Topic of this paper is competition on vertically integrated platforms and e-commerce platforms, and you could think about this as especially Amazon, but could potentially apply it in other settings that I'll discuss in a bit, serve at least two distinct functions. First, the sort of maybe the most well-known function is that they host marketplaces which match consumers with external sellers of various goods. But what they're also increasingly doing is producing their own house brand products which they sell directly to consumers on the marketplace in competition with external sellers. And now this dual role leads to a variety of potential conflicts of interest, in general the idea being that the marketplace oversight that the platform wields gives them potential the ability to advantage their own products over external sellers. Now there are a number of different directions in which you could think about conflict of interest here and the one that we're going to focus on for this paper is going to be the use of proprietary demand data. So what we mean by that is that by overseeing the platform, the sorry by overseeing the marketplace, the platform then gets to observe which third party products are selling well, how often customers reorder these products, so some notion of the kind of stability or longevity of demand, as well as what products consumers or folks are searching for. And all of this data can both hypothetically and in practice be used to target the introduction of house brand products in particular high demand segments. And so the kind of potential worry here from a competition standpoint is this practice which we'll refer to as insider imitation for this talk may potentially harm innovation or the introduction of new products onto these marketplaces by sapping profits from so-called long tail firms that sell mostly or only on a single platform. Now this is not just a hypothetical practice given the audience I probably don't need to motivate this much further but you know just to hammer this home. Last year the EU brought a sort of preliminary notice or published a preliminary notice that they were planning on bringing a suit against Amazon for breach of interest rules. There were actually a couple distinct practices that they noted in this notice but one in particular was specifically the use of this non-public business data in order to target competing products. Now you know just to kind of motivate this exercise even further you can ask kind of what's new you know why is this kind of private level competition particularly concerning for online platforms when this is a really commonplace practice in brick and mortar retailers like you know grocery stores department stores and so on. You know there's evidence going back at least to the 1870s with Macy's that department stores have been private labeling for a very long time and it's also very clear that the sales data in these brick and mortar stores is also routinely available. Now one answer to the what's new is that maybe this has always been a problem in all venues but some of the horses left the barn for brick and mortar but people are interested in regulating online so we have the chance to do something about it. I think that's a perfectly good answer but we think that actually another distinction that might make this a particular even theoretical interest is this idea of unlimited shelf space in e-commerce. So large retailers essentially by definition can carry only established products with a proven track record because they need to carry these products at scale. By contrast online marketplaces like Amazon can carry any product and in fact often pride themselves on having fewer virtually no barriers to entry and Amazon has even had problems with this where the kind of selling of stolen products has been difficult specifically because Amazon doesn't want to raise many barriers to sellers listing on the platform. And as a result of this of these this kind of low barriers to entry survey data suggests that most Amazon sellers are quite small often only make a few thousand dollars a year in revenue and sell either mostly or exclusively on Amazon. And so in terms of kind of what's new and motivating this exercise we think about online marketplaces as providing a novel venue for introducing and experimenting with new products and that raises distinctive antitrust concerns. So the basic question that we're going to tackle in this paper is how should a regulator think about restricting the use of marketplace data in order to kind of improve innovation and also to kind of maximize social welfare. And the key assumption that we're going to carry with us throughout this talk is that the platform is itself a strategic actor with commitment power. So we're going to assume that the platform can choose what we'll call an imitation policy strategically and can kind of commit to it in advance of the entry decisions that are made by entrepreneurs. And so the platform also makes a trade off that's sort of at least qualitatively similar to the regulators where it's going to trade off the benefits to its marketplace business of more intra-bound entrepreneurs against the benefits to its product division of imitating successful products. And so in order for regulation to serve a role it's going to have to do something beyond just solving a holdup problem of allowing the platform to commit when it couldn't otherwise. We're going to kind of take as given that the platform can solve that problem for itself by establishing a reputation. And we're going to then ask okay you know how does that leave incentives misaligned even with commitment power and can a regulator do something about that? Okay so this is a very short talk so I just want to kind of you know highlight that there's other work that's been done on closely related issues of sort of a vertically integrated online platforms and data usage even quite recently. And in fact I think most of the papers I'm citing have at least one author who's in the room right now. So there's been a lot of interest in recent work about regulation of so-called dual-moting or self-preferencing. Dual-moting is just the idea that a marketplace should can sell its own products at all and so papers have studied what would happen if you outright banned the sale of product of private little products on these online platforms. And also they have self-preferencing that you can somehow buy a search results or or recommended results to privilege private label products. There's also been a little bit of work about regulating the use of data collected from consumers which is sort of a complementary issue to ours. And so our contribution just to keep it very brief is we think about a slightly different issue which is not about kind of a restriction on vertical integration per se but about a regulation of collection and usage of merchant data in particular. So that's what we think of as being the kind of particular niche in which our paper sits. Okay so let's move right on to the model. So the models are quite simple. There's going to be a small number of players. There's going to be a platform and that platform is going to do a couple things one of which is to host a marketplace for an entrepreneur's product and then there's going to be an entrepreneur and that entrepreneur is going to develop a new product and potentially sell it on the marketplace. The platform is then going to have one additional thing that it can do which is that it can observe a product that's been introduced and it can imitate that product. Then there's going to be a regulator and that regulator is going to implement data protection rules and I'll tell you more about those rules in a bit. So there's going to be a couple key product market variables that are going to affect the entrepreneur's decision to enter and the platform's decision to innovate or to imitate. The first is going to be an innovation cost which is going to we'll denote by little k and it's going to be drawn from some distribution whose features aren't that important and the idea is that this innovation cost is going to determine whether a particular entrepreneur enters and it's going to be the private knowledge of the entrepreneur. The other important product market variable is a demand state which will denote alpha that's going to be drawn from some other distribution whose features will be important and we'll talk about that later on and this is going to basically determine how big the market ends up being for newly introduced product. Now the entrepreneur's problem is they look at their innovation cost k and then they decide whether they want to pay that cost in order to innovate by developing a new product and then if they decide to develop a new product then they're going to get some profits from selling that product in the marketplace. We're going to denote these profits by pi e for entrepreneur. They're going to depend on both the demand state and they're also going to depend on how much competition the entrepreneur faces in the marketplace. So we're going to assume that the demand state is sort of a positive indicator of profits so the higher demand is the higher profits are but we're also going to assume that the entrepreneur doesn't like being competed with very naturally so we're going to call the state where there's no imitation by a private label the monopoly state and when there is imitation the duopoly state and the entrepreneur doesn't like competition. Can I enter please Eric sorry we have a couple of questions so about the first one was about the commitment power of the platform so Doshin is asking Doshin and asking what is the rationale for the assuming that the platform can commit to its imitation policy. Okay so were there a couple of questions did you want to say all of them? Yeah just that one I think the other one we can keep for the maybe discussion because it's about a little bit more like how to think about it's about the you know I can ask it now so that you can think about it along the presentation and decide when to answer. So this is from Jack Kramer he asks amount of products that Amazon sells under private labels how many are really innovative trash cans are not so. Yeah okay good so to answer the first question about commitment I think this actually might be something that could also be good to to defer a little bit to the discussion section we think of there as being two reasons to focus on on commitment one is we think about this is just sort of a benchmark problem that says okay well there is always the potential for regulation to solve a holdup problem we kind of understand how that could work in a variety of settings but if we want to understand kind of the real conflict of interest between the platform and the regulator then we think about commitment as sort of giving us the the benchmark we need to really understand what the conflict of interest is there we also think it's reasonable that a large platform like Amazon sets policies that it you know it keeps consistent over time that seller that you know potential sellers in the platform internalize and that it can really develop a reputation by its practices and you know in fact it has developed in some sense a bad reputation for some of the the putative practices like you know downgrading sellers who don't use their fulfillment services for listing the buy box and things like that so you know maybe we can discuss more in the discussion section you know whether we think there might be a problem of it's kind of secret deviations from this but again we think about this is a good benchmark to understand conflict of interest then to answer jock's question um sorry can you repeat jock's question so it you can also answer it later on i mean the amazon sells under private labels many products but how many are they aren't really innovative yeah well okay so actually i think to the extent we think that amazon the private level products are not innovative we think about that is actually that that sort of fits with our model but the idea is specifically that the platform in our model is not an innovator all they can do is look at something that's already listed on the platform like say a trash can and then decide whether to to introduce that for itself now presumably for a trash can you know amazon doesn't need to see uh an entrepreneur enter in order to know that trash can exist um but you know as we see that amazon there are lots of different variations of products that you could think about as being innovative in the particular features or you could think about whether they're sort of a particular marketplace for for you know to sell trash cans online for instance as opposed to in a physical store is also something that there's some innovation in being willing to kind of take the risk of of experimenting with that um now maybe that's something that you again could think about amazon is being able to do without seeing an entrepreneur but we do like to think about there being a lot of products where amazon couldn't have uh wouldn't have had kind of a competitive advantage in thinking about this particular idea um and they really need to see an entrepreneur do it first and then they can imitate it at low cost if they want to um so one example that we like from a wall street journal investigation about these practices um was this small kind of two-guy company that introduced a trunk organizer um which is just basically a set of partitions that you put in the trunk of your car that allows you to kind of organize whatever things you put into the trunk um and that sold very well for a few years in amazon and then amazon came in after a couple years and imitated it so we think about that as the kind of thing that it's an innovation of the sense that it's not a product that that was kind of immediately obvious that you would kind of want to develop um and once the entrepreneurs came on and did it and it was a success then amazon kind of woke up and noticed it uh okay good so i don't want to keep you you know so slow at the start but uh i think that question is important to ask here uh about the exogenous cost of innovation how to understand this uh innovation costs being exogenous coming from lewing one uh i mean i guess the easiest and most literal way to understand it is just that there are going to be some products that are relatively costly to develop and some products that are going to be less costly to develop and so you know we don't model the specifics of individual products here but you could think about this as capturing some important amount of heterogeneity in the types of products that are being introduced so that's the way that we'd like to think about it here but you could also literally just think about it as they're also being heterogeneity in the efficiency of different entrepreneurs and this is really the simplest way to capture unobserved heterogeneity that will produce some smooth response and how much innovation happens in response to a policy change by the platform okay go ahead yeah all right great okay good so now i've described the entrepreneurs problem um which is quite simple it's really just a static decision about whether to innovate or not uh there we go so now next up we have the platform's problem um so if the entrepreneur decides not to innovate then the game ends and the platform collects zero from this product that didn't enter um if the uh entrepreneur does decide to enter then the platform gets to collect a stream of flow profits um which we like to interpret as commissions on the entrepreneur's marketplace sales um also at any time the platform may choose to introduce a competing product if it introduces this competing product it pays an invitation cost kp um which we're not going to model as random because it's from the point of view of the platform it's not uh and then its own flow profits which are going to be kind of an amalgam of the commissions and own sales are going to increase um from the monopoly level to some higher duopoly level and we're keeping all of the details of this competition very reduced form you know you can choose your favorite model of product market competition to get these um we're going to place very few restrictions on exactly what these numbers are um so just uh to kind of summarize the basic timeline the entrepreneur is going to draw their innovation cost and decide whether to innovate they don't innovate the game ends if they do innovate the product is introduced the demand state for the product is going to be realized um and at least in the benchmark models can be observed by the platform uh the entrepreneur is then going to sell their product over an infinite time horizon the time horizon here is actually not going to play much of a role in the benchmark model but will be important under regulation um there's going to be some discount rate which will not play much of a role and then the platform is going to choose a time at which they want to imitate uh potentially as a function of the demand state um and so you know whenever it chooses to imitate it pays this fixed cost and then the market structure is going to switch from monopoly to duopoly uh now as I mentioned before we're going to give the platform commitment power it's going to commit to an imitation policy and what we mean by an imitation policy is very simple it's just a mapping from the demand state for the product to a time at which the platform wants to imitate the entrepreneur is then going to get to observe the platform's imitation policy before it decides whether it wants to to innovate as I mentioned this commitment power is going to allow the platform to internalize the impact of imitation on innovation and so really going to allow us to clarify what the conflict of interest is between the platform and the regulator now the first result that I'm going to show you guys is going to be a very kind of blunt sort of a regulatory instrument the regulator can impose what we'll refer to as an insider imitation ban um and the insider imitation ban is basically going to sever this link between the demand state and the the imitation time so we're going to model the demand state very simply as being perfectly inferrable for marketplace sales data by default absent any regulation the platform gets to pass that data from the marketplace to its product divisions um and so what we're imagining in our our sort of first pass at this problem is that the regulator can mandate a Chinese wall between these divisions so the divisions just can't share data with each other this is something that's very common in in financial regulation uh and so the regulator's problem formally stated uh is should the platform be allowed to condition its imitation time t on the demand state alpha um so for the purposes of this talk what we're going to look at is uh whether this policy would increase the amount of innovation um and in the paper we do a sort of more nuanced analysis where we incorporate gains from competition um into this um but at least if the the the regulator's interest in new products is large enough um then the insights of from maximizing innovation are going to continue to hold um okay so also I'm I don't want to belabor this too much just to keep things simple um all of these profit variables that I've written earlier generically as functions uh we're actually going to take to be linear um when they're linear we can without loss normalize the demand so the average demand is one um and this linearity isn't crucial for much of our analysis but it really allows us to take our results more cleanly and powerfully it allows us to talk about uncertainty about profits independently of average profits uh given that potentially all these profit functions and non-linear functions you can't really renormalize things so that all the average profits stay the same as you move the demand around so linearity lets us do that okay so let's just talk very briefly about what the how the platform is thinking about designing an imitation policy absent any regulation and then I'll come and tell you how this thinking is going to change under the regulation so the platform's problem has two kind of simple components um one is it wants to kind of do a uh a gross scale trade-off between innovation and imitation which you can think about as targeting a particular innovation threshold kappa this is going to be the maximum fixed cost that an entrepreneur would be willing to pay to enter the market um but then it's also going to be able to tailor his invitation uh to the particular demand state and so it can design uh an imitation policy that optimally implements you know a given target threshold um so the second part of the problem you can think about as a classic sort of cost minimization step so how do you optimally imitate kappa in a profit maximizing way um and it turns out that absent regulation you always use a sort of bang bang policy um and the bang bang policy says in good demand states you annotate immediately in bad demand states you never imitate um so we could have written the no regulation version of this model without as sort of a static problem without any time dimension and just thought about it as which markets does the platform enter or not and you wouldn't really lose anything by doing that um and so I just want to introduce one kind of really crucial concept uh that both motivates this result but also will be useful for explaining our main results so the reason why the platform invitates only when the demand is high is that it thinks about this conversion rate between the entrepreneur's profits and its own profits so the way it siphons off profits from the entrepreneur is by imitating um and it turns out that the rate at which it convert it can convert the entrepreneur's profits into its own profits is going to be increasing in the demand state um basically because it it the fixed cost becomes a lower portion um of the the variable profits and so it gets to siphon off the entrepreneur's profits most efficiently in good demand states and that's why it's going to concentrate all of its invitation um in good demand states you know holding fixed a given amount of profit that is siphoning off and I'll skip over this last point um so then once the platform uh you know has chosen the demand uh threshold to implement a given kappa then it has to optimize over this kappa I'll call the optimal kappa threshold kappa star um and it's going to trade off two very natural forces on the one hand if you increase kappa then uh or sorry if you uh if you decrease kappa by imitating more then your profits are going to increase by imitating additional products but you're going to suppress entry and so you're going to have uh less entry of high cost entrepreneurs and and that you know the market is going to get established less often um and it's going to turn out that that these two uh margins the kind of uh you know we think about the first as like being a a marginal effect where you get a little bit more profit from all the existing products and the or that's that's sorry that's a marginal effect the inframarginal effect is that you get less entry um and there's going to be a distinction between the conversion rates and in each of these two uh parts of the first order condition um so if we think about the additional imitation of existing entrants then that's going to generate profits at what we'll call the marginal conversion rate and that's exactly going to be how you convert profits if you take a little bit more demand away from them or you or you imitate them in a a few more demand states um but if you get new entry of entrepreneurs then that's going to generate profits um at a different conversion rate what we'll call the inframarginal conversion rate and there you get to imitate them not just the marginal demand state but at a bunch of demand states above the margin and so you actually get to convert profits more efficiently for all of these kind of new entrants um versus for for an existing entrance and this will play you know this both determines exactly what the kappa star is but I won't tell you the details of that but it's going to play a really key role when we understand what regulation does okay so now let's just talk about how regulation impacts things so we're going to put in place an insider imitation ban this isn't going to prevent the the platform from imitating a product but it's not going to allow it to condition on this proprietary demand data um you know you can under the ban the platform's problem just reduces to a no uncertainty benchmark in this benchmark the demand state is going to just be fixed at one and because everything is linear so the expectations just take all the office to one um and the conversion rate is going to end up being fixed so you're just going to get this convert this fixed conversion rate um lambda bar which is going to be equal to the the conversion rate at the demand state so the question is how does this change in conversion rate affect the profitability of imitation it's going to turn out it's going to do different things to this marginal effect and this inframarginal effect so the marginal effect is that the conversion rate for new entrance is going to move from this average conversion rate uh to a different average conversion rate and I'll tell you on the next slide about what the comparison between those things is and then there's going to be an inframarginal effect which is the conversion rate for the existing entrepreneurs who are already entering is going to move from this kind of marginal conversion rate to this average conversion rate um and so it turns out those effects are going to be very different um on the margin and on the inframargin so the direction of the marginal effect is is going to be clear um this kind of a the conditional average demand state these are the average of all the demand states in which you imitate is always going to be higher than average because you're only uh you're only imitating in the good demand states and so the conversion rate for new entrance drops another regulation um and this force is going to mean that the platform has less of an interest in admitting new entrants because you don't get to convert their profits as efficiently because you can't target your imitation um and so that force is actually going to tend to push the optimal kappa down but the inframarginal effect is going to be less clear um so the marginal demand state need not be comparable to one it could be greater than or less than one um and so the direction of the effect is really going to depend on whether unregulated marginal demand is going to be above or below the average demand state um and so the the kind of net of these effects is that if the marginal demand state is low so you're imitating a lot of products including ones that are below average um then these two effects are going to point in the same direction um and this is going to be kind of the wrong direction from the regulator's point of view that this is going to be a bad outcome for the regulator where the platform is actually going to choose to imitate even more products when it can no longer tailor to demand on the other hand if the marginal demand state is sufficiently large then the inframarginal effect is going to oppose the marginal effect and can overwhelm it and that's the case in which the regulation is actually good and it increases innovation um and so our main results very briefly are just going to identify sufficient conditions on the distribution of demand which are going to identify the net effect of the regulation on innovation um and these are basically going to to affect how this marginal demand state alpha star is going to move around um so the concept that we use to kind of quantify this is what we call the upside demand potential um the upside demand potential is basically the kind of amount of demand that's in some given right tail of the product uh or of the demand distribution so we're going to call this uh we're going to refer to this relative to this function we're going to call it capital phi uh we're going to call this the alpha tail weight and the alpha tail weight is just going to be um the amount of the demand distribution uh to the right of a particular tail um so this alpha tail weight is going to be decreasing in alpha um all the weight is to the right of zero and none of the weight is to the right of infinity um and we're going to think about these alpha tailweights as quantifying upside demand potential that you know you have a total mass of one of demand or sorry you have a total demand of one but that demand can either be kind of concentrated around the mean or it can be kind of spread out and we think about the higher these alpha tailweights as being that's sort of the more spread out demand is and the more likely demand is to be very high or very low. So our first result is about products that we're going to call experimental these are products that have high upside demand potential and the former result is that there's going to exist some threshold demand state alpha bar such that if five alpha bar so this is the alpha right tail weighted alpha bar is sufficiently close to one so you have a lot of weight in this right tail then the regulation is going to stimulate innovation and so we think about this right tail weight as being large for experimental products that have significant upside demand potential and I'm going to skip over the details of this example so I can get to some other things that I want but this example is just a very simple two-point distribution that gives a case in which you have high upside demand potential and the key point here is that you either have very bad demand that's close to zero or you have very high demand that has that puts a little bit of weight on a large demand state and so this kind of polarization of demand produces high upside demand potential and in these sorts of markets the insider invitation ban is going to be good for innovation. Now the converse result for a very different sorts of products is that if the alpha tail weight at one so this is the weight of the distribution to the right of the mean is sufficiently close to zero then the regulation is going to reduce innovation and so we think about the upside potential demand potential is being small in markets for incremental products where you understand pretty well how the demand for that product is going to shape out and there isn't going to be a lot left to learn about demand and so the demand distribution is going to be kind of concentrated close to one and so for these sorts of products the regulation is actually going to hurt the amount of product entry under these platforms. Okay so let me skip that this example is just showing with the two more distribution as you move the points of the distribution close to one then indeed you get a situation where you have low upside potential and where you end up getting an incremental product where the ban is going to hurt innovation. Okay so that's kind of a those are our main results and that just gives a sense of some taxonomy of what's the linkage between the distribution of demand for new products when there's uncertainty about it and the impact of a regulation a very blunt regulation where you completely ban insider imitation and now in the last nine minutes of the talk I'm going to tell you about a couple alternative interventions so the first alternative is going to be a more nuanced intervention where you don't just ban data usage outright but you live in it in a particular way that I think is suggested by the name patent. So this is just by kind of a clear analogy with classic patent law which tries to balance the concerns for innovation and kind of the gains from competition by providing limited protection for new products and so analogously we can ask in our setting could the regulator do better than an insider imitation ban by providing innovators a limited data patent and so formally what we're going to allow the regulator to do is to ban insider imitation for a length of time TD after the introduction of a new product following which the patent expires and the platform can now use that demand data if it wishes to target introduction of any product. So here's the the result about how the platform is going to respond to the data patent as a function of its length so not surprisingly if the data patent is short then the platform is going to weigh out the data patent and is going to you know wait until it expires and then it's going to look at the demand data and decide whether to imitate based on the demand data but if the data patent is long then the platform isn't willing to wait until the patent expires and it just goes ahead and imitates prior to that time without waiting for the demand data and in terms of what happens to optimal innovation the key result here is that the optimal innovation threshold is going to drop discontinuously at this threshold time and also for times for for data patents that are longer than t-bar the patent is going to be a functionally identical to a complete ban the platform is going to move ahead of the expiry of the patent that means that imitation is just happening unconditional the demand state and that's going to look exactly the same as if the ban lasted forever and so what this result tells us is that you can do better in terms of stimulating innovation than a data ban even if the data ban is useful that it's even more useful to kind of calibrate a data patent so that you're just prior to this threshold so that the platform waits until the ban expires in order to use this data now of course that only tells us that a data patent can do better than a data ban you know it doesn't therefore tell us that some regulation is called for in this market and so this next slide is going to say that indeed it also expands the scope of markets in which regulation is actually going to be effective so here we're going to show that under some conditions data patents can be useful even when an insider imitation ban is not in when you had an incremental product market where the the regulation would have hurt the amount of innovation so the long the short of it is that when F satisfies a hazard rate regularity condition which i'm not going to show you because it's a little complex to describe but very roughly says that the hazard rate shouldn't be too large in in some kind of endogenous way then the amount of imitation sorry this should actually say innovation not imitation the amount of innovation that's induced by the regulation is actually maximized at this threshold time and while i won't tell you the condition what i will tell you is that all distributions in these categories are all going to satisfy this regularity condition so log normal uniform Pareto and gamma distributions are all regular in fact it was hard for us not to find distributions that don't satisfy it and basically those distributions have to look like distributions with atoms they have to have hazard rates that really explode at certain points and so just the kind of point here is that not that a regular regulation is always called for you can construct counter examples where no regulation is optimal those most easily those involved distributions with atoms but the point here is that there's much broader scope for applicability of data patents than there is for just an hour at data ban um i also want to very briefly mention one other kind of social welfare aspect which i haven't been mentioning so far in the talk but which is very relevant for data patents so one point that i mentioned but didn't spend much time on was that the short data patents they induced the platform to delay imitation intelligent access to demand data and a corollary of that is that there's this welfare boost from tailoring imitation so by tailoring imitation that's not just good for the platform's conversion rate of profits it's also good for social welfare because it eliminates this wasteful expenditure of fixed cost in low-demand states and so there's sort of a welfare boost that is granted above and beyond the change in in the innovation level so kind of taking given the amount of innovation when you get the platform to tailor to the demand data that's good for welfare and so a good data patent design is actually going to kill two birds with one stone it preserves the welfare advantages of data usage by convincing the platform to wait until the patent expires to tailor to demand while also minimizing the negative effects of this imitation on the amount of innovation on the platform so we think about data patents if the regulator has some ability to gather data and tailor it to individual product markets as being potentially a much more powerful tool than just an outright Chinese wall and then I just want to briefly mention one alternative intervention which is often discussed which is just divestment so whenever there's kind of you know popular critical discussion of vertically integrated platform practices you know forced divestment is often brought up as a possibility and you know some politicians have been very vocal about this like I think Elizabeth Warren in particular in the United States has been vocal for divestment the idea of divestment is just that you need to break up this platform into separate marketplace and product companies so that they can't coordinate in any way with each other and of course there might be good reasons to do that to solve other abusive practices like self-preferencing or or kind of a you know rigging of the search results or something which we're not going to touch in this paper but we can just ask a straightforward question of how divestment compares to data policy or to data privacy as a tool for as a kind of tool for regulating the usage of data now certainly divestment does do the same thing as a Chinese wall in the sense that now they're separate companies and so you can it becomes even easier to make sure that they don't share data with each other but it also just changes the nature of the problem that the product division faces because now it doesn't collect fees from the sale of products on the platform and so it's thinking about what products are worth imitating is going to really change versus the case where the there's a single platform that's making integrated decisions about the two divisions and so our result here is that divestment is going to reduce innovation compared both to an unregulated benchmark and even compared to an insider imitation ban so if your goal is just to stimulate innovation because of these concerns about you data usage divestment is a really bad solution for that particular problem and the reasoning here is that there are really two different forces that both turn out to point in the same direction so the first force is kind of a direct one that the divested product division now doesn't get any fees from third party products listed on the platform and so it just becomes a more aggressive competitor it's more willing to trade off less entry for more profits from the the products that do enter but there's an additional force which is interesting and subtle which is that basically these marketplace fees are kind of complementary to data privacy so it turns out that if you take the fees the marketplace gets gets from products to zero then all products become incremental so we showed you know one cut of the problem which is that you can think about experimentation versus experimental versus incremental products as being a function of the demand distribution but of course there also going to be a function of other product market variables which we left fixed and it turns out that if you take the monopoly profits of the platform to zero then every product is going to end up being incremental and so if you then bar access to data that has a further effect of further harming innovation and so you really get this kind of double whammy of negative effects from divestment in fact it's sort of exactly the opposite of what you might have been hoping to get which was that the data privacy you were getting from divestment was hopefully going to kind of help stimulate innovation on the platform and so this is just kind of you know highlighting that that directly kind of behavioral regulation of usage of data may be a lot better than structural regulation of you know the integration of these two services in one company for purposes of affecting innovation on these marketplaces. Okay so let me conclude so I don't go over time so as we've shown data usage for vertically integrated platforms has ambiguous implications for innovation because the platform at least partly internalizes the chilling effect of its own imitation on the entry entrepreneurs and so the the reaction to a regulation is going to depend critically on the shape of demand in particular this upside demand potential that we've identified as a key force and then what we've also shown is that insider imitation bans can be effective at improving the situation depending on the shape of demand and that they're an attractable alternative to blunt instruments like divestment they maintain this beneficent interest in collecting fees from new product introduction and you can actually refine them to do even better by creating limited data patents which eventually expire so I think I'm now a minute over time so thank you all for your forbearance and for your attention. Excellent Eric thank you very much indeed you're exactly on time we started one minute late so that's great so then I asked Justin for his discussion and then I had already collected a couple of questions for the Q&A. Would you like me to stop sharing my screen? Yes please I think then we can see yeah more screens of the people. Okay so let me just start with a big picture I think this is actually and I'm not always the type of discussion that says this so I really mean it when I say it I think this is actually a very important paper and it emphasizes that innovation is inherently a complex phenomenon and then we need to think a little more carefully before we jump into certain regulatory regimes as I'm sure you you gathered from the talk the essence here is that we may care about innovation measured or categorized in different ways so it's not just you know like a homogeneous product so to speak there's lots of ways of thinking about you know what he thinks what you know they think about here is of course the riskiness of the underlying innovation but of course you could also think about you know other types of innovation that maybe are less appropriate here like you know how easy is it to build on a past innovation so you know obviously innovation is a big topic and I think this is actually a really nice contribution okay and as hopefully you gathered the main argument is that you know if you restrict the ability of the platform to utilize demand data and it's vertical integration decisions then you actually in some cases can harm innovation rather than helping it okay what are the key ingredients first of all obviously uncertainty and second the commitment issue okay actually there's two types of uncertainty here one is revolving the innovation cost of the entrepreneur and the other is the demand state okay the commitment issue I just want to say I mean yeah obviously this is important I mean I think there was a question about that already I think it's quite clear that if you if you if you get rid of that assumption you can you know get different results in fact there's a paper that Andre Julian and and Todd have on that sort of same same comment or same same issue okay but at the same time I mean it's I think it's always appropriate to think about the commitment benchmark and I think it's pretty reasonable that a large firm like Amazon potentially would be able to establish some type of commitment okay so I personally don't have any problems with that but they are key key ingredients now I think the issue I've seen this paper several times and it always generates a lot of well what what about this you know what about that type of questions and you know I have a list here I'll I'll touch on you know there's all sorts of you know questions or concerns or potentially objections you might have to you know the particular assumptions they make sometimes with the paper I think that's really problematic because it suggests like that the issue they're looking at is on interesting or on stable in some way here I just think it actually suggests the opposite that this is actually a really important issue that we don't know very much about and yeah sure there are a bunch of questions like well you know what about this what about that I think those are important I think they those suggest and again I don't always say stuff like this when I discuss papers I actually think that there should be a lot of follow-up work that seeks to you know answer some of these questions so just you know just to give you a you know a flavor I'm sure there'll be others and the discussion or rather the after chat I'm not suggesting the author should do any of this by the way just to show that there's a lot going on lots you could think about you know what if the platform can optimize its fees maybe it doesn't matter in the linear model I wasn't quite sure but you know what if it can optimize its fees not just fixed fees and influence things at them you know at the margin but also kind of per unit or revenue sharing fees and influence extraction and say the higher demand states more broadly what if we forget about vertical integration but we think about the platform selling data between different types of firms so maybe there's already three firms in the market they're all generating some sort of data and the platform can now sell this data back to the firm so this actually happens you know Alibaba is kind of a leader and kind of marketplace intelligence and selling that that's a related very important question and then the other main striving to stay under five minutes here the other main issue I think this one is actually pretty interesting again not saying it should be done on this paper another assumption is that the there's sort of symmetric uncertainty between the entrepreneur and the platform regarding the demand state so they're equally uninformed about what demand will be I think it's pretty reasonable that the entrepreneur might be better informed I don't know if that matters for the results but I think you know it would be interesting to think about that I actually have kind of two final thoughts here I think are more important in a big picture level the first was not in the current paper I was thrilled to see that Eric talked about it which is okay fine so there's a problem with you know a simple type of data regulation what are better ways to regulate and it looks like Eric and his co-author are now working on that maybe it's data patents maybe it's something else but I think it's one thing to say okay there's a problem with this regulation but it's quite another to say hey here's a different solution a better solution and then the last comment I have I've mentioned this to Eric before so this is just kind of a more you know just as part of the discussion here a really interesting aspect about online marketplaces is that they're permissionless so if I want to go sell something on Amazon I don't need to be the manufacturer of that product I can go buy that product and then resell it on Amazon and in fact Amazon encourages this here I think it's important because you might have thought well maybe competition will solve this problem you know discipline platforms not you know cheat so much but it's not at all it's not all clear that that would happen and I guess I won't go into all the details just because I'm over the time but if you think about a world where as I said anyone can come and resell a product then it becomes hard to use market competition between platforms to discipline this type of behavior so again just in closing I think it's a really nice paper lots of questions concerns sure but I think in a good way here thanks


The Nintendo Switch is basically just an Nvidia shield with a screen stuck to it, attachable controllers, and of course, output to HDMI. It's because of this that it was pretty obvious that at some point people would get Android running on it, and they have. It's a ridiculously easy to do process. First of all, you download the image appropriately sized for your SD card of Lineage OS. Then, you download the G-Apps. You write that image to the SD card. You move the Google Apps zip to the SD card as well. You put the SD card in your Switch and boot it into RCM or Recovery Mode, and then you boot into Heckate, or Heckate, or Heckate. I don't actually know how to pronounce this. From there, you boot into Android, hold volume up, and boot into Recovery Mode. This one being the Team Win Recovery thing. From there, you can flash to Google Apps, and then reboot your Switch, boot back into Recovery Mode, boot back into Heckate, and reload Android, and boom, you're up and going. From here, I decided to run some benchmarks, but before I can do that, I actually have to, you know, go through the setup process, and you know it's good when the first thing you see when you boot up your Switch running Android is process system isn't responding. Luckily, this was a non-issue, and it was just a random message that popped up. Anyway, setup was normal from there, just a regular Android tablet, basically. Then came the Google Account screen, and I'm not gonna sign in, because sometimes when signing in and installing Google Apps' performance is hurt. After everything was done, the Switch booted up into what looks like a regular Android launcher. This is pretty much full Lineage OS running on a Nintendo Switch. It kind of looks a little weird, though. It's just that I never imagined Android running on this machine, despite the fact it is basically an Android tablet. I had to manually enable developer options, which is really weird. I've never actually used Lineage OS, but I'd expected developer options to be enabled by default, since, well, you had to unlock your bootloader, and pretty much do a bunch of other developer stuff to actually install Lineage OS. Anyway, first thing I did was go and grab the latest version of Dolphin Emulator, which is a GameCube and Wii emulator to test out performance. However, when booting up a game, things didn't really go so well. I got this error over here. Apparently, the Switch doesn't really work very well with OpenGL when running Android. Apparently, the way to fix it is to switch to Vulkan, but I haven't really found a way to do that. So sadly, there's gonna be no GameCube benchmarks today. N64, however, had absolutely no troubles running on the Switch. I was playing Mario 64 and everything ran at a perfectly smooth 60fps, with pretty much no stuttering. Not that I would expect anything less from such a powerful Android tablet. I mean, N64 games can run on pretty much every phone at this point. The last emulation thing I wanted to try out was Nintendo DS. This is because Android is quite famously a platform where there's loads of Nintendo DS emulators to choose from. I tried out Jurassic today, and while performance was pretty much top-notch, there were a couple of visual artifacts. However, I think this is purely up to the emulator. I decided to then run a Geekbench CPU test, and as you can see, you got a score of 240 for single-core performance and 825 for multi-core performance. Just for the hell of it, I ran the Geekbench Compute test with Vulkan, and it gave me a score of 1338. I would try out some mobile games where pretty much every single one that I tried started horribly and barely worked. There's loads of entropy here, maybe I'm getting different performance than other people for various reasons. The Switch's fans also started whirring up for no reason during random moments, not even during the Geekbench test, just randomly started activating the fans, like while downloading something. Anyway, in the end, Android on Nintendo Switch is still very experimental. I would have recommend using it as a tablet for gaming, but I heard stuff like PUBG Mobile works kind of well on it. The Tegra CPU is still pretty powerful, and maybe with some better optimization and a fix for that open GL error, this could be a very powerful gaming tablet that you can get for very cheap. I mean, really, it is basically a tablet, but it costs only $300. I mean, an Nvidia Shield costs $150 for just the Shield and a remote control. For $150 more, you can get a Switch, it comes with a screen, controllers, a dock to play it on the TV, and of course access to Nintendo's library, along with pretty much all the functionalities of the Nvidia Shield TV, except maybe some of the proprietary software for it. I'd say it's a pretty good deal, and it really makes sense that loads of these consoles are sold at a loss. Anyway, thank you for watching. If you want more videos about hacking the Nintendo Switch, then be sure to ask for them in the comments, and of course, obligatory advertising for the Discord server. It's a great fun time on there. Thanks for watching, and goodbye.


When we worked with Ryan Beer at the NRCS office to develop a grazing plan, one of our focuses was to retain as much rainwater or moisture on this ranch as we could. We were at six pastures when we started working with Ryan. We're now up over 60 pastures. By doing that, we give plants more rest. Therefore, we're developing more root system in the soil which helps water infiltrate into the soil and get our subsoil moisture at a higher level. We're in a kind of a fragile environment here. We don't get a lot of moisture. I think we're around that 14 inches of moisture per year normally. And this year we're in a drought. We're probably at a third of that. We have to be careful not to overgraze and lose the little bit of topsoil that we do have. We're using multi-species. We graze both cattle and sheep on this land. There's an overlap of about 40% of what sheep consume and cattle consume. Well, that leaves quite a little that one species or the other doesn't eat. So we try and take advantage of all of the stuff that we do grow on this ranch in some way or another to provide income for us. We always give pastures 45 to 60 days rest before we graze with the next species. In doing that, we're utilizing grasses that one species or the other does not utilize. Livestock numbers on this ranch, we've increased anywhere from, depend upon the year from 70 to 100% of what this ranch used to stock. With our multi-species, we do winter graze as much as possible. We used to plan on 2,500 pounds of feed per cow to feed our cattle through the winter. We've gotten that down to about 700. We try and graze as much as possible through the winter.  We used to feed six months out of the year. We're down to 2.5. The sheep, we usually don't feed them anything but a little corn supplement. They graze all winter. And the reason we can do that is because we give the grass rest so that it can regrow and produce more forage for us. It's amazing how much you can stockpile by giving that grass rest and not grazing it over and over again, taking second, third, fourth bites off of the plant as it's trying to regrow. By doing what we're doing and increasing our numbers and increasing the production of this land, we can afford the next generation an opportunity to produce food for the world.


Turkesteroin, the biggest scam in the supplement industry. Hey, what's up guys? It's Josh here. Today, I want to do a little review on this supplement called Turkesteroin. I had never heard of it. It sounds like turkey testosterone to me. You know, when I first heard it or like you found it from Turkey, something like that. So technically, this is supposed to be like an insect steroid that doesn't really react with our body. So technically, there's some studies and people claiming that it doesn't do anything because it can't react with our bodies. But then there's also a lot of anecdotal evidence. People trying and saying they get lots of benefits. And there's also another plant steroid called ectosterone that was discovered first and has had a lot of studies on it. And that also had a couple of conflicting studies. But as you can see from that click baity title, everybody who's posted a video about Turkesteroin, they started off by saying that it's a scam and everybody ends up pretty much low key saying by the end that it works and that it's great. Real quick to give you my back history with prohormone slash steroids. I've never talked about it on this channel. It's a weird kind of topic. I haven't taken them in many years. It probably would be a good 5 to 10 minute video for me to describe all the prohormones and steroids I've taken and when I've taken them long, long time ago. I've since stopped taking them basically probably until my natural test gets too low. But we're going to see what happens at that point. I'm going to push it as far as I could go. Anyways, I'm rambling too much. I tried a lot of them. I think there was like Max LMG, the old Superdrawl. They have like a new Superdrawl now. It's not that it's like the old Superdrawl methyl stem balloon, dimethazine. At the very end of me trying prohormones, I did run into SARMS, which again, I don't want to take too much time into it. But if I was going to do a prohormone or steroid again, the number one kind that I'll be looking into is a SARM.  I don't need to make it. So that means when you come off of it, you have depleted levels. SARMS kind of make it so you get most of the effects of the steroid, but it also doesn't replace testosterone 100%. So your body's like, you know, we still need to keep making testosterone because this isn't enough of what we need. On all this stuff, you're supposed to take like a PCT, some anti estrogens and some stuff for your organs. I had pretty decent testosterone back then and I do now.  So for me, I wasn't interested in anything that was steroid like whatsoever. So when they sent this to me, I was even like, the SARMS, like they're great, but I just don't really want to take a hormone booster right now. They say you don't need a post cycle treatment after, but it just kind of doesn't make sense to me because it's like, it doesn't register in your body, but then it works, but then it works, but then you don't need a PCT. Well, it's like, well, if it works, you do, I just doesn't make sense to me, you know what I mean? So I did get three bottles of this stuff. Each bottle contains 60 capsules. You take one capsule per day. So I have probably about six months of this stuff. I probably will be cycling on and off. I will kind of give probably a video a few months down the line once I've finished it with this stuff. But so far, I've had pretty much only pros. I pretty much for taking this for three weeks straight. I think for one, maybe two days, I forgot to take it. Didn't notice it at all. Normally, if you run like a pro hormone or a steroid and you're supposed to take it every day, you would totally notice it. I had just completely didn't notice it whatsoever, which was a very, very good sign. So anyways, guys, I've taken this for about three weeks. I'm about to probably any time now or maybe about a week, I'll take a little bit of a break, but I felt this stuff right away. I think like day one or day two, I started walking around. I started to feel just like I had extra juice in my body. Like my muscles felt a little bit more pumped. My joints felt just like I could just go like hit the punching bag randomly just hard. And I don't have to worry about anything. Just this kind of extra level of oomph. And I didn't feel bad before, but I just felt like an extra level of ready to go within my body like right away. So I knew something was kind of up with pro hormones and steroids. I get tons and tons and tons of side effects. It messes with your sexuality. I would get super, super, super angry, like ridiculously unhinged angry to the point that's probably why I could never take them again. I don't have any of those issues here. I don't really have almost any of the side effects here. You know, as far as me in the gym, I noticed pretty much right away the size increase. I have a detached AC joint up here. So there's only so much like oomph that I can put into certain exercises. I was kind of at my limit to where it's like, man, if I go any harder, it's either going to hurt the AC joint or kind of hurt my joints a little bit. I'm not about their life. I was kind of at almost like a plateau, especially with the stuff that I was taking. I definitely noticed a decent strength increase here and a decent size increase, just overall, like some very, very, very good results. I was actually very, very impressed with this stuff. And this is definitely going to be my go to for the time being until I need something stronger, if I need something stronger, but I'm going to try to hold out as long as I can, you know, on as much natural stuff as I can. And this is natural. It's like an all green capsule. Like, I don't think you can see that. Some like, some like red and green, like bio hazard colors on here, you know, I'm honestly really, really blown away with guys, because like I said, I didn't want to dip a toe into anything with anabolic. And if you were somebody who just wants an extra level of oomph from the gym, just everything as far as strength, size, endurance, performance, anything that a normal steroid would do nowhere near as strong, but some of those results, this is the number one thing that I would suggest. I have not tried other brands besides this one, but for the time being, I'm only going to go with this brand just because they're the one that I know, they're the one that I trust. It's like a lot about like the extracts on here that it is an extract. So like, maybe they do like something extra with it, or this brand is extra strong. I don't know. But I was absolutely blown away about size, strength and endurance. I got off this stuff. If steroids are a 10 out of 10, as far as strength, I would rate steroids a 10 Psalms. The ones I was talking about before, I would say as far as strength, probably about a four to an eight as far as what steroids would do. And I would rate this probably about a two to a three. Now that's actually very, very, very good. And you got to understand the more stronger it is, the more likely you're going to have side effects. If there's any weight lifters out there and you're somebody who wants to stay 100% natural, but you want some effectiveness, check this product out right here. And I will probably be again doing some more testing on the future. And feel free to ask me any comments down below because by the time you watch this video, I probably will have tried it out more and I will know a lot more about it. And I can answer your questions down there. But for me, I was absolutely freaking blown away by this stuff, guys. It is amazing for muscle building strength and all those goodies. I love it. And it's going to be a supplement I'm going to be using for a long time. So anyways, guys, we're on the road to 50,000 subscribers and I couldn't do without any of you guys help. You guys are the best. Having a great day out here, hopefully having a great day at home. See you all in the next video. Peace.


everybody forced here I'm doing a multi-random from 2021 flawless football hobby 2 box full case number five so we had a manning farve and Brady triple relic here gonna be random it is a game worn Brady and player worn on the manning and far between the Colts Pats and Packers I didn't even set up the random yet rookie move let me type it in here okay there we go Colts Patriots Packers good luck that's a two and a five seven on the random seven times whichever team is up top gets it very best of luck here we go one two three four five six seven and it's going to the Patriots congrats Patriots thanks again for joining the break guys congrats Pats get this out to you


Requirements for English teachers around the world. Asia. What do you need to be able to work as an English teacher in Asian countries? China. Qualifications. Minimum Teflor TESOL certificate. Bachelor's degree sometimes required. Native non-native speakers. Native speakers or fluent non-native speakers. Experience. No experience necessary. Visa. Tourist visa converted after arrival. Work visa before arrival. Other? 24 to 55 working visa age requirements. South Korea. Qualifications. Bachelor's degree required. Teflor TESOL certificate. Minimum of 120 hours required. Native non-native speakers. Native speakers. Experience. No experience necessary. Visa. Work visa before arrival. Other? Criminal background check required. Japan. Qualifications. Bachelor's degree required. Teflor TESOL certificate. Native non-native speakers. Native speakers. Experience. Some teaching experience preferred. Visa. Work visa before arrival. Thailand. Qualifications. Bachelor's degree required. Teflor TESOL certificate. Minimum of 120 hours required. Native non-native speakers. Native speakers or fluent non-native speakers. Experience. No experience necessary. Visa. Business visa before arrival or apply in country. Taiwan. Qualifications. Teflor TESOL certificate. Minimum of 120 hours required. Bachelor's degree required. Native non-native speakers. Native speakers. Experience. No experience necessary. Visa. Tourist visa can be converted. Indonesia. Qualifications. Teflor TESOL certificate. Minimum of 120 hours required. Bachelor's degree usually required. Native non-native speakers. Native speakers. Experience. No experience necessary. Visa. Work visa required. Malaysia. Qualifications. Teflor TESOL certificate. Minimum of 120 hours required. Bachelor's degree required. Native non-native speakers. Native speakers. Experience. No experience necessary. Visa. Work visa applied for in Malaysia. Cambodia. Qualifications. Teflor TESOL certificate. Native non-native speakers. Native speakers or fluent non-native speakers. Experience. No experience necessary. Visa. Business visa applied for in Cambodia. Laos. Qualifications. Bachelor's degree required. Teflor TESOL certificate. Minimum of 120 hours required. Native non-native speakers. Native speakers or fluent non-native speakers. Experience. No experience necessary. Visa. Work visa required. Other volunteer teachers do not need formal qualifications.


Hi, this is Wyatt from Scotland. You are watching Trucker George Vlogs on YouTube along with the new star of the show the diesel Mind the subscribe Good morning everybody today is the exciting day we've been waiting for Today's the day we go into Winnipeg for the IUI procedure. This is the procedure. I've talked about in the past That helps us with infertility because we're having trying we're trying to have a baby and And we've been having some difficulty over the past few years and today is the day when they take my guys and they drop them off at the finish line So we're hoping it's all gonna go well My wife here Brit has been In at the fertility clinic this whole day is mostly about her a little bit little I have a small part to play but So shoes in with the doctor about a week ago on Friday a couple days ago Just to see if she was ready and the doctor said she should be ready by now but he gave her some of these ovulation tests to take and They're saying that it's not time yet, but the doctor said it definitely should be time already To do this procedure. So we're going into the city today to see what's going on and hopefully Hopefully today's the day when we can get this done. If not they can What's it called again? They can Trigger her but in a good sense though not in the sense you're thinking Not in the sensitive millennial. Yeah snowflake kind of way. They can trigger her to surge is what it's called and then Pretty much we'd go back in tomorrow. I'm guessing and Do the procedure then they're gonna say today never got that smiley face on the stick. So So we're not too sure what to expect it. She it should have been ready yesterday all shouldn't get to go but These things are interesting. I'm learning a lot about the fertility process as we go through this and Try to have a baby ourselves. So it's sort of unpredictable Everybody's a little bit different. Every woman's body is a little bit different and And everybody who's gone through fertility stuff knows that your whole life is a waiting game We've been waiting for about two and a half years now Everyone's always like next month next month We get tired of waiting for next month to come So let's go into the city and see what the good doctor has to say I don't understand why 445 exists Yeah, we have to get up early for this early That's the biggest understatement I've ever heard Excited to go and find out what they have to say though We're here She's in a hurry They might be the day Might have to come back tomorrow Well, the Sun is back up there burning in the sky Still exists still it doesn't go up yet. No, it's a good thing So we have some good news and we're gonna be talking about fertility and the fertility process here in the next couple of clips So if that makes you uncomfortable or you don't want to hear about that Now's a good chance to skip ahead or just tune in tomorrow, but here's our story So we have some good news You want to tell them it's it's There's still eggs in there. They just haven't released yet They're taking their sweet time because they're stubborn like me. They belong to me So that's the good news. The bad news is that now Provided they gave me a blood test and provided that I'm not surging right now I get to give myself an injection tonight And that sort of forces the surge Yay So She went in for an ultrasound Was it Friday? Yeah a few days ago and they said yeah, you're pretty much just about to surge which means you're just about to ovulate and That means when that happens Like we have these little ovulation tests at home and it gives you a little smiley face if you're ovulating. Well, she is I don't It's never giving me a smiley face Never tried one though Live extras at home Wouldn't that be weird if I got a smiley face and you didn't and you can have the baby The amount of stuff ladies have to go through in the whole pregnancy process But you guys are amazing You guys are amazing so Anyway, so we're waiting for her to ovulate and it was supposed to happen by now And we were getting worried because day after day after day we weren't getting a little smiley face And we had an appointment today for the insemination which means they take my sample and they would you say artificially inseminate? You that would so that was called artificial. Yeah, cuz it's not the natural way They take my guys and my sample and they drop them off right at the fallopian tubes pretty much like right at the finish line so that they don't have to take the stairs and that They don't really have to work that hard cuz my guys are apparently a little bit lazy. They are my guys Then I'm not lazy. I used to be when I was younger though.  Yeah, they're still Anyway, so we've been having problems with that So this is the process we're taking to bypass the whole stairs take the elevator and have a baby our first baby and So We're waiting for her blood tests to come back right now If they say that she's already surging already ovulating naturally. We don't need to take this artificial trigger thing Hcg so it's called right Hcg is the Patient kit. Yeah Hcg is the hormone That releases when you're pregnant Okay, okay, so she's not naturally ovulating right now. They're going to induce it force it Make it happen tonight. My body is crazy fertile but It's just taking its sweet time and Josh is a trucker. So we got to speed this along Yeah, and they're being so helpful here. It's been really great. Yeah, but uh So there's two eggs ready to release one for those of you who know what this means I wouldn't have known what this meant before so it goes over your head. Don't feel bad It would have gone over my head too. One is that 25 millimeters big one. It's a big one because 20 is mature I think right around 20 20 millimeters the follicle the egg. It's that big 20 25 millimeters, right? And she has a second one that's 16 millimeters. I don't think they're that big no Just 25 is 25 is the measurement. I don't know what I don't really know what I'm talking about either. I'm not a doctor So there's two ready to go so very high chance that we might have twins with this whole process Plus it runs in both sides of our family. So yeah, and we're ready for toys Totally cool with that as long as they're healthy and you don't care if they're boys or girls Don't want four or five or six But there's only two releasing this time, right? So Yeah, the eggs can split. Oh Sneaky Oh Okay, well, I guess we'll see what happens So what we got to do now is we've got to hang around the city for the rest of the day It's 9 a.m. Right now and they said they'll have our blood her blood work back before 4 o'clock And they'll let us know if she isn't naturally surging and ovulating We have to buy a 200 dollar Injection the medication that'll go into this serenity On top of the 625 dollars for the Procedure on top of the 650 dollars to the registration. So if you guys are wondering how much this all costs Here you go On top of all the hormones that I take every single month on top of your swim up test That was 300 bucks the hormones are quite cheap because we have insurance. So they were only about 13 bucks a month But yeah, it it adds up quick. It's very expensive to do this and we're only doing iui in vitro is much more pricey So we've dropped a few thousand dollars for this and uh Yeah, if we got to go to the next step, which is IVF where they actually extract the eggs out of the woman out of the ovaries and then take my guys and they They put them together. Yeah, and uh, yeah Little science It's science and they fertilize the egg outside the body and then once the egg is fertilized They put it back in to her body and that's the last resort, but that's several thousand dollars as well. So Let's hope that this works. This should work because i'm so fertile there's We shouldn't have to do that, but you never know. Yeah We never thought we'd have to do this either No So that's where we're at. That's what we're waiting for now waiting for the blood tests and either way Whether or not we have to take the extra the booster shot. I guess you called The booster shot whether or not we have to go juice. No, no, she has to take that tonight or not We're coming in tomorrow for the insemination So tomorrow is the big day And then we'll be crossing our fingers hoping and praying when you guys watch this We won't know if she's pregnant yet So if you guys want to send your thoughts and prayers our way and even if I was we probably wouldn't tell you guys at the Early stages we've made that mistake once before true But we do appreciate any prayers you can send our way because we want to have a healthy pregnancy Healthy child. We don't care if it's a boy or a girl. We just want it to be healthy And uh This is our big day tomorrow's the big day. Hopefully Yeah, hopefully it works If not, we try next month in the month after that But as I said, I'm tired of hearing the phrase next month because next month never seems to come when you're in fertility stages So yeah, I don't think we've gone over the cost before of that So just those of you who apparently infertility is pretty common much more common than we thought So if you're wondering in canada, you might think that oh, I thought everything was free No, it's just basic medicare is free. This obviously isn't covered because why would they cover that that's not necessity It's not uh, it's not like life or death Yeah, but oh it's life. It's life That's very true Very true Maybe we should petition to have it covered No Then they'll just go overboard with everything But that's where we're at. So uh, yeah Let's go, uh, say hi to your dad. Maybe Yeah, sure. You want to say hi to him? Heck yeah, I always want to say hi to him Cool, he's just down the road at his work. He works at Chevy. Let's go look at some trucks Okay, yeah The very next day the big day There we go And I didn't need to end up taking that injection I was very happy about that But we have it on hand in case we need it for a next time So she surged naturally yesterday evening and we got a smiley face on the little ovulation test And today's the big day. So we go in today. I'd bring them a sample 8 a.m. This morning They do their Their thing with it clean it up a little bit. I guess just take what they need And then at 10 o'clock we go in for the actual insemination And then after that it's a waiting game waiting to get a Positive test result on our pregnancy test Yep You think that's what's going on, right? Nice boots Thanks Very nice On payment Genuine genuine leather Yeah, actually genuine kamek Chevy you're in charge buddy You're in charge you fierce lie in you you hobbling hobbler hobbit Whoa Can you handle it? It's a lot of responsibility It's all up to you, man Starts crying We'll be back soon. Just be a couple hours, bud. Just leaving. You doing okay? Yeah, just cramping I'm okay though I'll live Just get back to the vehicle here So it's all done Now we wait Find out the results later this month So we were here early this morning and dropped off my sample or gave them my sample And sort of cruised around tuxedo sort of a wealthier area of winter peg and looked at the big houses for a couple of hours and then came back and You went and Got the procedure done. I was there in the room and it looked uncomfortable And it was But it wasn't extremely painful. It's just uncomfortable. It's just some pain How are you feeling though? What do you feel the chances are? Is it a boy or a girl? Girl It's a girl So we're supposed to find out by the 23rd of this month And then we'll wait for it to uh We'll wait to get to the first trimester I guess before we would Tell anybody but don't expect an announcement anytime soon if we are Yeah, it'll be a few months before you hear from us yet on it If it doesn't work though, you'll hear and then we'll go in for another procedure Yeah Now we play the waiting game


another everyone and welcome. My name is Melissa Armel and I own the Stock Swoosh. And today I wanted to discuss with you about how you can simplify your trading. So many people think trading is very complicated. And I'm not saying that it's easy, but I'm saying that once you know what to look for in the market, then it becomes easy. So you have to learn a good system, a good strategy that you can apply every day so that you can make trading easy for yourself. I think one of the things that people find so difficult about trading the market is there's so many things out there. They're being bombarded by so much information on the internet, lots of other places that they don't have the focus. So simplify your trading life by focusing on one thing. So what I do is simple in the sense that I force myself to focus on one stock pick or two stock picks a day. So I will look at a bunch of things in the morning before the market opens, preparation is key. But again, keep it simple. When you're all over the place, trying to look at too many stocks, trying to go long and short of the same day of the same stock or looking at too many different time frames even, too many different stocks, too many different strategies, too many different indicators, you make it more complex, okay? Simplify what you're looking at. So for me, I only focus on gaps. I only focus on my system, which is a 26 point rating system that I created. And I only focus initially on shorts. And then I will look at longs if I don't see any good shorts, but I really go to the short side first because I prefer to not only focus on one stock but in one directional bias. So keep your trading life more simplified. Do not make it any more complex than it is. Many people find trading challenging because they're all over the place. I'm telling you, simplify your trading life by sticking to one thing, one system, one stock and even one directional bias. And it will make things a whole lot easier for you and it will also make trading less stressful. Thanks everyone, have a great day.


In this video I'm going to go through the top penny stocks under $1. We'll go through the information in this video. It seems to be a good series. The one that I did under $1 before seemed to went quite well in terms of people liking the video and subscribing and sharing the video around. So if you do like this series then do support it any way you can in terms of liking or sharing. And we'll go through some of the top penny stocks under $1 in this particular video. So we'll go through this. We'll go through the top three here. So that'll be fantastic. So I've got to say I'm not a financial advisor. I'm a business teacher in my daily life, but I'm by no means an expert on the stock market. Okay, let's go into the top three. So first of all, the one that I've got to talk about today is surge pair. So the ticker symbol for this is S-U-R-G-E. And this is essentially a FinTech company. It's involved in prepaid wireless top ups. It's got over 100 million prepaid wireless customers in the USA. They've got cutting edge technology. So blockchain based software. They've got wholesale products. A good part of this is, yeah, they have this FinTech part of the business, but also they actually provide direct pricing on the top 100 C-store products. So they can actually provide wholesale goods and actually sell to the small, you know, convenience stores, fairer products to them as part of their service as well. So they're involved in that kind of distribution side of it, but also the FinTech. They've got trusted partners, so audited and fully earned subsidiary of Sage Oldens. They've got support in terms of real time support from our team is only a live chat and call away. So it's involved in the FinTech industry, a growing industry. This one is a very interesting one and you don't see many penny stocks that are FinTech companies or FinTech related companies that are under a dollar. So I think that this one could have a chance to actually move up quite a fair bit. So let's have a look at the price. It's currently sat at 0.15. I mean, it's had a recent dip. Okay, you know, it's moved down to sort of 0.15. So it was at sort of 0.18. It's 52 week high is 0.35. Okay, so it's already kind of dipped down from its previous highs. Okay, and what I would say is if you can get this anywhere between 0.10 and 0.13, I think this is a buying opportunity to run the wave back up free of a double or triple up because I think that this will easily, you know, get to a double or triple its current price point. I think it will get to 0.40 quite easily. But I also think that it's got a lot of potential as well, right? Because of the industry it's involved in. It's one that you should maybe consider. I do like this one. Take a look at this one and do some further research into it. I think you'll find that it's actually quite an interesting company. Let me know your thoughts in the comments section down below on some of these picks here, right? So let's go into the next one. I've just got to say support the channel in any way you can in terms of always, you know, liking or sharing the videos. I'm much appreciated. I won't go on about it, you know, but I do really do appreciate all the support that's been given. I mean, we're over 10,000 now. We're getting closer and closer to 11,000 when we only just reached 10,000. So unbelievable support in the channel. So thank you for all that. And you know, remember at the end of the day, we're making videos also on my days off, right? This is the day off, right? But I'm still grinding. I'm still making videos. I'm still looking for place, right? Because, you know, if you want to win on the stock market, you've got to stay dedicated. So support the channel in any way you can. Just a quick announcement as well. We've got the channel membership now set up. So if you want to become a channel member and support the channel and also feel free, you know, it's optional, obviously. So it's up to you whether you want to do that. But, you know, being asked to, we would start a Patreon or channel membership. So that's there. What do you get? You get exclusive updates on stocks I'm interested in, occasional daily access to videos, exclusive informative blog style posts, channel membership badges, membership share posts for members to share different stock picks. And you get to support the growth of the channel. And it's very cheap as well. So if you would like to become a channel member, then do feel free. And, you know, you'll get your return back on investment anyways, in that sense with all the information that will provide in the channel membership option. Again, I've gone with YouTube membership rather than Patreon. It's just easier. It's two clicks of a button, right?  So it's just easier, right? And it's easier for me as well actually as a busy teacher, just to kind of keep track of things. So if you want to help out, feel free. Let's move into the second one. What we got? We got Luvu Brands, L-U-V-U. Now, you'll notice I actually talked about this stock quite a lot of times in the past. If you're an older subscriber between, say, 1,000 to 5,000, 6,000 subscribers. If you're one of the older subscribers, you'll notice that we did cover this one a lot. Take a symbol, L-U-V-U. Now, what they do? So they manufacture in America and also they met challenges head on with entrepreneurial spirit. And their brands have spread across their home time to more than 43 countries. And they manufacture goods that are made of particularly fabric related goods. They've got bean bags. They've got lingerie. They've got all sorts of different stuff that they sell as part of their Luvu Brands. And they've got a range of different mini brands underneath the Luvu company itself. And they've got more than 2,000 products available. They've developed more than 800 products and they've packaged and shipped over 75,000 products there. So they've done a lot. They've done a lot of this company. And, pardon me, I think for the price it's currently at, it's very, very cheap as well. Let's go through it. It is incredibly cheap. Another point is that I think that they've just had recently had their earnings call. I was working so I didn't manage to watch it live. But I'm looking into that when the upload is available on the website. I think they're putting a link up there. I'll be looking into it when they do that. Hopefully it's before the actual market opens so we can make assessments. But this one I do think is growing its revenue. I've got no negatives to this company. I think it's going to grow its revenue in terms of what it's doing. I think they're producing really good quality products you can tell by looking into what they produce. Have a look on the website, right? And do some research into it yourselves. But this is one that I think has got a lot of potential. Current price is at 0.17. So you know I mentioned in terms of that earnings that maybe some earnings are coming out soon or they already have. There has been a bit of a run up on the inclination of that, which is a good sign. So it's 0.17. It's actually starting to move now. This Louisville brand is starting to move. It was hovering around sort of 0.11, 0.14 for absolutely ages. Well, I say ages. It was only about four to six weeks. And now we're starting to have that pop. And I know they're both different companies, right? But Gaxi, another one that I, this is one of my favorites. But another one of my favorites is Gaxi. Take a symbol, G-A-X-Y, Galaxy Next Generation. That was hovering around 0.21, 0.25 for a few weeks as well before it popped. 0.0, 0.021, right? I wish it was, you know, it's a sub penny stock really. But it moved. It was, you know, it was consolidating for ages and then it popped. Okay, so you want to be in when it's just either about to pop or, you know, it's in the consolidation phase, right? Because that says that it's got a good level of support here. And, you know, it's eventually going to pop based upon the fundamentals of the company and as it grows, you just got to give it time. So this is the one that I think's popping past possibly next week or the week after. This will reach over 0.20 and beyond. I wouldn't be surprised if, you know, in the long term this doesn't reach a dollar. Okay, I wouldn't be surprised if this stock doesn't reach a dollar in the long term. I'm talking within the next to 12 to 18 months. I think this could reach a dollar. Okay, let's go into the next one. But this one, keep an eye on it. I really do like this one. This is one of my favorites. Gaxi is my favorite, but this is one of them. I've got quite a few penny stock favorites to be honest. These are the ones that I'm liking at the moment. And then the next one we're talking about. I made a video on this, IQST, Ixtel. I made a dedicated video on this already. So I'll go through this one quite fast. So it's a US-based publicly listed company offering leading edge technical communication and technology services for the global market. Brilliant company. It's involved. Well, I'd say it's brilliant. It's got its problems as any penny stock does. Ultimately, it's involved in a lot of growing industries. You know, telecoms in terms of 5G, they're involved in FinTech, they're involved in blockchain, a range of industries that are growing. They've got some interesting revenue figures on the website. You know, I think that this one has got a lot of potential. Take a symbol IQST. Current price on this one, 0.79. It's had a bit of a dip. Okay. And I think that this one's incredibly volatile. So you've got to be prepared for that. So, you know, what I'm doing is I'm buying an increment. So I'm buying a little bit and I'm buying a little bit more, right, rather than putting my position in one trade because it's ultimately incredibly volatile this stock. And as it dips more, it's like brilliant time to buy a little bit more. If it dips a bit further, brilliant. Then it starts to move up and then it actually works out right. So this is one that is a really good one. If you look into it, look into the website, look at Ixtel. IQST is the ticker symbol for this one. So that's the end of the video for today. If you did enjoy the video, then do leave the like on the video. It does appreciate the channel and appreciate all the support, right? So much appreciated on that. And I'll see everybody in the next one.


Hey guys, it's Steph. I'm sister number four and I'm with you in my kitchen today to show you the best Thanksgiving side dish no matter where we're eating Thanksgiving. It never misses a year So stay tuned to watch how to make this super easy recipe We love candy yams Literally a side dish of candy dams has been every Thanksgiving. I've ever been to my entire life So this is our version without corn syrup. They're still really sweet They're still really soft and they are still sprinkled with marshmallows So, you know, they're still going to taste good Before we get started, don't forget to hit subscribe below and let us know in the comments Do you like yams or do you hate yams? I feel like there's really no in between on this one Let's get started. You'll need a 9 by 13 inch pan that's been coated with cooking spray to that You'll add about 6 to 8 yams that have been boiled until they're soft to the touch On top of that, you'll spread 1 fourth cup of butter that has been cut into very small pieces The more pieces you cut it into the better because you want to make sure it gets all over your yams To that you're going to add a half a teaspoon of cinnamon. Make sure you sprinkle it everywhere and Then you'll add one half cup of brown sugar all over the top You can also do this in layers, but you don't have to on top you'll put one and a half cups of mini marshmallows and then Bake it in the oven at 400 degrees for about 25 minutes Until your marshmallows are a nice toasty brown and your yams are heated all the way through and There you have it the perfect side dish for your Thanksgiving meal Don't forget to hit that subscribe button if you want to see more recipes just like this one and We wish you all a very happy Thanksgiving


Welcome folks, we have the now industrial trading down 573, you get the Nasdaq down 212, S&Ps off 70. Gold, gold contract down $32 trading at 1821, you get silver down 89 cents at $20.67, light sweet crude, flat $106.30 a barrel, notes and bonds. The tenure note up 23 ticks trading 120 flat, 30 year up a full point plus 9 ticks at 1931 and $king dollar, this is it I think folks, we're going to 121 and this is going to be pretty intense man. So you get king dollar up 975 ticks, 104, 819, euros 103, yen's 128.14, the British pound is at 122, where are you, 121 to 1 US dollar. Well you know when we take a look at the S&Ps folks, bottom line is that you know we broke the 1 to 1 ABC structure, it's going after a 1 to 1.382, that gives you a 366 price projection, right now you're at 386. NDX100 is just grinding its way to that 1 to 1, bottom line you get the Q's out here at 286, the low was 284, 281's the number, so this is going to get interesting watching this whole thing shake out. My take is that you know as I said many times is that the NDX leads up leads down all around and we'll see how this finishes off a 1 to 1 because what's real possible here is that it'll be such a disaster that this will finish off a 1 to 1, the S&P will be close to 1 to 0.382 and then that's where you'll see you get some kind of a bounce. My take at this particular point is that you're still going to have a rough couple days here you know and this is unfortunately just the beginning of a bear run that I suspect is going to be a couple years. We take a look at the gold contract, Gold South 220,000 contracts, this 1788 is game, that's the ABC structure down right now, you're at 1821 and what that's going to be all about is this, if this dollar folks gets to 121 you will see conniptions in this marketplace that you've never seen. You know it's strictly a currency correlation what I expect you'll also see is implosions, we're going to see implosions that's because the currency inside of the debt market meaning the bond market as well as the currency market the amount of leverage you can put on is overwhelming and these big firms that's how they make so much money, you always hear like how much they make well the bottom line is that now you're going to hear how much they lose. Stay right there folks come right back.