So now we have time for questions. Okay, let's start here. Thanks. It's a question for Vito. So I was a little confused about two possible ways of looking at migration of individual A from origin to destination, which is it can be with the same income. You can carry your income with you or you can change your income, right? You go to this other place and your income changes.  With his own income. Now in my mind there couldn't be any changing global inequality if that happens, right? Because a global inequality you there's anonymity so you're here now you're there but you're the same observation in the vector. So there could be changes in within country of course but not in global inequality. So when you have changes in global inequality presumably that is because say in your empirical exercise you're observing the migrant with a different income. Now the same person has moved but the income has also changed. So in some sense there are two factors at least on within country inequality. There is a factor of location so it could be rich in Syria but poor in Italy or something like that but there's also the change in income effect. I don't know if you did separate those and in my mind for the global inequality I mean empirically there'll be issues around PPPs and all kinds of things messing up but conceptually if you observed everybody its incomes in dollars just moving a person from one place to the other should have no effect so all of the effect on global inequality should come from changes in incomes of the migrants. Right. Thank you. Thank you Chico. So the paper doesn't have a theoretical model there so we do this empirical analysis and that's the result. Then I was thinking what's the theory from the point of view of inequality measurement. So my point was that when you move an individual from one country to another country you're moving a mass. So apart from the movement of the income so the effect of income you're moving a person so the population size is changing and that is an effect on inequality at least on the between countries inequality and then there is an effect on the within country inequality. It is not clear to me what is the sign of these two effect. Of course on the world inequality in time there is an inequality in the population of the world where there is no attach to each of us what is nationality nothing changed. Of course unless we assume that the income can change but in the between groups those are groups okay in the between groups and within groups the fact that the size of the groups is changing is something that affects inequality. That is why I was puzzled puzzled and I don't have an answer so that that's and then there is a problem of course what is the income that the individual expects to have when he moves from one country to another country and that is an empirical of course an empirical issue. Just a quick follow-up I mean just again so suppose we are in a world where you what you were thinking of which is you take your income with you so there's no change in income individual eye has the same income at origin and destination then we know that that global inequality does not change and therefore the effect in between group and within group will have to add up to zero which is what you find. Not exactly but that's probably because you are observing them with other incomes you're observing the empirical result I'm just saying you see what I'm saying my question is to the natural disasters yes because we are having like a similar paper tomorrow in the climate session but we are having all the disasters in the in the paper we are having like a kind of integration between the climate and disasters and the inequality and the growth it's integrated modern so we are we are having like the number of people affected by the disaster is one of the important variables that we take into consideration while we're doing this so it's not just the number of the events the frequency but also the intensity of this event so that's my suggestion okay thank you. If someone was raising the hand before okay okay thank you so two comments one for Vito is if you have like an issue of causality from inequality to migration and the other way around yes it's at least one let's say in the theoretical part if if if you can let's say if you have only one causality then you don't have an issue with with causality in your regression but the first question is can you can you control let's say can you deal with that issue of causality the second is different people may have different incentives for for migrating so you may want to check for for heterogeneous effects like I know different coefficients for different levels of income I don't know and one for for Flaviana I think maybe more than than inequality itself there is an issue of polarization income polarization if you have clear defined groups then you have trust issues so so maybe and and what you found regarding between inequality maybe suggesting that so so you may want to to check if if you can use a measure of polarization and then the other issues people let's say trust may be related to what the government is doing for income distribution so you may want to to use the difference between pre taxes inequality and after taxes inequality thank you so we take a few questions and then all the answers because there are many people yeah this is in line with the suggestion about polarization I think it's a very good suggestion I was wondering a bit about whether the underlying theory so you said you didn't just want to use the aggregate genie you wanted to break it up into like percentile bands or something and then you were using the genie within the band but I think that some of the theory around the around trust is is you almost need like what what's the reference group for each person along the percentile I'm wondering you know like could you you could think that and I don't know what what the how you define a reference group like that but say it was like a 10% either way of any anybody at a certain percentile you could define the genie coefficient per percentile in that way if that's the if that's the theory because if you just cut it up into percentile groups then there's a problem at the boundary you know how do you think about the people at the boundary you know the people just below are in a group and the people just above are in a group but I think the theory of the trust that you're trying to get to something like a relative you know relative to a group around around you or something like that so maybe that's that's something you could do there's some issues on the boundaries of course when you get right down at the bottom but if you're the poorest you're the poorest that's what it is yeah thanks thank you go to the other side that hi thanks a really interesting set of papers I don't want to pile on but I also had a question for Flaviana so I'm wondering about the role of institutional quality I mean if you're in a country where you don't where the institutions don't work well you probably shouldn't trust government so I wonder if you how do you take that into account in looking at this relationship thanks for a very interesting set of papers I have a question for Flaviana and one for Vito for Flaviana I was wondering if so you show that interpersonal trust and trust in institutions are two different phenomena and I was wondering if you can dig in more into interpersonal trust because I wonder if there are different phenomena within that and I don't know if the whole value of service allows you to do it but I wonder if trust in persons of your own group whatever the group is and trusting persons from outside your group are different and actually opposite phenomena where trust in institution declines trusting people from other groups declines as well and therefore trusting people of your group increases relatively you see so and increasing trust in people of your own group is not necessarily a positive phenomenon I don't know while trusting people from other groups may be a positive phenomenon for social cohesion I don't know if you are able to look into that or not and and then for Vito you get a very strong effect of migration on within country inequality and I was wondering how you got to that because the literature the empirical literature I know on this is not so conclusive it's because low income workers and migrant workers that are low income are are not necessary competing with each other more complimentary and so I don't know I've seen a more nuanced literature on this topic and I'm wondering how you get your result I think that's more a philosophical okay maybe I should stop yes I had a question for Vito Peragini as well one could speculate that for the migrants what is maybe more relevant than the overall distribution of the destination country is the distribution of the incomes of migrants of the same country or region in that destination country and I had the sense that in the second part of your paper you have a way to recover those distributions at least at a regional level knowing in its destination country so I was wondering if you could like take those that data to the first part and check whether if it matters for migration decisions how much it matters and how much then they aggregate distributional characteristics of the destination country matter once including information on the particular in form distribution for the migrants which is probably what they where they can more realistically expect not to end there was any other question here no I have a one question for fellow Viana yes very interesting paper so I guess there may be public some different between short-term inequality and long-term inequality right say for example long-term inequality if you look at inequality every over the past 10 years or even 15 years so right then so I just wonder you know if there any different you know in the impact of short-term inequality and long-term inequality on your reasons and also for longer term inequality I guess you may also and you have to worry less about you know end of the 80s right because and this is not given and and for the whole value survey it's like a the value right now so so in a way you know it can help a bit with your problem yeah thank you thank you very much a lot of questions I have a question for yeah when in the first part in the first part of the results when you don't have a control I think for the net of migrants in the destination country the people but if that is a big phenomena that may be affected the inequality in the in the destination country and I'm not it maybe like it may be interacting with your result but in nothing contradictory ways but yeah the net because they may be increasing the genie in the in the destination country even even without you find your results but I was wondering how that may be interacting okay a few minutes for you to reply let's start with Vito okay so let me start with them the easy ones which are so a one question was it would be interesting to see to into I mean to include in the first part of the paper when we study how inequality affect the migrations okay to include some information on the path of the distribution where migrants are expected to land after the moment and this is part this is in the in the models we I didn't discuss them but are there also there you know the last columns of the table we have the average income of the 40% the average income so yeah that's something we do and in fact is in the right direction we say so the results are consistent and we don't have I mean there are many things for which we do not control this is a very parsimonious model if you want but I agree that that is very important so there are different mechanisms that we can be behind you know and yeah so now on the reverse causality you know I'd like to say that the second part of our paper is an answer to the problem of reverse causality in the first part but in fact it is not the right answer because in the last part of the paper what we have is that the global inequality doesn't change but there is a positive effect of migration on inequality within countries and that is exactly the kind of inequality for which we test in the first part so yeah I'm afraid to say that I mean you know that model which is a panel fixed effect model maybe maybe maybe you know in a sense I mean it's sensible to this criticism and and the second part if I understand well in which we show that in fact that my migration increases the inequality within countries you know in a sense gives significant to this to this problem and we don't have the composition of skills if that was the question of skills and heterogeneity although that has been very discussed very much discussed in the literature we if they don't know if that was the question taking into account the heterogeneity of the migrants to see so not not only the income so the position income distribution but also some other characteristics that we don't do that I mean that is very well discussed in the literature so we just focus on you know on the income of the of the of the origin and we you know we obtain it in the second part of the country we don't control for that and there is another question the effect of migration on inequality we do know how how is it that we have this a strong effect I don't know I mean we should discuss in detail what the way we we construct what was your question the way we constructed it counterfactual distribution because the you know that result comes from comparing the actual distribution with the distribution without migration it might be you know that the way in which this contract is usually constructed and we can discuss later because you know may bring those strong results it may be possible but that is the basically the driver of that way that strong result yes I'll stop you okay so thank you for all this question and suggestion I start with the polarization issue yes we will check and add some polarization measure in fact the results about the between inequality component is maybe suggesting that there is an issue of polarization but still I just want to underline that the final result of the impact of aggregating equity on institutional trust is driven actually by the within inequality component so still the profile of inequality matters is just not only polarization but for sure we will add a section by considering polarization difference between pre-tax and post-tax yes Carlos I don't remember if the database has also information on pre-tax is only post-tax so maybe we have we will explore other database that provide pre-tax distribution institution are quite a yes at a certain time I remember that we are the control and then we decided to leave it out because I think we were losing too many information but I will check it again interpersonal trust no we didn't go into detail of because our focus was institutional trust but for sure there is something to to to look and to to to be said about this about the trend of both internet interpersonal trust and similar story for short-term and long-term inequality we didn't check the difference between the two so we just we are just considering unitemperate inequality and just so it's short-term inequality and we will we will also consider consider adding and looking at what happened when we also had a long term inequality then there was a question about the boundary I'm not sure I understood when what you were referring to but the way in which we are computing this profile is we are dividing distribution into three parts so we compute this within inequality in the bottom 40 percent then between the 41 and the 80 percent and then eighty hundred percent and we take then the between between between equities within this group so I'm not sure I got your question about boundary but maybe we can discuss yeah after so I think that's it okay so the comment on number of people affected by the natural disaster I think that's a really good comment I do not have data on that but I think indirectly I'm able to look into that by constructing percentiles of fiery to do power show to the extent that higher percentiles reflect higher intensity of the disaster right so that might be one way to indirectly you tease out the channel that you're talking about but that's really good so this and I'll certainly look into that thank you okay any last very last question or comment or reaction I think that on it because I think too easy so I have a question for Veronica this economic complexity thing I don't know maybe I missed it did you have a theory or a hypothesis for mechanisms for example I was thinking that suppose you think of increasing economic complexity in a cosmopolitan way you know there's more activities maybe higher income in some of them maybe you'd expect the market side of things to make inequality greater I could think of counter arguments as well but let's say but then but then you know there's demand for public insurance and countries develop welfare states and as they are more economically complex actually perhaps the market income wouldn't change very much but they have developed these mechanisms and is that something that that was in your model at all or in your empirics at all no the basic idea for the complexity index the idea for the complexity index is like we can think of like a proxy of technology incorporation because basically the Asian countries are the ones that move forward so what we have the question about the development of the welfare state and how that may be related I mean I have not control for that and I am just now thinking that we have as I said after taxes genie so maybe there is some story like some more complex story in the in the argumentation but the main message that we have up to now is that this basic idea that that making economies more complex through the incorporation of technology may lead to lower inequality yes but not for poor countries up to a certain stage up to do reach there so that's like the basic the basic intuition behind the model so what what may be happening then it's like more complex I'm not sure but we what we are trying to to understand is that this virtue cycle technology inequality technology or complexity in in the type of production that we are doing also we have some limitations because this economic complexity index is based on x in on on exports basically so it's if it depends also on the time of x for not necessarily your your production your domestic production is reflected there in another way but it's the only kind of measure that you may have that synthesizes the concept because that's that's the main idea so okay so thank you very much all the presenters


Hey everyone, it's Gabrielle over here at Oxmer Toyota. You guys, I wanted to show off the beautiful 2021 Toyota Camry. This is the XSE. So you guys, this one is relatively rare. This is one of the first ones within about 250 miles in this supersonic red with the black roof. So you've got that two tone. It's a very sporty look. I mean, it's absolutely gorgeous. You've got these black accents. You have these 19 inch alloy black wheels, very beautiful. Now you are going to have the black mirror caps.  Moving on to the back, you have a black spoiler. And then you've got the dual exhaust. All right, guys, why don't we jump on in the front? Now on this vehicle, I do want to show off the sunroof. You have a panoramic sunroof. So it's going to be that whole top. So you have in the front, the back, very beautiful. It's going to bring in a lot of light. So you have the black leather seating. Has that sport look with that mesh insert. So there is a wee charger in there. There's all-weather floor liners, which will be nice for the winter coming up. So you do have a beautiful multi-information display right there, a touchscreen. All right, guys, so this one is rare. So if you are interested, I would say come on out, take a look at it. It's not going to last very long. All of our contact information is going to be listed right here, guys. Thanks so much for watching and I'll see you all soon. Bye.


I was just about to say, welcome to the end of the week. And if you're listening to this, it is the end of the week, but not for me and Piers, because we're actually recording this on Wednesday. So if something spectacular does happen between recording now and when this episode goes out, kind of two days later, you must forgive us, please. But what are we gonna cover here? We're gonna talk about a quite stark divergence between some of the giants on Wall Street and what they think about this recent record high we've had in US stocks. We've got JP Morgan, very bearish, and Goldman's and Bank of America are very bullish. So we'll look to dive into some of the rationale and what the arguments are for each case, and we'll let you decide with the poll we'll put with this episode, whether you fit within the ball camp or the bear camp. Then we're also gonna talk about record highs for both Bitcoin and gold. So what's underpinning that? Why, what might happen next? We'll look to unpack that. And then if we have time, we're also, there's lots of other things going on in the market. I think China's come out with its annual targets. They've come out and talked about 5% growth, a lot of question marks, whether they'll be able to hit that. We've also got super Tuesdays just happened at the point we're recording this. But I guess if you're in America, you probably already know what happened. But if you're in Britain and you're English, you'll probably be thinking, what is super Tuesday? So I'll do a quick explainer of that as well at the end of the episode. Andrew Powell's got an important speech to deliver as well this week. But Piers, to start then, this kind of battle on Wall Street for who's right, who's wrong. So JP Morgan, what do you think? They're pretty bearish. Yeah. I mean, well, it's kind of, it's kind of Goldman's versus JP Morgan, isn't it? They've, you know, in recent days of the kind of chief economists have kind of come out with pieces that are very starkly opposite. And you've got JP Morgan thinking that this recent, you know, very pretty phenomenal gravity defying bull run that we've seen, you know, particularly in US stocks. But globally, more broadly, we were talking about this on the podcast the other week, weren't we, where off the back of the NVIDIA results, you had new all-time highs being set in stock indices in the US, in Europe, in APAC. But the US, let's just kind of focus on the US here. So the S&P 500 has had a phenomenal run of it. So coming off the October low, which was around about 4,100, we're now, you know, above 5,100. And it's just been stellar.  And they're kind of defining this with a year-end target. And this is where the kind of stark contrast is the most obvious to see. So JP Morgan's year-end target, so in like 10 months time, they think we're gonna drop from above 5,100 now back to 4,200. So almost right the way back to that low point we saw in quarter three of 2023, Goldman's exact opposite. They think, don't be silly, don't fight this trend, bet against these mag seven at your peril, the stock market's gonna march higher and they've got a 5,400 year-end target. So it's quite, I'd say it's pretty unusual to get, you know, the big, big, big boys so contrastly sort of delivering expectations about what's coming next. So I thought, yeah, we could kind of drill into the bull case and the bear case a little bit. Which do you wanna go first? Yeah, let's go, you know, let's get the doom and gloom out of the way. So why is your man at JP so pessimistic? Yeah, so there's like, for fundamental reasons, the kind of bear case and like this guy at JP Morgan. So their chief market strategist is a guy called Marco Kolanovic. Okay, so he's their chief guy in the U.S. Now he's come out and said, look, volatility's low, froth is building and basically equities, I'm quoting him here. He said, equities have moved up this year even as bond yields rose and rate cut expectations unwound. Investors may be assuming that the increase in yields is reflective of economic acceleration but earnings projections for 2024 are coming down and the market appears too complacent on the cycle. I'd say you gotta think about like, is the market gonna come down? Well, question one, well, why did it go up? And those forces, are they done? You know, have we gone too high now? And basically he's saying we've gone too high. The key thing is the interest rate expectations. So a key catalyst for this big rally and the last year into the start of this year was where the Fed made a big pivot and they actually started to say, we're gonna cut rates quite consistently throughout the whole of 2024. And you'll remember, seems a bit crazy to think about it now but literally a month ago, markets were pricing six rate cuts in 24, the first one being in March, like in a couple of weeks. Okay, so that was literally a couple of months back we were thinking that. And now markets, the expectations have gone from six cuts to maybe four or not even four, maybe three and then you got some out there like the Larry Summers of this world were saying, forget cuts. The next move is gonna be a hike because the economy is so strong. Okay, so that's the big kind of, absolutely the most I guess compelling argument in the bear camp is to say the interest rate situation has completely changed. And ultimately that all shakes out to mean because inflation has been dropping, it all shakes out to mean that the real interest rate, so that's the Fed's interest rate minus the inflation rate is actually now the highest since like 2007. So real rates are up there tightest. And ultimately JPMorgan is saying, look, when real rates are that high, I don't care how strong the economy looks at this point, it just can't survive that for any period of time. So that's that kind of central argument. Do you think that the bear case gets a added benefit of human behavior in a sense of the narrative gets so much more promotion, if you like, because I got an email alert from the Daily Telegraph because I get the newsletters from every major publication, literally. And I always kind of- I thought you normally just deleted the telegraph. Well, I canvassed the headlines just to see like what the general mood is. You've changed. I find it, well, yeah, the guardian is still in there and the observer. But the point being is that that headline that they led with was Bitcoin bubble frothy market inbound. And I clicked on the newsletter, so it caught my attention. I was like, okay. Yeah, well, did its job. And then it just cited only one side of this discussion that we're having. Right, well, yeah. That's bad journalism straight up. Yeah, but that's, is that bad journalism when they all do that? I mean, that's just default. So who's to blame here?  Isn't it the, I mean, isn't there a bit of emphasis on us as consumers, as people, to have a little bit more better understanding of like we need to not just believe all the information that just falls on our doorstep. We've got to be a little bit more. We're straying into a different conversation here, but I mean, the mainstream media now, the way it's gone digital, of course, has meant that it's just about clicks. That's the single measure that they have. And don't forget that clicks means money because of course they're generating advertising revenues. So they need more clicks, more eyeballs, more money. So it's about clicks. So you have to go sensational and the human nature is that you look at a kind of catastrophe disaster type headline and the stats don't lie. There's always more clicks than there are for headlines with positive stories. That's human behavior.  They've kind of dug their own grave a little bit. If you've got a negative headline, you've obviously got to justify why you've put that headline with then some arguments, right? And this is the failing of, I think today's mainstream media, you're getting fed one side of the narrative where proper journalism should be presenting a full 360 viewing. You're not getting that anymore. And so this is why you need to, I think the onus is on the individual to do even more digging and your own research. You've kind of got to do the job yourself now rather than relying on mainstream media. So my question then is, does that play a part in this? Do you think when it comes to this idea of market direction, when we start to get to these more extreme levels? Well, I think it definitely does. I mean, history tells us that the media does play a role or certainly has played a role in shaping sentiment. And so now you're starting to get more noise because you've got to have, like to justify that article, there's nothing better than the J.P. Morgan chief market strategist to quote in your article, right? Because now you're getting proper heavyweights kind of fall on that side of the fence. So yeah, I mean, I think there is a possibly, this is a compelling argument for the bear case for sure. There is a couple of other kind of items on that bear case list. Number one is something we've been banging on about forever and it's getting really boring, but it's the market concentration thing. It's the fact that this rally is so narrow where it's mainly being driven by the magnificent seven. Although I saw, have you coined a new term here or did you get this from someone else? Because I was really looking through a couple of your slides and do you want to talk about your new? I can't remember what I've donned it now. The eight. Enormous eight. It's not the nice seven anymore. It's actually Chung's enormous eight. And basically just throw Netflix in the mix as well, right? Well, yeah, it was there to start. I felt a bit bad for Netflix after it had a bit of a boom period. So let's just pop it back in and let's just roll out enormous. Answering Chung's enormous eight. So basically because the market rally is so narrow, it's more vulnerable. And so I guess it's super vulnerable to anything negative about Nvidia, basically. If you get bad news on Nvidia, fine. That's like the pin that will just prick this little bubble. Is it a bubble? I don't know, but certainly it'd be a deflation factor if something bad happens to Nvidia. Okay, so is there any other on your checklist for bear? Yes, one more, China. Just economically vulnerable. And yeah, just a little bit nervous about how that all shakes out in 2024. And of course, it's such a monster economy that if it does go pear shaped a little bit, then that's gonna have a spillover effect on everyone else. So yeah, that's certainly something to be monitoring and the bears are all over that. Yeah, I mean, one of the things actually talking about the enormous eight that slide that I put together, the quote that I saw was quite good one was that the rising tide is not lifting all boats. Right. So even amongst the mag seven, let's say. Yeah. You're actually starting to see a bit of discrepancy where Tesla is getting whacked year to date. I think they're probably down 20% plus now because I think they fell again yesterday. Apple are down and Apple have had a bit of a rough time of it actually in the recent week. They've been slapped with a two billion euro or they're about fine from the EU over music streaming. They came out. I saw a report this week as well that their iPhone plunged in China in the first week weeks of 2024. Just to give that some context, iPhone sales dropped 24% in the period apparently. So they're under pressure from all of the local. And that's their second biggest market, right? Right. And that's the one where there's a lot of focus to grow as well. So yeah, they're down and I think Alphabet might be down as well, just a fraction or pretty flat at this point. So then on the year, they've got issues like around kind of roll out of Gemini and they're trying to, they're trying to compete on the AI race, you know, off the heat, you know, on the heels of open AI. And they've just had a bit of a crisis internally, I'd say on the sort of DEI front where the DEI team in Google seem to be, basically seem to have had grown into this position where they've got outsized influence over everything. And this is now fed through to the product that's now sat in front of the user.  So if you search things like, you know, give me an image of, I think, what was it? The media were talking like, give me an image of George Washington.  So that's factually incorrect, but it's the way that the DEI layer of their tool is kind of, it's gone too far where you now get those, you know, quite ridiculous outcomes that are factually wrong. And I think in the end, that's what users want. They want to trust the product to give them correct information. And so if you're getting something that's factually 100% wrong, well, then you question everything else that it spits out as well. So I think, yeah, another stumble, another one, because there's been multiple now in this kind of AI race from Google side. So yeah, they're not doing so well. So the concentration has gone from almost seven even more. And really we're talking two stocks, there's NVIDIA and META. You might put Microsoft in there as well.  I mean, you can't not have Microsoft there, but META and NVIDIA more so, but you're right, the concentration's narrowed, which makes it all the more vulnerable. So yeah, so a good argument in the bear case. Okay, well look, I'm a half glass full kind of guy. So I'm not gonna add any more bearish points. So look, I've jumped in the cab, I've gone around the block and I've jumped out at GS's HQ in New York. What's Dave Costin, the kind of peer or the equivalent of JP Morgan's person saying? So he's definitely got an opposite case here. And again, this is what makes it difficult, some really compelling arguments on this side of the defense. So I don't know where to start here. Let's talk about sentiment at the moment. So fine, you're talking about negative media articles, well, it hasn't damaged sentiment yet, at least. So a couple of things like US consumer confidence data last week came out with a quite a notable increase. And it's notable because it's been below this kind of 75 kind of level, which is kind of the cutoff with this consumer confidence data. If it's below 75, it's considered to be like overall bearish. On average, people are more negative than positive and above 75, the opposite. And we've been below 75 for 29 straight months, okay? From August 2021. And so 29 straight months in the bear camp and in February, it's gone back above 75 for the first time. And actually there was a big jump on the month, largest monthly gain in that sentiment indicator since December, 2005. So you're getting these kind of crazy positive sentiment scenarios going on. That could be fed by just the fact that wage, real wage growth is continuing to stay strong because inflation's dropped. Yes, interest rates are still high. So fine, that is a drag, but it does mean wage growth is now much higher than inflation. So in real terms, the consumer's getting more wealthy again relative to the speed at which the cost of living's increasing. So it's eight months in a row now of real wage growth in the US. So maybe people are feeling a bit flush. What recession? People are worried about a recession. It just hasn't happened. Maybe they own a bit of NVIDIA stock and they're thinking, we're here more likely they own a bit of Bitcoin and they're thinking, sweet, I've unloaded. And so from a sentiment point of view, the consumer seems strong. If you think about investors, there's this thing called the American Association of Individual Investors Survey. It's actually in the top decile. The top, it's like the 99% sort of decile in terms of bullishness. Now you could spin that and say, well, that's a bad thing then because well, if everyone's bullish, they've bought already. And I guess another point for the bear camp possibly because valuations are this high, you're probably seeing people not sell if you've already bought in the past, not sell because there's positive cases to hold, right? But new buyers coming in now. I mean, it's expensive, right? And there are some more bear case noise coming through. So yeah, maybe that stat on the top decile of that survey is not necessarily a positive thing. So anyway, sentiment's positive, right? Backed up by the economy. You're getting strong kind of labor market numbers and we've got another non-farm payrolls report this Friday but that's straight really strong. I've talked about wages, GDP trackers are still looking at two to high twos or even low 3% GDP growth in the US. And it's like, wow, the resilience is just ongoing, right? So the final and I think more interesting case is this AI revolution and what does it actually mean? Fine, outside of Nvidia and Meta now, let's leave those for a sec. Is AI leading to productivity gains? And I think the idea had been that not yet was the answer except something came out last week that maybe has flipped the argument. I don't know if you saw the news from Clana last week. So Clana, Scandinavian, massive, by now pay later burn, right? They've started to use, so through, they've got an AI assistant that's powered by open AI, okay? And they use this for customer service calls, right? That's been live globally on their platform for one month in the month of February and they released a load of stats about how it's gone. And their AI assistant dealt with 2.3 million conversations in one month. That was two thirds of Clana's entire customer service chats is now automated. Basically, the AI's done the work of 700 full-time human agents in February. Now, okay, you might think fine, okay, but is this AI any good? It might be handling a lot of volume, is it doing it in a way that's good for consumers? And basically, they're saying that actually, customer satisfaction scores have stayed exactly the same, indicating that they are doing it as good, but actually when you drill into the stats, so they're saying that it's more accurate in errand resolution. So the AI involvement has led to a 25% drop in repeat inquiries. It's resolved errands in less than two minutes on average compared to a human, which is 11 minutes. And I mean, so it's taking over 700 human beings. They've got this in 23 markets, it's available 24 seven, of course, the AI doesn't need to sleep and it's available in 35 different languages. So this news from Clana and they've said, Clana have said, as a result of this successful rollout, our profits will go up by $40 million in 2024. So I think that's quite an interesting, I think the first major case where we've seen AI actually translate into real proper productivity gains within a firm, which is leading to margins going up and profits improving. And ultimately the best thing of all, the customer being more satisfied. So look, it's obviously still early days and Clana find an early adopter, if you're a consumer facing business that's handling a lot of inbound from your customer, find this AI tools spot on for you. I'm not saying all businesses have that business case, they don't, but still it was interesting to see those numbers coming out of Clana. And then just going back to, I got one point on the ball case on valuations and then another on something Bank of America said, who are also equally as bullish as GS. So one thing that the strategist at Goldman said was that this time is different. And whenever a sentence starts like that, it always makes me slightly nervous. It's like that famous saying in trading, right? This time is different. It's the time you get slapped around the chops by the market more often or not. But he said this time is different than other periods in history when stock prices have moved abruptly, typically beyond their value. So much in the context of now. He said that unlike prior such instances, the breadth of extreme valuations is far more contained this time with the number of stocks trading at those types of multiples down sharply from that kind of COVID peak that we had in 2021. So do you think that plays into it as well? I think that is true. So it's kind of, you're making the concentration argument but in a positive way now, where you're basically saying it's just the mag seven that got crazy valuations. But the key point here is another, you've got to go one step further. And that is to say that, okay, yes, those seven have, or not even the seven now, we've set like, let's talk about the three. And so the argument is, well, are those three companies, are their valuations justified at these levels? And the big difference is, yes, the fundamentals straight up back it up. We talked about, well, that example, Metta buying 350,000 H100 chips for $14 billion. There's real hard cash changing hands here. This isn't expectations, wild expectations of something happening in the future. It's absolutely tangibly happening. So that's why it's a little bit different to the bubbles of the past and why the Goldman's camp is saying that don't bet against this yet. I mean, there's no evidence to suggest that this invidia-driven rally is gonna stop yet. Yeah, and then as I said, Bank of America, they also upped their target from 51 to 5,400 this week. The main reason for that was that they said surprising margin resilience amid the dramatic step function change in rates and inflation. But the point I wanted to make was, they said that while the target is being raised, they warned the likelihood of a near-term pullback is high. And I think that's something that people miss when we throw around or you read about these year-end targets. They are year-end targets. I think people get so confused, not maybe the professionals doing this day-to-day, but just your casual reader of finance thinking, right, it's now a good time to invest or not. And then all of a sudden they invest and the market drops 5%. And you're like, oh my goodness, but then actually staying invested, the market ends up at 6,000 at the end of the year. And so just to be clear, I mean, I think the summary of what we're saying is, the market probably is ripe for a pullback. Yeah. I think that would make some sense. Moreover, typical pre-election volatility heading into November, I got a stat here. So the figure is that in the run-up to the election, the VIX typically increases 25 percentage points. Right. A big risk event. Basically, people position themselves for extraordinary bout of volatility forthcoming, but then post-election day, history would show us that it's generally positive. They're removing the uncertainty in markets rally. So look, we are in March and there's still the best yet to come for the year. So I think it's about not getting too blinded by, because people always say to me, oh, this bank's terrible. We've already hit the year end target. Right. And I kind of think like, son, you better buckle up because we might end at 5,400 and we might be close to 5,400, but I can tell you what, we're certainly not gonna be a smooth level line till the year end. So yeah, I just wanted to make that point crystal clear. Exactly. All right, well, look, let's move on. The other kind of talking points of the week, I mean, you can't really get away from it at the moment because people are starting to, I caught someone peered over their shoulder this morning on the commute in and he was checking on his Coinbase. How's that Bitcoin overnight while I was asleep? And Bitcoin hitting record highs and perhaps we can kind of intertwine gold as well within this conversation. Yeah, I mean, yeah, Bitcoin, crypto's back, baby.  That was only, what was that, only six months ago? Yeah, probably less than that. Maybe not even that, right? But whatever, yeah, Bitcoin, so it's made a new all-time high. I mean, that's the kind of headline news and hit the all-time high yesterday, okay? But I mean, this thing's lethal. Crypto, just generally, I mean, the fraud, that is not for the faint-hearted. Hit a new all-time high, $69,063, okay? Just pipping the 2021 high, all right? Do you wanna know what happened next? It got slammed $10,000. It sold off down to $59,000. So just nicked a new high and then came off, I mean, that's like eight, nine percent, like bang. However, overnight and why your mate on the tube was probably kind of relieved when looking at the chart this morning, it snapped back most of that and we're back up to like $67,000 now.  Another stat for you, just if that guy is listening, I've got another bullish stat. Bitcoin's price has doubled in 18 days or less in three of the four times that it's hit a new all-time high. Okay, so 100K in two weeks. Here we go, here we go, baby. Well, look, let's just kind of break this stat. There are some fundamentals driving this, dare I say it, because that's been the big skepticism within the financial industry around crypto generally is the fact that it's very hard to kind of, because crypto hasn't been adopted as a currency, you know, full stop. I mean, certainly not in the mainstream, right? So it's like, well, it's hard to fundamentally assess it as an asset and as a kind of, you know, a holder of value and a medium of payment. It's very hard to fundamentally assess it. However, there are two key fundamentals that are driving this. So there was a big event earlier this year where in the US, the US regulator approved spot ETFs with two of the big guns. So Fidelity and BlackRock, who are two of the very, very biggest, I mean, they're both, I think they're, I think they're number one, BlackRock and number three, I think Fidelity in terms of the world's biggest asset managers. They've both had Bitcoin ETFs approved. Okay, this was a major game changer. What's led, not following that, and that was at the start of the year, we've had five weeks of strong inflows into these ETFs since the first day of trading began and we've had about $7.5 billion worth of capital flowing in. Mostly from institutional investors. This is basically given institutions that are kind of safe away of accessing this asset. You don't have to go through the FTXs anymore, right? You've got these crypto exchanges where there's a certain amount of risk around on their own. And so here now you can trade Bitcoin essentially through BlackRock. Okay, so it removes one of the big risks. So like an ETF, is it just institutional or can a retail participant access an ETF? So are we saying this is institutional inflow or is it not? I think that it's both but the majority would be institutional of that seven and a half bill but there's definitely consumer money there as well. Yeah, you can absolutely buy into this ETF through BlackRock if you want as an individual. But I think for the first time, institutions, let's put it this way, have basically remained on the sidelines of this whole story and this has given them a vehicle to say, well, okay, fine. At least one of the big risks is eliminated. Maybe they're now thinking, well, okay, the Bitcoin story and crypto more broadly over the long term, maybe we can see it becoming something meaningful and being adopted. And so, right, this is a kind of vehicle where they can start to kind of get involved, okay? That's number one fundamental. Number two is an event that's gonna take place in April. It's the halving event, okay? Now, the way this system works is basically every 210,000 blocks that get mined, we have a halving event which basically means the reward to the miners for mining a block, halves, all right? So the last time it halved was on the 11th of May, 2020 where it went from 12.5 Bitcoins as the reward for a block being successfully mined. It dropped to 6.25, right? That was in May, 2020. In April, we're coming up to again another 210,000 blocks being mined. So the reward's gonna drop by half to 3.125 Bitcoins. So the idea and the thought is that well, if the reward halves, you're gonna get less mining. Less mining means less Bitcoins. So the supply factor here is helping to drive the price up, okay? Just as an aside, every 210,000 blocks, right? The way it's set up at the moment and the way we're tracking, it's expected that the last halving event, there's gonna be many, many more, but the last halving event will be in 2140 as in the year 2140. Look forward to that. When the maximum number of Bitcoins, well, so the maximum number is capped at 21 million, I don't know if you knew that. So as soon as the number of Bitcoins in circulation hits 21 million, stop. No more mining, that's it. So that's on track to hit that in 2140. Anyway, this halving event in April, again, there's another reason why for supply side arguments, there's a bull case and that's helping to drive Bitcoin up. Another interesting angle within this that I read was talking about the, this is a money opportunity from a revenue source for some of these financial institutions. And one thing was Bank of America, Wells Fargo's brokerage units, they've started offering these US listed spot Bitcoin ETFs. These two, what they call wire houses, I've never really heard of that term, but a wire house is a full service broker of any size, essentially. So these two would fit within that. But the way of which this actually works from a legally compliant way, which I didn't know this, was that they've approved the ETFs on an unsolicited basis. So I don't know what the case is for BlackRock or Fidelity, it could be different. But for these two, what that means is that financial advisors cannot suggest this product to their client. It must be that the client requests information about it for them to then present it. Now, this opens up then this idea that ETF firms are gonna have to spend a lot more money on marketing so that then the consumer sees it to then contact their financial advisor to request information or in fact, participate within the ETF. And what this then means is this isn't just marketing because we're talking about crypto, which naturally a lot of these more, let's say sophisticated investors are more skeptical. And so therefore you're gonna have to spend more money on marketing because of the brand side of things that you need to promote the product giving its association with risk. And I was looking at some of the numbers, that's a pretty painful reality from a business case point of view because actually one of the things is that the majority of spot Bitcoin ETFs charge 0.3% or less in terms of the fees. So when you take out the marketing side of things this could be a loss leader, which could be okay for BlackRock or Fidelity because it's so large, but some of these others maybe not. So yeah, I just thought that was quite interesting. And the interesting point, what this means, so perhaps it does impact some of the big boys is the fact that the spot Bitcoin ETFs having received full kind of throttled approval and that's made a net $8 billion or so taken the ETF since its launch. If you think about it, that is even more impressive because what you're saying is is that a lot of people haven't even, a lot of these companies haven't just gone out there with an army of advisors to push this product. A lot of this has actually been fairly passive and inflows have come from perhaps retail investors or self-directed financial advisors, which makes it even more kind of bullish in that sense because they haven't been able to take the fall, hasn't been completely kind of a free-for-all as yet. Well, don't, but in the short term and it might not last, but the media's basically doing their marketing for them in a way, right? Certainly when that ETF got approved, I mean, that was big global headline news, front page FT kind of stuff. Now, obviously when Bitcoin's hitting a new high, fine, there's a huge wave of media attention on that. But I guess your point is, and it's correct, you can't rely on the media story and narrative being focused on Bitcoin for very long. And so, yeah, they might have to then return back and rely on their own marketing initiatives. Yeah, that's interesting. I didn't realize.  And so perhaps you can run a loss leader program where you market some of these products and you attract them in, but then you actually end up taking their pension and other long-term investments. And so you use it as a marketing vehicle rather than looking for a real source of revenue via the ETF, perhaps. I like it. Now you're talking. Yeah, yeah. All right. Well, look, Gold, what's going on with Gold? Because that also has squeezed higher as well. Gold knew all-time highs. I mean, everything's new all-time highs, it seems, doesn't it? But it broke 2000, it actually broke 2000 bucks. It was actually the end of last year, and it's been continuing to March. March North, we're up above, we're actually trading at 21.26 now. So that's $2,126 per Troy ounce is the unit that we use for Gold. But it's a bit funny because, well, hang on, stocks are all-time highs, interest rates are really high. Both of those things are quite negative for the price of Gold in theory. I.e. Gold is one of its attributes is a safe haven. Well, hang on, people are bullish, look at stocks. Why is Gold so high? With interest rates, that just means you can earn a big yield on your cash now, put it in your bank account, buy some short-term money market products, and you can earn 5%. The famous thing about Gold, it doesn't pay you any, it yields zero. You buy a block of metal, and that's it, it doesn't yield anything, right? It's a big negative for owning Gold. So normally in a high interest rate environment, that's bad news for Gold. And yet it's all-time highs. So why? So I think there's a couple of really strong underlying drivers that are probably a bit unusual, I would say, and have maybe gone a bit under the radar. So firstly, record buying by central banks, okay? So, and particularly in the emerging market space. Now, this is a function and a bit of a side effect of, we believe, US sanctions against Russia. Way back it feels like when that conflict began, when Russia invaded Ukraine, the US started to implement sanctions and one of their kind of, they weaponized the dollar as it's being described, where they basically used, well, firstly, they were blocking dollar-held reserves that were held in US financial institutions. They kind of sanctioned and ring-fenced. They also used the international payment system, SWIFT, to try and make it difficult for Russian institutions and indeed the government process transactions, right? And they kind of called it weaponizing the dollar. So what a lot of emerging markets have said is, well, look, I don't want to be vulnerable to that. So they've kind of taken their dollar reserves because a lot of them hold US dollar reserves and instead they bought gold, it seems.  That's been ongoing, right? For the last, obviously, as I said, since when Russia invaded Ukraine, you had a big pop to the upside and that seems to still be fueling it. The other thing is more recent and that's Chinese consumers. There's been a really big demand coming through from them for two reasons, really. Number one, well, just broadly, there are economies in a bit of trouble, but the property market specifically is in huge trouble. So you've got some Chinese looking to switch out of some of their property investments and put it in gold or it's safe. And also more broadly, the stock markets had a shocker in China. Talk about all-time highs in most places, not in China. And so there's been a shift in an asset allocation switch to gold at a consumer level, it would seem. So I'd say those seem to be the two fundamental drivers that have seen demand increase, which have really outweighed the negative forces on gold, which is stocks all-time highs and positive sentiment and interest rates still really high. And what I've got, if stocks were to see a 10% downturn, yeah. I mean, is this a case of gold? Gold's kind of pretty robust in all circumstances at the moment. Yeah, if gold can be at all-time highs now, I mean, yeah, just think what will happen if some of these kind of, yeah, if the sentiment shifts, if I don't know, Nvidia comes out and suggests, hang on, demand is softening or you get Powell saying that we're gonna hike rates, although maybe that'll be negative for gold, actually. Anyway, yeah. I mean, gold's outside of a hawkish pivot from the Fed and Powell's talking this afternoon. So who knows? I mean, that's incredibly unlikely. Powell's message is gonna be, guys, higher, we're not, we're gonna stay at these levels. It's gonna be more about be patient for rate cuts. Not, that'll be the message. Not, boy, we might start hiking, you know. Yeah, well, let's finish on those final points then. So Powell and this Super Tuesday in US politics. So let's continue with what you just said with Powell. So do you think that Powell and the rest of his board of governors are aware of some of the mood at present? And so therefore what's required here is just a stable hand, really not too much movement from an official language perspective. This is just about a reassuring speech that he needs to deliver on this major platform today. It's a tricky one, because when you got this positive market sentiment, if stocks are going up, then a lot of the US, a big portion of the US public own stocks, right? So if their asset values are increasing, then this can lead to positive consumer sentiment. We saw that on the consumer sentiment data. It can lead to more consumption, because people feel wealthier, which leads to more inflation, which then leads to, well, hang on, we might need to raise rates again, right? So he's got to tread this fine lot. He's got to basically say, we're not going to cut rates as quickly as you want and hope that that's enough to kind of cool off this sentiment scenario. If sentiment isn't dampened by that message and it hasn't been yet, it can't go on much longer. And if sentiment continues to say super positive in the end, Powell is going to have to go one more step than the hawkish side and say, well, look, we might entertain another hype. And I think if that were to happen at any point in the coming months, then that would be a kind of moment where you're going to get a pullback in markets where they think, okay, actually the Fed may well hike further. But I can't see that happening yet. And so I'd say today it's more about, more of the same from Powell. Rate cuts are coming, but not until the second half of the year. Okay. And then the summary of Super Tuesday, Joe Biden, Donald Trump election. Okay, good night. See you later. Yeah. So yeah, just for, as I said at the top of the show, for any of the Brits who are listening and not so familiar with this, very quick kind of skinny of what this is. So millions of US voters went to the polls for the primaries in 15, 15 states, indicated that Americans will likely face a basically a rematch between the unsurprisingly Donald Trump and President Biden. So the background is these primaries do not ultimately decide on who will become president or even directly pick candidates for the election in November. Instead, they simply award delegates who will vote for the winning candidate at each party's nominating convention this summer. So yeah, the November election is almost certain to be a rematch at this point between Biden and Trump. And as per usual, and we will talk about this, I'm sure in the months leading up, it's not about all of these states. Again, it's very much a focus on swing states and six in particular, which we will talk about in future episodes, I'm sure. So yeah, nothing's really changed a great deal, I would say, for what we all thought and think the battle will be in the coming months. Yeah, I mean, last night he smashed Nikki Haley, who's the rival for the Republican candidacy. The only rival left absolutely smashed her across all 50. Well, she got close in Vermont where the vote was 49.9%. I know, sorry, she won Vermont, apologies. She won 49.9% versus Trump's 45.9%. But basically most of the states, Trump was like 70, if not 80% of the vote absolutely destroying her. So yeah, Trump will be up against Biden. And I'm sorry, I said it, I don't know, two months ago, I said it a month ago, I'm gonna carry on saying it. This will be the least interesting election of my lifetime in the U.S. Donald Trump will not only smash Nikki Haley, Biden can't compete with Trump. It's too old, there's too many problems. Trump will smash Biden. It'll be the most one-sided election, decades. Wow, I've got a strap. I need to take that 10 seconds. That's gonna be like the TikTok of this podcast. All right, with that bombshell. That is the end of the episode. Thank you very much. As I said, we started with the Ball Bear case for stocks talking about JP the Bear and GS the Ball. Let us know what you think. I'll drop a poll on the podcast episode. It's specifically for listening on Spotify or just leave a comment or review and let us know what you think. We'd love to hear from you. All right, Piers, thanks very much. Thanks to everyone. See you next week. Cheers, Sam. See you later.


Thank you all for coming. My name is Barbara Rechenbach, and I'm the Interim Associate University Librarian for Collections and Services at Columbia. And I'm just going to do a few framing remarks for the presentation today to kick it off, so if you want to advance. So anyone who's studied literature or history will remember the late 19th century debate about the woman question. This phrase, the woman question, was used in connection with the social change in the latter half of the 19th century, which questioned the roles of women. And it would sort of dominated cultural discussions in newspapers and intellectual circles. Something wasn't working, and there was a desire to make changes to address this question. In planning for the session, all of us had a call, and I couldn't help but think that we're in the moment of the e-book question. Something isn't working. We are in a moment of social change and reading habits around digital scholarly monographs. And this session acknowledges that the digital electronic monograph is not working from either the perspective of the author or creator or the user. The goal of this session is to bring multiple perspectives together to offer potential solutions for this problem. Alex Humphries, head of JSTOR Labs, and I will talk about a collaborative project between JSTOR and Columbia to reimagine the monograph and the process we use to do that work. And then we'll be followed by Lisa Macklin, director of scholarly communications at Emory, and Charles Watkinson, the director of the University of Michigan Press and AUL for publishing at the University of Michigan. And they will address how publishers can best support and sustain digital scholarship through a publishing platform optimized for digital scholarship. So with that, I'm going to hand it off to Alex. Do you want to? Okay, so as Barbara said, I run the JSTOR Labs, which the JSTOR Labs is a fairly small team. There's about three of us, three to four or five, depending on the time of day. And what we do is we test out new ideas for JSTOR. We then share that work with the community. Those ideas are proofs of concept. They're prototypes that we do really quickly. Ideally, we're possible with a partner, either a library or a publisher or scholar, and then share what we've learned. And our process is inspired by design thinking and lean startup and new product design approaches. We're essentially an agile scrum team in operation. This is the overall process, but rather than sort of step you through it and sort of talk about the questions that we ask and how they change during the rapid iterations of a project, the easiest way to see it and to understand it is to see it. So we have a little video of the project Barbara mentioned, reimagining the monograph, which we'll dive into a little bit afterwards. So now we're going to see if videos work and sound works and all that kind of complicated things that labs people like to do. At Columbia University at Butler Library, where we've been working with our partners to re-enact the monograph. Monographs are long form scholarly arguments of books that you find in the stacks here at Columbia Library. We have a theory hypothesis that some of the value in those books is getting lost as they migrate to a digital world where people are doing research digitally. And this project is about working with the community to imagine and build new ways of unleashing those things that are lost and making them even more powerful. The process we've taken for this project is inspired by the lean startup and design thinking communities. It's geared towards rapid prototyping, learning as quickly as possible and getting ideas and thoughts in front of users and getting their feedback in order to end up with a final site or application that delights the future. The Columbia University Library has for several years has had a real interest in e-books and in their views and in the platforms that our users are engaging with. So when JSTOR approached us about this possibility, we knew it would be something our users would value and that our staff could really engage with in thinking how best ways to serve users in the future around the world. We started with a series of six user ethnographies where we watch individual users using books and monographs and their native habitats. And we learned from that to see where they're struggling with their context or et cetera. We then held a workshop where we brought members of the community along with the JSTOR Labs team where we brainstormed what the typical monograph reader or scholarly book researcher, what their pains were, what their gain or what the success looked like for them and what are their tasks. The next day, we drew up some of those ideas to figure out where the biggest opportunity was. And based on that input, we chose one of the ideas and worked to build it. And then in a final week at Columbia, we honed the idea with interviews with users every day. Some of those were formal half-hour interviews and some of those were what we called gorilla testing where we set up a desk out in front of the Columbia Library and asked people to give us 15 minutes. It's a great way of getting lots of feedback really quickly. The outcome of this project, re-imagining the monograph is first and foremost the prototype, the topic graph, which is a website that allows users and researchers to see and visualize all of the topics contained in long-form scholarly argument. It also will include a white paper. We're writing a white paper that will share what we've learned during the research and at the workshop with the community and will open source all of the code that is powered to prototype so that others can benefit more from it. The great thing about videos is I don't actually have to say nearly as much as, I mean, that was basically, I'm done now. Any questions? You're not that lucky. So what I'll do is I'll dive into some of the details that that little video skipped over and then we can go from there. So I mentioned at the very start that this, we work rapidly. I think it's helpful to have an understanding of how rapid that is. So the bulk of the project, we did the user research, sitting with those six historians in August and September, but the real meat of the project happened, all of it happened after I submitted the proposal to give this presentation. So that was due in some time in October and a week later we held the workshop where we brainstormed a thousand ideas to reimagine the monograph and then over the following month, month and a half we built that topic graph, which I'll show you and here we are. So it's a bit of a leap of faith that we'd have something or I'd just be presenting a terrible story. So some details that sort of came out. This user research that we did using ethnographies was a tremendous way to understand the entire context for users. All of these ethnographies have been visualized in these pretty little one-pagers and they're all available in the white paper and that alone makes, to me, makes the white paper pretty valuable simply because those specific user stories are wonderful. You hear a lot of stories in e-bookland about kids these days don't wanna read print books or everybody, nobody reads digitally and they're very grandiose broad statements and by having six individual profiles you see the diversity and breadth of use cases and experiences and preferences, which is really valuable when you're trying to solve them. The key findings from that sort of emphasized that diversity but also how much each individual user really owned their processes such that it would be kind of hard for us to change those processes. We have to work within the processes that they have and those processes aren't only digital or print, they span a variety of different media devices, tools. This little, this person here used a napkin midway through the session so that was documented. They're very sophisticated processes. Their preference for print versus digital was strongly held but it wasn't as canonical as I prefer print or I prefer digital so we tracked four different uses of monographs across all the users, mining citation, extracting specific information, close reading and reuse or revisiting a text and each one of those they would have a different preference of print versus digital but then the users were different. Some of them all preferred one and some preferred the other and they would range. They also were forced outside of their preference zone very often by the systems that we all provide them. So using all of that information we presented that to a workshop of some really smart cookies. Vote publishers, librarians, scholars, there was a data visualization expert, data scientists in history, languages across everything. It was a really wonderful and inspiring group and in one day basically in the morning we had, we talked about and learned about the user research and then in the afternoon we did two sketch exercises during which everybody drew, you have 10 minutes to draw eight ideas to reimagine the monograph for things you can do. We shared those and do it again. It's called the design jam and we probably walked away with over 100 different ideas that we could build. We then worked by testing with users in those pencil drawings you saw in the video the different ideas to choose the one to build and we went and built topic graph. So let's see if I can show it to you. This is all live on the labs.jstore site. You can go to it and I should say we did build this in about a month. So it's imperfect. There are things that we want to improve and personally I'd love to hear from each of you what specifically you think we should improve how we can make it better. This began, this currently houses all of the OA books that are in JStore and it's envisioned as a way to quickly understand all of the topics that are covered within a book. One of the rules that we set for ourselves was we wanted to deal with the books as aggregators and publishers have them in the archive. So we're dealing with sort of raw PDFs. We're not expecting a big investment per book to get highly tagged EPUB or anything like that because we wanted to be able to scale this to include to deal with a large corpus of content. The little connection here is a little teensy slow so happily I've reloaded it. The way this works is this topic graph and I'll tell you a little bit about topic modeling in a second. You can click on every page within it, theoretically. Maybe we'll do it here. And it will bring up the page with the highlighting of all of the terms associated with the topic highlighted in the relevant color and it'll load in a second once the internet's caught up. So let me step back a little bit and this is not a technical talk by any stretch but the idea behind topic modeling is it's a digital humanities tool that looks for, that's machine intelligence that looks for clusters of terms within texts that appear together frequently enough that you can have a guess that they mean a particular thing and JSTOR has been working on a topic graph for a topic model based on its corpus of scholarship and the highlighting isn't coming through right now, unfortunately. So you can see what those terms are here. So for the publishing industry, all of the terms, you know, publisher and book and sell and sold and best seller are highlighted in the interface as you browse through and the same thing for bourgeois, it's just different topics, different terms that describe that topic. Okay, oh, they're there. Now you can see the highlighting and so you'd see that on every page, all of the terms related. What are the goals for this? So users talked about when they're evaluating a monograph or a book to whether or not it's going to be useful to them. They look at five to 20 pages of a book before they download the chapter to download the book and our goal for this project was to find a way to make that process better. Let them look at the right five to 20 pages as opposed to guessing based on chapter name or something like that. Get to those pages faster and be able to see more if that's what they want and that's what we were trying to build. Oh, one last thing. So I mentioned there are 50 or so books within this. We also, because you might not find one in your interest area and you're interested in testing out the topic model, so we built a make your own topic graph. So if you have a PDF of anything, you can upload it and it'll email you a link to that document with the topic model applied to it and the graph next to it. So it's a way to experiment and test it out. I'd be interested to hear what you think. Okay, so what's next for this? I mentioned we're releasing it. The labs team is all about learning. We learn a heck of a lot while we're building these prototypes and we've shared some of that in the white paper but we're gonna continue to learn and some of that comes from user feedback and usage and all of that and I'm interested in what you think. One of the areas that we definitely need to continue to invest and develop and where we need your feedback is the topic model itself. In some cases, and you'll see this if you poke around and look at different books, some books, the topics that come up are really great and interesting and really good and others not as good. They're a little bit wonky or they're a little silly and that often happens because of the training set that we used which is the JSTOR corpus which is not everything ever written. So it's got disciplinary strengths and other things. So we need to learn a little bit better which ones are trustworthy and we can present and are helpful and find ways to hide or minimize, decrease the number of weirdnesses. The other thing I should say is, so this is an idea and I do think in the user testing we have is that it does improve the evaluation but as it is of a monograph before download but I wanna temper that a little bit because as it is now, it's not gonna do that because it's not hitting the user at the point of their problem. The point of their problem is when they're in their catalog system or in JSTOR and trying to decide do I wanna read this book or not, they're not gonna jump over to another website to evaluate it. So for this to be really, if it's deemed valuable for this to actually impact users, it needs to meet the users at the point of their need. So all of that was one of the hundred ideas that came out of this workshop. There's a lot more that was discussed and more things that we could do. So we've tried to document some of that conversation which was a really powerful conversation in a white paper that white paper is available on the lab site. It's available as a draft for comment. We're very interested in making that as valuable as possible. So I'd love to hear your thoughts on it. Well, it'll be open for a draft for comment until the end of January. And then we'll take your comments under, take your comments and then revise and publish later in the spring. One big portion, sort of the centerpiece of that white paper is a set of principles that emerged almost organically in the conversation at the workshop around reimagining the monograph based on these user needs. So these are they and I'm not going to walk through them here. And in the white paper, there are pictures and each one is sort of dived into with more detail than is here. I just wanna say that Topograph touches on two or three of these ideas and it's just sort of scratches the surface of a few of them. This is just what we could do in a month. I'm really interested in seeing what we can do looking across all of these. And I think it's especially important. This isn't going, JSTOR isn't the one who can solve all of those. We could Topograph and Evaluation and Discovery is something that's sort of in a JSTOR-like sweet spot. But the community has to do all of these together. There are other things that are not right for JSTOR to fix. And we need to work on together. And so I'd love to have additional partnerships to build things like Topograph or for others to keep building really interesting solutions for reimagined monograph. With that, I'll hand it over to Barbara. All right, thank you, Alex. And now I'm gonna tell you why you're gonna want to partner with Alex and the team. We had such an excellent experience. And this all grew out of JSTOR has been holding a series of 2020 meetings. I don't know if any of you in the room have had the opportunity to have JSTOR come and visit your campus. But 20 years out after JSTOR's founding, they're having these conversations at campuses to find out what's going on, what are the big concerns in libraries and in higher education. And that's really where this idea grew out. If Alex was at that meeting, we were talking a lot about the things that we care a lot about in the Columbia University Libraries. And eBooks came up as a very important topic for us. We've had a long investment, as I mentioned in the video in eBooks. We hired in 2013 an eBook program development librarian for two years. And we were sort of placing a bet that we needed more information about the way in which we are investing our resources. We were buying more and more eBooks. But we were also hearing anecdotally that our users didn't particularly like them. And we were spending a lot of money. And so we said, we need to take a step back here and actually do a deep dive. And so we had a lot of focus groups. Our eBook librarian, Melissa Gertzen, met with faculty, with students all over campus and really had a chance to figure out what people were doing with eBooks. And we found out some really interesting things. They were using eBooks. They weren't reading eBooks. I think that's a really important distinction. And they wanted both. They wanted eBooks for some uses and print books for other uses, which is a tough place for libraries to be when we know that we can't just buy everything in every format. So we learned a lot during that time. And when Jay started talking to us about this project, we saw it as a way to kind of close the loop on some of that user engagement. So one of the things we did is we put a call out after that initial workshop. We put a call out to our PhD students in English, comp lit, and in history. And we asked them, would they come and spend some time with the labs team when they were on campus? And we had a surprisingly good response, I thought. A number of students wanted to come over, sit down with the labs team, look at the prototype, and work through it. And in some of them were users that we had worked with previously. So the idea was, we care about this topic, now we're gonna try to do something about it. And that was a really nice way to continue that user engagement. You also saw in the video that image of the kind of gorilla testing in the Butler Library lobby. That was also really interesting for our users to kind of engage and know that we care about that topic and that we were able to work with them in that way. So I mentioned the eBook librarian. We had, there's a report at the bottom of the screen. But if you just Google eBook program development study, I really recommend this report. I think she did a really great job. So one more point I'll make before we turn it over to our colleagues. And that is that it's very interesting to bring a team like JSTOR Labs into a library to really model a kind of IT culture that's a bit foreign to our libraries, as I think many of you will know.  And that is not how we work in libraries and IT. And so it was very interesting to have this team come into the Columbia libraries. Some of our IT folks worked with the team and I think it modeled a different way of working. And I don't imagine that our IT department is gonna scrap years of ways of doing things and take on this kind of approach. But it does model in small ways how we can begin to do projects. And I think it was so nice to be able to iterate on it and work with the users and be responsive so quickly. I think this is a lesson to us as we think about ways to structure IT culture in our libraries. So with that, we'll turn it over to our colleagues. Thank you. So while we're getting set up, I will give you a little preface. One is that we don't have anything quite so as exciting as a video. But we're also gonna change perspective a little bit in that we're gonna move really from focusing on the readers, not that they are not unimportant, I think the work that's being done at JSTOR Labs is definitely important to all of us who have e-book collections, certainly recognize that. But we're gonna move a little further upstream, if you will, and really focus on some of the needs of authors and really focus on really how we can support authors with new tools around what we've kind of framed as long-form scholarship. So it's the monograph, but it's the monograph as it moves into the digital age. And so, uh-oh. The slides have gone awry. Are they the right ones? Yeah, they're the right ones. I could just say transfer pretty much. Yeah, so all right, well, I will tell you what was on the slides, apologies for that. So there were two Melon grants. One was at Emory University, one at Michigan and Indiana, and both were really looking at the question of kind of the future of the monograph, if you will, moving into the digital era. And the different perspectives were that Michigan and Indiana both have presses. Emory University does not. We have not had a press. And as you can imagine, we do have a lot of authors who do publish monographs on a regular basis. Something we're definitely very vested in. And in both cases, we ended up recommending that we really look to the future to having open access distribution of monographs and also really look at institutional subventions for monographs, so that we can provide some equity, if you will, in the system. Emory not having a press would be considered kind of a free rider, if you will, in the university press system. So part of this is really looking at building a whole new set of tools as we're moving into a digital realm where you may have a long form scholarship, but you're not necessarily talking about something that's based in text. It may be something that's multimodal that really can't even be replicated in a textual monograph that could be represented as an EPUB or an ebook or PDF. And with that, I will turn it over to Charles to talk about some of the work at Michigan. Yeah, so the work of Michigan is sort of focused on the idea of, sort of narrowly focused on the idea of building a new platform for the presentation of the long form scholarship that we publish. And it's under the name of Fulcrum. And this is a hydrophedora based, it's a hydra haired over a fedora repository. It's sort of built on the same infrastructure as our data repository, et cetera. So there's lots of, there's some technological innovation there which is interesting. But what's far more interesting to me is the way in which the idea of platform is something that's much more encompassing around the idea of policies, processes, as well as technology. And this is where the sort of linkage between the Fulcrum project and the project that Lisa is leading, which she's going to talk a little bit more about, really comes into focus. So if we could just forward Lisa. So currently the main type of publishing of sort of different forms of material that we're doing is pretty straight sort of facsimiles of print in e-book form. And this is an example of one of the open access books that we've published this year. In fact, 15% of our list of monographs in 2016 have been published open access through Knowledge Unlatched, through Institutional Subvention, things like that. And in fact, on the JSTOR labs site, you will find 13 of the 50 University of Michigan Press books. So this is what we're sort of doing at the moment. This is a book where you can read for free online. You can buy downloadable versions for Kindle, et cetera. When you read for free on the web, you're actually going to DLXS, which is an old Michigan build platform from over 15 years ago now, which is pretty robust, but it's a fairly flat presentation. But when we start to do contracts with authors with this sort of book in mind, suddenly we start to run into a number of issues around the way our contracts are currently formulated and the realities of an open access environment. So if we could move forward. Fulcrum, as we develop it, is really sort of, it's going to allow us to render these simpler e-books better. But it's really preparing us and the publishers that we're working with for a place where text is not the primary artifact in one of these long form publications. This is a platform optimized for multimedia, for video, audio images, but really increasingly things like 3D visualizations, GIS, things like that, and helping to integrate those in an environment where the key values are the flexibility of Hydra, so the ability to take and give back to the community on various apps that can play things, the durability of the fact that it's built on an institutional repository infrastructure which itself feeds down into things like AP Trust. So really good preservation standards and the discoverability and the fact that it's standards-based and interact with all the major abstractors and indexes. So moving forward, this is the first project on Fulcrum which will really sort of demonstrate this new style of publication. This is a project, the Mid-Republican House from Gabbiee, which is an archeological excavation report. It's the report on a project that was excavated digitally from the very start, so using a lot of digital tools. And one of the primary tools this group used was spatial visualization to try and really give a sense of the excavation as it was happening and allow people to sort of strip away layers and build up the house that they're publishing in this volume. So it has a 3D element coming from the Unity 3D software gaming engine. It has the data set and it has the general narrative that's more familiar. So we don't have a nice video, but we do have, slightly older technology, a lenticular car that you can reconstruct the house with. So they're down at the front if you want them. And also it's an information about Fulcrum. But just to say that this type of publication presents even more in the way of challenges to our contracts and to the various bits of paperwork we have around rights and permissions and roles and responsibilities and all of that stuff. And so one of the things we rapidly realized working on publications like this is that we needed new tools that went beyond technology to really capture all these elements of the author-publisher relationship. And that's what I'll hand over to Lisa for. So as Charles mentioned, really digital publications challenge all of us in a host of ways. They challenge the notion of authorship because seldom is one person really the sole creator of a work like Gabby E. There are often projects that involve a whole host of people with a whole different set of skills. So how do we give credit to those? Kind of who owns what within that? Sometimes these sorts of projects can be very expensive. So how do you kind of plan for that financially? How do you account for that? How do institutional subventions perhaps play into that scenario? How are these things discovered and acquired by libraries? Just because something is open access we all know too well. It doesn't mean that it's automatically in the hands of the people who want it and need it. And then preservation is a really key role here. One of the things I personally think is exciting about the Fulcrum project is it is kind of combining those two aspects of publishing something and also preserving it in a repository. And then versioning is also an issue that comes up. All of us have had the experience of writing something and then coming back to it several months later and thinking, oh, I could have done this or that so much better. And in a print world, once you produce that physical print artifact, you kind of understand it's done. But in a digital world, being done can have an entirely different sort of meaning and connotation. So it's kind of those sets of considerations that really led us to look at a new publishing contract because the current book contracts being used really do not address the questions of institutional subventions. They don't really kind of address some of the aspects of really long form scholarship and they really don't always address open access. So thanks to generous support from the Andrew Mellon Foundation, we had a process by which to really develop what we're calling a model publishing contract. And I will say that is model as an example, not model as in perfect. And so we had a meeting of really a lot of committed folks. They spent literally a day and a half at Emory in the spring talking about contracts. And it really was a much more interesting discussion than what you might think. And it included folks from the University of Michigan, University of Georgia Press, University of Minnesota Press. There were some lawyers in the room, some lawyer librarians in the room. And we really kind of talked through what didn't work with current book contracts and then where we kind of wanted to go. From that we hired an attorney because this really is a contract intended to be adopted by anyone who wants to adopt it so it's not belonging to any particular press. And we have now launched a website which I'll tell you more about with a final draft and we're really looking for public comment from folks. So to delve a little bit more into it, the traditional book contract that we know really deals with kind of the things that you would expect of author delivering a manuscript that will be published on X timeline. And that's perfectly fine. We're not suggesting that that is not a perfectly good way of producing print books but once you've kind of moved into a digital realm where you're talking about open access and Creative Commons licenses, the current contracts don't really accomplish what you really need to do. So what we were looking for as goals was really to have this be kind of a fundamental building block in this transition if you will to publishing digital long form scholarship. It is being released with a Creative Commons zero license that is intentional while the focus has really been on the work of university presses. We think that there is definite utility within digital scholarship centers that are doing digital projects that this contract might help. We're also really focused on being more balanced between the author and the publisher. And we've tried to do that in a couple of ways. One is less legalese. Now there's still some legalese I will tell you but there's less legalese than you commonly see in book contracts. It's also shorter. It is essentially a core contract of four pages with a series of schedules that allow us to accomplish several things. So one of those things is we were talking about authorship and ownership and how you properly give credit being one of the challenges kind of in the digital long form scholarship. And one of the ways we've done that is there is a schedule that specifically is for defining what is the work. Because the work is no longer just delivering a manuscript of X number of words. There needs to be taken into account what are the technical requirements for hosting the work. I mean Charles mentioned the various platforms that University of Michigan has but you can imagine a plethora almost of a combination of open source tools being used, GIS being used, et cetera. So we need to basically prompt the author to think these things through and prompt the press to take that into consideration and then put that down on paper so that everyone understands what the expectations are. Preservation and revision is another area that really kind of fall within defining the work. How is the work going to be distributed? Will it have a Creative Commons license? If so, which one? We make that an option. Really identifying who the various contributors are and what their role was in the project. So this I kind of liken to the movie credits that we've all seen and I joke that I have no idea what a key grip is but I know you need one to make a movie because there's always a key grip listed. So we really do wanna give credit to the various folks who work on making these long form scholarship projects successful with the skills that they bring. Versioning was another thing where we wanted to kind of bring clarity in a few different ways. One is as with any publication, the content that's not original to the author needs to be identified. We tried to be a little more flexible there in accommodating for use, things used with Creative Commons licenses. What is the publication schedule and setting correct expectations between both the author and the press around realistically when milestones can be accomplished and what actually constitutes a new version. So the folks at Minnesota with their manifold project are really exploring iterative publishing and they were very helpful in giving input. And what we've put as a default that can be changed within the contract is that a new addition or new version is 30% new content. But of course that could be adjusted depending on circumstances but it is something that particularly as we move into the digital realm we need to be aware of. And then again, financial planning. So who are the other stakeholders? If you're getting an institutional subvention or a subvention that's being provided by a foundation, do they have requirements? Do they want to be listed in the acknowledgments? If so, then the author needs to let the press know that. Revenue sharing and royalties. So the first example that Charles showed of Just Vibrations, which is an open access book that is also published in print and sold. So we wanted to be able to accommodate those kinds of situations where you do have open access distribution but you also have royalties that may be paid back to the author. And so we have ways of accomplishing that as well. And then we also really wanted to clarify the marketing efforts of both the publisher and the author. Authors can be the best advocates for their work. And so this was a way to kind of give the press a chance to show what they're going to be doing with this work and the author to kind of step up to the plate, if you will, and show what they are going to be doing to market their work. I will say, particularly with this financial planning, one of the key pieces of this is the digital monograph costing tool that Nancy Marin and other colleagues have worked on. Also another Mellon funded project. And there is now the tool available on the AAUP website. So we really kind of looked at that as kind of a key essential tool that fed into the model publishing contract and into the greater transition that we've been talking about. So the model publishing contract with two examples, just vibrations being one of the things we use kind of as an example. And then a purely digital monograph that had no print component is the other example. That's available at this website and we're really seeking feedback from folks who are librarians, publishers, technologists to really kind of look through the contract, tell us what works, what doesn't work, language you found helpful, language you found confusing. But through these various projects, I think we have really discovered that the value that humanists place in the long form argument is very much there, is very much valued by humanities authors. And that an evolution to kind of leveraging, if you will, the advantages of digital distribution to be able to do multimodal work. The Gabby E. Project is a primary example of something that could not be done in text in nearly the same way. But we need a whole new set of partnerships and tools to kind of help with this transition, if you will. So we're hoping that the model publishing contract is one of those tools. And so these are the various URLs of the things that we have mentioned. And with that, we're gonna open it up for questions for basically all of us on the panel. So thank you.