Welcome to Access a Trader, the number one community for those who are committed to taking control of their trading in order to achieve success, profitability, and longevity. Thank you for joining us. Here's Dan Shapiro to help you find your edge, master your process, and own your future. Hey, guys. Good evening, everybody. Welcome to another edition of theaxxasatrader.com. Nightly update show. Hope everybody is doing well. Hope everybody had a good day of trading. Hopefully, everybody's just happy, right? That's the most important part, happy and healthy. If you are brand new to the channel, guys, welcome to work. Thank you very much for spending a couple of minutes with us as we embark on this never-ending journey of technical analysis and stock market worship, right? I'm joking around by that, but yeah, a lot of people do worship the stock market. It's pretty much very unhealthy. So let's talk about today, right? So the Dow, the Nasdaq, and S&P for the first two weeks of the year pretty much flat. Nasdaq started the year, the first two weeks, up about two tenths of a percent, which I mentioned in the weekend update was a good thing. The fact that we didn't come in two, three, four, eight percent in the start of the year is actually a very, very good thing. And the reality is we had four days that the QQQs broke below the 20-day moving average, and after that was right back to the regular schedule program of really, really aggressive buying. I believe that, again, there was a lot of fund managers who underperformed last year. We talked about this in prior videos, and now that the stocks are really getting aggressive again, especially notably a name like NVIDIA. We'll get to AMD in a second, but they're really chasing performance now. That's why you're seeing some really aggressive, really exaggerated candles. And if you traded, for example, AMD today, and again, we'll get to the pivots in a second, you'll notice that what was the last time you saw AMD put up a $12-$13 candle on pretty much on an upgrade, right? Upgrade usually good for two, three, four points in this environment, because I believe they are chasing performance, and they did underperform last year. They're exaggerated, right? They're exaggerated candles, and we saw that today. If you looked at today's trading date, the numbers are really not going to really jump at you. The Dow Jones Industrial down 250 points. The culprit here was Boeing. Boeing has had a really, really hard time since it peeked out all the way up in December down another 17 points today. That's obviously a big weigh-in on the Dow stocks on the S&P as well. But if you look at the Nasdaq down 28 points, what's interesting about the Nasdaq and the QQQs, you look at the semiconductors, right? You look at the semi-names, and you're saying to yourself, well, what led this market higher today, right? If you look at, for example, a lot of the names today, Meta was down. Amazon got down. Netflix, right? Down. Microsoft, it was up on a big upgrade, came all the way in, back red, got green at the end of the day, right? So you have a lot of mixed bag here. The two names that really stood out today, one continues to stand out, is Nvidia.  It's just nonstop. It really is. It hasn't put in a lower high since the big breakout above that 487 level and confirmed the yearly highs above 505. It hasn't looked back, and today was AMD. AMD got upgraded today. I believe it was with $170 price target. Look at the move AMD did today. This is not usually supported on an organic day-to-day basis, but this is the type of a chasing environment that we're in when institutional money flow has to play catch up. Because again, if you're running a fund and your fund is up 6, 7, 8, 9, 3% for the year, and the NASDAQ 100 is up 54%. Your shareholders are not going to be happy. Your customers are not going to be happy. So what do you do? Well, I'm not going to miss it this time around, and that's exactly what's happening. And these two names really did very, very well. And if you look at the rest of the NASDAQ composite, it's really, really tough to get excited by a lot of names because they didn't participate. Does that mean the market now is going to go heavy and only two stocks are taking up the market? No, it means absolutely nothing. A lot of days you have meta resting, and the next thing you know, meta wakes up the next day and goes up 10. You have names, for example, like Microsoft that had a chance to really explode today, right? Really, really had a chance to explode today off the open, and it didn't. Matter of fact, I bought the first dip on Microsoft. It didn't hold out. I lost a buck on that in the dip. It actually went right on the day before rallying back, but the money flow is still very, very strong with these technology names. Is that going to continue going into tomorrow? Yeah, very possibly, right? Very, very possibly. Even a name like Tesla, right? So Tesla is now day one, two, three, four, five days under the 50-day moving average. Day three, underneath the 200-day moving average. Like I said on the weekend video, does it have to go down every single day? It doesn't have to. What it did do, and this news came out after, well, technically it was a news, it was more of an Elon Musk tweet, but the news came out after, I think it was on Sunday afternoon, Elon Musk basically tweeted out that if he doesn't have at least a 25% ownership of Tesla, there's no point for him to allocate his attention there. Or work on AI products somewhere else. Obviously, the cult leader of one of the biggest cult-following stocks out there with potential leaving where stepping down or maybe taking a little bit less attention away from the natural course of action of Tesla. Shareholders got worried, got very, very worried, especially at the open. You could see the drop here right at the open. It was down about six and a half, seven points. Congratulations to all you guys who came in short. I was up at four o'clock in the morning. Stock came all the way down to like, what was it? Down to 2.13 pre-market and then ultimately went down to 2.12. Tesla's loud out of the woods by any stretch of the imagination. For this thing to start reclaiming any type of balance, it needs to reclaim back the 5-day moving average. We're still about six, seven points away from that. Again, don't mistake a dead cat balance with, well, that was the bottom now, the stock is going to go higher. Maybe it was, maybe it is, maybe it does, but the point is you have to see the levels start getting taken advantage of and start reclaiming before you can start talking about bottom or potential rounding bottom. Do I believe that it could give another maybe one more day of potential little balance? Yeah, sure, but I think the lawler continues to stay below the 50, below the 200-day moving average. It's just a recipe for new lower prices. Is it possible they could start running this thing up ahead of earnings? Look, anything's possible. Guys, like we always talk about, nobody's trying to guess what happens the next day, right? We're prepared for the market. We do our research, all that good stuff, but you know what, stocks tend to sometimes trade the complete opposite direction of what you think, and that's okay, right? Again, it's not the point of being right. It's the point of being prepared, and every single day, we are definitely prepared on both sides of the market. So the question is, let's look at the dynamics, right? Let's look at the dynamics of the overall market. Semiconductor is absolutely strong, right? You have NVIDIA, AMD, and then names, for example, like a CLAC, right? A CLAC definitely needs one more day to get above this channel, a name like NTNX, right? NTNX has a really, really great run of VAGO, which is an impossible trader, you know, holding up fairly, fairly well, but then you have names like AMAT in the same group, right? Pretty much, you know, pretty much stock, kind of an environment, trying to reclaim back the 50-day moving average. So even though the semiconductors look great, and this is the highest close in this whole formation, again, led by NVIDIA and AMD, again, some of the names that had big, big rallies are just getting tight, or at least in the middle of their consolidation or distribution stage, and that's not a really great recipe for an overall sector to go higher. Does it mean we can't go higher? Of course not. Again, market has a lot of momentum, market has a lot of structure right now, but the point is definitely keep an eye on both sides of the market because there's definitely names that just don't look good, right? Look at a name, for example, like PayPal, right? Look at PayPal. PayPal, I use PayPal, you use PayPal, everybody looks PayPal. PayPal close on the 50-day moving average, right? Look what happened to the last member of the club that lost its 50-day moving average, right? That was Tesla, lost its 50-day when straight down. Look at PayPal, look how close this thing is to losing the 50-day moving average. It's light blue line, if it loses the 50-day, this thing can get hit. Look at a name like Roblox. Same thing, right? This is the first close below the 50-day moving average. In case the market gets more heavy tomorrow, you know, keep an eye on this thing. If this thing starts confirming today's channel, maybe this thing gets hit, right? It looks pretty bad. It doesn't look good. Look at a name like iRobot. I believe this was earnings, right? They blew up on earnings, now going sideways. If it starts losing its earnings lowest in the next couple of days, maybe this thing can get hit as well. And even a name like Meta, and again, I'm not saying these things get killed by no search of the imagination, but this is its first close on the 50-day. If you see the last time it touched the 50-day bounce, now what happens tomorrow if it doesn't? If it doesn't bounce tomorrow and it loses the five-day? Isn't this a recipe? Or if you're losing the five-day, the shortest-term sentiment that it could get hit as well? So again, you want to watch the market on both sides. DDOG, beautiful-looking chart, you know, beautiful-looking chart, looks ready for the next couple of days. If it starts getting going, a name like ISRG and to its surgical, they make all these prosthetic legs and arms and all that good stuff. Consolidating looks pretty good. MU in the semiconductor space came out with some 89... 89 weeklies. It was either weeklies or for next week's expiration, but that looks good as well. But the name in the game is still AMD, NVIDIA, Microsoft. Those names are holding up very, very well. AMD right now is basically trading after hours at day's highs, right? At day's highs, NVIDIA is basically a couple of bucks away from the day's highs as well. So the bulls are still in control. There's a lot of names, though, that are acting as poorly as you probably don't want them to act in this type of environment. But again, that's the whole point. There is no such thing as the stock market, right? It hasn't been such a thing as a stock market. It's a market of stocks. There's bulls, there's bears, there's donuts, there's hand grenades. There's something there for everyone that you can put, formulate an opinion based on your individual process that you can make things right. You can make things fit. And if you are a bull market trader, there's still a lot of names that are looking very, very good. But on the flip side, as we talked about the PayPal's of the world, the iRobots of the world, the Roblox of the world, there are names that are starting to get soft as well. And if they start confirming bigger levels, you can see a pullback. So going into tomorrow, here's the question, right? Here's the million dollar question.  The Metas, the Apples, the Amazons, the Microsofts of the world, or is everything else going to pull them down? That's the question going into tomorrow. It's impossible to answer until we see the market open and a trend starts to confirm. The most important part is, guys, always stay ready. Stay ready on both sides of the market. Don't fall in love with any ticker. Don't fall in love with one side of the market. As we've seen, especially in the start of 2024, things change just like that. And the key is, don't get caught with your pans down. Guys, God bless everybody. Have a great night, and I will see you all tomorrow. Take care.


All right, I'm expecting a lot from this trailer. If you're seeing this video, that means that I was impressed. So let's see what happens. Oh, okay, okay. Technically you got by by not showing any new characters. Oh, that was Super Saiyan 3. That's gone. Oh, Super Saiyan 3 looks so good. Intros, you got him with the scouter. You got him with the scouter though. Kaioken is sick. He even said Kaioken. No way. Great aim. OK, they're showing everything. That looks so good. That looks so good. Oh, OK. OK, are we going to see UI though? Super Saiyan, I'm expecting every form now. Big Bang animation looked amazing. Oh, they even got the OG Super Saiyan. Super Vegeta. That animation looked good too. Did him like he did sell. Super Saiyan 2. So I mean, Super Saiyan 2 is all right. We get some good references, I think. Are we going to see Majin? That's Vegeta's technique. That look cool. Hey, Super Saiyan, that is sick. Oh my god, we do see Majin Vegeta. Wow, that looks really good. We're going to see more. Oh, no. The god. But we've already seen blue though. If you don't show me UI, you're holding back. God looks really good. They even have the stun animation, the Resengan. The god Resengan. And we've seen this. This is old news. This is old news. That animation looks great. You're stopping it there. Come on. Come on, show us UI. SSB Kaioken. No UI though. You're holding back. You're holding back. Give me a little teaser. No release date either. Man, what are we talking about here, man? No release date. No UI? Not the craziest trailer that is legitimately it. Yeah, it was hype. It was hype. I could have done with it a little bit more, though. A little bit more. I'm still excited for Dragon Ball Sparking Zero. I'm really hoping they show off some new character soon, some new transformations we haven't seen. I think that's what's going to really have me tapped in. Also, they showed the Kakarot thing. But how are you feeling about Dragon Ball Z Sparking Zero? I'm still excited, as always. Like, subscribe. We're going to be doing a ton of it here on the channel, whatever it is that it does come out. And I'll see you then.


I have to lower that because my mouth isn't in my forehead yet. Good afternoon, distinguished guests, ladies and gentlemen, and especially students. I won't replicate what's in the program. That's pretty clear. She has an amazing background, Dr. Thompson does. And just to fill in a few little blanks for you, in addition to being the chancellor's professor and chair of the Department of Gender and Women's Studies at the University of California, she's also the director at the Center for Science, Technology, and Medicine in Society, the director of the Lee Ka Shing Program in Gender and Science at the University of California, Berkeley. And she is also an RQIF professor of Department of Sociology, London School of Economics and Political Science. She is a trained scientist in her undergraduate and continuing interest throughout her graduate career. She has been an assistant professor at the University of Illinois, Urbana-Champaign, at Harvard University. And as I said, she is now a professor at the University of California, Berkeley. She has visited institutions worldwide, and she is the author of books Making Parents, the Ontological Choreography of Reproductive Technologies, and Good Science, the ethical choreography of stem cell research. In addition to numerous articles on scientific research, biomedicine, biodiversity, social justice, and governance. Her 2005 book, Making Parents, which won the 2007 Rachel Carson Prize from the Society for the Social Study of Science, introduced the concept of ontological choreography in which she lays out the different order of things, such as emotions, politics, technologies, and clinical measurements, and how they work together in medicine, and for her concept of promissory capital and biomedical mode of production. She serves on the stem cell research oversight committee of the University of California, Berkeley, and previously served on the Children's Hospital of Oakland research institute stem cell research oversight committee. She is currently a member of a group that has already been discussed here several times, the Nuffel Council on Bioethics and the Working Group on Genome Editing. She has been a panelist at several international meetings, and she is a frequent contributor to the public discourse and policy discussions of biotechnologies, the role of science and society, and most recently, CRISPR technology and human genetic engineering, as well as systems of stratification that limit technology access. Her work reaches across disciplinary boundaries and has found a broad audience of academics and non-academics alike. Dr. Thompson's work continues to broaden the biopolitical paradigm by asking iconic questions about the social transformation and entanglements of the social material environment as we move forward with new and evolving technologies. Please join me in welcoming Dr. Thompson. That was so nice, thank you. Gosh, good afternoon, and thank you, Professor Jenner, for that lovely and very generous introduction. I'd like to start in general, actually, by thanking everyone who's made this conference so fantastic. The organizers, especially Professor Helke and Hong and Bob Blassen-Taylor and Professor Bergman, but also all the other faculty and staff and students, donors and audience members whose interventions have been so valuable, and also a special thanks to my team, Professor Jenner, Lindsay, and Rachel, who've attended to my every need and more, and whose company has been so delightful these past two days. This event has modeled the very best that can result from terrific organization putting to public good the values of a liberal arts ethos and the power of making happen a conversation of vital importance to us as a society today. I guess I should say congratulations to you all for that, and what a wonderful university. Unfortunately, I can't see you because of the lights, but I'll imagine people. I turn now to my talk, and I want to begin with a statement of my commitments, and the reason that I want to spell them out at this point in the conference is just sort of to fess up to a couple of things, and to make it clear that I don't think that they're the only ways to go, but they are the point of view that I'm going to be speaking from. So a couple of things where I stand. I have a background in the life sciences, philosophy, transnational, intersectional, feminism, and science and technology studies. I'm therefore predisposed to seek to combine the knowledges and practices of each in how we navigate science and technology. I'm very much pro-science and very much pro-social science, despite the deep and lasting damage that both have promulgated in their long histories, and we forget those at our peril. Labeling, in my opinion, labeling people as pro or anti-science is a way of dismissing or silencing people's views, and it's antithetical to having a deep and rich conversation that actually produces results. A corollary of this is that I believe in sorting out the various kinds of concerns where appropriate. In particular, I'm very invested in arguing for different mechanisms for collecting data on and reviewing the social issues that arise and the clinical issues that arise and taking society-wide patterns of discrimination, of racism, sexism, and ableism, and classism, off the backs of individual patients and their clinicians. Commitment number three. I can't emphasize this enough. You don't know how you're going to react in a given situation unless you've been there. From many years of field work around reproductive and genetic technologies, there's a process that people often go through when they're diagnosed with something, and a very typical one around IVF would be starting off by saying when you find that you can't as easily get pregnant as you might like to, taking, perhaps, going to see a physician, perhaps being willing to take a drug to help you ovulate, and people will frequently say during that period, oh, I would never do in vitro fertilization. Comes the time when that's their only way to get pregnant, very, very often, they radically change their point of view. The last thing I mean to be implying is that people are hypocritical or that there's anything wrong with this. I think these things are journeys. I have enormous compassion for people going through these journeys, but just that remembering that you don't know how you're going to react and your views quite likely will change when you have a particular conditioning question. And finally, I personally believe that context is everything, and some of the previous speakers have emphasized this. And so for myself, rather than drawing hard lines against particular procedures, I find it's better to pay detailed attention to what would be best in a given set of social and technical circumstances. I realize that this places me in a minority among both scientists and social scientists. For example, I'm not prepared to say, oh, we should do germline genome editing, or we should do somatic genome editing. I'm much more interested in looking at the particular circumstances in question, although I do greatly respect and appreciate the reasons why many people think that lines in the sand are important. Okay, so much for my commitments. Okay, so I've been tasked with addressing the history and future of reproductive technologies and their impact on society. So let me start with perhaps one of the greatest hits, and at least some ways to think about the topics at hand. So as Professor Hong pointed out yesterday with her examples from antiquity, a concern with fertility is not new. It's neither tropically new in terms of stories we tell about fertility, nor is it new temporally. And one place that North American fertility clinics often begin the story is with Sarah and Abraham in the Bible. Through their belief, they were eventually able to conceive despite their advanced ages, reputedly 90 and 100 respectively, at the time of conception, and despite Sarah's previous infertility. I don't know if you can see the caption at the top of this children's book here, but it says that learning Bible stories is fun. I don't necessarily disagree with that, but I'd like to draw your attention to the fact that this image flies in the face of what most practitioners would consider responsible messaging. Fertility declines with age, and it precipitously declines with age in women. Even egg freezing should be done when you're young if it's going to hold out a good chance of preserving your fertility. The point here in this story, in the Abraham and Sarah story, is more the miracle of birth in the context of belief. And there are many, many more such stories in the Bible, Isaac and Rebecca, who gave birth to the twins Esor and Jacob, Rachel and Jacob, Elizabeth and Zachariah and Luke, and so on. It's worth taking a detour just for a minute to think about the gendering of infertility diagnoses. Most English translations of the Bible use the expression baron and attribute it to the woman. Baron is a horrible word, right up there with sterility, premature ovarian failure, and other words that infertility patients hate, but it gives us a glimpse into just how deeply women's reproductive capacity, youth and worth are mutually entangled. When you Google baron, this is the kind of picture you get. I don't think most women with facing infertility feel that that's a good representation of their entire selves. Thanks to a far better understanding of reproductive biology and of the causes of infertility, we now know that the causes and prevalence of infertility vary in different countries in part due to environmental and workplace toxins, but that overall there are approximately as many cases of male infertility as female infertility, with both partners being implicated in some cases and unknown etiology in others. Typically, clinics treat the couple for that reason. Lest we think that only women are subjected to an elision between their reproductive capacity and their gender identity, consider another image I've seen used in infertility information sessions, the crane. This image incorrectly equates a man's capacity to sustain an erection with his ability to father a child. We know from many cases, including fathers who are paralyzed below the waist and others who've used electrojaculation, sperm aspiration, or pre-advanced illness or pre-surgery sperm freezing to get pregnant, that this is not necessarily the case. But something we can all do right away is to interrupt these harmful associations that traffic in gender and sexuality stereotypes when we hear them. It can be extremely painful being involuntarily childless and suggesting that a woman's creativity, generativeness, and femininity are compromised because she's not pregnant or that a man's sexual prowess and masculinity are being questioned through the careless use of language and metaphor is something that we should all resist. Would we say that those who choose not to have children are barren or impotent or might they be carers of other kinds, for the earth and environment, for their life's work, for the children or dependents of the community? In addition, we should assume that fertility is important to gender non-conforming and non-binary individuals and couples and to LGBT individuals and couples for whom the stereotypes and barriers to treatment often carry the additional burden of being heterosexist. Assisted reproductive technologies are often the only or the best way to become biological parents. Gay fathers, for example, have been pioneers of cross-border reproductive care and have turned to surrogacy despite their frequent ambivalence about the privilege and possibilities for exploitation contained in third, fourth and fifth-party reproduction necessary for them to have biological children. Comprehensive trans-reproductive healthcare also has a long way to go, but it's a growing area of reproductive medicine and activism and fertility preservation is a major focus, especially for transitioning youth and their carers. And I should point out that it's not even legal to preserve your fertility when you're transitioning in all countries. But it's not enough to stop with gender and sexuality. Gender and sexuality play out in very different ways in relation to fertility as in relation to all things. When race, class, disability and citizenship are taken into account. Sorry, I missed a slide. The picture here says the path to parenthood is not always straight. Gender and sexuality and fertility is complex. I return now to the story of Abraham and Sarah. I started with this story because it's so frequently alluded to in the world of infertility and because it shows that although in vitro fertilization and more recent ARTs may be newish, infertility and concerns with gender, genealogy and biological clocks is anything but new. In the spirit of this entire conference, however, I want also to remind people that the links between fertility and stratification, gender, race, nation, class are also not new and in fact, co-emerge with the social and biological facts of infertility itself in every instance. Remember that 14 years prior to conceiving Isaac, Abraham had a child with Hagar. In Genesis 16.1, the new American Standard Bible says, now, Sarah, Abraham's wife, had born him no children and she had an Egyptian maid whose name was Hagar. So Sarah, I said to Abraham, now behold, the Lord has prevented me from bearing children. Please go into my maid, perhaps I will obtain children through her. In other translations, she's referred to variously as a slave, servant, a servant girl or a handmade. In this short passage, we see that third party reproduction was already a thing and as it followed a logic whereby Hagar's reproductive laboring body and indeed her own fecundity could be owned or at least appropriated by the procreative intent of Sarah and Abraham. If second wave feminism often ignored the racialized and immigrant child care labor making it possible for middle class white women to enter the workforce, the framework of reproductive choice often ignores the race, class and national dynamics of stratified reproductive labor too. In my book, Making Parents, I came up with the phrase selective pronatalism to point to the logics whereby some people are contraceptive and some are procepted. Actually originally I combined it with biodiversity conservation but I was talked out of trying to do animals and humans in the same format. PhD logic there. And also to refer to the processes whereby some people become intended parents and others become parties to the reproduction of intended parents. I've learned throughout my career from the brilliant work of my colleagues in the social studies of science and medicine and transnational and intersectional feminisms. Of particular importance to our theme at this conference is the concept of reproductive justice. At once a scholarly and activist movement, reproductive justice was pioneered by US women of color, collaborating or in close kinship with many colleagues and allies from gender identity, disability justice, indigenous studies and environmental justice activism and scholarship and increasingly has joined up with colleagues around the world. Reproductive justice expands the concept of reproductive choice that Sarah and Abraham should have options in my allegory today to encompass the case of Hagar and her right to her own reproductive autonomy to have and to raise her own children and to do so free from economic and other kinds of structural violence including enslavement, the removal of children by intended parents or the removal of children by the state. In my second book, Good Science, I focused in on the innovation economies. These embryos are my children by the way on these books or two of my children. I focused in on the innovation economies and geopolitics of human pluripotent stem cell research. In that book, I investigated how human embryonic stem cell research became permissible by combining three elements that hadn't been necessary to articulate for the private treatment based rise of the fertility industry. So it's very important that in the US, the fertility industry developed mostly in the private sector, mostly free from scientific regulatory apparatus. That was not true at all of stem cell research and is not true at all of genome editing. In this book, I looked at, I talked about something called the procurial frame and I analyzed it as being composed of three things. The moral imperative of being procures about which disability justice and racial justice scholars have taught us to be extremely wary. The use of public measures and funds to de-risk the social and ethical risks surrounding the procurement of human eggs, sperm, embryos and embryoid products into the lab. And bio curation, keeping tabs on chains of custody and the future potential reversibility for these materials. So these are the frames through which I think about the history of US reproductive technology. I'm going to turn now to landmark aspects of the history of reproductive technologies in the United States. In an article earlier this year in the IVF Global History Special Issue of Reproductive Biomedicine and Society Online, a paper that I called Between Rock and a Marketplace, I documented the rise and unique characteristics of the US reproductive technology field. The US has played and continues to play a distinctive and significant part in the history of in vitro fertilization and assisted reproductive technologies worldwide. US IVF emerged in the scientific context of contraceptive and fertility research, in the social context of a wealthy nation without universal healthcare, something that's come up so already. And in the political context of the abortion, IVF had its first clinical success in the US in 1981. Since then the US has become known for procedure date and its impact on federal versus state funding and regulation, involving third, fourth and fifth parties, sperm donors, egg donors and surrogates in the practice of IVF. It's also been one of the pioneers in domestic and transnational deployment of IVF for same sex and trans intended parents at a pioneer of the social egg freezing revolution, since the American Society for Reproductive Medicine and the Society for Assisted Reproductive Technologies declared the egg freezing procedure no longer experimental in 2013. US IVF has been marked by debate about such things, has really characteristic things that it's been marked by. In particular debate about such things as poor success rates, misleading patients with undue hype and hope, poor insurance coverage and organizations like Resolve, a patient activist, patient advocacy organization, have lobbied from the very, very first days for universal health coverage for fertility. Only 15 states to this day offer or cover infertility for those who have health insurance. Then there's all those who don't have health insurance and all the exemptions that there are on that allow people not to offer this coverage. It's also been marked by extraordinarily high payments for the right kind of egg donor, upwards of $50,000 for the rights, race, ethnicity, IQ, and musical talent and such things. Beautiful music at this conference, by the way, thank you. And that doesn't mean go and get an expensive egg donor. And the gendered, racialized, and class-based access to and meanings and experiences of infertility and ARTs. The exploitation of surrogates and donors, postponed childbearing, and donor offspring searches for their genetic parents. There have been waves of public debate and public controversy around each of these things. Debates about the biomedical interface, including the use for stem cell and gene editing research of supernumerary embryos created during IVF is also a signature aspect of US IVF. And you might remember President Bush's adage, there's no such thing as a leftover embryo. And there are places that freeze leftover embryos and you can adopt an embryo and bring it to term. Two Time Magazine articles, nicely bookend the rise and mainstreaming of US IVF. US researchers were the first to publish evidence of human fertilization in vitro, with the publication in 1944 of John Rock's, that's the rock in the hard place, Mock in the Marketplace, and Miriam Mencken's report in Science, of the creation of two two-celled and two three-celled human embryos. The Time Magazine article that reported on Rock and Mencken's paper famously summed up the framing of the public debate at the time in terms of man against nature and an affront to womanhood and motherhood in the face of scientific reproduction. The quote reads, man will never be happy until he has proved that he is at least as smart as nature. One thing he would like to show the world is that he can reproduce himself scientifically. Artificial insemination was one step. He took another step last week with the first recorded fertilization of a human ovum outside the mother's body. In Science last week, Harvard gynecologist John Rock and his assistant, Miriam F. Mencken, note the gender division of labor there, reported the scientific affront to womanhood. It's worth noting that this framing as a scientific affront to nature and to woman was very short-lived and the major concerns quickly became concerns from religion and concerns about safety, monstrosity, and so on. Fast forward to 2007, and a Time Magazine article from December 2007 asked what the 10 best chores to outsource might be. At the top of the article, it said, outsourcing brings to mind big factories and call centers, but entrepreneurs around the globe now offer services from tutoring to sculpting a bust of your grandpa to regular folks for a fraction of the cost in the West. Thought the world was flat before? Well, now you can hire someone in India to carry your child. Task number one, best chore to outsource of December 2007, pregnancy. Just for your interest, task number two, playing a video game to reach higher levels. Really similar, lots of hashtag false equivalences going on here. Three, illustrating a book your mother wrote for her grandchildren. Four, tutoring your son in math, because you just better get that edge. And five, making a video tribute for your sister's wedding. Okay, pregnancy is not only ranked number one. The fact that now you can hire someone in India to carry your child is taken by the article's author as a sign that the world is flat. Economic, regulatory, religious, legal, and ethical gradient all push people, scientists, clinicians, patients, instrument manufacturers around the world in search of third party reproduction. To portray these gradients as evidence of flatness rather than evidence of profound structural inequality and sustained structural inequality often shored up by imperial and colonial relations of today or yesteryear. And to fail to skip a beat in referring to hiring someone to be pregnant for you or to imply that it would obviously still be your child, she gave birth to, is to suffer from an intense form of neoliberal blindness. So gender, sexuality, race, class, disability, nation, markets, and innovation are the story of infertility and of IVF in the US. It seems vital to ask how we can go about science policy differently as new technologies are developed and deployed if we're going to take care of all of these concerns. We need some way to make the right kinds of conversations happen as this conference is doing where real listening and learning across specializations can occur. I'm going to turn now to the case of genome editing and ask how we should be thinking about onboarding new technologies in this area. Genome editing is a great case for this because there's almost universal agreement that an older model of science policy doesn't work in this arena. So the older model that you may or may not be aware of is one that's, but you'll recognize it even if you don't know it by this name, is that that's called the public understanding of science. The public understanding of science is sometimes slightly derisively called the download model because or sometimes the Charles River model because it's thought to come from policy schools at Harvard and MIT where knowledge flows in one direction from experts to lay people. This model was based on two premises about scientists and two about the public. The two about the scientists were that experts know best, they understand the science in question and second that experts also know best how to think about the issues raised by the science in question. The two premises about the public were the following. The public needs to be educated because they don't understand the science. There's a gap or a deficit or a failure of understanding. And the second premise that the public will agree to and see the wisdom in science policy based on scientific expertise once they are educated. It's a highly hierarchical model, it puts experts above lay people and scientific knowledge over other kinds of knowledge. It's paternalistic and it gets described as having a democratic deficit. This model works well when the position of science in government is secure and when there's good reason to trust that the science in question is tried and true and will be broadly speaking beneficent. This model has been largely replaced in science policy circles, however, by another model. And this model is called public engagement in science. So instead of understanding of, the emphasis is now on engagement in science. So public engagement in science is sometimes called the co-production model after Professor Sheila Jasanov's work where all kinds of stakeholders should have a say in science policy. This model is also based on two premises about scientists and two about the public. The first one is identical. Experts know their own fields best. Scientific experts, other kinds of experts know their own field best. But the second premise is that they have no special understanding of other fields. Experts are neither any better nor any worse prima facie than anyone else in knowing how to think about the various kinds of issues raised by the science in question. The two premises about the public are also slightly different. The public in this model is conceived of as having many areas of expertise, including many other kinds of science, but also all kinds of non-scientific kinds of knowledge and experience, such as law, ethics, humanity, humanities and life and environmental experience and exposure. And the second premise about the public is that the public's knowledge is relevant to science policy. For example, in provenance of materials, who's an eggs and embryos? Who are going to be your donors? Procurement, how do we move these precious humanoid materials around the place? And disposition, what do you do to get rid of no longer wanted embryos, for example. It also contains the idea that including these kinds of expertise might improve the science in question. Public engagement in science is a much less hierarchical model where different actors and kinds of knowledge are important. It's participatory by design and it has a democratic circuit which can make it unruly. It works well when the case for the science in question needs to be made, where limits are at stake and or where there are reasons to be concerned about ethical and social risks such as becoming or perpetuating a selecting society. The famous cartoon The Peasants Are Revolting captures the cost of what Professor Benjamin was referring to yesterday as social illiteracy, just as much as the commentary about cross-border reproduction as evidence of the world's flatness did. The king cannot see the threat posed by his own illegitimacy as ruler precisely because he cannot see the power of the people. A second cartoon plays on the theme of the same cartoon. It riffs, it assumes everyone knows the earlier cartoon and shows what happens when you apply that kind of logic to healthcare priorities and health funding. The guy, the supplicant says The peasants are revolting and the king says, I know, put deodorant and mouthwash in the healthcare bill. So this cartoon highlights the way in which the wrong medical priorities are funded if the social problem is incorrectly diagnosed and if there is an excess of social illiteracy. So rather than the public understanding of science, in the case of human genome editing calls for a different model, that of public engagement of science, and the call for this has been made far and wide. What might the public engagement in genome editing look like? The key questions then, if we take that model of public engagement seriously, fall into two groups. First, who are the public? Does the public mean stakeholders? It's important to note that who sits on these committees varies from country to country. So Professor Murdoch was telling us earlier about what gets called the great and the good in England. People who rise to a position of power in their respective fields are considered to be good spokespeople about issues that they don't necessarily have expertise on, whereas in the US it's much more domain specific and they look for what are considered to be the relevant expertise to make up a committee. So the first question is, who are the public? Does the public mean stakeholders or does the public mean everyone? And perhaps there's a case to be made, especially for those who are not stakeholders who don't have an interest in the science in question and who thus might be seen as better at judging the public good. There's a long standing body of social theory suggesting that people who have less of an interest, less to lose, see better the reality of the social problems at hand. Who are the stakeholders in genome editing? They're scientists, clinicians, politicians, patients, those living with disabilities, those marketing the technologies, those profiting from the technologies and their value-added add-ons, those currently or historically underserved by the medical or technical fields of the technologies in question. They're ethicists, religious leaders, social scientists, and so on. Can everybody play a role? And then as Professor Saxton pointed out, can anyone speak for the entire group for which they're representative? And how do you represent those who cannot represent themselves? Unborn generations who might be edited before their birth, those who don't have the power of speech, and so on. How do you weigh up, for example, the risks of becoming an ever more selecting society versus the right or need for biomedical cures? How do you balance, in other words, individual good versus societal harm? How do you factor in economic activity and its downstream effect on the public? So with that bio-innovation model that I talked about earlier from my book, Good Science, the premise was not just that pro cures would come up with cures, but it was regenerative in two senses. It would also regenerate and revitalize the economy and that would have trickle-down effects which would enrich the taxpayer and would enrich the state in general, paying into public coffers and public benefits from which everyone stands to benefit. Second, how should the public engage? How do you or whoever incentivize the public to participate? Harder than you might think. It's actually very hard to get, for example, non-stakeholders to come and have a say, to weigh in about particular issues. What's meaningful participation? Is it being surveyed by experts? Is it commentating during public comment periods? So, for example, in the UK, we're really good at surveying people's opinions, but we're not necessarily that good about bringing people's opinions to bear on the eventual solutions we come up with. Is it comment which you might think is a good thing or you might think is a bad thing? Is it commenting during public comment periods? Is it just having a voice? Is it the fact of being heard? Is it protesting and resisting? So, many different ways that you can have meaningful participation. What's the goal of the participation? Is it having a chance to speak and be heard in an official forum? Is it or is it something more substantive? Is it influencing the framing of the debate from the outset? Or should there be a traceable impact? So, this is kind of from the least having a voice and being heard to framing the terms of the debate or should it go all the way to having a traceable impact on how the science is done? What studies to carry out? For what conditions? And with what clinical interface? And with what consequences to morality and to the economy? Perhaps it's only real engagement if the public's voices in a wide range of opinions are visible in the resulting legislation. But then you have to ask, are there any views that are too extreme to be included? Are there any that are outside the polity writ large? Are there things that are intrinsically violent or dangerous? Things that should be ruled out of bounds from the start. Again, this is one with a big difference between the US and the UK. The free speech and First Amendment, things make more things acceptable to say in the US than they do in most places in Europe. The kinds of things we know we need to do if we're to add real social justice goals to excellent science are things that those of us at a group called Science Fair tackle. So Science Fair is an informal group of us, anybody who'd like to is more than welcome to join. And Fair stands, it's a bad pun, and it stands for feminist anti-racist equity. It's a data sciences initiative to argue for data infrastructures and collecting good data on social and political and economic factors, separately from or independently from the clinical context. So we know that we need robust and ongoing public consultation when there's a question of public trust and when the risks and efficacy of technologies are still at stake. We know that we need funding models that internalize things that have previously been thought of as externalities to healthcare funding and innovation funding. We would also argue that we need panels selected to oversee the goals that are being monitored and that these should have very strong representation. For example, Professor Saxton referred to as disability consultants, people who have experienced conditions in question, people who have particular kinds of insight into the phenomena that we're collecting data for. So for example, this group, Science Fair is interested in collecting data on sexism, racism, ableism, classism and so on in technologies that are mostly being rolled out in an individualist clinical context. You need government buy-in. At least the government needs not to be opposed to it. We need health infrastructure and data analytics buy-in. We need robust and regularly updated measures of increased well-being. And in particular, we need marching rights to call for new research or tweaks in ongoing research in the light of findings. So the interventions, the findings of such groups must be robust. They must have teeth. There must be ways to tweak what we're doing in our science policy writ large on the basis of the social patterns that we're finding. And remember from all the talks we've heard so far, we know that we have a predisposition to normalize and naturalize certain orders that are anything but flat. So the work it takes to reveal these things and to bring to light the patterns of discrimination, patterns of selection and their mitigation is significant and substantial. I also want to emphasize that there is a very, very strong continuity between what's going on in things like genome editing and non-invasive prenatal testing, pre-implantation genetic diagnosis, but also how we segregate our schools, the differences between privatized and public childhoods, the numbers of resources we put into preparing different children for college, the emphasis we put on quote, unquote, meritocracy. All of these things are leading us to an increasingly selecting society in my view and I don't believe that we should make, put any more or any less weight on any one locus than on any other. Today's technologies promise what is perhaps, so that's the right to say about that. Today's technologies promise what is perhaps an even greater series of challenges and opportunities to how and whether and for whom we think about reproduction. So in my forthcoming book, Getting Ahead, Automation, Augmentation and Deselection, I turn my attention to the coming together of a suite of emerging technologies which perhaps even more radically will affect what it will mean to be human and for whom in the years to come. Gene editing and the newest versions of reproductive technologies are emerging not just with bioinformatics and personalized medicine, but also with 3D printing, drones, the internet of things, hyperloop, blockchain, autonomous cars, robotics, big data, computation and the financialization and digitization of everything. At the same time these technologies have been deployed in a time of rising populism that in many ways sets itself against technology elites and in a time of unprecedented and unstable inequality that will likely maintain if not exacerbate all the digital and other technology divides. They are also co-emerging in a time of environmental and migratory crisis. In some parts of the world, we'll be confronted with questions such as the possibility of an end to bodily pregnancy. Many of my students think that sounds absolutely fantastic. Have a robot that does the pregnancy for you and that will be a great help in gender equity. The possibility of life extension, a lot of money and it's easy to dismiss the singularity and other things by saying well, what about the multiplicity? But there is actually a lot of money going into views into science and technology related to life extension. What do we do with life extension, especially in the light of an aging population and the economic dynamics of aging populations? Will life extension, if it's red, becomes even somewhat widely available, reduce the need for frequent reproduction entirely? And what about the possibility of routine cognitive and physical super enhancement and hybridization with AIs? Will that attenuate the biological relationship that parents and offspring feel for one another? Will it mean that we're all closer to each other and further away from one another in a way that disrupts the seeming imperative of the nuclear family and the urge to reproduce? So if any of these things come to pass, what will they mean for gender reproduction and family and what will they do to the relations among the haves and the have nots? And in general, will a biological, family-based reproductive drive still exist? And if so, how will it be realized? What will we value and how will we live a good life if the stages encompassed by the reproductive and non-reproductive life course are phased out? And if we are all augmented and our children selected for, in what will we seek individuality, worth, competition and compassion? If there are two things that the history of reproductive technologies has taught us, it's that they are always already stratified and that reproduction always already stands in for things that go far beyond the biological facts of the matter and a moment of individual reproductive choice. I should emphasize that I don't only see gloom and doom. I'm very excited by many of these developments. I see great promise, for example, to many kinds of assistive technologies designed by and with our disability consultants and I look forward enormously to the day when naturalized models of cognitive and bodily worth are disrupted beyond recognition. But it will take an enormous and collective act of will to steer these so-called fourth industrial revolution technologies rather than away, to steer them toward rather than away from thriving planetary and human futures and towards rather than away from curiosity-driven science and justice for all. If we don't remember our histories and if we don't dream of better futures, we are doomed to get carried along by the selective pronatalisms and other technological divides we inherit. Please join me in this struggle and thank you for your attention. Okay, slight regroup. Because of the time I propose that we have our seventh inning stretch now. So go ahead and take a brief break and we will reconvene in about 10 minutes and we will seamlessly connect the Q and A.


At one time, the highway department wanted to build an access road, basically an expressway from I-10 to Callahan Road through Culebra, and we organized against it, because it was going to split up all the parishes, St. Holy Rose, Holy Family, West End Baptist, Price the King, so we organized all of those, and we stopped it. And then after that, that's why 151 is there.


The nature of the fight in the Indo-Pacific is a maritime fight. The fourth Marines created JWX as a rehearsal opportunity in a demanding environment, which is going to force us into the jungle, it's going to force us into the water. We're constantly changing, we're constantly learning, we're improving our processes and JWX allows us to do that. Our objectives were to take concepts, these new concepts that the Navy and the Marine Corps is working on, to actually rehearse them, to exercise them. When an adversary has to look at what we bring to the table, they've got to account for the entire team. We've got a ground combat element, logistics combat element, aviation combat element. We're all figuring out ways that we can organize for the fight and for the missions that we're being called upon, and that provides a very good base unit for the Joint Force to bolt onto. We have emerging capabilities that are being filled in all the time, but our mindset remains the same. If you're an adversary and you're watching us, we're light, we're agile, we move around, we bounce from position to position, we're going to be successful, we're going to win the fight.


Welcome everyone. I would like to thank you already for the organization team for inviting me and also for setting up such a great moderation team which is very, very related to my research. So it's my pleasure to present to you some research that I do on the recommendation behavior of Amazon which I do together with Matthias and Tagium. So please let me know about any comments or feedback that you might have so you can send me an email or you can also just write in the chat. Okay, so what is the motivation for my talk? So we depart from that online marketplaces host a large number of suppliers and products and they facilitate consumers to find what they are looking for by giving recommendations. And when giving these recommendations it's natural that platforms also consider their own interest and however this raises some discussion. So especially in the context of hybrid sales platforms which are platforms which operate a marketplace but at the same time act as a retailer on these platforms on the same marketplace have become into an intensive discussion. So specifically there is the fear that the interest of consumers in hybrid platforms might not be well aligned and this fear is also reflected in current legislative initiatives such as the Digital Market Act or the American Innovation and Choice Online Act. And part of these initiatives is to prevent very large platforms from self-referencing their products at the expense of competitors. Of course as a very large platform which is hybrid Amazon is a case in point and there's also a growing literature studying potential self-referencing behavior with respect to how Amazon might favor its own sales department by the so-called buy box. So I think there will be also some people present here which study this question but in our paper what we want to do is we want to study a more subtle form of self-referencing and as I will explain to you this will consist in first not providing any supply recommendation for a specific product and then in the second step if then using distorted product recommendations just to consumers elsewhere. But what we have in mind is maybe better explained with a series of screenshots how Amazon works. So on Amazon once you are interested in a specific product so you end up at some product page. So here for instance we have a very popular card game called UNO and this product page then gives you a lot of information about the product characteristics and also user feedback in the form of ratings and stars. So what you see here. Furthermore as for this product here often there are plenty of suppliers available so for instance as you can see right here it's not not that big you can see that this product is offered by 39 distinct suppliers. Of course most consumers do not have the time or also not have the interest to look at all of them and therefore Amazon pre-selects one of them on the right hand side of the page. So this is here and this is what we call the buy box. So for instance in this buy box what you can see is there is one seller promoted or featured so which is called in prime time and this is also a seller which uses Amazon to do the fulfillment. And once Amazon features a supplier at this buy box it's very likely that a consumer interested in this product will choose to buy from it and there are different studies which claim that over 80 percent of transactions are done with suppliers that are inside this buy box and this should highlight to you the importance of this design element to see a consumer. Without surprise it's many suppliers as well as researchers have been wondering about how to get into that buy box because if you are one of the 39 you might imagine okay they have a high interest to be there given that all the most transactions are done over this design element. And a second question which is more related to the policy perspective is if this buy box is somehow biased in favor of Amazon's own state department and this is also what others for instance my discussion studies. In our paper we would like to study we would like to take a different route because we think there are many more ways to stick consumers towards other products and this is what I will explain in a minute. So what's remarkable is that in a significant number of cases Amazon does not feature any seller. So this can be seen here in this example on this slide so here we have a very similar product and it's just a different variety of the same card game you know that I have shown you before and what you can still see you have the consumer feedback you have the stars you have the rating and you can also see that this product is available from 21 different suppliers. However on the right hand side you don't see any buy box so Amazon decided not to feature any of these suppliers and hence something is different about this page design. So in case you still want to buy this product what you would need to do is you would need to click on see all buying options and then you could compare the 21 different offers available. It's debatable if this makes the purchase of the product more complicated but at the same time most people would agree that this deviation from the default design has the potential to create some confusion among consumers and this confusion may even lead to a situation where some consumer thinks something is wrong so maybe the quality of the supplier is not good or maybe there's another problem associated with this with this product and this might be dead to meant to fall the sales. Of course one might wonder why would Amazon do this because Amazon so if this is detrimental sales why would Amazon make it more difficult or make it create a situation where consumers are confused. So what Amazon claims is that they do that in the interest of consumers so specifically in particular they say they may remove the buy box when it be when they believe to observe harmful pricing practices and harmful pricing practices could be that the price of a product is higher than prices on or of Amazon. Of course this is of course a nice behavior so if we say Amazon is very interested in getting a lot of consumers then this could be a good strategy but we still believe that this is not straightforward and we believe this behavior is interesting for two reasons. So one thing is that as Amazon is a hybrid sales platform one might wonder if Amazon applies the same standards regarding harmful practices especially in the cases when they also supply the product. So the question would be is there a situation where Amazon thinks the price is too high when Amazon is also among the sellers if not this could hint an unequal treatment which would be in favor of its own sales department. The second aspect why we think this is interesting is that if consumers are indeed confused by the absence of the buy box as I said before it could have the potential to reduce the product sales and so even if this policy is only applied to third party suppliers Amazon potentially foregoes commission payment and if consumers turned to buy somewhere else then Amazon would even make losses in the short run. However the letter point may only matter if Amazon then really loses these consumers. In practice Amazon might achieve to increase the sales of other products by redirecting consumers to them and specifically consumers who search for a given product but do not see a buy box might be more attentive to other recommendations of Amazon for alternative products. And one might wonder then if this is the case that consumers get more attentive to recommendations for something else if Amazon is then using this channeled as a means to favor products where it's also selling itself. So these are essentially the aspects that we want to study in our paper and for this we have we do conduct an empirical analysis of web script data from Amazon in the US over a period of four months and then we proceed in two steps. So the first step we study we investigate the buy box oppression itself. So specifically what we are interested in is to analyze under which circumstances Amazon really removes the buy box. So for instance if this is really related to two high prices and also if these factors are different for Amazon's own office. Furthermore we want to quantify if the removal of the buy box has an effect on sales because then this would also give a price to the suspicion that you effectively steal consumers away from the stock of product. So this is what I will focus mostly in my talk today. In the second part of the paper what we want to do is to analyze potential follow on effects of the buy box removal. In particular we would like to know if consumers once the buy box is not shown are more attentive to alternative product recommendations and if this materializes also in more sales of other products. And for this exercise we will focus on one type of organic product recommendations which is called compare with similar items that I will explain later. And we will for this design element we will also study if we confirm that consumers are more attentive to these recommendations. If Amazon is leveraging this situation to steal consumers more towards products for which it is a supplier. Okay so for those including Jacques who will not have much time I do a preview of the results. First regarding the buy box oppression so we as I said we study frequency determinants and effects of buy box suppression and what we find is that Amazon always shows the buy box without telling itself a product which suggests or which could hint already an unequal treatment. However if Amazon is not among the suppliers we observe in our sample that it removes the buy box in 39% of the cases of our sample particularly if it considers prices to be too high. Now that we know that or that we found out that Amazon is not that Amazon is never removing a buy box in our sample when it's selling itself we try to apply the same policy for products when Amazon is among the suppliers and if we do this kind of simulation we find that Amazon would not be able to show a buy box in 13% of cases. Third what we also find is that indeed the buy box oppression has an effect on pay up. So we work with different approximations that we may discuss later and what we find is that if a product does not show a buy box then this reduces sales by 25 to 30%. Okay this is for the first part of the paper. On the second part of the paper whether there are follow-on effects essentially what we find is that products recommendations on pages where there's no buy box are associated with higher sales. So whenever you end up on a page where there's no buy box then apparently consumers are more attentive to recommendations to other products and this materializes in higher sales and the effectiveness is almost twice as high and regarding the question whether Amazon leverages this situation what we see is that Amazon tends to steer consumers more towards product itself if the buy box of the focus product and this is an indication of a very subtle step preference thing. Okay do you have any questions so far? If not then I would just move on. Sorry have you tried to look at the FBA versus FBN? So we will discuss this as well but so maybe we can come back to that when we in the analysis. Okay so our paper relates broadly to three streams of literature. So first there's a large theoretical literature in an industrial organization highlighting that intermediaries may have incentives to distort recommendations and it's clear that Amazon is an intermediary and of course because of the commission income may also distort recommendations towards something that consumers would not be their first choice. Second closely related there's an increasing theoretical literature about distortions that may emerge from hybrid sales platforms so hybrid sales firms that act both as a marketplace but also as a retailer and this literature shows that businesses which might have these businesses might have incentives to distort recommendation in favor of their own product and services. However the impact on welfare is not so clear because the degree of distortion might be less severe than in the case of a separated business. So naturally Amazon is a hybrid sales platform and we contribute to this literature by showing empirical evidence about this kind of steering behavior towards own products. Third on the empirical side there's an empirical large literature in marketing which highlights that in the online context the presentation of a product so whether it's made very prominent or whether it receives a very good ranking may drive higher sales and specifically for Amazon there's a growing literature also from marketing and as not only economics which mostly studies with how Amazon enters product markets and how Amazon makes the decision to who to feature in the buy box and as well as how other product recommendations may be used for that preference and we believe that we can contribute to this literature by providing like a new discussion on first the unequal treatment that Amazon may apply when deciding whether to show a buy box or not and second a more subtle form of steering which is by the combination of the absence of a supplier recommendation in order to make to make a product recommendation more visible to consumers. Okay I will describe now the data that we use in our analysis so what we do is we use web script data from the US version of Amazon for a period of about four months and our initial sample consists of the 50 best-selling products in 50 categories on Amazon. As we are somehow also interested in competition with other platforms specifically with prices on other platforms we have further expanded this sample to include the same set of products which are very popular on the biggest competitor in the US which is Walmart.com and by including the very popular products on Walmart.com we end up with a sample of almost 4,000 products and for these products what we have done is we have collected daily data on product characteristics on the decision to show a buy box and on the availability of distinct sellers and the prices that they set and we have done essentially the same collection also for the platform Walmart although we use mainly here the price level on Walmart for the same products which are available on Amazon. Finally we have also amended our data set with third-party suppliers like the Keeper and others. Okay for our analysis we don't exploit the full detail of the data that we have so in principle we have more detail but to simplify a little bit the exposition we use data a data set where one observation is one product on a specific date and by this we have then around we have more than 270,000 observations where yeah over the period of four months for these around 4,000 products. There are distinct things that I would like to highlight about our sample so in a bit more than half of the cases we see that Amazon is acting as a supplier so you see there are even among the top selling products there are a lot of products which where Amazon is not active. However it's relatively rare that a product is only sold by Amazon so for 92% of products in our sample there's at least one third-party seller so this in turn means that only in 8% of the cases only Amazon is available. For these third parties which are in the sample this can be FBA sellers so sellers which use the facilities of Amazon to do the fulfillment or they or sellers which are called FBM which use their own logistics to do the fulfillment. Okay most importantly for our study the bybox is visible in only 83% of cases of course this might at first sight look look like a very high number but what I will show you the following that if you condition the data set in a relevant way it's very frequent that the bybox is not visible. Okay finally anybody might be wondering indeed in 93% of the cases when bybox is shown and Amazon is available as a seller in our sample Amazon is getting the bybox in 93% of the cases. Okay for our analysis we also disposed of a lot of other controls like the sales rank which is a good proxy for sales for those which don't have data from Amazon itself or from a large merchant. We also have information about the number of sellers offering the product which can range to up to 280 different offers and then we have other pricing information so we have the manufacturer recommended sales price and also we have information whether the product is available at Walmart or eBay and we have also information about the respective prices. Okay in this slide I want to contract contrast a little bit first the differences of product day observations where the bybox is shown and where it's not so on the left hand side you see statistics for the product day observations when the bybox is visible and here on the right hand side on the middle columns you see the same information when the bybox is invisible. So one thing that we see first is that when the bybox is missing so these columns this never coincides with Amazon selling the product itself and if we take this information into account that the bybox is only missing or can only be missing if Amazon is not selling the product then we can calculate that the bybox is not visible in 39% of these cases where Amazon is not among the sellers. Second aspect that I want to highlight is the suppression of the bybox does not relate too much to the presence of suppliers which use the fulfillment by Amazon. So if we look at the relative frequency of an FBA seller being available FBA the sellers being those which use Amazon facilities so we see that the bybox is visible when the bybox is visible then in 48% of the cases these high quality sellers are available while it's only 44% when the bybox is in physical. So there is a difference but the difference is not relatively large or at least it's not that large that would as it could explain that the bybox is not shown. What is a bit surprising is even that the number of offers when the bybox is not shown is substantially larger so while we have here when the bybox is visible around eight offers on average this number increases to four teams so we have 14 different offers available when the bybox is invisible in our sample and this makes it even more questionable why Amazon thinks none of them should be promoted because among the 14 especially if we also see that the frequency or the presence of FBA sellers is not that low one could wonder why there's no one promoted. Finally what we can also see what we can also deduct a little bit also from the descriptives although it with these type of variables not always that clear the negative clock sales rank which could be a measure of our sales we can see that products which don't have a bybox apparently are less sold or are less often sold as reflected by this higher sales rank or by this more negative sales rank. Of course this could be related of because the product is not popular but it could also be an effect and it could be an effect of the bybox removal but this is of course something that we need to study econometrically. Okay what's more important probably is how the suppression of the bybox relates to prices and for this I have prepared this nice chart so what we see here is the fraction of pages or product day observations without bybox in relation to the price difference of the minimum amazon price compared to different bench numbers. So for instance we compare the minimum price on amazon to the price on walmart on ebay or to this manufacturer recommended sales price and what we can already see here is that there is an increasing pattern so the more expensive products are on amazon the more likely this bybox is removed and specifically for the case of walmart what we can see is if the amazon minimum price is 10 percent higher than the price on walmart we already see that in 80 percent of the cases there's no bybox and this should highlight that apparently amazon cares a lot about the relative price difference to other competing marketplace. Okay as these descriptive tables cannot account or this chart cannot account for the identity between products and sellers and their availability we also confirm these initial observations econometrically so specifically we estimate linear regression model to explain the decision of amazon to show the bybox or not. So the dependent variable is an indicator taking value one if the bybox is shown and the external variables briefly speaking are product market characteristics such that the availability of certain supplier types elect measure for demand and the minimums nymph price so the more we control for prices on competing products and sales channels and as well as for product and date fixed effect. Okay and when we do this then our result that we got from the graph are largely confirmed but let's maybe go one by one so first of all what we see is that the presence of a high quality data has some effect on the presence of the bybox so here depending on the specification we see that the likelihood that the bybox is shown increases by 7 to 9 percentage points and surprisingly we see that there is a negative correlation between the bybox presence and the number of sellers but yeah these effects are not that large what may be more important is the effects of the price here we see strong effects so for instance the higher the minimum price on amazon the less likely a bybox is shown um we can then also see how the comparison to some benchmarks also affects the likelihood of a bybox being shown for instance what we see here is that if the price on amazon is higher than the manufacturer recommended sales price this also decreases the likelihood of a bybox being shown the same goes for a comparison of the product price with an average of other products on amazon and finally what we see and where we also see the largest effect if a price if the price of a product on amazon is higher than the price of the same product on walmart or ebay then we see that the that the likelihood of the bybox being shown is reduced by 22 percentage points okay so it seems that that the competition between the marketplaces has the largest impact on the bybox operation compared to within platform orders okay there are no questions i would should we go on so now that we have seen before that the bybox is always shown when amazon is among the suppliers one might wonder whether this stems from that fact that amazon is always acting as a good supplier so amazon using its own fulfillment could be considered as high quality supplier and also amazon as a large platform or as a large seller could also offer very competitive prices so which in turn could justify that we observe in our sample that the bybox would never be removed however to see whether this is the case we want to do an exercise where we essentially proceed in the following steps so we first take the observations where amazon is not a seller to calibrate a model which predicts if the bybox should be shown or not and then we take this model in the second step to the data where amazon is among the sellers to do an auto sample prediction if a bybox should be shown or not and if we essentially see that the bybox could always be shown by this prediction then we know okay amazon or this could be an indication for an equal treatment of amazon's own sales department and third party suppliers but if we don't see that then this could be an indication of unequal treatment okay so step by step so if we so here in this table what we have is we have on the left column one to three we have the observations where amazon is acting as it's not acting as a seller and as i said before we see that the bybox is only visible in around 64 percent of the cases we use different models like ols or lotted in order which are very similar to the model that i have shown you before and we find also that the predictive power of these models are relatively good so we correctly predict whether there should be a bybox or not with 85 to 87 percent of the cases on column four and five we then have the data where amazon is is a seller where in the really existing data we have observed that amazon is always showing a bybox when it's among the sellers if we use then these different models from the data where amazon is not selling to predict in the cases where amazon is selling whether there should be a bybox then indeed we see there should be cases where amazon should not recommend themselves um specifically what we see so in the different models is that the bybox should not be shown in 13 to 14 percent of the cases why this is the case so on the one hand we also observe that amazon is sometimes not the cheapest phase channel available on the internet so even if um so for instance walmart or ebay might be cheaper and then even then amazon close the bybox possibly promoting its own offer and second what we have also done is we treated amazon as a seller that is as good as a fulfillment by fulfilled by amazon like an fba seller which also decreased this number however if we would treat amazon as a fbm seller for instance then this these numbers would be much closer to these numbers here okay i see in the chat there is a question okay i don't know that was okay um as a final step of this analysis of the bybox suppression we can wonder if consumers do not see a bybox whether this affects the likelihood of a purchase and unfortunately of course i don't have state data available as most researchers which do research about amazon and therefore i have to substitute this data by other data that is available and uh like in other studies i use the sales rank which is published regularly by amazon um which is information for each category of network which which are the most frequently sold product and the good thing about this variable is that it negatively correlates with the sales and um what we do is then we adopt a model similar as rimers and bald fogel last year an a or a study about book sale and estimate a conditional adjustment model which is uh we explained the minus log negative might also explain the negative log sales rank by the lack negative log sales rank an indicator of taking value one when the bybox is not available and then essentially controlling for the same factors as before so product market characteristics um product fixed effects and then combinations of the category and the day fixed effects okay if we do this uh what we can observe is that indeed when the bybox is invisible this decreases the the sales so um how good do we get to this interpretation so as i said here the dependent variable is the next sales rank so first what we need to do is we need to do an adjustment for the conditional adjustment model and if we do this then we would find we would be left with a message that the bybox depression worsens the sales rank by 50 to 60 percent um as this is very difficult to interpret what this means 50 to 60 percent of the sales rank uh we then in the second step apply a conversion uh which is based on previous studies which had access to to amazon data or amazon sales data which approximate the relationship of a sales rank change to to sales change and what comes out is that essentially a good approximation is that um that the effect has to be has to be divided by two such that we end up with a with a decrease of sales of 25 to 30 percent what's interesting is um that we get to this conclusion even when controlling for the fact that okay that the price might be higher so prices indeed have a uh decreasing effect on sales we also see that if a price is relatively high to the amazon category then this also decreases and specifically we also see that apparently uh there is we can also measure interplatform competition here so if the price on on amazon is higher than the price on walmart or ebay this also decreases thing and even then we still find a sizable effect uh of um 25 to 30 percent um when doing this okay good so i would be done with the first part of of the paper which was about the buy box itself so in the second part of the paper what we aim to study is the question whether there are follow-on effects of this buy box suppression why because as i have explained in the beginning um it is a bit surprising that amazon would make it more difficult or more complicated for a consumer to to buy the product specifically in the market for for third-party products and why is this puzzling because amazon is essentially making its income from commissions then and if a product is not sold uh then this of course decreases decreases this commission income and then could also lead to a decrease in profit however if amazon is able to to steer consumers to other products this might not be this might not be uh too relevant and this is essentially what we want to study in this second part um so our idea here is that when there is no buy box for a specific product i so remember the uno game like the the lila one without buy box then the purchase process is more costly for buyers or at least consumers are are somehow incentivized to think that something is wrong and therefore they for don't want to buy this product um it's possible that consumers will partly continue to search on amazon and then recommendations to other products might then become more relevant and especially what we have in mind is that on each product page so even the product pages without a buy box might have a lot of recommendations to other products and what we would ask now the first step is for the recommended products j which received more product purchases when being recommended on a product i page without a buy box okay and just minder you have finally needs left okay um okay and to see what we have in mind so um there are different recommendations to other products and one that we will focus on is this compare with similar items table and what is good about this one is that it's a non-sponsored form of recommendation so manufacturers cannot buy in order to be promoted there and it's also present for most of the products and it's directly accessible for from the top of the page and what it does it allows an easy comparison of with up to five other similar products and for the products that we have considered where we have were in the first part of the paper interested whether they have a buy box or not we have also collected this information about the comparison page table which is shown on this table and so first thing that we want to establish is whether recommendations of the on these tables have an effect on sales of these other products for this we have estimated a similar sales model then that we did before for the buy box oppression and what we indeed see is the more a product is recommended on other pages within this compare with similar items tables then we see that the sales are higher and the second step we distinguish whether these recommendations come from pages of products which have a buy box and those which don't have a buy box and what we observe is that essentially recommendations from tables which are below are on a page without buy box are more effective so what you can see here is that the effect of the recommendations of of another product page without buy boxes almost twice a time finally we would like to see whether amazon is using this higher visibility of the comparison table to promote more often its own products and our idea would be an independent marketplace operator should not make the composition of these comparison table products dependent on the buy box present or not and while an integrated operator so like amazon may enter the composition as the demand effect is higher without buy box so what you see here is yes does amazon say how it sets the comparison table because this is not through ads so i was wondering does it say anything about how it chooses those products um we have to look into this specifically but if it's not about uh if it's not about ads then usually they would say this is about prices and product quality again no okay um so what this chart to show you is that products that amazon sell are then more often in the comparison table especially when the buy box is not visible so what you see here is the proportion of products which are on this buy amazon which are on this comparison table compared to the proportion of compared to to the presence of these products in the product space and what you why you can see already here that even when the buy box is visible there is some kind of over representation of amazon products in this comparison table this over representation of amazon product is much more severe in the case that there was no buy box especially when the frequency or the relative frequency of amazon products in the product space is relatively low so if you have for instance 20 of amazon products in one category what you can see is that um amazon tends to steer consumers much more to awards its own products when there's no buy box okay we can also compare confirm this with the regression analysis but this one i will skip so let's get to my conclusion so i stay more less in time so the findings of our study are as follows so amazon tends to hide the buy box if it receives prices too high but only when it's not acting as a better second finding that i would like to highlight the buy box suppression decreases demand for a product on the amazon marketplace and amplifies the sales enhancing effect of product recommendations on the same page and third when in such a situation we also observe that amazon tends to steer more towards own products when the buy box is suppressed um what do we take from these findings so we would like to highlight some some aspects about this so first one is that the fact that we only observe that the buy boxes can be suppressed when amazon is not present is an indication of a different treatment of third party offers and is also a sign of intransparency of the of the behavior of amazon second the fact that we observe that purchases are less likely when there's no recommendation highlight that there's a search friction for consumers so consumers which would be interested in buying this good are somehow confused uh by this policy of amazon and this might also be detrimental for consumers third um another risk of this policy from our perspective is that if amazon conditions uh the prominence of products or the prominence of of uh supplier offers uh on prices on competing marketplaces this can provide similar incentives as a price very close so we don't say that amazon is replicating a price very close or something like this but we just want to highlight that this can be the risk that it provides similar incentives to to condition the prominence of products on prices somewhere and finally for the part that I have shown you very briefly we also provide subtle evidence for a subtle form of step referencing so we see that when amazon abstains from recommending a specific seller then this makes consumer more attentive to other product recommendations and amazon effectively uses this also by distorting the recommendations more towards its own product with this I'm gladly done in time so thank you very much for your attention and I'm happy about any of your comments yeah so thanks Urik for your great presentation and so we are happy to have DaVish to discuss this paper and meanwhile if you have any question welcome to type in the chat or you know talk after the discussion um hold on one second let's see all right thank you all for um for this so I really enjoyed this paper I thought it was very clear this is something I've actually wondered a lot about and I think I learned a lot from this paper but what amazon is doing so I want to start with some screenshots so these are screenshots that took about a year ago maybe a year and a half ago this is some kind of tire sealant and you can see in green the buy box is suppressed but if you look in red there are a bunch of sellers and if I go the next slide you know there's a FBA seller for $29.99 which is you know has a lot of ratings and a large percent positive there are other sellers as well so it's sort of a a mystery of why is it that amazon is not allowing you to just buy the product in the buy box so I also looked at the comparison table and what you find here is that all the comparisons are to different sizes of this product sold by amazon itself um so everything's exactly in line with Ulrich's paper um and so finally yesterday I looked back since the year and a half later and now there is a buy box but it is amazon selling the product and for maybe about a 10 higher price the third parties are selling a year and a half so this seems to be a pretty ubiquitous thing so the question is what's the what's the mechanism what's driving it um so I'm going to give you two different views and I think my my advice to Ulrich is to maybe talk a little bit more about the second view the first view is that amazon wants to prevent price gouging now Jeff Bezos has said well he doesn't care about price gouging but consumers care a lot about price gouging in congress now there are multiple bills about price gouging as you might imagine amazon doesn't want to get the appearance that its marketplace is price gouging so there are a couple of things it could do one is it could sell the product itself where it can set the price itself um it could potentially match other marketplaces the second is that it can reduce the sales of products that are being price gouged either by suppressing the buy box or by steering away through recommendations and Ulrich has shown evidence of both of these um now when I can make a couple comments here first of all there's a as Ulrich said a subtle form of self-preferencing only amazon is allowed to price goug and it's very clear here you know if you know anything about amazon's pricing amazon does price match competitors and as Ulrich showed showed us they are monitoring ebay and walmart but they seem to have made the decision not to price match ebay ebay and walmart marketplace which is why their product is often priced higher but they're punishing third parties that price higher so you could imagine a strategy of amazon to price match ebay and walmart and then this idea of self-preferencing would in some sense go away because amazon would have the same prices and there's another view of this um which is that this is about sort of a moose favorite nation clause to the back door um so the dc government recently found a lawsuit which i'd recommend the authors to look at um calling this an antitrust violation and if you look at the history of this amazon used to have price parity provisions which basically said you can only sell on our platform if you're not selling below on competing platforms so if you if you're a seller you sell on less on walmart or ebay you're not allowed to sell on amazon that was eliminated first in europe and then in the us because of regulator concerns antitrust concerns and instead they have what they call a fair pricing policy um but part of that is that they suppress the buy box if it's a higher pricing competitor websites it's not clear if this is different than the price parity provision or not but this could be a maybe a more broad way to do it where there's not an explicit agreement on the price to the third party sells but you can get the same thing um so what i was wondering is it is a dc in the dc lawsuit case they say they basically say their fair pricing policy is just the same as the mfn contracts as before this is the anti-competitive and a violation of the antitrust laws um so there's a question of is dc right or not and then what's going on with consumer welfare so if you think about the first mechanism of price gouging you would think that suppressing the buy box might lower prices because it's going to force the third parties to lower their price so they're going to lose a huge amount of sales but if this is about third parties selling for cheaper on ebay or walmart this might raise prices because you know they they get the message and they raise their prices of other platforms um so i think that the main thing that i would like from this paper is to talk a little bit more about who is selling on ebay and walmart is it the same merchants as amazon which would be sort of like a pp like the price provision thing or is it others so this is for example walmart is selling for less walmart the retailer is selling for less than amazon it looks a lot like the price gouging whereas if this is the third party amazon is also selling on walmart marketplace it looks a lot more like the second view um there's a lot of regulatory interest on the second view and i think um if i were the authors i'd play that up more but the more you can get at that you know so if you could have a statistic of when amazon suppresses the buy box x percentage of the time third parties on the amazon platform are selling for cheaper on walmart ebay um i think that would really inform a lot of the interest debate um so with that i have to thank uh i encourage all you to read this paper and i it uh it is very nice and clear so thank you