[User] Please transcribe this text

[Assistant] Which is the wage, uh, which is the wage to rental rate ratio.


[User] Please transcribe this text

[Assistant] Alternatively, I don't know if anyone besides me likes this intuition. We can rewrite this as, uhm,


[User] Please transcribe this text

[Assistant] MPL over W equals MPK over R. My bang for the buck formulation that I like. That the next dollar of wages, if you ask


[User] Please transcribe this text

[Assistant] Should I spend the next dollar on wages or machines? You should do it until the next dollar of wages delivers you the same return as the next dollar of machines.


[User] Please transcribe this text

[Assistant] Okay, this is what you get for the next dollar of wages, MPL over W. This is what you get for the next dollar of machines, MPK over R. You want to continue to straight off machines and workers until that condition is true. Okay?


[User] Please transcribe this text

[Assistant] So let's actually now solve for this, okay, for our, for our example. Let's actually solve for that.


[User] Please transcribe this text

[Assistant] If we solve for this, we know that the marginal product of labor, which is dq dl,


[User] Please transcribe this text

[Assistant] Over the square root of 0.5 times L, over the square root of K times L.


[User] Please transcribe this text

[Assistant] Okay, just, just taking the derivative of the, all I did was take the derivative of the production function. Okay, so therefore,


[User] Please transcribe this text

[Assistant] Putting these together, we know that marginal rate of technical substitution in this example is equal to minus k over L.


[User] Please transcribe this text

[Assistant] That's not a general formula, okay? That's just this example.


[User] Please transcribe this text

[Assistant] We also know we want to set this equal to the negative of the wage rate. The, I'm sorry, the wage, the wage rental rate ratio. Okay? And we know that's negative a half. Then we know that's a half. So, cause that's five and that's ten. So we want to set


[User] Please transcribe this text

[Assistant] The marginal attack constitution to the wage rate, rental rate ratio, which means we set minus k over hell equal to minus a half. Or at the optimum,


[User] Please transcribe this text

[Assistant] Okay, that means that uhm, your labor, your capital, at the optimum, that means the amount of capital should be half as much as the amount of labor. In this example,


[User] Please transcribe this text

[Assistant] We just solved for the efficient combination of inputs. The efficient combination is you should use capital to labor in a ratio of a half. You should use half as much capital as you use labor. Okay. So let me pause there and let's talk about where this is coming from. Yeah.


[User] Please transcribe this text

[Assistant] That, that's a graphic intuition. Let's come to the economics intuition. The economics intuition is the following.


[User] Please transcribe this text

[Assistant] The production function delivers this relationship. That the marginal rate of tax substitution equals minus k over L. In other words, when you're producing goods,


[User] Please transcribe this text

[Assistant] Given this production function, you're indifferent between the next machine and the next worker. That's just the way this production function worked out.


[User] Please transcribe this text

[Assistant] That one more machine delivers you the same amount as one more worker. Okay? Now I've just told you that one machine costs half of one more worker. So which do you want more of?


[User] Please transcribe this text

[Assistant] No. One machine delivers the same return as one worker. You want more workers. Workers cost half machines. They're equally productive. So you want more workers.


[User] Please transcribe this text

[Assistant] So the awful amount of machines is going to be half as many as the number of workers.


[User] Please transcribe this text

[Assistant] You want more workers because you're indifferent. Look at that production function. You're indifferent.


[User] Please transcribe this text

[Assistant] Okay? You don't give a shit about L versus K. They're the same to you. Okay? You're a hard capitalist, man.


[User] Please transcribe this text

[Assistant] Machine or worker, you don't care. But the market's telling you, you can get a worker for half the price of a machine. So you as a good cost minimizing capitalist, get, take twice as many workers as machines. Okay? And that's the outcome that you get here.


[User] Please transcribe this text

[Assistant] Okay, so that's basically, uhm, what we do, uh, to, to derive this. Now, what I want to do is take this and then derive


[User] Please transcribe this text

[Assistant] Our ultimate goal, which is, what is the long run cost function?


[User] Please transcribe this text

[Assistant] That's sort of what we, why we started this lecture. What is the long run cost function? Let's do the math. We'll do the math in five steps. Step one. Q equals square root of k times L.


[User] Please transcribe this text

[Assistant] We know from up there that k over l equals w over r. We derive that. Right?


[User] Please transcribe this text

[Assistant] Leading us to the conclusion that K leads to the conclusion that K equals one half L. Okay, we just derived that.


[User] Please transcribe this text

[Assistant] Therefore we can rewrite Q, we can rewrite Q.


[User] Please transcribe this text

[Assistant] As the square root of 1 half times L squared. Just substituting in, because k equals 1 half L.


[User] Please transcribe this text

[Assistant] It's gonna be square root of 2 over 2 over q.


[User] Please transcribe this text

[Assistant] L is square root of 2 over q, k is square root of 2 over 2 over q. I'm sorry, I'm sorry, no, not over q. I'm sorry, that's my bad. Error. Error. Go back.


[User] Please transcribe this text

[Assistant] Should always look at my notes. It's square root of 2 times q. My bad. L is square root of 2 times q.


[User] Please transcribe this text

[Assistant] Okay. Therefore, armed with this l and k, we can rewrite our cost function. So step five is that the cost function, given this stuff, the cost function


[User] Please transcribe this text

[Assistant] Equals r times square root of 2 times q.


[User] Please transcribe this text

[Assistant] I'm sorry, r times square root of 2 over 2 times q plus w times square root of 2 times q.


[User] Please transcribe this text

[Assistant] I just derived the cost function. This is what we came here for. This is what you got up this morning wanting to see. Okay? You got up this morning and you said, I want to know.


[User] Please transcribe this text

[Assistant] How does the cost of a firm vary with the quantity it produces? And I've just told you.


[User] Please transcribe this text

[Assistant] This tells you how the, the cost that you pay vary the quantity you produce. And I did that by, by deriving the optimal mix of L and K you want to use.


[User] Please transcribe this text

[Assistant] And then simply imposing the prices of those two and I get a cost function. Yeah.


[User] Please transcribe this text

[Assistant] No. This is all dependent on that production function I wrote down. What's generally true is this. Okay, or actually k wouldn't be, what's generally true is just c equals, in the long run, what's generally true is c equals wl plus rk. Okay, that's what's generally true.


[User] Please transcribe this text

[Assistant] I just made, but what I've showed you is given three things. A production, all I gave you is a production function, a wage rate, and a rental rate.


[User] Please transcribe this text

[Assistant] Given those three things, you can then derive the cost function. Okay? Given those three things, you can derive the cost function.


[User] Please transcribe this text

[Assistant] In fact, given two things you can derive the cost function, you could actually derive the cost function given one thing.


[User] Please transcribe this text

[Assistant] Given just the production function, derive the cost function as a function of these input prices.


[User] Please transcribe this text

[Assistant] Okay, so that is a lot of, uh, that's a lot of results from one function, from one production function. Okay. Now, other questions about this? Other math I got wrong. Sorry about that. Yeah.


[User] Please transcribe this text

[Assistant] So I'm with the production function, that mathematical equation, this mathematical equation, W and R, I'm done. Everything I've done this lecture comes from those three things. The math is hard and annoying. We will have you practice it. You will not like it. Okay? It's just, it's just kinda what you gotta do.


[User] Please transcribe this text

[Assistant] The bar on the, you shouldn't see a bar on a K over here. That's all over here when I was doing the short run. Yeaah.


[User] Please transcribe this text

[Assistant] Oh, you're right. That's a good point. I needed an R and W back here. You're right. It's a good point. Good point.


[User] Please transcribe this text

[Assistant] But I still could have done this whole thing as a function of r and w if I wanted to. If I wanted to really, you know, screw up my math. Alright.


[User] Please transcribe this text

[Assistant] Okay, so now armed with this, let's talk about what happens when input prices change.


[User] Please transcribe this text

[Assistant] We talked about with consumer theory what happens when the price of pizza and cookies change. What happens when the price of labor and capital changes? What does that do? So let's talk about changes in input prices. Okay. Let's go to figure 6-4.


[User] Please transcribe this text

[Assistant] With the same production function, square root of L times K, we're not changing our production function. We're gonna change the wage-rental ratio. So, line, the, the, the, uhm, we have our initial,


[User] Please transcribe this text

[Assistant] We have our initial line, our initial, uhm, wage-rental ratio.


[User] Please transcribe this text

[Assistant] Which is that basically, uhm, you have a wage rate, uh, this, uhm, but that, but, but the budget constraint essentially that's flatter.


[User] Please transcribe this text

[Assistant] Is our original budget constraint. The flatter budget constraint is our original budget constraint. That's a budget constraint with a price of capital of 10 and a wage of 5. And that intersects our isoquant at point X.


[User] Please transcribe this text

[Assistant] We chose five units of labor and two and a half machines. Okay, that was our original, this is sort of a messed up graph, but our original intersection was at point x.


[User] Please transcribe this text

[Assistant] The cost minimizing combination that got a square root of 12.5 production.


[User] Please transcribe this text

[Assistant] Was to have five workers and two and a half machines.


[User] Please transcribe this text

[Assistant] Now let's say the price of workers rises to 10. The wage rate rises to 10 bucks an hour. So now workers and machines cost the same. What is now the optimal mix of workers and machines?


[User] Please transcribe this text

[Assistant] We know we still want to produce the square root of 12.5. So we want to stay tangent to the same isoquant. Okay?


[User] Please transcribe this text

[Assistant] So basically what we're saying is, this is as if we said to consumers,


[User] Please transcribe this text

[Assistant] Keeping utility changed changed the price. What do we call that?


[User] Please transcribe this text

[Assistant] Anyone remember what we called that? Keeping utility constant and changing the price? Anyone remember what we called that? Who said that? Alright, raise your hand next time. Be proud. Substitution effect. That's the substitution effect. It's the same idea here. We want to know


[User] Please transcribe this text

[Assistant] For a given level of production, what happens is the price of the inputs change. And so we shift along the isoquant for point x to point y. And you'll see we choose a mix where we use fewer workers and more machines. And just as the substitution effect is always non-positive,


[User] Please transcribe this text

[Assistant] This shift as these, as the wage rate rises, the price of the good of the x-axis rises, you'll unambiguously use no more and almost certainly less workers. Okay? And you can see that graphically.


[User] Please transcribe this text

[Assistant] Think about graphically, you're looking for the tangency between this curve and the line. The slope of the line just got steeper.


[User] Please transcribe this text

[Assistant] Therefore, you must move to the left on the curve. It's the same proof as we use the substitution effect, why the substitution effect was always non-positive.


[User] Please transcribe this text

[Assistant] Okay, it's the same intuition here we use for why a rise in the wage rate will lead you to hire fewer workers and more machines. Guess what? You just entered the debate on the minimum wage.


[User] Please transcribe this text

[Assistant] Any file with a minimum wage, what do people say? Well, if you raise the wage, you have to pay workers. They'll be replaced by machines. That's this.


[User] Please transcribe this text

[Assistant] This is the math, this is the mathematical and graphical intuition behind the debate on minimum wage, which we'll get into later in the semester.


[User] Please transcribe this text

[Assistant] But the basic idea of that debate is, gee, if you force firms to pay more to workers...


[User] Please transcribe this text

[Assistant] They're gonna substitute towards machines. That's exactly right, in theory. In practice, there's a lot of complications. But this gives you the theory of why people make that argument. Yeaah.


[User] Please transcribe this text

[Assistant] Ah, great point. Because here I'm drawing a new iso- I am drawing the isocost that I would use while still producing square root of 12.5.


[User] Please transcribe this text

[Assistant] So that's, it's just the substitution effect. I'm just, I'm not drawing the full set of isocosts at the new...


[User] Please transcribe this text

[Assistant] Christ, I'm just saying, to produce the same amount, what's my new, if I want to produce the same amount, what combination do I now have to use? Okay? Yeah.


[User] Please transcribe this text

[Assistant] The total cost of production, let's see. It used, yeaah, it has to be, uh, no. Uh, let's see, no, total cost doesn't have to be the same. The total cost used to be five workers at five bucks an hour, that's 25. It used to be 50. Now what is it? It's now, it's 70. The total cost has gone up.


[User] Please transcribe this text

[Assistant] Exactly. It's not like the budget constraint of your income is fixed. That's what's hard about producer theory. Because basically, the budget constraint was sort of asking, you know, keeping your budget fixed. This is like asking, keeping your total production fixed. And so now you have to pay more to get, to get that level of production. Okay?


[User] Please transcribe this text

[Assistant] Good questions. Okay. Now ultimately what does this lead to?


[User] Please transcribe this text

[Assistant] This, so, so that gives us our change in input prices. Okay, other questions about that?


[User] Please transcribe this text

[Assistant] Now, remember when I said at the beginning of the lecture, we are first going to solve for what is the cost minimizing combination of inputs for a given quantity. We derive that up there. It's half as much, going back to our old prices, it's half as much capital as labor.


[User] Please transcribe this text

[Assistant] Now we want to ask, how does your cost change as the quantity changes? And we call that the long run expansion path.


[User] Please transcribe this text

[Assistant] The long run expansion path, which is how do your costs expand as you produce more? And we see that in figure six, five. In figure six, five, we show the particular case.


[User] Please transcribe this text

[Assistant] Of a linear long run expansion path. That's what you get in this example. It's a particular case.


[User] Please transcribe this text

[Assistant] What this case says is at any given level of production.


[User] Please transcribe this text

[Assistant] In other words, you always want to have, given, essentially, given the price of labor is half the price of capital, you always want to have twice as many workers as machines.


[User] Please transcribe this text

[Assistant] So if you want to produce square root of 12.5, you want five workers in two and a half machines. If you want to produce square root of 50, you want 10 workers in five machines. If you want to produce square root of 100, 12.5, you want 50 workers in seven and a half machines.


[User] Please transcribe this text

[Assistant] So the long run exp- given this production function, the long run expansion path is linear. You always want the same ratio of workers to machines. Yeaah.


[User] Please transcribe this text

[Assistant] Is because we're not given a fixed constant we are with consumers.


[User] Please transcribe this text

[Assistant] Consumers were saying, look, you've got a resource, you've got a constrained maximization. We haven't constrained their maximization yet. There's another constraint we need. They have an extra degree of freedom relative to consumers.


[User] Please transcribe this text

[Assistant] Now in fact, consumers have a degree of freedom too. When you grow up, your parents don't give you money, you decide how much to make. So in reality, consumers will have the same degrees of freedom.


[User] Please transcribe this text

[Assistant] But we started with the easy consumer theory case, where you constrict, we took away a degree of freedom. Now we're adding it back, which is you can choose how much to produce. Like you being able to choose your income as a consumer.


[User] Please transcribe this text

[Assistant] Okay, and that leads to a long run expansion path. Let me go on, because I want to make sure I get through this stuff.


[User] Please transcribe this text

[Assistant] Okay? Now, the long-wear expansion path does not have to be linear.


[User] Please transcribe this text

[Assistant] So think about, look at figure 6, 6-5B and 6-5C.


[User] Please transcribe this text

[Assistant] So 6.5b is a long run expansion path for a production function such that


[User] Please transcribe this text

[Assistant] Capital becomes less productive the more you produce. I don't have the example of a production function, but when you write down production functions, which have the feature,


[User] Please transcribe this text

[Assistant] That the more you produce, the less productive capital becomes.


[User] Please transcribe this text

[Assistant] The less each addition you have of capital helps to help each additional unit of workers. Okay?


[User] Please transcribe this text

[Assistant] That kind of, you know, that basically there's so much stuff to do where workers can efficiently share tasks and things. These are just a worker that the marginal product of labor essentially diminishes less quickly than the marginal product of capital. Okay?


[User] Please transcribe this text

[Assistant] On the other hand, in figure 6-5C, we can have a long run expansion path where labor becomes less productive relative capital. Think of this like heavy machinery. Where like basically all workers can do is run the machine.


[User] Please transcribe this text

[Assistant] You need the worker to flip the switch. That's all they do. So the second worker, you're already flipping the switch.


[User] Please transcribe this text

[Assistant] So really adding more machines is a more productive way to expand, expand output. None of these is right or wrong.


[User] Please transcribe this text

[Assistant] We're just saying that the shape of this expansion path can basically vary with how much, uh, with different production functions. Okay? But they're all the same idea. Now, so basically,


[User] Please transcribe this text

[Assistant] That tells you, uhm, and the bot, but here's the bottom line that we wanted to come to.


[User] Please transcribe this text

[Assistant] That long run expansion path is a long run cost curve.


[User] Please transcribe this text

[Assistant] If you want to ask how do my costs vary with how much I produce, this curve tells you.


[User] Please transcribe this text

[Assistant] Because what it does is it says for every level of production, it'll tell you the optimal combination of L and K. Given the price of L and K, that will tell you the cost.


[User] Please transcribe this text

[Assistant] And so you trace out the cost with every level of production. This is your cost curve.


[User] Please transcribe this text

[Assistant] This long run expansion path tells you what the costs are for every level of production.


[User] Please transcribe this text

[Assistant] And it tells you that because you've made, you're doing the efficient level of production. Okay? That's what the Long Range Expansion Pass is telling you. Okay, this is hard. I'm about to make it harder. Which is we're now going to talk about the relationship


[User] Please transcribe this text

[Assistant] Is that long run costs are everywhere lower than short run costs. Without looking at the figure, because the figure doesn't help with this. Why does that make sense? Why are long run costs


[User] Please transcribe this text

[Assistant] Why, if you can optimize over the long run, will you always have costs that are no higher and in general lower than optimizing the short run?


[User] Please transcribe this text

[Assistant] I think this Le Chatelier's principle, is that right for the chemists among us?


[User] Please transcribe this text

[Assistant] That basically, like, essentially an extra degree of freedom means the more you can optimize over, the better you can do in optimizing. In the short run, you could train by the size of the building.


[User] Please transcribe this text

[Assistant] In the long run, you can choose. So let's, to see that, let's go to figure 6-6.


[User] Please transcribe this text

[Assistant] Okay, consider a firm with three possible sizes of plants. They're gonna build a plant. So the capital here is the building. And there's three possible sizes, small, medium, and large. Okay?


[User] Please transcribe this text

[Assistant] What does that curve mean? That means that the small plant, I'm sorry, the small plant is SRAC1, the medium plant is SRAC2, and the large plant has SRAC3. Compare SRAC1 to SRAC3.


[User] Please transcribe this text

[Assistant] What this is saying is, for small quantities of production,


[User] Please transcribe this text

[Assistant] Right? For small quantities of production, if you extend SRAC3 out, you see it levels of production like Q1 or even Q2. SRAC3 is way above SRAC1. When you go to a level of production like Q3,


[User] Please transcribe this text

[Assistant] SRAC1, if you extend that dashed line out, it's gonna be much, much higher than SRAC3. So the right and SRAC2 is in between. So essentially for different levels of production, these give the different optimal short run cost curves. In the long run, you get to choose.


[User] Please transcribe this text

[Assistant] So the long run average cost curve is the lower envelope of the short run average cost curves.


[User] Please transcribe this text

[Assistant] Because in the long run you say, well, here's my production level. I know in the long run. So if I know I'm gonna build a lot of things, I choose SRAC3. I choose, I choose the biggest plant.


[User] Please transcribe this text

[Assistant] If my production is going to be low, I choose the smallest plant.


[User] Please transcribe this text

[Assistant] But I can optimize in the long run by choosing the right size plant for my production level.


[User] Please transcribe this text

[Assistant] This is hard, and I'm almost out of time, so let me end with an example that perfectly illustrates this. Tesla. Elon Musk, everybody's favorite guy these days. Okay. Tesla, when they came out, had to decide how big a plant to build. How many batteries to make. Batteries are the key input in Teslas. Okay.


[User] Please transcribe this text

[Assistant] And they expected to make, have demand for 20,000 cars by the year 2017.


[User] Please transcribe this text

[Assistant] They built a plant that was the efficient plant to produce 20,000 cars.


[User] Please transcribe this text

[Assistant] The problem is, demand was for 200,000 cars. As a result, there's a three year waiting list to get Teslas. It turned out that was not the right size to produce. They lost money.


[User] Please transcribe this text

[Assistant] Now he's saying, wait a second, people want way more cars. Well producing them at the tiny plant was exorbitantly expensive. I had to run it over and over time, pay workers over time. To produce 200k cars at that tiny plant just was exorbitantly expensive. That's if you take that dashed line and send it way the hell up.


[User] Please transcribe this text

[Assistant] The SRAC-1 line, they sent it way the hell up and incredibly expensive. So what is Musk doing now? Building the largest battery plant in the world.


[User] Please transcribe this text

[Assistant] In Nevada, he is building a battery plant that can produce batteries for 500,000 cars.


[User] Please transcribe this text

[Assistant] He's now saying in the long run, I can more, if I'm gonna boost 200,000 cars, I can do that more efficiently with a giant battery plant. And that's what he's doing. So he's re-optimizing. Now, what if Musk is wrong?


[User] Please transcribe this text

[Assistant] What if it turns out Tesla suck? And people are like, I don't want them anymore. Someone else, or you know, Chevy finally figures it out and makes a good electric car. Okay? Then what's gonna happen is he's gonna have made a mistake in the long run. Then in the third period, he'll go back to a smaller plant again.


[User] Please transcribe this text

[Assistant] So Tesla's an example of this sort of long run, short run dichotomy.


[User] Please transcribe this text

[Assistant] Anyway, it's a lot of stuff for one lecture. We'll come back next time, talk more about costs, and then we'll start getting into competition.


[User] Please transcribe this text

[Assistant] I'm getting too nervous. Hey, alright, so where do you want this? Uh, right over here is fine.


[User] Please transcribe this text

[Assistant] Hey, so Pete, yeaah. I, uh, kinda gotta talk to you about something I can't tell my mom about. What's up? Well, uhm, weird things have been going on. Like physically. Oh.


[User] Please transcribe this text

[Assistant] I think it's, I think it's better that I just, uhm, I show you.


[User] Please transcribe this text

[Assistant] You're Spider-Man. You can fix this. Your way.


[User] Please transcribe this text

[Assistant] Chief chemist. And Roxanne killed him.


[User] Please transcribe this text

[Assistant] That was a hell of a way to tell me you're Spider-Man.


[User] Please transcribe this text

[Assistant] He said the only thing you knew how to do was sell other people's ideas.


[User] Please transcribe this text

[Assistant] Nothing about Roxanne surprises me anymore.


[User] Please transcribe this text

[Assistant] Can you check that computer?


[User] Please transcribe this text

[Assistant] First we need to escape the security wing. I'll go down to the floor, see what we're dealing with.


[User] Please transcribe this text

[Assistant] That security door is our way out, but I'll have to hack a few terminals to disable the alarm.


[User] Please transcribe this text

[Assistant] One thing, did you ever think about calling me to tell me you were Spider-Man?


[User] Please transcribe this text

[Assistant] After my parents died, Rick basically had to become my dad. I thought I'd miss having a brother, but I had you.


[User] Please transcribe this text

[Assistant] Probably millions of dollars in untaxed money. You clear the floor, I'll work on overriding security so we can get out. I can still access machinery if you need me to move anything.


[User] Please transcribe this text

[Assistant] Thanks. I know this place. This is Krieger's lab.


[User] Please transcribe this text

[Assistant] Mostly for show, but yeaah. Rick showed me a photo once.


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[Assistant] Does this job ever get easier? Some of it, yeaah. Some things never get easier though.


[User] Please transcribe this text

[Assistant] More like sell yourself. It's the worst. You got any advice? Well, take me. Hi, I'm Peter Parker.


[User] Please transcribe this text

[Assistant] I'm 25 years old, I have a bachelor's degree in biophysics, and I'm in love with the greatest woman ever. Humble brag. Who is the best journalist in this city? Word. Go MJ.


[User] Please transcribe this text

[Assistant] I try to live up to the example set by the other greatest woman ever, my Aunt May.


[User] Please transcribe this text

[Assistant] And a while back I was bitten by a radioactive spider and got superpowers.


[User] Please transcribe this text

[Assistant] I've used my powers to help the city and stop a lot of people who wanted to hurt it. Like my mentor, Dr. Otto Octavius.


[User] Please transcribe this text

[Assistant] His lab got shut down by an old colleague, Norman Osborn. Otto retaliated by releasing a bio weapon. In the end, I managed to stop Otto, but not without great cost. That was pretty good. But I'm no Peter Parker. Exactly. You're Miles Morales. You're 17 years old and a student at Brooklyn Visions Academy. What else?


[User] Please transcribe this text

[Assistant] Yeah, why don't you just start another draft?


[User] Please transcribe this text

[Assistant] I missed you too. I heard about Mei. I'm so sorry.


[User] Please transcribe this text

[Assistant] It was. Past tense. We think it's in remission.


[User] Please transcribe this text

[Assistant] Actually, I was hoping you might have time to go for a ride with me. Sure. I'm surprised the frugal flyer


[User] Please transcribe this text

[Assistant] Where are we going? We'll see. Come on! Try to keep up!


[User] Please transcribe this text

[Assistant] You sure you're up for this? Oh yeah, I'm feeling good.


[User] Please transcribe this text

[Assistant] Hey, I read about your old boss, Dr. Octavius.


[User] Please transcribe this text

[Assistant] Huh. Well, I mean, it seemed like you guys were doing good. She was at your house? We'll figure it out.


[User] Please transcribe this text

[Assistant] Lots happened in the last ten years. It's a miracle I'm even sitting here now.


[User] Please transcribe this text

[Assistant] I got a second chance. And I'm gonna take advantage of that.


[User] Please transcribe this text

[Assistant] Wow, this is, uh, a lot to process.


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[Assistant] Why don't you come by the lab tomorrow? Check it out. You have a lab already? I'll show you some of my ideas. And we can talk about it some more. Deal? Sure.


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[Assistant] Good morning class. We have a new teacher joining us today.


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[Assistant] Adventure 9, The Adventure of the Three Students.


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[Assistant] It was in the year 95 that a combination of events into which I need not enter caused Mr. Sherlock Holmes and myself to spend some weeks in one of our great university towns, and it was during this time that the small but instructive adventure which I'm about to relate with Phyllis.


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[Assistant] It will be obvious that any details which would help the reader exactly to identify the college or the criminal would be injudicious and offensive.


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[Assistant] I will endeavour in my statement to avoid such terms as would serve to limit the events to any particular place, or give a clue as to the people concerned.


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[Assistant] We were reading at the time in furnished lodgings close to a library where Sherlock Holmes was pursuing some laborious researches in early English charters—researches which led to results so striking that they may be the subject of one of my future narratives.


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[Assistant] Here it was that one evening we received a visit from an acquaintance, Mr. Hilton Soames, tutor and lecturer at the College of St. Luke's. Mr. Soames was a tall, spare man of a nervous and excitable temperament. I had always known him to be restless in his manner,


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[Assistant] But on this particular occasion he was in such a state of uncontrollable agitation that it was clear something very unusual had occurred.


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[Assistant] I trust, Mr. Holmes, that you can spare me a few hours of your valuable time.


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[Assistant] We have had a very painful incident at St Luke's, and really, but for the happy chance of your being in town, I should have been at a loss what to do.


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[Assistant] I am very busy just now, and I desire no distractions, my friend answered.


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[Assistant] I should much prefer that you called in the aid of the police.


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[Assistant] No, no, my dear sir, such a course is utterly impossible. When once the law is evoked, it cannot be stayed again, and this is just one of those cases where, for the credit of the college, it is most essential to avoid scandal.


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[Assistant] Your discretion is as well known as your powers, and you are the one man in the world who can help me.


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[Assistant] I beg you Mr. Holmes, to do what you can.


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[Assistant] My friend's temper had not improved since he had been deprived of the congenial surroundings of Baker Street. Without his scrapbooks, his chemicals, and his homely untidiness, he was an uncomfortable man.


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[Assistant] He shrugged his shoulders in ungracious acquiescence, while our visitor in hurried words, and with much excitable gesticulation, poured forth his story.


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[Assistant] I must explain to you, Mr. Holmes, that tomorrow is the first day of the examination for the Fortescue Scholarship. I am one of the examiners.


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[Assistant] My subject is Greek, and the first of the papers consists of a large passage of Greek translation, which the candidate has not seen.


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[Assistant] This passage is printed on the examination paper, and it would naturally be an immense advantage if the candidate could prepare it in advance.


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[Assistant] For this reason, great care is taken to keep the paper secret.


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[Assistant] Today, about three o'clock, the proofs of this paper arrive from the printers.


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[Assistant] The exercise consists of a half a chapter of Thucydides. I had to read it over carefully, as the text must be absolutely correct. At 4.30 my task was not yet completed.


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[Assistant] I had however promised to take tea in a friend's room, so I left the proof upon my desk. I was absent rather more than an hour.


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[Assistant] You are aware, Mr Holmes, that our college doors are double.


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[Assistant] A green bays one within and a heavy oak one without. As I approached my outer door I was amazed to see a key in it.


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[Assistant] For an instant I imagined that I had left my own there.


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[Assistant] But on feeling in my pocket I found that it was alright.


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[Assistant] The only duplicate which existed, so far as I knew, was that which belonged to my servant Banister.


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[Assistant] A man who has looked after my room for ten years, and whose honesty is absolutely above suspicion.


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[Assistant] I found that the key was indeed his, that he had entered my room to know if I wanted tea, and that he had very carelessly left the key in the door when he came out.


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[Assistant] His visit to my room must have been within a very few minutes of my leaving it.


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[Assistant] His forgetfulness about the key would have mattered little upon any other occasion, but on this one day it has produced the most deplorable consequences.


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[Assistant] The moment I looked at my table I was aware that someone had rummaged among my papers.


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[Assistant] You amaze me. How could you possibly know that? Pray continue your very interesting statement.


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[Assistant] For an instant I imagined that Bannister had taken the unpardonable liberty of examining my papers. He denied it, however, with the utmost earnestness, and I am convinced that he was speaking the truth.


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[Assistant] The alternative was that someone passing had observed the key in the door, had known that I was out, and had entered to look at the papers.


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[Assistant] A large sum of money is at stake, for the scholarship is a very valuable one, and an unscrupulous man might very well run a risk in order to gain an advantage over his fellows.


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[Assistant] Bannister was very much upset by the incident.


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[Assistant] He had nearly fainted when we found that the papers had undoubtedly been tampered with


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[Assistant] I gave him a little brandy and left him collapsed in a chair while I made a most careful examination of the room.


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[Assistant] I soon saw that the intruder had left other traces of his presence beside the rumpled papers


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[Assistant] On the table in the window were several shreds from a pencil which had been sharpened.


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[Assistant] A broken tip of lead was lying there also.


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[Assistant] Evidently the rascal had copied the paper in a great hurry, had broken his pencil, and had been compelled to put a fresh point to it.


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[Assistant] Excellent!" said Holmes, who was recovering his good humour as his attention became more engrossed by the case.


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[Assistant] This was not all. I have a new writing table with a fine surface of red leather.


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[Assistant] I am prepared to swear, and so is Bannister, that it was smooth and unstained.


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[Assistant] Now I found a clean cut in it about three inches long. Not a mere scratch, but a positive cut.


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[Assistant] Not only this, but on the table I found a small ball of black dough or clay with specks of something which looks like sawdust in it.


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[Assistant] I am convinced that these marks were left by the man who rifled papers.


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[Assistant] There were no footmarks and no other evidence as to his identity.


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[Assistant] I was at my wits end when suddenly the happy thought occurred to me that you were in town, and I came straight round to put the matter into your hands.


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[Assistant] Do help me, Mr. Holmes. You see my dilemma. Either I must find the man, or else the examination must be postponed until fresh papers are prepared.


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[Assistant] And since this cannot be done without explanation, there will ensue a hideous scandal, which will throw a cloud not only on the college, but on the university.


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[Assistant] Above all things, I desire to settle the matter quietly and discreetly.


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[Assistant] I shall be happy to look into it, and to give you such advice as I can," said Holmes, rising and putting on his overcoat.


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[Assistant] The case is not entirely devoid of interest


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[Assistant] Had anyone visited you in your room after the papers came to you?


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[Assistant] Yes, a young Daulat Ras, an Indian student who lives on the same stair, came in to ask me some particulars about the examination. For which he was entered? Yes. And the papers were on your table? To the best of my belief, they were rolled up. But might be recognized as proofs? Possibly. No one else in your room?


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[Assistant] Did anyone know that these proofs would be there? No one saved the printer. Did this man Bannister know?


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[Assistant] He was very ill, poor fellow. I left him collapsed in the chair. I was in such a hurry to come to you. You left your door open? I locked up the papers first.


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[Assistant] Then it amounts to this, Mr. Soames, that unless the Indian student recognised the roll as being proofs, the man who tampered with them came upon them accidentally without knowing that they were there. So it seems to me. Holmes gave an enigmatic smile. Well, said he,


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[Assistant] Let us go round. Not one of your cases, Watson. Mental, not physical. All right, come if you want to. Now, Mr. Soames, at your disposal.


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[Assistant] The sitting room of our client opened by a long, low, latticed window onto the ancient, lichen-tinted court of the old college. A gothic arched door led to a worn-stone staircase. On the ground floor was the tutor's room. Above were three students, one on each story.


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[Assistant] It was already twilight when we reached the scene of our problem.


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[Assistant] Homes halted and looked earnestly at the window.


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[Assistant] Then he approached it, and standing on tiptoe with his neck craned, he looked into the room. He must have entered through the door.


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[Assistant] There is no opening except the one pane," said our learned guide.


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[Assistant] Dear me, said Holmes, and he smiled in a singular way as he glanced at our companion.


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[Assistant] Well, if there is nothing to be learned here, we had best go inside.


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[Assistant] The lecturer unlocked the outer door and ushered us into his room. We stood at the entrance while Holmes made an examination of the carpet.


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[Assistant] One could hardly hope for any upon so dry a day.


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[Assistant] Your servant seems to have quite recovered. You left him in a chair, you say. Which chair? By the window there. I see. Near this little table.


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[Assistant] He carried them over to the window table, because from there he could see if you came across the courtyard, and so could effect an escape.


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[Assistant] As a matter of fact he could not, said Soames, for I entered by the side door.


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[Assistant] Well, he carried over this one first and he copied it. How long would it take him to do that, using every possible contraction?


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[Assistant] You were not aware of any hurrying feet on the stair as you entered the outer door? No, I can't say I was.


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[Assistant] Well, he wrote so furiously that he broke his pencil, and had, as you observe, to sharpen it again. This is of interest, Watson.


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[Assistant] And the piece remaining is only about an inch and a half long.


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[Assistant] Look for such a pencil Mr Soames and you have got your man.


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[Assistant] When I add that he possesses a large and very blunt knife, you have an additional aid.


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[Assistant] Watson, I have always done you an injustice. There are others. What could this n-n be? Is it the end of a word?


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[Assistant] You are aware that Johann Faber is the most common maker's name. Is it not clear that there is just as much of the pencil left as usually follows the


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[Assistant] He held the small table sideways to the electric light. I was hoping that if the paper on which he wrote was thin, some trace of it might have come through upon this polished surface.


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[Assistant] No, I see nothing. I don't think there is anything more to be learned here. Now for the central table.


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[Assistant] This small pellet is, I presume, the black doughy mass you spoke of, roughly pyramidal in shape, and hollowed out, I perceive.


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[Assistant] As you say, there appear to be grains of sawdust in it. Dear me, this is very interesting. And the cut—a positive tear, I see. It began with a thin scratch and ended in a jagged hole.


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[Assistant] I am much indebted to you for directing my attention to this case, Mr. Soames. Where does that door lead to? To my bedroom. Have you been in it since your adventure? No, I came straight away for you. I should like to have a glance around. What a charming old-fashioned room!


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[Assistant] If anyone were forced to conceal himself in this room, he must do it there since the bed is too low, and the wardrobe too shallow. No one there, I suppose.


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[Assistant] As Holmes drew the curtain I was aware from some little rigidity and alertness of his attitude that he was prepared for an emergency. As a matter of fact, the drawn curtain disclosed nothing but three or four suits of clothes hanging from a line of pegs. Holmes turned away and stooped suddenly to the floor.


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[Assistant] It was a small pyramid of black putty-like stuff, exactly like the one upon the table of the study.


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[Assistant] Holmes held it in his open palm in the glare of the electric light.


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[Assistant] Your visitor seems to have left traces in your bedroom as well as in your sitting room, Mr Soames. What could he have wanted there?


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[Assistant] I think it is clear enough. You came back by an unexpected way, and so he had no warning until you were at the very door. What could he do? He caught up everything which would betray him, and he rushed into your bedroom to conceal himself.


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[Assistant] Surely there's another alternative, Mr. Holmes. I don't know whether you observed my bedroom window.


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[Assistant] Lattice-paned, lead framework, three separate windows, one swinging on hinge and large enough to admit a man. Exactly.


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[Assistant] And it looks out on an angle of the courtyard so as to be partly invisible.


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[Assistant] The man might have affected his entrance there, left traces as he passed through the bedroom, and finally finding the door open, have escaped that way. Holmes shook his head impatiently.


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[Assistant] Let us be practical," said he. I understand you to say that there are these three students who use this stair and are in the habit of passing your door. Yes, there are. And they are all in for this examination.


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[Assistant] Yes. Have you any reason to suspect any one of them more than the others? Soam's hesitated.


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[Assistant] One hardly likes to throw suspicion where there are no proofs.


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[Assistant] In a few words the character of the three men who inhabit these rooms


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[Assistant] The low of the three is Gilchrist, a fine scholar and athlete, plays in the rugby team and the cricket team for the college, and got his blue for the hurdles and the long jump. He is a fine manly fellow,


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[Assistant] His father was the notorious Sir Jabez Gilchrist, who ruined himself on the turf.


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[Assistant] My scholar has been left very poor, but he is hardworking and industrious. He will do well.


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[Assistant] The second floor is inhabited by Dalit Ras, the Indian. He is a quiet, inscrutable fellow, as most of those Indians are. He is well up in his work, though his Greek is his weak subject. He is steady and methodical.


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[Assistant] The top floor belongs to Miles McLaren.


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[Assistant] He is a brilliant fellow when he chooses to work. One of the brightest intellects of the University. But he is wayward, dissipated and unprincipled.


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[Assistant] He was nearly expelled over a card scandal in his first year. He has been idling all this term, and he must look forward with dread to the examination. Then it is he whom you suspect


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[Assistant] Dare not go so far as that, but of the three he is perhaps the least unlikely. Exactly. Now, Mr. Soames, let us have a look at your servant Bannister.


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[Assistant] He was a little, white-faced, clean-shaven, grisly-haired fellow of fifty. He was still suffering from this sudden disturbance of the quiet routine of his life. His plump face was twitching with his nervousness, and his fingers could not keep still.


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[Assistant] We were investigating this unhappy business, Bannister," said his master.


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[Assistant] I understand," said Holmes, that you left your key in the door. Yes, sir. Was it not very extraordinary that you should do this on the very day when there were these papers inside?


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[Assistant] It was most unfortunate, sir, but I have occasionally done the same thing at other times. When did you enter the room?


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[Assistant] It was about half past four. That is Mr. Soames' tea time. And how long did you stay?


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[Assistant] When I saw that he was absent, I withdrew at once. Did you look at these papers on the table? No, sir, certainly not. How came you to leave the key in the door?


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[Assistant] I had a tea tray in my hand, and I thought I'd come back for the key, then I forgot. Has the outer door a spring-lock? No, sir. Then it was open all the time? Yes, sir. Anyone in the room could get out? Yes, sir.


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[Assistant] When Mr. Soames returned and called for you, you were very much disturbed.


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[Assistant] Yes, sir. Such a thing has never happened during the many years that I've been here. I nearly fainted, sir. So I understand. Where were you when you began to feel bad?


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[Assistant] Where was I, sir? Why, here, near the door.


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[Assistant] That is singular, because you sat down in that chair over yonder near the corner. Why did you pass these other chairs?


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[Assistant] I don't know, sir. It didn't matter to me where I sat.


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[Assistant] I really don't think he knew much about it, Mr. Holmes. He was looking very bad, quite ghastly. You stayed here when your master left?


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[Assistant] Only for a minute or so, then I locked the door and went to my room. Whom do you suspect?


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[Assistant] Oh, I would not venture to say, sir. I don't believe there is any gentleman in this university who is capable of profiting by such an action. No, sir, I will not believe it." Thank you. That will do," said Holmes.


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[Assistant] Oh, one more word. You have not mentioned to any of the three gentlemen whom you attend that anything is amiss. No, sir, not a word. You haven't seen any of them? No, sir. Very good.


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[Assistant] Now, Mr Soames, we will take a walk in the quadrangle, if you please.


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[Assistant] Three yellow squares of light shone above us in the gathering gloom. Your three birds are all in their nests, said Holmes, looking up. Hello! What's that? One of them seems restless enough.


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[Assistant] It was the Indian whose dark silhouette appeared suddenly upon his blind. He was pacing swiftly up and down his room.


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[Assistant] I should like to have a peep at each of them," said Holmes. Is it possible? No difficulty in the world, so Holmes answered.


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[Assistant] This set of rooms is quite the oldest in the college, and it is not unusual for visitors to go over them. Come along and I will personally conduct you.


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[Assistant] No names, please," said Holmes, as we knocked at Gilchrist's door.


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[Assistant] A tall, flaxen-haired, slim young fellow opened it and made us welcome when he understood our errand.


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[Assistant] There were some really curious pieces of medieval domestic architecture within. Holmes was so charmed with one of them that he insisted on drawing it in his notebook, broke his pencil—had to borrow one from our host—and finally borrowed a knife to sharpen his own


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[Assistant] The same curious accident happened to him in the rooms of the Indian, a silent little hook-nosed fellow who eyed us a sconce and was obviously glad when Holmes's architectural studies had come to an end.


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[Assistant] I could not see that in either case Holmes had come upon the clue for which he was searching.


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[Assistant] Only at the third did our visit prove abortive.


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[Assistant] The outer door would not open to our knock, and nothing more substantial than a torrent of bad language came from behind it.


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[Assistant] I don't care who you are. You can go to Blazes.


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[Assistant] roared the angry voice. Tomorrow's the exam, and I won't be drawn by anyone.


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[Assistant] A rude fellow, said our guide, flushing with anger as we withdrew down the stair.


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[Assistant] Of course he did not realize that it was I who was knocking, but nonetheless his conduct was very uncourteous.


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[Assistant] And indeed, under the circumstances, rather suspicious. Holmes's response was a curious one. Can you tell me his exact height? he asked.


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[Assistant] Really, Mr. Holmes, I cannot undertake to say he is taller than the Indian, not so tall as Gilchrist. I suppose five foot six would be about it. That is very important," said Holmes. And now, Mr. Soames, I wish you good night.


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[Assistant] Our guide cried aloud in his astonishment and dismay.


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[Assistant] Good gracious, Mr. Holmes. You are surely not going to leave me in this abrupt fashion. You don't seem to realise the position. Tomorrow is the examination.


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[Assistant] I must take some definite action tonight. I cannot allow the examination to be held if one of the papers has been tampered with. The situation must be faced."


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[Assistant] I shall drop round early tomorrow morning and chat the matter over. It is possible that I may be in a position then to indicate some course of action. Meanwhile you change nothing. Nothing at all. Very good, Mr. Holmes.


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[Assistant] You can be perfectly easy in your mind. We shall certainly find some way out of your difficulties. I will take the black clay with me, also the pencil cuttings. Goodbye.


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[Assistant] When we were out in the darkness of the quadrangle, we again looked up at the windows.


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[Assistant] The Indian still paced his room. The others were invisible.


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[Assistant] Well, Watson, what do you think of it? Holmes asked as we came out into the main street.


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[Assistant] Quite a little parlor game. Sort of three-card trick, is it not?


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[Assistant] There are your three men. It must be one of them. You take your choice. Which is yours?


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[Assistant] A foul-mouthed fellow at the top. He's the one with the worst record. And yet that Indian was a sly fellow also. Why should he be pacing his room all the time?


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[Assistant] There is nothing in that. Many men do it when you are trying to learn anything by heart. He looked at us in a queer way.


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[Assistant] So would you if a flock of strangers came in on you when you were preparing for an examination next day, and every moment was of value. No, I see nothing in that.


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[Assistant] Why Bannister the servant? What's his game in the matter? He impressed me as being a perfectly honest man.


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[Assistant] So he did me. That's the puzzling part. Why should a perfectly honest man? Well, well, here's a large station as we shall begin our researches here.


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[Assistant] There were only four stationers of any consequence in the town, and at each Holmes produced his pencil chips and bid. All were agreed that one could be ordered, but it was not a usual size of pencil, and that it was seldom kept in stock.


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[Assistant] My friend did not appear to be depressed by his failure, but shrugged his shoulders in half-humorous resignation.


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[Assistant] No good, my dear Watson. This, the best and only final clue, has run to nothing. But indeed I have little doubt that we can build up a sufficient case without it.


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[Assistant] By Jove, my dear fellow! It is nearly nine, and the landlady babbled of green peas at seven-thirty.


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[Assistant] What with your eternal tobacco, Watson, and your irregularity at meals, I expect that you will get noticed to quit, and that I shall share your downfall. Not, however, before we have solved the problem of the nervous tutor, the careless servant, and the three enterprising students.


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[Assistant] Holmes made no further allusion to the matter that day, though he sat lost in thought for a long time after our belated dinner.


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[Assistant] At eight in the morning he came into my room just as I finished my toilet.


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[Assistant] Well, Watson," said he, it is time we went down to St. Luke's. Can you do without breakfast? Certainly.


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[Assistant] Soames will be in a dreadful fidget until we are able to tell him something positive. Have you anything positive to tell him? I think so. You have formed a conclusion?


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[Assistant] It is not for nothing that I have turned myself out of bed at the untimely hour of six. I have put in two hours hard work and covered at least five miles with something to show for it. Look at that!


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[Assistant] He held out his hand. On the palm were three little pyramids of black doughy clay. Why, Holmes, you only had two yesterday.


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[Assistant] And one more this morning. It is a fair argument that wherever number three came from is also the source of numbers one and two, eh, Watson?


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[Assistant] Well, come along and put friend Soames out of his pain.


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[Assistant] The unfortunate tutor was certainly in a state of pitiable agitation when we found him in his chambers.


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[Assistant] In a few hours the examination would commence, and he was still in the dilemma between making the facts public and allowing the culprit to compete for the valuable scholarship.


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[Assistant] He could hardly stand still, so great was his mental agitation, and he ran towards Holmes with two eager hands outstretched.


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[Assistant] Thank Heaven that you've come! I feared that you had given it up in despair. What am I to do? Shall the examination proceed? Yes, let it proceed by all means. But this rascal—he shall not compete. You know him? I think so.


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[Assistant] If this matter is not to become public, we must give ourselves certain powers and resolve ourselves into a small private court martial.


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[Assistant] You there, if you please, Soames. Watson, you here. I'll take the armchair in the middle.


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[Assistant] I think we're now sufficiently imposing to strike terror into a guilty breast. Kindly ring the bell.


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[Assistant] Banister entered and shrank back in evident surprise and fear at our judicial appearance.


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[Assistant] You will kindly close the door, said Holmes.


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[Assistant] Now, Bannister, will you please tell us the truth about yesterday's incident? The man turned white to the roots of his hair. I've told you everything, sir. Nothing to add? Nothing at all, sir.


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[Assistant] Well then, I must make some suggestions to you.


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[Assistant] When you sat down on that chair yesterday, did you do so in order to conceal some object which would have shown who had been in the room? Banister's face was ghastly.


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[Assistant] It is only a suggestion, said Holmes suavely.


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[Assistant] I frankly admit that I am unable to prove it. But it seems probably enough. Since the moment that Mr. Soames's back was turned, you released the man who was hiding in that bedroom." Bannister licked his dry lips. There was no man, sir. Ah, that's a pity, Bannister. Up to now you may have spoken the truth. But now I know that you have lied. The man's face set in sullen defiance.


[User] Please transcribe this text

[Assistant] There was no man, sir. Come, come, Bannister. No, sir, there was no one. In that case you can give us no further information. Would you please remain in the room? Stand over there near the bedroom door.


[User] Please transcribe this text

[Assistant] Now, Soames, I'm going to ask you to have the great kindness to go up to the room of young Gilchrist, and to ask him to step down into yours.


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[Assistant] An instant later the tutor returned bringing with him the student.


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[Assistant] He was a fine figure of a man, tall, lithe, and agile, with a springy step and a pleasant open face. His troubled blue eyes glanced at each of us, and finally rested with an expression of blank dismay upon Bannister in the farther corner.


[User] Please transcribe this text

[Assistant] Just close the door," said Holmes. Now, Mr. Gilchrist, we are all quite alone here, and no one need ever know one word of what passes between us. We can be perfectly frank with each other


[User] Please transcribe this text

[Assistant] We want to know, Mr. Gilchrist, how you, an honorable man, ever came to commit such an action as that of yesterday.


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[Assistant] The unfortunate young man staggered back and cast a look full of horror and reproach at Bannister.


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[Assistant] No, no, Mr. Gilchrist, sir. I never said a word. Never one word!" cried the servant.


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[Assistant] No, but you have now," said Holmes. Now, sir, you must see that after Bannister's words your position is hopeless, and that your only chance lies in a frank confession.


[User] Please transcribe this text

[Assistant] For a moment Gilchrist, with upraised hand, tried to control his writhing features. The next he had thrown himself on his knees beside the table, and burying his face in his hands, he had burst into a storm of passionate sobbing.


[User] Please transcribe this text

[Assistant] Come, come, said Holmes kindly. It is human to err, and at least no one can accuse you of being a callous criminal.


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[Assistant] Perhaps it would be easier for you if I were to tell Mr. Soames what occurred, and you can check me where I am wrong. Shall I do so? Well, well, don't trouble to answer. Listen and see that I do you no injustice.


[User] Please transcribe this text

[Assistant] From the moment, Mr. Soames, that you said to me that no one, not even Bannister, could have told that the papers were in your room, the case began to take a definite shape in my mind.


[User] Please transcribe this text

[Assistant] The printer one could of course dismiss. He could examine the papers in his own office.


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[Assistant] The Indian I also thought nothing of. If the proofs were in a roll he could not possibly know what they were.


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[Assistant] On the other hand, it seemed an unthinkable coincidence that a man should dare to enter the room, and that by chance on that very day the papers were on the table. I dismissed that. The man who entered knew that the papers were there. How did he know? When I approached your room I examined the window.


[User] Please transcribe this text

[Assistant] You amused me by supposing that I was contemplating the possibility of someone having in broad daylight under the eyes of all these opposite rooms forced himself through it. Such an idea was absurd.


[User] Please transcribe this text

[Assistant] I was measuring how tall a man would need to be in order to see, as he passed, what papers were on the central table.


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[Assistant] I am six feet high, and I could do it without an effort. No one less than that would have a chance. Already, you see, I had a reason to think that if one of your three students was a man of unusual height, he was the most worth watching of the three.


[User] Please transcribe this text

[Assistant] I entered, and I took you into my confidence as to the suggestions of the side table.


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[Assistant] Of the centre table I could make nothing, until in your description of Gilchrist you mentioned that he was a long-distance jumper. Then the whole thing came to me in an instant, and I only needed certain corroborative proofs, which I speedily obtained.


[User] Please transcribe this text

[Assistant] What happened with this? This young fellow had employed his afternoon at the athletic grounds where he had been practising the jump.


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[Assistant] He returned carrying his jumping shoes, which are provided as you are aware with several sharp spikes.


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[Assistant] As he passed your window, he saw by means of his great height these proofs upon your table, and conjectured what they were. No harm would have been done had it not been that, as he passed your door, he perceived the key which had been left by the carelessness of your servant.


[User] Please transcribe this text

[Assistant] A sudden impulse came over him to enter, and see if they were indeed the proofs.


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[Assistant] It was not a dangerous exploit, for he could always pretend that he had simply looked in to ask a question. Well, when he saw that they were indeed the proofs, it was then that he yielded to temptation. He put his shoes on the table. What was it you put on that chair near the window? Gloves, said the young man. Holmes looked triumphantly at Bannister.


[User] Please transcribe this text

[Assistant] He put his gloves on the chair and he took the proofs, sheet by sheet, to copy them.


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[Assistant] He thought the tutor must return by the main gate, and that he would see him. As we know, he came back by the side gate. Suddenly he heard him at the very door. There was no possible escape.


[User] Please transcribe this text

[Assistant] He forgot his gloves, but he caught up his shoes and darted into the bedroom. You observe that the scratch on that table is slight at one side, but deepens in the direction of the bedroom door.


[User] Please transcribe this text

[Assistant] That in itself is enough to show us that the shoe had been drawn in that direction, and that the culprit had taken refuge there.


[User] Please transcribe this text

[Assistant] The earth round the spike had been left on the table, and a second sample was loosened and fell into the bedroom.


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[Assistant] I may add that I walked out to the athletic grounds this morning, saw that tenacious black clay is used in the jumping-pit, and carried away a specimen of it, together with some of the fine tan or sawdust which is strewn over it, to prevent the athlete from slipping. Have I told the truth, Mr. Gilchrist?" The student had drawn himself erect. Yes, sir, it is true, said he.


[User] Please transcribe this text

[Assistant] Good heavens! Have you nothing to add? cried Soames.


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[Assistant] Yes, sir, I have, but the shock of this disgraceful exposure has bewildered me. I have a letter here, Mr. Soames, which I wrote to you early this morning, in the middle of a restless night.


[User] Please transcribe this text

[Assistant] It was before I knew that my sin had found me out. Here it is, sir. You will see that I have said I have determined not to go in for the examination. I have been offered a commission in the Rhodesian police, and I am going out to South Africa at once.


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[Assistant] I am indeed pleased to hear that you did not intend to profit by your unfair advantage," said Soames. But why did you change your purpose?" Gilchrist pointed to Bannister.


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[Assistant] There is the man who set me in the right path, said he.


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[Assistant] Come now, Bannister," said Holmes, it will be clear to you from what I have said that only you could have let this young man out, since you were left in the room and must have locked the door when you went out. As to his escaping by that window, it was incredible.


[User] Please transcribe this text

[Assistant] Can you not clear up the last point in this mystery and tell us the reasons for your action? It was simple enough, sir.


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[Assistant] If you only had known, but with all your cleverness, it was impossible that you could know.


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[Assistant] Time was, sir, when I was butler to old Sir Jabez Gilcrest, this young gentleman's father.


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[Assistant] When he was ruined I came to the college as servant, but I never forgot my old employer, because he was down in the world.


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[Assistant] I watched his son all I could for the sake of the old days.


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[Assistant] Well sir, when I came into this room yesterday, when the alarm was given, the very first thing I saw was Mr. Gilchrist's tan gloves are lying in that chair.


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[Assistant] I knew those gloves well, and I understood their message. If Mr. Soames saw them, the game was up.


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[Assistant] I flopped down into that chair, and nothing would budge me until Mr. Soames he went for you.


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[Assistant] Then out came my poor young master, whom I dandled on my knee, and confessed it all to me.


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[Assistant] Wasn't it natural, sir, that I should save him? And wasn't it natural also that I should try to speak to him as his dead father would have done, and make him understand that he could not profit by such a deed? Could you blame me, sir?


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[Assistant] No indeed, said Holmes heartily, springing to his feet.


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[Assistant] Well, Soames, I think we've cleared your little problem up, and our breakfast awaits us at home. Come, Watson,


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[Assistant] As to you, sir, I trust that a bright future awaits you in Rhodesia. For once you have fallen low.


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[Assistant] Really? But, I was gonna give him a tour. Let him decide for himself.


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[Assistant] Diverted my salary into research projects, installed a diverse board to keep us on the right track, and made sure all research aligned with our mission. To heal the world.


[User] Please transcribe this text

[Assistant] First I want you to meet Dr. Young, who's a leading entomologist. You know what entomology is.


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[Assistant] Uh, any chance it can wait a sec? It'll be quick.


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[Assistant] We have a particle accelerator? We use it to send proton beams to that treatment room downstairs.


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[Assistant] It's being repaired right now, but I'll have it back up in no time.


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[Assistant] The things you've seen today are just a small taste of what we're working on.


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[Assistant] When I was in treatment, all I could think about was, what would I do if I survived?


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[Assistant] And I kept hearing my mom's voice telling me that our planet is in trouble. And it's my responsibility to save it.


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[Assistant] Come on. Let's make my mom and me proud, huh?


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[Assistant] Whatever time you need. It's all about balance right?


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[Assistant] Some of our researchers have field experiments set up around the city. If you have time, check them out. I've sent you the location, and your badge will grant you access.


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[Assistant] And help us to be wise.


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[Assistant] Be careful. Genki, let's get uptown. I'll make you something good for dinner.


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[Assistant] Just got off the phone with MJ. She says she'll head over to Connor's soon.


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[Assistant] Yeah, just making sure. You said the Steel Foundered, right?


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[Assistant] Huh, ee, ee.


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[Assistant] Shuh. Shuh.


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[Assistant] Suh, suh, ee-yai.


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[Assistant] Let's go, man, before he gets too far. No. He's mine. You sure?


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[Assistant] Okay. What's going on?


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[Assistant] I couldn't save you. Or Finn. If Spiderman can't protect everyone, who will?


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[Assistant] I can't jump. I am very hungry.


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[Assistant] If you enjoy your experience, please tell your friends and feel free to visit one of the many mysteriums now open around the city. Our free trial ends soon.


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[Assistant] No serum. These drugs are for late stage chemotherapy. Are these Cravens too?


[User] Please transcribe this text

[Assistant] No craving yet, but I think I know where he is. How's the suit? Weird. It's like it knows me. Don't worry, buddy. I'll take care of everything. Promise.


[User] Please transcribe this text

[Assistant] It was totally... I didn't want to worry Harry.


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[Assistant] Huh. If we could beam a few electrons in there, it'd stabilize it, but it's not like we have a... HARTICLE ACCELERATOR?


[User] Please transcribe this text

[Assistant] Relax and be a good proton. Huh?


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[Assistant] Okay, before we get down to business, there's something I have to tell you.


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[Assistant] Your dad's right. Foundation doesn't matter. What? This was our dream. And it still is.


[User] Please transcribe this text

[Assistant] But right now we have to focus on finding Dr. Connors and getting you healthy again.


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[Assistant] Dual sinks, but only ones used.


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[Assistant] Connors was here alright, and fighting for his life by the looks of it.


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[Assistant] Genetic recombination research? He came home to cure himself. Hard to do experiments in your living room though.


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[Assistant] These blood samples are for a genome analyzer. Does Connors have a lab here?


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[Assistant] This lab probably kept him and his family safe whenever he had an episode.


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[Assistant] Reptile terrariums for Connor's initial limb regeneration tests. I never did find out how he lost his arm.


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[Assistant] Couldn't hold him. Exactly how Craven likes it.


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[Assistant] Dude, MJ told me you almost died. You okay? Hey, Miles. Actually, never felt better. I should've been... Wait, really? Positive. Hey, let me call you back. Got some bonfires to crash. Uh, okay.


[User] Please transcribe this text

[Assistant] So today we're going to have sort of a different kind of class since it's the last class. Today I'm going to talk.


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[Assistant] The set of issues we've talked about this semester to a real world topic.


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[Assistant] And actually how it plays out in policy and practice. And I'll draw on some of my own experience.


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[Assistant] Having applied the kind of tools we learned in 1401 to the field of healthcare economics.


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[Assistant] Led me to be able to help in the development of healthcare policy in the U.S. And talk about sort of where healthcare stands, uh, healthcare policy stands at this point.


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[Assistant] Let's sit a little bit of background about health care in the U.S. Uhm, uh, basically about health care in the U.S., we have to recognize that, uh, the U.S. spends by far the most money on health care of any developed nation in the world.


[User] Please transcribe this text

[Assistant] We spend about 17.5% of our gross domestic product on healthcare. That amounts to almost $10,000 per man, woman, and child, every man, woman, and child in America.


[User] Please transcribe this text

[Assistant] Uhm, that dwarfs the rest of the world. The typical European nation spends about two thirds as much as a percent of GDP on healthcare. Uh, England spends less than half as much, uh, on healthcare. So basically, we spend a lot on healthcare, uh, as a share of our economy. Uh, and, uhm, what do we get for it?


[User] Please transcribe this text

[Assistant] Well, uhm, the evidence here, the first fact is clear. The evidence that we get for it is a little bit mixed. So, if you look at the typical thing on the web, you know, US healthcare is terrible, our money is wasted, you'll see that on things like


[User] Please transcribe this text

[Assistant] Infinite mortality we rate like 20th in the world. Or life expectancy we're like 20th in the world. So by those metrics we don't do very well.


[User] Please transcribe this text

[Assistant] Uhm, but in fact those metrics are misleading because we also have, we have the most unequal healthcare system in the world.


[User] Please transcribe this text

[Assistant] The right way to think about it is to think about the haves and the have-nots. The haves, which is us, and most people in America, people who are well insured in the system, actually get probably the best healthcare in the world.


[User] Please transcribe this text

[Assistant] Okay, now, that might be disputed by many people, but I think about this like an economist would think about it, which is, how would you decide whether people prefer product A versus product B, whether they buy product A versus product B?


[User] Please transcribe this text

[Assistant] Every year one million people come to the U.S. to get treated for the health care problems. No one leaves.


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[Assistant] No one's going to England for surgery. No one's flying from the U.S. to England for surgery. They're coming here.


[User] Please transcribe this text

[Assistant] Uhm, if you're in the system, we have the best healthcare in the world.


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[Assistant] Unfortunately, if you're out of the system, we have some of the worst healthcare in the world.


[User] Please transcribe this text

[Assistant] So a white baby born in America today, there's roughly a slightly more than 0.5% chance the baby will die in their first year.


[User] Please transcribe this text

[Assistant] That's comparable to, uhm, that's comparable to Northern Europe.


[User] Please transcribe this text

[Assistant] If you look at a black baby born in the US, the odds they die in the first year are about twice that, which is worse than Barbados.


[User] Please transcribe this text

[Assistant] So the problem in the US is not that our outcomes are bad. The problem is that they're very unequal. Okay? That we're spending all this money.


[User] Please transcribe this text

[Assistant] We're delivering good, but not exceptional outcomes for people in the system, and bad outcomes for people out of the system. So clearly we're not getting a lot of value. It's not like we deliver exceptionally good outcomes to people in the system. We're slightly better, despite fending a lot more, and we're worse for many Americans who are left out of the system. So, that's sort of the setup.


[User] Please transcribe this text

[Assistant] Of where we are, which is really have two fundamental problems in healthcare in the US. Our spending is too high, and our access is too unequal.


[User] Please transcribe this text

[Assistant] Okay, now, what's, so I wanna focus today's lecture on those two aspects and think about how can we bring the kind of lessons we've learned in this course to thinking about addressing those problems. So let's, so I'm gonna focus on the access problem and the cost problem. Let's start with the access problem. Now, in America, before 2010,


[User] Please transcribe this text

[Assistant] We had about, or before 2014, we had about 50 million uninsured Americans.


[User] Please transcribe this text

[Assistant] 50 million people who did not have health insurance in the US. We're the only nation in the world, only developed nation in the world, with significant uninsured population.


[User] Please transcribe this text

[Assistant] Now, the fact that 50 million people are uninsured, is that a problem?


[User] Please transcribe this text

[Assistant] On its face. If I just said, here's a fact, 50 million people in America don't have health insurance. Based on that fact alone, can you tell me whether there's a problem or not?


[User] Please transcribe this text

[Assistant] You know, many more people than that don't have flat-screen TVs and don't own homes.


[User] Please transcribe this text

[Assistant] Why do we think that we should care if people don't have something? The answer would be, we would only care if what? Under what condition? When do we, yeah.


[User] Please transcribe this text

[Assistant] Now, think about it because they could be richer, but that's not our problem. Given their budget, they're not buying it. What?


[User] Please transcribe this text

[Assistant] Under what condition is the market not, under what type of conditions would the market not deliver the best outcome?


[User] Please transcribe this text

[Assistant] If there's a market failure. So the fact that people aren't insured doesn't matter.


[User] Please transcribe this text

[Assistant] Except, A, if there's a market failure, or B, for redistribution purposes. Remember, that's the two reasons we want the government involved. So if, if health insurance markets were perfectly functioning,


[User] Please transcribe this text

[Assistant] And people who are uninsured were roughly equally distributed income as everyone else, there'd be no cause for worry. But in fact, that's not true. We've talked in this class about why markets like health insurance won't function well, which is a problem of adverse selection.


[User] Please transcribe this text

[Assistant] Okay, there's information failures which will lead health insurance markets not to function well.


[User] Please transcribe this text

[Assistant] And the people who aren't insured tend to be much poorer than the people who are insured. It's also a redistributional concern. So the reasons we care about the uninsured are both because of market failures.


[User] Please transcribe this text

[Assistant] And for redistribution, that they tend to be lower income. What's interesting is the uninsured don't tend to be the poorest in society. They tend to be the near poor. So here's the way sort of health insurance coverage works in the U.S.


[User] Please transcribe this text

[Assistant] For the vast majority of Americans, 60% of Americans, 600 Americans have what's called employer sponsored insurance. So.


[User] Please transcribe this text

[Assistant] Like most of your parents, like me, they get health insurance from their employer. Okay. The typical upper income American gets health insurance from their employer. Typical average income American does, about 60% of Americans. Okay. Then...


[User] Please transcribe this text

[Assistant] And I'm gonna do this sort of pre-ACA, so before 2014, before the big change that was put in place by the Affordable Care Act, you had about another maybe 6%


[User] Please transcribe this text

[Assistant] Compared to ESI, and the reason is because of exactly the average selection problem we talked about. Think about yourself as an insurer.


[User] Please transcribe this text

[Assistant] And think about what your goal is. Your goal as an insurer is to essentially absorb risk in a way that allows you to make a profit.


[User] Please transcribe this text

[Assistant] So what you want is you want to live off the law of large numbers. You know that with a large enough group,


[User] Please transcribe this text

[Assistant] You could be able to predict what their costs will be, and therefore you can just make a profit on top.


[User] Please transcribe this text

[Assistant] So insurers love when MIT comes to an insurer, they're delighted. They're like, look, you got between MIT and, and Lincoln lab stuff, you got about 10,000 employees. I with great certainty.


[User] Please transcribe this text

[Assistant] Can predict what the cost will be next year for a group of 10,000 employees.


[User] Please transcribe this text

[Assistant] And so I as an insurer can know I can just charge that plus X percent and I'm golden.


[User] Please transcribe this text

[Assistant] But when John Gruber walks in the door, they're like, why are you coming to me, individual? Maybe because you know you're sick, maybe because you love skydiving. I don't know, but I'm wary of you. So I'm going to charge you a lot of money to get health insurance.


[User] Please transcribe this text

[Assistant] As a result, most, very few people bought health insurance on their own. And in particular,


[User] Please transcribe this text

[Assistant] The reason they didn't is because insurers would not offer health insurance to people if they were at all sick.


[User] Please transcribe this text

[Assistant] They would do things like having what we call pre-existing conditions exclusions.


[User] Please transcribe this text

[Assistant] These were features of insurance contracts which said, look, you walked in the door, John, and you want health insurance, but I know in the past you've had cancer or asthma or knee surgery.


[User] Please transcribe this text

[Assistant] I'm gonna tell you, I'm gonna insure you, but not for any expenses that might arise from recurrence of those past injuries.


[User] Please transcribe this text

[Assistant] Did you have cancer in the past? Anything that comes up in the future, because you had cancer, I'm not gonna cover.


[User] Please transcribe this text

[Assistant] So I'm gonna give you essentially partial insurance. There's gonna be a market failure. I'm gonna insure you, but only for part of what you need. Okay? Alternatively,


[User] Please transcribe this text

[Assistant] They could use pre-distinct condition exclusion. They could use what was called medical underwriting.


[User] Please transcribe this text

[Assistant] Which was basically saying, okay, John, come in, I'm gonna give an exam, and if you look sick, I'm gonna deny you insurance. Or if you look sick, I'm gonna charge you a hundred times more than someone else. So these were not illegal or even immoral.


[User] Please transcribe this text

[Assistant] These were just ways insurers came up with to try to deal with the adverse selection problem.


[User] Please transcribe this text

[Assistant] And not immoral, I mean this is just they're maximizing their profits. It's what companies do.


[User] Please transcribe this text

[Assistant] And the point is that when they did this, what this meant was, if you did not have employer-sponsored insurance, or insurance for the government, which I'll come to next.


[User] Please transcribe this text

[Assistant] Then you were subject to the fact that if you got sick you might not be able to get insurance. Which is sort of weird. Insurance is supposed to cover if you were sick. But in fact, if you were sick you might not be able to get it.


[User] Please transcribe this text

[Assistant] Okay, so that was the fundamental market failure we had here through adverse selection.


[User] Please transcribe this text

[Assistant] Now we also, that was employer sponsored insurance, so that was about two thirds of the population.


[User] Please transcribe this text

[Assistant] Probably more like 20%. 20% of the population, government sponsored insurance. The two big programs here are called Medicare.


[User] Please transcribe this text

[Assistant] Now, if you ever take my 1441 class, I will only hold your response for one thing if I ever meet you ten years later. Would you remember the difference between these two programs?


[User] Please transcribe this text

[Assistant] Okay. Medicare is health insurance for the elderly. Medicaid is health insurance for the poor. And those are our two big public insurance programs. Okay. And about 20, and if you're in those programs, you're also set. They don't have any of these features. If you're in, you're covered for everything.


[User] Please transcribe this text

[Assistant] Okay, so about 20% of people are there. And then finally, if you add up the numbers, we had about 15, um, numbers don't quite add up, but you had about 15% of the population was uninsured.


[User] Please transcribe this text

[Assistant] 15% uninsured. So you have about two-thirds private, about one-fifth public and about one-sixth uninsured.


[User] Please transcribe this text

[Assistant] The typical uninsured person is like what we call the working poor. Someone who's got a job, but it's a crappy job that don't offer health insurance, but they make enough money they can't qualify for being in the low income program.


[User] Please transcribe this text

[Assistant] So your family's struggling at like forty, fifty thousand dollars a year.


[User] Please transcribe this text

[Assistant] High enough income that they're not qualifying for Medicaid, but not a good enough job they're getting health insurance. That's your typical uninsured family. Two-thirds of the uninsured.


[User] Please transcribe this text

[Assistant] Our families that are headed by a full-time, full-year worker.


[User] Please transcribe this text

[Assistant] Okay, they're not the, typically the unemployed down there luck people. They're really the people who are trying to play by the rules they say in politics, but typically can't get a job with health insurance. Okay? So that's your basic landscape.


[User] Please transcribe this text

[Assistant] And what we know from that landscape is that a lot of the access problems were because of this group and this group. Because the people who couldn't get in this market and as a result were often uninsured. That was a lot of what drove the access problems. So that was sort of the first


[User] Please transcribe this text

[Assistant] That's the first, that's one of the two big problems that faced our system.


[User] Please transcribe this text

[Assistant] And for many, many years we know we had that problem, and for about a hundred years,


[User] Please transcribe this text

[Assistant] And probably about every 17 years on average there was a big attempt to reform healthcare and they always failed.


[User] Please transcribe this text

[Assistant] And they always failed because they failed between two extremes. There were two extreme views that could never quite meet in the middle ground.


[User] Please transcribe this text

[Assistant] And they come to what I talked about last time, which is how do we solve the problem of market failures in insurance markets?


[User] Please transcribe this text

[Assistant] Well, one version of solving that, I described, was subsidization.


[User] Please transcribe this text

[Assistant] You could remember with my MIT program if I paid the healthy guys 500 bucks they'd all buy two. And I'd solve the problem. So one version was subsidization. The problem is, subsidization


[User] Please transcribe this text

[Assistant] Only works if it's big enough to overcome these problems.


[User] Please transcribe this text

[Assistant] And no one ever opposed subsidization big enough to overcome these problems. In my MIT example, I was going to give $400 to every healthy- First of all, it means giving money to healthy people, which is sort of politically a little difficult. Like, hey, the healthier you are, the more money you get. Seems a bit weird.


[User] Please transcribe this text

[Assistant] Also, it's just hard to solve these problems by just subsidizing people. Insurance companies are still too good at trying to get rid of the sick people.


[User] Please transcribe this text

[Assistant] And even if you subsidize people to come in, insurance companies will always have an incentive. They'll say, great, healthy people come and subsidize, still want to avoid the sick.


[User] Please transcribe this text

[Assistant] Which is saying, look, let's just have the government provide health insurance to everyone. We have the government provide social security to everyone.


[User] Please transcribe this text

[Assistant] In Canada, everybody gets government-provided health insurance. Why not just do it here?


[User] Please transcribe this text

[Assistant] Let's get rid of all the crap with insurance companies we don't like after all.


[User] Please transcribe this text

[Assistant] Insurance company administrative costs are about 15% of medical spending. So boom! We can lower 15% of medical spending.


[User] Please transcribe this text

[Assistant] That is, you know, that's like $500 billion a year. Boom, it's gone.


[User] Please transcribe this text

[Assistant] Okay, so basically why not, so single payer is something a lot of people have advocated for. Let's just have one giant universal health insurance program. Now the problem with this,


[User] Please transcribe this text

[Assistant] The problem with the single payer approach is largely, there's pros and cons of economics perspective, but the problems here are largely political. Which is that to make single payer happen,


[User] Please transcribe this text

[Assistant] You have three enormous political barriers, which come back to economics. Everything comes back to economics. But they play their way out in the political system. Okay? The first problem is paying for it.


[User] Please transcribe this text

[Assistant] Single payer to get, have the government give everyone health insurance means a massive expansion in the government, which means a big increase in taxes. And we know taxes have deadweight loss, and we know taxes are politically unpopular. Now,