This is real. Relationships are universal. The question I have is when you grow to this scale and both your businesses are at what do you stare at or what do you fear in your business? I don't really have any fears to be honest with you but if I had to say something and I hope to get there one day but mine would be that I don't reach my full potential which is not going to happen because I'm giving it everything. I'll go to bed. I'll pass out because I've left it all on there, all on the table every single day. Every drop of energy is expended and I've got to a point where I don't know how to manage it to where I know when to give it. I know how to manage it to a point where I know how to give it my all day long to where at 5 o'clock I can start slowing down and try to start to turn my brain off from that side of it and switch over to family time, me time starting to recharge. But then by the time I go to sleep I leave myself just enough energy where I can barely enjoy that moment before I go to sleep.  I'm not focused on what that potential is. I'm just focused on am I going to do what I need to do today. And so my only fear when I wake up is am I and I already know the answer but am I going to give it everything I've got right now? We don't stop because we are motivated to get to this certain point. The thing is though is that you believe you work hard and you adapt. But then there's this little thing at the end called patience. And I think that as you're going through your days and you're trying to create this balance I wish I could just put something inside of your brain right now. Because looking back I was the same exact way. I would work till midnight every night. And if I would have only understood how abundant this business is and how me working until midnight is not like the leads and all the little things that I'm trying to do at night wasn't sure. There's moments where in the beginning maybe you need to work till midnight. There's certain things going on. I get it. But as far as a general routine that wasn't my make it or break it. That just wasn't my make it or break it. I thought it was. You think it is. But the business is so abundant that you don't have to if you don't recharge if you don't spend that time with your family you're going to look back and say and then you're going to understand you're going to say to yourself you know what I could have gotten where I am without working all those hours I could have spent more time with my family and you're going to start to think I wish I would have done that but now that I'm here and understand this now that's what I'm going to do. That's what happened to me. And I wish somebody would have told me that you don't have to do that. You know because it comes back to being efficient. If you can't get done between eight and five okay what you need to do to exponentially grow then we need to reevaluate what we're doing and why we're working till midnight. What's something you did for either one of you before you let's say have money to invest and put into like for new agents for instance like or someone just starting out What kind of stuff did you do? You call your friends and family that's nothing. All your Facebook friends that's free. First sale by owners are free. Door knocking is free. Meeting people with brokers is free. All this is free. You could build a business from literally with a spending zero if you really wanted to. The only reason people spend money is because you know you buy products and technology makes things easier like the dialer. You know where you can find numbers like that from your office and then call them with a snap of the finger and then just call them one after another automatically.  So you buy for you do those things for convenience to speed the process up but you can literally just walk around in public and meet people. There's people that do this by the way. I know people that are made to live in off of meeting people at bars at night and go up shores and selling huge like multi multi-million-dollar houses and developments and stuff. And just people that they met going out drinking at bars.  But I don't drink at all by the way. But but does that make sense? Like all those things are free. You don't have to have any money to do that. Like the name of the game is just contact people. Connections. You know and you can pay a little bit to contact people. You know or like even the red X and the dollars couple hundred dollars. Right. I think where we get in trouble is Zillow leads two or three hundred dollars for a lead realtor.com. You know things like that. You know where you're buying these leads where you're basically buying a random person in your market's contact information for several hundred dollars and you could have just met them a program for free. See what I'm saying. So I think that's where a lot of people are getting in trouble. They feel like they have to buy leads that these leads are the only leads that are doing anything. Nobody else is doing anything. This is the only source of leads. I think that the industry has kind of got us a lot of newer agents kind of thinking in that am I wrong.


Li Wenlang died at 258 on February 7th, 2020. We deeply regret and mourn this. When Wuhan's central hospital announced the death of the 34 year old ophthalmologist on the social media site Weibo, there was an outpouring of sadness and anger in China. It was one of the first signs that something far more troubling was happening in the city of Wuhan than the Chinese government was letting on. When Wenlang had tried to alert his colleagues via WeChat that he was witnessing an alarming spike in respiratory illnesses, the local government forced him and eight other people to sign apologetic admissions of rumormongering. And that was the beginning of a disinformation campaign by government leaders around the world that would help turn a crisis into a global pandemic. Chinese officials later assured the public they'd found no human to human transmissions of the viral pneumonia he'd posted about and that the disease was preventable and controllable. In January, government officials shut down the food market where they suspected the disease had originated. Nine days later, a 61 year old man who regularly shopped at the market became the first known fatality. But what government officials failed to tell the public was that his wife, who had never visited the market, also caught the virus meaning that it was transmittable among humans. As the hospital ward filled and workers began to fall ill, China's politicians still refused to acknowledge for weeks that human to human transmission was happening, even staging a 40,000 family potluck in Wuhan and instructing hospitals not to use the words viral pneumonia on lung scan reports. As the death toll in China climbed, the national government pointed fingers at local authorities and Wuhan's mayor said his hands were tied by a national law requiring approval from central authorities before declaring an epidemic. In late January, Lee texted a New York Times reporter from his hospital bed that, if the officials had disclosed the information about the epidemic earlier, I think it would have been a lot better. There should have been more openness and transparency. Concern for the need to better guide public opinion on the issue turned out to be President Xi's rationale for refusing to disclose human to human transmission for several weeks. In February, authorities jailed an activist who dared criticize Xi's handling of the crisis. A journalist reporting stories inside Wuhan critical of the government's response disappeared, as did a wealthy tycoon who publicly blamed the Communist Party's speech restrictions for worsening the spread. As the first U.S. patients began testing positive, the Trump administration downplayed the threat. It's going to disappear. One day it's like a miracle it will disappear. There's no reason to panic because we have done so good. Is it real? It absolutely is real. There is no question about it. But you saw the president the other day. The flu is real. 14 people and the 15 within a couple of days is going to be down to close to zero. They think this is going to be what brings down the president. That's what this is all about. This is their new hope. The lag in testing was in fact a failing. Do you take responsibility for that? No, I don't take responsibility at all. But there's also an enormous difference between a country that jails dissenting voices and the U.S. with its strong First Amendment protections. The media and political class have derided Twitter and Facebook for lacking adequate gatekeepers. But it was through these platforms that medical professionals, technologists, epidemiologists and everyday citizens bypassed the media and the government to implore their fellow citizens to act. A Twitter thread from a member of a Seattle-based medical team that defied the CDC to run tests and discovered an outbreak in the city and sequenced the genome got the word out about the value of social distancing long before the federal government did. Yale social scientist and physician Nicholas Krstakis explained the science of disease spread to further promote social distancing and self-isolation in threads shared thousands of times. On January 30th, technologist and venture capitalist Balje Srinivasan asked on Twitter, What if this coronavirus is the pandemic that public health people have been warning about for years? And then began encouraging the cancellation of events, pleading for more early testing and warning about the lack of reliable information coming out of China. He was critical of early media coverage that often downplayed the threat of the outbreak with facile comparisons to the flu and flippant dismissals of companies in Silicon Valley that began taking precautions early. And of course, this non-issue turned out to be very much an issue, and they weren't simply getting the story wrong, but they were actively attempting to shame and silence people getting the story right. And some of them, you know, later insincerely apologized. Others wrote, you know, columns about how they got the story wrong and this slightly went sincerely. And then once Trump started adopting their early talking points about how the virus was just the flu, several of them have reversed themselves and pretended, you know, they've been taking it seriously all along. As the government continues to stumble, the decentralized response has been forthcoming with individuals voluntarily self-isolating after the flatten the curve chart was circulated widely on social media. Doctors in Seattle defied the federal government to test for COVID-19. States like Colorado implemented their own drive through testing stations and mayors and governors began taking extreme measures to protect the spread within dense city centers. But to keep the decentralized response going, information channels will need to remain as open as possible. To this day, the centers in China are being muzzled or worse, and the Trump administration has classified several top-level coronavirus meetings. American social media is chaotic, confusing, and full of bad actors and misinformation. Wild speculation abounds, but that wild, free-wheeling conversation keeps us safer than a censored press or even a free press controlled by professionals. The contagious spread of information in a race against the contagious spread of the disease remains a powerful weapon when confronting this global emergency.


Today's Cobb highlight is the Redline Series Carbon Fiber Subaru EJ Alternator Cover. Suburbers in general are quirky cars and that extends to the way that the engine bay looks. They're not the prettiest thing in the world. So we wanted to add some parts that give it a little more style to go with the performance. And we put a lot of effort into making this alternator cover more than just some flat thing that you stick on top of the engine. It's got really nice contours that follow the natural shapes of the alternator and the other parts around that area. And it's also made of really high quality material. It's a 2x2 twill weave carbon fiber with a really nice glossy PPG coating that looks nice but it also protects against fading and discoloration. Plus we have the cool red line badge that you only get within the Redline Series. And also for those that have a 2015 or newer STI and have our carbon red line intake, we specifically designed the shape on this side to match the flow of that air duct so they look like a perfect combo to go together. But it also fits really well with some of the other EJ engines that don't use that intake.


Good morning, everyone. Welcome you to lecture number four of our course, Collective Dynamics of Firms. I see four students here, so I'm not so sure what kind of message I should deduce from this. Maybe it's a message that no one is really interested in this course, then I stop teaching it, right? That's the only consequence. The loss is then mostly on the side of those who want to get the credit points. You should think about this. Maybe you talk a bit to your fellow students if this is a message you want to convey or not. If it is not the message, then you may think of maybe a better strategy. If no one is willing to come to the score, then I certainly do not continue to teach here. What did we do last week? We were talking about distributions and we addressed the issue of how to specify the parameter dependencies. Our assumption was that distribution should be described by a mean value and by a variance, and the question was how should we calculate this? There was a maximum likelihood estimation. You should understand the meaning of this. Then we talked about skew distributions and the two best candidates for this, the log normal and the power law. There are other forms of skew distribution. You should not worry about it. It is simply that the two play a major role in this course, but there are other candidates. We also talked about a specific way of finding out if some values belong to a distribution or not. That was the sift plot where we had to stop at the very end. You understood that it was a way of projecting out the deviations of the large values from the distribution. We are going to improve our slides on this. I thought of a little figure to enlighten why the rank is linked to the cumulative distribution. That was basically the meaning of it. Then most importantly, we also focused on the stylized facts about firm size and firm growth. We started with firm size. We found that firm size is following a skew distribution. This was not very impressive, but then we discussed why we cannot be more specific about it. I hope you recall this. It depends on the data set that we have, or it depends a bit on the way it is analyzed. I did not finish with the second stylized fact, so I will continue with this in a moment, and then we will also continue to talk about these growth rates. Let me switch back to the stylized facts about firm growth. That is why we stopped here last week. We first have to define what a growth rate is in order to determine the stylized fact. The growth basically is the change of the size over years. That means, from my perspective, I would define the growth rate like this. This is the change of the size between consecutive years, t minus delta t, with delta t as one year. That is the size of the previous year. That is the size of this year. We divided by the unit of time, or by the time interval, which is in most cases for firm data, it is one year. This growth rate has a unit, something over time. If we measure growth in number of employees, then this is change of employees over time. There is a time unit involved. This is inconvenient in most cases, but the rate, if you come from physics or chemistry, so the rate is the number of events per time unit. This is not very convenient. Therefore, people instead of defining this as the absolute growth rate, defined it as a relative growth rate, which is the ratio between the size of consecutive years. You see there is a formal relation, if I define it like this, to the absolute growth rate. That means, if you are given the relative growth rate, this capital R, then you are able to think about the absolute growth rate. The most common way of using it is the log growth rate. That means, I take the logarithm of this, and then I end up with a small r, which is, again, a ratio, but even the logarithm of a ratio, like this. You see how it is related to the size of the firm in different years, and to the absolute growth rate. I want you to think about this in a moment, although when you recapulate the lecture at home, because in most cases, the small r is used. In some cases, the capital R is used. We have to make sure that we always talk about the same way we define the growth rate if we want to compare with the data. The natural thing is this one. Now, what do we find if we analyze growth rates like this? We find that growth rates follow a Laplacian distribution. That is the stylized fact number one. It's a well-confirmed fact. What is a Laplacian distribution? It is a symmetric distribution, like this double exponential distribution that I have plotted here. You see there's an exponent. In the exponent, there is an absolute difference. Mu is the mean value, x is the size of the firm, b is then identified as a variance, of course. You see because of this absolute value, it should be symmetric, at least from the theory. We will find out later whether this is true or not. This stylized fact is different from stylized fact number one, because we talk about a different distribution, but also because we are very specific about the distribution. In stylized fact number one, we could only agree on there is a skew distribution, but we didn't agree on what kind of skew distribution. Here it's different. We are precise about the kind of distribution. That is a noticeable difference, and no one argues that this is the right distribution. Here are a few examples where it holds on aggregated, but also on disaggregated sectors. If we plot the Laplacian distribution in a double logarithmic plot, that's the same distribution as before, then it looks like a tent. You see this? This is not in a double logarithmic plot, if I'm sure this is a log-normal plot. Anyway, it looks like a tent. This is very characteristic here. You see that this shape of the growth distribution is the same for different sectors of the industry. What is the printerry and furniture are the examples here? This can be a bit of a surprise to you, because why should the growth rate of such very different industry be the same? The first issue in order to project this out from the data, one has to do a little bit of rescaling, and we will talk about this in more detail today. You see, instead of these absolute growth rates, they have plotted here the difference of the small s, and the small s is the logarithm of the capital s. S is a measure of the size. In this particular case, size was measured by sales. We start from sales numbers, then we take the logarithm of these sales numbers, we get the small s, and then the difference in the logarithm is calculated.  But this, at the same time, is also normalized against the mean value. This is very important, because otherwise you wouldn't have this symmetric distribution that you can compare. That's the first surprising fact. It holds, obviously, for different ways. But what does it mean? What's the difference between a Laplacian distribution as we have it here, a double exponential distribution, and a normal distribution? Who wants to comment on this? How would a normal distribution look like in this graph? Parabolic, yes, it's also symmetric, that's correct, yes. But what would be the most noticeable difference? Who has a clue? What would be the most noticeable difference here? Normal distribution. The normal distribution would be here, very much centered around zero, very small variance here. It would be in this area here only. If you just go for the small growth rates, you would probably not distinguish, be able to distinguish between a normal distribution and a Laplacian distribution. But if you look into these tails of the distribution, this tent-like shape, then you notice there's a huge difference between the normal distribution and these tent-like tails. And that, what's the message of this, yes? A normal distribution with a large standard deviation would also look symmetric, but it would not have these straight lines, so that's number one. And number two is, I mean, of course, we can define a normal distribution where the variance is 100 times larger than whatever the average value, right? What would be the meaning of this distribution? That's another question. So what we see here from these tent-like shape is that negative growth rates and positive growth rates are much more likely than compared to the normal or random case, right? That's the message. To have large negative or to have large positive growth rates is much more likely than drawing a random number. That's an interesting finding in case of an economic argument, right? Because then you have to find reasons for explaining these large growth rates. We will come to this in a moment, but you understand this, so you will recapitulate its symmetric, you see these huge deviations from the normal distribution, and you also please understand that it takes a bit of data rescaling to get this nice plot. It's not simply like this, yeah? Okay. With this, we go back to the lecture of today. Before we start further analyzing the growth rates, that's what we do today. We have to look into tests for the distribution. It's extremely nice that we now know there is a Laplacian distribution underlying the growth rate. How can we verify this? You have the data in your self-study exercise, and you have to do this yourself. Can you find out whether this is the Laplacian distribution or not? Of course, you can plot the data, but there should be some test to tell you this is the Laplacian distribution or it is something else, right? This test is the test we use here is a Kolmogorov-Smirnov test. Usually, as I wrote on the first bullet points, you test distributions by creating a null hypothesis. That is your hypothesis that you want to reject. That means if you want to go for the growth rates, your null hypothesis cannot be or shouldn't be. The null hypothesis could be the normal distribution, which should be rejected. If you have the null hypothesis it's Laplacian distribution, then you probably cannot reject it, right? Because, as we now know, there is a Laplacian distribution behind it, but you can also not tell that this is the distribution. What you do instead is you argue the other way around. You take all other candidates in mind, you keep all other candidates in mind, you take them to the Kolmogorov-Smirnov test and round by round reject other candidates. This is what you do. Please understand this very carefully. You do not prove that some data follows a distribution. This is not what you do. The Kolmogorov-Smirnov test tells you that some data does not follow other distribution. This is something you really have to understand. This is important. The null hypothesis is the hypothesis that you have to reject. As you already mentioned, the standard null hypothesis is of course the random growth, which would be according to the normal distribution. I mentioned on this slide that there are other useful tests, which we do not discuss in this lecture here. We are not going to apply these. We are even not going to tell you all the mathematical details about the Kolmogorov-Smirnov test. Instead, we want you to use it and not to fully have the statistical derivation of this test. I had an argument with Pavlin last night. Some of you may have seen that we had other slides prepared for the Kolmogorov-Smirnov test, but I thought this is not really needed. It is not needed that you go through the statistical details of the test. It is much more important that you understand this test is available. I can apply it to my data with the following R command. Then I get an output that I am able to interpret. That is what we are concentrating on. The basic idea of the Kolmogorov-Smirnov test is to compare an empirical distribution with a theoretical distribution, which would then be your null hypothesis. You compare these two distributions not on the level of the density, but on the level of the cumulative distribution. Then there is a difference between the one sample and the two sample Kolmogorov-Smirnov test. You should also understand this. The one sample Kolmogorov-Smirnov, or one side Kolmogorov-Smirnov test, compares empirical data to some theory. The two side or two sample Kolmogorov-Smirnov test compares samples among each other. That is the difference. If you have two samples and you would like to know do they belong to the same underlying distributions, then you use the two side Kolmogorov-Smirnov test. For today, for what we discussed today, the one sample, one side Kolmogorov-Smirnov test is important. I mentioned that if we just talk about normal distributions, there are more specific tests you should use. I mentioned two on this slide. This is just for your reference. For specific classes of distributions, there are more specific tests, which give you maybe all the better statistics, but the Kolmogorov-Smirnov test is quite general on this. For the Kolmogorov-Smirnov test, we have to calculate a statistics, which is based on this F. F is the cumulative distribution function. That is something you have to understand. It's not the density, it's the cumulative distribution. There are two. Fn is the empirical, where you have n observation, and F, without the subscript, is the theoretical distribution function. You already know how to calculate the empirical distribution function, cumulative distribution function, simply by looking into the number of elements that are up to a given value x. You sum up over, you calculate the total number of elements up to the value x, and you divide it by the sample size. That's the way you calculate this. This is the distribution you want to test, for example, the normal distribution. Then you see, dependent on the value of x, you get then a number of differences here, absolute differences.  That means you look into the lowest upper bound of the differences, something that comes from above and bounds these differences. If the two distributions are the same, then this D converges to zero. But in most cases, it looks like this. This is the example that we want to use. We have a blue line here that is the true theoretical cumulative distribution function, the one that we use as a hypothesis. This black line, with the steps, is the empirical distribution function. How is the empirical distribution function calculated? As I said, you sum up two specific values of x. In this particular case, all the time, if you find a value that is below this, you add one here. If you go and look here, I hope you can see this. This is your first x that you got from the sample. Then you get a second x. That means the step height of this is always one. If you found a new x that fits into this boundary of x, if you find a new xj that is lower than x, then you make a jump here and you add a one here. There are always steps of one. You see this? If there are two values, then, of course, you have larger steps. What you see here, of course, it's not the same. There's a minor difference, but it looks like a larger jump. Then the question is, what is the maximum difference between these two curves? This is the sequence of the D. The Kormogorov-Svernov test calculates for you this sequence of the D and returns the lower upper bound of it. Just basically, if you want to get this in an i-figurative idea, it's a bit like the maximum of the difference. It also has to tell you, and that is what the Kormogorov-Svernov test actually does for you, whether this is a significant number or not. Let's assume the maximum difference is 0.01. What does it tell you? You don't know. This is what the test is about. It tells you if this is compared to the distribution we are talking about, is significant difference or not. Here is how it is implemented as an example. This is something you should basically do at home. You can do it by typing exactly what I show you on the slide. We need some empirical data to make sure that the data is not according to our null hypothesis. We have chosen a complete different distribution, which is in our case a t-distribution. Now we generate empirical in quotation marks data, artificial data that follows obviously a different distribution. Students t-distribution. We do it in a very simple way. This is the only command that we use to generate the data. Very convenient. Then we create a null hypothesis. Remember the meaning of the null hypothesis is something that needs to be rejected. Our null hypothesis is the most common one. It follows a normal distribution. You already know that this is not true because I told you where the data comes from. In other cases you don't know. You test this distribution. Here you see how the Kolmogorov-Smirnov test works. You have here the sample that comes from this distribution. Then you have here your null hypothesis, which is a normal distribution. The output looks like this. You get the maximum d-value, which is not the matter. It's the lowest upper bound of the set, formally. Then you get this value here. What's the meaning of this p-value? We explain it on the next side. The p-value, as you have seen, is a very small number. It's e to the minus 16. We assume for each of these texts that there is a significance level, which is in some cases 0.05. We assume for each of these texts that in statistics you learn what is the meaning of the different significance values. You choose of course different significance values for refining your results. Let us assume for our test that the alpha is a small number. If the p is less than the alpha, it means that there is no significance for the fact that the sample comes from the normal distribution. That is the meaning. That means you have to reject the null hypothesis. That's the important message. Because there is no significance that the data is equal to the value of the null hypothesis. In our little example, this is not a surprise because we have chosen a different distribution to generate the data. That's the idea. You see from this that you cannot tell what is the real distribution the data follows. Then the next step that you have to do is check other distributions. That's the next step, and so on and so on. You see if you go here for the Kolmogorov-Smirnov test again, that this p-norm defines our null hypothesis, but we need to have two other values here to characterize this. Namely the mean and the variance. The mean and the variance have been calculated in this particular case from the data. You hopefully remember that if I go for other distributions, then of course these are the two parameters that also determine how the other distribution should look like. What you have to do now is to go through different distributions and find out is there a distribution where I cannot reject the null hypothesis. Hopefully you come up with the idea of testing the t-distribution. In order to get this, you first have to plot the data and then look into your mass book what looks like this. Maybe is it symmetric? Is it asymmetric? Does it have only positive values or negative values? If it has negative values then it's obvious it cannot be the log-normal distribution and so on. Then step-by-step you reject certain ideas and then you test the significance of this. Now with this knowledge we go back to the fact, the last fact number two, namely the distribution of the growth rates. There was this finding that we already discussed, namely there is a tent-like shape in a log-log or in a log-normal plot. At least on one axis there should be a log, otherwise you don't see the straight line. The interesting finding is that we are very specific about the distribution that means it should occur independent of the data sets that we are using. We go for other sectors of the industry then we should see the same distribution. We look into small firms and big firms, we should see the same distribution and so on and so on. Remember what the consequence of this is. A stylized fact means it is a statistical truth because it is observed in very many different data sets. That's the important message of a stylized fact. Let us now talk about two other dependencies here which I will refer to the next week in more detail. The first one is, no, I think on stylized fact number three I refer today. The first one is that the growth rate itself is a function of the size. We are not saying what function it is, we will investigate this by looking into the data, but the growth rate is nothing that is independent of the size. You could already have seen this when you look into data that comes from different size classes, I didn't show it to you. There is a plot next one. The second stylized fact is growth rate also depends on the age of the firm. If you are a firm that is already for 200 years in the market, then you have a different growth rate than a firm that has been established two years ago. This will be specified next week in more detail. In order to understand, we have to find out the dependency on the size. How do we do this? I go with you step by step through these steps that you have to do in the self-study exercise. I explain it here and you do it afterwards yourself in the self-study exercise. Here we have put up the stylized fact. The volatility of the growth rate decreases with size. That is the stylized fact number three. What does it mean? It does not mean that the growth rate decreases with size. You can say this. That means the variance, the deviation in the growth rates decreases with the firm size. If you are a startup company of 20 people, then your growth rate can heavily fluctuate. Understand, if you get rid of 10 people, then your growth rate is already minus 50%. If you hire another 10 people, then your growth rate is plus 50%. Why is it that huge number? Because you only have 20 people. That means if you then on this low level change a bit in the number or in the size of the firm, the number of employees, for example, then you have heavy changes in the growth rate. If you are a company like Nestle and you hire 10 new people, then there is probably no effect in the growth rate because 10 is way too small compared to the whole company. That means even if you have larger changes in the company, because the company is that big, you will not see big fluctuations negatively or positively in the growth rate. That's a message. Or as you said correctly, you can better predict the growth of larger companies and you have problems to predict it with smaller companies. We have to find out how this dependence looks like. That is the important thing. The start-up effect does not tell us a precise like equation, how it depends. We find out with the data. I hope that you understood that we do not talk about the absolute value of the growth rate. We just talk about the deviations, the annual deviations dependent on size. How do we get this? We do the following. We have the data and this is actually what you have to do at home when you do the self-study exercise. You are given data that looks like this matrix here. In the column here, you have the N firms. I think in our case, N is equal to 10,000. We have the size over different years. That means you are given the size of a firm in year one, whatever year one is, 10,000 different sizes. Then you are given the size of the firm in year two. We are talking about the same firm. From this, you can calculate the growth rate that you follow. Of course, there is a change in the number of employees that you can calculate. As we already discussed on the previous slide, the growth rate in this case is not just defined as a change in the number of employees, but there is a ratio between these sizes. There is a logarithm of this ratio. That is the small r. r is basically the logarithm of the relative growth. Did you get it? That means you have then two values assigned to each of the firm. Actually, yes. It is the size of the firm. The growth rate, which appears twice at year one and year two. The growth rate, which is of course only one value because you have taken the difference. What you have to do then is you pool the data. What do we mean by pool? We neglect the year the data comes from. We treat everything as coming from the same years. We pool it together here, as you see. By this, we ignore the time dependency. We have a larger statistic. Then in the next step, you do the following. You take the pool data, where the year is of course gone as an information. You sort this according to the size. There is a very easy command. It is named sort. It does the same for you in r. You get a sorted pooled vector. You understood why we pooled the data, because we are not interested in the actual year of the size, but more or less in the distribution of all the sizes. That is the first thing. Then we sort it. Because each of the firm has a particular growth rate, then we can of course assign or keep the information of the growth rate with the firm. We sort along the size of the firm, and not along the growth rate. That is the important information. Did you get it? Yes. What we do then in the third step, we divide this sample of sorted values into size classes. A good size class number could be, for example, what would be a good size class number? 3, 10, 50, 10,000? You can think of it. It is the same problem as we have already discussed when we talked about binning of data. If you choose a bin to large and everything falls into one size class or into one bin, then you get a complete flat distribution, because the bin was too large.  The same is true for the size class. If you have two size classes, then you will see afterwards that you can fit any sort of curve to these two values. If you have 10,000 size classes, then you do not have a good statistics. Let's say 20 is a good value. Then you have now 10 or 20 of these values and you calculate the average size in this size class. For the same firms, you also calculate the standard deviation of the growth rates. You have all these numbers and for each of the size classes, you calculate the standard deviation of the growth rate. Why do we talk about the standard deviation of the growth rate? Are you still with me? Because that was standard effect number three, right? Standard effect number three was talking about the volatility or the standard deviation, so how it depends on size. Therefore, we look into this. This is what we get. That's the result. We copied it from a paper here. You see people took a log log plot. We will have a theoretical argument while we take a log log plot later, but that's the result. The standard deviation, where does the growth? You see it's a straight line. There's a linear fit that nicely follows this data. What does it mean? It means that there is an exponential relationship between the size and the standard deviation. The slope here, this beta can be determined empirically. That's what we do in the next slide. This is the important finding. It's, by the way, a robust finding. We will see this afterwards. Let us interpret this now. This is a log log plot. We talk about the logarithm of the volatility and we talk about the logarithm of the size cloud. This is the average size of the size class. Let me remember this as we have defined on the previous slide. Because we see a straight line, then we write this up as our hypothesis as a straight line, but on the log log plot. X is equal to a constant plus linear dependency. This is z and this is x. It's a linear relationship. The x is, in this particular case, log of the average value of the size class. You see that underlying this equation, there is a linear relationship. Just we wrote it for the log because that's what we have seen in the data. We do not take the logarithm, then it looks like this. We rewrite it and then we have this dependency. The sigma is the volatility and the j refers to the size class. It depends on the average size of the size class in such a way as we write here. From here we get into this. That's this exponential dependence. That means the straight line on this plot is exactly equivalent to having a volatility proportional to the xj to the minus beta. The beta is a slope of that graph. Did you get this? Yes. It was a log log plot and you have seen a straight line in the log log plot. That means there's a linear relationship on the log log axis. That's the linear relationship. I just rewrote this linear relationship in this way and then I end up with this relation between the sigma and the x. This tells me exactly what I was interested in. The sigma is the measure of the volatility and I told you the volatility depends on the size class and the larger the firm, the smaller the volatility. That's exactly what we found out. That is a particular instance of Stylite's fact number three. Let us have a break of 10 minutes here. 10 minutes and 10 after 11 we continue. I think we can continue. We already explained how we got this relationship. I hope that you are able to follow this discussion. We get this relationship artificially increasing our data set by ignoring what year the data comes from. This is an important issue. We have a small sample. You can still get this relationship if you pool the data the way we have described and keep in mind the growth rate for each of these size values that you have obtained. This is a binary vector. There is a size in a given year and the growth rate between this year and the next one as I have described on the slide with the pooled data. This means that we cannot infer on time dependence here, but this is all the normal we are talking about. We are talking about the volatility or the variance of the growth rate dependent on the size which is measured as a size class. You can do the same as I think I wrote in the note here by instead of looking into the volatility of the growth that you look into the growth rates yourself. Then you do the same thing and try to find out whether there is a dependency on the size of the firm on the size class. Then you find there is no dependency. Maybe it is a good exercise if you do this at home. You can also test this to find out what you see. There is no relation between the growth rate and the size. That's important. Starlight's fact number three is on the volatility of the growth and the size class. Please keep it in mind that's an important difference. Then we found this beta which is approximately 0.2, but not exactly. If it is a starlight's fact then we should find something similar in different data sets. Keep it in mind and we will test this before. Now I do the same thing again by discussing some of the findings. Let's assume that we did this now with different data sets. We first go for the data set of the US companies. I showed you last week when we talked about this very nice log normal distribution. You remember that I showed your data set from US manufacturing firms over 20 years. Remember this? We are investigating this now again by looking into the one year growth rates and how volatile they are dependent on the size class. Remember this is the findings that we want to discuss. The variance of the growth rate depends on the size class. Here again this is just to remind you on the definition of the size of the growth rate. You see there is a log growth rate that's a small r and the s0 is just ln of x. x is the size of the firm because in some of the coming slides in the figures there is a small s and that's a small s. What we are interested in is this distribution of the growth rate conditional on the size class or the size in a given year. We do exactly the procedure as I have described before. This is the finding. The same finding if you want so as you have seen in some of the previous slide but these were from different people. The previous slide was from Bottacci and Seki and this is from Amaral and Co-Workers. Different data set, different people analyzing it but the same stylized fact. You see this tent-like shape here of the distribution which reminds you on the Laplacian distribution that we have discussed. That's the first finding. Secondly you see that dependent on the size class I'm talking about I have different slopes here in this distribution. You see this. They have chosen these three size classes here but in the investigation that I'm going to show they have investigated about 20 size classes. There are only three shown. They are not just three taken. That's a different thing. If you choose all of them then the figure looks a mess. This is the expression also for this particular distribution function. You notice this double exponential distribution here. You see this. It's a difference to the normal distribution. You want to see it here in this absolute values of course. There was no x square. Now they did the same exercise to find out how the volatility of the growth rates depends on the size class. In this picture you already see that for some size classes the volatility is much larger because these flanks of the distribution, these tails here go much more to the left and to the right. You see these where we have the little triangles here. This is the smallest size class. For the largest size class where we have these squares the things look a bit different. You see already by looking into the distribution of the growth rates you see that for different size classes this has to be different. Now they investigated the same way as we did before and get this kind of plot. You see this distribution, this dependency here. We remember S0 was defined as ln x. Basically it's the same functionality of the volatility dependent on the size as we have discussed before. Does someone remember what Mr. Botatzi found? What was the value? Two slides back. 0.19. What do these people find? 0.19. That's a very interesting fact. It's not a trivial thing. Even more interesting you see they have proxied size now by very different measures. Before we were talking about employees mostly. They have different measures. Sales, the data set that Louis Amaral used was based on sales. We saw the log normal distribution. These are the sales data but they have all the proxies with assets. This was what was PPA. I've planned property and equipment. Don't ask me what this was. That was another measure of size. This is cost of good sales and this is number of employees. What you find is that the relationship between the volatility and the size is always the same. No measure how you proxies the size. This is the important finding. It doesn't change if you go from number of employees to sales value. You find the same pattern. This is why we call it a stylized fact. If we find a complete different pattern then this is not a robust observation. It's an observation we only have if we look into sales data. Here you see we find this always no matter if we proxied by sales or number of employees or asset value or something like that. That is the meaning of a robust observation. You see that the beta is although more or less the same. That means the dependency is constant over all of these proxies. That's the first important message here. You see it does not really depend on the proxy of the size. There is another interesting finding. It could be different of course for different markets or for different business areas of the firms. Why if you are a pharmaceutical firm why should the growth rate for pharmaceutical firms be dependent on size in a similar way as the firms for whatever computer hardware or something like that? This is a non-trivial thing. What they find here is that if you go for different sectors of manufacturing which means basically if you talk about firms at very different markets you always find this here. I think in the notes I wrote that there is a difference in the size of the market. As I see between 2000 and 3000 is manufacturing food, textile, paper, chemical whereas 3000 to 4000 is electronic transportation equipment, primary materials, machinery and so on. This is a non-trivial thing. The economist tells you that of course your growth rate depends on various macroeconomic conditions. It also depends on your competitors how much you can grow. That means it depends on the market you are in. If you are in a market that is not saturated like let's assume some eastern European markets then you may expect much larger growth rates than doing the same business in south of France. Of course the market has been established for a very long time already. The interesting finding is if you look into the change and this means basically also the predictability of how firms grow you find a similar relationship for all of these. Across different markets, across different proxies of the size. Now we go one step further. We want to understand in detail how the dependency on the size is. We already discussed the sigma proportional to x to the minus beta. That's the equation you should keep in mind. If you want to remember an equation then that's the equation to remember. We want to use this finding now to rescale this. This is a technique that tells us if we understood something of the underlying distribution or not. You see we can now assume this is our reference figure that I want you to keep in mind. You see here whether I get this slope or that slope depends on the size class. But I know already from this finding here how the volatility depends on the size class. This was my finding. Then I could use basically this relation to rescale my volatility. The different size class instead of having a sigma that just depends on this I put in this result that I just got. That's what we see here. We have rescaled the distribution function by making use of the sigma dependent on x relationship. Basically what we do is we normalize small and large firms. We put them into the same distribution by taking into account that small firms have a much larger variance of the growth. It's in large firms. Then you see something very nice. It's exactly the same figure as I have shown you before. You see that all curves collapse on the same curve. This is called a master curve. It means we understood how the growth rate depends on the size. Otherwise we wouldn't get this collapse. No matter whether I talk about large firms or small firms all of them follow the growth rates for all of them follow the same distribution. In order to get this we had to rescale our value which is the log of the growth rate. We scale it here as you see by the sigma one dependent on s which where s was the proxy for the size, right? ln x. If we scale it by this then we can get all these together. Of course you have seen that the maximum here was also different. The maximum dependent on where you are in and what size class you are in. This has been also rescaled here by the volatility and then everything gets to the same height. You understand what the meaning of scaling is. We found a relationship namely how the first of all the distribution depends on two parameters mean value and variance of volatility, right? Now we understood how the volatility changes if I consider different size classes and then we put this into the dependence of the variance, how the distribution depends on the variance, right? That means we rescale the two important values here in order to match everything on a master curve. That is the idea and only if we see that all the data points collapse on one curve we claim that we have understood something. I take here a complete different example to show you that we are talking about if you want some fundamental way of looking into data. I take one example here from physics. Let's assume you are doing experiments.  Then you have various ways of controlling the experiment. There is a difference. For example, change the temperature in the simplest way though higher, lower temperature then you can change the initial concentration of the chemical you put in then you see sometimes the process is faster, sometimes the process is slower and so on. That means you have like different variables that you can change, right? Or parameters that you can change, boundary conditions of your experiment. Then you want to understand how does the outcome really depend on what I have changed, right? There is some, of course there is some dependency for example on the number of particles that you have observed or the size of the system on this kind of thing. There is also some dependency on initial condition like what's the initial concentration or something like this. But you have to find out how the whole experiment really depends on this. The idea is to use this kind of scaling relations in order to find this. Usually you test only for a particular variable. That's what I wrote here.  You use different dependencies like log or exponents and these kinds of things. This is one example that I have taken from a physics book that I own. It's not important what the experiment behind this is. We don't care. But in this particular case, you measured some quantity c experimentally dependent on another parameter n. You do this experiment for n equals 100, 300 and so on. You get these different curves here. Here you plot lnc versus lnr. I don't talk about r and c and what's the meaning of this. It doesn't matter. It's the same problem as we have discussed before. You see if you vary n, you get these different curves. Now you need to understand what is the influence of the n on the output here. That means you try to find out how these different curves depend on n. No one tells you, but you can basically have a number of guesses that you can then try. If you find the right dependency, then all these curves simply collapse into one master curve. This is a visual confirmation that you got it the right way. Because these ten curves or whatever it was collapse into one master curve. Why did it collapse? Because you rescaled your axis. These two axes in a way that depends on n, of course. But in the right way that depends on n. Here you had to put in n to the minus 1 over d, whatever d is. That was another parameter that you could adjust. I think in the notes we wrote, if you cannot read this here, we wrote what you have to use for the scaling. It's not trivial to guess this dependence. I agree. It's not as simple as we have seen it before. It's a more complicated dependence. But once you find this dependence on n, how r and c depend on the influence of n, then you can scale the axis by this influence and then everything collapses into a master curve. This is basically the visual proof that you understood how the whole process depends on n. Because you were able to get rid of n. It was the same example that we used in the very beginning when we talked about men's shoe size distribution and women's shoe size distribution. After we understood how the shoe size distribution depends on gender, we were able to remove the influence of gender from the distribution. Here it's the same after we understood how these processes depend on n. We were able to remove n from this figure by rescaling the axis the right way and then at the end simply get this master curve. We understand what we are talking about. In our case, the relation is a bit simpler than this one, but the idea is the same. The idea is shown here on this slide, that all our data collapses into one master curve. In order to get this, we have to rescale the x and the y the right way. This is what I wrote here. In our case, I also described precisely how we got to know how to scale it, because we found this dependency how the sigma depends on the s before. This was x to the minus beta. This knowledge was used to rescale the data axis. With this, let me come to the last example. Nicely enough, we finished a bit earlier today. If this is a robust observation in firms that we have a size dependence of the growth rate, then we can also ask how robust it is. If we go for other economic entities, do we see similar things or not? That's an interesting question. Once you've got this idea, then you can test other sets as well. You have already seen that Bottazzi and Amaral found the same relationship in different data. In Bottazzi's case, if I recall correctly, it was also on US data, so then you can say, well, that's maybe country-specific or something like this. It's not, but that argument you could probably bring up. But you can also look into other data sets. You can look into specific markets only where you then distinguish the sig numbers you already have seen that this doesn't play a huge role. You can use it for different proxies. You have already seen it doesn't play a role. And you can go also for other economic entities. If you think instead of a firm of a country, then people have tested whether the growth rate of a country or how the growth rate depends on the size of the country. Basically, the volatility of the growth rate depends on the size. I come to this maybe here already. These are the results. The second example is the growth rates of R&D expenditure of US universities. US universities are another economic entity that decides every year how much to spend on R&D, research and development. And this of course depends on the income of the university, the number of endowments that they got in the previous year, the interest rates that they got on their existing endowments and all these kind of things. So it's hard to predict things, right? The question is how does the growth rate of these university changes over time? Or the same question is how does it change for countries? You look into a country like Greece, and you see this has a negative growth rate for a couple of years. But this was not always the case. In other years it had a positive growth rate. That means you have a time series of growth rates, and you can look into how does it depend on the GDP as a measure of the size of the country. I'm not sure if people have used a number of inhabitants, but that's a bad proxy, I have to say. The number of GDP is certainly a better proxy for the size of the country. GDP means gross domestic product. Brutal social product. And here you see how these growth rates for countries depend on size. Here we only discussed two different size classes, small GDP and large GDP, and we do not look into the growth rate directly. We look into the volatility of the growth rate. That means the predictability. And this is what we get. It's very interesting to notice that the GDP is first of all the same relationship, the volatility dependent on size as for the other economic entities. That's the first finding. And even the scaling exponent, the beta, is about the same before we talked about 19. 0.19 and here we see it's 0.15. That's very close. And here in the other example, I think this one here, they looked into the growth rates of universities. And you see the 10 shape is the same. The exponent is a bit larger, which means it's even harder to predict because you can have a larger range of growth rates over here, over the year. But apart from this, everything is the same, right? That's a nice finding. You would not expect this. You can be a bit surprised. What's the underlying dynamic that makes countries and firms and universities so similar with respect to this one feature? And of course, in this course, we will address this question after we understood everything about the empirics and we will say, okay, what kind of dynamics is it really? What's underlying these data? There is some kind of universal dynamics that captures the generic features of a growth process, independent of whether I talk about a firm or a country, right? And we are able to highlight this independent dynamics and of course we understand why it creates this kind of universality. That's the idea, right? I hope that everyone got this message and I think this is also the last message I have of today or not. So, yes. So, your self-study talks is exactly what I have described today. You have to bin the data. You have to plot histograms. You have to test the distribution. As I have described before, you do not need to understand how the Kolmogorov Smirnov test works precisely. You should recall what the command is, that you have to type in. That's the important thing and then you should be able to interpret the outcome. If R tells you the outcome, then you should be able to interpret this. This is the idea and that's also something we certainly ask in the exam, right? Just for your guidance here. Then we come to these questions here where I'd like to recommend that you pay some attention to this because only if you are able to answer these questions then we make sure that you also follow the course to some extent. We will have our first online test very soon. We are now after lecture four. That means in the next week you have to do the online test. The online test is a simple test. It's an open book test. You can do it at home. You can spend as much time as you like on doing the online test. It's simply for us and for you to get an idea of whether you are following the course or not. That's the idea. You have to pass the online test in order to take the exam, which is obvious, right? I cannot imagine that someone who fails in the online test completely changes everything in the exam. I would be hardly surprised. Regarding the exercise, Pavlin told me that there are not more people than here, right? Obviously, the solution that you found is that you send a representative of your group who has then to volunteer to do the exercise. If you think that this is the best way, then I don't know. But what we would like to achieve is that you take this as a chance to discuss more about the lecture, more about the findings, more about the art, that you use this really as a way to improve your knowledge, to double check, to ask questions, and so on, also to your fellows. Yes, Pavlin told me this. I am fine with changing the date of the time slot of the exercise. There was no problem with this. Every time slot is a compromise. The question is not whether we can change the time slot. Of course we can. The question is whether we find a better compromise. Maybe we find another time slot, but then you cannot attend. Okay, so that's maybe the problem. Pavlin has opened this discussion as far as I understood already, so you can make suggestions if you like. And we find a time slot where more can attend. That's fine with me. This provides a seminar for you, not for us, right? You have to understand this. That means we want to offer you the possibility to get here. We can certainly adjust and find another room. But I want to repeat that this is not... You shouldn't see these exercises as another way to push you, right? We do not want that you spend more time and effort in this. We want you to really learn something from the course. That is why we have structured the course the way we did it.  This is not what we have in mind. We want to make sure that you draw some useful knowledge out of this. That is our important thing. And therefore, we want you to do these practical circumstances. Later on, if you are in industry or academics and you have to analyze data, then you immediately know, okay, I got used to this program and even if it is a different data set, I can try. I can try. And then you educate yourself, right? This is the first step that you get confident in yourself, that you are able to do this, right? And that you get something out of it. And if you want to do your PhDs, then, of course, then you learn what to do with this, how to test a model against data and all this kind of thing. It's useful for everyone. Okay, with this, I stop here. I see you next week again and we continue then on the last Dalat's fact. And then afterwards, we move into modeling. Okay, thanks.


Welcome to the Jackie Boswell show. I'm Joe Wamba, joined now with Jackie Boswell, head coach of the women's basketball team. And coach, last week you guys dropped the first Commonwealth conference game with Albright, but bounced back in a big way, defeating Widener 87-78. Yeah, I mean, two tough conference games. And we were hoping that our four non-conference to start the season would get us ready for a conference play. And I do think they did. Even though we fell a little short from Albright, we think we learned a lot and got some production on the defensive end, holding them only to 61 points. The problem was just being able to convert some open shots, which we didn't do. And got to give Albright all the credit for all the shots that they did make. But I agree with you. I thought that showed a lot of courage and a lot of strength to come back on Saturday to go on the road to Widener, who's playing pretty well and can shoot the ball very well. And they had a great game on Saturday against us. And a person with a great game is Sarah Tarbert, having 49 points and 16 rebounds. Yeah, I mean, it's pretty incredible. I thought it was a lot of fun to watch. If that was the game that had to be done, it had to be done in that game. Because like I said, I thought Widener played a fantastic game. They hit a lot of threes and really made us really work on the defensive end. But Sarah started off pretty quickly with the first 16 points of the game. And our team responded in the right way. I mean, they gave her the ball. And I think Hannah Chris ended up with six assists off of her. So I give a lot of credit to the rest of our team as well for knowing who the person was that needed to have the ball in their hands at that time, and at least in this game. And Sarah. A huge point of emphasis that you've had on your season and on your team is the rebound game. And you guys get a lot of production not only out of Sarah, but off of the bench out of Niavani and Cameron Ward. Yeah, I think both of them combined were for 15 rebounds. So that is big coming off the bench. And that has to be the point of emphasis. Because when you don't shoot the ball very well, like we did against Albright, we've got to be able to increase our opportunities. And sometimes those offensive rebounds are easy putbacks. So we have to get a little bit more rebounding minded. Those two are always like that. And I felt really good that they were able to come out and do the things that we needed them to do and rely on them. And moving into this week, you guys traveled 11 on Valley and then have Arcadia at home, followed closely back up with Elizabeth Town on Sunday. What is it you're going to be your point of emphasis this week to get those Ws that you're looking for? Well, you know, we just got to stay focused and take one game at a time. And that's what we talked about when we got to Weidner. And after the Weidner game, that that was just one stepping stone in the end result that we want. So I think that Lebanon Valley, we have to focus on being able to defend on their floor. They're going to shoot the ball well on their floor. And we just got to rely on that defense and see what comes on the offensive end. Well, Coach, we wish you the best of luck, this tough stretch of really good competition in games you guys got this week. Yes, thank you. She's Jackie Boswell. I'm Joe Wamba. This has been the Jackie Boswell Show.


Welcome to the Crimson Engine. My name is Rebedium. We are continuing our ongoing series of interviewing other YouTube creators, other filmmakers, other photographers. I'm very happy to have Sean Tucker here today live from London to talk to me a little bit about, you know, his photography, his filmmaking, his channel and, you know, all things Sean Tucker. Thanks for, thanks for making the time, Sean. You're very welcome. So I wanted to start with asking what first motivated you to pick up a camera? What was the, you know, what was the spark that made you not necessarily want to be a professional photographer, but, you know, wanted to capture an image? Well, I was probably about seven or eight years old, actually. My dad had left home and my mom had remarried and I was sort of part of this family that I didn't feel much a part of. So, you know, there's a new child on the way. I was going to get a sibling, but they felt like a unit and my brother and I sort of felt on the side of things were on the out. So we went down to the seaside one day. I remember it was just a little holiday here in the UK and we were, we were by the beach and I had a little point and shoot camera, like one of them with the little zip zip wind on and I kind of snuck up on this seagull and I managed to get quite close. Even though I had a wide angle, I managed to get quite close and snap the shot. And when we took it to get developed and, you know, there are a bunch of other photos in there, but that one photograph, my mom looked over my shoulder. She went, that's, that's a great photograph. Maybe you should be a photographer and it's one of those weird things where it wasn't a conscious choice at that point and I wouldn't be a photographer for years and years after that, but it sunk in somewhere and I think getting that little bit of affirmation when I was really, really small was, was quite a big deal. Yeah.  You know, I had whole other careers before I came back around to photography and I was working for, I was working for the church back in South Africa. I used to be a pastor and the church didn't pay very well, so to make ends meet and to pay the bills on the side, I decided to start filming and I did video work. So I ran like little youth camps and on these camps, which was like development camps for teenagers, this was like back when survival was big. So early 2000s and I had a little, a little handy can with a flip out screen, you know, the DV tapes and everything and I would not only just run these camps, but also because we need to make a bit of money to keep the camps going, I'd shoot the, the camp and we did it in like a survivor style. So we split them in tribes and they had to build their own shelters and do challenges in the day and everything and at the end I'd, I'd cut together like a one hour survivor episode of their camp with them, with, you know, you did it in like iMovie or something or you did it in the, in the camera with like back and forth kind of recording. No, it was, it was a iMovie. It was something like iMovie. I don't think it was at that time because I was on PC. So it would have been some, some cheap version. I remember, I can't remember, you know, it might have been pinnacle back in the day, one of the early pinnacle studios and, and I cut this thing together and they could buy these little DVDs, which was like a survivor episode and that was, you know, I hadn't been doing photography at all, but I sort of came back to video and started to film and edit for things and that's kind of where I, that was the entry point back in, was on the side of another career to do something to make a bit of extra money. You know, you have a channel on YouTube that I'll link to of course in the description if people haven't heard of it. I'm sure they have. It's, you know, kind of really breaking out and becoming super popular and it's, it has a, your channel has a much different tone to, I would say the vast majority of not just photography YouTube channels, but YouTube channels in general where you, you know, you, you speak very much from the heart. You're very sincere. You're very genuine. You don't do a lot of videos. You do, you know, one every couple of weeks maybe kind of what was the, the process of arriving at that voice? Is it something that came very naturally? Is it something you decided you'd do no matter how it was received?  It comes a lot out of what I did before. So for the whole way through my 20s, I, like I said, I was a pastor in churches and a lot of the reason that I got involved there was because I got a real kick out of speaking to especially young people. So I was sort of in charge of 35 year olds and under and speaking to them and getting them to sort of push themselves and better their lives somehow. So that was, there's always been that part in me and I did a lot of training to get as good as I could at that, to be a decent storyteller and to communicate well. And then I was eventually fired from the church for being a heretic. So, you know, as you do, there was just stuff I was saying that they, they weren't happy with. It was definitely time to move on anyway. And I was really sad at that point that I'd lost my context to do this thing, which I kind of fallen in love with doing and it took a long time to come back around. But then I sort of sunk myself into full time photography and filmmaking and did some full-time jobs for companies for a few years and tried to do a bit of freelancing. And this thing was always in the back of my mind, like I wonder if I could bring that skill set out again and do it in a different form because I really do find it super fulfilling. And it was about, I think it was January 2015 that I was, at the time I was working for a company, my full-time day job was shooting large products for an e-commerce company in London here. And that meant that I was running the studio shooting like sofas and dining sets and like big products on set and having to get through a lot in a day. And because I've been doing that for four years, I kind of knew how to do it now. You know, I had a system that worked well and that I could push through a lot of stuff in a day and I thought, maybe the start of a YouTube channel because I felt like I should try something there. I wasn't really sure what, but maybe it could be teaching that stuff. So I did three videos in January 2015 and I put that out there and it did all right. It got passed around a lot because I kind of watched it back and felt very cold. It just felt like a tutorial that there's a lot of tutorials. It wasn't anything different really to what was being done, even though it filled a specific niche with product photography. So I left it. Like I abandoned the whole channel idea and then in May 2016 I was taking a break from the job and taking a week off and going to Wales to Snowdonia to shoot, just to shoot landscapes for myself, just for fun, just to do something totally different because I felt like this kind of heavy product load and very technical studio photography. I was falling out of love with photography. It was becoming a job and I needed to do something about that. And yeah, I mean, that's a really fascinating thing of, you know, like you get into something because you like it or I feel a calling for it, but then at the same time it becomes the thing that you do every day and it's no longer a calling.  Yeah, and it was, I chose landscapes specifically because I'm not very good at them. So it's not my thing. Like I don't know, I kind of know technically how to do it from watching other photographers do it, but I have no experience in it. So I'm no good at it. And I felt like it would be a good challenge because I was thinking at the time, like how, how, how in love you are with photography at the start when you don't really understand stuff and you're discovering things. And I thought I needed to do something to jumpstart it. So jump into an area of photography that I didn't understand and would need to jumpstart myself again and have all those discoveries. And then almost as a last minute thing, I decided I'd film it and just shoot a vlog. And, and I was there for four days, five days. And the first, I mean, the first day I just totally chickened out. I didn't film anything.  I thought I can't bring myself to do this. So I didn't film anything on the first day.  And then I filmed one little tutorial thing on a hillside another day. And then it came to the very last day and I got some B-roll and this one little tutorial. I thought, okay, let me just film stuff. And if I don't use it, I don't use it. And I sat, I went to three areas that were really, really close to this little hut I was staying next to the river.  Exactly the stuff I've just been saying, you know, fall back in love with photography and try different things that you're not good at. And then even driving home, I thought I haven't really got a video here. I'm not really going to bother. And then I kind of taught myself into pulling it all up on a computer and throwing it in a timeline. And I realized, actually, I might have something. It's too long. It's 17 minutes. It's, it's a bit waffly and self-indulgent and slow-paced. And I thought, I don't really care because I like it for what it is. And it is what I feel. Like it's as vulnerable as I can be on camera. And it is what I think. So I put it online. And that video did really, really well and got passed around a lot. And the important thing for me at that point was I cared about it like it felt closer to what I used to do in talking to people and trying to inspire them to do things that push themselves and better themselves and the rest of it. So that was like the genesis of the channel. And then I just made a deal. I kind of said that I would do one a month. And I knew, you know, if I was going to take YouTube seriously, that's not what you do. You have to put a lot out. You have to put at least one or two a week so that you can build an audience and put as many hooks out there as possible in your videos to draw in as many subscribers as possible. I just thought like, I think I would end up. I don't, I don't have an interesting thing to say every week. Definitely not more than once a week. So if I did that, I would end up taking a little bit of content and stretching it over a bunch of videos and it would start to feel very thin. I want to talk a little bit about the future of your channel. You know, what are your ambitions as far as your work? You know, both photography and film, would you like to do larger pieces?  Where would they be distributed?  But, you know, build a community around the work that you do. Now, where would you like to take that? I mean, long term goal for me is to move into narrative more and more. So, and that's for stills and for video really. So I've got a few projects in mind. And this is for personal work. And obviously it would go into the channel in that I would talk about this work and I'd share the lessons I'm learning along the way. But I would love to do more storytelling visually.  But some kind of team that could get together because I realized this is work I couldn't really pull off on my own. But a team that could go away and that we could shoot stills. We could shoot portraits. We could shoot environmental shots of a place. We could film interviews of a place and we could sort of produce a documentary that could also run with a photo story and tell important stories. And and again, like, I mean, I've got I've got ideas for my own personal work in terms of stories I'd like to tell. And I'm still trying to work out the logistics about how to do that. But definitely being part of something bigger that goes out and tell stories that need telling and it really I haven't even got such a narrow idea of what that should be. But yeah, it's definitely it's definitely narrative. That's what I want to move towards because so much of the work before has been very unnarrative and I do love storytelling. So it's been, you know, product photography or or portraits. But a lot of the portraits that I've done previously have been very kind of, you know, models and actors and that kind of stuff. And I'm less and less interested in that and more interested in in finding interesting people to photograph who have a story and their portrait even gives some of that across. Last couple of questions. If you could and I'm sure you will go anywhere where would you? Where would you love really love to photograph and film? Oh, gosh. I've got a list. I really want to do Mongolia. I'd love to head out there. I've got some kind of I've got a fascination with Scandinavia. So I'd love to get up into sort of the northern reaches of Sweden and Norway and places like that to go and check things out there as well. Just I mean landscapes and people sort of become really interesting in that part of the world. Gosh, where else? I've got a thing for Chile. Chile's been on my list for ages as well. Just for landscapes there. It looks absolutely stunning. So so that would be amazing as well. Yeah. But I mean, funny enough that the big project that I've kind of got in mind that I want to work on is in the UK. It's right here. It's just that this is where the story that I want to tell it. So again, like there's there's places I'd love to see to see places that I might not have the story worked out there yet. And then there's stories I need to be shooting at home and hopefully, you know, they all flesh out into something in the long run. Thank you so much for making the time. Please take out Sean's channel if you haven't already. He makes really wonderful videos. Takes really wonderful photographs. I really appreciate you taking the time to sit down and talk with us today. And hopefully we can do it again. Sometime in the future when you're you've found that story. You've created that that that film and you're getting it out into the world. Thanks, mate. Yeah, this has been great. Cheers.


This is the VOA Special English Agriculture Report. Most kinds of rose plants are native to Asia. But roses also grow in other parts of the world, including Northwest Africa, Europe, and the United States. In 1986, Congress and President Ronald Reagan declared the rose as America's national floral emblem. They proclaimed it the national flower, in other words. But whatever the name, the choice of the rose did not smell very sweet to supporters of other popular flowers. Some say roses are difficult to grow, but you have a better chance of success if you start with a few suggestions from experts. First, choose a place to grow your roses, where they can get sunshine, for about six hours on bright days. You can buy roses from a garden center or by mail. You can buy potted roses, also known as container roses, or bare root plants. Each kind has its fans. Some gardeners say potted roses are easier to plant. They say the roots develop better if you start with potted roses. But others point out that bare root roses come without soil, so they weigh less to transport, and that can save money. The University of Illinois Extension advises getting bare root roses as close to planting time as you can. If they arrive before you are ready to plant them, make sure the packing material is moist. Keep the plants in a cool, dark place. You plant the roses while they are dormant. The resting plants have no leaves, but still need water. When growing roses, the soil should feel moist deep down. Watering should be done in the morning. You can avoid problems like black spot and mildew that way. Be careful not to water roses too much. Too much water can cause unhealthy discoloration on the leaves. After heavy rains or too much watering, try temporarily pulling away mulch from around the roots. Doing that will help dry the soil. Placing mulch around rose plants is normally a good idea. Mulch suppresses weeds and holds moisture in the soil. You can use mulch made from bark, pine needles, cotton seed, or oak leaves. If the soil is very dry, you can add peat or compost to help condition it. If aphids, thrips, or other insects invade your rose bushes, you may be able to force them off with just a strong spray of water. For VOA Special English, I'm Mario Ritter. Share your secrets about growing roses at voaspecialenglish.com.


Welcome to Julissa news. My name is Rob and today instead of doing a live stream I needed to break it down what's going on with the Fed and if they're going to pivot and there was this great Interview and it was with Mary Daly and she is a president of the San Francisco Federal Reserve And she asked and was asked and answered a lot of good questions one of those was Will you pivot and when will you stop the next one was when is restrictions enough? And what's the Fed's overall plan next one was how long could you be raising rates? And this is the one that is I think is most important So if you think it's gonna happen or stop anytime soon, I think you're wrong And also, how do you know when you've gone too far? And this is one of their biggest criticism forward versus lagging indicators and lastly we're gonna talk about the question was what about the global economy and the UN calling for you to stop and the Bank of England and other Fed reserves across across the World Which are starting to pivot why don't you guys and will you pivot so the first question is very simple? Mary Daly was asked will you pivot and when will you stop? And this is important to understand because right now a lot of people are calling for the Fed to pivot They believe that they will pivot I don't know if that is true and I was gonna have you listen to the snippets about a minute long And I'll be right back We are resolute at raising the interest rate into restrictive territory so that we can bring inflation down Which is causing millions of Americans to suffer a real pain. Everyone's experiencing it It's also very damaging to the economy to have this level of inflation So we're committed to bringing it down and staying the course until we're well and truly done So yeah, Mary bladed out pretty eloquently there is they are not going to pivot anytime soon So it made a lot of sense to me Let me know what you think about that in the comment section But the next question is I think what's on everybody's mind is is what is restrictive enough? How far are they going to go and what's the Fed's overall plan some of what Mary just described you exactly what's going on here? Well when we were speaking before the interview you said that the futures market hump shape The idea that you go up and then you come back down next year is wrong Yeah, I don't see that happening at all I see us as raising to a level that we Believe is restrictive enough to bring inflation down and then holding it there until we see inflation truly get close to two percent and and Demonstrate that price stability is restored. So pretty much what she just said there was look We know that there's some problems coming up And we know that there's some issues But if you're thinking like a lot of different Market makers and people who are taking a look at this analyst and saying well There's gonna be a big hump It's gonna go up and it's gonna go back down and that's it Mary here is saying no throughout next year We will probably continue to raise rates until we see it at our goal level, which is two percent So that would answer your question as to like is this gonna happen or it's taper off anytime soon And the next question that that she was asked was look How do you guys do this because it seems to me like you're looking at a lot of rear-facing Data that is not forward-looking and are you guys just going forward until something breaks and going okay? Put the brakes on and it's a pretty thoughtful out answer here. So just take a listen So we definitely don't raise rates until something breaks We actually are forward-looking very forward-looking you have to be and that's one of the things about data dependence You can look at the data that's published Those are tend to be backward-looking But a lot of the time I spend in my job is actually out talking to business leaders community members Small businesses workers and asking where are we in the economy? You put that together and you're constantly calibrating through this data dependence To risks not doing enough and having inflation expectations drift because inflation just won't die off or Doing too much over Oversteering essentially and causing unnecessary downturn that isn't relevant for the economy and the conditions we're trying to achieve So we're constantly monitoring both of those risks And I guess the main thing I want your listeners to know is it's not a backward-looking enterprise It's a forward-looking enterprise. We don't want to wait till we found we got there and then say uh-oh We want to get there as smoothly as possible This is the tool we have and it is a blunt tool and that's why we're so careful about how we're thoughtful about the data We are constantly out talking to people looking at the information we have It's not just about looking at a model and making a rule It's really that's you know the regional reserve banks that there's 12 of us. We spend a good portion of our time Talking to leaders business leaders community leaders workers groups to try to find out what the economy is and and here's what I'm hearing to a person That when you're out there talking in the windows just in Boise, Idaho People right now are worried about inflation and they want inflation to come down they recognize that's a major pillar of a Well-functioning economy and one young man said to me I have plenty of jobs But I only have 24 hours in a day and seven days of the week and every time I work I lose money because I go to the store I can't afford anything so really the main message here is inflation is problematic and we are committed to restoring price stability So whether you agree if that's the right approach or not It seems like the Fed is resolute and they have a vision as to where they want to go And the last thing really comes down to this is the question which is what about the global economy? Because as we do this and the dollar increases in strength it starts to to crash everything around it So what about the global economy doesn't America have a responsibility and we've seen the Bank of England pivot as they start to do Quantitative easing we send the UN call for America to stop So the question is what does this mean for the Fed and will they start to pivot just because of the global economy? So just take a listen we really have a domestic charter That's what Congress gave us and so we have to stay on our case The other thing I know is that when the US is it is a reserve currency of the world and there are many things going on besides global tightening we have the war in Ukraine China continues to struggle through COVID There's slowing across the globe and that often brings people into US currency So those are just things that happen as a natural part of the economy and the dynamics there But the Fed stays resolute to its mission the mission that Congress gave us price stability full employment We're obviously achieving on the full employment side. We're missing on the price stability side We have to bring inflation down and basically that's it. We understand there's a global economy We understand what's going on as far as the Fed reserve does but they're like look We have a mandate and we're going to hit that mandate And that is our goal here and that's pretty much it So let me think about that in the comment section and then just to follow up There was a question that came about like well, what about would this lead us into a recession and I think we're already kind of there I think we're already there period and I know that there's there's been some some different definitions But again, if we take a look at a recession's recessions don't last forever I mean, this is back in 1969 usually about 18 18 to 24 months, but after those time frames What do we see? We see big huge economic growth and we've had quite the run Since 2009. I mean 13 plus years take away coronavirus and for me if what Mary was talking about is like We're gonna keep continuing into 2023 and then maybe even 2024 to actually get out of it. That's not two years That's not two years and for me. I Don't really I see it as the same thing as we talked about before as far as the the four-year cycles I mean if we go from 2012 2015 having all-time high-dip resets having all-time high-dip reset from 2016 by 19 2023 if we're going to a recession 2023 and 2024 and then we're kind of getting out of it in 2025 What does that mean economic boom just like we saw over here? Does that mean it's going to definitely happen? I can't say that for sure because all models work until they don't and that's what leads me to my last point which is This is of course Mr. Wonderful Kevin O'Leary and he's just going to give his his opinion about where the Fed is going and also don't forget Kevin's gots his Fingers on the pulse of business and he sees a lot of data points about what's going on So just take a listen if you think that the what he thinks as far as a recessionary woes and if the Fed's gonna Pita look at it this way 75 basic points is coming. It's built into the market already So far it's had no effect on inflation still north of 8% so why would the Fed stop? I think people that think the Fed's going to pause our dreaming and so as investors You have to make a decision you can't time the market You know with certainty that there are days when you make two and a half three percent gains You just don't know which ones and at some point and I don't know what it is Maybe it's Q1 You're going to get a signal from Powell that he's going to slow down and on that day if you're not in the market You'll lose 30% of the gains of what's going to happen. So I have no choice, but to I can't control the Fed No, we control the Fed I'll tell you one thing that's not disturbing me that is really Perplexing me. I have data every week on top-line revenue and cash flows of private companies over 50 of them We are supposedly slowing down. We're supposedly in a recession. You don't see it Nowhere nowhere do I see it right? So there's this very weird irony Which is that all these CEOs say actually I don't feel it right now, but I think it's coming later That's where you are. Well the Fed is in its dialogue telling me to reduce my capex telling me to reduce my inventories Telling me to stop hiring people and by the way, that seems to be working. Well, yeah, except I don't see it I should see it. I get weekly revenue. I get weekly cash flow We've had the best quarter ever 17 and a half percent pre-tax cash flow when before the pandemic is 15 the economy is still working So, you know, it can take another 75 basis points. You know, that's from Kevin himself. Look his business looks to be doing pretty good I think other businesses might be doing pretty good. I think some are also lagging I think for people like you and me just the basic retail investor We may be feeling it at the pump at the grocery store and things like that as we see inflation Which I think is why inflation should come down. Now, let me know which thing about that in the comment section But here's what it is and Kevin said it perfectly two things first of all You can't control it. You can't control the Fed So just worry about the things that you can actually can control and what he talked about was those 10 days So those days to be in the market There was a great piece over on CNBC and it goes it talks about if you get out of the market for And you miss the 10 best days. How much you go down? This is a chart shows how $10,000 invested in the S&P 500 for the 20 year period between 1999 and 2018 What have performed under various scenarios? If you fully invest it didn't touch it for 20 years You'd be it you go from 10,000 to 30,000, which is not bad for doing nothing But average annual return even in the dark days, you would still be up 5.6 percent annualized That's year over year. If you miss the 10 best days, it drops by more than half 2 percent If you miss the 20 days, you're down. You miss the best 30 days You're down 2.4 miss the best 40 days you down 4.5 and so on and so forth So for me, you might take a look at it time in the market is a little bit more important than timing the market We try to time it a little bit But usually we're never gonna hit the tops of the bottoms and now will lead me to my very last point Which is you know what I'm doing. I'm sticking around. I'm staying in and this is what I'm doing This is from my coinbase account and I buy crypto every day Do I buy the same amount that I would when we're on you know We're on an uptrend and there's not fed tightening and there's not Fantastic inflation going around and there's some more stronger fundamentals. No, I am dollar-cost averaging But what I call micro DCing, which is I'm still buying but not as much as I did before and Bitcoin Ethereum They're my blue chips and I buy them every single day But it's less than what I usually would buy because I'm just waiting and I'm seeing I don't want to be totally out of the market I'm not gonna go all in and there are some alts down here that I buy Sometimes every day sometimes every week and I'll do those in a separate video But that is it for today now look and I went a little bit long But I wanted to make it crystal clear about where the feds mission is where they see things and what's happening There's things that we'd like to see I just don't know if we're actually going to see them again You can't fight the Fed and you want to control the things that are right in front of you that you can't control and that's it So look, thanks so much for stopping by I appreciate you like today's video Give it a thumbs up consider subscribing and that's all so I appreciate you stopping by and I'll see you on the next one


Bemidji baseball in the spotlight today, beginning their section 8-3 tournament against Little Falls, just a five team section, so only three wins needed to make state. Otto Grimm on the mound for the Jacks, recently named a finalist for Mr. Baseball. Today he showed why. Jacks down once and nothing in the first until Grimm rockets one into right. Down to get many good pitches, but he makes them count one-to-one tied up. In the same inning, Cole Tatra with a run around third, grounds one-to-third. The throw goes into Matt Fitzgerald's back, and kickball he's out in baseball, that's a run. In the third for Little Falls, Joel Holm turns one-to-left, allows a run to score, and we're tied at two. Bottom half of the inning, Henry McCalichick lays down the bunt. It brings home Hunter Olsen, they try and throw McCalichick out at first. Throw gets away, Matt Fitzgerald scores, they're up four-to-two. Last chance for Little Falls, down four-to-three.  And in the ninth, Anthony Vetter with two on hits one. The game-winning RBI, another run would score, Little Falls gets the win six-to-four.