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[Assistant] Generation-ers, uh, like me, who are headed towards retirement in another 20 or 30 years, and they're gonna bankrupt the system. There are gonna be so many of those old geezers hanging around, needing to get paid, that young people like you won't be able to carry the burden.


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[Assistant] And so it's our fault. And then there are the people, the naysayers, uh, Gore, uh, chief among them, but many others.


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[Assistant] Obama now, similarly saying, slightly similar thing, saying, well privatization doesn't make any sense, it's logically impossible, because if you take the, the taxes that the young people are paying,


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[Assistant] And tell them those social security taxes they can invest in private accounts, the stock market with their own name on it.


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[Assistant] So those are the three most standard things people say. All three of them have some truth to them but on the whole are substantially wrong, completely wrong, and once we clarify what


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[Assistant] Advances made by FDR and I just thought I'd play the tape of him signing it into law.


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[Assistant] And behind him is the Secretary of Labor, Francis Perkins.


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[Assistant] In my generation, there are many women named Francis, uh, because they were, their parents named them after her.


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[Assistant] Okay, well it was regarded as historic for all time.


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[Assistant] And, uh, let me now explain what Social Security does and what the rules are. And, uhm, I have to open another file.


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[Assistant] Alright, so after passing legislation, a massive publicity campaign was launched, convincing people that this was a good idea.


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[Assistant] And not everybody thought it was such a good idea, by the way. And so, uhm, you know, this is an example of the advertisements that were plastered everybody, you know, telling you about how much you were going to get money after you retired.


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[Assistant] Now what was the idea of the, uh, of Social Security? The idea was that lots of people


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[Assistant] When they were young, didn't really have the foresight to imagine what life was going to be like when they were old. In fact, in the late 1930s, the most destitute of Americans were the old.


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[Assistant] And, and so the idea was to force them to save when they were young and then the money would be there, uh, would be invested in U.S. Treasury bonds and accumulate interest and the money would be there when they got old. So it was, uh, it was imagined to be a pre-funded system. Pre-funded meaning the money comes in,


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[Assistant] Uh, and it's that money which is gonna then pay people later when they get to old age.


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[Assistant] The average person, recipient, retiree would be receiving 40% of the average wage of the average young worker.


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[Assistant] And you know, the idea is that when you're older, you have less expenses, you probably already own your house, your children are out of the house.


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[Assistant] Uhm, so 40% should be enough to keep, uh,


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[Assistant] You know, to keep you going was certainly more than the average old person had at the time.


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[Assistant] Well, there's a side part of the program, which he mentioned, which is ensuring people, you know, who, uh, of disabilities, things like that, but that's a, that's a one out of the 12.4% tax, it's something like 1.8%.


[User] Please transcribe this text

[Assistant] But the main part of the program is insurance against what? Yes.


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[Assistant] Living too long. You get the money your entire life, no matter how long you live, you're being insured against living too long. Exactly. So, what happened was, uh,


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[Assistant] Uh, and the second year they were paying more taxes, and of course they were still not retired yet, so they weren't actually giving out the money to anybody, and this, uh, fund was starting to accumulate. So Francis Perkins said,


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[Assistant] Well, what are we doing here? The whole point is that we've got all these old people, you know, it's just the Depression, we haven't come out of the Depression yet, World War II hasn't started, it won't for the U.S. start for a few more years by the way.


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[Assistant] So there are all these old people sitting here, totally destitute.


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[Assistant] And, uh, we've got all this money also just sitting here. It really doesn't, uh, make sense, she said. What we ought to do is take the money the young people are paying in and give it to the old people who are already there.


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[Assistant] Uhm, that way we're gonna be saving these old people and the young people, when they get old, they'll be saved themselves because there'll be more young people coming along afterwards who can pay taxes in when they're young and today's young, when they're old, will then collect those taxes and they'll be reimbursed for what they contributed when they were young.


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[Assistant] Well, so Roosevelt, uh, resisted for a while. Francis Perkins was apparently an incredibly persuasive person.


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[Assistant] Uhm, one person at the time named Witt, uh, who was on the Social Security Commission, uhm, tried to tell if that was going to lead to budget problems. She said that he was a half Witt and that he...


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[Assistant] You know, we had a more urgent problem. Not that she doubted him, but the problem was more urgent. And so that was the only way to solve the problem, the current crisis.


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[Assistant] So they finally agreed to, to move the system from a pre-funded system to a pay as you go system. So the money coming in from current workers is used to pay directly to the old workers.


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[Assistant] So then, as I want to get into all these, uh, little variations that got made.


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[Assistant] Okay, so the program is called the Old Age Survivors and Disability Insurance Plan. Insurance is mainly for living too long.


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[Assistant] But also in case you get disabled or in case you, you know, you have children and you're disabled, you die, they get something. Okay, so it's a combination of insurance, disability, but the main insurance is for living, uh, a long time. So as I said, the tax now is 12.4%.


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[Assistant] So, if you make more than $106,800, you pay


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[Assistant] You know, 13,000 or something and then nothing more on social security.


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[Assistant] Okay, so the social security tax stopped at 106,800. You know, the income tax, you know, I'm losing track of what it is.


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[Assistant] Uhm, and whether we're gonna go back to the old one, but let's say it was 38%, 39%, something like that.


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[Assistant] If you had to pay that income tax and on top of it pay social security tax of 12.4%, you'd be over 50% tax.


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[Assistant] So needless to say, some of the people who think we can solve our social security problem by eliminating the cap would be imposing a gigantic marginal tax rate on a lot of people who don't have a marginal tax rate. So uhm,


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[Assistant] So 94% of workers are below the cap anyway. So we're talking about the top 6%, most of which you'll, you know, you'll be in that top 6%.


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[Assistant] You won't have to pay on the margin, uh, social security tax.


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[Assistant] So there's the taxes, 12.4%, and then there's the benefits. Now how, how do the benefits work? How are they determined?


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[Assistant] Uh, so the tax, as I said, you just take your income below $106,000 and you pay 12.4% tax. So, how do the benefits work? This is a little bit, uh, complicated, but it's actually a very interesting plan. What they do is they look


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[Assistant] at every year what the fraction of your wage was relative to the economy-wide average.


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[Assistant] So it's your relative earnings. So when you're young, it's probably going to be .8, .9.


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[Assistant] If you go to Yale, it's probably going to be 1.5, 2, you know, soon thereafter.


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[Assistant] Okay, and it might get even higher, but anyway, so that's the relative earning you have every year.


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[Assistant] So then they say, let's average your 35 best relative earning years.


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[Assistant] And, and just divide, you know, take the sum, divide by 35. So what is your average lifetime, uh, relative wage? You know, for the average person, it's gonna be 1.


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[Assistant] For some people it might be .8, for some people it might be 2 or 3 or 10.


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[Assistant] Okay, now, the first, so, alright, so you've got your average relative lifetime wage. What do you make when you retire? Well, when you hit an age, which, you know, they're adjusting now, but it used to be 65. When you hit 65,


[User] Please transcribe this text

[Assistant] When you hit 65, you're going to be paid a fraction of the average wage in the economy when you're 65.


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[Assistant] Well that depends on what your historical average wage was. So let's just see what the, uh, function is.


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[Assistant] Was one equal to the average wage of the economy, your entire...


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[Assistant] Your relative wage historically was 10% of the average wage in the economy. You would get 9% of the average wage at the time you retire.


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[Assistant] So you can see that this is what's called a concave function, just like our utility function is diminishing marginal returns. So it means that if you're earning


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[Assistant] You know, the ratio of relative wage historically to what you get of the young people's wage is going to be 0.9, then the slope drops to 0.32, then it drops to 0.15, and after your double, actually I think 1.99, after your double the, uh, average


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[Assistant] If your historical average wage was more than twice the average wage in the whole economy, doesn't do you any good. Might as well have been twice. If it's four times, ten times, you don't get any increase. Okay, so you can see why it makes some sense to have a cap to the taxation, because people who are making


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[Assistant] Ten times the average wage, you know, let's say their average, they're making a million dollars nowadays.


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[Assistant] You know, they aren't gonna, they aren't gonna see any benefit from having contributed so much to Social Security. Their benefits would be just the same as if they only made $200,000 a year. So if you, if you, if you pass, you know, so, okay, so,


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[Assistant] Anyway, so you see that there's a connection between how much you made when you were young, how much you get paid when you're old, but the connection is a concave thing, so it redistributes income.


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[Assistant] People who had very low average wages when they were working get a better deal from Social Security than people who had high relative wages while they were working.


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[Assistant] A cornerstone of the idea it's meant to help protect, uh, the people who are very poor.


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[Assistant] Uhm, and so it doesn't pay everybody equally, it's still, you get, you know, you get, uh,


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[Assistant] After the first year you retire, so when you're 65, you're getting 40%.


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[Assistant] Your way, your Social Security benefit is indexed to inflation.


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[Assistant] So it grows at the rate of inflation. The wage is probably growing faster than the rate of inflation. So as you get to be 75, 80, and 85, your average, uh, benefit, your benefit


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[Assistant] Is, you know, down to 36% of the...


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[Assistant] Average wage of the workers when you're 85.


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[Assistant] But it starts off at 44%, assuming you were averaged your whole life.


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[Assistant] Uh, it will be 44% and gradually decline if wages grow faster than, uh, prices.


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[Assistant] Insuring you against living long because the benefit carries on forever. It's insuring you against inflation because it's inflation corrected.


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[Assistant] It's, in a way, insuring you against, uh, you know, feeling like a loser because you're always gonna get a, you know, start off with a fraction of the new


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[Assistant] Average wage. So if your children, you know, if the whole country grows incredibly fast and your children are incredibly, uh,


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[Assistant] That's the idea of the system. Are there any questions about how this mechanically works?


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[Assistant] Okay, now, it's very hard to do this in the market, by the way. If you wanted to, if you, uh, for example, now think to yourself,


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[Assistant] An annuity in the private market that's going to tell you that, you know, when you hit 65, it'll continue paying you for the rest of your life. You can actually find them, but they're an incredibly bad deal.


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[Assistant] Okay, and the reason that they're bad deals is that, uhm, the people


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[Assistant] Social Security is doing something that is very hard to obtain in the market. Okay, now uhm,


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[Assistant] Oh, I should say a couple other things about it, some other interesting things. You can choose to retire early if you want.


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[Assistant] If you retire before 65 and you want to get your payments starting earlier,


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[Assistant] They say, okay, you can do that, but we're gonna cut the payments every year, and they make some actuarial calculations so that it's fair, you know, based on how long the average person lives. So if you start at 63, you get two extra years, but they know if you live the average amount of time,


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[Assistant] You know, how much to cut it down so that on average you'll get the same amount as if you did the normal thing retiring at 65. They've actually raised this age to 67.


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[Assistant] Okay, now another thing is to the, uh, spousal benefit. This is actually quite amusing, I think.


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[Assistant] So I had a secretary 20 years ago who, uhm,


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[Assistant] Got married three times, and so she consulted me about this. So if you're a spouse...


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[Assistant] Uh, you know, traditionally, one of the spouses wouldn't work, typically the woman, and so the, the wife would


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[Assistant] Then you can either take your own social security benefits or if they're bigger you can take 50% of your spouses. And if you've been married to several different guys,


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[Assistant] She tracked down her first husband. She hadn't talked to him in 30 years. Anyway, so.


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[Assistant] Okay, so let's give you an idea of the size. This was in 2005. Okay, so, you know, 157 million workers are paying into it. There are 48 million beneficiaries and, uh, okay, so, you know, it's a gigantic program. So imagine the cost of keeping track


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[Assistant] Get the taxes, you have to make sure people are paying the right amount. It's a, it's a, it's a huge thing involving, you know, basically the entire population. You know, a couple hundred million people almost.


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[Assistant] How much does it cost? How much money do they waste running the whole thing? Less than 1% of the, of the benefit.


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[Assistant] Okay, so it's one of the most efficiently run, uh,


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[Assistant] Programs, maybe the most efficiently run program government has ever devised.


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[Assistant] So, it isn't that the thing is going broke, as you might...


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[Assistant] Believe listening to George Bush because somehow they're pissing away the money and just losing it.


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[Assistant] It's, that's not the reason. Something else was happening, because they don't waste much money, uh, running the program.


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[Assistant] Okay, so this just gives you an idea just of the size of it. So this is 2005. You have to add, you know, 20% or something or 15%. So the benefits, the contributions, 593 billion.


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[Assistant] Remember, there's a tax rule of 12.4%, then you have to pay out the benefits, which is some other rule, depending on what you earned in the past.


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[Assistant] So there's no connection between the two, and in fact the contributions are bigger than the benefits. So the trust fund was growing. And you know, look at the administrative expenses. Tiny, and there are some, you know,


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[Assistant] The railroad, when the law was first passed, railroads were in trouble and so, you know, in order to get some senator to vote for it, they had to hand money over to the railroad. So anyway, something's going on with the railroad. But the other, the other, the other benefit is, uh,


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[Assistant] The trust fund, you know, makes returns, because it's a two trillion dollar trust fund. It was a little smaller than that in 2005, so it's earning interest. That's part of the extra returns. You see why the surplus is quite big now, but it's headed to be quite small.


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[Assistant] Okay, so now what is the argument about? What is the crisis of social security?


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[Assistant] Uh, what do the Democrats and the Republicans say? Well, you've just heard what the program is. You know, how attractive do you think it is? And do you really think it's such a good program?


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[Assistant] The main thing is it's going broke. Okay, so now why is it going broke? It's not broke now, it's making more money than it's paying out.


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[Assistant] But in the future, it's very obvious that, you know, you can just look at the demography, it's very obvious that it's going to


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[Assistant] The benefit formula is going to be so big and there are going to be so many people retired that the payments you make to the old, when I'm retired, are going to be way bigger than the payments that the young are making in taxes, provided the rules stay the same.


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[Assistant] So, that's the main problem. Somehow the system got out of whack.


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[Assistant] And we have to figure out, uh, how to put it back together. Now it's no surprise in one sense that there's an imbalance because there's nothing about the rules that balanced it.


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[Assistant] I mean, the taxes you pay, we'll look at this a second, you know, that's just some formula, 12.4%.


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[Assistant] What's that got to do with the benefits you're paying? Well the benefits are...


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[Assistant] You know, 40% on average of what new, young people are making. So basically, somehow the system must have assumed that there'd be three young people for every old person. So the young people paying 12% each, the old guy getting 40%,


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[Assistant] You know, 3 times 12 is, you know, 12.4. It's close to 40%.


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[Assistant] But if that ratio is very different from that, the system, for example, two young people for every old person, the system's gonna be way out of balance.


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[Assistant] And the formula has nothing to do with how many children people are having or anything like that. So it's not so surprising it's gotten out of balance.


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[Assistant] But we're gonna see there's a much more fundamental reason why it's out of balance. Okay, so the first problem is it's not in balance. But, there are many other problems. So why do Democrats like it so much?


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[Assistant] Democrats say, oh, it's wonderful, you know, we're helping poor people, we're redistributing things on the basis of lifetime earnings.


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[Assistant] Benefits, if the young are booming along, making huge wages, then the, then the old guys keep up with them, and, uh, and, you know, so there's less, you know, so it's not only insurance, but it's kind of less jealousy and less disparity in the population.


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[Assistant] And then, uh, you're protected against long life, you're protected against, uhm, inflation.


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[Assistant] And you can't blunder away your money because you have no choice about where your investment goes. The government's taking care of it. So if you're a bad investor, you can't suddenly find that you've lost all your money. So now, what do Republicans say? Republicans, uh, uhm, and, and, surprisingly, I'm going to


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[Assistant] You'll be surprised where I come out on this, I think. So the Republicans say, you know,


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[Assistant] This stuff is much less good than it sounds at first glance.


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[Assistant] There are no property rights. I mean, who's to say what I'm gonna get when I'm old? I mean, nobody even knows what they're gonna get when they're old.


[User] Please transcribe this text

[Assistant] I defy you, a Republican would say, to tell me exactly what your benefits are gonna be, or even to explain to me how they're calculated. Virtually nobody in the population can do that. That's just not right. That, uh, and you have no control of them. So what's gonna happen, a Republican would say, one of these days the Democrats are gonna say, oh, the system's broke.


[User] Please transcribe this text

[Assistant] We can't afford to pay everything, so we're going to cut all the benefits in half or something.


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[Assistant] So a Republican would say, you know, they're not even my benefit. In order for me to really feel good about participating in Social Security, I want to know that when I put the money in, what I'm getting out, and I want to own it. I don't want it to be some vague promise that can be taken away from me, and probably will be taken away from me.


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[Assistant] Uh, then another thing is even if you did know this formula that I just explained,


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[Assistant] So, and I know, you know, how much of my, how much money today is, how big my savings are relative to what the market values of my social security benefits.


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[Assistant] So the Republicans say, you know, people just don't have any idea what the value of their benefits are. Even if they happen to know the formula I just explained, nobody really can calculate what it is.


[User] Please transcribe this text

[Assistant] Can you tell me how much, you know, what's the effective tax rate? Let's say I'm in the top tax bracket. What is my effective tax rate on Social Security? I mean, what fraction of my taxes are being taken away from me?


[User] Please transcribe this text

[Assistant] You know, because my benefits are going to be less good because I was one of the highest earners.


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[Assistant] Who can, you know, there's virtually no one in the country who's able to answer that question. A Republican would say, what kind of a program is this? If nobody knows, you know, okay, they'll say, you know, we may sign on to the idea that the rich should give some of their money to the poor, but at least tell us how much of our money we're giving to the poor. We don't even know.


[User] Please transcribe this text

[Assistant] Okay, and then the Republicans say, we'd like, uhm,


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[Assistant] Equity like returns, you know, why is it, this is what George Bush said, why is it that our returns are going to be 2%? I'd like to know that my money is being invested in the stock market and earning a really, you know, what I can get in the stock market. I mean, I'm, you know,


[User] Please transcribe this text

[Assistant] Anyway, uh, Republicans are, you know, everybody, you know, they're savvy investors like to think that the money that they're being forced to save can get the same rate of return as they get in the, uh, they could get for themselves. Why should they be cheated in their government? Nevermind that they have to give away part of the money, the part that they're not giving away, at least, auto-earn equity returns. And then there's the, uh,


[User] Please transcribe this text

[Assistant] Republicans tend to think that choice is a good thing, not a bad thing, and so if you let people decide how to invest, it'll be better, uh, you know, be better for them than if you, uh, even if some of the people make mistakes.


[User] Please transcribe this text

[Assistant] Yes, but no, I don't think so because...


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[Assistant] Uhm, so his question is, since we don't know how long we're going to live, how can you pro- and the program continues to insure you for your entire life and pay you for your entire life, how can you figure out what the market value of that is?


[User] Please transcribe this text

[Assistant] Well, the fact is, there is no general market you can look, so you're right, there is no general market you can look at now, today, that will tell you


[User] Please transcribe this text

[Assistant] Suppose we pay, you know, a fixed amount of money for the average person in the economy, what's the present value of that? However, that doesn't mean we couldn't have a market like that.


[User] Please transcribe this text

[Assistant] My solution is going to involve precisely a market like that.


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[Assistant] So, uh, it could be, you could, so to answer his question, it's not inherently impossible to attach a market price to paying somebody for the rest of his life.


[User] Please transcribe this text

[Assistant] After all, you could imagine Social Security now paying, you know, let's say 50 million people, the people who are now retired, each of them is going to be paid for the rest of his life according to a rule which we all understand, you know, it just goes up with inflation. So you could say,


[User] Please transcribe this text

[Assistant] Suppose you owned 1% of all those payments.


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[Assistant] Every 70 year old today is going to get from Social Security. That's a well defined thing. It could trade in the market. You're going to see that the market, as soon as we start talking about this, there are an incredible number of


[User] Please transcribe this text

[Assistant] Derivative instruments, they're called, that trade in the market, that could be a new one, and uh, then there would be a market price. So you're going, so you're,


[User] Please transcribe this text

[Assistant] The rules of Social Security are whether you think they're bad or good for some other reason. I've tried to list the ones that struck me as the most important. Yeaah.


[User] Please transcribe this text

[Assistant] Okay, so if you, uh, you can always do this calculation, which I'm going to, which has historically been done, I'm going to show you those numbers in a second.


[User] Please transcribe this text

[Assistant] You can always look at how many ta- let's take some, let's take, you know, someone my father's age. My father actually died. But you know, a couple years ago, my father, he could look back at his whole life and he could look at how much money he paid in Social Security taxes when he was young.


[User] Please transcribe this text

[Assistant] And then he could look at what benefits he got every year until that point, and let's say it was the day after he died, I could look at what he got every year until he died. So then I could just, you know, that's just a sequence of cash flows. We know how to calculate the internal rate of return of that sequence. That's his rate of return.


[User] Please transcribe this text

[Assistant] Okay, and I showed you in the graph a long time ago that that was actually done. We've done that. Leimer did that.


[User] Please transcribe this text

[Assistant] So Bush is saying, why should you, young Yale undergraduate, why should you be satisfied with less than 2%? If instead of paying those taxes to Social Security, you put them in the stock market, you'd get lots more than 2%, probably. So why, why be satisfied with 2%?


[User] Please transcribe this text

[Assistant] Okay, and Gore would say, well now that we've got this pay as you go system, you can't possibly put it in the stock market because then the old people aren't going to get it.


[User] Please transcribe this text

[Assistant] Any other reasons you think the program is good or bad? Social Security, should we get rid of, you know?


[User] Please transcribe this text

[Assistant] What's your feeling about whether we should get rid of it or not or privatize it or change it?


[User] Please transcribe this text

[Assistant] Other problems with Social Security is that it discriminates.


[User] Please transcribe this text

[Assistant] Uh, it doesn't treat everybody equally, so who were the most, so for, his first example was


[User] Please transcribe this text

[Assistant] Who, who among you can you predict is gonna, are gonna live less long than others among you, at least historically? Presumably that will stay true. Yup. Men.


[User] Please transcribe this text

[Assistant] Okay, so men live six years less long than women, so men are getting a bad deal. Now,


[User] Please transcribe this text

[Assistant] Historically, although this is rapidly changing, but black men lived even less long than, you know, even worse by a considerable degree. So you'd think it discriminated against black men. Uhm, so, uh,


[User] Please transcribe this text

[Assistant] Black men also tended to be poor on average though, so they were getting a better return per year than rich white men. But anyway, that, uh, uh, black men with the same earnings


[User] Please transcribe this text

[Assistant] Got a much worse deal historically on social security than a white man with the same earnings, because on average the black man died before the white man.


[User] Please transcribe this text

[Assistant] I regard those as less critical, though, than the problem of thing going broke, and these things, which have split the country, it's been impossible, so basically everybody thinks this is a logical contradiction. You can't do what the Republicans want, at the same time do what the Democrats want. So, what did you do?


[User] Please transcribe this text

[Assistant] Anything else anyone wants to say? Someone else had their hand up before.


[User] Please transcribe this text

[Assistant] Why did the system become such a mess? How did it go broke? How should we have expected it to go broke?


[User] Please transcribe this text

[Assistant] So, did it all happen because there was the baby boom?


[User] Please transcribe this text

[Assistant] Alright, so I want to tell a little parable. So, that has nothing to do with the baby boom. Okay, so.


[User] Please transcribe this text

[Assistant] Imagine that, uh, and I'm saying it in terms of fathers and sons, but I could say mothers and daughters, okay, so historically it was


[User] Please transcribe this text

[Assistant] Men working most of the time, but let's do, let's do mothers and daughters, okay? So suppose a mother, uhm, is old now and sick.


[User] Please transcribe this text

[Assistant] And she has, she needs an incredibly expensive operation. A million dollars to, uh, you know, to save her. Or, you know, to extend her life.


[User] Please transcribe this text

[Assistant] My daughter wouldn't have any life, and her daughter wouldn't have any life, and her granddaughter wouldn't have any life, and you know, my mother's responsible for not just me, but all these generations after me. Why should I be the one to pay the entire benefit of my mother's operation? So you know what I'm gonna do? I'll give her the million dollars.


[User] Please transcribe this text

[Assistant] I sacrificed for your grandmother, my mother. I put up a million dollars to save her. I'm broke now. You know, maybe I don't need an operation, but I'm basically totally broke because I spent so much money rescuing my mother.


[User] Please transcribe this text

[Assistant] You know, why don't you do the same for me that I did for my mother and give me a million dollars?


[User] Please transcribe this text

[Assistant] And by the way, you know, you can use the same argument with your daughter, and so it's really not so bad because you're gonna get the money back when you're old. And so the granddaughter agrees, you know, the granddaughter agrees and pays the daughter, and then when she gets old she does the same thing. It goes on for generation after generation. Now, let's say it went on forever. How did this happen? Have we just, I mean, did the money get


[User] Please transcribe this text

[Assistant] Where did the million dollar operation get financed? How did that happen?


[User] Please transcribe this text

[Assistant] Well you got a million dollars back, remember, when you were old. Okay.


[User] Please transcribe this text

[Assistant] Exactly, so that's the whole point. The point is that the daughter made, the dollar paid, so just to repeat what he said, the dollar, in case you couldn't hear it, the dollar, the first daughter paid a million dollars when she was young. Yes, she got the million dollars back, but when she was old, and that's a long time later, so that whole time she made a zero percent interest. Now maybe the interest rate,


[User] Please transcribe this text

[Assistant] Was, let's say over that many years, you know, uh,


[User] Please transcribe this text

[Assistant] You know, we're talking about 30 years or something, or 40 years, a large amount of time. So the interest rate...


[User] Please transcribe this text

[Assistant] Could easily, you know, if 40 years, you divide 40 into 72, you know, it's like one and three quarters or something. So if the real interest rate was one and three quarters, something like that, you could easily imagine that the, that was the doubling rule, that, you know, $100 could have been turned into $200.


[User] Please transcribe this text

[Assistant] So, the first daughter, who took her $100, could have had $200 by the time she was old, she only got $100 when she was old. So in present value terms, she gave up $100 when she was young, she got, in present value terms, $50 when she was old. So she gave away $50. She paid for half the population, half the...


[User] Please transcribe this text

[Assistant] Operation. Now where did the rest of the fifty dollars come from? Well the granddaughter


[User] Please transcribe this text

[Assistant] Gave a hundred when she was young, got a hundred when she was old. So in present value terms, she also lost fifty dollars, exactly the same amount as the first daughter. She gave up the same thing. Her contribution was just as big. Of course, it came a lot later. So in present value terms, at the time of the operation, the granddaughter's contribution is discounted twice. So it's a half of a half.


[User] Please transcribe this text

[Assistant] So, she only contributed, in present value terms, $25 to the, to the operation. And then the, the great granddaughter, you know, she, she herself looks at it as a $50 contribution, but from the point of view of the original operation, it's discounted three times, so by eight, so it's twelve and a half.


[User] Please transcribe this text

[Assistant] So fifty plus twenty-five plus twelve and a half plus six and a quarter, blah blah blah, forever, adds up to the hundred. So basically, this


[User] Please transcribe this text

[Assistant] This parable shows how a daughter could share the cost of, uh,


[User] Please transcribe this text

[Assistant] Her mother's operation, uh, among all the descendants,


[User] Please transcribe this text

[Assistant] Pay completely for the operation with each generation making the same contribution, the same sacrifice. Of course the generations who make it earlier are helping the mother more, but the point is every generation has made exactly the same sacrifice and paid for, and that's how the mother's, the original mother's operation got paid for.


[User] Please transcribe this text

[Assistant] Okay, now, what happens in Generation 10? The great-great-great-great-granddaughter is gonna say, you know, I never even heard of this original mother. I couldn't care less about her operation. It wasn't a very good operation anyway, because she died three years later, because medicine was so bad at the time.


[User] Please transcribe this text

[Assistant] So, you know, why should I be paying my contribution to that original mother's operation? Yes, I know that she's responsible for my life, but you know, I don't know anything about her. I don't even know her name anymore. How come I have to keep paying for it? So, I just want to stop. Now, why is it going to be actually difficult for that, that generation to stop?


[User] Please transcribe this text

[Assistant] You know, I just don't want to give my money to that first generation. I couldn't care less. Let's stop right now. Yup.


[User] Please transcribe this text

[Assistant] Exactly. So you can't stop just in the middle because it's like the gift is being reenacted every generation. So the new generation that stops, you know, even if they understand where the whole thing began, they're not going to screw


[User] Please transcribe this text

[Assistant] Okay, so that's what, uh, okay, so to say the parable, uh, very shortly, the first generation that got taxes


[User] Please transcribe this text

[Assistant] Uhm, got benefit without contributing anything, that was a huge amount of money that they got. Every generation after that lost money, but that was all, you know, a way to pay the first generation, and you can't stop the cycle without screwing your own parents. And so it's very difficult for the thing to be stopped. Okay, so, now you could


[User] Please transcribe this text

[Assistant] Make it a little bit more realistic by saying, uh, every people, every generation, uh, the interest rate, you know, there's a growth


[User] Please transcribe this text

[Assistant] You know, wages grow every generation, say between 1 and 2 percent in real terms. That's the historical rate of growth.


[User] Please transcribe this text

[Assistant] By the way, it hasn't been so great lately, but anyway, historically,


[User] Please transcribe this text

[Assistant] So on an annualized basis, if you did the same thing with each generation paying, uhm,


[User] Please transcribe this text

[Assistant] 12% of their youthful wages you would find


[User] Please transcribe this text

[Assistant] That, now we're coming closer to reality, that every generation was going to get


[User] Please transcribe this text

[Assistant] dollars out of a hundred, say, but their daughter, that lady's daughter, you know, thirty years later, the wages would have grown by one to two percent every year for those thirty years. So twelve percent of that bigger number would go to that daughter, and so there would be a rate of return between one and two percent.


[User] Please transcribe this text

[Assistant] And that's exactly what Bush and everybody else is predicting. So now you understand where the 1-2% comes from that, uh, you're, you're going to get. The population is stable, the ratio of young to old, the rate of return on Social Security is going to be equal to the rate of growth of wages.


[User] Please transcribe this text

[Assistant] So it's not zero, as in the first parable, because wages are actually going. It's between one and two percent.


[User] Please transcribe this text

[Assistant] Which is a bad return compared to the rate of interest. And that's why every generation is losing. Okay, so let's, uh, let's do one more thing. So the baby boom. Does the baby boom make things worse or better?


[User] Please transcribe this text

[Assistant] Okay, so how can you think about this? It's so simple minded to think this way. It's amazing. The public is so confused about this.


[User] Please transcribe this text

[Assistant] So, suppose you have a baby boom generation. So you're going on with all these mothers. We're back to the mother and the daughter with, uh, you know, one million.


[User] Please transcribe this text

[Assistant] And so let's say there's one mother, one daughter, you know, one person in every generation. All of a sudden, there's a baby boom generation. But of course, to be old, you have to be young first.


[User] Please transcribe this text

[Assistant] So let's say in some generation, after a bunch of times, there are two daughters instead of one daughter.


[User] Please transcribe this text

[Assistant] Uh, they're each paying, uh, okay, there are two daughters instead of one daughter. So each of them is now going to pay, uhm,


[User] Please transcribe this text

[Assistant] Half of their money goes to paying off the, the mother when she's old, just like it would have usually happened. But the baby generation, because, you know, it's young first, that means when I was young, my generation was making a huge amount of money, paying a huge, we're doing it now, that's why there's a surplus.


[User] Please transcribe this text

[Assistant] There's this gigantic surplus being put into social security. And so when my generation gets old, the dollar that didn't have to go to the mother because there was an extra dollar there, that could be earning the rate of interest. And so at the end, when we get old, there are going to be two of us.


[User] Please transcribe this text

[Assistant] I'm saying that, you know, every generation, you give one, you get one. You give one, you get one. You give one, you get one. All of a sudden, there are two people.


[User] Please transcribe this text

[Assistant] So they're, they're each giving one, so none, none of them is better off, is worse off than before. When they get old, they're gonna need to, you've only got one person contributing. So here's the baby boom, you know, double the size of all the other generations.


[User] Please transcribe this text

[Assistant] So, but, you know, when they're old, it looks like a problem. Who's gonna take care of these people? They're not enough young people to take care of them. But remember, they were young before they were old. So since this mother only needs one, and there are two of these daughters, the extra one could be invested, and the one that's invested is gonna make a rate of return and become more than one, like close to two.


[User] Please transcribe this text

[Assistant] And so you'll have the one from the new daughter plus almost two from, from, that was, you know, saved and earned up, you know, that's in the Social Security Trust Fund and earning interest.


[User] Please transcribe this text

[Assistant] So basically, what did the baby boom do? It staved off the original problem. So let's just think about this one more time.


[User] Please transcribe this text

[Assistant] Suppose that you have a slightly more realistic example, the kind you're going to do in the problem set. So suppose that you've got, uhm, uh,


[User] Please transcribe this text

[Assistant] People live from 20 to 80 and they pay taxes and work from 20 to 60 and they retire from 60 to 80.


[User] Please transcribe this text

[Assistant] Okay, and everybody's paying 12 dollars in social security taxes when working and there's a zero rate of interest and they're getting 24, well maybe I have a rate of interest, they're getting 24 dollars.


[User] Please transcribe this text

[Assistant] So the system balances every year because in every generation,


[User] Please transcribe this text

[Assistant] These are the birth years. The thing started, you know, in 1938. And then,


[User] Please transcribe this text

[Assistant] They collect the taxes for a little while and then they start, I mean they, uh,


[User] Please transcribe this text

[Assistant] So you see in 1940, the people who are 60, the 1880 generation, would have two decades to live. So these guys...


[User] Please transcribe this text

[Assistant] You know, the, the, the 60 year olds and the, you know, the 60 year olds here are, and these 70 year olds, the 70 year olds and the 60 year olds are collecting money.


[User] Please transcribe this text

[Assistant] Now the original start of Social Security was a little fuzzy, they didn't want to pay people who had never contributed, but, so these 70 year olds weren't really getting anything, but,


[User] Please transcribe this text

[Assistant] Okay, let's just keep for the simple steady state. So the, uhm, so the point is the 60 year olds are collecting 24 dollars.


[User] Please transcribe this text

[Assistant] And then here are the contributors. This new generation has now come on board to contribute. So again, there's balance every generation. Okay, but now who are the winners and the losers?


[User] Please transcribe this text

[Assistant] Well, you know, it's much worse than it sounded at first glance, because people live longer than one year. So you see, these people, from the 40s, in the 40s, all these guys were gaining. So this generation of 1870s, they have almost an infinite rate of return. They made no contribution.


[User] Please transcribe this text

[Assistant] Okay, these guys, they also made no contributions and they're getting huge returns because they're getting them, you know, 20 years of returns.


[User] Please transcribe this text

[Assistant] But it's worse than that, because when we go to the next generation, these guys, they actually contributed when they were in their 50s, but that wasn't very long before they retired, and they got, you know, 24, they got, uh,


[User] Please transcribe this text

[Assistant] You know, they've got $24 of benefits and $24 of benefits having only paid $12 of taxes. Of course, this came later, but 48 compared to 12, they got a gigantic rate of return. Well, you go to the next generation, and they're making out like bandits too, because they only contributed twice.


[User] Please transcribe this text

[Assistant] Okay, so in the forties, a bunch of people were making money, getting benefits who didn't make any contributions.


[User] Please transcribe this text

[Assistant] In the 50s, a bunch of people were making, uh, getting benefits who made no contributions. In the 60s, there are still people getting benefits who made no contributions. It's not until you get to the 70s, 40 years, or the 80s, what am I talking about, the 80s, once people are fully retired, you know, have made


[User] Please transcribe this text

[Assistant] 40 years of contributions. So from 40 to 80, it's not until the 80s.


[User] Please transcribe this text

[Assistant] If you just do the distributive law of arithmetic and you realize that all of these things go off to infinity because they're discounted, you can sum them in either direction. You can sum down the column.


[User] Please transcribe this text

[Assistant] But therefore, if you add up the rows, which is what each generation gets, they also have to add up to zero. But you see, there's so many original generations. This one got a huge benefit. This one got huge benefits. The present value of their contributions relative to their benefits is incredibly positive for that generation.


[User] Please transcribe this text

[Assistant] For this generation, it's still incredibly positive. Remember, this is 12-12, and this is 24-24. For this generation, it's probably still positive. Not till you get to here has it become, you know,


[User] Please transcribe this text

[Assistant] Well, not until you get to here does something change. So this is a positive number, this is a big positive number, this is a gigantic positive number, and this is a huge positive number. You add up over all these things positive,


[User] Please transcribe this text

[Assistant] Present failure, the same bad rate of return. And there were so many generations that got positive amounts of money. So in today's dollars, can you tell me how much money that we gave away to these early generations? Can you take a guess? Just quarter of magnitude.


[User] Please transcribe this text

[Assistant] Couple trillion. Does someone want to guess? A reasonable guess? Does someone want to make another guess? Just so you get an idea of the size of the program. Does anyone know what GNP is in the country?


[User] Please transcribe this text

[Assistant] Okay, 14 trillion, something like that is GNP. So what, so what do you guess this number is? Okay, it's 17 trillion.


[User] Please transcribe this text

[Assistant] So he guessed 2 trillion. It's 17 trillion. So we made a staggering transfer of income


[User] Please transcribe this text

[Assistant] To these people at the beginning, and now somebody's gotta pay for that. And it's not a trivial thing to pay for because 17 trillion. If everybody gave every corporation, every, you know, person in the whole country gave away all the money they made for a year, we still wouldn't pay it down. Okay? So, that's a big amount that's hanging over everybody.


[User] Please transcribe this text

[Assistant] Okay, and that's what the social security problem is. We gave away so much money at the beginning, if we keep the system in balance, we're never, we have to gradually give all the money back.


[User] Please transcribe this text

[Assistant] Power in the 50s and 60s, you know, when we're still helping all those people. But the point is there's a tremendous transfer of wealth to the retired generations of that era.


[User] Please transcribe this text

[Assistant] And that's the reason why everybody's getting such a bad rate of return now. It's not because the money's being lost or thrown away. Okay, so what I'm telling you now is not controversial.


[User] Please transcribe this text

[Assistant] You know, I don't think many people understand it, but it's, you know, everyone who does understand it agrees with it. When we come to my solution, they're not going to agree. But anyway, so what I'm telling you, so...


[User] Please transcribe this text

[Assistant] So just to compute, so in the problem set you have to compute an example. So let's just see how this would work.


[User] Please transcribe this text

[Assistant] So suppose, from the point of view of a 10 year old, just doing the example I did, suppose there's a 15% rate of interest.


[User] Please transcribe this text

[Assistant] And you're going to contribute in your 20, 12 dollars, 12 percent, 12 dollars,


[User] Please transcribe this text

[Assistant] In your, so no growth in the economy, so it's $12 all the time when you're in your 20s, 30s, 40s and 50s.


[User] Please transcribe this text

[Assistant] You discounted at the 15% rate of interest. You're going to be doing, you know, contributing $12 in your 30s. You discounted at 15% twice.


[User] Please transcribe this text

[Assistant] In your 40s, you're giving up $12, you're discounting it to 15% three times, and in your 50s, you're doing it $12, you're discounting it four times. So that's the present value of what you've


[User] Please transcribe this text

[Assistant] You know, from the point of view of a child, you know, a young girl looking at what you're going to be forced to contribute over your life, those are the taxes you can expect to pay in present value terms. Now, of course, when you get old, in your 60s and 70s, you're going to get


[User] Please transcribe this text

[Assistant] Uhm, you know, you can get benefits, $24, much higher than the taxes you paid, but by then, it's five decades later, so you're just counting it five times, and when you're in your 70s, you're just counting it six times.


[User] Please transcribe this text

[Assistant] So, you have to divide, you know, discount by 6, and so the present value of that is way less than the present value of this.


[User] Please transcribe this text

[Assistant] So, you see that the loss in present value terms is going to be $11, $11 out of 34, which is what your benefits were, so your loss is something like 34%.


[User] Please transcribe this text

[Assistant] So this is just, uh, you know, I just made up these numbers. The actual loss, which I computed for the real economy,


[User] Please transcribe this text

[Assistant] So that's how, how big each generation, a quarter of the typical, so here it's 34%, a quarter to a third, in this example, of each generation's social security taxes are simply being lost, frittered away, and handed, you know, basically used to pay off that, that origin, the debt from the original generation. Okay?


[User] Please transcribe this text

[Assistant] Now, if you want to look at the total present value loss, starting in the 1930s,


[User] Please transcribe this text

[Assistant] You have to, for people born in the 1930s, you have to look at


[User] Please transcribe this text

[Assistant] Okay, and so I just do one, but you're gonna do that anyway. Alright, so, that's it. So now, if you look at which generation's got the most money,


[User] Please transcribe this text

[Assistant] Uhm, you know, at the beginning there weren't many people in social security, and so the amount of money that they got, the benefits the really old got, there were so few of them, that if you added up it wasn't that big. But, so the generation that benefited the most was my father's generation, and James Tobin's generation.


[User] Please transcribe this text

[Assistant] Tobin, as we'll see in a second, he used to tell me all the time, he was Yale's most famous economist.


[User] Please transcribe this text

[Assistant] And, uh, he also went to the same high school he went to in Illinois.


[User] Please transcribe this text

[Assistant] He used to tell me that the problem with Social Security was we didn't have enough, you know, I didn't have enough children.


[User] Please transcribe this text

[Assistant] And I would tell him the problem with Social Security was we gave him his birthday too much money. And that's why we're all suffering now.


[User] Please transcribe this text

[Assistant] So you can see all the generations and which one, which generation got the most amount of, uh, money. So that's also calculated. Okay.


[User] Please transcribe this text

[Assistant] So, now, this parable is not exactly accurate.


[User] Please transcribe this text

[Assistant] So by the way, so you know in the parable we should have had a crisis in 19, uhm,


[User] Please transcribe this text

[Assistant] 70. It should have been 1980. I told you the people in 1980 should already have, uh, realized the generation


[User] Please transcribe this text

[Assistant] Starting to work in 1980, so born in 1960, a lot


[User] Please transcribe this text

[Assistant] What kept the gen, you know, what kept the problem from manifesting itself until now, well what kept the problem from manifesting itself until now is actually quite easy to see, is there were a lot more young people per old in, in the historical generations, and you know, now we're leveling off and everybody thinks they're gonna be


[User] Please transcribe this text

[Assistant] You know, two young people for every old. The story I sort of told, working from 40 to 60 and retired from 60 to 80 in a stable population, that's coming close to what we're converging to. So they're going to be two young for every old. There used to be three, four, and five. Now how could there have been so many young for all the old?


[User] Please transcribe this text

[Assistant] Well, you know, women at the beginning weren't, you know, in this period, they were working. Women were surging into the labor force. There were a huge number of young women working, but their mothers actually weren't working. They had not worked, and so they were getting half their husband's benefit.


[User] Please transcribe this text

[Assistant] Not the full benefit that these daughters who are working their whole lives are gonna get. They're not gonna take half their husbands benefits, they're gonna take all their own benefits, which are gonna be a lot higher.


[User] Please transcribe this text

[Assistant] Uhm, you know, then there was the baby boom generation. The baby boom generation, far from causing the problem, has deferred the problem. You know, there are all these surpluses because we were working so long. Okay, so uhm, I don't want to get into all the details. Uhm, people are living longer now too, so that's another problem.


[User] Please transcribe this text

[Assistant] Okay, so uhm, too much complication here. Okay, so what can you do about this? Well, there are obvious things to do, which


[User] Please transcribe this text

[Assistant] So one thing you could do is you could say, well if we're supposed to be paying the average young person forty percent,


[User] Please transcribe this text

[Assistant] You know, we can maybe shave the 40% down a little bit. Let's make the social security tax 18% instead of 12%. So you could raise it by 6%.


[User] Please transcribe this text

[Assistant] Okay, that would be, uh, a shocking, okay, so I think that nobody wants to actually do that because, you know, an increase of 6% in the tax rate is an astronomical increase.


[User] Please transcribe this text

[Assistant] And, uh, you can read in the newspapers how low Congress is to raising taxes. Okay, another obvious thing you could do...


[User] Please transcribe this text

[Assistant] If you could reduce the, you know, raise retirement age.


[User] Please transcribe this text

[Assistant] So instead of saying it's 65 because people live, you know, till 80, make it start at 67. So you don't have to pay them, you know, for 15, 13 years instead of 15 years. In this example, you know, 18 years instead of 20 years. So you can keep raising the retirement age. The problem with that is,


[User] Please transcribe this text

[Assistant] Not the retirement age, the age you start collecting social security. The problem with that of course is that people retiring


[User] Please transcribe this text

[Assistant] Not later and later, even younger and younger, believe it or not. And so you're getting this huge gap between when people retire and when they start getting Social Security. So there's a limit to how, so obviously you should do a little bit of that, but there's a limit of how much, how much of that you can do.


[User] Please transcribe this text

[Assistant] Okay, so what does it mean to privatize? How are they going to do that? So what Bush had in mind, what Bush had in mind was the following. He said, uh,


[User] Please transcribe this text

[Assistant] What is privatization? It involves three things.


[User] Please transcribe this text

[Assistant] It's the creation of individual accounts. Your name goes on it. So it's your account, your money, and no one can take it from you.


[User] Please transcribe this text

[Assistant] Okay, so it's individual accounts and pre-funded, and it's diversification. You're supposed to invest not just in government bonds, but in the stock market, whatever else you want. So those are the three cornerstones of, of, uh, at least the way I've explained them, of privatization. Three things it accomplishes. It gives you your name on the money.


[User] Please transcribe this text

[Assistant] It's your money, that's the money that's going to pay you when you get old, pre-funding.


[User] Please transcribe this text

[Assistant] And you can invest in a wider range of assets, especially the stock market, which is what everybody seems to want to put their money in or at least used to until we had this last crash. On the other hand, the stock market is going up a lot, so people are getting less nervous about it. So, uhm, those things are different. They're not the same.


[User] Please transcribe this text

[Assistant] So Bush said, I want to privatize. I want the 12% that you pay in Social Security taxes. I want 4% of that.


[User] Please transcribe this text

[Assistant] For you to be able to put that in your private account in the stock market with your name on it.


[User] Please transcribe this text

[Assistant] Okay, so Gore said, well that's ridiculous. If there's only 8% then left to give to the old people, the old people are gonna get cheated.


[User] Please transcribe this text

[Assistant] So, how, how is Bush gonna handle that? What do you think he's gonna do?


[User] Please transcribe this text

[Assistant] So, and I don't know what Gore was thinking, especially since I was


[User] Please transcribe this text

[Assistant] You know, but he just said in the debate, that's impossible, you'll just screw all the old people, so.


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[Assistant] Okay, so you're supposed to be giving 12%, you know, 12 here, minus 12 here.


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[Assistant] And then getting some return over here, and now Bush is saying, I only want to give you eight dollars, and the other four you put in your, uh, social security account, and then you're gonna get maybe a much bigger return here, cause, uh, sorry, in your


[User] Please transcribe this text

[Assistant] So Bush would say, yes, let people put their names on it, force them to save and contribute the whole $12. Bush agrees that you gotta force young people to save because they're not gonna do it otherwise. So you put the $4 on their private account, but it's their name on it, and then it grows to some huge number, you know, 20, instead of just doubling by the time they get old. And so that huge account is their money. So uhm, that's what Bush would say.


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[Assistant] So, Gore would say, well if you take these four dolls and give it to them, how are you going to, uh,


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[Assistant] How are you gonna, you know, pay the old people who need the 12 right up here?


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[Assistant] Well, it's not like Bush and his advisors didn't think about that. That would, you know, that would be a colossal blunder. Of course they thought about that. So what were they going to do?


[User] Please transcribe this text

[Assistant] Okay, these people. So if this, if you chose


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[Assistant] You have to pay the interest at the end of 30 years. So this 8 comes out of your 20.


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[Assistant] Okay, so, so the government, so these people, uh, so these people pay back the aid.


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[Assistant] So basically, to say it in a different way, the way you get to invest in the stock market according to the Bush privatization rules is these people effectively, they're giving the $12 anyway to the old, but they get to borrow $4 and put it in their private


[User] Please transcribe this text

[Assistant] Stock market account, and then they have to pay back the amount they borrowed with interest, which is eight.


[User] Please transcribe this text

[Assistant] But if the stock market does better than the, uh, than the interest rate, then of course they make out better. They get 20 minus 8, which is 12.


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[Assistant] Okay, so is that clear how he did it? So it's not a logical impossibility.


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[Assistant] The problem with the Bush plan, of course, is that if the money you put in the stock market earns the same rate of return that you would have gotten anyway, uh, you know, if the stock market earns the same rate of return 100% over all those years as the bond market, then this


[User] Please transcribe this text

[Assistant] Private stash you have just goes back to paying off the government debt and you haven't gained anything in the stock market. You get the same social security benefits you would have had anyway. So these guys who opt into the privatization program, let me repeat the Bush plan, if you opt into the privatization program you effectively


[User] Please transcribe this text

[Assistant] Do everything you did before in social security. You pay your taxes that go to the old people, you get the benefits just like you did before, but on top of it you have the right to borrow four dollars from the government, invest it in stocks, and then at the end you have to pay back the government out of your stock market returns.


[User] Please transcribe this text

[Assistant] So, you're betting on whether the stock market is going to do better than the bond market. That's effectively what the plan was. Okay?


[User] Please transcribe this text

[Assistant] And then you'd have to owe the government eight. Where is the government gonna get the other two dollars from? It's gonna reduce your social security contributions by two. So that's the government's plan.


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[Assistant] Okay, so in the problem set, I gave you three problems. You can only do two of them now. The last one, I didn't get far enough to have you do, so just do two of them.


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[Assistant] And I'm going to give my own, uh, plan next time and also


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[Assistant] Do a little mathematics. It's going to turn out that it's quite interesting to, to math this up a little bit.


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[Assistant] So I'm, I always make fun of consultants and business school students because I was both. But it has that very consulting, but it really is the kind of one plus one equals two. If I do this, the company does this. If I do this, the company does that. It's a sign.


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[Assistant] You know, it's the art of administering a business so that people, I'm sorry, the art of accomplishing more than the science of management teaches you is possible.


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[Assistant] To follow what the mission is, what you want, to follow whatever it is with true enthusiasm.


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[Assistant] And it is that enthusiasm which transcends science, which makes it seem like 1 plus 1 equals 3, not 2.


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[Assistant] So people talk a lot about, well, how do you get there? How do you be a great leader? And I think it's a lot of things and I don't pretend to have the answer.


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[Assistant] But it's a bunch of things about who you are as a person. People follow people they respect and trust. If you don't have respect and trust, you don't have any hope of being a great leader.


[User] Please transcribe this text

[Assistant] Some of the stuff are skills you can learn. People look at people who are great speakers, who are really convincing orators, and they say, wow, I want to follow that person. People who can tell great stories, great narratives.


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[Assistant] Which is the purpose of the leadership. And that's what I mean by scaling a vision.


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[Assistant] That you can lead all day long, but if you're leading people to, you know, make soap.


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[Assistant] In a previous video, you've already seen all the basic building blocks of the inception network.


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[Assistant] In this video, let's see how you can put these building blocks together to build your own inception network.


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[Assistant] So the inception module takes as input the activation or the output from some previous layer. So let's say for the sake of argument, this is 20 by 28 by 192, same as our previous video.


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[Assistant] The example we worked through in DEF was the, uh, one by one followed by five by five layer.


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[Assistant] And this was the example we worked through on the last slide of the previous video.


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[Assistant] Then to save computation on your 3x3 convolution, you can also do the same here.


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[Assistant] And then, uhm, maybe you want to consider a one by one convolution as well. There's no need to do a one by one convolution followed by another one by one convolution. So there's just one step here and let's say this outputs 28 by 28 by 64. And then finally is the, uhm, pooling layer.


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[Assistant] So here we're going to do something funny. In order to be able to concatenate all of these outputs at the end, we are going to use the same


[User] Please transcribe this text

[Assistant] Type of padding for pooling, so did the output height and width.


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[Assistant] It's still 28 by 28, so we can concatenate it with these other outputs.


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[Assistant] But notice that if you do max pooling, even with same padding, uh, 3x3 filter is right at 1. The output here will be 28x28.


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[Assistant] It'll have the same number of channels and the same depth as the input that we had here.


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[Assistant] So this seems like it has a lot of channels. So what we're going to do is actually add one more one by one conv layer to then do what we saw in the one by one convolutional video to shrink the number of channels so as to get this down to 28 by 28 by let's say 32.


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[Assistant] And the way you do that is you use 32 filters of dimension, one by one,


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[Assistant] So that's why the output dimension has a number of channels shrunk down to 32.


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[Assistant] So then you don't end up with the pooling layer taking up all the channels in the final output.


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[Assistant] And finally, you take these, all of these blocks, and you do


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[Assistant] Channel concatenation, just concatenate across this 64 plus 128 plus 32 plus 32, and this, if you add it up, this gives you a 28 by 28


[User] Please transcribe this text

[Assistant] By, uhm, 256 dimensional output. Where in channel concat there's just, you know, this, uh, concatenating the blocks that we saw in the previous video.


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[Assistant] So this is one inception module.


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[Assistant] And what the inception network does is, more or less, put a lot of these modules together.


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[Assistant] And you notice a lot of repeated blocks in this. Maybe this picture looks really complicated, but if you look at one of the blocks there, that block is basically the inception module that you saw on the previous slide.


[User] Please transcribe this text

[Assistant] And subject to little details I won't discuss, you know, this is another inception block. This is another inception block.


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[Assistant] Uhm, there's some extra max pooling layers here to change the dimension of the height and width, but that's another inception block.


[User] Please transcribe this text

[Assistant] But the inception network is just a lot of these blocks that you've learned about repeated, uh, to different positions of the network. But so if you understand the inception block from the previous slide, then you understand the inception network.


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[Assistant] It turns out that, uh, there's one last detail to the inception network, if you read the official research paper, which is that there are these additional side branches that I just added.


[User] Please transcribe this text

[Assistant] Well, the last few layers of the network is a fully connected layer.


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[Assistant] Followed by a softmax layer to try to make a prediction. What these side branches do is it takes some hidden layer and it tries to use that to make a prediction. So this is actually a softmax.


[User] Please transcribe this text

[Assistant] And you should think of this as maybe just another detail of the inception network, but what it does is it helps to ensure that the features computed, even in the hidden units, even at intermediate layers, that they're not too bad for predicting the output cost of a image. And this attempt disappears to have a


[User] Please transcribe this text

[Assistant] Oh, and by the way, this particular inception network,


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[Assistant] was developed by authors at Google who called it Google Net, spelled like that, to pay homage to the La Net network that you learned about in an earlier video as well.


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[Assistant] So I think it's actually really nice that the deep learning community is so collaborative.


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[Assistant] And that there's such strong healthy respect for each other's work in the deep learning community.


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[Assistant] The inception paper actually cites this meme for we need to go deeper and this URL


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[Assistant] is an actual reference in the inception paper, which links to this image.


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[Assistant] And if you've seen the movie titled The Inception, maybe this meme will make sense to you, but, uh, the authors actually cite this meme.


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[Assistant] as motivation for needing to build deeper neural networks and that's how they came up with the Inception architecture.


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[Assistant] So I guess it's not often that research papers get to cite internet memes in their citations, but in this case, I guess it worked out quite well.


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[Assistant] So, to summarize, if you understand the inception module, then you understand the inception network, which is largely the inception module repeated a bunch of times throughout the network.


[User] Please transcribe this text

[Assistant] Since the development of the original inception module, uh, the authors and others have built on it and come up with other versions as well. So there are research papers on


[User] Please transcribe this text

[Assistant] Newer versions of the inception algorithm and you sometimes see people use some of these later versions as well in their work like inception v2, inception v3, inception v4. There's also an inception version that's combined with the resonant idea of having skip connections and


[User] Please transcribe this text

[Assistant] That sometimes works even better. But all of these variations are built on the basic idea that you learned about in this and the previous video of coming up with the inception module and then stacking up a bunch of them together.


[User] Please transcribe this text

[Assistant] And, uh, with these videos, you should be able to read and understand, I think, the inception paper, as well as maybe some of the papers describing the later variations as well.


[User] Please transcribe this text

[Assistant] So that's it. Uh, you've gone through quite a lot of specialized neural network architectures. In the next video, I want to start showing you some more practical advice on how to actually use these algorithms to build your own computer vision system. Let's go on to the next video.


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[Assistant] Welcome to this course on chat GPT prom engineering for developers. I'm thrilled to have with me Issa Fulfett to teach this along with me.


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[Assistant] She's a member of the technical staff of OpenAI and had built the popular chat GPT retrieval plugin. And a large part of the work has been teaching people how to use OM or large language model technology in products. She's also contributed to the OpenAI cookbook that teaches people prompting. So thrilled to have you with you.


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[Assistant] A lot of that has been focused on the chat GPT web user interface, which many people are using to do specific and often one-off tasks.


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[Assistant] I think the power of OMS large language models as a developer to that is using API calls to OMS to quickly build software applications. I think that is still very under appreciated.


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[Assistant] In fact, my team at AI Fund, which is assisted company to deep learning AI, has been working with many startups on applying these technologies to many different applications. And it has been exciting to see what LLM APIs can enable developers to very quickly build.


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[Assistant] So in this course, we'll share with you some of the possibilities for what you can do, as well as best practices for how you can do them.


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[Assistant] So in the development of large language models or LLMs, there have been broadly two types of LLMs, which I'm going to refer to as base LLMs and instruction tuned LLMs.


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[Assistant] So BaseLM has been trained to predict the next word based on text training data, often trained on a large amount of data from the internet and other sources to figure out what's the next most likely word to follow.


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[Assistant] So, for example, if you were to prompt this, once upon a time there was a unicorn, it may complete this, that is, it may predict the next several worlds are, that lived in magical forest with all unicorn friends.


[User] Please transcribe this text

[Assistant] But if you were to prompt us with what is the capital of France, then based on what


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[Assistant] articles on the internet might have, it's quite possible that the base LLM will complete this with what is France's largest city, what is France's population and so on, because articles on the internet could quite plausibly be lists of quiz questions about the country of France.


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[Assistant] In contrast, an instruction-tuned LLM, which is where a lot of the momentum of LLM research and practice has been going, an instruction-tuned LLM has been trained to follow instructions. So if you were to ask it, what is the capital of France? It's much more likely to output something like the capital of France is Paris.


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[Assistant] So the way that instruction-tuned OMS are typically trained is you start off with a base OMS that's been trained on a huge amount of text data and further train it, further fine-tune it.


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[Assistant] Because instruction-tuned LLMs have been trained to be helpful, honest, and harmless. So for example, they are less likely to output problematic text such as toxic outputs compared to a base LLM.


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[Assistant] A lot of the practical usage scenarios have been shifting into what Instruction TML earns.


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[Assistant] Some of the best practices you find on the internet may be more suited for a base OOM, but for most practical applications today, we would recommend most people instead focus on


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[Assistant] Instruction-tuned LLMs which are easier to use and also because of the work of OpenAI and other LLM companies becoming safer and more aligned.


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[Assistant] So this course will focus on best practices for instruction tune over homes.


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[Assistant] Which is what we recommend you use for most of your applications.


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[Assistant] Before moving on, I just want to acknowledge the team from OpenAI and DeepLearn.ai that had contributed to the materials that Easy and I will be presenting. I'm very grateful to Andrew Ming, Joe Palermo, Boris Power, Ted Sanders,


[User] Please transcribe this text

[Assistant] And then in one from OpenAI that were very involved with us brainstorming materials, vetting the materials to put together.


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[Assistant] the curriculum for this short course. And I'm also grateful on the deep learning side for the work of Jeff Ludwig, Eddie Hsu and Tommy Nelson.


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[Assistant] I think of giving instructions to another person. Say someone that's smart, but doesn't know the specifics of your task. So when an LLM doesn't work, sometimes it's because the instructions weren't clear enough. For example, if you were to say, please write me something about Alan Turing.


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[Assistant] Well in addition to that it can be helpful to be clear about whether you want


[User] Please transcribe this text

[Assistant] The text to focus on his scientific work or his personal life or his role in history or something else.


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[Assistant] And if you specify what you want the tone of the text to be, should it take on the tone like a professional journalist would write? Or is it more of a casual note that you dash off to a friend that holds the OM generally what you want?


[User] Please transcribe this text

[Assistant] And of course, if you picture yourself asking, say a fresh college graduate to carry out this task for you, if you can even specify what snippets of text they should read in advance to write this text about Alan Turing, then that even better sets up that fresh college grad for success to carry out this task for you.


[User] Please transcribe this text

[Assistant] So in the next video, you see examples of how to be clear and specific, which is an important principle of prompting OMS. And you also learn from either a second principle of prompting that is giving LDLM time to think. So that let's go on to the next video.


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[Assistant] In this video, we'll learn how to split complex tasks into a series of simpler subtasks by chaining multiple prompts together.


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[Assistant] You might be wondering, why would you split up a task into multiple prompts when you could achieve it with one prompt and chain of thought reasoning like we learned in the previous video?


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[Assistant] We've already shown that language models are very good at following complex instructions, especially the more advanced models like GBT-4.


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[Assistant] So let me explain why we would do this with two analogies comparing chain of thought reasoning and chaining multiple prompts. The first analogy to compare the two is the difference between cooking a complex meal in one go versus cooking it in stages.


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[Assistant] Using one long complicated instruction can be like trying to cook a complex meal all at once where you have to manage multiple ingredients, cooking techniques and timings simultaneously. It can be challenging to keep track of everything and ensure that each component is cooked perfectly.


[User] Please transcribe this text

[Assistant] Chaining prompts, on the other hand, is like cooking the meal in stages where you focus on one component at a time, ensuring that each part is cooked correctly before moving on to the next.


[User] Please transcribe this text

[Assistant] This approach breaks down the complexity of the task, making it easier to manage and reducing the likelihood of errors. However, this approach might be unnecessary and overcomplicated for a very simple recipe.


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[Assistant] A slightly better analogy for the same thing is the difference between reading spaghetti code with everything in one long file and a simple modular program.


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[Assistant] The thing that can make spaghetti code bad and difficult to debug is ambiguity and complex dependencies between different parts of the logic. The same can be true of a complex single step task submitted to a language model.


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[Assistant] Chaining prompts is a powerful strategy when you have a workflow where you can maintain the state of the system at any given point and take different actions depending on the current state.


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[Assistant] And so an example of the current state would be after you've classified an incoming customer query, the state would be the classification. So the, it's an account question or it's a product question. And then based on the state, you might do something different.


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[Assistant] Each subtask contains only the instructions required for a single state of the task, which makes the system easier to manage, makes sure the model has all the information it needs to carry out a task, and reduces the likelihood of errors, as I mentioned.


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[Assistant] This approach can also reduce in lower costs since longer prompts with more tokens cost more to run and outlining all steps might be unnecessary in some cases.


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[Assistant] So, to summarize, because this was a long explanation, instead of describing a whole complex workflow in dozens of bullet points or several paragraphs in one prompt, like in the previous video, it might be better to keep track of the state externally and then inject relevant instructions as needed.


[User] Please transcribe this text

[Assistant] And what makes a problem complex? I think in general, a problem is complex if there are many different instructions and potentially all of them could apply to any given situation, as these are the cases where it could become hard for the model to reason about what to do. And as you build with and interact with these models more, you'll gain an intuition for when to use this strategy versus the previous.


[User] Please transcribe this text

[Assistant] And one additional benefit that I didn't mention yet is that it also allows the model to use external tools at certain points of the workflow if necessary. For example, it might decide to look something up in a product catalog or call an API or search a knowledge base, something that could not be achieved with a single prompt. So with that, let's dive into an example.


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[Assistant] So we're going to use the same example as in the previous video where we want to answer a customer's question about a specific product. But this time we'll use more products and also break the steps down into a number of different prompts. So we'll use the same delimiter that we've been using in the previous videos. And let's read through our system message.


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[Assistant] You will be provided with customer service queries. The customer service query will be delimited with four hashtag characters.


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[Assistant] Output a Python list of objects where each object has the following format. Category, which is one of these predefined fields.


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[Assistant] Or products. And this is a list of products that must be found in the allowed products below.


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[Assistant] Where the categories and products must be found in the customer service query.


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[Assistant] If no products or categories are found, I'll put an empty list. And so now we have our allowed list of products. So we have the categories and then the products within those categories.


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[Assistant] Now our final instruction is only output the list of objects with nothing else.


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[Assistant] So next we have our user message. And so this message is, tell me about the smart X pro phone and the photo snap camera, the DSLR one. Also tell me about your TVs. So we're asking about two specific products.


[User] Please transcribe this text

[Assistant] And also this general category of televisions.


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[Assistant] And both of these products are mentioned in the allowed products list. Then we have a television section as well.


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[Assistant] Then we format the system message and user message into the messages array and then we get the completion from the model.


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[Assistant] So as you can see, for our output we have


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[Assistant] So we have a list of objects and each object has category and products. So we have the smart X pro phone and the


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[Assistant] This structured response is that we can then read it into a list in Python, which is very nice. And so let's try another example. So our second user message is


[User] Please transcribe this text

[Assistant] My router isn't working. And if you notice in the list, we don't actually have any routers and then let's format this correctly and get the completion.


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[Assistant] And so now that we have this step to identify the category and products,


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[Assistant] If we find any products and categories, we want to load some information about those requested products and categories into the prompt so that we can better answer the customer question. And so.


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[Assistant] In our workflow, the state now after this prompt is run is either products have been listed or they haven't been listed.


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[Assistant] And in that case we wouldn't try to look anything up because there's nothing to look up.


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[Assistant] Also, if I were to actually build this into a system, I might use category names, maybe something like computers and laptops or something.


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[Assistant] To avoid any weirdness with spaces and special characters. But this should work for now.


[User] Please transcribe this text

[Assistant] So now we want to look up some information about the products that the user mentioned. So about this phone, this camera and about the TVs in general.


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[Assistant] And so we need to have some kind of product catalog to look up this information from. So here we have our product information that I just pasted in.


[User] Please transcribe this text

[Assistant] So as you can see, we have a large number of products available at our store.


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[Assistant] And so for each product, we have a couple of different fields. We have name, category, brand, warranty.


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[Assistant] And so the product is just a dictionary from product name to this object that contains the information about the product. Notice that each product has a category.


[User] Please transcribe this text

[Assistant] So remember we want to look up information about the products that the user asks about. So we need to define some helper functions to allow us to look up product information by product name. So let's create a function, get product by name, input the name, and then we're going to return


[User] Please transcribe this text

[Assistant] Products dictionary, and we're gonna get the value for the item with the...


[User] Please transcribe this text

[Assistant] Name as the key and then our fallback is just going to be none. And so we also want to define another helper function to get all of the products for a certain category. For example, when the user is asking about the TVs we have, we want to load all of the information about all of the different TVs. So get products by category.


[User] Please transcribe this text

[Assistant] Input the category name string. And to do this we want to loop through all of the products in the products dictionary and check


[User] Please transcribe this text

[Assistant] First we want to loop through each product and we have to get the values because we need to actually access the category which is in the value and then


[User] Please transcribe this text

[Assistant] We'll return this product if the product.


[User] Please transcribe this text

[Assistant] So let's do an example for each of these helper functions. So first, we have a product called the TechPro Ultrabook. So let's get the product information by name.


[User] Please transcribe this text

[Assistant] And let's do an example to get all of the products for a category. So let's get all of the products in the computers and laptops category. So here you see we fetched all of the products.


[User] Please transcribe this text

[Assistant] Let's continue our example and just to remember where we are, let's print the user message. So the user message was, tell me about the Smart X Pro phone and the camera and the TVs. And then the initial output from the model from the first step was this.


[User] Please transcribe this text

[Assistant] And so what we also need to do is read this output from the model, which is a string. We need to pass that into a list so that we can use it as input to the helper functions that we just wrote. So let's write a helper function to do this.


[User] Please transcribe this text

[Assistant] So we're going to use the Python JSON module.


[User] Please transcribe this text

[Assistant] And we're going to write a function called readStringToList, a very descriptive title, an input string.


[User] Please transcribe this text

[Assistant] And so first we'll just check if the input string is none.


[User] Please transcribe this text

[Assistant] In case something in a previous step failed, we're just going to return nothing.


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[Assistant] And now we're going to have a try accept block to make sure that we catch any errors.


[User] Please transcribe this text

[Assistant] And so first we replace any single quotes with double quotes in the input string to make sure we can pass the JSON.


[User] Please transcribe this text

[Assistant] And then we're going to use the JSON loads function to read the input string into the array or the list. And then we're going to return this. And if there's a decode error, we're going to print the error and then return none. So let's try this with our example. So.


[User] Please transcribe this text

[Assistant] As you can see, it's just the same thing except now the type of this variable is actually a list instead of a string.


[User] Please transcribe this text

[Assistant] So the whole point of what we're doing is to get the product information into a list that we can add to the next instruction to the model, which is going to be the instruction where we ask it to answer the user question. And so to do this, we need to put the product information into a nice string format that we can add to the prompt. And so let's also create a helper function to do this.


[User] Please transcribe this text

[Assistant] So we're going to call it generate output string and it's going to take in the list of data that we just created. So this.


[User] Please transcribe this text

[Assistant] So now I'm going to paste in some code and show you an example and then we'll talk about what this function is doing. So.


[User] Please transcribe this text

[Assistant] We're going to get the product information from our first user message. And so we're going to use this helper function, generate output string on our category and product list, which if we remember was this.


[User] Please transcribe this text

[Assistant] And so here we have all of the product information for the products that were in the mentioned in the user message. So we have the phone that they mentioned.


[User] Please transcribe this text

[Assistant] And this is information that will be helpful for the model to be able to answer the user's initial question. And if you're interested in how this function works, I'll give a brief overview, but


[User] Please transcribe this text

[Assistant] You can feel free to pause the video and read it more thoroughly. So it basically just loops through