There's a reason why Xtrades is currently the fastest growing application on the market for sharing financial ideas. With over $2.5 million paid in the last two years to contributors, users are flocking to see what trades the top traders on the leaderboard are sharing in real time. If you're looking to grow your reputation as a trader on the internet or discuss your trading ideas with other reputable investors, click the link below and get connected with a trading mentor today, completely free of charge. Alright, what's up everybody? This is Alex from Xtrades and welcome back to another weekly trade ideas list. I hope everybody had a wonderful trading week and also survived the FOMC last week. It was a very wild week in the stock market. Powell basically reiterated potential rate cuts in early 2024, even though he was talking about holding higher for longer, even though last meeting they said they were not even thinking about rate cuts, it seems like they basically just caved in and the market loved it. And last week, the trade ideas list was pretty good. We had a couple long setups. We had Tesla that could go either way. It actually triggered some downside early in the week. Had a nice about 3% dump to the downside once it broke out the wedge. And then also it actually broke out of the wedge later in the week to the upside. So it paid both ways pretty much for anybody. There's people in the chat who had their own call outs for Tesla calls that did pretty good and as well as the puts did really good at the beginning of the week. Netflix did really good off the demand zone. We were looking at SoFi had a killer week. As soon as the FOMC came out and people thought they could be cutting rates, SoFi had a killer couple days, ran up almost like 15%, broke out of the channel and had everything we were looking for for that setup. So hopefully we have a couple more setups this week that are going to be just as good. But before we get into that, we're going to go ahead and get into the economic calendar real quick. So I'd say there's really no market moving data, probably until existing home sales here on Wednesday. That always has the potential to move the market, so pay attention to that. And then Thursday, the most important is going to be the GDP obviously. You can also pay attention to the Philadelphia Fed Manufacturing Survey. This can move the market depending on how extreme the reading is. And then Friday is going to be the most important and it's going to be personal income, personal spending in the PCE index, which is the Fed's preferred inflation gauge over the CPI. They prefer this over the CPI and we will want to see that continuing trend of inflation cooling. That way we know their hiking is working and that ray hikes are doing their job as they're supposed to. And then we also have new home sales and consumer sentiment as well. I would say consumer sentiment is probably the second most important. This always can move the market pretty big. Just depends on the reading and on to the seasonality. I actually have a new little tool that we can start using here similar to the season acts that we used to use in past videos until they cut me off from the free edition. I now found a new tool that's almost just as good and also has lots of years where the data that you can go up to, you can raise this bar all the way up to 25 years if you want. You can go back down to 10 if you want more recent trends, which is probably what we're going to be looking at. Want to pay attention to the most recent seasonality. We want to see what the market's following lately rather than all throughout history. Obviously, the longer term trends are good to pay attention to as well. But for the short term and the medium term, I feel like these 10 years to 15 year readings are probably pretty good for seasonality if you're trying to get readings on a week to week basis. So I got the spy here. We're looking at a potential dip from the 18th to the 22nd, which is our trading week. And then that goes up to Friday, the 22nd, and we do have a small little dip here, nothing crazy. And then after this week, it's kind of when we start seeing that Santa Claus rally and you see that run up into the end of the year historically. So this little potential dip that kind of averages throughout these last 10 years, it kind of makes sense because we did just have that monthly expiration in December and there is potentially a window of weakness after those options expiration. After so much money has expired, you have a lot of rebalancing the week after and that kind of can bring some weakness and you will see that window of weakness potentially. So pay attention to that. We do have a potential dip, nothing crazy. You can see that just a little bit of a drawdown. Obviously, we didn't follow it last week or the week before and you kind of do have a little downtrend here that we did not follow. We actually had upside. So just keep that in mind. Seasonality is not going to be 100% accurate all the time. Conditions are different each year and sometimes it's just not going to follow. So but keep in mind that options expiration, window of weakness week after, you will see some rebalancing sometimes and you can see some weakness in some names and honestly, the indexes look like they are ready for some type of cool down, whether it's consolidation or a short-term pullback. But to be fair, they have looked like they've been ready for that for a while now. So got to be careful with that. Be careful shorting over the 9 and 21 EMA combo on the indexes. It's a good way to get wrecked. So just be careful. Keep this in mind. Looks like we do get a little dip potentially, but nothing crazy. And onto the setups. This first one we're looking at CVX to the upside. I like this one for calls. You can kind of see it broke out of this little downtrend thing that I had going right here. You can see it was like a test one, test two, test three rejection. Finally broke out of that little wedge. Also had this 140 low hold and that 140 comes from right here. I'm sorry that 140 comes from right here and held it up pretty good. They actually sold off pretty aggressively from their last earners report and it's kind of just been down and chopping since. But now starting to get some momentum after the FOMC.   And it looks like energy is trying to catch a bit now. So I wanted to pay attention to that. Obviously, this might need to get over 150 flat. So this 140 990s, you could just round that up to 150. That's coming from right here. It's also support right here. Need to get over that and you can start filling this gap up. There's also this one day 50 EMA, which is this line right here. We need to get over that. And then you can probably get up to the one day 200 EMA overall. MACD is positive. That's a good sign. Obviously, it's been positive for a while. So it looks like the momentum indicators were able to hold despite any weakness down here. So just pay attention to that. Look for a move over 150 maybe, that 150 move and one day close over 150. You can start entering this gap. That could be your confirmation to fill up all the sale amounts that's right here. You really don't have any supply until way up here. You got a one day supply candle right here. This is a rally-based drop supply zone, simple as that. You got the rally-based drop, big sale imbalance right here. So that's a pretty clear zone. And you got another gap right here as well. So lots of sale imbalance, lots of gaps to fill for energy or at least CVX here. I'm not sure about the Exile and the Exom. I'm guessing they have probably a pretty similar pattern since CVX and Exom do make up the whole ETF, basically at least half of it. And a lot of energy, they kind of move the same or similar. So just pay attention to the energy sector if you can. Exile had a nice little bullish wedge that was breaking out of as well. So, but I really like CVX here, holding up supports, broke out of your downtrend, needs to get over 150 and you got a couple of gaps to fill. So CVX, looking at calls, make sure you buy time on your contracts. I say it every single week if you're gonna swing trade, 30 plus days of expiration at minimum, maybe even go longer. Day trades really stick to like probably three to seven days of expiration at least, but you need to be flat by the end of the day. Do not swing three to seven days of expiration overnight. It's just risky. Unless you're way deep in the money with way less risk, way less money up front, because otherwise that will put you at risk. And any drawdown three to seven days of expiration is just not good overnight. So that's for CVX, looking at calls, be patient. And onto number two, we got SQ here. So this is pretty clear. You got to test one, test two. This is a test three downtrend line. So now I want to look for some potential downside. Just want to look at put scalps on this. Obviously if you wanted to do puts, it's a little bit riskier. You got seasonality going against you. Maybe you could just trade down to this. So if you needed to hold overnight to get to this point at 70, 67, that could work as well. I feel like eventually it might try to come down and test this one day nine EMA. And it just does it every time, right? It goes up, it consolidates or drops for a little bit and then it taps the nine and then it goes higher and it just keeps going and going and going. So eventually the nine is going to get tapped. That's a given. Whether it's going to be consolidating and it's going to meet all the way up here because that nine EMA lags, whether it's going to be after a big drop and test it all the way down here, I have no idea, but the nine EMA will get tested eventually. So that's just one thing to keep in mind as well as you have this big downtrend line. If we even looked at it on the one week timeframe, you had a pretty clear downtrend test right here, test one, test two, test three. You don't have a confirmed breakout yet and you don't have a confirmed one week bar rejecting here yet either. So you want to keep that in mind. This one day bar, this little short term one is the only one you have and it's really not a bearish bar at all just because it's red doesn't mean it's indicating a reversal. It's not indicating really any strong cell pressure. This is more of a consolidation candle. So one thing you will want to see is SQ taking out the Friday's lows. It's going to be at 73.66. Keep it simple if it breaks under that. I could probably start filling up these one day bars right here and this little short term gap. I'm not going to market it just because it's pretty small but that's a gap right there from 70.50s to 71.70s. So SQ looking at puts for day trading, probably mostly scalping. I'm not sure how I feel about swinging puts on this especially with the market pretty much expecting rate cuts in early 2024. A lot of these zombie companies, a lot of these beat down tech names and crypto names, all the junk pretty much they got slapped in 2022 is getting bit up, short squeezed, everything you could think of. So that's why with these names you might just want to stick to like scalping puts if you're going to try to time tops or catch reversals at the top. You got to be thinking more short term just in case it ends up holding the trend which is obviously pretty common lately. You just got it holding the nine and 21 combo here and just keeps marching higher. So once it gets back down to the nine you want to be careful. So that's for SQ looking at puts mostly scalps way for it to get under 73.66. So it takes out that Friday's low. That's a good chance I can start feeling back down again. And for our last individual ticker I only have three this week because there really wasn't that many setups that I liked. Lots of the market already broke out. Lots of stuff is already hitting highs there just really wasn't many trend breaks or downtrend lines being tested. No uptrend lines really being tested. A lot of stuff was just already at highs or way over their moving averages. So longs really weren't that good. There's a couple high growth names. They kind of like zombie companies that are at its low like a lot of solar names like SPWR that's at lows. It looks pretty good for maybe further out calls like at least like four to six months of expiration for calls. Maybe even leaps which are a year or longer. You can look at that. So check out SWR or really any of the beat down solar names. Those kind of do good in a low rate environments and with the market trying to price in rate cuts a lot of them are trying to do good now. And I'm not even saying like the rate cuts are already here or anything. It's impossible to fit funds ready to stay exactly where it was they paused. But you do have the bond yields trying to come down and that's basically the market trying to price in any rate cuts in the future. Market is very forward looking. So that's why a lot of these zombie companies are doing really good. Especially high growth names like Uber has been doing really good. Solar has been doing good. Beat down EV names. China names had a bounce last week. We actually had Baba calls a couple weeks ago and I finally got to close those on Friday for a nice game. So congrats if you were able to be patient on that. Just because these setups maybe don't play out the week after we look at them. Keep it on watch if it hasn't done anything yet. Cause a lot of them will come back to life. Like B&Y that wedge we were looking at a couple weeks ago that just hit our price target from a couple weeks ago last week. So and then Baba same thing as well. It just needed to chop around at lows for a little bit. And then Friday just had a big move up and we were able to cash out on those calls and it just took a couple weeks. So be patient. You know, don't think bad of me just because the setup doesn't play out the first week. That's all I'm saying. But Uber here, we do have a clear trend break even if you went down to the four hour. Same thing. You got test one, test two, test three, test four. Try to test five, broke down, came up. Try to get back over it and then close back outside of it again on Friday. It actually had a big dump towards the end of the day. Really big volume and it closed at 364 million volumes. This is the biggest volume it's had in a really long time. Specifically this bar, somebody looks like traded 201 million shares just in this after hour period right here right at the close. Really big volume, likely due to options, expiration and also it's joining the S&P 500 I believe. So there's probably a lot of rebalancing right there. That's why you got this big volume spike.  It could be a market on close order. Somebody buying at the market price at close, we have no idea. But this is huge rebalancing volume. So definitely worth noting. Overall, you still do have it holding your nine EMA, nine to 21 combos. So just keep that in mind. It will need to get under the one day nine EMA to even get to the 21. So if you want to wait for a break under that, that's understandable. Wait for the MACD to kind of go negative as well. You do have the four hour MACD going negative right here. So that's a good sign. But Uber I'm looking at puts same thing kind of with SQ. I'm not exactly sure about a swing here but definitely looking at scalps, day trades, something I can get in and get out, you know, at least by the end of the day. Uber, I kind of do like the downside here better for a swing trade than SQ just because SQ still has the potential to break out. You got Uber actually breaking a trend here. So that could be a good further out swing, I guess. You just got to be careful in these coming months because this December to January, the markets can get a little bit crazy seasonality wise. And then you also have this all-time high resistance here at 64. And that's kind of what we rejected off of on Friday. Obviously didn't hit it to the penny, but general area, obviously it was good enough for everybody and they kind of brought it down towards the end of the day. So Uber looking at puts, keep this all-time high resistance in mind, trend breaking, make sure it breaks under the one day nine EMA, keep it simple and just be patient. All right, not to the indexes. So last week, we were just focused on this 462. We really needed to see a breakout over that for me to even feel bullish about getting up to any higher price targets. And honestly, 462 was probably the highest I was looking for just because I had no idea how it would react to resistance, at least until you got a one day close over that, which we did on Tuesday. And that kind of led you into this blast off up into 473, which was actually that peak that we marked the last time all the way from over here. So we need to get over that 462 to get to 473. That was kind of your clear path. This is your free space right here, which is interesting because we actually did hit that 473. So that's amazing. Obviously, I didn't capitalize off of it. I did not go long the spy up here. We had our other stuff. We had like Netflix and SoFi and Baba and other stuff that were a little bit more discounted and not at highs or anything. So I did not long into this, but it's cool to see the technicals played out. And we just needed that 462 break in order to get higher. And you can see there's no big rejection on 462 either and no close back under the 459.44 or the 460 level that bears needed. So this uptrend is just holding very good. You got your nine holding very cleanly. No breaks on the nine except for this one day and it gapped up the next day reclaimed and it's just been up ever since. One thing we did get was some slight resistance at the 473, which ironically is also this 1.272 Fibonacci. So not only was it the 473 all the way back from 2022 or 2021, it was also this 1.272 Fibonacci from this measurement right here. So from this peak right here down to this low, the 1.272 extension or the first breakout target was also 473.10. So that just aligned perfectly with the old resistance that we marked last week right here, this level matched right up with the actual Fibonacci breakout target, which is amazing. Obviously about a 10 cent difference, but still it was very cool to see that. That's exactly where price rejected. If you look at it in the short term, you can see there's a double top at the 473. It's also the 1.272 Fib extension and it's just been kind of going down since, chopping down nothing too crazy, but there was some downside. So now it's hard for me to see past 473 and obviously it was hard for me to see past 462 as well, but I can't really project any higher until it, you know, had a one day close over 473, then you can start thinking all-time highs. Otherwise, this is kind of just basing out. You got your one day nine e-mail down here. It might need to come back down to this and test for dip buyers to show back up and take it back up to the 473 level. Obviously it could just consolidate here as well. I honestly, I would recommend looking at other things other than the indexes, maybe even look at the QQQ. The QQQ probably has a better volatility right now than the SPY.  So we need more volatility, especially if you're gonna day trade the indexes. Obviously late-day SPX plays and stuff like that. That's gonna have volatility regardless. But in terms of swing trades and stuff like that, there's really not enough volatility right now to really aim for more upside with further out contracts. And also there's not enough volatility to really aim for downside right now and further out contracts as well. So swings are kind of tough right here at the moment because you have no signal to the downside. And also you already hit the big breakout target from 473, or I'm sorry, from 462 up to 473. So gotta be careful with that. Like I said, if it can come back down to maybe 462 one day, look for a dip buy down there. Or simply if it can come back to the one day 90 May, you can look at that as well. Obviously I'm still open for day trades on the SPY. I trade the SPY and the QQQ regardless of how high it is. Just depends on how the camera lipivots are looking. And maybe go watch my video on camera lipivots and you can figure out how to day trade with those. But in terms of swings, I really don't like anything up here. Maybe a potential pullback into the nine and you can look for a dip buy down there. But that's about it. So you have 462 up to 473. That's the current trading range. There's really no other resistance or support or anything. Obviously you could watch these short-term levels. Like you could watch this 468. That's the level to watch if it stays above that. The structure is still kind of bullish with a max low of 468.40s. So this little 468.80s to 468.40s is just short-term. Needs to hold above that probably because if it breaks down that, you got these big buy imbalance candles that can fail down. So make sure it stays over this short-term. If you want to see a run back up to 473, that's kind of the short-term thing I could see right now at the moment, but that's about it. So that's for spot. Just be careful up here. Pay attention to your one day nine moving average as well as your one day 21 EMA, which is all right here. Still trending over it. Once price pulls into it, it could be worth a watch for some upside. All right, and on to the QQQ. So last week we were just focused on the 388 as usual. This 388 was kind of our focus practically the past couple of weeks. I think we had a fib drawing there as well. I could probably re-add it here. Let me get rid of this low and this high real quick. You can go ahead and add a Fibonacci. So start here, go down to the low. That's the 388 we were focused on. And then add a Max 1.272 Fibonacci target at 439 right there. And I mentioned that I need to get over that 394 in order to get to that. And I finally was able to do that on Monday first thing. So we had a pretty bullish close on Friday, but the max upside I could only see for the time being was at 394, which is the strong resistance right here. And I was able to close over that on Monday. And you can see once it closed over that Monday, that was your straight shot up to the 1.272 extension target at the 400s. And then I went even more after that once FOMC came out. But honestly, this 1.272 is the highest I could see. That was it. So you had 394 up to 400. That was my personal high at 400 and it cleared that. And now we're basically at the all-time high level. So the all-time high is at 408.71. That's this peak right here. So that's probably the max I could see right now. Now that we've gotten over that 400 and we started closing over that, if it wanted to go higher, that's probably the most I would look for. Cause once it got up to that 408, it probably could see some big resistance on the short term at least. But we are kind of just clearing resistance as guys. I mean, it's not really seeing any resistance once we get up to any major levels on anything. Other than the short term, you might see a quick pullback, but we're not seeing anything major. Even with the closes under 388, that wasn't enough to fill this little gap right here. And it's just been going up ever since. But I really would be cautious at this 408. I mean, this is a huge level guys. It's all-time highs and it's a big psychological level and it could be really hard to clear now that we're actually there or actually near it. So just be careful of that. That's the max upside I could see. If it breaks over the 408, we'll go over some Fibonacci extension targets next week, but I'm not going to draw that out right now because we haven't broken over that yet. But if we do get that one week close or one day close over 408s, I'll go ahead and do a measurement from this high down to this. And then we can get some 1.272 extension targets, which is going to be way higher. So basically your trading range right now is just 438, which is the 1.272 Fib or this low right here that bounds perfectly. So your 1.272, oh, it's all-time high of 408. That's the trading range currently. So max high I could see the 408s. And max low I could see is obviously the 400s. I would really like to see it come back down to 400. I would definitely buy right here for a quick scalp or something. And then if it can come up to the 408s or near it, I'd be willing to try and put scalp there as well. So I want to see it pulling into one of those levels first before trying to do any day trades or anything. But obviously it just depends on camera low pivots as well. So the camera low pivots can look way different than one day levels and they work just as good. So we'll see how that goes. But these big crucial levels, 408s and 400s, I want to see something there. I really like to go long once it gets down here just for a quick day trade or go short or go with puts once it gets up to 408s. Simple as that. This mid range is just tough to go long or go short because you really don't have confirmation of anything. And you really don't have any major level here. I mean, I guess you could maybe mark this as a potential resistance. This 406 30s kind of rejected off of it on Friday, little bit towards the end of the day. And then a big pullback right here about 1%, nothing crazy, but compared to the pullbacks we've had lately, this is a pretty decent one. So watch that 406 30 as well. So that's another level to watch actually because you got one reaction right here, a small reaction right here. So it's worth noting. No idea if it will continue off of this, but just pay attention to the 406 30, the 408 71, which is the all time high. And then you want to see it pulling into support eventually, hopefully at the 400s. And last but not least, we are going on to DeVix. So last week we're just focused on the 1273 as usual. We did get that close under 1273 on Friday, which gave you that signal that it would go down to the 2019 or the 2020 blows eventually. I kind of just mentioned it could just slow grind down, which is kind of what we've been doing. Cause you can see we have one big bar and then we've just been consolidating ever since. So we had this one big bar on Tuesday and that was it. And we also hit loads of 1180. So it was about half a point away from hitting the actual 2019 to 2020 blows at 1142. It's kind of still just making its way down there. You can see that the nine EMA that we've been kind of going over the last couple of weeks is still just trending below it. Still trending under your 21. You don't have a single one day close over this, except for this one attempt right here and also one right here. They just had to follow through afterwards. No convincing close over the one day nine EMA and no convincing close over the one day 21 EMA. No convincing close over the 1273 resistance either, except for maybe this one bar right here that could have had some potential. That's about it. You can see actually the VIX came all the way up to 1273 right here, wicked off of it. And it looks like we just stayed even lower after that. So it rejected off this. See it hit the 1273 way lower and it's been staying under since. So this 1273 is a clear level you want to pay attention to and exactly why I marked these big volatility extreme points. Anything with a circle, anything with an arrow is a volatility extreme point you want to pay attention to. So it's the same thing as this week. There's really no change here. This was last Friday's close. This is this Friday's close. I have the same outlook training under the nine and 21. Obviously I kind of still have to expect volatility to go lower here at least to the 2019 to 2020 lows and maximum until it can start getting back over 1273 plus this one day nine EMA. If it can start getting over both of those, you already know my opinion, volatility could see a short term spike. So that's about it. Need to wait for that close over 1273 plus the one day nine EMA. Otherwise it looks like it's just gonna keep grinding lower down to the 2020 lows. So as simple as that guys, just pay attention to it. Fix is kind of at a stall out point here, not really going too much lower, also not going higher. You can see it hasn't really gone anywhere, which are perfect conditions for a meltup in the stock market, which we've seen on the spy, we've seen on the QQQ. You have volatility going nowhere, not really breaking down aggressively, not going up aggressively either. This is perfect conditions for a meltup. That's exactly what it's been doing. So you want to see it getting back over those volatility extreme points or getting down to a volatility extreme point, which is gonna be 2019 of 2020 lows or getting back over 1273, simple as that. So hope you guys enjoyed this video. Make sure you like, comment, and subscribe to our Xtrees YouTube channel. I'm gonna get this chopped up, sent out. I love you guys and I'm out.


Hello and welcome to the Grand Line Review, your source for everything one piece. Today we have a review of episode 881, Going Into Action, the Implaceable New Admiral of the Fleet, Sakazuki. And while this episode happened, I mean it was another 15 minutes at least of solid shoehorned Toei filler, but people have a tendency to think I'm overly negative when reviewing episodes like this, so we're going to start with the like 5 minutes of actual new material that was in the episode and there are actually some nice positives. For example, I really enjoyed the opening shot of the camera panning over the waves as we approached New Marineford. I love the look of the water and the whole thing is just a very nice and simple piece of animation, simple but ever so effective and a really strong way to start out the episode. And then things continue pretty positively as well as I really like the establishing shots of New Marineford. They very much conveyed the scale of the facility quite well and definitely made me feel like it was a huge upgrade from the original Marineford, which is an impression I did not quite get from the manga, like it was an impressive two page spread, but it left me with the overall impression that the facility was equal to or even smaller in scale than the original. So well done to the anime for that. Then we get into the Sakazuki shouting stuff though and I've mentioned this in a prior review, but it really is as if the anime is paying no attention whatsoever to the emotional intention behind a scene or a performance.  So just in case anyone has forgotten, this is Fleet Admiral Sakazuki. He murdered Luffy's brother, Port Gasty Ace, and is the figurehead of absolute justice, but you would be forgiven for thinking that this image here is of a special needs patient who has somehow escaped his carer at a cosplay convention. Like just what are his eyes even doing? Nobody's eyes do that. I mean unless you're dragging from the last episode, there's one exception, and I mean somebody very correctly pointed out in the comments of a previous episode review that this is not technically a criticism of the animation, but it should be directed as a criticism to the art. So yeah, fair point, and look, this art is abysmal. This man is supposed to be one of the most feared characters in the entire series, and he is consistently being turned into a bit of a joke. Speaking of jokes though, and we'll have some more negativity here, but it will be followed by something nice and positive, so just stay with me. But the part where Sengoku appears from behind the wall didn't really land for me, and I believe that's entirely to do with the way that it was presented. You see in the manga there is a small panel at the bottom of a page featuring Sakazuki looking really angry in the foreground, with T-Bone looking shocked in the mid-ground, and then way in the background we have this tiny drawing of Sengoku and his goat just popping in to say, you know, it's not so easy being the fleet admiral, is it? And this was quite hilarious because it's amazing to see a character of his renown just popping in for a quick quip at Sakazuki, along with his goat friend, too, and then we just don't see any more of him. I'm not saying it was the most hilarious thing that's ever happened in the series, but it was a cute little moment, and now what this episode did was turn that cute little moment into an elongated and heavily focused on part of the scene. And sadly, that cute little joking moment just isn't quite able to hold its weight when it is the sole focus of a shot.  That was pretty funny, so I guess it's just the nature of adaptation. We lost the joke in one way, but gained the joke in another, so I suppose that's balanced. But this brings us to my favorite part of the episode, really, which is the filler scene featuring Sengoku, and the reason why I love it so much is simply because of the fact that he is feeding Luffy's wanted poster to his goat. It's hard to explain why, but this moment it just works for me. I mean, if we're going to make Sengoku all introspective and set him up for a flashback, this is a wonderful vehicle to do so, because it gives you that excuse to bring up Luffy with something visually interesting happening on screen at the time and not have everything happen in a completely unnatural way. Although that whole unnatural thing did also happen during this episode when it was Sakazuki's turn for a flashback. It just felt weird and out of character for someone with his fiery disposition to take the time out of his busy day to stare out into the sea and essentially begin lamenting his failures. Like, are we really talking about the same character who reprimanded Kobe for wasting valuable seconds on the battlefield? Sakazuki is the kind of guy who moves on and takes action constantly, striving to implement his ideal justice. He isn't the kind of dude who just stands there and says brooding things like, on that day two years ago, it was supposed to end. We of the Navy thought we ended it. This poor antagonist just gets so watered down, quite literally in this shot actually, see the water effect everywhere. And to be frank, that's kind of the episode. Talking in an office for five minutes and then everything else is a rehash of Impel Down and some of Marineford. In fact, I think the biggest slap in the face of this episode was that it ended mid-recap. Like, the to be continued screen is generally meant to come after some sort of dramatic episode ending, but instead here it comes as Luffy is falling onto the battlefield that we saw 10 years ago or so I think. There's no excitement here and I mean even if you're a newer viewer, which is actually very fun and quite difficult to say, newer viewer, newer viewer, newer viewer, there's no tension in this because earlier on in the episode, we had a flashback of how Ace died. So it's not as if you'll be sitting there going, oh, I wonder if Luffy will be able to save Ace in this flashback. It's more like, oh, I wonder how much of this flashback I'm going to need to watch before I get to the bit that they already showed in the beginning. It just shows a complete and utter disregard for creating a solid product because even if you subscribe to the argument that Toei Shueisha and Fuji TV are doing this to inform newer fans that might not have been following the series for so long, rather than using this past animation to extend this series into all eternity and suck every yen that they can out of the children's companies advertising to their audience, they're still doing it horribly. And then there's the other argument of no, we need filler because it allows the manga to get further ahead of the anime. And no, we don't. We just need a business model that isn't from the middle ages. Make One Piece Seasonal. It works for every other modern property of Weekly Shonen Jump and the increased episode quality might even allow One Piece to get a primetime TV slot again, which means significantly more money from much higher paying advertisers, rather than airing on Sunday mornings and relying on whatever crap is being sold to children. In any case, my feelings are more or less summed up with the following comment. Next time on One Piece, we recap the recap, so you can recap while you recap. And that pretty much does it for episode 881. If you enjoyed this video and the content this channel produced in general, then please do consider donating to the Grand Line Review Patreon because the support of all of your amazing people is what continues to make this channel possible. Also do check out my Teespring store for your instant shirts, witties, and other miscellaneous items, with the proceeds going directly to support the channel as well. And if you'd like to join the fun at any time then please do head over to my Discord server where a wide array of shenanigans takes place on a daily basis. And finally please do comment with your thoughts on the chapter. This has been the Grand Line Review and I'll see you next time.


Hey guys, welcome back to my YouTube channel. I'm continuing this week my adventures in low-fat eating, just ordered for the first time in my entire life. I exaggerate not. Sushi, this is sushi place in downtown Jerusalem and I'm just going to show you guys what is a cuisine that is naturally low in fat because there's all these like low-fat products like seitan and all these little things, I thought it made a lot more sense to just find a culture that naturally eats low-fat food and then like cook a lot of that food. I think Japanese was people's main recommendation so therefore I decided sushi would be a good evening meal tonight so I just ordered myself a sushi bowl here and I'm going to see how this tastes. Hey here's what we've got going on here, we've got some salmon, some sweet potato and this place also if you're in Jerusalem looking for sushi or Japanese food they have a sahi dry beer which I can confirm is pretty good. If you're doing low-fat you've got to live a little as well. And also we're having a sushi bowl that's here also really good. That's pretty good though this is my second time eating fish today, earlier meal was a tuna sandwich which was also really good and 5 grams fat per 100 grams so it's definitely possible to eat low-fat instead of getting the usual food I would falafel, shawarma, hamburgers, just go for something like a light sandwich, read the nutritional label and if it's low-fat enough. So far another successful day in my low-fat eating adventure here in Jerusalem, that sushi place check it out, good as sushi Japanese food. My wife and I are going down through Jerusalem now and looking at restaurant menus and trying to just season through different eyes of like what's low-fat, I still think there are good options and it's fun, that was a really good meal and I feel great. So this is fun discovering all these new ways to eat here in Jerusalem, subscribe for more videos.


Thank you very much Nancy and thanks. Thank you very much to the organizers it's a great honor for me to be here today and Thank you everyone for coming to listen to what I have to say Okay, so as Nancy said I would like to talk about savings and wealth inequality and I would like to start from some very important open questions So first of all why are some people rich while some others are poor and others are in the middle? What to what extent can the government affect inequality and what instruments should they use? so In very important stepping stone to answer these questions and many others is to understand why people save The goal of this talk is to understand why people save and how this saving behavior leads to wealth inequality So let me give you a road map of this talk The first thing I would like to do is to provide some basic facts. This is not a seminar about Empirical evidence because I only have about 50 minutes So I just would like to give some basic facts to get all of us on the same page Then I would like to talk about the basic Beauty hug it a yagari more or ugly model that I will call beauty for short from now on Which is the building block of many many modders of wealth inequality And I would like to try to explain what are its main mechanisms and what it can and cannot deliver and why Then I would like to talk about richer models that I've already been developed and taken to data I Would like to talk about what I think we have learned so far from this body of literature And then I would like to conclude with what needs to be done and what I think are very important avenues for research both from the standpoint of topics, but also I will conclude with some methodological thoughts So let's get started with the basic facts as I said this will be a very short review First of all, I'm sure nobody here ignores that wealth and earnings are very unequally Distributed with a lot of poor people and a very thick tale of richer people both in earnings and wealth But wealth is much more concentrated than earnings So for the talk I will talk about US data, but these facts hold across many countries in this table You see wealth from the 1989 Soviet consumer finances data. The reason I take this period I will talk a little bit about changes over time But is that many of the models will be calibrated to this period if you look at the richest 1% About 20 30 percent of total net worth is held by various very small group of people And if you look at the richest 5 percent over half of total net worth is held by these people If you look at earnings and here report the distribution of earnings for working-age people I don't want to include people who are not working For wealth it doesn't matter a whole lot because the distribution of wealth is almost the same across the working age And the whole population So if you look at earnings and the richest 1% makes 6% which is still a concentrated distribution But much less than the distribution of wealth So one important question is going to be what kind of saving behavior or forces in the model give rise from your Concentration of earnings to the one we observe for wealth an important point that You know many people have discussed is that if anything since 1989 things have become more concentrated And so the puzzle certainly has not gone away The other important piece of evidence that I would like to mention is that Rich people you know consistently with these facts not only have high income and earnings They also have a high saving rate. Okay, so they save a larger proportion of their flows of earnings And this happens both before retirement and after retirement so I Just wanted to flash this light. There are many. This is just a partial list There are many valuable contributions about the facts and but I want to focus on the models today Okay, so let's talk about the big the building block the the beauty model of wealth inequality It's a characterized by two very important pieces one is preferences Here I'm picking the version of a basic beauty model in the life cycle But if you want to think about the infinitely lived one, you just have to let he go to infinity Okay So the person the household maximizes expected the future utility from consumption in all future periods Because I do the life cycle. There is a survival Probability there is a random variable s which is one if you're alive and zero if you're not alive and its distribution Evolves over age to be consistent with life expectancy and you derive utility from consumption The other important piece is the budget constraint so here the first important assumption is that there is one asset and it has a risk-free rate of return are and You consume these are your accrued Assets and returns and this is where the uncertainty comes from. Okay, so this is your earnings it's a stochastic process and What people do here? Even if they are identical ex ante exposed they are hit by earning shocks. They experience different earning histories And therefore they are exposed to terogeneous. Okay, so what we typically do is we close the model Computing either a steady state in which there is a constant distribution of people over state variables and people's Circle through these are states and people typically are impatient because to have a finite support people are impatient and Circle through this distribution of wealth So what I would like to do is to discuss what kind of saving behavior this kind of models deliver So here this is the saving rate by age and wealth for a median earning person And I'm going to do it by age because I'm using the life cycle version So here you have current assets and you can see that the black line is the one for a 25 year old If the 25 year old has little assets, they have positive savings But for these given earnings level as assets increase the saving rate goes to zero and actually becomes negative Okay, as people age the retirement saving motive kicks in so these saving Rates move to the right and the crossing point is higher But the bottom line is that when your health gets to be quite high your saving rate becomes a negative Okay, so the precautionary saving behavior in these kind of models is a self-insurance against earnings risk against longevity risk and then there is savings for retirement and The important idea is that there is this buffer stock of wealth Okay, so once you reach this buffer stock of wealth your savings turn negative and you stop saving and this is what is really in contrast with the Empirical evidence Especially in the US there is strong empirical evidence that people when they're very rich they keep saving at a very high rate Okay, so this is the intuition why in the beauty model and I will show you some tables later It's hard to get the very rich people because when you get rich you stop saving and you don't get truly rich okay so What are the limitations well first of all? The saving behavior is counterfactual compared to the data and second these kind of models don't generate very rich people In addition, I think there is something else These are very simple models that miss a lot of risks or other elements other saving motives And so we might miss or mischaracterize even the savings of people. We think we understand Right, not only it's clear that we are missing the rich But it's possible that we think we understand the middle income or the poor why we really don't because we mis-specified the environment And the reasons why people save is very important. So let me give you an example To try to drive this point home that it's not that we don't understand the rich It's not clear that we understand the saving behavior of other people or at least all of their main saving motives Suppose as in reality that at least in the United States Out-of-pocket medical costs and long-term care costs are a significant people risk that people face especially during retirement So what is going to happen is that the people who have low lifetimes are insured by the government through mean tested programs But upper middle income people we really face a lot of medical expense and long-term care risk So if you abstract from these risk in the models typically what we do we try to match our Average or total or some measure of assets because we want to match the resources and the economy So we are going to typically going to assume these people are very patient But then you when you evaluate government insurance What you're going to have you evaluate government insurance on people who are very patient as opposed to people who save a lot Because they face a lot of risk. So you are going to get Evaluations about these government programs that are very misguided because you are misunderstanding the savings of a lot of people So for going from the standard beauty model, I would like to talk about six important ingredients There will be others But I need to make some choices and I will try to be clear about why I think that these six ingredients are important So I would like to do You have seen these equations before and I would like to show you what are the new pieces Okay, so the first one is when we introduce bequest motives and that transmission of human capital across generations Well when you introduce bequest motives you add To the utility function some value from the assets that you leave to future generations Okay, so you are changing preferences The other thing that you do is at some point people receive this bequest when their parent dies and If you allow for transmission of human capital one very easy reduced form way to do it is to link the earnings of parents and children, okay So the second kind of story that I would like to discuss is Eterogeneous preferences, okay in this kind of story You also modify preferences, but you just assume that people have different discount factors and risk aversion The third story is going to be that it's not really preferences that are What we work on but they are rate of returns instead of everyone facing the same rate of return We have a rate of return that is idiosyncratic to the person and is stochastic The fourth story is entrepreneurship and here what we do is people can choose to run a business In which case they have a production function in which they invest some capital to run the business Or if they don't run the business they get the same earnings fluctuations. They get in the basic beauty model So this is also essentially about how you model resources here the fifth story is That maybe we are not getting the earnings risk, right? Okay, and so it's also in the budget constraint and the sixth story is that well Maybe we are getting this risk right But there are other risks for instance coming from medical expenditures and long-term care that we don't allow for in the budget constraint But they are important for a large fraction of people So let me go to the first story Why did I pick these six stories besides the fact that I wrote Four of those stories I brought on I think a model is always a very simplified version of reality and How you decide how you should make your model complex should be Grounded in empirical evidence. Okay, so for each of these stories. I will first you see these little dots here So this is the empirical evidence that underpins that story and that makes you think yes This is something important that I should actually introduce in the model So for each story, I will have the empirical evidence than the model and the results so Big question human capital so There is a lot that it was a lot of debate between Coplico fan summers and Modriani that was later settled in my opinion The idea is that when you look at capital today You can distinguish the one that has been saved by people who are alive today And the one that has been inherited by people who are already dead and in the aggregate Notice it doesn't tell us anything about distributions in the aggregate the amount of capital that is transferred across Generations we can debate if it's 50 percent or 60 or 70, but it's large Okay, so there is a lot of capital that is earned by our Parents and transferred to us the second one is more at the individual level There is a lot of empirical evidence that earnings education Social economic status of parents and children are linked, right? So this is a evidence about the human capital part while this is about physical Physical capital and why this is aggregate. This is at the individual level. Okay, so that's why I think it was worth exploring This story there is a lot of evidence that this can be important in understanding wealth and savings So let me tell you what I did in this paper What the first part that you have seen the olg with the retirement period and earnings and lifetime Uncertainties the basic model I have shown you so far what I would like to point out is that typically you have Earnings risk and you have mortality risk. So people save in these risk-free assets. There are no annuity markets As people don't typically buy them in the data in many countries. So people Happens some people live die early and they leave accidental bequests Right accidental in the sense that they would like to consume them, but they die early on and so they leave bequests So when introduce a bequest motive and I will be much more explicit about what this means You not only have accidental bequest. Those don't go away. They are still there But you also have voluntary bequests because you derive utility from transferring some resources to your children Okay, so you will have two types of bequest in this environment So I would like to remind you what the problem is you have this additional term in the utility function the intergenerational correlation of earnings and the received bequests and I would like to tell you more about what I did for the bequest motive So I picked a warm glow altruism in which you derive utility from living assets And there are two important things to say While this is a typical CRRA function this parameter It's very important if this parameter is zero the marginal utility of even as of a small bequest is very large So even poor people would try really hard to leave something to their descendants But this is a very has very come to our factual implications Because many a large fraction of people in the US if you look at people without the surviving spouse Which would be the counterpart in the model of 30% have little to no value Okay, so you need to make sure that the distribution of bequest in your model looks like something in the data The other thing we could have thought about is to have a more altruistic model You know the problem is that the altruistic model also has very strong Implications about intergenerational risk sharing so while I think the the question of how we should model bequest is important and still open I think this is a very good starting point and it matches the absurd distribution of bequests So importantly, I do not pick model parameters to match wealth inequality For instance, I pick parameters of the bequest distribution to match moments of the bequest distribution So what you get for wealth is sort of an identifying restriction is not something that you try to match by construction Okay, so let me show you The results from this model So the first line you have already seen except that I'm adding the wealth genie concentration So here you have the data from the 1989 SCF and you have the how much wealth as the richest 1% in this line What you have is I actually want to say something so This line. It's actually two different models that have the exact same implication This is the model you have seen with no bequest motive whether you give the bequest to everyone alive The accidental bequest like we usually do in many models or you give them to the children you still get these exact numbers Okay, so what is going on is that some people get a large bequest some people get a small bequest It's like winning the lottery. They're saving behavior doesn't change. Nothing changes for wealth inequality Here with the blue you see when we start adding I here I want to add this warm glow bequest that is a luxury good and you can see that the Concentration of wealth in the richest 1% doubles. What is going on here is that? This non-homotic city in the bequest motive implies that when you have either high-earning Seastories or you get a large bequest. That's when you want to leave a bequest So some of the wealth gets concentrated because it's passed on across generations And that's how some rich families arise The other thing I want to point out is that this model does a really poor job on matching the poor people right in the data about 6% has zero or negative wealth and In a life cycle model in which people start with little to no assets like in the real world These models over predict the front and you know the adding bequest that nothing good on this side of the distribution You over predict the number of poor people Okay, so when you add the human capital inheritance You have that not only some families leave more assets their children and these propagates, but they're human capital Their earnings are correlated. So there is a little more concentration So let's summarize the findings from this first Story accidental bequest and voluntary bequest and human capital So first of all, you know, sometimes you read all Bequest are actually Equalizing and so on and so on so forth What this paper tells you is that it's not as much the receipt of the bequest that matters is Whether you have a saving rate that is increasing in wealth Okay, so it's really the bequest motive rather than the passive receipt of an inheritance that changes wealth Concentration because it changes saving behavior You have also seen that adding ability Correlation of ability across generations help generate the breacher families over time But the big thing is that even with bequest and intergenerational links the wealthy are not wealthy enough and the poor are too poor Okay, so this is making progress, but it's not all of the story and you know I think when you try to calibrate these parameters to other moments You can get a Better idea why to how much this can explain.  Well, I think there is a Very large body of evidence from the applied micro literature The preferences are heterogeneous You can look at different methods Euler equation versus life cycle method of simulated moments maximum likelihood You can look at US data and the PSID we have Danish registry data There is a lot of evidence that people are heterogeneous in their patients and their risk aversion So what do we have in terms of models people have studied heterogeneous preferences. I'm sure the paper 99% the people in this room know is cruz Allen Smith So what cruz Allen Smith did they took an infinitely the agent model and they found that a little bit of heterogeneity in data goes a Low way, you know in the macro tradition. You try to say it's you only need a little because we are very skeptical of heterogeneous preferences But it's still you know it makes sense, but the rich you cannot match the upper tail It's not like you know Bill Gates is twice as patient as everyone else, right? So it's there, but it's probably not the reason why the rich are so rich Even more interesting. I think as I will argue later I think using a life cycle model introduces much more discipline in what you are doing And papers by Hendricks and Gonzalo Paz pardo used life cycle models and found that first of all in life cycle model You need much more preference at a journey and what happens is that yes You can increase the rich that can be a little richer But then the poor will be poorer right because you have this gap in in preferences And they also looked at both beta and sigma at least Gonzalo did as opposed to cruz Allen Smith That mostly played with beta So what do we have on this story? So, you know on the one hand I think the evidence from the applied literature is fairly Substantive that there are these differences. So I don't think we should completely discount this explanation But I don't think that this is the reason why the rich the super rich are very rich So I think this is an important mechanism that we can use in conjunction with others But not the whole story So my third Story is about the Theruginus returns So this is a new paper that came after the models that I will tell you about And this has an exceptional Norwegian data So that you can use these are very rich data to compute returns from stocks from bonds from private equity And these authors found find that returns are very Theruginus For instance 200 basis point across the 10th and the 90th percentile of the distribution They are also Theruginus within asset classes. So whether they are bonds or risky assets And interestingly they are correlated people with higher wealth tend to have higher returns and people with the private equity Entrepreneurs also tend to have higher returns So These papers came before the evidence. So they were ahead of their time So what? Well, yeah, it's even if it's 2015. I think it was in the works well before we heard about the Norwegian data so this paper what it does essentially is to choose the The these Process for the rate of return at the individual level to match wealth inequality And the conclusion is that first of all the rate of returns alone cannot match the distribution of wealth you also need be quest motives and In my opinion, this is a very interesting explanation, but we need to move forward. Okay, so this is sort of a Perhaps an interesting Mechanism that works, but it's a bit of a black box. First of all, I think it's very important to be more serious about matching these Implied returns to data, but more importantly these rate of returns are endogenous They are endogenous to your portfolio choice. They are endogenous to entrepreneurial choice They are endogenous to a variety of behaviors that you cannot just assume that they fall from the sky especially if you want to evaluate taxation So papers that haven't endogenized to some extent and you know, they did it they didn't Mean to do it because we weren't thinking about these particular problems where paper on Entrepreneurial choices starting with the chance of quadrini There is a recent paper by Kan and Kim that thinks about this an optimal stock and bonds portfolio with the participation cost So you get an endogenous rate of return depending on if you're rich enough You get more stocks you get stocks and also you might think that perhaps there is a story about Investors sophistication and that rates of return might be related to different knowledge about different assets But the point I want to make is that I think it's important to go beyond this idea that rates of return heterogeneous and try to think where do they come from So that's why I would like to I would like to talk about this story Because I think it not only endogenize this rate of return But there is also a lot of other empirical evidence that entrepreneurship is strongly related to wealth So besides the fact that the paper is mine, of course So Let me start with the facts again Okay regarding entrepreneurs many entrepreneurs are wealthy and many wealthy people are entrepreneurs so far This is a correlation. So this is my paper with Marco Cagietti. As usual, you know, entrepreneurship is like pornography So what we try to do is we have a specific notion of what an entrepreneur is in the model And what we do is we try to go to the data and be as close as possible to that notion So when you in the survey of consumer finances when you consider an entrepreneur someone that not only is self-employed But only business and actively manages it What you find is that in the richest one percent of people over half are Entrepreneurs according to this very restrictive criterion. If you are more generous, that's 80 percent Okay, so you can see at least that many rich people are entrepreneurs according to this definition There are other I think important facts about entrepreneurs The first one is that exactly like the rich people they have high saving rates Specifically to entrepreneurship. They have a high saving rate before they enter entrepreneurship Maybe they're trying to save to get their business started and after to develop their business There is also a lot of evidence that at least part of the entrepreneurs face borrowing constraints so they need some skin in the game to be able to enter and to expand their firm and In addition, they hold very undiversified portfolios So these are all facts that the model I will show you we reconcile very nicely even though it's a very simple model So as I mentioned briefly before at every period people decide whether to be a worker or an entrepreneur So there is an occupational choice if they are an entrepreneur They are endowed with some ability theta that is persistent over time, but it's stochastic and they run the production function You can add the hiring workers. Nothing really changes What is important is that if you invest capital K there is a decreasing return And then you get back on the appreciated capital Then the other part that is very important is that there is a collateral constraint So a are your assets and K your is your working capital in the firm This simple the question is simply meant to say if you have more assets So you save more you can expand your business more Why are rates of return endogenous in this framework? Well, first of all rate of returns are the marginal product of capital for the firm So there are decreasing rates of return. So they depend in firm size and second Until entrepreneurs are constrained they will invest everything in the firm But after they will split their assets between the risk-free and the firm and so there will be that part too So this model Which I also didn't calibrate to match the wealth inequality, but we'll match other important facts of entrepreneurs Also matches wealth inequality very well Okay, and notice that the fraction of entrepreneurs that in the SCF that satisfies our definition is 7.5 percent So it's an overall small fraction of the population and you know I don't have the time to show you everything but it matches a lot of other facts that you observe in the data So let me summarize what we learn from this entrepreneurship story first of all they can generate a lot of rich people and the key mechanism is that Being able to invest in your firm is potentially very profitable So you need to save to to get this high rate of return if your firm size for at least some entrepreneurs is High the large Optimal firm size, then you will keep saving even when you're rich, right? So this is the mechanism that drives high saving rate even for richer people And the model I think Rationalizes in a very nice way all of the facts. I showed you about entrepreneurs It rationalizes a very undiversified portfolio because initially you're constrained and your return is very high The high saving rates you want to enter and expand your business and the high wealth So let's talk about the fifth story It's about do we really understand earnings dynamics or are we really with our little a one process or something like that They really representing well the risks that people face okay, so Again, I want to start from the facts. Okay, there is a lot of applied Micro-papers that have you know, I Said a few that are recent and particularly suited to make this point But there are hundreds of papers trying to understand how earnings dynamics evolve and basically one very important feature Is that they're very rich much richer than when we have a symmetric year one or even a transit or impermanent component and Importantly high earners face a lot of risk Right, you can see that if I'm a high earner and I face a lot of risk So my risk depends on my earnings level. I might save a lot. Okay, so there is an open question as to whether these is realistic method Really driving savings to a large extent or not? So let's talk about models so back in 2003 Castaneda Diaz Jimenez and victorious rule used exactly this method and the goal of their game was I want to pick why and its Stochastic properties to match cross-sectional moments of earnings and wealth So very clear exercise. Can we do it? Okay? The answer is yes We can match wealth concentration Perfectly as long as you choose the appropriate earnings process However, this is what you get right? This is what you need to do that your earnings level are normalized to one at the lowest level three ten 60 okay, so if you are a thousand at 60 like the awesome state or Tiger Woods You have a 20 percent probability of dropping of having a 99 percent or 99.9 percent depending on which of the other states you fall to and you can see here if you are super rich The only week you can smooth your marginal utility of consumption to save it on because tomorrow exogenous You lose it and it's gone.  What we do is we go to data Okay, and we use a nonparametric earnings process that has dynamics over the life cycle persistent that the persistence changes over the life cycle and it's very flexible in terms of your earnings level can have very different risks Q-ness and kurtosis and we use this process to match moments from tax data. Okay, so What we find is that, you know, we think that this goes a long way to solve in this criticism that you don't really have the higher nurse In the survey data. So what we find is that one victory, okay? So remember how the poor people are too poor in this life cycle beauty models, right? So here with this kind of risk you have that the poorest 60% of people actually start looking like in the data. Okay, so there is something about understanding this earnings dynamics However, we don't find those kind of extreme drops for the higher nurse. Yes, they have more variance Yes, they have more skewness, but it's not enough to really generate bill gates or even the 1% without talking about the super rich So at least the poor people are realistically poor and When you think of another measure of how people can self-insure you can look at the variance of a consumption as people age Right, if the variance of consumption with people age spreads out a lot that there is some less capacity of self-insurance and these earnings process can match it while the previous year one has a Very large increase in inequality over the life cycle very counterfactual so My view from the earnings risk is that it is important to better think how we should move away from this year one Methodology, especially we you know we propose a very simple way to do it But this is not really why rich people save and you know this data set doesn't have the entrepreneurs This is earnings data, but if you really think it's entrepreneurship, I think an entrepreneur is not a wage shock I think you should go beyond that and you should model it explicitly, okay? So the last the last of the six stories I would like to tell you about it's a bit different It doesn't come from a standard modern which we are trying to match with inequality But I think it has a lot of convincing ideas that this is important Fact one These are our young chicks 74 year olds, okay, we are looking at people who are retired They age we observe them until very advanced ages we have a lot of them and here we are plotting their out-of-pocket so what they spend after government insurance and private insurance space and By age and we do that by permanent income, okay? You can think by education that would look very similar what is going on is that medical expenditure after age nine They really start to rise, but especially so for people with high permanent income Notice that these are also the people who live longer, right? So they can be hit by the double whammy of living a long time and being stuck in a nursing home for a number of years So they face a lot of risk, okay The other piece of evidence I would like to bring to the table to convince you that this is something important is we also have The same chicks here 74 year olds. I'm showing you two courts some started 74 some start at 83 These are they are savings by permanent income So what you see is that the high permanent income people start out return well I mean they have been retired a while, okay start out in our sample with high Assets and they really hold on to them. It's not like they start the accumulating until they are very old The poor people they never save because they rely on government programs like Medicaid and Social Security Oops while the middle income people are the one that show most this saving So what did we do with Eric and John? Well, what we did we Estimated the structural model of retirement and we took the medical expenditure from data And we took them as a shock as other people have done before to do your resources We also allowed for a consumption floor because there are government programs that do provide one So let me tell you what are the key findings from this model. So we have our 74 year old chicks, okay This is one court the dashed line is our baseline model So if you take the 74 year olds, that's how our model would predict they did save during retirement with medical expenditure and long-term care risk, okay so what you see is that you know You have this line for each permanent income level the solid line is what happens if we remove If we remove medical expenditures completely, okay So what is this graph showing you this graph is showing you that once you estimate the model like this medical expenditure Explained a large portion of savings in retirement So there is the question is you know, you're trying to think are we talking about 1% are we talking about the 5%? Are we talking about the middle income? So there is a very interesting paper by these authors that uses a data set that actually looks at upper to middle income people right, you might think well, how far up does this saving motive spread and they find that First of all they find that the same interesting dichotomy that we do say pick a A 55 year old so someone very young for us and if they have below a hundred thousand dollars What is going to happen with long-term care risk is that they know they are likely to get against the government insurance? Which is tested so they actually start this saving faster On the other hand if you have a hundred thousand or more This long-term care risk makes you save more because you're very unlikely to rely on the government consumption floor So this is introduces a very interesting inequality in savings in all the age the combination of long-term care and this kind of government insurance interestingly in terms of what I was discussing of How rich are these people really for which this matters well my answer is fairly rich So what they find in their data set again very representative of upper to Fairly rich people is that the effect in percentage terms is strongest for those at the top 20th percentile of financial wealth, which is much more concentrated than total wealth, okay So it spreads fairly high up in the income and wealth distribution So my conclusion from this is that we don't have yet a model of wealth inequality that allows for this medical expenditure But they are clearly important. They are large and they really affect the savings behavior in retirement and supposedly before And I think they it's very important to understand things. So let me Go to the conclusions and directions for future research part I first would like to say what I summarize what I think we have learned from these six stories The first thing is well, you know, I will surprise you now not everyone is middle-aged Okay, so, you know in the infinitely model everyone is middle-aged Everyone has some assets and is facing disordnance risk, but that's a really big Abstraction if you want to think about these issues In addition, you know precautionary savings is not the only reason why people save and again That is the lion's share of savings in the infinitely lead agent model So I think it's really important to model the life cycle and it's really important because we also did to do A better job of modeling retirement. So the way my friend Cristonetti puts it.  Okay, and I think Especially with increasing medical expenditures and aging we really need to worry about this even if you want to think about inequality Intergenerational links we have seen are important because the human capita I think We have seen that entrepreneurship has the potential of explaining a lot of wealth inequality and that house of earnings dynamics matter So let me go to the part where I pontificate On the beautiful papers that we should all write And Victor use rule told me I should be very inspirational. So hopefully, you know So I would like to talk about the contents and then I would like to talk a little bit about the methods as Nancy said I really think that a lot of value comes from You know the contents and building being really serious about how you go in about these models So first of all I'd like to talk about human capital Health the family rates of returns on wealth and changes in inequality over time So human capital well, we assume earnings are exogenous Maybe some papers have a little bit of labor supply, but wages are still exogenous If you're writing a theory on how inequality in wealth comes up I think it's fairly important that we start thinking how inequality in wages Translates into inequality in wealth and how these two forms of inequality come up Clearly if we think about policy, the two are going to be very interrelated. You're not going to just affect one Okay, so I think this is very important to health Well, Gary Baker told us it is a very important part of human capital when you're young health affects both your capacity to work and You know your savings through medical expenditures and the capacity to earn and when you're old Well, we I have already told showing you what happens after age 74 So I think you know first of all thinking about the whole life cycle and thinking How health evolves and how that affects labor supply and inequality in wealth and earnings is very important Going beyond that. I think we need to be more ambitious and think about what determines health and how the Determination of health interacts with wealth and inequality. It's difficult But you know, we are getting better at understanding things and we should really try to think about this I've shown you how bequests and human capital really are important Okay, so well, the family is much more than bequests and human capital I really think we need to think harder about what the family does at least more. There's some important aspects of the family for instance The family has the labor supply of both Partners with young and the wages of both partners with young and the medical expenditure of both partners when old and There is the labor supply that you can use to some extent to self-insure and there are marriage and divorce risks So I think it's very important to better understand how inequality is affected by the family Reads of returns on wealth. I think it's very important that we are better understand how they are determined Is entrepreneurship a big one? I think so, but you know, it's always good to show that your beliefs Hold have some empirical counterpart and what are the other important determinants in the paper that I mentioned about We's Opistafari and co-authors about 25% of returns. It's either a fixed effect of the person or observables So there is a significant amount of this variation. We can I try to explain Dynamics of the time super important. We have some excellent papers, but there are just two or three So, you know Understanding why Richard people are getting richer and maybe the middle class is getting squeezed. I think it's very important I have talked only about the static picture, but I think it's really important to think also about the dynamics And I would like to conclude with methods We have seen one or two story at the time I think it's about time that we try to have richer models We you know if you only have one story at the time It's first on the one hand you might be over estimating because you think it's all entrepreneurship But the other hand there might be some interesting Synergies if you have more than one in which two explanations have some kind of multiplier effect So I really think it's important to have much richer model of saving behaviors You know, this brings the question of how are we going to identify these different saving models? I think it's really important to be serious about what drives your model and what aspects of the data identify your model So this is where I think that you really digging into House of level data set is going to really help First of all, you can try to establish new facts the individual or the group level that the model has to match and Second, I think it's you know as we have seen in the superstar or the Tiger Woods shock You match cross-sectional facts you get really counterfactual Risk from the standpoint of the individual over the life cycle, right? So these are micro level data sets are incredibly important to give you a sensible Representation of the risk that people face which after all is one of the key reasons why they save


So, problem 3, 2 kg of water, so m equal to 2 kgs at 25 degrees, T1 equal to 25 degrees in degree in a frictionless piston change arrangement as in the figure. The mass of the piston is such that it exerts a pressure of 100, so that is directly the piston pressure is given, adhesive pressure also is same. So, that means, when the piston has to float, the pressure should be at most the pressure plus piston pressure that is 200, at least it should be this, when the piston has to float. So, at state 1, the pressure should be 100 plus 100 is equal to 200 kilo Pascal's, so that is the thing. Heat is slowly added, when the piston just touches the stops, so let us say that is state 2. State 2 is when piston just touches the stops, so that means I will say V2, sorry P2 will be the same 200 kilo Pascal's and V2 we can calculate, so that we can ensure this, because at till that point it will be a constant pressure process. Then after that, state 3 here temperature finally reaches, heated until the temperature finally reaches 1000 degrees centigrade, so T3 will be 1000 degrees centigrade, so these are the states which are given to us. So, here when the piston just touches the stops, T2 is also given, 240 degrees centigrade temperature. Pressure should be constant at this point, then pressure should increase, because piston cannot move upwards, the pressure will increase, constant volume will occur then, so this is the problem. So, let us see state 1 properties, state 1, state 1 is 200 kilo Pascal's, 25 degrees centigrade. So, we have to go to the tables, so here if you go to the tables, 200 kilo Pascal's is 2 bar, so we are in 2 bar now, 2 bar and we will see that the temperature is for 2 bar saturation temperature is 120.2 ok, at 2 bar T sat saturation temperature is 120.2 degrees centigrade, but T1 is 25 degrees centigrade, so the state is sub cooled liquid, so sub cooled liquid state is the state 1. So, this means properties of saturated liquid at the given temperature can be taken. So, what I am saying is V1 will be equal to Pf at 25 degrees centigrade, U1 equal to Uf at 25 degrees centigrade, so that is the main, because it is sub cooled liquid ok. So, what we are saying here is, here the saturation lines are like this in a TV diagram, so the pressure will be like this, but for the given pressure of say 2 bar, you are here somewhere, this is 25 degrees centigrade and this is 120.2 degrees centigrade. So, you should not take the value corresponding to the pressure, but take the value corresponding to temperature, this is 25 degrees centigrade. So, for sub cooled liquid, the properties of saturated liquid at the given temperature should be taken, so that is what we are trying to get here. So, V1 will be equal to, so we will see the properties again. So, go to the, now we should not take the entries from the saturated pressure tables, but go to the entries in the saturation temperature based tables. Here you can see that for 25 degrees, the value of Vf, Vg are given, but we have to take value of Vf, which is this and the Uf ok. So, now, V1 equal to 0.001, 0,032 meter cube per kg, very small value volume. Similarly, U1 will be equal to 104.9 kilojoule per kg. So, these are the initial state properties. Process 1 to 2 occurs at constant pressure, because the distance is free to move. So, P2 will be also equal to the same as P1, so 200 kilopascals. T2 is given as 240 degrees centigrade. So, T sat at 200 kilopascals equal to 120.2 degrees centigrade. Now, we see that T2 is greater than T sat at P2 implies the state 2 is superheated state.  So, like this, in the previous case this is 120, but given for state 2 it is 240. So, you are in the superheated state. So, state 2 is in superheated state from superheated tables for 2 bar. We will see the entries now. So, here this is the table. We are talking about 2 bar table, temperature is 240. So, here you can see that we have to interpolate between these two entries, 200 and 250 are given, but given temperature is 240. So, we have to interpolate. So, we know that at 200 the specific volume is 1.08, at 250 it is 1.199. So, for 240 what will be? So, that we can take here. So, interpolating between 200 degrees centigrade and 250 degrees centigrade entries, we can get V and U for 240 degrees centigrade at 2 bar. So, by doing that we get V2 equal to 1.175, 2 meter cube per kg, U2 equal to 215.6 kilo kilo joule per kg. So, these are the values. So, state 2 is fixed. Now, state 3 process 2 to 3 occurs constant volume. Why? Because the piston is now hitting, it stops. So, it cannot move upwards. So, constant volume. So, here we can say V3 will be equal to V2 equal to 1.175, 2 meter cube per kg. So, one value is fixed and what is given is temperature is given, final temperature is given as 1000 degrees centigrade. So, these are the two state, two state properties available. That is at 1000 degrees centigrade, the volume is 1.152. So, what is this state? So, 1000 degrees centigrade. So, the state should be superheated. Already, it is superheated in the previous state. So, further heating has done, has been carried out. So, it should be superheated state. State is superheated vapor. But now the problem is, I do not know the pressure. We know that all the superheated tables are given in terms of pressures. So, once the pressure is known, we can go to the particular table and do. But now we have to go to a particular table where the temperature is 1000 degrees. And the specific volume is 1.1752 meter cube per kg. So, we have to go to the table and get the value of pressure and u from that table. So, we go here 1000 degrees. So, if we say, for example, 2 bar, it is actually it should be more than 2 bar because already we are in 2 bar in the state 2. So, now 3 bar if we take, 3 bar I find the volume is still high. 4 bar it is 1.469. What is the volume required is 1.175. So, now we can see these two are higher. So, go to higher pressure where the volume reduces. 5 bar it is for 1000, it is 1.175. So, it is very close to our value. So, this is the state for us. The pressure has reached 5 bar and the temperature has reached 1000 degrees where the specific volume is 1.175 which we have calculated. So, we can take the u value as 4052. So, here 1000 degrees 1.175 pressure is 5 bar and u3 is 4052 kilojoule per kg. So, this is the value. So, now all the states are fixed. Ok, now what you should do is you can apply the first law q122 equal to w122 plus u2 minus u1 delta ke equal to delta pe equal to 0. So, from this I can say, first we will calculate work. Work is when there is a change in volume which has occurred in the process 1 to 2. So, I can say work here is p into 2 into 200 this mass into p into v2 minus v1. So, I will write the value such as 1.175 2 minus 0.0010032. This is v2 minus v1. So, that will be equal to 469.6 kilojoules. So, delta u122 will be equal to m into u2 minus u1 which is 5221.4 kilojoules. So, q122 can be calculated as 5691 kilojoules. So, now for process 2 to 3 delta v equal to 0, so w equal to 0. So, q223 equal to u3 minus u1 or u2 u3 minus u2. So, that means you will see this is m into u3 small u3 minus small u2 which is equal to 8363.8 kilojoules. So, now let us try to draw the diagram, tv diagram. Saturation lines we have to draw for all the things. First is 200 kilo pascals or 2 bar 200 kilo pascals. So, here initially 25 degrees centigrade is the temperature. So, here 25 degrees centigrade. So, state was somewhere here sub cooled then that is state 1. Now, state 2 was 240 degrees. So, maybe this is this is 120.2 degrees centigrade saturation then say 240 degrees somewhere here this is 240. So, this is the state 2. So, the state 1 first the liquid sub cooled liquid goes to saturation saturated liquid then plate in heat is added then it becomes saturated vapor then it becomes super saturated vapor. So, that is the state 2. Then what happens? Pressure increases volume remains constant. So, volume remains constant from now. So, this is the volume V2. V2 can say this is V1. So, now what is the final pressure? 2 to 3 final pressure is 5 bar. So, something here only qualitatively I am showing here something like this. So, this is actually 1000 degrees centigrade may not be the scale please understand 1000 degrees centigrade actually should be much higher than the critical point. The critical point temperature is 370. So, I will draw it properly. So, this is 1000. Okay, now constant volume. So, 2 to 3 here is this process superheated to superheated. So, this is 3 states. So, this is the way first constant pressure process sub cooled liquid at 200 kilo Pascal which because temperature is 25 degrees less than the saturation temperature. Now, it is heated becomes saturated liquid at 200 kilo Pascal then later it is added becomes saturated vapor then it goes to superheated vapor in final state constant pressure process occurs till that. Then constant volume process occurs where the volume remains constant and pressure increases. So, this is 5 bar. So, 2 bar to 5 bar final pressure is 5 bar we have estimated correct. The temperature is given was given. So, corresponding to this temperature and this V2 was 1.175. So, based upon this we have calculated this pressure as 5 bar. So, this is the state diagram. Always in the draw state diagram you have to show the saturated lines. Saturated this is called saturated liquid line and this is the saturated vapor line. Saturation line should be shown. So, this completes this problem. Now, this fourth one a cylinder in which a piston is retained by a spring. So, as a spring is having this is retained by this always the spring is connected to the top push of the piston. This contains water. Water means it can be liquid in vapor initially contains 0.01 meter cube. So, V1 is 0.01 meter cube. The mass of the water is also given 0.5 and the cross sectional area of piston is given AP is 0.05 meter cube. Mass is 0.5 kg. Okay. Now, mercury manometer is connected to the cylinder initially reads height difference between its limbs this and this. It has 37.5 centimeters as shown in the figure. Water is now slowly heated ok heated until the piston rises through a distance of same 37.5 centimeters. So, from this it goes to some height. So, this also is 37.5 37.5 centimeters. So, now at any instant during this process bottom surface of the piston this bottom surface of the piston and the mercury meniscus in the openly more at the same level. So, when the piston goes here mercury meniscus also will be in the same level. So, this will be the meniscus level that is the from this point the meniscus also would rise to 37.5 centimeter. Similarly, this will drop to 37.5. So, the final state we can say somewhat here and here could be meniscus levels. Okay. Assume that the pressure excited in the left limb. So, here the left limb pressure excited in the left limb of the manameter due to the pressure presence of water above the mercury column. So, this is the darker region is mercury and this is water. So, this water has some height and it has density of 1000. So, it can also put some pressure on this point correct, but it is told that assume that the pressure excited in the left limb of the manameter due to the presence of water above the mercury column is negligible. So, you need not take into account of that because the mercury density is 13600 kilogram per meter cube water density is 1000. So, we can neglect it. So, 13 times more density. So, we can neglect it. We can also include it if you want, but this clarity is shown. So, given that no, don't need to take into account of the water hydrostatic pressure in the left limb. Now, G is 9.81 and the atmospheric pressure is 1 bar. So, this is the problem. So, the pressure at every point can be calculated as rho G H plus P atmosphere here of this opening here the atmospheric pressure acts that is 1 bar. Okay. Now, what find the initial and final pressure of water relationship between pressure and volume the heat and work interactions. Okay. So, this is the problem. So, let us do this solution initial pressure equal to atmospheric pressure plus density of mercury into G into H1 which is equal to 1 into 10 power 5 because 1 bar plus 13600 into 9.81 into 37.5 into 10 power minus 2. So, that will be equal to 150,000 Pascals or 1.5 bar. Okay. So, that is the initial final pressure P2 will be equal to P atmosphere plus rho HG G into H2. Now, what is H2? H2 will be when the when the piston moves to 37.5 the miniscule in the right limb will also go up to the height of 37.5. But the left limb will also descend from the given position to 37.5. So, the total H will now be 2 times the 2 times of 37.5. Okay. So, that we have to take into account. So, when the piston goes up by a height of H the difference between the miniscule levels in the right and left limb will go by 2 H. So, that we will take into account and calculate this as 1 into 10 power 5 plus 13600 into 9.81 into initially the difference was 37.5 plus due to the descent of mercury by in the right limb by 37.5, left also will go down by 37.5. So, there is increase of 2 H. So, that into 10 power minus 2. So, this is the H2. Now, that is our delta H you can say. So, now, that will be equal to 250,000 Pascals or 2.5 bar. So, the initial and final pressures are got from pressure based saturation tables for P equal to 1.5 bar. We can take Vf equal to 0.001053 meter cube per kg Vg equal to 1.159 meter cube per kg as we have done in the last time. Similarly, Uf in Ug, Uf equal to 466.9 kilojoule per kg and the Ug equal to 2520 kilojoule per kg. So, these are the values. Now, how will we calculate the value? V1, first we have to say V1, we have to calculate as what? 0.01, this initial volume V1 meter cube divided by M. M is 0.5 kgs in kg. So, you will get this as that is initial specific volume as 0.02 meter cube per kg ok. Now, you see that the Vf is less than V1 is less than Vg correct. Because you can see V1 is more than Vf less than Vg. So, that means, state 1 is saturated mixture of liquid and vapor that is it ok. So, now, I can find the quality. What is quality at state 1? It will be equal to V1 minus Vf divided by Vg minus Vf which is equal to 0.0163625. So, that is the quality. So, from then I can find U, U1 equal to Uf plus x1 into Ug minus Uf. So, that will be equal to 500.5 kilojoule per kg ok. So, state 1 and the properties are fixed now. State 2 P equal to 2.5 bar. Now, again for this what is V2? We can calculate V2 as V2 that is total volume is V1 plus A into delta H which is equal to 0.01 plus 0.05 meter square area into 37.5 into 10 power minus 2 because piston goes up by 37.5 centimeters. So, that is the piston that in centimeters 37.5. So, convergent meters I put this. So, V2 will be equal to 0.02875 meter cube. So, I can find the specific volume as V2 by m which is equal to 0.0575 meter cube. Now, from saturation table for 2.5 bar I can find Vf equal to 0.001067 meter cube per kg and Vg equal to 0.746 meter cube per kg. So, now, we can again find that V2 Vf is less than V2 is less than Vg. So, the state 2 is also a saturation mixture of liquid and vapor ok. So, I can again find the quality. X2 will be equal to V2 minus Vf divided by Vg minus Vf. Vg Vf etcetera are from 2.5 bar which we have written earlier. So, from this I can find the X2 equal to 0.07575 and now Uf equal to 532.8 at 2.5 bar. Similarly, Ug equal to 2537 kilojoule per kg at 2.5 bar. So, that means, U2 can be calculated as Uf plus X2 into Ug minus Uf which is equal to 684 for 0.71 kilojoule per kg. So, now, we can apply the first law and define the values that is for work and heat interaction. So, what is W equal to integral p dv ok. For that I need for doing this I need p versus v relationship ok. So, now, for doing that let us write pressure at any point is equal to p atmosphere plus 13600 into 9.81 that is rho hg into g into delta h which is equal to 37.5 plus 2h into 10 power minus 2 ok. Because from the initial point when piston goes up by a height of h then the difference between the meniscus in the right and left limbs will become 2h. So, the total height difference delta h will be 37.5 by 2h centimeters or 37.5 plus 2h into 10 power minus 2 meters. So, that will be the pressure at every point. Now, I will connect volume, volume at any instant after the initial state v minus 0.01 this is v1. So, that will get written as 0.05 this area of the piston into delta h into 10 power minus 2. So, that means, I can say h equal to or delta h equal to so, I will say h itself h is better. So, h equal to what 2000 into v minus 0.01 ok. So, now, we can see that I am writing h in terms of v. So, then I can write pressure in terms of v directly. So, pressure is written as substituting the value 100 kilo Bascals. So, 10 power 5 minus 3335.4 into sorry plus 5336640 into v substituting this I get this value. So, that is the relationship between pressure or I can say which is equal to 96664.6 plus<PHONE_NUMBER> into v. So, that is the relationship between the pressure and this. So, now, I can integrate this because pressure and volume relationship is going. So, I can calculate w1 to 2 as integrating this. Instead of pressure you put this volume based expression here and get w1 to 2 as 3751 joules or 3.751 kilo joules. Similarly, q1 to 2 can be calculated as w1 to 2 plus m into u2 minus u1 delta kepe equal to delta ke equal to 0 ok. So, now, this will be equal to 3.751 into mass is 0.5 into u2 is 684.71 minus u1 is 500 500.5. So, that will be equal to 95.856 kilo joules ok. Here you can see the pressure increases linearly with volume and state 1 say to both are saturated states. So, this is the problem number 4.