Well, I'm very excited to be with you today to do this webinar, to give you an update on the CLSA, and I'm very pleased to present it on behalf of Carmen Durena and Christina Wilson and the entire CLSA research team. It's a really exciting moment for us at this particular time. Well, to be honest, it's another exciting moment for us at the CLSA. There's never a dull moment at the CLSA. But it's a really important time in our history as we release for use the baseline data on all 50,000 plus participants for use by the research community. So the focus of my presentation today, as you can see, is really about the practical. It's really going to focus on how to use the data and how to apply for the data. And I hope it will be useful to you. I will, and I apologize in advance, go over some of the baseline information and the study design and content. I think it's essential to do that just to orient everyone, but I know there's lots of people on the call who've heard this before. I'll try and go over it fairly briefly and then get into the really exciting stuff, which is how to access the data. So as you know, the CLSA is a strategic initiative of CIHR. And at its inception when we were developing the study, there were more than 160 researchers and collaborators from 26 institutions. That number has increased dramatically now. But when we were very first designing it, that was the compliment that was involved. It is highly multidisciplinary and we would go so far as to say interdisciplinary and hopefully even transdisciplinary. And involves elements from biology, genetics, medicine, economics, epidemiology, the whole gamut of disciplines that have an impact on aging. And it really is the largest research platform of its kind in Canada, given its depth and breadth. To remind you of the study design, we initially planned to recruit 50,000 men and women who were 45 to 85 who were living in the community. And those people were followed in two ways. 20,000 were randomly selected from the 10 provinces and they were followed by telephone and completed a questionnaire. Another 30,000 were randomly selected from within 25 to 50 kilometers of 11 sites in seven provinces across the country. And those people were followed both in their home. And then they came to a data collection site where they provided physical assessments and also bloodnearing. The design is to follow participants for 20 years. We do a full follow-up every three years and we do a maintaining contact in between. At the baseline, we actually went back to participants at 18 months and collected data from them. We have now changed that so that in the second wave or sorry, in the first follow-up, we will collect all of the information when we go back to them at once. And then the intent is to link to numerous administrative databases over time. This just gives you an idea of where we are now. You can see that the baseline data collection was finished in 2015 and we're midway through the first follow-up at this point in time. With respect to recruitment and I put this slide in here because I think it's very important. One of the things that is somewhat unique to the CLSA is that people were randomly selected to participate. So we use three separate sampling frames but all of them can be rolled up. We partnered with Statistics Canada initially and recruited people through the CCHS Survey on Healthy Aging. We also partnered with Provincial Ministries of Health and utilized the health card registration databases and recruited participants that way. And then we supplemented that with random digit dialing. What this led to is a study that is both national in scope and has a representative sampling frame. And if you look at all the blue dots, that just indicates that people were randomly selected from each of those provinces to participate in telephone interviews. And you can see in red the data collection sites where people participated in home interviews and came to a data collection visit. Another thing about the CLSA that makes it quite innovative and also very unique is that it's completely, the data is completely captured electronically. So participants provide questionnaire data, lists they're recruited into the CLSA and whether it's done by telephone or whether it's done by home interview, it all gets stored at the statistical analysis center. You can see also that there is a data collection site visit and there's a lot of processing that occurs in terms of those physical assessments and also in terms of the biological data that's collected, the biological data, the blood and urine is stored at the Biorepository and Bioanalysis Center at McMaster. Eventually all of that data will become alphanumeric once it is analyzed. Some of it may actually never be alphanumeric, but what will become alphanumeric then goes to the statistical analysis center and that's where the data gets disseminated to researchers. I'll spend a little bit more time on this later in the presentation, but just to give you an idea of the depth and breadth of the information that's collected in the CLSA, you can see that there's a wide range of questionnaire modules. Things ranging from education to vision to cognitive status to depression to satisfaction with life, caregiving, retirement planning. And also too, you can see that we have a veteran identifier. So we actually have a fair bit of information on veterans in the CLSA. People who come to the data collection site, we also do a wide range of physical assessments. And again, this is done in 11 sites across the country. It's all completely standardized. We do physical assessments like height and weight and BMI. We have bone mineral density, body composition and aortic calcification. We have blood pressure, ECG, carotid intimamedial thickness, pulmonary function, both vision and hearing tests, as well as performance tests. We also do a neuropsychological battery. And as you can see here, we collect blood nearing as well. There are 42 aliquots of blood that is stored per participant. We do some basic hematological tests on site, and those results get recorded. But the remainder is actually processed and frozen within two hours and then gets shipped weekly by cryo shipper to the Bioreplosatory Center at McMaster, where it's stored, as you can see, in these large liquid nitrogen tanks for future use and analysis. I do want to just give you a little bit of an idea where we are with respect to the first follow-up. To be honest, that's the subject of an entirely separate webinar, but I'll just give you a very brief idea of some of the changes that we made at the first follow-up. So there was a number of new areas of content that we added. And you can see in particular, we've added an entire module on child maltreatment and also elder abuse, and that was in conjunction with the Public Health Agency of Canada. There's a number of other things that we've added, things like unmet healthcare needs, workability. We've added a question on sexual orientation and gender identity. We've also asked people about their own subjective sense of cognitive decline. The other thing that we're working on is a decedent questionnaire so that when people die, we will go to their next of kin and ask them about the last three months of a participant's life. The other major issue with respect to the follow-up that I want to make you aware of is that we have engaged in a number of accommodation strategies. So as you can imagine, we had a fairly rigid criteria about who could enter into the CLSA, and so they had to be living in the community at baseline. They had to be able to come to a data collection site if they were in the comprehensive arm of the CLSA. Obviously, they had to be within the age range, but they also had to be cognitively intact in order to sign a consent. As people age within the CLSA, we are doing everything that we can within our ability to allow for flexible participation and to be able to accommodate people so that they can stay in the CLSA over the long term. So there's a number of strategies that we've had to employ to address things like people migrating out of the area if they're in the comprehensive side and come to the data collection site. We had to deal with cognitive impairment, and so we put a whole process in place around getting an indication of participants' wishes for how they would like to participate in the future if they're no longer cognitively able to, and put in place a process for getting proxy consent. There are all kinds of accommodations that we've made with respect to physical impairment, and that includes developing a DCS at home visit and also numerous strategies to deal with sensory impairments like hearing loss or vision loss. And the other aspect is that although people had to be living in their own homes when they entered into the CLSA, as they move into institutions, we will do our best to follow them. And there are a number of strategies that we have employed to be able to follow them as they go into institutions as well. What I really want to get into and spend the most time on is how to access the data, how to access data and biospecimens. And this is, I think, a very timely thing to do because we do have a lot of data now available. The fundamental tenets that we work with in the CLSA, and these are obvious, these are fairly obvious, is that the rights and privacy and consent of participants must be protected and respected at all times, that the confidentiality and security of both data and biospecimens must be safeguarded at all times. And it's also important, particularly for the biospecimens, that they're, well, I shouldn't say that. For both the CLSA data and biospecimens, there are resources that we want to be able to use optimally to support research to benefit all Canadians. But the reason I highlighted biospecimens in particular is because they're a depletable resource. They have to be very conscientious about how we utilize those over time. And given that our major objectives in the CLSA are to look at longitudinal aspects, we need to make sure that we have this information over time. Another fundamental tenet is that there is no preferential or exclusive access to the data by anyone. The PI, the CLSA team, and as well as our partners and stakeholders. So with respect to who can apply, basically researchers who are based in academic settings and research institutes in Canada are eligible to apply. International researchers may collaborate with Canadian researchers in order to access data or biospecimens, as long as the data or biospecimens are analyzed in Canada. And you can see that I put an asterisk there and said that that piece is currently under review. And it is our intent to be able to release the data, certainly the data first, potentially biospecimens, but we have to work through all of the issues that are involved. For the actual alphanumeric data, we suspect that this will be resolved fairly shortly. And also, I would like to point out that both graduate students and postdoctoral fellows based at Canadian institutions are eligible to apply for the data. Graduate students have to apply under their supervisors, but postdoctoral fellows can apply on their own. So the data that are currently available, there are in total 51,338 participants in the CLSA. And baseline data from these participants is available to the research community this spring. So that includes all of the baseline questionnaire data, as well as the majority of physical assessment data and hematological biomarker data from 30,000 plus participants who came to the data collection site. And I'll be very specific as we go through as to what data is available, but also to you will be able to go onto the website and see what's available. So there are a number of elements, and I separate them out like this for you because they're also separated out in the database on the website. So I just thought it would make it a little bit easier for you to figure out. So there are 60-minute telephone interviews that were done with the 21,241 tracking participants. And you can see here all of the questionnaire modules that are contained in that 60-minute telephone interview. There are also in-home-based-to-face interviews that were done by 30,097 comprehensive participants. So we went into the home and collected this information. You'll see that there's a lot of overlap with the previous set of information. So in fact, it's available on all 50,000, but it's separated by database at the moment. You can see here, this is the data that's currently available for people who are comprehensive participants and came to the data collection site. And the list is slightly different than the earlier list that I showed you. There are some aspects of the data collection site visits that will not be available for release right away. But you can see that in terms of physical assessment, there is height, weight, waist-tip ratio, blood pressure, ECG, spirometry, hearing, 4-meter walk, time-dup and go, standing balance, chair rise, visual acuity, tenometry, and grip strength data available. And this is all data that's available as alphanumeric data. There are no raw images that are being released at the present time. You can see in terms of biomarkers that we have hematology variables, and I'll show you those in a sec. There are also questionnaire-based information that comes from the data collection site visit. So there's information on social networks, social support availability, social participation, as well as disease symptoms and contraindications for some of the tests. And those contraindications are also useful variables. The neuropsych assessments have not yet been released for the comprehensive, but they will be released very shortly. They should be released by the fall. And so if you are thinking about putting in a data application, it is possible to request the neuropsych assessments as well. They're not currently available on the checklist, but you can add them in the comment section. I've just listed the four tests here that are also available in the tracking component, but there is in fact a larger list of neuropsych assessments that will be released this fall. And here you can just see a list of the hematological data that's available on comprehensive participants. So white blood cells, lymphocytes, monocytes, red blood cells, transcripts, platelets, a number of factors that may be of interest to you. And again, this is all as alphanumeric data. Now as I said earlier, in the baseline we went back and did a maintaining contact interview where we also took or got 30 minutes worth of information from participants as well. And it's both the first set of data and this 30-minute interview that comprises the entire set of baseline data. And so this was done on all 51,000 plus participants. And you can see here that there's some information that's very essential to you as you might want to analyze the earlier data set. So things like physical activity or built environment or wealth or oral health, a number of variables that will be very key. So this just summarizes the data by data set. And again, I show it to you in this way because this is the way that you can search it on the website. So there's the tracking baseline 60-minute telephone interview and the baseline tracking maintaining contact interview. There is also the comprehensive baseline in-home interview and data collection site visit and the comprehensive maintaining contact questionnaire. I want to spend some time now just going over how you would actually apply for data access. And this is all on the web, but I think it's useful to review it and hopefully it will be helpful to you. There's a large amount of information on the website and I would encourage you to spend as much time as you can going through it. But hopefully I'll give you enough of an orientation now that it'll be a lot easier when you do it. So the first thing that you should do really when you're preparing an application is to consult the data and sample access policy and guiding principles because you really do want to be aware right at the outset what can and can't be done with this data. You will also want to review pertinent sections of the CLSA protocol and also look at the CLSA questionnaire. And again, you know, there's such a large volume of data available. It can be quite overwhelming. And so I do encourage you to spend some time with it because I think it will help you orient yourself a lot more when it comes to actually preparing the application. The other thing that you can do is visit the data preview portal and that's also on our website. And what it does is it allows you to search databases directly. So I'll go through this in a minute with you. But it's very, very useful because you can see exactly what's in there and how it's completed. So if you want to know how many people in the CLSA report that they have sleep problems, for example, or report that they have cardiovascular disease, or report that they have some chronic disease, or you want to look at education level. You can go in and see exactly what proportion of CLSA participants have responded to these, how they've responded to these questions. But really the first actual step is to complete the data and or biospecimen request application. And I will show you that again in a sec. Just as a note up front, we will not share identifiable information. And so that includes six digit postal codes, names, and contact information. Any data that you will get will have an ID and all of the rest of the information, but you will never be able to directly access participants themselves. And I'll just give you the website that's French. If you have queries about the alphanumeric data specifically, you should send them to access at clsa-elcd.ca. And if you have queries related to the biospecimen, and in this case I'm referring to the raw biospecimen, you should send them to bbc at clsa-elcd.ca. So this is the front page of our website. And again, I would encourage you to spend some time on it. I think that there are two bars, as you can see the bars up on the bar at the top that have been circled. The researchers bar and the data access bar.  So the very first thing that you want to do is review the application process. So you can see that under data access, the data access bar, there is a bar that says data application process. And then also data application document. And so I would probably start here. You can see that I've also circled the link to the data and sample access policy and guiding principles. And again, I would encourage you to start there. There are two specific data application documents that you need to complete. One is the actual application. And I've just put the front page of that application on for you here. The other is the data checklist. And again, I've just put the front page of it on for you here so that you can have an idea of what it actually looks like. These are on the website. They're downloadable forms. And they're both required for your application. So again, one of the first things that I would suggest that you do is actually go and look at the protocols. So if you go under the researchers bar, you'll see that the protocols are right on the website. There's a shortened version, which is the executive summary. And then there's the full study design at baseline. And again, you don't have to read the entire protocol, but you can search the protocol for the sections that are most relevant to you. I would also encourage you to use the data collection tool. And I think it's really important to use the data collection tool in conjunction with the data preview portal. And so the data preview portal is really good, but it can be confusing. And if you see how the data is actually collected by questionnaire, I think it will be a lot easier for you. The interesting thing is or the helpful thing is that you can actually download the questionnaires. And not only can you download them, but you can actually search them for any terms that you're interested in. So you can search the entire questionnaire for nutrition or food or poverty. And you will get variables come up throughout the data set. But you can see it in the format that it's asked of the participant. Then I would suggest that you go actually into the data preview portal. And again, this is under data access. And the data preview portal really is the gateway to access the data. And it has a variable search mechanism that allows you to search and get simple descriptive statistics. You won't be able to do anything complex, but it really is very useful to see what the data looks like. It's currently only available for the alphanumeric data. So once you get into this, once you get into the data preview portal, you want to click on data set. And it will take you into the data preview portal. And I'm just going to give you a couple of tips now. I don't want to pretend like I'm the expert in the data preview portal. And if I thought I was an expert before, we just moved to a new version of the data preview portal on Friday. And I'm still learning my way around. But it's still an extremely useful tool. And I would encourage you to spend some time with it. It's on a backbone called Mica. And Nailstrom has developed this for us. And it is used by a number of cohorts internationally around the world. And it allows all of the cohorts to record their data or have a database that's in a similar format. So sometimes it doesn't seem particularly useful to us in that it doesn't mirror the questionnaire exactly. But the nice thing about it is that when we use it across data sets, it will be very, very helpful to us. So this is just the front page here. And you can see that there is a variable search tool. And it really is we use it to locate items that we're interested in. We also have a link for variables not yet available. And I will just go through it quickly. But you can see that primarily the data that is not the variables that are not available to you yet are labor force variables and medication variables that have to be coded. These are open-ended text. There are other labor force variables and medication variables in the available data set. But not specifically these ones. But you can look through this first because it's much smaller. If you then go into the variable search, you can see that you can get a fair bit of information. And I will just point out that there are some smart tips for you to get you started right at the very beginning. But this is what the preview, the data preview portal looks like. So you can see that there are, so you can see that right now we've got the comprehensive maintaining contact questionnaire, the comprehensive questionnaire, the tracking maintaining contact questionnaire, and the tracking main questionnaire. As all of the questionnaires are up there, and you can see that there's 5,197 variables. And all of those variable names are currently, will currently be listed. So there's the variable name, the variable label, and you can see which data set it comes from. If you now click on Data Set over on the left-hand side, and I think you do have to actually click on Data Properties and then acronym as well to bring down the box that shows you which data sets are available. So there's the four data sets, as I said, the comprehensive maintaining contact questionnaire, the comprehensive main questionnaire, the tracking maintaining contact questionnaire, and the tracking main questionnaire. And you can click on or off of those boxes as you like. And you can see here that just the comprehensive data sets are clicked. And so then just the comprehensive data collection, comprehensive data shows up. And you can see now that there are 3,362 variables. And under Data Set, all of them come from the comprehensive. In terms of searching, there are a number of ways that you can do it. So again, we're under the Variables tab on the left-hand side. And you can see at the top, you can either use the search bar, and the search bar gives you areas of information or scales. You can use the search fields on the left-hand side. And so you can go by a variable label, like food, or you can go by a variable name. But if you've gone into the actual questionnaire and you know the variable name, you can just type it right in there. You can also use the drop-down menu on the left-hand side. And that gives you areas of information and also construct. And again, I would suggest that you play around a little bit because it can be a little bit hard to not figure out, but to really fully understand. So for example, let's say we can see here that we've typed in depression, and it will give us areas of information. So when we type in depression, areas of information come up. And you can see that we've got the CESD scale and also a scale for psychological distress and emotions. The areas of information really just group together variables that are around a common theme. So these ones are scales, but they can also be categorized around a common theme. But in order to pick it up, it has to be coded, and not every single thing gets coded into an area of information. So just to be aware of that and utilize different mechanisms. You can see here, this is searching by variable name. So now under name, we've just typed in DEP. And what will come up is, for example, variables that come from the CESD depression scale. You can see that there. And again, this time, we'll only get 12 variables coming up. We can also search by variable labels. The thing about going with variable labels is that, again, these have to be coded, and you're not likely to get an exhaustive list. So here, under the label depression, you can see, again, there was a label match for 92 variables. I just want to highlight quickly that you can use an and or function. And so this will allow you to search, for example, we've used depression here. And what happens is if you enter it as an area of interest and also a variable label, then you put it and you're going to see that you only get one variable that comes out. And so it's both identified as a variable of interest and a variable label. If on the other hand that you do it by or, you can see that you'll get much more variables. So this time, they either have to have the label depression or they're part of the psychological distress and emotion area of interest. And in this case, you'll get 120 variables being reported back to you. And you can see over on the right-hand side that by using the advanced basic link here, you can switch how that operator works. And lastly, once we get here, you can see that we get some actual descriptive statistics. And you can see at the top, and I apologize, I know it's not all that clear, but you can see that it gives the question for you. We typed in depression and what came out is from the comprehensive database, has a doctor ever told you that you suffer from clinical depression? And you can see below under statistics that 16.4% of comprehensive participants responded yes to that question. And so this can be very, very useful information to you as you're planning your study. I know this has been very, very quick and very, very brief, but I hope it's been useful. If you do have more information under the data access bar, there is an FAQ section which goes through a lot of this information and should be very useful to you. I do want to just go through the data access feeds with you. We operate on a partial cost recovery model. It's $3,000 for a straightforward alphanumeric data set, and that's for all 51,338 participants. If you require more complex customization, then there are additional fees. There is no cost to graduate students who use the CLSA data for their master's thesis or their PhD thesis. And postdoctoral fellows are eligible to apply for one-three data sets. And it is our intention that if you use the data and you derive variables that would be useful to others, that we'll then return those derived variables to the CLSA data set as appropriate. This just gives you an idea of the process of once you submit an application. So when you first submit an application, it goes through an administrative review at the National Coordinating Center. That's just to make sure that the application is all completely filled out. And it also goes for a quick statistical review at the Statistical Analysis Center. And again, that's to make sure that the variables that you've requested we actually have and that kind of thing. It then gets sent to the Data and Sample Access Committee. And the Data and Sample Access Committee review the application and make a recommendation to the Scientific Management Team. The Data and Sample Access Committee does not do a full scientific review on applications if they have already undergone scientific review. If they haven't, then they will do that for you. Once that happens and your application has been approved, then we require a Data and Sample Access Agreement to be signed. The Data and Sample Access Agreement is something that is signed between your institution and McMaster as the host institution. And that's the only part of this equation that we have no control over. It may take a little bit of time to get the agreement signed between the two institutions. Once we have that signed and we have proof that you have ethics, then the request goes to the Statistical Analysis Center to prepare your data set and deliver it to you. And that process is relatively quick. It's usually done within, I think, 7 to 10 working days. And then once you have the data, you are available to utilize that data on your desktop. You have to be aware of the Access Agreement that you sign. And there are very strict guidelines about how you can use that data. It has to be in a locked room and a secure location. So just make sure that you understand the requirements very strongly. And then at one year, we will ask you for either an annual report or a final report. I do want to highlight to you that there is a funding opportunity to analyze the data set currently available. The Canadian Institute of Health Research has a catalyst grant available to researchers at the moment. In total, there is $1,205,000 available to fund up to 17 grants. The grants can be $70,000 for up to one year, and it's for Alton numeric data only. And I just wanted to...the application is on ResearchNet, but I just wanted to make you aware that it does include a CLSA data request, data access request application as well. We also...the next...you can also apply just directly to the CLSA to use the data. And the next deadline for applications, I believe, is October the 17th. I just want to remind you of my co-conspirators in crime, Tina Wilson and Carmen Durena. And obviously, the CLSA cannot be done without a huge contribution from numerous, numerous people. These are the operations committee and the scientific leads. And anytime you put a list up with people's names, you know that you run the risk of not mentioning somebody really important. But these are the PIs, the site investigators, the working group leads, and key co-investigators. We have a large number of CLSA funders and partners. And of course, we could not do this study without our participants. So, thank you. I understand from Mark there's a number of questions. So, I hope this was useful and I'm happy to answer any questions. Great. Thank you very much, Susan. Yes, there are some questions. I think some of them we can answer fairly quickly. So, let's start going through them. First question from Wasem. Is medication use coded using drug identification number or other coding system? You can just pretend just one of these two, Mark. It's coded using drug identification number. Great. And there's another question. Will the webinar be made available? Yes, we'll have a slide on that shortly. So, stay tuned. When will data from the first follow-up be available? Good question. We won't finish collecting data until mid-2018. So, I would say that at the very earliest, it would be late 2018, early 2019. Great. What were the baseline exclusion criteria? So, off the top of my head, you have to be in the living in the ten provinces. Individuals who were on reserves or crown lands or in the Canadian Armed Forces were also excluded. Susan, any others that I didn't remember? You had to be living in the community at base lighting. You had to be not cognitively impaired. That's my closer to importance. Community dwelling and not cognitively impaired. Are the data going to be accessed through data research centers? I think that this question came early on in the presentation. And you already answered that, so you apply directly to CLSA. There is some talk about putting some data in the data research centers. We haven't been...we are in conversation as to whether we will do this. But one of the things that we wanted to do was make the data more accessible to everyone. And that's why we've gone this route directly. Which isn't to say we can't use the data research centers as well, but it requires some working hours. Great. Another question. Can you provide a brief overview of the short diet questionnaire from the comprehensive component? No. I think it would be much better time spent to look it up in the questionnaire. It's on the website. Yeah, that's right. Sorry. Go ahead. Sorry. No, I was just apologizing for saying no. Right. The short diet questionnaire it contains just really briefly. It contains several questions asking about your frequency of consumption of various different foods. So it could be things like poultry, white meat, red meat, the stuff that you drink. It's a very intensive questionnaire off the top of my head. I can't identify all of the various food groups that it asks about. It's very comprehensive. So you're asked, do you consume this particular type of food? And if yes, what is your frequency of consumption? And is that daily, weekly, et cetera? But yeah, the questionnaire is available in the Data Preview Portal for viewing. So you can go to it. And Susan has already provided information on how to access that information. Are there any differences in questionnaire contents between telephone interviewing and in-home interview? Are the contents similar? And I think I can answer that very quickly to say that there is overlapping contents. So everything that we ask in the telephone interview questionnaire is also asked at the in-home interview. But there are some additional questions in the in-home interview or of participants who go to the data collection sites. So for example, everyone is getting the REI, mental alternation tests and animal naming tests. Those are cognitive tests. But people who are in the comprehensive component are getting additional cognitive questions. So I think unless, Susan, you have something to add to my answer, the best way to understand what the differences are is to go to the Data Preview Portal to see the questionnaires which are available. Yeah. And I would like the data, I would go to the actual questionnaire because you can download the questionnaire, you can search by the questionnaire. Once you're familiar with the questionnaire, then it's good to go to the Data Preview Portal. It's hard to go to the Data Preview Portal right away because it doesn't run through the questions in the way the questionnaire does necessarily. Yes, that's right. I keep thinking Data Preview Portal because I'm on it a lot myself. But no, you can download the actual questionnaires and that would be a great starting point. It appears serial biospecimens will be collected every three years. And the guess is that we could confirm that that's the case. Participants in the comprehensive component will visit the data collection site every three years.  Yes, they will. We won't always process it in exactly the same way all the time because, for example, there's some genetic pieces that we don't need to keep doing over and over again. But there are other things like metabolomics or epigenetics or the hematological markers that we do want to have repeated. Is there a special procedure to request for linkage with administrative data such as hospitalizations and deaths? It's a very good question. At the moment, we're working very closely to try and create linkages with administrative data so that you become able to request the administrative data and the CLSA data at the same time. We're working closely with Kaihai. We also have relationships with all of the provinces. We don't have it worked out completely yet. At the moment, and specifically for the call by CIHR, you can only request alphanumeric data, the alphanumeric data that's available. There is no opportunity to link to administrative databases through that call. But if you would like to link within province to an administrative database, what I would suggest is that you get in touch with us either through the access or the info line. Great. Okay. So will the CIHR CLSA catalyst grant support access to biospecimens? The answer is no. It is only for alphanumeric. That is questionnaire-based data. Let's see. How long will it take for the application process? I guess that's the data access application process. Susan, would you know what the average timeframe is from submission of a request to getting an answer? It's very hard to say definitively.  So you can see that it goes for the administrative and statistical review pretty much right away. But then there's a little bit of a timeline as the data and sample access committee reviews the application. Then they actually meet. Then there's a turnaround time when they have to make the recommendations that goes to the scientific management team. That whole process takes about six weeks. But where it gets hung up and where we can't guarantee the timeframe is around signing the data and sample access agreement. If I could give anybody any bit of advice, that's the place where you have to be completely on top of things because it can fall through the cracks between institutions. McMaster is really good because they're used to doing these all the time. But your own institution may not be, and you need to be really engaged in that process. Again, once that's signed, it's a very short period of time before the data set can be prepared for you and released to you. Great. Does the project require approval from CLSA before it can be submitted to the CIHR funding call? And I believe the answer is no. You don't need prior approval to apply to CIHR in August for the catalyst. Is that correct, Susan? Okay. Here's a comment. Someone has received this data earlier. Do we automatically get, this is an individual, they've gotten data from the telephone sample. Do we automatically get the rest of the data from the in-person group as well? And I would imagine the answer is no. You would have to submit a follow-up data access application. Is that correct? Yes, you would. Okay. So Andrew Wister, one of our local principal investigators from British Columbia, he's asking, he says, is there a glitch in the search function in the data preview portal? And then he talks about some issues that he had. So we'll have to look into that. I would just, let me just say for a sec that for those of you who are used to using the data preview portal, we did upgrade it on Friday so that it's a newer version. It's actually easier to use but it's different than it was before, slightly different. So some of you may have, you may just have to adjust to getting used to it. But as far as I'm aware, there's no glitch. I've been using it a lot this morning and it seems fine. Okay. Aside from the list of tests provided today for the biospecimens, is there a full list of tests you intend to conduct in the future or does this depend on requests from potential investigators? That's probably referring to analyzing of the biospecimens, that question. Well, I don't really know how to answer that. There is a full list of all of the tests, the physical assessment tests that get done and the hematological analyses that get done and they're on the website. There are large, so there are also a number of analyses that we're going to be doing and funding for to do as part of the first follow-up. So we will be doing some genetic tests. We will be doing some epigenetic analysis. We'll be doing some metabolomic analysis. Those will not be available until 2018. Great. Thanks. When will the titles or topics of approved projects be released to researchers who have made a primary application? We'll be released to researchers who have made a primary application. So all of the topics of approved projects are posted online and you can go there and see them. I don't know if there's anything you want to add to that season, but they are available online. Yeah, no. There are a number of projects that were submitted for the, I think it was the March deadline that will be, the results will be conveyed to applicants very, very shortly. And then there are a number of applications that came in for the June deadline and they will be reviewed or they have, I'm getting confused now. Yeah, they will be released to, the results will be released to investigators shortly. Great. Great. Is there a variable to examine rural versus urban? There isn't currently a variable for rural versus urban. And it is possible to generate it, but we actually don't release the full postal code. If someone is interested in categorizing that, we can, well, actually, I believe that we have started to do some work on that. I actually have to respond to that individual person myself unless you know the answer right now, Mark. Well, I can tell you what I did. I was given access to the first three digits of the postal code and using that information, you can categorize urban rural, but that's based on Canada Post's definition of urban rural. Right. And so upon request, we can release the first three digits of postal code, but it's not part of the regular data set. It requires generating itself. And I do believe that some people are doing this. The problem is, as you see, there is, you know, we can return some of those derived variables to the data sets, but people haven't finished analyzing their data yet, and we couldn't add it to it till the next wave. So I think people will have to generate it individually until that point. Yeah, and it's fairly easy to do that. The drug names were not coded. Can we consider submitting a project, including drugs to CIHR catalyst grant, and when will the drug info be available? I don't know the answer to when the drug information is available. I know that it's being worked on, but it's very labor intensive. I will get back to that person. I just got a text from a mentor who said that urban and rural is available. Great. So, question. Someone is wondering if the interviews were conducted in English and French only, or were participants offered interpretation, and the answer is English or French only. Can the impending legalization of cannabis, given that, has there been any thought given to integrating more detailed questions on cannabis use into follow-up? So can you repeat that, Mark? Given the impending legalization of cannabis, has there been any discussion of including more detailed questions on cannabis use in future waves of the study? It's not discussed in the past. It's not currently on the table to be added, but it's certainly worth considering in its possibility. How will participant death be captured? Through death certificate matching or some other means? Yes, it will be done using vital statistics, but we often learn of participant death by having family members contact us as well. But it will all be done through linkage with vital statistics. Can data access be expedited in any way for graduate students, especially master's students? No. It's already a reasonably expedited process. It's no longer than applying for any other data set, I would say, and it's certainly a lot faster than collecting your own data. So unfortunately, it's such a process that's required in terms of review that we really only can do it in a regimented fashion like that. Beyond administrative application errors, can an application be rejected based on study design or significance? Policy is to reject as few applications as possible. We always make sure that it's either undergone scientific review or we will do a brief scientific review ourselves if it hasn't undergone scientific review. And we always want to make sure that it stays within what our participants can send it to. That said, it's really difficult to think of a research question that wouldn't fall within the consent process. That would be outside of understanding adult development and aging. So on occasion, we will ask an applicant to resubmit something if we think that it hasn't been fully articulated, but we rarely reject it completely outright. Great, thanks. So we're at 105. There's a couple of questions left over. We'll have to defer the questions about cannabis to another time. If there's a question about REB ethics approval, you'd have to check with your local REB to determine what they require for access to secondary data. So I think that we're over time and I'm getting pressure to put an end to this webinar. So Susan, this was really informative, I think, especially given the upcoming CIHR Catalyst Grant. This webinar has really helped orient people towards what the CLSA can offer and it will be very helpful information with respect to applying for that grant opportunity. The deadline is August 30th for that CIHR Catalyst Grant opportunity. Susan, on behalf of all of us here at CLSA, thank you very much for your very informative presentation today. Great, thank you very much. Thanks, and you can see, for final reminders, there's information on the CIHR funding opportunity at the links that you see pasted there. The presentation slides and webinar recording will be posted to the CLSA website. You've got the links there and we'll be back with the seminar series in September 2016 and we will hopefully be having researchers present the results of their projects that have involved the use of CLSA data. So check our visit for websites and on behalf of everyone at CLSA, thanks for making the webinar series a success and we'll see you in September.


Okay, so I think we'll get started. Thank everyone for coming to this presentation. So my name is Patrick Newman, and I'll be talking about panics and depressions in early America. So the basic gist about this presentation is we'll be talking about business cycles before the Federal Reserve. So the Federal Reserve was founded in 1913. And since the financial crisis of 2008, you've had a lot of discussion about central bank monetary policy, and just in general, counter-psychological policy, expansionary fiscal or monetary policy. And some of this debate or some of this discussion has centered around, you know, can we survive without the Federal Reserve or just the country in general? Can it survive without the central bank? There's been a lot of blame placed on central banks for causing the financial crisis and a weak recovery. And usually when those attacks are sort of levied against the central bank, the response is, well, we still have to have one. It's better than the alternative, right? And because the alternative is almost always described as well before the Federal Reserve, as I'll explain, we had very severe business cycles, they were very frequent. And it was only when we had a central bank, this was the third central bank that we had basically, that we were able to basically iron out those business cycles. So that's just sort of the general motivation. And what I'll be going through is sort of a brief overview of business cycles and panics in the 19th century. And then I'll be going through one case study in particular, the Panic of 1873, as well as the Depression of 1873 to 1879, okay, in depth, all right? So the ensuing depression. What makes this depression significant is that actually according to modern recession dating, it's the longest recession on record. So the Great Depression, even though we all know it started in 1929, ended due to World War II, right? That's what we're taught here. Ended after World War II. The actual recession phase at the beginning was at least only the Great Contraction of 1929 to 1933. So this recession or this depression, excuse me, actually beats it in terms of length, at least the way it's traditionally defined, okay? So the general myths that you hear about when you discuss life before the Fed was that America suffered from frequent and severe business cycles in banking crises, okay?  It took the depressions, it took the long time for the economy to recover and then almost always intertwined with these recessions were these very harsh banking crises. So we discussed like a banking panic. A panic just sounds awful. You know, you have a panic, everyone's in pandemonium. You know, they're trying to get money from a fraction reserve bank, they don't have enough money, the banks have to close, the contagion spreads, et cetera. All right? And what's the way it's almost always described is to say, oh, these severe business cycles in these banking crises were due to the unregulated financial sector, supposedly, as well as general laissez-faire macroeconomic policy. So the idea is that, well, before we had governments step in to sort of clean up the financial sector and the mess, you had all these problems. And it's a very similar narrative to whenever you hear about the financial crisis, say, oh, well, before the financial crisis, the federal reserve and all sorts of regulators, they turned a blind eye and you had all this deregulation and you had this commercial investment banks that were allowed to join forces once again. Bernie Sanders sometimes rails against that and then you had this whole financial crisis and then you had to have Dodd-Frank and all sorts of other stuff to really fix it up. So this might come as a surprise, lecture for me is you, but I'm gonna argue that both are wrong, okay? In fact, the financial sector was too heavily regulated and it was actually, in a sense, sometimes not laissez-faire macroeconomic policies, right? And also the business cycles were not nearly as frequent or severe as there have been commonly portrayed, okay? At least modern research has shown this, though modern researchers still somewhat refuse to somewhat grapple with the new data. So we talk about business cycles and panics in the 19th century, older analysts, both contemporaries, so someone in the 1870s, as I'll describe, as well as someone, let's say in the 1950s or 1960s, looking back on the period, they relied on faulty economic data, right? So one of the things that, if you take it a macroeconomics course, you know, there's statistics like GDP, unemployment, inflation, various production indices and so on, those haven't been around forever. Basically, people had better things to do before they started to collect this data, particularly in the 1920s and the 1930s. So when you look back, you know, in older periods, you have to use basically less reliable data, as well as contemporaries during those periods when they're trying to find some sort of measures of the economy, they might be saying, all right, let's look at the stock market or let's look at prices. Consumer prices are falling, wages are falling. Okay, that must mean that real wages or real prices, you know, are falling. People are getting poor or, oh, okay, the financial sector is contracting, et cetera. As we'll see, it was generally relying on faulty economic data and they often confused deflation with actual economic growth, all right? And so the NBER chronology, which is the National Bureau of Economic Research, they're basically the organization that is in charge of dating expansions and recessions in the American economy. They still sort of use this old faulty data. They have not revised their business cycles from the past, even though much more improved data, statistical methods, et cetera, has come out recently that has actually shown that a lot of these depressions were nearly as severe as we thought they were. So here's just a sample of some of the papers and the research that's come out. And notice that again, these are in mainstream professional journals, journal of economic history, journal of macroeconomics. If you don't have access to these articles, you know, just the great place you can go is measuringworth.com, all right? So it's very easy to look at. It has a continuous GDP series. You'll get things like real prices, wages, all sorts of things, even comparing it to different countries. It's a great website. And these sources really show that actually it's not as bad as we thought they were, all right? So there's the old saying. It's that a picture is worth a thousand words. And I'll explain this and there's another slide that will show it much more blown up. So the top part of the picture is the annual NBER recessions sort of from expansion to recession. In the black bars, you can't really see the numbers at the bottom, but it starts at 1790 and it goes to 2000. And the teal right there, that is the 1870s that we'll be talking about and it's also down here. So those black bars represent recessions, all right? So the Federal Reserve was created right around here. So just notice looking at the old NBR data. Look how many more recessions, how much more severe they were, et cetera, with now, right? So there's a lot less, the black bars are much less frequent and they're also much thinner, okay? So that's the old NBER recessions, you know, they're dating. And when you look at it, you say, well, the actual, the evidence is pretty convincing then, at least it did seem like business cycles were much more severe. However, here is the alternative recessions dating created by Joseph Davis. He was one of the people on the PowerPoint slide before. He created a new continuous industrial production series from about 1792 right before the Federal Reserve and used it to basically date recessions. So for how long did the economy contract, okay? Before it started to recover. And just look at the enormous difference. And I mean, look, it looked like it was a giant depression and now it's gone, right? And then you can even see elsewhere. So this has remained the same, but this has not, okay? It looks much different, all right? And you can, in fact, so this is just the pre-Federal Reserve, it just shows it, you know, a little bit bigger. You can see that, okay, again, in the 1870s, you know, the NBER said it was from 1873 to 1879. Well, Joseph Davis, as well as we'll be talking about later, said that the depression lasted from only 1873 to 1875. And there's many more examples. Even the period are in the 1820s. Again, that's just drastically gone.  And then just to at least go back briefly, this paper right here, the New History of Banking Panics in the United States has done a similar thing. So from 1825 to 1929, right before the Great Depression, developed a, you know, trying to study basically whether or not what was considered banking panics back then really qualifies what we called banking panics. And he found, this paper found, you know, similar results that were a lot of banking panics were not actually banking panics, okay? So even banking panics and recessions were both less severe and less frequent than we thought, okay? And this is now, instead of relying on older, faulty economic data, basically relying on new methods, at least GDP series, et cetera, to show that this was not the case. All right, so we got that. And all right, so when we talk, at least now we'll go into a little bit more of the theory instead of just the data. This is me as university after all. We talk about business cycles. I'm not, hopefully I'm not surprising anyone here when I say business cycles in bank crises are due to government intervention. And of course the general framework that we all know and love is Austrian business cycle theory. So what causes a depression, all right? Or what's now called a recession, as we say, well, it's due to some sort of expansionary monetary policy from a central bank, okay? Or from some other sort of government entity, all right? That allows fractured reserve banks to engage in credit expansion. This artificially lowers interest rates, sets in motion an unsustainable boom, all right? Then if it continues, you're either going to have hyperinflation or you're gonna have some sort of contractionary monetary policy, okay, either caused by the gold standard or something deliberate, put the brakes on the credit expansion and then you have the bust, okay? And that in general, that just naturally puts pressure on the banking system, okay? Why would it put pressure on the banking system? Well, as we know from Austrian business cycle theory, when the bust occurs, the unprofitability of the higher orders are revealed. So you have all these malinvestments in these sectors of the economy, these industries have generally been borrowing money so they can't pay back their loans. So that means banks now have to write off these loans, right? So they have to take a cut on their equity and they can teeter more towards insolvency so you could have a prominent bank that might fail, just in general from a business cycle and that in turn could cause other banks to fail, could put pressure on the banking system, et cetera. However, in many cases, banking banks can be at least in the free market, they can be quite resilient. What adds to this is it was particularly acute in the United States and what's made these bank failures and bank panics so frequent was that you had a whole plethora basically of government intervention on top of the expansionary monetary policy that basically made the banking system very fragile, okay? So on one hand, you had various branch banking restrictions, okay? So a branch banking restriction, basically a bank could normally open up a branch across state lines or even sometimes in another, in the existing state. Instead, and this is strategically lobbied for by banks, is that they blocked out competition from other states or sometimes from other regions of a state. So instead of a branch banking system, which is what we have now, you can go to a Bank of America, not just in California, but also in New Jersey, Florida, et cetera. You often had a unit banking system, which basically meant that the banks were poorly diversified. They didn't make loans in other areas of the country. They generally stayed local. So for example, if a crop harvest went bad, you had a lot of small banks that basically they didn't diversify their loans and they were really hurt by this.  Imagine if retail stores could not open chains across states. You'd have a lot of inefficient companies, basically. On top of this, you had various laws that incentivized banks to hold part of their fractional reserves in the form of deposits at other banks, okay? So as we'll be talking about, there was something known as the national banking system back in the day, where you had a system of country banks holding part of the reserves in reserve city banks and these reserve city banks were holding part of the reserves in central reserve city banks. So you actually had, instead of each fractional reserve bank pyramid and credit on top of its own reserves, they were all pyramid and credit on top of sort of centralized reserves, somewhat similar to the Federal Reserve now, okay?  And we'll talk about this. And then lastly, or at least this is not really the last restriction, it's just the one I'm gonna talk about here, is that many banks were basically forced if they wanted to issue notes, before the day the Federal Reserve issued actual handheld currency, banks used to issue bank notes, then that meant that if they wanted to issue notes, they would have to invest some of their money in government bonds. And the idea was that, oh, government bonds, if the bank fails, they'll be able to sell these credit worthy, these strong instruments, these are solid investments, they were not, particularly at the state level, in that many of these banks were fragile because of that, but if the public wanted to hold less deposits in more notes, many of the banks, they say, well, we have to buy more bonds, we can't buy any more bonds now, so you'll have to actually take our reserves right now in terms of our species, such as gold and silver. And so that put pressure on the banking system, made them closer to becoming illiquid, all right? So all of these factors, and many of these were really unique to the United States, or at least they were unique in their, in how often they were practiced, really made our banking system quite fragile, all right? So there were other banking systems that were not pure free market systems, but they behaved much more efficiently during this time period. They still suffered from problems, but they performed much better, such as Scotland or Canada, even though again they had problems, they were much better than the United States, and they suffered from far fewer banking crises. So basically the general outline for a business cycle back in the pre-federal reserve era was you had some sort of expansionary monetary policy caused by the government. This creates the Austrian business cycle theory we all know and love. And then on top of a fragile banking system, this would cause a bank panic, okay? So if anyone's seen one of the old episodes of The Simpsons, I always love this episode where Mr. Burns, he's very sick and he goes to the doctor, he tries to check on his health, and the doctor basically explains that he has so many diseases that they're all blocked through the door. And if one of them were to get through, they would all basically be able to go through. And what Burns says is, oh, that means what you're saying is I'm indestructible, right? And the doctor says, oh, no, no, no, I'm saying even in fact a slight breeze, and then Mr. Burns just walks out of these, indestructible basically, you know. He just sort of walks out, you know, like he cares about nothing. Now it's really the banking system where all it would take is just one minor thing and the whole thing tumbling down, but people just said, oh, well, just, you know, everything would have to happen in a certain way. So, you know, it's just never gonna happen. And it would frequently happen. Mr.  And that's just how I've always imagined this as well. Okay, so I have actually done some of my earlier work. I've done a lot of work on American business cycles and banking regulations. I would tell you it's the least favorite part of my talk, but I would be lying. So here is some of my work on the depression of the 1870s, which we'll be talking about. The depression of the early 1920s, as well as the origins of the national banking system. So sort of going through the special interest motivation behind what actually caused the national banking system that later led to the depression of 1873. So I'll be going through basically both of these right here and I highly encourage you to read them. If you're up at night and you can't go to sleep, just print one of these out.  So it's the tried and true method, I swear. So anyway, okay. So we go to, now we'll look at a case study in depth. So I've gone through saying, all right, the actual business cycles were not caused by laissez-faire. They were not nearly as severe or as frequent as we've all been told. So now let's actually look at one in depth. Okay, so to really prove to you I'm not lying. All right. So the general outline, I've said of the 1870s business cycle was that in the early 1870s you had a boom, particularly in railroads. So railroads was a new sort of transportation back then. It was a higher order lengthening the production structure. Okay, it allowed people to travel distances much faster but more importantly, goods and services linking the West to the East and so on. In this boom in railroads during this time period was primarily due to credit expansion following the national banking system. So as we'll talk about the national banking system was created in the midst of the civil war, some sort of massive government overhaul as we all know, wartime crises always make for great opportunities for passing legislation. And that was true then, it's true now, it was true then. And as we'll talk about the system, much like the Federal Reserve was due to special interests in private bankers. So for those who have read Murray Rothbard's work on the origins of the Federal Reserve, he sort of goes through how bankers, trying to organize a cartel, they met at Jekyll Isle and then they pushed through a bank bill through Congress, et cetera. This is in a sense fairly similar, okay, as we'll see. The panic of 1873 was due to basically the inevitable slowdown in credit expansion combined with the fragile banking system. And then the contemporary, excuse me, both contemporary and as well as many present accounts say that, all right, it lasted until 1879. This very long six year depression. But in fact, as I'll show, there's really just a mild recession that lasted only until about 1875. Then there was recovery. So the panic of 1873 often ushers in something that's known as the long depression.  Of course, that's not true. Basically the long depression was really a myth. It was just deflation. There were banking crises punctuated during that, but it wasn't some continuous depression, okay. All right, so let's go into a little bit more in depth. Okay, so we're at the Civil War. So the year, it's no longer 1784, it's 1861. Remember it like it was yesterday. So 1861 to 1865, you had various Civil War interventions. So in late 1861, the United States, and bear in mind, I'm concentrating on the union here, the Northern and Western States, suspended species payments. So there were various reasons for their pressure on the banking system. The government was basically trying to borrow too much from them. So then the government and the banks suspended species payments, so they were no longer, we were on a gold standard back then, but now they were no longer honoring their liabilities and converting them into gold and silver, okay. So we're really on, as you could say, a fiat system, or more particularly, known as credit money, because they knew we would resume species payments in the future. It actually took us nearly 20 years to do so. Then beginning in 1862, we started to print, the US government started to actually print their own handheld paper money until about 1864, and they were known as green backs. And the green tent, that's where we actually get the, you think of the actual dollars you hold now in your wallets, it had that green came from the green backs of your, all right. They were bigger sized though, but that's a whole different story. All right, so then in 1863 and 1864, we'll go through the motivations behind this in more depth later, but you had the national banking acts, all right. So the United States, if for those of you who remember your American history, you know, under Alexander Hamilton, we had a first bank of the United States, or we had the Bank of the United States, it's charter, got vetoed, excuse me, it chartered in pass, then we had the second bank of the United States and Andrew Jackson famously vetoed that bank. And so beginning at the Civil War, we did not have a central bank, all right. But now that during the wartime emergency, wartime crisis, we basically try again and we'd push for a quasi central banking system. All right, so we did get that and we got the national banking acts. And this created federally chartered national banks. So instead of a system of state banking, you now had federal banking where banks had to apply for charters from the federal government. And if they wanted to issue bank notes, which was basically the lifeblood if you wanted to be a bank, you had to, they had to be backed by federal bonds, right. State banks could still exist, but it also just so happened that if you wanted to issue state bank notes, you had to pay a 10% annual tax on those, which basically totally removed, there are no state bank note currency anymore. State banks still survive due to deposits, but that's a whole different thing. It also created a credit pyramid that's three tiered structure that I spoke about before. You had country banks holding part of their reserves in reserve city banks. Then you had reserve city banks holding part of their reserves in central reserve city banks. And at the very beginning, the only central reserve city bank was the center of civilization on the earth. That was New York city, at least in my opinion right there. So New York city basically acted, a lot of the banks in there acted as a, in a sense, central banks. You had the comptroller of the currency, which we still have today, and they had to institute capital and reserve requirements. And these capital requirements in particular, where you had to hold a certain amount of equity, made it very hard for smaller banks to operate.  And then quite crucially, it basically perpetuated in a sense, accentuated the unit banking system, where it was hard for banks to branch. Where basically by the turn of the century, you had like 11,000 banks, but there was like less than 100 branches, in terms of commercial banks. So any fish in banks, banking system. Okay, why is this, oh, here we go, why that was there? All right, slight panic, slight banking panic just hit me right there. So all right, so we talked about the special interest behind this legislation. So you have an investment banker, Jay Cook, and secretary of the treasury, Salman P. Chase. So Jay Cook is a prominent investment banker in Philadelphia. He was the forerunner, he was the country's most prominent banker until J.P. Morgan in the 1870s. So Jay Cook was located in Philadelphia at that time, Philadelphia was a very prominent financial center of the country. So there was a various connection between Jay Cook and his brother, Henry Cook, and they lobbied to make Salman Chase Treasury Secretary at the beginning of President Abraham Lincoln's term. All right, so they spent a lot of money beforehand in the 1850s elevating his candidacy. He was governor of Ohio and all sorts of other stuff. And then they basically lobbied to make him secretary of the treasury. And then, what would you know? It just so happens that Chase was later impressed by Cook and Co's ability to market, sell government bonds and he grants Cook and Co, the investment bank, the monopoly on selling bonds, okay? So selling federal bonds, the U.S. government, they had to sell their debt to basically prospective buyers. And Cook and Co would basically, you know, buy the debt, then sell it and sort of pocket a commission, okay, from the government. So then Cook supports Chase's subsequent plan for a national banking system. And this was to, you know, why would Cook do that? Well, if Chase's plan for a national banking system involved banks, if they wanna issue bank notes, they have to buy federal bonds, all right?  Well, it was these guys right there. So they realized, okay, this is a way of making, basically my life easier. So, you know, that was one of the main reasons why they supported it. And you go through the record and we'll sort of go through a quote. They really engage in a lot of newspaper propaganda. They talk to senators. They really try and persuade them, et cetera. And then what do you know? Cook sets up his own national banks including in New York City. So by 1870, Jay Cook had actually, you know, transformed himself from a relatively, you could say renegade investment banker to really the most prominent banker in the country. And there was something known as the House of Cook. So much like you have the House of Morgan and so on. You know, later on you'd have the House of Cook. So for example, the fourth national bank of New York was really Cook's Bank and it just so happened to be the largest bank. It was the first largest bank in terms of assets. And then the second largest bank in terms of bankers balances. So acting as that sort of quasi central bank at Central Reserve City Bank. Okay. And then he had many other banks, et cetera. So he was in a sense, he helped set up this system and then he was directly profiting from it. Okay. So here's an interesting quote. So this is part of the paper that I wrote. So this is February 12th, 1863. So this is right around when they're trying to lobby the national banking system. So Henry Cook writes to his brother. He says, it'll be a great triumph Jay one to which we have contributed more than any other living men. The bank bill had been repudiated by the house and it was not sponsored in the Senate. And was this virtually dead and buried when I induced Senator John Sherman to take hold of it. And when we went to work with the newspaper. So I have that bolded. So he goes, I induced. All right.  All right. There's some sort of advantage there for Sherman. And then we went to work with the newspaper. So he said, all right, we're going to spread the gospel of the national banking system. And then he says, I'm very sure that Chase appreciates our efforts and knows that he can never fully know the value and efficiency of our services services. And there's many other letters between them around this time period. They're basically saying like, hey, we were crucial in making, in shepherding this bank bill through Congress and really getting everyone to support it due to the crisis of wartime emergency. And in fact, John Sherman, when he was talking about this bank bill in the Senate, he was saying, well, if we had a national bank banking system before 1861, there would have been no civil war. I was just a little bit of a stretch, but you know, again, he wants to argue for something. He's free to do so, I guess. So, okay, in 1870 to 1873, after the civil war, there was some slight contractionary monetary policy. And overall the money supply was roughly constant. Then from 1867 to about 1870, there was very limited credit expansion, which basically meant that the money supply was increasing by less than 3% yearly, which is not a lot. It's not perfect, but it's not, not a lot, okay? Then from 1870 to about 1873, there was much stronger credit expansion. And this was basically due to the national banking system maturing. Banks were slowly pyramiding their credit and basically this crucial year, 1870, where enough of them did so, it was a lot easier for them to engage in credit expansion. And you just look at the jump, the money supply increased from about 3% yearly to about 10%, okay? So a much significant increase in credit expansion, okay? So this boom was created during after the national banking system, took a couple of years, but they got the job done, all right? In a sense, almost similar to the Federal Reserve, we think about in 1913, it was created, then it kind of couldn't work its magic, at least in terms of an Austrian business cycle theory because of World War I, but then right after it did and you had the Depression of 1920 to 21, okay? So somewhat similar analysis here. All right, so now looking at some more statistics in particular, there are major malinvestments in railroads and sort of higher related, in sort of related higher order goods, excuse me. So you had things like metals, fuels, machinery, and so on, all right? So we listen to Austrian business cycle theory, we say the lower interest rates increase the present value of long-term production processes in various higher orders the most. So businesses are going to be incentivized to embark on those, okay? So we look at a money supply figure. So again, just notice the difference yearly, about 3% increase to then about 10%. Notice the pickup in GDP from about 3.2% annual growth to about 7.2%. The CPI, so we'll talk about that in a second. So wholesale price indices in the Joseph Davis production index shows the relative movements really explainable by Austrian business cycle theory. So we know Austrian business cycle theory is that the relative prices of the higher orders are increasing as compared to the lower orders. And on top of this, basically the relative prices of the lower orders and consumer goods are increasing compared they were to before, all right? So we know from sustainable growth that consumption declines, all right? Lower order prices of lower orders fall and then the higher orders rise, so sort of that fulcrum. In this case, consumption is not declining. The public hasn't saved anymore and said it's just credit, okay? And you can see this at least through the CPI, so even though prices are still falling, they're still deflation, it's falling by less, okay? So it signals that basically there isn't actually savings going on, at least there's one quick way of looking at it and the economy. And then you look at a production index for machinery that's just one we'll look at. So in the paper, I have a whole list of basically comparing lower orders and higher orders, et cetera. You look at the jump from about 6% growth during this time period to 11%. And in this component, the major component of machinery was railroad locomotives. So this is really a proxy for railroads, okay? All right. So then you have the panic in recession. So by late 1872, 1873, there's the end of the boom and then there started to be investor uncertainty. So there was a slowdown in credit expansion. All right, this led to a decrease in railroad profitability. This led to a decrease in the value of railroad bonds. Then this led to an increase in the losses of banks. So around this time period, the US government were no longer in the Civil War. They're no longer running deficits. So Jay Cook and Co, they don't need to sell as many government bonds. So what do they do? Well, they switched to selling railroad bonds, okay? So they were invested heavily.  Jay Cook and Co, they decided to flex their muscles, get involved in selling Northern Pacific bonds. Just so happened that they had way too many of them. It was a huge loss for them. Again, so they were really hoisted by their own patard in a sense that they were, they had to fail because the banking system they helped create later caused a business cycle that ruined them. All right, particularly Jay Cook. So the investment bank Jay Cook and Co fails in September 1873 and a panic begins. Okay, so it was around September 20th. The stock market closed for 10 days and cash payment was suspended until October 1873, all right? And what this actually does is aggravated uncertainty. Some of you will hear about how suspending species payments allows banks to sort of shore up their balances and so on. But it made it harder actually for people to buy goods and services because they couldn't get money from the banks. And it actually just led to, oh, if banks are gonna suspend, this bank's going to suspend, et cetera. And then the contagion sort of spread. And so you look at comparing 1870 to 1873, the money supply still actually increased because the government tried to print some greenbacks to shore up the situation, but it wasn't really successful. GDP drastically declined where, it didn't actually decline in absolute terms, but it was still inching along, eking out at existence. The CPI fell, okay? So this is just from the decline in nominal spending, sort of the uncertainty that accompanies a business cycle. And then look at machinery, railroads fell off a cliff where instead of rising 11% each year, they fell by nearly 18% in both years. So not just 18% in total, it's 18% each year of this. So it's a huge decline. So railroads, the classic higher order good that was hit hard. And what's important is if you actually look at the other series, the higher order goods were hit the hardest in the lower orders, not so much. So some of them actually increased. And this shows, this is the relative price movements. Not everything gets hit the hardest, excuse me, not everything gets hit equally is bad. So like the housing sector was hurt much more than other sectors of the economy. That's sort of that structure of production analysis of our stream business cycle theory. So then we go to the long depression. So 1875 to 1878, this sort of period to quote the long depression, you can go to 1879, but in 1879 growth actually increased. So significantly regardless of what series you use. So just to be charitable, just just look at 1878. So it makes it look like I'm not artificially trying to inflate my numbers. So when you actually look at the series, the recovery began in 1875, although prices continued to fall and there was actually monetary contraction. So now enough banks fell, whereas we'll see that there were bank failures and the money supply declined. And there was a laissez faire counter cyclical policy, which meant that there was basically no counter cyclical policy. There was no expansionary monetary policy and there was no expansionary fiscal policy. Now, what happened is you had this nominal illusion from deflation. So as I mentioned before, prices were falling, prices of stocks were falling, wages were falling, incomes were falling, profits, interest rates, et cetera, in this fooled contemporaries. Because people were only concentrating basically on their output prices. So they didn't realize input prices fell or if they were a worker, they didn't realize that consumer prices fell. So if your income falls by 5% a year, you might say, oh, I'm getting 5% poorer, but if the prices of good you buy also falls by 5% or even more, then you're actually getting, you're actually better off. You can buy more goods now. But this is sort of harder to realize. Instead the nominal illusion, so to speak, generally affects deflation because people like to see things go up, not go down. So a historian, and this was even back in the 50s and he admitted as much, he said, contemporary appraisals of the intensity of the depression tended to be more, the more alarming by their very vagueness and contributed to the prevailing pessimism. So everyone was just like, oh, we have this really bad depression. As we'll see, they were sort of had these faulty economic statistics. People didn't really know what was going on. So they just sort of exaggerated the problem and this made it seem very bad. So if you look at just the example statistics, this organization estimated that New York unemployment 1873 was 25%, right? Which is astronomical unemployment. The issue though is that they calculated in a way that we would totally find faulty and that they just looked at the amount of people going to a relief organization each month. They said all of them were unemployed, like a charity relief organization and they just added up each of them. So like if 10,000 people went in January, 10,000 people went in February, 12,000 people went in March and so on and they had the total amount, then they divided it by the workforce in New York City and they said that was the unemployment rate. Well, of course, what they don't realize is that they're assuming that each 10,000 people are completely new. Like if it's just the exact same 10,000 people, then the unemployment rate is not 10,000 plus 10,000 plus 10, it's just 10,000 or whatever it is, okay? The Chronicle newspaper, prominent newspaper at the time, they said that the amount of people who were unemployed in the construction industry was actually, they said it was this large amount and what estimates later showed is that it was actually more than the total amount of people employed in construction, which is a little bit impossible, basically. They said more people are unemployed than even what existed in the construction industry. More people are unemployed in construction than in that industry, so it makes no sense. So there are no doubt that people were mad and many workers in the railroad industries did have it very tough during this time period, but again, that was just due to the reallocation of resources from the railroad industry, but just in general, this was sort of part of the beginning of an enormous change. You had the development of large-scale farms, department stores, inequality, so the rise of these very rich entrepreneurs, et cetera, during the 1870s, and people are mad at that and so they don't really know what they're mad at, they're just mad at it, and they're starting to channel their anger and they say, well, it's the depression, okay? So when in doubt, you basically blame it on the depression, okay? So again, looking at the 1870s, so the money supply each year declined by nearly 3% during this time period, but then look at, okay, GDP was less than growing at less than 1% each year, and now it's growing at about 4% each year, so 4% GDP growth, remember you hear now, obviously things are different, but President Trump's like, oh, I can get to 3%, okay? So, well, we got to 4%, we weren't even trying, basically, you know, the government wasn't even trying at this time period, and then this was actually fairly comparable to other, you know, the overall average GDP or per capita GDP growth during this time period, you know, in the 1800s. So prices continued to fall, there was some weak growth in railroads, but other industries caught up, and that's what Austrian business cycle theory basically teaches us, right? You have to let these unprofitable industries continue to contract. If not, you're just sort of going to prolong the economic stagnation, and there was no economy-wide depression, okay? Then a sequel, sort of if you want to go on, you say in 1879, we finally resumed species payments, there was a large gold inflow that led to credit expansion in the panic of 1884. Murray Rothbard in his History of Money in Bank in the United States talks about this briefly. So this is, you know, again, just sort of the general kind of narrative of that, right? So in conclusion, the severity and the frequency of business cycles and banking panics in the 19th century, they've been exaggerated due to faulty economic statistics. They were not due to laissez-faire, but instead due to misguided government regulations, okay? So you frequently hear the narrative, it's that, oh, these business cycles, they were due to life without the Federal Reserve, and if only we had the Federal Reserve, everything would have been great. In fact, again, they were due to very many perverse banking regulations that made the banking system more fragile, as well as government monetary policy that made it susceptible to these periods of excessive credit expansion in then these busts. In the panic of 1873, in the depression of 1873 to 1874 is a classic example of this, so good case study, and you should tell all of your friends about it as well as my paper. All right, so thank you very much. Thank you.


This video will demonstrate a workflow in which a pixel-based unsupervised classification is used to map land cover from landsat imagery. Unsupervised pixel-based classification approaches work by partitioning the input imagery into a set of output classes based only on the spectral values. Because they're only making use of the spectral information, they are limited in their utility. They cannot make use of spatial information such as size, shape, or even texture. One can use the isocluster unsupervised classification geoprocessing tool to perform an unsupervised classification, but a more streamlined approach is to use the image classification wizard. To do this, first select the image dataset in the table of contents that you want to classify, and then from the imagery tab, choose classification wizard. The first step is to configure the classifier. Under classification method, I'm choosing unsupervised, and for the classification type, I'm going to use a pixel-based. Now, I already have an existing classification schema, so I'm going to load that here. If you don't have a classification schema, you can always select default and modify it later. Once you've finished entering the configuration parameters, you can click Next. This will move to the next step in the image classification wizard, which is called train. For an unsupervised classification, the train phase is where you'll enter some of the key parameters. The most common parameter you'll want to modify is the maximum number of classes. This is the total maximum number of output classes that you can have in the resulting classification. You want this to be higher than the number of classes you need, but note that this is the maximum and it may not be achieved. I'm going to adjust the maximum number of classes to 10 and leave all the other defaults in place. Clicking Run will produce the initial classification. You'll want to review your output here, and you may want to go back to the previous stage and make some adjustment to the input parameters, such as adjusting the number of classes. If you're happy with the output, simply click Run in the Classified window to move to the next phase. This will produce another raster data set with the same number of classes, and in the Classified window, you can now click Next to transition to the next phase. In the Assign class phase, I'm going to assign a LAN cover class to each one of the categories in my unsupervised classification. It's important to note that although classes may be specially dissimilar and then have unique values in the unsupervised classification, they could belong to the same LAN cover class. Prior to assigning classes to the unsupervised classification, you may want to make some modifications to your classification schema. For example, you can right-click and edit the properties of the schema, giving it a new name and entering descriptive information. You can make modifications to individual classes, adjusting their name, color, and default numerical value. No two classes should have the same numerical value. You can also add or remove classes from the existing schema. Then finally, if you're happy with your classification schema and are considering reusing it in the future, be sure to save it using one of the Save options. Assigning the appropriate LAN cover class to each one of your unsupervised categories may take some time. You'll want to compare your unsupervised classification to at least your input imagery and perhaps even to some other reference data that you may have access to. To associate each LAN cover class with its corresponding unsupervised category, you'll want to select the class in the Assign Class dialog, click on the Assign Class button, and then click on a pixel belonging to that class in the unsupervised classification. The unsupervised class assignment information is updated in both the Assign Class dialog and in the Table of Contents. Clicking on a class in the Assign Class table will highlight that particular class in the unclassified raster, helping you understand what pixels belong to that particular category. As was mentioned earlier, one or more unsupervised categories could have the same LAN cover class. You'll want to continue this process of associating LAN cover classes with unsupervised categories until all of your unsupervised categories have an associated LAN cover class. As I'm working with Landsat data, which is at an angle, I even have a background category that I need to consider for my classification. Once you've completed the class association process, you can click Next to move to the reclassifier stage. For an unsupervised classification, it's unlikely that you'll need to apply any reclassification routines, so you can click Run to finalize your classification. This produces the final classified raster, which is stored inside your project to your database. You have the option of removing any intermediate products from your Table of Contents to clean up your ArcGIS project. Unsupervised classification is an easy and straightforward way to getting it feature extraction. However, it should generally only be applied to multispectral imagery, and in those cases where the LAN cover classes can be clearly separated by only spectral information.