very fluid process and has been a very efficient line for them. The results from this line that we've seen so far at Derek's significant ROI from their packaging system. The ROI for the entire line including the UR-5 was slightly under a year which is very impressive considering all the hardware that was required for that line and the system has allowed them to scale their manufacturing from two days a week to four days a week for the designated part numbers on that line which really has allowed them in the busy times of the year to build more quickly and fill those larger orders that come in around the Christmas time and fill gaps that they have within their manufacturing process because of labor shortages. And then one of the best aspects that I like of this is that they were able to retask the employee at the end of line there to be the robot supervisor for the main day shift. There's an online academy that we have called Universal Robots Academy where you can go online and learn how to program for free. They have their workers on each shift that we're doing this. Go online, do the online academy and then the woman who won the programming contest she became the robot supervisor for that shift. This is Brittany. She's the robot supervisor. She knows how to do basic programming. She can recover the collaborative robots from a stop for any reason and get them going so you don't have to pull Sam out to work on them, very self-sufficient in that aspect. If there's anything that happens with the robots like a box is rejected on the checkware she can come over and grab it. She'll fill in and load the pallet as needed and move the pallet to the warehouse when it's full. Really it's just a flexible worker that can supervise these robots and make sure they're always running at full capacity and there's no downtime because any downtime is lost revenue for the company. What's next for Derek's? A few slides ago I showed the overall cell. At the end of line on the top of the page there you can see where the boxes are palletized. That's a key area that they're working on implementing here within the next fiscal year is implementing a large UR10, so a 10 kilogram robot to palletize onto that pallet there to eliminate Brittany having to come over and help as needed to palletize boxes once they stack up there so making it a more automated system. Then they're looking at replicating this line throughout their facility to address the continued labor shortages they're seeing within their facility. Thank you for letting me present this case study to you guys today. This has been a phenomenal application from Derek's and they're seeing continued success. I would say the key takeaways for me from Derek's is that really was having the buy-in from Matthew and John all the way down to Sam that this was something that wasn't kicked around for a long time. It was something that they executed on quickly and efficiently and didn't kick around for a year before finally pulling the trigger. They saw immediate benefits because of how quickly they moved. Now I'll take it over to Ron for any questions. Thank you, Ben. Excellent job. I appreciate that. Also, I just want to make mention, Ben gave us the case study. We focused on specific application. If you needed more information about collaborative robots in general, we decided not to do that at this webinar. We have an earlier webinar that has as plus Ben has information on their universal robot sites for that. We do have some questions coming in. It's kind of interesting. I guess the first one is one that's probably near and dear. Do I need a programmer to program these? What level of education? Give us a sense, the audience here, how sophisticated a person you need to do this programming? Yeah, absolutely. Something that's really nice about collaborative robots is that they're all inherently easy to use, at least most of them that are commercially available on the market, especially the universal robots. It's down to almost an operator level right now that you can learn how to program these. Like Derek, for example, yes, Sam did the majority of the programming on this, but using the he didn't go to any form of in-person training or anything like that. It was all something he went online and did our free online academy to learn how to program these. It takes about 90 minutes. From there, he used the skills he built online in a simulated environment to use on his robots and really be self-sufficient from there because our collaborative robots, the majority of them on the market, don't require any sort of code to program them. So it's all moving the robot by hand to different positions and setting waypoints which are locations in space. And so that lack of having the right code really brings it down to a less sophisticated level of experience required to program them. Like Brittany, for example, who's the robot supervisor, she had zero robotics experience going into it or even automation experience and she was just an operator and she was able to really build her skill set and gain a new resume item. Okay, excellent. You said these robots don't need cages. How safe are collaborative robots? And how do you say that you don't need cages? What is the mechanism to help keep them safe for workers? Yeah, absolutely. So collaborative robots are force and speed limiting robots. So within the joints of this robot, there's the servos that are always monitoring the current of the robot. So when the robot's in movement, it feels that resistance against it and it's a triggered stop that when it gets to a certain level of force against the robot, it triggers a protective stop where the robot kind of pauses and that creates a pop-up on the interface screen or the teach pendant of the robot where the programming is done letting you know the robot's in a protective stop and you have to go over and enable it to be able to continue its program from there. The robots also, they're lower payload than a lot of industrial robots that are on the market currently. So like Universal Robots, our largest robot commercially available is 10 kilograms and so that's a lot lower weight than what you see in the industrial world where it can go up to hundreds of pounds. So a lot less weight you have to worry about there and you can actually regulate on the robot how safe it is. So you can have it extremely restrictive where it takes only a brief touch to stop it or you can have it at least restrictive where it will still stop on a dime but it requires a little more force. So that allows you to really, based on your risk assessment, regulate what speeds with the robot you want to move at and what forces are required to stop it. But at the most, it's like a small bump when the robot hits you in the shoulder or in the waist, it doesn't leave a bruise or anything like that. Okay, and you got a good point. You mentioned about the size. You talked about a 5 kilogram or 10 kilogram. How do you size a robot? Is it application-based or how do you do that? Yeah, it's application-based. So there's a lot of applications that pop up, especially within the packaging world where your product might be one to two kilograms but you need our largest size, the UR10, because it's got a 1.3 meter reach. So the different size, usually as you go up in size with a robot, usually there's a bigger reach involved with it. So sometimes people are listing very light products but you need that reach to get into the case. So it's really about looking at your application and identifying how heavy is the product that we're lifting and what is the distance from where we could mount the robot to where it needs to drop the product. Okay. Here's another interesting one and it's all about technology as you start it off. What essentially is the life cycle of a robot? When do they become obsolete? You've got to replace a robot if I spend the money for this one. How long can I expect it to last before I have to replace it? Yeah, absolutely. So most, or I will say all robot companies on the market don't publish any sort of meantime to failure or anything like that because it varies so much from application to application across the board. So our robots are specced and rated in designs for 35,000 hours of continuous use. So that's what they're specced for, but if you're using our robot 24 hours a day, seven days a week with full 10 kilograms and very fast, jerky, high accelerations, you're probably going to experience a shorter life than somebody that's using it five days a week, slower motions, lower payloads. So it really just depends on the amount of abuse the robot's seeing. It's hard to give an exact number, but that's something where working with us in local distribution, we can help make sure your program is running efficiently as possible to minimize the amount of stress that the joints are experiencing. Here's another good question, and you know this one was common. What kind of budgets do you need to have to implement, let's say, a basic collaborative robotic program, kind of like what Derek's did, or something similar? Can you give us any guidelines on that? Yeah, I can't first say how much Derek's line costs them because I don't have the exact numbers right now on how much the case sealer and the checkware and those conveyors cost them. But I would say if you're doing a similar packaging application with a UR5, a five kilogram robot, I would say budgeting 60 to 70,000 would probably be sufficient just for the robot, the hardware, and potentially the checkware plus conveyor there to get that set up. Okay, that's a good number. Thank you, appreciate that. Okay, that's another one here. Oh yeah, how in the word of automation, that's where we kind of started this whole thing off in automation. Where do you make the judgment to automate, let's say a machine that you have versus putting in a robot? Is there a template, a worksheet, or something you guys have, or somebody has to say, hey, this is when you can automate the machine versus putting in a collaborative robot? Yeah, absolutely. I would say the easiest way to identify it is to look at your, especially within the packaging world, look at your throughput. If your throughput is very low mixed, high volume, you would probably want to go towards a fixed asset at that point that's dedicated machine in the floor, can just do a couple of products very, very quickly. If you have a higher mixer, you need something more flexible with lower product volume so you can have a little bit slower speed. That's where I go towards a collaborative robot where you can have the flexibility of having to move a little slower, but it gives you the capability to move it around your facility and have it work on different lines and different tasks. Okay, all right, here's a good question. Somebody's interested in kind of getting more information and getting started on collaborative robots. How does somebody get started? What's the jump off point and what are the information they need to get started? Yeah, absolutely. So I would really recommend going on to the Universal Robots website. We have a lot of good white pages on there with different information on how to get started. It's all right on the homepage, how to get started.  And then after reading those, if that's something that interests you, either go online and do our free online academy or even reach out to us directly via the website. There's, you know, put in your contact information. We'll contact you and chat to you about your application and, you know, needs and that sort of thing and move from there. Okay, great. And you talked about the online training. There's a question here. How do you access the online training? Is it, you know, once you become a customer, you get access to the training systems or what tells a little bit about that? Yeah, absolutely. So the wonderful thing about the online academy is that you could go online and you don't have to be a customer. You could go online and do it for free today. You know, tens of thousands of people have done it worldwide. It's great. It takes about 90 minutes. And we actually have two different portions of the academy now. We have the basic academy that takes you literally from getting the robot out of the box to doing programming on a simulated pick and place off a conveyor line in 90 minutes. And then we have a protract, which teaches you how to do a little more complicated things like polishing or palletizing and that sort of thing, all within a simulated environment.  So it's completely free to do at any time for people. Just go onto our website, Universal Robots Academy, very straightforward. Excellent. Excellent. Well, that's good advice. We'll have to make sure we collect those links on the follow-up. So Ben, thank you very much. Definitely appreciate your time today. Thank all of you who are attending the webinar. We definitely appreciate it. Again, there will be a short survey that comes out as soon as you log off today. We close out the webinar. Please take it. It's about one or two questions and we'll answer it. Ben, thank you again. And good day, everybody. Have a good one. Thank you very much, everyone.


Hi everyone, Sean with jaspyscasebrakes.com here doing 2020-2021 prison fast break, one box break, random team, two random teams each, number four, where we're giving away a $1,500 mixer spot. So, 15 spots in the break, everyone gets two random teams, no vet commonship, and at the end of the break we'll randomize all 15 names, random top name gets the $1,500 mixer spot. Here is the list of customers, Dmitri Yov, down two, Sean Matic, last spot mojo, and the teams from the hawks to the wizards. Dice roll to randomize both lists and pair them up, five and a four, nine times, separate dice roll, separate randomizer after the break to give away those spots. Oh, never mind. We're going to restart this one, because every spot gets you two teams. So, we got to double up that list. I forgot that step. So, one time, and two. So, there's Sean Matic last spot mojo once, and twice. Now, we'll do nine times on the randomizer. Ninth and final time, Timothy. Timothy. And nine times on the teams. Ninth and final time. Pacers. Jazz. Nine times on the teams. Nine times on the dice. Nine times on the customers. Nine times on the dice. No trade window. We're not going to read through the full list, since this is a filler break, but we'll stop and look at all the highlights, potential highlights we can have in here. Kings. Tyrese Halliburton, Sean Matic. Nix. Obitop and Sean Matic. James Weissman, Warrior of Timothy. Tim Rolfe, Anthony Edwards, Kobe Chan. Hornets. So, let's print this list. And pop this box open. Good start for the Lakers. LeBron James, Kobe Tribute. Lakers, Timothy. Richard, there are still. Yeah. If you go to the site, take a look and see. There should be some stuff that is still in play tonight. We've been doing a lot of fillers like this for the basketball mixer.  Team Randoms. See if there's anything on there that you like that you might be able to help out with. Like I said, so first up LeBron James, Kobe Tribute for the Lakers. Timothy Innes. Enes Cantor, Celtics. Timothy Innes. Yanis for the Bucks. Kobe Chan. And Otto James Johnson to Timberwolves, Kobe Chan. Allen Iverson, 76ers, Kobe Chan. Frank Niddlekeena to 175 for the Knicks. Sean Matic. And Obi-Tompon for the Knicks and Sean Matic. John Wall, Wizards, Kobe Chan. Damien Lillard, Lazer, Sean Matic. Precious, Ashua. Heat, Dimitri. And Thaddeus Young to 125 for the Bulls, Kobe Chan. Tony Snell for the Pistons. Timothy Innes. And Paul George for the Clippers. Timothy Innes. Tobias Harris for the 76ers, Kobe Chan. That is to 175. Isaiah Stewart for the Pistons. Timothy Innes. And Obi-Tompon for the Knicks again, Sean Matic. That is a photo variation as well, Sean. Paul George for the Clippers. Timothy Innes. And Sadiq Bay Blue to 175. Pistons. Timothy Innes. Davis Bertons, Wizards, Kobe Chan. Donovan Mitchell-Jazz, Timothy Innes. Avery Bradley for the Lakers. Timothy Innes. Marvin Bagley, Kings. Sean Matic. Steph Curry, Wizards. Kobe Chan. Alright, Warriors. Timothy Innes, my apologies. Josh and Kobe to 75. Timberwolves. Kobe Chan, and there you go. That finishes off the box. Now, the important part. The $1,500 break spot. Let's pop back over here. So here is that customer list again from Dmitri back down to Sean Matic. The original 15 names. Top name wins the $1,500 spot. So top name after four and one five times. Good luck, everybody. One, two, three, four, five, and fifth and final time. Kobe Chan. I think that's the second one tonight already. Five times on the randomizer. Five times on the dice. Kobe on top. There you go. So that was 2020-2021. Prism fast break, one box break. Two random teams each. Number four from jaspyscasebreaks.com. I'm Sean, jaspyscasebreaks.com. Thanks for hanging out. We'll see you next time.


I'm a car salesman, not necessarily by choice. It's something that just kind of fell into place. I work at a local dealer near the mall in town, one my dad's been running since before I was born. He loves cars, and he loves to sell them. I guess which sums up about why I work there. It's not a bad gig, and I've been doing it long enough to get pretty good at it. I've had some pretty good sales in the past. Some felt a little scummier than others, but hey, it's the way the trade works. I don't make money if the cars don't sell, so it's in my best interest to do so. I was sitting at my desk when it started. It was a decent but overall a slow day at the lot. The weather was nice, but I didn't get much of a chance to enjoy it, because I hadn't had any sales all day. Not one. No young couple looking for a cheap ride. No bachelor looking for a lifted truck or sports car. No family scouting or replacement minivan. Nothing. So, I spent most of the day at my desk, twiddling my thumbs, listening to the radio, eyeing the lot in case someone happened to wander over. I was ready to get a sale, be productive, instead of sitting around. My dad was out for the day, something about a golf outing with a competitor. He loved that stuff. Since I had the building to myself, I was hoping to put in some decent numbers, but on this particular day, there was no one. I've had a lot on my mind recently, and the business would help clear the chaos in my head. So there I sat, dicking around as the hours crept by. That was until he showed up. I watched him arrive by bus, getting off the shuttle at the stop through the window. I had nothing going on, so I watched him after he showed up. He got off the bus and looked straight at the dealership. A slow limp of a walk, starting as soon as he saw my building. My first thought of him was your stereotypical boomer dad had to be pushing 60. What was left of his wispy hair was gelled and combed across a colossal bald spot. His eyes were shielded by bronze aviator glasses, and his outfit looked like it was ripped straight from an 80s business catalog.  He had his hands stuffed in the pockets of the jacket, and he was on a mission. While he drew closer to the dealer, I combed my hair, spit out my gum, and straightened my tie. It was evident he was coming this way, but he was heading straight to the lot, probably to browse. I just took that as my cue to meet him. I pushed through the door and into the sunlight, feeling the breeze for the first time today. He was just standing there now, scanning. He didn't linger on anything very long, I would have to do some digging to get this sail moving. How are we doing today? I asked. The name's Mark, I'll be your liaison today. Something I can help you find? He just stood there, ignoring me for a second, looking at the cars at his own uninterrupted pace. With the aviators and the double chin, he looked like a grumpy frog. There was something unsettling about him from the get-go. I'm looking for a car. He said, can't seem to find it. This is my third dealer today. He said plainly, very to the point. I clapped my hands together, ready to start the routine I'd done a hundred times. Well, this is your lucky day. We currently have an abundance of... I'm not here for the sales pitch, kid. He cut me off, taking slow steps towards the shiny hatchbacks. Ah, well, certainly some way I can help. I know all the makes and models in this lot, we happen to be sitting on quite a bit, and let me tell you, now is a good time to be on the market. I started, turning around to face the luxury sedans. We looking for something sleek, for cruising, maybe something a little sportier, surely an old top like yourself would. I turned back to see he was already walking away. I felt a twinge of frustration. I'd have to work a little harder to get this guy to play ball. I scratched my head and caught up with him, walking quicker so I could lead. The mini SUVs are one of our most popular items. Plenty of room for passengers, cargo in the back, all wheel drive too. I can't recommend that enough. You know how winter can be around here. I said, the guy just looked ahead, chewing his lip a little. The wind blew at his combed strands, but he didn't seem to mind. Heated seats, Bluetooth, some of these even have TVs in them. They make the commute more enjoyable, satisfying. No interest on the first 12 months, if you buy before the fall. We're at the tail end of our summer sale, but there's still time. Even if you need time to think something over, I'll get you taken care of. What do you do for a living, if you don't mind me asking? Finally, he looked my direction, but his face was still blank. Almost like stone. I was a car salesman. I'm retired now. He kept walking. Ugh, you gotta be kidding me, I thought to myself. This guy already knew the game, the tactics wouldn't work here. I'd have to follow him around like a lost dog and lap up whatever he fed if I wanted any kind of sale today. We could be here all day and he still might not get anything. I kept the enthusiasm going. You don't say anywhere around here, maybe you know my father. I asked. The man had finally stopped at our little cluster of trade-ins from past transactions. The man looked them over one by one, the sun shining off his sunglasses as he panned slowly like an owl. He came to an old Buick and stopped, fumbling in the pockets of his windbreaker. He pulled out a pack of cigarettes and a zippo. Oh, sir, I'm afraid I'm going to have to ask you to put those away. We have a smoke-free premises. I said, but he continued anyway. He leisurely fished a cigarette out of the pack and lit it, taking a big drag before pointing to the Buick. The Le Sabre. Get me the keys. He blew the smoke out at me and I waved it away. I looked at the car, a creeping anxiety washing over me. It was a 98 Buick Le Sabre limited silver with tinted windows. This car of all things, was this a game? Pardon? The trade-in? I assure you we can find something better to suit you. Walk with me. Yet I can show you. What's the deal with the price? 8,000? It blew books for five tops. I know you heard me. Get the keys. He looked at me and took another drag. His tonal shift was alarming. I found myself glancing around. We were still alone. Uh, yes, yes, right away. Let me get those for you. I briskly returned to the office for the key. I was sweating a little, trying to wrap my head around the old man's choice. The Le Sabre of all cars. I thought about calling my dad, but decided against it. He'd flip out, ask too many questions, want to know every little detail about what was going on. When I came back, he was peering through the windows of the car. His cigarette snubbed out on the pavement. I wanted to scowl at it, but I kept cool. I still wanted to get something out of this guy. I just had to figure out how. It looks clean. Really clean. He said as he peered through the driver's side window, they all are. I said, scratching the back of my head as I looked across the lot. The SUVs wouldn't do it. He was too old for a really smart car. I had a feeling the digital stuff would scare him away. Maybe the Lincoln's? The old man was looking at the paper that was taped to the inside of the windshield. The one below the large for sale sign. It displayed the mileage and terms of sale. As is no warranty. Stuff like that. It was a trade-in after all. Well, let's take a look, shall we? He was leaning on the car now, waiting for me to unlock it. Sure thing. I smiled and worked the key, looking inside myself to make sure nothing was amiss. The front seat floors were still covered with paper shields to ward off shoe scuffs and the back seat was nice and clean, just as I hoped. Here she is. I reluctantly held the door for him and he ducked his head in. It was hard to tell what he was thinking. It was those damn sunglasses. You couldn't see anything behind him. He chewed his lip as he looked up and down the dash than his gaze held on the seats. They were tan leather, a clean shine still catching the light. Leather seats, very clean. Back in my day, these used to drive the girls wild. He said under his breath and I started to feel uncomfortable. So many skirts in these back seats. He clicked his tongue and ran a finger down the clean leather. He looked at his fingertip and rubbed it against his thumb as if trying to feel dirt. Oh yeah? Is that so? I played along, wiping sweat from my brow. Yes, of course. He continued, you ever get a dame in the back seat of one of these kids. He asked, a grin forming on his wrinkled, stubbled chin. It gave me the chills. Ha, I'm afraid not. I gave a nervous laugh. Say a few rows down we have the Lincoln Town Car, a few of them actually. Even in silver too. Of course he wasn't listening. With a labored wheeze he leaned in and hit the button for the trunk. There was a soft clink when the trunk popped and he immediately walked around me towards the rear of the car. He stood in front of the trunk with it cracked a little as if he didn't want to open it. He ran his thumb over the paint then over the temporary license plate. When his thumb reached the dealership sticker, he stopped for a moment. The sticker was the silhouette of a diamond. He looked at me, still frowning. There it is, namesake of the business. You know, diamond deals is my dad's idea. I still think it's pretty cheesy if I'm being honest. I said, unable to shake the feeling of nervousness. The old man seemed taken aback. Ah, yeah, there's that. He looked at the sign then lifted the lid of the trunk. As he stared in the trunk, I found myself following in looking as well. The same silent stare. The trunk was empty. And there was a clear view to the little carpet hatch that led to the spare. The old man ran a finger over the carpet. Plenty of room in there, isn't there? He asked. He was looking tired. Perhaps the sun was getting to him. Yeah, I suppose there is. Good going to town car, I guess. Plenty of room for groceries. Or a woman. He spoke. It stood the hair up on the back of my neck. Excuse me? I asked. You heard me. He looked at me, then slammed the trunk shut. It was loud and I jumped a little. Sir, I think it's time I asked you to leave. I stammered, feeling a ball in my throat. I instinctively felt for my phone, and the old man stared and scratched at his stubbled chin. Leave. Who said I was leaving? I'm not finished here. He shoved past me and walked back to the driver's side. He dug in his pocket again, this time pulling a full half pint of whiskey. He twisted the cap and broke the seal before taking a swig. I could only watch in disbelief.  I'm calling the police. I pulled out my phone while he screwed the cap back on. Don't worry. I'll call him. He said, pocketing the whiskey and pulling out his own flip phone. What? I asked. I said I'll call him. Don't worry about it, kid. I'll handle it. He flipped his phone open. I mulled over it in my head, sweating more and more as my confusion built. Wait, that's not necessary. We can figure something out, I'm sure. I put mine away and held my hands up. The old grumpy man looked at me, and after a time his phone snapped shut. He ducked out another cigarette and lit it. I scratched my sweaty head and looked around, but it was just us on the lot. When's the last time you drove this kid? He asked. What? The Buick. You ever take her for a spin? After hours when daddy's not looking? He growled, his voice getting lower like he was whispering. What? No, I've never driven this car before. I told him. Huh, that's funny. All these cars in the lot and the only one without dust on the paint is this one. It's still got shine on the tires. Why spray that on there? Why pretty it up? Why do that if it's overpriced?  He was moving closer. The cigarette smoke dancing on the wind.  Another thing, kid. The paper on the window said it's got 130,715 miles. He said, yeah, and I demanded dash says 747. He took another drag. So what? The car fax is off then. It's 32 miles.  I'll print out a new one. I told you, the cars get moved around. Yeah, maybe a block or so, unless there's a maintenance issue. But you know what? There's a couple of bars in town, only a few miles away. You ever take her out for a spin, kid? Clean her up? Impress the girls. He was taking another drink. My hands were starting to shake. I don't know what you're talking about. I said, sure you don't. Let me tell you a story, kid, real quick. Like I said, I was a salesman, too. I get it. I was there once. I was damn good, too. But I'll tell you what else.   Not really. They were just things that happened. The cars, on the other hand, I lived for those cars. That's what I loved. When I got divorced a long time ago, me and my wife never talk. My daughter, she must be a little younger than you. He said, anyways, my ex calls me yesterday. We haven't spoken in years, tells me our kid hasn't come home. She thinks she's missing, said she's called the cops.  Yeah? So what? What's that got to do with me, huh? I was getting loud, my voice echoing in the lot. I could feel the anxiety setting in. Thing is, her date picked her up that night. Like a gentleman, car all done up, spick and span. That was the last time she saw her, said she was driving an old silver Buick, said there was a sticker on the bumper, like a symbol. She wasn't sure it was dark out. But as it turns out, on the third dealer, I found it. I looked at the large spinning sign, the glimmering letters, diamond deals. I couldn't breathe. So I think it's time you come clean. He took a last drag and snubbed it out. I don't know what you're talking about. Yeah, I think you do. Maybe your daddy ain't quite figured it out, but he will. Does he know you marked the price up on that car? To keep the eyes off of it.  Sure, you only put a couple miles on it. Bar's not that far. But I'll tell you what. You know what else isn't far away? The river. You could be there in ten minutes. So what happened, kid? You pushed too far? She reject you? You're wrong. I said tears welling in my eyes. No, I'm not. This car is probably the cleanest one on the lot. It's been vacuumed at least three times, and the outside's shiny and new. We don't wash the trade ends, kid. They're not worth the effort. How long till daddy finds out? You think he'll like that? You messing up that bad. It was an accident. Brat, like yourself, maybe not used to hearing the word no. She hurt your feelings. Take it down a peg.  I told you it was an accident. I felt my knees buckling. Suddenly it was hard to stand. Sure thing, kid. I'm gonna cut to the chase. I'm calling the police either way, either now, or when I'm on the way home on the next bus. They'll take everything I know, and they'll find her wherever she is. It ain't gonna be quick. They'll drag you and your father through the mud through the whole process. You'll be finished. He said, and my legs could no longer hold the weight of the stress and guilt. I started sobbing, burying my face in my hands. What do you want me to do? I can't undo what I've done. At some point, he was standing next to me, getting one last thing from his pockets. Through tears and shame, I could see the pistol, a little 38. I won't tell you I was there for my daughter growing up. I wasn't, and I know that. But she was still my daughter. I'm just doing what's right. It's the least I can do. I'm no killer. You want to know what I want you to do? Atone for your mistake. Make sure it doesn't happen again. It seems like a better alternative to carrying the weight and rotting in prison, don't it? Either way, it's up to you. I did my part. He said, and held the gun out. I took it and cradled it in my trembling hands. The old man sighed, retrieving the whiskey and downed it in a large gulp. He winced behind his glasses, and there was the glimmer of a tear behind the lens. He tossed the bottle into the parking lot and walked away, lighting a cigarette without another word. Through puffy eyes, I watched him go, the same limp taking him to the bus stop he arrived on. Without as much as a look back, he sat on the bench and waited for the next shuttle. I climbed to my feet and went back in the office. I collected my things and locked up for the day, closing the dealership early. When I came back out, he was gone. The bus bench was empty, like he was never even here. The pistol in my pocket and the booze bottle in the parking lot reminded me of his visit, almost assuring me of what had to be done next. I got in the Buick and drove home. It took me some time to process it all, but by the time I got home, I knew what I had to do. I sat down and wrote this, hopefully to clear up any questions for those that come looking for me.  I'm sorry. Really. I was in denial at first and I tried my best to cover it up because I was scared, scared of what I did and the repercussions that would follow if it was discovered.  I've made my decision. There's a little bar on the outskirts of town called the Sixth Shot with a little red neon sign. If you head east for eight miles or so, there's a small bridge with a river running underneath. She's under the bridge. I tried to use some rocks to weigh her down. I hope she's still down there. I'm sorry. She deserved much better. Well, that's about all I have to say. I've learned from my mistake. I won't hurt anybody again. I got the pistol on the desk now and once I post this, I'm gonna take the deal the old man gave me. At least that way, I can try and set things right. He did say he was a damn good salesman. Goodbye.


Thank you very much. In this presentation, I am going to talk a lot about different methodological challenges of estimating intergenerational mobility and from the economics point of view. So this is a bit, you know, limited in scope and I am also going to follow a lot the outline of the chapter that we had done for the book but by this time while doing this presentation, I was thinking that it's almost time to write another one because literature really exploded. So the starting point of the chapter was that if you look at any development textbook, every textbook has a chapter on inequality but very few of those chapters talks about inequality of opportunity or inequality at the intergenerational immobility. There is not a whole lot of interest in it before, you know, this huge rise in income inequality that we had seen after globalization and following that, you know, there is quite a bit of explosion of literature on this. I should also mention here that much of the literature actually closely follow the literature that's being developed in the context of developing countries. So there are three issues that I am going to focus on for a very first one would be that what would be a good indicator of economic status. Of course in the context of developed countries permanent income is considered to be the most informative of all economic status. Chico in the morning told you why, you know, we can't really go down that road because most service in developing countries don't have income data, particularly for parents generation when parents are mostly employed in agriculture and informal sector. And then, you know, there are studies which relies on, you know, one round of survey or so on. These things leads to quite, you know, quite a bit of attenuation bias. So it would actually look like income ability is much better than it actually is. So here are some of the examples that I had put in there. I am just going to hand wave and go to the next. So in the case of developing countries, we had to come up with some indicators of economic status and what is better than education that is more or less, you know, widely accepted. You can also think of parents occupation. That's another and in fact, you would see that there are two parallel literature in developing countries, one focusing on education, one focusing on occupation. Here also the question is that, you know, you would think that both of these indicators are important as Chico pointed out in the, you know, keynote in the morning. Then the question is how do you combine them? There are ways to combine them. For example, DHS does this wealth index. This kind of, the problem with this kind of combining is that you can't really interpret the weights. I mean, it does not really have economic interpretation and you can't also link it to some, you know, economic theories and makes it difficult to interpret results. There is also the other approach that Luebowski and Wittenberg had suggested and there are a couple of papers on that. I am just going to skip that quickly also, you know, if you want, you can look at the details of that in the chapter. So next, I am coming to the measurement of it. So there are three, four different standard measurements that's being used in economics literature. The very fast one is that you regress children's education on parents' education and the slope coefficient gives you what is known as relative persistence or it would be like relative immobility. You can standardize both the children's education and parents' education run the same regression. You're going to get the Pearson correlation, which is the wrong one. And with the, you know, with the popularity of Rashid's work, there is also this rank-rank regression, which we are just calling it IRC. Basically, you take the rank of children's distribution and rank of parents' distribution and you just run one regression, which is given there. Now, remember that in developing countries, we are dealing with a very limited data. So what kind of problem you are going to be expecting there? First of all, you know, the measurement error. If there is measurement error, of course, we are going to have attenuation bias and this is going to look, you know, make things look rosier than actually. And there are some evidence of that in the context of developed countries. But for developing countries, particularly when we work on education, we kind of wave out the measurement error issues. I think maybe waving out for the children's generation is fine. I do have a bit of doubt whether that's fine for parents' generation. Okay. The second bias, and this is also going to cause attenuation bias, is in most context, actually, I should qualify as co-residency. So the idea here is that you are going to be using some sort of household survey data. And typical household survey data will have people who are resident in the same dwelling, which means a child who migrated for some reason, work, study, whatever, you're not going to see them there. That causes attenuation bias, which is known as co-residency bias. And we had done some work on this and basically we find that among different measures, the three measures that I had pointed out, IGRC performs the worst when you're looking at the estimate of the slope coefficient. Okay. The IGCI, IRC tends to do a bit better job. There are, you know, the opposite evidence, particularly for income. In the case of rural India, here is the paper for that. Now, given this, next question I am going to ask is that if we see that relative persistent in a country, one country is more than other, can we just say that one country has better opportunity for its children than other? Here I am going to take the example of China and Indonesia. The persistence estimate, that better estimate for China is .34, Indonesia is .78. From the face of it, it would seem like that children in China are having a lot more mobility. And here I grafted. So what you see is that for each of father's education, you see the expected son's schooling. And here from the, once I give you the picture, figure, you can see that actually in terms of absolute mobility, Indonesia is doing much better than China, right? There are a couple of other examples in this graph. There could be situation that in certain part of distribution one country is doing better than the other and so on. So, up short of all this discussion is that you can just look at relative persistence. You do need to look at the absolute mobility too. In other words, you also need to look at the intercept estimate. And there is a lot less evidence on, you know, how intercept estimate performs under different types of data constraints. But overall, it does look like the rank-rank regression performs much better. Okay. Now, here I'm going to qualify a bit. And this is not in the chapter. This is from the, sorry. Or maybe this is my old, okay. Before I go to the, so I have a slide missing in here. So let me just tell you what the idea there. So what measure, when are you going to use what measure? It depends on the context. What the rank-rank regression or IGC regressions are doing is that it is standardizing the distribution, parents and children's distribution in some way. In case of IGC, you are using normal distribution. In the case of rank-rank, you are using uniform distribution. And advantage of that is that you are actually, you know, kind of wiping out all the shifts in the marginal distribution for this. So if you are interested to know what happened over time in a country in terms of persistence, yes, those works better. But if you're looking at government policies, which actually the purpose is to move the marginal distribution. Think of improving school access. That's going to improve the marginal distribution for the children's generation. For that, you want to stay with the IGRC model. The other advantage of IGRC model is that it is, it can be linked to the economic theory very readily. And you can explore different mechanism through which policies changes absolute and relative persistence. Now finally, I know that I'm running out of time. There is the final concept that I talked a little bit this morning. So maybe I will just skip this and go into the interpretation of the parameters. Now we know that, you know, this is a classic case where there is omitted variable, which is that children's ability is not known. And we do know that parents and children's ability might, you know, correlate positively. And because of that, we are going to have, you know, some indigeneity biases in the estimate of the better one. And in other words, you know, these are not causal parameters. First of all, I should point out that if you're doing intergroup comparison, I would not worry about it. Is there a reason why, you know, ability of distribution for girls at birth would be different from boys? No, actually. So if we're doing that kind of comparison, we are fine. Now for the causal interpretation, you actually need some exogenous variation in parents generation. And there are a couple of new papers which looks at intervention like school construction that shifted mother's education and looked at, you know, how that affected children's education. So that's one approach. The second approach is basically doing some sensitivity analysis. And actually, Chico has a paper on this. Idea there is that if we know what is the correlation between parents and children in terms of ability, we can use it to bound the estimates and tell you, you know, how much it matters. Okay. Last slide. So that's the paper by Chico and his co-authors. I should also tell you that when you're doing policy impact, I think maybe you don't need to worry as much about this ability correlations. Because, you know, the distribution of the ability is not going to change because of the policy. I mean, because of the construction of a road or construction of a school. So that's another way that people have gone into. Finally, there is also an emerging literature right now which focuses using more of the structural models to give a lot more structure on different mechanisms. And those models also have a lot of degrees of freedom to, in fact, figure out what are these ability parameters are going to be. And on the basis of that, doing policy simulation. And that is my last point. Thank you.


Item number SCP-799, Object Class, Euclid. Special Containment Procedures. Each specimen of SCP-799 is to be kept in a separate biocontainment cell at Biosite 66. It has been determined that Biosite 66's maintenance staff remove significantly more than the necessary quantity of dust from the D-class dorms monthly. This dust is to be collected in the usual dust-pan bags and delivered as necessary. No more than 500 grams per specimen is necessary or advised. Should any throw pillows or similar objects appear, they are to be placed in separate containment immediately and the presiding researcher notified. The objects are not to be removed from containment, except as authorized for testing. Description. Members of SCP-799 vary in size, shape, and superficial appearance, but always retain the appearance of a knit or woven item of bedding, made from an unfamiliar but very soft natural fiber resembling high quality marina wool blend, but identified as data expunged. An individual's mass ranges from approximately 0.5 to 6 kg and does not change except through ingestion and excretion, so any immediate increase in area comes at the expense of thickness and fabric weight. The blanket retains heat unusually well. Its coloration and markings are highly variable. It tends strongly towards pastel hues and patterns resembling stylized neotinous animals, particularly when its mass is less than about 2 kg. The items are living organisms, most similar in physiology and structure to data expunged. Although alternate hypotheses suggest fungal origins, full notes may be found in document 7991A. Although well insulated, they are apparently cold-blooded, hence their adaptation for use by intelligent homeotherms. Instances of SCP-799 are usually torpid and incapable of movement, requiring little nutrition. What they do need, they draw from the organic detritus present in household dust. One specimen is believed to have survived for years on end stored in a damp, ill-maintained attic, living on heat from the house below, and detritus from the wooden rafters above. Excretion is minimal. Excretion resembles tiny, very dry data expunged. If forced to survive for long periods without sufficient nutrition though, SCP-799 is capable of metamorphizing into a predatory form. The resultant structural changes are invisible to the casual observer. They consist of modifications to the digestive tract and feeding orifices. The latter shift from minute, dispersed filter-feeding mouths into a single large mouth, lined with data expunged. The organism also develops contractile tissue similar to animal muscle. Once this metamorphosis is complete, the SCP-799 individual waits for a large animal to wrap up in it and become quiescent. Sleep is common, before opening its mouth, tearing off a few kilograms of biomass, and consuming it. The biomass is apparently reduced to thin slurry almost immediately, as even seconds after feeding, there is no visible bulge in the blanket. The mouth is completely resorbed ten minutes after feeding. At this point, no evidence of the blanket's nature is available, except by x-ray, or by observing the sudden increase in mass. By 40 minutes after feeding, the entire carnivorous digestive system is resorbed and the organism returned to filter-feeding phase. In carnivorous phase, the organism will not feed on inanimate objects or cold-blooded animals. Dr. Wendt hypothesizes that its senses are entirely limited to touch and heat, and that it does not recognize cooler objects as organic. SCP-799 reproduces by budding. When an individual's mass reaches a certain minimum, very slowly except in cases of carnivory, it takes on a more quilt-like appearance. Gradually, over the course of several weeks, the quilt squares along one margin puff up and slough off to become several small doilies or throw pillows. The offspring are clones of the individual, identical to it in every way, and will grow to a similar size and reproduce in turn given suitable conditions. The first of the Foundation's captive SCP-799 population was retrieved on f*** from f***, where it had been residing in the home of notorious germaphobe after it metamorphosed and apparently fed on his infant-granddaughter. A standard biological sweep revealed the blanket's nature, and it was taken into containment without resistance. It is not known how many instances of SCP-799 exist outside Foundation containment, as they are very difficult to detect, except by genetic testing. Extrapolation of growth rates and reproductive weights from the specimens in containment suggests that a filter feeding SCP-799 organism reproduces once every 50 to 60 years. Note, any wild blankets should be destroyed upon detection. This species is fascinating, but we have a large population in containment, and the wild ones pose too much risk of public exposure. Further research into a possible special connection with SCP-1626 is ongoing. Dr. W... Lesson complete. If you missed the previous orientation, go watch SCP-798, Cortex Rat, right now. Or for the complete course, watch this playlist.